Datasets:

luzimu
/

github-issues

Dataset card Viewer Files Files and versions Community

Split (1)

train · 1k rows

url stringlengths 61 61	repository_url stringclasses 1 value	labels_url stringlengths 75 75	comments_url stringlengths 70 70	events_url stringlengths 68 68	html_url stringlengths 49 51	id int64 1.42B 1.84B	node_id stringlengths 18 19	number int64 5.16k 6.14k	title stringlengths 1 290	user dict	labels list	state stringclasses 2 values	locked bool 1 class	assignee dict	assignees list	milestone dict	comments sequence	created_at timestamp[s]	updated_at timestamp[s]	closed_at timestamp[s]	author_association stringclasses 3 values	active_lock_reason null	draft bool 2 classes	pull_request dict	body stringlengths 3 33.9k ⌀	reactions dict	timeline_url stringlengths 70 70	performed_via_github_app null	state_reason stringclasses 3 values	is_pull_request bool 2 classes
https://api.github.com/repos/huggingface/datasets/issues/6088	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6088/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6088/comments	https://api.github.com/repos/huggingface/datasets/issues/6088/events	https://github.com/huggingface/datasets/issues/6088	1,825,665,235	I_kwDODunzps5s0XDT	6,088	Loading local data files initiates web requests	{ "login": "lytning98", "id": 23375707, "node_id": "MDQ6VXNlcjIzMzc1NzA3", "avatar_url": "https://avatars.githubusercontent.com/u/23375707?v=4", "gravatar_id": "", "url": "https://api.github.com/users/lytning98", "html_url": "https://github.com/lytning98", "followers_url": "https://api.github.com/users/lytning98/followers", "following_url": "https://api.github.com/users/lytning98/following{/other_user}", "gists_url": "https://api.github.com/users/lytning98/gists{/gist_id}", "starred_url": "https://api.github.com/users/lytning98/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lytning98/subscriptions", "organizations_url": "https://api.github.com/users/lytning98/orgs", "repos_url": "https://api.github.com/users/lytning98/repos", "events_url": "https://api.github.com/users/lytning98/events{/privacy}", "received_events_url": "https://api.github.com/users/lytning98/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[]	2023-07-28T04:06:26	2023-07-28T05:02:22	2023-07-28T05:02:22	NONE	null	null	null	As documented in the [official docs](https://huggingface.co/docs/datasets/v2.14.0/en/package_reference/loading_methods#datasets.load_dataset.example-2), I tried to load datasets from local files by ```python # Load a JSON file from datasets import load_dataset ds = load_dataset('json', data_files='path/to/local/my_dataset.json') ``` But this failed on a web request because I'm executing the script on a machine without Internet access. Stacktrace shows ``` in PackagedDatasetModuleFactory.__init__(self, name, data_dir, data_files, download_config, download_mode) 940 self.download_config = download_config 941 self.download_mode = download_mode --> 942 increase_load_count(name, resource_type="dataset") ``` I've read from the source code that this can be fixed by setting environment variable to run in offline mode. I'm just wondering that is this an expected behaviour that even loading a LOCAL JSON file requires Internet access by default? And what's the point of requesting to `increase_load_count` on some server when loading just LOCAL data files?	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6088/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6088/timeline	null	completed	false
https://api.github.com/repos/huggingface/datasets/issues/6087	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6087/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6087/comments	https://api.github.com/repos/huggingface/datasets/issues/6087/events	https://github.com/huggingface/datasets/issues/6087	1,825,133,741	I_kwDODunzps5syVSt	6,087	fsspec dependency is set too low	{ "login": "iXce", "id": 1085885, "node_id": "MDQ6VXNlcjEwODU4ODU=", "avatar_url": "https://avatars.githubusercontent.com/u/1085885?v=4", "gravatar_id": "", "url": "https://api.github.com/users/iXce", "html_url": "https://github.com/iXce", "followers_url": "https://api.github.com/users/iXce/followers", "following_url": "https://api.github.com/users/iXce/following{/other_user}", "gists_url": "https://api.github.com/users/iXce/gists{/gist_id}", "starred_url": "https://api.github.com/users/iXce/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/iXce/subscriptions", "organizations_url": "https://api.github.com/users/iXce/orgs", "repos_url": "https://api.github.com/users/iXce/repos", "events_url": "https://api.github.com/users/iXce/events{/privacy}", "received_events_url": "https://api.github.com/users/iXce/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "Thanks for reporting! A PR with a fix has just been merged." ]	2023-07-27T20:08:22	2023-07-28T10:07:56	2023-07-28T10:07:03	NONE	null	null	null	### Describe the bug fsspec.callbacks.TqdmCallback (used in https://github.com/huggingface/datasets/blob/73bed12ecda17d1573fd3bf73ed5db24d3622f86/src/datasets/utils/file_utils.py#L338) was first released in fsspec [2022.3.0](https://github.com/fsspec/filesystem_spec/releases/tag/2022.3.0, commit where it was added: https://github.com/fsspec/filesystem_spec/commit/9577c8a482eb0a69092913b81580942a68d66a76#diff-906155c7e926a9ff58b9f23369bb513b09b445f5b0f41fa2a84015d0b471c68cR180), however the dependency is set to 2021.11.1 https://github.com/huggingface/datasets/blob/main/setup.py#L129 ### Steps to reproduce the bug 1. Install fsspec==2021.11.1 2. Install latest datasets==2.14.1 3. Import datasets, import fails due to lack of `fsspec.callbacks.TqdmCallback` ### Expected behavior No import issue ### Environment info N/A	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6087/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6087/timeline	null	completed	false
https://api.github.com/repos/huggingface/datasets/issues/6086	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6086/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6086/comments	https://api.github.com/repos/huggingface/datasets/issues/6086/events	https://github.com/huggingface/datasets/issues/6086	1,825,009,268	I_kwDODunzps5sx250	6,086	Support `fsspec` in `Dataset.to_<format>` methods	{ "login": "mariosasko", "id": 47462742, "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "gravatar_id": "", "url": "https://api.github.com/users/mariosasko", "html_url": "https://github.com/mariosasko", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "repos_url": "https://api.github.com/users/mariosasko/repos", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "type": "User", "site_admin": false }	[ { "id": 1935892871, "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement", "name": "enhancement", "color": "a2eeef", "default": true, "description": "New feature or request" } ]	open	false	{ "login": "alvarobartt", "id": 36760800, "node_id": "MDQ6VXNlcjM2NzYwODAw", "avatar_url": "https://avatars.githubusercontent.com/u/36760800?v=4", "gravatar_id": "", "url": "https://api.github.com/users/alvarobartt", "html_url": "https://github.com/alvarobartt", "followers_url": "https://api.github.com/users/alvarobartt/followers", "following_url": "https://api.github.com/users/alvarobartt/following{/other_user}", "gists_url": "https://api.github.com/users/alvarobartt/gists{/gist_id}", "starred_url": "https://api.github.com/users/alvarobartt/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/alvarobartt/subscriptions", "organizations_url": "https://api.github.com/users/alvarobartt/orgs", "repos_url": "https://api.github.com/users/alvarobartt/repos", "events_url": "https://api.github.com/users/alvarobartt/events{/privacy}", "received_events_url": "https://api.github.com/users/alvarobartt/received_events", "type": "User", "site_admin": false }	[ { "login": "alvarobartt", "id": 36760800, "node_id": "MDQ6VXNlcjM2NzYwODAw", "avatar_url": "https://avatars.githubusercontent.com/u/36760800?v=4", "gravatar_id": "", "url": "https://api.github.com/users/alvarobartt", "html_url": "https://github.com/alvarobartt", "followers_url": "https://api.github.com/users/alvarobartt/followers", "following_url": "https://api.github.com/users/alvarobartt/following{/other_user}", "gists_url": "https://api.github.com/users/alvarobartt/gists{/gist_id}", "starred_url": "https://api.github.com/users/alvarobartt/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/alvarobartt/subscriptions", "organizations_url": "https://api.github.com/users/alvarobartt/orgs", "repos_url": "https://api.github.com/users/alvarobartt/repos", "events_url": "https://api.github.com/users/alvarobartt/events{/privacy}", "received_events_url": "https://api.github.com/users/alvarobartt/received_events", "type": "User", "site_admin": false } ]	null	[ "Hi @mariosasko unless someone's already working on it, I guess I can tackle it!", "Hi! Sure, feel free to tackle this.", "#self-assign", "I'm assuming this should just cover `to_csv`, `to_parquet`, and `to_json`, right? As `to_list` and `to_dict` just return Python objects, `to_pandas` returns a `pandas.DataFrame` and `to_sql` just inserts into a SQL DB, is that right?" ]	2023-07-27T19:08:37	2023-07-28T15:28:26	null	CONTRIBUTOR	null	null	null	Supporting this should be fairly easy. Requested on the forum [here](https://discuss.huggingface.co/t/how-can-i-convert-a-loaded-dataset-in-to-a-parquet-file-and-save-it-to-the-s3/48353).	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6086/reactions", "total_count": 1, "+1": 1, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6086/timeline	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6085	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6085/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6085/comments	https://api.github.com/repos/huggingface/datasets/issues/6085/events	https://github.com/huggingface/datasets/pull/6085	1,824,985,188	PR_kwDODunzps5WlAyA	6,085	Fix `fsspec` download	{ "login": "mariosasko", "id": 47462742, "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "gravatar_id": "", "url": "https://api.github.com/users/mariosasko", "html_url": "https://github.com/mariosasko", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "repos_url": "https://api.github.com/users/mariosasko/repos", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	null	[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006031 / 0.011353 (-0.005322) \| 0.003579 / 0.011008 (-0.007429) \| 0.080862 / 0.038508 (0.042354) \| 0.056660 / 0.023109 (0.033551) \| 0.388285 / 0.275898 (0.112387) \| 0.422270 / 0.323480 (0.098790) \| 0.004651 / 0.007986 (-0.003335) \| 0.002895 / 0.004328 (-0.001433) \| 0.062767 / 0.004250 (0.058517) \| 0.046491 / 0.037052 (0.009438) \| 0.389918 / 0.258489 (0.131428) \| 0.434650 / 0.293841 (0.140809) \| 0.027265 / 0.128546 (-0.101281) \| 0.007946 / 0.075646 (-0.067701) \| 0.261207 / 0.419271 (-0.158065) \| 0.045057 / 0.043533 (0.001525) \| 0.391977 / 0.255139 (0.136838) \| 0.418525 / 0.283200 (0.135326) \| 0.020705 / 0.141683 (-0.120978) \| 1.459271 / 1.452155 (0.007116) \| 1.516935 / 1.492716 (0.024218) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.174659 / 0.018006 (0.156653) \| 0.429627 / 0.000490 (0.429137) \| 0.003714 / 0.000200 (0.003514) \| 0.000070 / 0.000054 (0.000016) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023255 / 0.037411 (-0.014156) \| 0.073463 / 0.014526 (0.058937) \| 0.083000 / 0.176557 (-0.093557) \| 0.146704 / 0.737135 (-0.590431) \| 0.084419 / 0.296338 (-0.211919) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.392222 / 0.215209 (0.177013) \| 3.902620 / 2.077655 (1.824966) \| 1.903056 / 1.504120 (0.398936) \| 1.753423 / 1.541195 (0.212228) \| 1.874547 / 1.468490 (0.406057) \| 0.495947 / 4.584777 (-4.088829) \| 3.084680 / 3.745712 (-0.661032) \| 4.235064 / 5.269862 (-1.034797) \| 2.626840 / 4.565676 (-1.938837) \| 0.057273 / 0.424275 (-0.367002) \| 0.006457 / 0.007607 (-0.001150) \| 0.466018 / 0.226044 (0.239974) \| 4.648264 / 2.268929 (2.379335) \| 2.520293 / 55.444624 (-52.924331) \| 2.339393 / 6.876477 (-4.537083) \| 2.538848 / 2.142072 (0.396775) \| 0.592018 / 4.805227 (-4.213210) \| 0.125041 / 6.500664 (-6.375623) \| 0.061038 / 0.075469 (-0.014431) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.244285 / 1.841788 (-0.597503) \| 18.411576 / 8.074308 (10.337268) \| 13.850100 / 10.191392 (3.658708) \| 0.131904 / 0.680424 (-0.548520) \| 0.016824 / 0.534201 (-0.517377) \| 0.328931 / 0.579283 (-0.250352) \| 0.364801 / 0.434364 (-0.069563) \| 0.376298 / 0.540337 (-0.164039) \| 0.525045 / 1.386936 (-0.861891) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006059 / 0.011353 (-0.005294) \| 0.003693 / 0.011008 (-0.007315) \| 0.062982 / 0.038508 (0.024473) \| 0.062155 / 0.023109 (0.039046) \| 0.389467 / 0.275898 (0.113568) \| 0.437046 / 0.323480 (0.113566) \| 0.004823 / 0.007986 (-0.003163) \| 0.002935 / 0.004328 (-0.001393) \| 0.062679 / 0.004250 (0.058429) \| 0.049676 / 0.037052 (0.012623) \| 0.418054 / 0.258489 (0.159565) \| 0.442467 / 0.293841 (0.148626) \| 0.027652 / 0.128546 (-0.100895) \| 0.008146 / 0.075646 (-0.067501) \| 0.069414 / 0.419271 (-0.349858) \| 0.042884 / 0.043533 (-0.000649) \| 0.387167 / 0.255139 (0.132028) \| 0.418684 / 0.283200 (0.135484) \| 0.022419 / 0.141683 (-0.119264) \| 1.460606 / 1.452155 (0.008451) \| 1.514204 / 1.492716 (0.021487) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.200523 / 0.018006 (0.182517) \| 0.415970 / 0.000490 (0.415481) \| 0.003202 / 0.000200 (0.003002) \| 0.000069 / 0.000054 (0.000014) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025836 / 0.037411 (-0.011575) \| 0.078859 / 0.014526 (0.064333) \| 0.088523 / 0.176557 (-0.088034) \| 0.141572 / 0.737135 (-0.595563) \| 0.090258 / 0.296338 (-0.206080) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.416548 / 0.215209 (0.201339) \| 4.155278 / 2.077655 (2.077623) \| 2.126683 / 1.504120 (0.622563) \| 1.963762 / 1.541195 (0.422568) \| 2.029018 / 1.468490 (0.560528) \| 0.499005 / 4.584777 (-4.085772) \| 3.063503 / 3.745712 (-0.682209) \| 4.250800 / 5.269862 (-1.019061) \| 2.642634 / 4.565676 (-1.923043) \| 0.057815 / 0.424275 (-0.366460) \| 0.006784 / 0.007607 (-0.000823) \| 0.492481 / 0.226044 (0.266437) \| 4.914306 / 2.268929 (2.645377) \| 2.601582 / 55.444624 (-52.843042) \| 2.337863 / 6.876477 (-4.538614) \| 2.462854 / 2.142072 (0.320782) \| 0.593738 / 4.805227 (-4.211489) \| 0.127030 / 6.500664 (-6.373634) \| 0.064206 / 0.075469 (-0.011263) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.326919 / 1.841788 (-0.514868) \| 18.728929 / 8.074308 (10.654621) \| 13.903681 / 10.191392 (3.712289) \| 0.162670 / 0.680424 (-0.517754) \| 0.016913 / 0.534201 (-0.517288) \| 0.337504 / 0.579283 (-0.241779) \| 0.339786 / 0.434364 (-0.094577) \| 0.384955 / 0.540337 (-0.155383) \| 0.514358 / 1.386936 (-0.872578) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c5c31b492c45e01c6f4593ada2b84517a75a5c7c \"CML watermark\")\n", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6085). All of your documentation changes will be reflected on that endpoint.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007610 / 0.011353 (-0.003743) \| 0.004616 / 0.011008 (-0.006392) \| 0.100330 / 0.038508 (0.061821) \| 0.084450 / 0.023109 (0.061341) \| 0.386610 / 0.275898 (0.110712) \| 0.418479 / 0.323480 (0.094999) \| 0.006085 / 0.007986 (-0.001900) \| 0.003800 / 0.004328 (-0.000529) \| 0.076248 / 0.004250 (0.071997) \| 0.065175 / 0.037052 (0.028122) \| 0.387154 / 0.258489 (0.128665) \| 0.425484 / 0.293841 (0.131643) \| 0.035946 / 0.128546 (-0.092601) \| 0.009901 / 0.075646 (-0.065745) \| 0.343015 / 0.419271 (-0.076256) \| 0.060965 / 0.043533 (0.017432) \| 0.390585 / 0.255139 (0.135446) \| 0.405873 / 0.283200 (0.122673) \| 0.026929 / 0.141683 (-0.114754) \| 1.767916 / 1.452155 (0.315761) \| 1.893431 / 1.492716 (0.400715) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.237888 / 0.018006 (0.219882) \| 0.503949 / 0.000490 (0.503459) \| 0.004769 / 0.000200 (0.004570) \| 0.000088 / 0.000054 (0.000033) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031553 / 0.037411 (-0.005859) \| 0.096950 / 0.014526 (0.082424) \| 0.110374 / 0.176557 (-0.066183) \| 0.176754 / 0.737135 (-0.560381) \| 0.111703 / 0.296338 (-0.184635) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.449232 / 0.215209 (0.234023) \| 4.510247 / 2.077655 (2.432592) \| 2.188547 / 1.504120 (0.684427) \| 2.007530 / 1.541195 (0.466335) \| 2.095650 / 1.468490 (0.627160) \| 0.563262 / 4.584777 (-4.021515) \| 4.062412 / 3.745712 (0.316700) \| 6.338350 / 5.269862 (1.068489) \| 3.844669 / 4.565676 (-0.721008) \| 0.064517 / 0.424275 (-0.359758) \| 0.008536 / 0.007607 (0.000929) \| 0.553872 / 0.226044 (0.327828) \| 5.530311 / 2.268929 (3.261383) \| 2.835109 / 55.444624 (-52.609516) \| 2.493900 / 6.876477 (-4.382577) \| 2.728412 / 2.142072 (0.586340) \| 0.680161 / 4.805227 (-4.125066) \| 0.155831 / 6.500664 (-6.344833) \| 0.070359 / 0.075469 (-0.005110) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.504852 / 1.841788 (-0.336936) \| 22.806335 / 8.074308 (14.732027) \| 16.598386 / 10.191392 (6.406994) \| 0.207857 / 0.680424 (-0.472566) \| 0.021425 / 0.534201 (-0.512776) \| 0.474069 / 0.579283 (-0.105214) \| 0.472263 / 0.434364 (0.037899) \| 0.542195 / 0.540337 (0.001858) \| 0.782871 / 1.386936 (-0.604065) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007443 / 0.011353 (-0.003910) \| 0.004465 / 0.011008 (-0.006544) \| 0.076268 / 0.038508 (0.037759) \| 0.086607 / 0.023109 (0.063498) \| 0.443295 / 0.275898 (0.167397) \| 0.472819 / 0.323480 (0.149339) \| 0.005841 / 0.007986 (-0.002144) \| 0.003727 / 0.004328 (-0.000602) \| 0.076015 / 0.004250 (0.071765) \| 0.063188 / 0.037052 (0.026136) \| 0.450555 / 0.258489 (0.192066) \| 0.478532 / 0.293841 (0.184691) \| 0.036258 / 0.128546 (-0.092288) \| 0.009869 / 0.075646 (-0.065777) \| 0.083786 / 0.419271 (-0.335486) \| 0.056546 / 0.043533 (0.013013) \| 0.449647 / 0.255139 (0.194508) \| 0.457588 / 0.283200 (0.174389) \| 0.027197 / 0.141683 (-0.114486) \| 1.769991 / 1.452155 (0.317836) \| 1.859905 / 1.492716 (0.367189) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.268637 / 0.018006 (0.250631) \| 0.492860 / 0.000490 (0.492370) \| 0.008574 / 0.000200 (0.008374) \| 0.000140 / 0.000054 (0.000085) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.037679 / 0.037411 (0.000268) \| 0.108258 / 0.014526 (0.093733) \| 0.117850 / 0.176557 (-0.058707) \| 0.181611 / 0.737135 (-0.555524) \| 0.120901 / 0.296338 (-0.175437) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.485780 / 0.215209 (0.270571) \| 4.851289 / 2.077655 (2.773635) \| 2.486068 / 1.504120 (0.981948) \| 2.299417 / 1.541195 (0.758222) \| 2.387093 / 1.468490 (0.918603) \| 0.568826 / 4.584777 (-4.015951) \| 4.163426 / 3.745712 (0.417713) \| 6.224964 / 5.269862 (0.955102) \| 3.255619 / 4.565676 (-1.310058) \| 0.067081 / 0.424275 (-0.357194) \| 0.009065 / 0.007607 (0.001458) \| 0.580449 / 0.226044 (0.354405) \| 5.786394 / 2.268929 (3.517465) \| 3.057780 / 55.444624 (-52.386844) \| 2.764339 / 6.876477 (-4.112138) \| 2.880718 / 2.142072 (0.738645) \| 0.681376 / 4.805227 (-4.123851) \| 0.157858 / 6.500664 (-6.342806) \| 0.072481 / 0.075469 (-0.002988) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.590704 / 1.841788 (-0.251083) \| 23.141929 / 8.074308 (15.067620) \| 17.001141 / 10.191392 (6.809749) \| 0.203790 / 0.680424 (-0.476634) \| 0.021766 / 0.534201 (-0.512435) \| 0.475309 / 0.579283 (-0.103974) \| 0.466448 / 0.434364 (0.032084) \| 0.551470 / 0.540337 (0.011132) \| 0.727876 / 1.386936 (-0.659060) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#61b19eea7fc5cf484e8cdf41d6ae035f94d8a671 \"CML watermark\")\n" ]	2023-07-27T18:54:47	2023-07-27T19:06:13	null	CONTRIBUTOR	null	true	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6085", "html_url": "https://github.com/huggingface/datasets/pull/6085", "diff_url": "https://github.com/huggingface/datasets/pull/6085.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6085.patch", "merged_at": null }	Testing `ds = load_dataset("audiofolder", data_files="s3://datasets.huggingface.co/SpeechCommands/v0.01/v0.01_test.tar.gz", storage_options={"anon": True})` and trying to fix the issues raised by `fsspec` ... TODO: fix ``` self.session = aiobotocore.session.AioSession(**self.kwargs) TypeError: __init__() got an unexpected keyword argument 'hf' ``` by "preparing `storage_options`" for the `fsspec` head/get	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6085/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6085/timeline	null	null	true
https://api.github.com/repos/huggingface/datasets/issues/6084	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6084/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6084/comments	https://api.github.com/repos/huggingface/datasets/issues/6084/events	https://github.com/huggingface/datasets/issues/6084	1,824,896,761	I_kwDODunzps5sxbb5	6,084	Changing pixel values of images in the Winoground dataset	{ "login": "ZitengWangNYU", "id": 90359895, "node_id": "MDQ6VXNlcjkwMzU5ODk1", "avatar_url": "https://avatars.githubusercontent.com/u/90359895?v=4", "gravatar_id": "", "url": "https://api.github.com/users/ZitengWangNYU", "html_url": "https://github.com/ZitengWangNYU", "followers_url": "https://api.github.com/users/ZitengWangNYU/followers", "following_url": "https://api.github.com/users/ZitengWangNYU/following{/other_user}", "gists_url": "https://api.github.com/users/ZitengWangNYU/gists{/gist_id}", "starred_url": "https://api.github.com/users/ZitengWangNYU/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/ZitengWangNYU/subscriptions", "organizations_url": "https://api.github.com/users/ZitengWangNYU/orgs", "repos_url": "https://api.github.com/users/ZitengWangNYU/repos", "events_url": "https://api.github.com/users/ZitengWangNYU/events{/privacy}", "received_events_url": "https://api.github.com/users/ZitengWangNYU/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	null	[]	2023-07-27T17:55:35	2023-07-27T17:55:35	null	NONE	null	null	null	Hi, as I followed the instructions, with lasted "datasets" version: " from datasets import load_dataset examples = load_dataset('facebook/winoground', use_auth_token=<YOUR USER ACCESS TOKEN>) " I got slightly different datasets in colab and in my hpc environment. Specifically, the pixel values of images are slightly different. I thought it was due to the package version difference, but today's morning I found out that my winoground dataset in colab became the same with the one in my hpc environment. The dataset in colab can produce the correct result but now it is gone as well. Can you help me with this? What causes the datasets to have the wrong pixel values?	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6084/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6084/timeline	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6083	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6083/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6083/comments	https://api.github.com/repos/huggingface/datasets/issues/6083/events	https://github.com/huggingface/datasets/pull/6083	1,824,832,348	PR_kwDODunzps5WkgAI	6,083	set dev version	{ "login": "lhoestq", "id": 42851186, "node_id": "MDQ6VXNlcjQyODUxMTg2", "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "gravatar_id": "", "url": "https://api.github.com/users/lhoestq", "html_url": "https://github.com/lhoestq", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "repos_url": "https://api.github.com/users/lhoestq/repos", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6083). All of your documentation changes will be reflected on that endpoint.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006049 / 0.011353 (-0.005304) \| 0.003698 / 0.011008 (-0.007310) \| 0.080614 / 0.038508 (0.042106) \| 0.060955 / 0.023109 (0.037846) \| 0.337119 / 0.275898 (0.061221) \| 0.369544 / 0.323480 (0.046064) \| 0.004681 / 0.007986 (-0.003305) \| 0.002892 / 0.004328 (-0.001436) \| 0.062907 / 0.004250 (0.058657) \| 0.049235 / 0.037052 (0.012183) \| 0.338842 / 0.258489 (0.080353) \| 0.371172 / 0.293841 (0.077331) \| 0.027016 / 0.128546 (-0.101530) \| 0.007940 / 0.075646 (-0.067706) \| 0.260902 / 0.419271 (-0.158369) \| 0.044566 / 0.043533 (0.001034) \| 0.342354 / 0.255139 (0.087215) \| 0.359829 / 0.283200 (0.076629) \| 0.020801 / 0.141683 (-0.120881) \| 1.444111 / 1.452155 (-0.008044) \| 1.515595 / 1.492716 (0.022879) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.183446 / 0.018006 (0.165439) \| 0.437071 / 0.000490 (0.436581) \| 0.003124 / 0.000200 (0.002924) \| 0.000067 / 0.000054 (0.000013) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023760 / 0.037411 (-0.013651) \| 0.072812 / 0.014526 (0.058286) \| 0.082790 / 0.176557 (-0.093766) \| 0.146330 / 0.737135 (-0.590805) \| 0.084469 / 0.296338 (-0.211870) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.395215 / 0.215209 (0.180006) \| 3.953023 / 2.077655 (1.875369) \| 1.914268 / 1.504120 (0.410148) \| 1.710195 / 1.541195 (0.169001) \| 1.782594 / 1.468490 (0.314104) \| 0.503651 / 4.584777 (-4.081126) \| 3.039656 / 3.745712 (-0.706056) \| 4.364691 / 5.269862 (-0.905171) \| 2.597762 / 4.565676 (-1.967915) \| 0.057384 / 0.424275 (-0.366891) \| 0.006419 / 0.007607 (-0.001188) \| 0.467214 / 0.226044 (0.241169) \| 4.661425 / 2.268929 (2.392497) \| 2.341957 / 55.444624 (-53.102667) \| 1.977598 / 6.876477 (-4.898878) \| 2.178005 / 2.142072 (0.035933) \| 0.588492 / 4.805227 (-4.216735) \| 0.124972 / 6.500664 (-6.375692) \| 0.060902 / 0.075469 (-0.014567) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.243092 / 1.841788 (-0.598695) \| 18.369971 / 8.074308 (10.295663) \| 13.939700 / 10.191392 (3.748308) \| 0.149275 / 0.680424 (-0.531149) \| 0.016873 / 0.534201 (-0.517328) \| 0.334245 / 0.579283 (-0.245038) \| 0.353832 / 0.434364 (-0.080532) \| 0.382720 / 0.540337 (-0.157617) \| 0.534634 / 1.386936 (-0.852302) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005933 / 0.011353 (-0.005420) \| 0.003695 / 0.011008 (-0.007313) \| 0.063457 / 0.038508 (0.024949) \| 0.062347 / 0.023109 (0.039238) \| 0.412370 / 0.275898 (0.136472) \| 0.450399 / 0.323480 (0.126920) \| 0.004627 / 0.007986 (-0.003358) \| 0.002822 / 0.004328 (-0.001507) \| 0.063819 / 0.004250 (0.059569) \| 0.049154 / 0.037052 (0.012101) \| 0.428196 / 0.258489 (0.169707) \| 0.464109 / 0.293841 (0.170268) \| 0.026967 / 0.128546 (-0.101579) \| 0.007876 / 0.075646 (-0.067770) \| 0.068479 / 0.419271 (-0.350793) \| 0.041080 / 0.043533 (-0.002453) \| 0.399817 / 0.255139 (0.144678) \| 0.426900 / 0.283200 (0.143701) \| 0.019931 / 0.141683 (-0.121752) \| 1.461642 / 1.452155 (0.009487) \| 1.529314 / 1.492716 (0.036598) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.230256 / 0.018006 (0.212249) \| 0.423442 / 0.000490 (0.422952) \| 0.002492 / 0.000200 (0.002292) \| 0.000072 / 0.000054 (0.000018) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025798 / 0.037411 (-0.011613) \| 0.077361 / 0.014526 (0.062836) \| 0.088454 / 0.176557 (-0.088102) \| 0.142137 / 0.737135 (-0.594998) \| 0.088213 / 0.296338 (-0.208125) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.417656 / 0.215209 (0.202447) \| 4.157095 / 2.077655 (2.079440) \| 2.132863 / 1.504120 (0.628743) \| 1.967220 / 1.541195 (0.426025) \| 2.020505 / 1.468490 (0.552015) \| 0.496835 / 4.584777 (-4.087942) \| 2.989251 / 3.745712 (-0.756462) \| 2.849315 / 5.269862 (-2.420546) \| 1.848941 / 4.565676 (-2.716736) \| 0.057307 / 0.424275 (-0.366968) \| 0.006825 / 0.007607 (-0.000782) \| 0.489103 / 0.226044 (0.263059) \| 4.904776 / 2.268929 (2.635847) \| 2.593914 / 55.444624 (-52.850710) \| 2.253384 / 6.876477 (-4.623093) \| 2.426384 / 2.142072 (0.284312) \| 0.592467 / 4.805227 (-4.212760) \| 0.126122 / 6.500664 (-6.374542) \| 0.063160 / 0.075469 (-0.012309) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.313020 / 1.841788 (-0.528768) \| 18.343984 / 8.074308 (10.269676) \| 13.763060 / 10.191392 (3.571668) \| 0.146312 / 0.680424 (-0.534111) \| 0.016980 / 0.534201 (-0.517221) \| 0.339572 / 0.579283 (-0.239711) \| 0.351310 / 0.434364 (-0.083054) \| 0.397616 / 0.540337 (-0.142721) \| 0.536879 / 1.386936 (-0.850057) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#73bed12ecda17d1573fd3bf73ed5db24d3622f86 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009979 / 0.011353 (-0.001374) \| 0.005024 / 0.011008 (-0.005984) \| 0.096566 / 0.038508 (0.058058) \| 0.081181 / 0.023109 (0.058072) \| 0.398415 / 0.275898 (0.122517) \| 0.513971 / 0.323480 (0.190491) \| 0.006716 / 0.007986 (-0.001269) \| 0.004350 / 0.004328 (0.000022) \| 0.071418 / 0.004250 (0.067168) \| 0.065002 / 0.037052 (0.027949) \| 0.424791 / 0.258489 (0.166302) \| 0.442369 / 0.293841 (0.148528) \| 0.054540 / 0.128546 (-0.074007) \| 0.014067 / 0.075646 (-0.061580) \| 0.368930 / 0.419271 (-0.050341) \| 0.082468 / 0.043533 (0.038935) \| 0.419875 / 0.255139 (0.164736) \| 0.508308 / 0.283200 (0.225108) \| 0.050411 / 0.141683 (-0.091272) \| 1.582271 / 1.452155 (0.130116) \| 1.842033 / 1.492716 (0.349317) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.290427 / 0.018006 (0.272420) \| 0.594736 / 0.000490 (0.594246) \| 0.007058 / 0.000200 (0.006858) \| 0.000149 / 0.000054 (0.000095) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027085 / 0.037411 (-0.010326) \| 0.087626 / 0.014526 (0.073101) \| 0.094299 / 0.176557 (-0.082257) \| 0.160169 / 0.737135 (-0.576966) \| 0.101474 / 0.296338 (-0.194864) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.545845 / 0.215209 (0.330636) \| 5.674389 / 2.077655 (3.596734) \| 2.489065 / 1.504120 (0.984945) \| 2.166674 / 1.541195 (0.625479) \| 2.166925 / 1.468490 (0.698434) \| 0.791244 / 4.584777 (-3.793533) \| 4.944878 / 3.745712 (1.199165) \| 4.121628 / 5.269862 (-1.148234) \| 2.701262 / 4.565676 (-1.864415) \| 0.087609 / 0.424275 (-0.336666) \| 0.006945 / 0.007607 (-0.000662) \| 0.668478 / 0.226044 (0.442434) \| 6.552813 / 2.268929 (4.283885) \| 3.164698 / 55.444624 (-52.279927) \| 2.447333 / 6.876477 (-4.429144) \| 2.608271 / 2.142072 (0.466198) \| 0.954202 / 4.805227 (-3.851025) \| 0.187730 / 6.500664 (-6.312934) \| 0.063229 / 0.075469 (-0.012240) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.461042 / 1.841788 (-0.380746) \| 21.601409 / 8.074308 (13.527101) \| 18.553604 / 10.191392 (8.362212) \| 0.234571 / 0.680424 (-0.445853) \| 0.027119 / 0.534201 (-0.507082) \| 0.423448 / 0.579283 (-0.155835) \| 0.556397 / 0.434364 (0.122033) \| 0.493958 / 0.540337 (-0.046379) \| 0.711345 / 1.386936 (-0.675591) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008637 / 0.011353 (-0.002716) \| 0.014450 / 0.011008 (0.003442) \| 0.084135 / 0.038508 (0.045627) \| 0.080513 / 0.023109 (0.057403) \| 0.557941 / 0.275898 (0.282042) \| 0.563199 / 0.323480 (0.239719) \| 0.006475 / 0.007986 (-0.001510) \| 0.004407 / 0.004328 (0.000078) \| 0.088537 / 0.004250 (0.084287) \| 0.060871 / 0.037052 (0.023819) \| 0.593077 / 0.258489 (0.334588) \| 0.615572 / 0.293841 (0.321732) \| 0.050157 / 0.128546 (-0.078389) \| 0.014313 / 0.075646 (-0.061333) \| 0.091784 / 0.419271 (-0.327487) \| 0.065649 / 0.043533 (0.022116) \| 0.532569 / 0.255139 (0.277430) \| 0.580775 / 0.283200 (0.297575) \| 0.036434 / 0.141683 (-0.105249) \| 2.080051 / 1.452155 (0.627896) \| 1.907430 / 1.492716 (0.414713) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.297763 / 0.018006 (0.279757) \| 0.670408 / 0.000490 (0.669918) \| 0.000467 / 0.000200 (0.000267) \| 0.000082 / 0.000054 (0.000027) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030297 / 0.037411 (-0.007114) \| 0.100310 / 0.014526 (0.085784) \| 0.113158 / 0.176557 (-0.063398) \| 0.149599 / 0.737135 (-0.587536) \| 0.102620 / 0.296338 (-0.193718) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.616588 / 0.215209 (0.401379) \| 6.572262 / 2.077655 (4.494608) \| 2.830748 / 1.504120 (1.326628) \| 2.478441 / 1.541195 (0.937246) \| 2.573017 / 1.468490 (1.104527) \| 0.844154 / 4.584777 (-3.740623) \| 5.161625 / 3.745712 (1.415913) \| 4.541114 / 5.269862 (-0.728748) \| 2.907804 / 4.565676 (-1.657872) \| 0.097044 / 0.424275 (-0.327231) \| 0.008692 / 0.007607 (0.001085) \| 0.806640 / 0.226044 (0.580595) \| 7.620521 / 2.268929 (5.351593) \| 3.587100 / 55.444624 (-51.857524) \| 2.901319 / 6.876477 (-3.975157) \| 3.091288 / 2.142072 (0.949215) \| 1.056109 / 4.805227 (-3.749118) \| 0.209860 / 6.500664 (-6.290804) \| 0.079575 / 0.075469 (0.004106) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.966194 / 1.841788 (0.124407) \| 28.040515 / 8.074308 (19.966207) \| 25.848647 / 10.191392 (15.657255) \| 0.255472 / 0.680424 (-0.424951) \| 0.036154 / 0.534201 (-0.498046) \| 0.515168 / 0.579283 (-0.064115) \| 0.696092 / 0.434364 (0.261728) \| 0.602712 / 0.540337 (0.062374) \| 0.781091 / 1.386936 (-0.605845) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6f641aca7fbb1f21da48c087a5c10e76f4c6be35 \"CML watermark\")\n" ]	2023-07-27T17:10:41	2023-07-27T17:22:05	2023-07-27T17:11:01	MEMBER	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6083", "html_url": "https://github.com/huggingface/datasets/pull/6083", "diff_url": "https://github.com/huggingface/datasets/pull/6083.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6083.patch", "merged_at": "2023-07-27T17:11:01" }	null	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6083/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6083/timeline	null	null	true
https://api.github.com/repos/huggingface/datasets/issues/6082	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6082/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6082/comments	https://api.github.com/repos/huggingface/datasets/issues/6082/events	https://github.com/huggingface/datasets/pull/6082	1,824,819,672	PR_kwDODunzps5WkdIn	6,082	Release: 2.14.1	{ "login": "lhoestq", "id": 42851186, "node_id": "MDQ6VXNlcjQyODUxMTg2", "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "gravatar_id": "", "url": "https://api.github.com/users/lhoestq", "html_url": "https://github.com/lhoestq", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "repos_url": "https://api.github.com/users/lhoestq/repos", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6082). All of your documentation changes will be reflected on that endpoint.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007215 / 0.011353 (-0.004138) \| 0.004101 / 0.011008 (-0.006907) \| 0.085884 / 0.038508 (0.047376) \| 0.085375 / 0.023109 (0.062266) \| 0.351610 / 0.275898 (0.075712) \| 0.399284 / 0.323480 (0.075804) \| 0.005598 / 0.007986 (-0.002388) \| 0.003405 / 0.004328 (-0.000923) \| 0.064906 / 0.004250 (0.060656) \| 0.059000 / 0.037052 (0.021948) \| 0.354589 / 0.258489 (0.096100) \| 0.406070 / 0.293841 (0.112229) \| 0.031627 / 0.128546 (-0.096919) \| 0.008597 / 0.075646 (-0.067049) \| 0.291050 / 0.419271 (-0.128221) \| 0.054120 / 0.043533 (0.010587) \| 0.366242 / 0.255139 (0.111103) \| 0.375975 / 0.283200 (0.092776) \| 0.025608 / 0.141683 (-0.116074) \| 1.473514 / 1.452155 (0.021359) \| 1.543226 / 1.492716 (0.050510) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.198068 / 0.018006 (0.180062) \| 0.450583 / 0.000490 (0.450093) \| 0.005368 / 0.000200 (0.005168) \| 0.000102 / 0.000054 (0.000047) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028323 / 0.037411 (-0.009089) \| 0.089058 / 0.014526 (0.074533) \| 0.097718 / 0.176557 (-0.078839) \| 0.154546 / 0.737135 (-0.582590) \| 0.098224 / 0.296338 (-0.198115) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.386292 / 0.215209 (0.171083) \| 3.846222 / 2.077655 (1.768567) \| 1.858695 / 1.504120 (0.354575) \| 1.685885 / 1.541195 (0.144690) \| 1.790727 / 1.468490 (0.322237) \| 0.486771 / 4.584777 (-4.098006) \| 3.658363 / 3.745712 (-0.087349) \| 5.345236 / 5.269862 (0.075374) \| 3.215942 / 4.565676 (-1.349734) \| 0.057580 / 0.424275 (-0.366695) \| 0.007382 / 0.007607 (-0.000225) \| 0.464174 / 0.226044 (0.238129) \| 4.640848 / 2.268929 (2.371920) \| 2.383152 / 55.444624 (-53.061472) \| 2.013288 / 6.876477 (-4.863188) \| 2.244142 / 2.142072 (0.102069) \| 0.585408 / 4.805227 (-4.219819) \| 0.134698 / 6.500664 (-6.365966) \| 0.060641 / 0.075469 (-0.014828) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.258414 / 1.841788 (-0.583374) \| 19.825848 / 8.074308 (11.751540) \| 14.644025 / 10.191392 (4.452633) \| 0.169198 / 0.680424 (-0.511226) \| 0.018180 / 0.534201 (-0.516021) \| 0.395100 / 0.579283 (-0.184183) \| 0.411543 / 0.434364 (-0.022821) \| 0.463364 / 0.540337 (-0.076973) \| 0.628613 / 1.386936 (-0.758323) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006860 / 0.011353 (-0.004493) \| 0.003981 / 0.011008 (-0.007027) \| 0.065589 / 0.038508 (0.027081) \| 0.082460 / 0.023109 (0.059350) \| 0.362980 / 0.275898 (0.087082) \| 0.394837 / 0.323480 (0.071357) \| 0.005298 / 0.007986 (-0.002688) \| 0.003372 / 0.004328 (-0.000957) \| 0.064918 / 0.004250 (0.060667) \| 0.058033 / 0.037052 (0.020981) \| 0.367259 / 0.258489 (0.108770) \| 0.403122 / 0.293841 (0.109281) \| 0.031566 / 0.128546 (-0.096980) \| 0.008583 / 0.075646 (-0.067063) \| 0.071287 / 0.419271 (-0.347984) \| 0.049586 / 0.043533 (0.006053) \| 0.359252 / 0.255139 (0.104113) \| 0.378519 / 0.283200 (0.095319) \| 0.023412 / 0.141683 (-0.118271) \| 1.494522 / 1.452155 (0.042367) \| 1.559176 / 1.492716 (0.066460) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.228396 / 0.018006 (0.210390) \| 0.441865 / 0.000490 (0.441375) \| 0.000395 / 0.000200 (0.000195) \| 0.000054 / 0.000054 (-0.000000) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031169 / 0.037411 (-0.006242) \| 0.093427 / 0.014526 (0.078901) \| 0.100673 / 0.176557 (-0.075883) \| 0.152817 / 0.737135 (-0.584319) \| 0.102226 / 0.296338 (-0.194112) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.437032 / 0.215209 (0.221823) \| 4.376078 / 2.077655 (2.298423) \| 2.346928 / 1.504120 (0.842808) \| 2.168573 / 1.541195 (0.627378) \| 2.261024 / 1.468490 (0.792534) \| 0.497080 / 4.584777 (-4.087697) \| 3.594402 / 3.745712 (-0.151310) \| 5.090361 / 5.269862 (-0.179501) \| 3.034750 / 4.565676 (-1.530927) \| 0.058538 / 0.424275 (-0.365737) \| 0.007892 / 0.007607 (0.000285) \| 0.517643 / 0.226044 (0.291598) \| 5.173174 / 2.268929 (2.904246) \| 2.825917 / 55.444624 (-52.618708) \| 2.542593 / 6.876477 (-4.333884) \| 2.716290 / 2.142072 (0.574218) \| 0.598253 / 4.805227 (-4.206974) \| 0.135610 / 6.500664 (-6.365054) \| 0.062113 / 0.075469 (-0.013356) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.389554 / 1.841788 (-0.452233) \| 20.412868 / 8.074308 (12.338560) \| 14.539988 / 10.191392 (4.348596) \| 0.162046 / 0.680424 (-0.518378) \| 0.018508 / 0.534201 (-0.515693) \| 0.398840 / 0.579283 (-0.180443) \| 0.400902 / 0.434364 (-0.033462) \| 0.463647 / 0.540337 (-0.076691) \| 0.612921 / 1.386936 (-0.774015) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#45bef1810d9341ba4cb27547d748fddb97843792 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005943 / 0.011353 (-0.005410) \| 0.003582 / 0.011008 (-0.007426) \| 0.080030 / 0.038508 (0.041522) \| 0.057458 / 0.023109 (0.034349) \| 0.390783 / 0.275898 (0.114885) \| 0.430926 / 0.323480 (0.107446) \| 0.003207 / 0.007986 (-0.004778) \| 0.003592 / 0.004328 (-0.000737) \| 0.062468 / 0.004250 (0.058217) \| 0.046739 / 0.037052 (0.009687) \| 0.394343 / 0.258489 (0.135854) \| 0.435912 / 0.293841 (0.142071) \| 0.026812 / 0.128546 (-0.101734) \| 0.007954 / 0.075646 (-0.067692) \| 0.261415 / 0.419271 (-0.157857) \| 0.044665 / 0.043533 (0.001132) \| 0.403454 / 0.255139 (0.148315) \| 0.418946 / 0.283200 (0.135747) \| 0.022247 / 0.141683 (-0.119436) \| 1.456387 / 1.452155 (0.004232) \| 1.508234 / 1.492716 (0.015518) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.182487 / 0.018006 (0.164480) \| 0.416343 / 0.000490 (0.415854) \| 0.001404 / 0.000200 (0.001204) \| 0.000062 / 0.000054 (0.000007) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023643 / 0.037411 (-0.013768) \| 0.071798 / 0.014526 (0.057272) \| 0.083623 / 0.176557 (-0.092933) \| 0.146023 / 0.737135 (-0.591112) \| 0.083094 / 0.296338 (-0.213245) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.417484 / 0.215209 (0.202275) \| 4.157393 / 2.077655 (2.079738) \| 1.950438 / 1.504120 (0.446318) \| 1.766639 / 1.541195 (0.225444) \| 1.807382 / 1.468490 (0.338892) \| 0.496061 / 4.584777 (-4.088716) \| 2.975001 / 3.745712 (-0.770711) \| 3.340608 / 5.269862 (-1.929254) \| 2.236293 / 4.565676 (-2.329384) \| 0.056946 / 0.424275 (-0.367329) \| 0.006506 / 0.007607 (-0.001101) \| 0.480377 / 0.226044 (0.254332) \| 4.788525 / 2.268929 (2.519597) \| 2.430139 / 55.444624 (-53.014485) \| 2.154145 / 6.876477 (-4.722332) \| 2.321623 / 2.142072 (0.179551) \| 0.584040 / 4.805227 (-4.221188) \| 0.124508 / 6.500664 (-6.376156) \| 0.060828 / 0.075469 (-0.014641) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.201641 / 1.841788 (-0.640146) \| 18.066232 / 8.074308 (9.991924) \| 14.022304 / 10.191392 (3.830912) \| 0.146573 / 0.680424 (-0.533850) \| 0.016892 / 0.534201 (-0.517308) \| 0.333259 / 0.579283 (-0.246024) \| 0.357795 / 0.434364 (-0.076568) \| 0.391265 / 0.540337 (-0.149072) \| 0.551378 / 1.386936 (-0.835558) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005706 / 0.011353 (-0.005647) \| 0.003448 / 0.011008 (-0.007560) \| 0.063146 / 0.038508 (0.024638) \| 0.056292 / 0.023109 (0.033183) \| 0.355533 / 0.275898 (0.079635) \| 0.394996 / 0.323480 (0.071517) \| 0.004270 / 0.007986 (-0.003716) \| 0.002790 / 0.004328 (-0.001538) \| 0.063033 / 0.004250 (0.058783) \| 0.044684 / 0.037052 (0.007631) \| 0.370621 / 0.258489 (0.112132) \| 0.401074 / 0.293841 (0.107233) \| 0.026737 / 0.128546 (-0.101809) \| 0.007872 / 0.075646 (-0.067774) \| 0.068815 / 0.419271 (-0.350457) \| 0.040976 / 0.043533 (-0.002557) \| 0.370733 / 0.255139 (0.115594) \| 0.387418 / 0.283200 (0.104218) \| 0.018854 / 0.141683 (-0.122829) \| 1.479834 / 1.452155 (0.027680) \| 1.536388 / 1.492716 (0.043672) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.222125 / 0.018006 (0.204119) \| 0.408007 / 0.000490 (0.407517) \| 0.000367 / 0.000200 (0.000167) \| 0.000055 / 0.000054 (0.000001) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025100 / 0.037411 (-0.012311) \| 0.076617 / 0.014526 (0.062091) \| 0.088311 / 0.176557 (-0.088246) \| 0.143785 / 0.737135 (-0.593350) \| 0.088349 / 0.296338 (-0.207989) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.419246 / 0.215209 (0.204037) \| 4.172413 / 2.077655 (2.094759) \| 2.199355 / 1.504120 (0.695235) \| 2.025158 / 1.541195 (0.483963) \| 2.074491 / 1.468490 (0.606001) \| 0.495893 / 4.584777 (-4.088884) \| 2.998858 / 3.745712 (-0.746854) \| 2.770531 / 5.269862 (-2.499331) \| 1.817497 / 4.565676 (-2.748179) \| 0.057317 / 0.424275 (-0.366958) \| 0.006723 / 0.007607 (-0.000884) \| 0.491062 / 0.226044 (0.265017) \| 4.906155 / 2.268929 (2.637226) \| 2.654916 / 55.444624 (-52.789708) \| 2.299873 / 6.876477 (-4.576604) \| 2.451438 / 2.142072 (0.309366) \| 0.585048 / 4.805227 (-4.220179) \| 0.124778 / 6.500664 (-6.375886) \| 0.062067 / 0.075469 (-0.013402) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.298239 / 1.841788 (-0.543549) \| 18.090238 / 8.074308 (10.015930) \| 13.822568 / 10.191392 (3.631176) \| 0.130560 / 0.680424 (-0.549864) \| 0.016662 / 0.534201 (-0.517539) \| 0.333337 / 0.579283 (-0.245946) \| 0.348493 / 0.434364 (-0.085871) \| 0.386049 / 0.540337 (-0.154289) \| 0.511156 / 1.386936 (-0.875780) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#029956a347b0306cd27f693e12cf9a82acf4ef80 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006014 / 0.011353 (-0.005339) \| 0.003623 / 0.011008 (-0.007385) \| 0.080500 / 0.038508 (0.041992) \| 0.057713 / 0.023109 (0.034603) \| 0.325976 / 0.275898 (0.050078) \| 0.359986 / 0.323480 (0.036506) \| 0.004709 / 0.007986 (-0.003277) \| 0.002933 / 0.004328 (-0.001395) \| 0.063457 / 0.004250 (0.059207) \| 0.047514 / 0.037052 (0.010462) \| 0.331629 / 0.258489 (0.073140) \| 0.382048 / 0.293841 (0.088207) \| 0.026949 / 0.128546 (-0.101597) \| 0.008043 / 0.075646 (-0.067604) \| 0.262152 / 0.419271 (-0.157119) \| 0.045271 / 0.043533 (0.001738) \| 0.333355 / 0.255139 (0.078216) \| 0.347996 / 0.283200 (0.064796) \| 0.020814 / 0.141683 (-0.120868) \| 1.460723 / 1.452155 (0.008568) \| 1.488845 / 1.492716 (-0.003872) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.193735 / 0.018006 (0.175728) \| 0.431433 / 0.000490 (0.430943) \| 0.002494 / 0.000200 (0.002294) \| 0.000066 / 0.000054 (0.000012) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023762 / 0.037411 (-0.013650) \| 0.072680 / 0.014526 (0.058154) \| 0.081687 / 0.176557 (-0.094869) \| 0.143224 / 0.737135 (-0.593911) \| 0.083083 / 0.296338 (-0.213255) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.397393 / 0.215209 (0.182184) \| 3.954643 / 2.077655 (1.876989) \| 1.950038 / 1.504120 (0.445919) \| 1.760551 / 1.541195 (0.219357) \| 1.871165 / 1.468490 (0.402675) \| 0.508645 / 4.584777 (-4.076132) \| 3.114379 / 3.745712 (-0.631333) \| 3.474554 / 5.269862 (-1.795307) \| 2.090126 / 4.565676 (-2.475551) \| 0.058008 / 0.424275 (-0.366267) \| 0.006465 / 0.007607 (-0.001142) \| 0.475009 / 0.226044 (0.248965) \| 4.767981 / 2.268929 (2.499052) \| 2.372050 / 55.444624 (-53.072574) \| 2.038094 / 6.876477 (-4.838383) \| 2.072819 / 2.142072 (-0.069253) \| 0.591913 / 4.805227 (-4.213314) \| 0.125002 / 6.500664 (-6.375662) \| 0.060055 / 0.075469 (-0.015414) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.234171 / 1.841788 (-0.607617) \| 18.121476 / 8.074308 (10.047168) \| 13.727313 / 10.191392 (3.535921) \| 0.136021 / 0.680424 (-0.544402) \| 0.016505 / 0.534201 (-0.517696) \| 0.331400 / 0.579283 (-0.247883) \| 0.346019 / 0.434364 (-0.088345) \| 0.378985 / 0.540337 (-0.161353) \| 0.522606 / 1.386936 (-0.864330) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006035 / 0.011353 (-0.005318) \| 0.003584 / 0.011008 (-0.007425) \| 0.061953 / 0.038508 (0.023445) \| 0.059416 / 0.023109 (0.036307) \| 0.359380 / 0.275898 (0.083482) \| 0.396842 / 0.323480 (0.073363) \| 0.004716 / 0.007986 (-0.003269) \| 0.002825 / 0.004328 (-0.001504) \| 0.061697 / 0.004250 (0.057447) \| 0.049009 / 0.037052 (0.011956) \| 0.363099 / 0.258489 (0.104610) \| 0.403672 / 0.293841 (0.109831) \| 0.027722 / 0.128546 (-0.100824) \| 0.007966 / 0.075646 (-0.067680) \| 0.067455 / 0.419271 (-0.351816) \| 0.042530 / 0.043533 (-0.001003) \| 0.361257 / 0.255139 (0.106118) \| 0.388957 / 0.283200 (0.105758) \| 0.021845 / 0.141683 (-0.119838) \| 1.431989 / 1.452155 (-0.020166) \| 1.503131 / 1.492716 (0.010415) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.241493 / 0.018006 (0.223487) \| 0.429319 / 0.000490 (0.428829) \| 0.002604 / 0.000200 (0.002404) \| 0.000074 / 0.000054 (0.000019) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026227 / 0.037411 (-0.011184) \| 0.077177 / 0.014526 (0.062651) \| 0.085840 / 0.176557 (-0.090717) \| 0.142280 / 0.737135 (-0.594855) \| 0.088465 / 0.296338 (-0.207873) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.434912 / 0.215209 (0.219703) \| 4.339664 / 2.077655 (2.262009) \| 2.242495 / 1.504120 (0.738375) \| 2.091353 / 1.541195 (0.550159) \| 2.161425 / 1.468490 (0.692935) \| 0.501647 / 4.584777 (-4.083130) \| 3.075326 / 3.745712 (-0.670386) \| 4.091557 / 5.269862 (-1.178304) \| 2.776425 / 4.565676 (-1.789251) \| 0.057338 / 0.424275 (-0.366937) \| 0.006767 / 0.007607 (-0.000840) \| 0.506882 / 0.226044 (0.280837) \| 5.059074 / 2.268929 (2.790146) \| 2.706665 / 55.444624 (-52.737959) \| 2.370253 / 6.876477 (-4.506224) \| 2.505421 / 2.142072 (0.363348) \| 0.590289 / 4.805227 (-4.214938) \| 0.125990 / 6.500664 (-6.374674) \| 0.062778 / 0.075469 (-0.012691) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.361287 / 1.841788 (-0.480501) \| 18.500726 / 8.074308 (10.426418) \| 13.844459 / 10.191392 (3.653067) \| 0.144416 / 0.680424 (-0.536008) \| 0.016987 / 0.534201 (-0.517214) \| 0.336237 / 0.579283 (-0.243046) \| 0.357116 / 0.434364 (-0.077248) \| 0.402062 / 0.540337 (-0.138275) \| 0.543066 / 1.386936 (-0.843870) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#029956a347b0306cd27f693e12cf9a82acf4ef80 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007559 / 0.011353 (-0.003794) \| 0.004379 / 0.011008 (-0.006629) \| 0.089702 / 0.038508 (0.051194) \| 0.065104 / 0.023109 (0.041995) \| 0.362016 / 0.275898 (0.086118) \| 0.376768 / 0.323480 (0.053288) \| 0.006538 / 0.007986 (-0.001447) \| 0.004167 / 0.004328 (-0.000161) \| 0.074138 / 0.004250 (0.069888) \| 0.052753 / 0.037052 (0.015701) \| 0.366367 / 0.258489 (0.107878) \| 0.389121 / 0.293841 (0.095280) \| 0.042820 / 0.128546 (-0.085727) \| 0.012560 / 0.075646 (-0.063086) \| 0.359235 / 0.419271 (-0.060037) \| 0.074250 / 0.043533 (0.030718) \| 0.384051 / 0.255139 (0.128912) \| 0.385450 / 0.283200 (0.102250) \| 0.046270 / 0.141683 (-0.095413) \| 1.593275 / 1.452155 (0.141120) \| 1.704207 / 1.492716 (0.211490) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.249390 / 0.018006 (0.231384) \| 0.614347 / 0.000490 (0.613857) \| 0.012641 / 0.000200 (0.012441) \| 0.000126 / 0.000054 (0.000072) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029099 / 0.037411 (-0.008312) \| 0.090966 / 0.014526 (0.076440) \| 0.102273 / 0.176557 (-0.074284) \| 0.167564 / 0.737135 (-0.569571) \| 0.106118 / 0.296338 (-0.190220) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.536122 / 0.215209 (0.320913) \| 5.448464 / 2.077655 (3.370809) \| 2.461977 / 1.504120 (0.957857) \| 2.081506 / 1.541195 (0.540311) \| 2.091509 / 1.468490 (0.623019) \| 0.810307 / 4.584777 (-3.774470) \| 5.161304 / 3.745712 (1.415592) \| 4.525070 / 5.269862 (-0.744792) \| 2.886313 / 4.565676 (-1.679363) \| 0.093992 / 0.424275 (-0.330283) \| 0.008516 / 0.007607 (0.000909) \| 0.691978 / 0.226044 (0.465934) \| 6.834665 / 2.268929 (4.565737) \| 3.284355 / 55.444624 (-52.160270) \| 2.496803 / 6.876477 (-4.379674) \| 2.814387 / 2.142072 (0.672315) \| 0.985300 / 4.805227 (-3.819928) \| 0.210343 / 6.500664 (-6.290321) \| 0.075459 / 0.075469 (-0.000010) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.436073 / 1.841788 (-0.405714) \| 22.722401 / 8.074308 (14.648093) \| 19.988521 / 10.191392 (9.797129) \| 0.229757 / 0.680424 (-0.450667) \| 0.029672 / 0.534201 (-0.504529) \| 0.479914 / 0.579283 (-0.099369) \| 0.605106 / 0.434364 (0.170743) \| 0.511668 / 0.540337 (-0.028670) \| 0.800281 / 1.386936 (-0.586655) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008665 / 0.011353 (-0.002688) \| 0.006009 / 0.011008 (-0.004999) \| 0.073377 / 0.038508 (0.034869) \| 0.077188 / 0.023109 (0.054079) \| 0.451422 / 0.275898 (0.175524) \| 0.484640 / 0.323480 (0.161160) \| 0.006266 / 0.007986 (-0.001719) \| 0.004129 / 0.004328 (-0.000200) \| 0.063102 / 0.004250 (0.058851) \| 0.064653 / 0.037052 (0.027601) \| 0.439521 / 0.258489 (0.181032) \| 0.458964 / 0.293841 (0.165123) \| 0.046018 / 0.128546 (-0.082528) \| 0.014109 / 0.075646 (-0.061537) \| 0.095727 / 0.419271 (-0.323544) \| 0.070133 / 0.043533 (0.026600) \| 0.440143 / 0.255139 (0.185004) \| 0.502468 / 0.283200 (0.219269) \| 0.034582 / 0.141683 (-0.107101) \| 1.656282 / 1.452155 (0.204127) \| 1.784641 / 1.492716 (0.291925) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.303111 / 0.018006 (0.285105) \| 0.599194 / 0.000490 (0.598705) \| 0.000411 / 0.000200 (0.000211) \| 0.000073 / 0.000054 (0.000018) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033061 / 0.037411 (-0.004350) \| 0.096073 / 0.014526 (0.081548) \| 0.095347 / 0.176557 (-0.081209) \| 0.161004 / 0.737135 (-0.576131) \| 0.111544 / 0.296338 (-0.184794) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.615695 / 0.215209 (0.400486) \| 5.794243 / 2.077655 (3.716588) \| 2.594720 / 1.504120 (1.090600) \| 2.566255 / 1.541195 (1.025060) \| 2.573653 / 1.468490 (1.105163) \| 0.873653 / 4.584777 (-3.711124) \| 5.353323 / 3.745712 (1.607611) \| 4.604974 / 5.269862 (-0.664887) \| 2.901282 / 4.565676 (-1.664394) \| 0.099614 / 0.424275 (-0.324661) \| 0.010368 / 0.007607 (0.002761) \| 0.775490 / 0.226044 (0.549446) \| 7.245449 / 2.268929 (4.976520) \| 3.740165 / 55.444624 (-51.704459) \| 2.986132 / 6.876477 (-3.890345) \| 3.092510 / 2.142072 (0.950438) \| 1.022461 / 4.805227 (-3.782766) \| 0.212137 / 6.500664 (-6.288527) \| 0.084534 / 0.075469 (0.009065) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.687983 / 1.841788 (-0.153805) \| 23.491808 / 8.074308 (15.417500) \| 20.722165 / 10.191392 (10.530773) \| 0.231011 / 0.680424 (-0.449413) \| 0.028309 / 0.534201 (-0.505892) \| 0.436911 / 0.579283 (-0.142372) \| 0.583126 / 0.434364 (0.148762) \| 0.559712 / 0.540337 (0.019374) \| 0.820645 / 1.386936 (-0.566291) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#029956a347b0306cd27f693e12cf9a82acf4ef80 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006538 / 0.011353 (-0.004815) \| 0.003952 / 0.011008 (-0.007056) \| 0.084183 / 0.038508 (0.045675) \| 0.070616 / 0.023109 (0.047507) \| 0.320491 / 0.275898 (0.044593) \| 0.352021 / 0.323480 (0.028541) \| 0.005330 / 0.007986 (-0.002656) \| 0.003400 / 0.004328 (-0.000928) \| 0.066392 / 0.004250 (0.062141) \| 0.052529 / 0.037052 (0.015477) \| 0.329581 / 0.258489 (0.071092) \| 0.374437 / 0.293841 (0.080596) \| 0.031379 / 0.128546 (-0.097167) \| 0.008576 / 0.075646 (-0.067070) \| 0.288621 / 0.419271 (-0.130650) \| 0.052748 / 0.043533 (0.009215) \| 0.319911 / 0.255139 (0.064772) \| 0.358169 / 0.283200 (0.074970) \| 0.023128 / 0.141683 (-0.118555) \| 1.479578 / 1.452155 (0.027424) \| 1.566351 / 1.492716 (0.073635) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.217616 / 0.018006 (0.199610) \| 0.471546 / 0.000490 (0.471056) \| 0.003880 / 0.000200 (0.003680) \| 0.000085 / 0.000054 (0.000031) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027716 / 0.037411 (-0.009696) \| 0.081718 / 0.014526 (0.067192) \| 0.095457 / 0.176557 (-0.081100) \| 0.150746 / 0.737135 (-0.586389) \| 0.096061 / 0.296338 (-0.200277) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.406811 / 0.215209 (0.191602) \| 4.062757 / 2.077655 (1.985103) \| 2.060658 / 1.504120 (0.556538) \| 1.870944 / 1.541195 (0.329749) \| 1.908984 / 1.468490 (0.440493) \| 0.489053 / 4.584777 (-4.095724) \| 3.571038 / 3.745712 (-0.174674) \| 3.255351 / 5.269862 (-2.014511) \| 2.007078 / 4.565676 (-2.558599) \| 0.057078 / 0.424275 (-0.367197) \| 0.007240 / 0.007607 (-0.000367) \| 0.485641 / 0.226044 (0.259596) \| 4.841657 / 2.268929 (2.572729) \| 2.569676 / 55.444624 (-52.874949) \| 2.151119 / 6.876477 (-4.725357) \| 2.330337 / 2.142072 (0.188265) \| 0.581721 / 4.805227 (-4.223506) \| 0.132591 / 6.500664 (-6.368073) \| 0.060491 / 0.075469 (-0.014978) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.237699 / 1.841788 (-0.604089) \| 19.460306 / 8.074308 (11.385998) \| 14.123006 / 10.191392 (3.931614) \| 0.155669 / 0.680424 (-0.524754) \| 0.018385 / 0.534201 (-0.515816) \| 0.393330 / 0.579283 (-0.185953) \| 0.408890 / 0.434364 (-0.025474) \| 0.457348 / 0.540337 (-0.082989) \| 0.640293 / 1.386936 (-0.746643) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006582 / 0.011353 (-0.004771) \| 0.003950 / 0.011008 (-0.007059) \| 0.064636 / 0.038508 (0.026128) \| 0.077651 / 0.023109 (0.054541) \| 0.365505 / 0.275898 (0.089607) \| 0.393370 / 0.323480 (0.069890) \| 0.005466 / 0.007986 (-0.002520) \| 0.003314 / 0.004328 (-0.001014) \| 0.064960 / 0.004250 (0.060710) \| 0.057355 / 0.037052 (0.020302) \| 0.377773 / 0.258489 (0.119284) \| 0.408394 / 0.293841 (0.114553) \| 0.031698 / 0.128546 (-0.096848) \| 0.008575 / 0.075646 (-0.067071) \| 0.070390 / 0.419271 (-0.348881) \| 0.050035 / 0.043533 (0.006502) \| 0.360461 / 0.255139 (0.105323) \| 0.384862 / 0.283200 (0.101662) \| 0.025380 / 0.141683 (-0.116303) \| 1.484429 / 1.452155 (0.032275) \| 1.542944 / 1.492716 (0.050227) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.190193 / 0.018006 (0.172187) \| 0.468996 / 0.000490 (0.468506) \| 0.003012 / 0.000200 (0.002812) \| 0.000091 / 0.000054 (0.000036) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031488 / 0.037411 (-0.005923) \| 0.088673 / 0.014526 (0.074147) \| 0.101886 / 0.176557 (-0.074670) \| 0.156774 / 0.737135 (-0.580361) \| 0.102818 / 0.296338 (-0.193520) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.428019 / 0.215209 (0.212810) \| 4.271369 / 2.077655 (2.193714) \| 2.271530 / 1.504120 (0.767410) \| 2.085172 / 1.541195 (0.543977) \| 2.143439 / 1.468490 (0.674949) \| 0.493468 / 4.584777 (-4.091309) \| 3.569030 / 3.745712 (-0.176683) \| 4.777962 / 5.269862 (-0.491900) \| 2.872115 / 4.565676 (-1.693562) \| 0.058200 / 0.424275 (-0.366075) \| 0.007657 / 0.007607 (0.000050) \| 0.502874 / 0.226044 (0.276830) \| 5.026721 / 2.268929 (2.757792) \| 2.734301 / 55.444624 (-52.710324) \| 2.396072 / 6.876477 (-4.480405) \| 2.574322 / 2.142072 (0.432249) \| 0.593855 / 4.805227 (-4.211373) \| 0.135134 / 6.500664 (-6.365530) \| 0.061491 / 0.075469 (-0.013978) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.320522 / 1.841788 (-0.521265) \| 19.933221 / 8.074308 (11.858912) \| 14.055921 / 10.191392 (3.864529) \| 0.149620 / 0.680424 (-0.530804) \| 0.018590 / 0.534201 (-0.515611) \| 0.399550 / 0.579283 (-0.179733) \| 0.410463 / 0.434364 (-0.023901) \| 0.469872 / 0.540337 (-0.070465) \| 0.616481 / 1.386936 (-0.770455) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#029956a347b0306cd27f693e12cf9a82acf4ef80 \"CML watermark\")\n" ]	2023-07-27T17:05:54	2023-07-31T06:32:16	2023-07-27T17:08:38	MEMBER	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6082", "html_url": "https://github.com/huggingface/datasets/pull/6082", "diff_url": "https://github.com/huggingface/datasets/pull/6082.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6082.patch", "merged_at": "2023-07-27T17:08:38" }	null	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6082/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6082/timeline	null	null	true
https://api.github.com/repos/huggingface/datasets/issues/6081	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6081/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6081/comments	https://api.github.com/repos/huggingface/datasets/issues/6081/events	https://github.com/huggingface/datasets/pull/6081	1,824,486,278	PR_kwDODunzps5WjU0k	6,081	Deprecate `Dataset.export`	{ "login": "mariosasko", "id": 47462742, "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "gravatar_id": "", "url": "https://api.github.com/users/mariosasko", "html_url": "https://github.com/mariosasko", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "repos_url": "https://api.github.com/users/mariosasko/repos", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006680 / 0.011353 (-0.004673) \| 0.003987 / 0.011008 (-0.007021) \| 0.084677 / 0.038508 (0.046169) \| 0.076800 / 0.023109 (0.053691) \| 0.358338 / 0.275898 (0.082440) \| 0.386573 / 0.323480 (0.063094) \| 0.005370 / 0.007986 (-0.002616) \| 0.003323 / 0.004328 (-0.001005) \| 0.064238 / 0.004250 (0.059988) \| 0.057859 / 0.037052 (0.020806) \| 0.355408 / 0.258489 (0.096919) \| 0.388302 / 0.293841 (0.094461) \| 0.030784 / 0.128546 (-0.097762) \| 0.008381 / 0.075646 (-0.067266) \| 0.287971 / 0.419271 (-0.131300) \| 0.053078 / 0.043533 (0.009545) \| 0.352719 / 0.255139 (0.097580) \| 0.370319 / 0.283200 (0.087119) \| 0.023064 / 0.141683 (-0.118619) \| 1.480661 / 1.452155 (0.028507) \| 1.555711 / 1.492716 (0.062995) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.211289 / 0.018006 (0.193283) \| 0.466957 / 0.000490 (0.466467) \| 0.003760 / 0.000200 (0.003561) \| 0.000076 / 0.000054 (0.000021) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028552 / 0.037411 (-0.008859) \| 0.084469 / 0.014526 (0.069943) \| 0.096027 / 0.176557 (-0.080529) \| 0.152170 / 0.737135 (-0.584965) \| 0.096513 / 0.296338 (-0.199825) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.382940 / 0.215209 (0.167731) \| 3.841735 / 2.077655 (1.764080) \| 1.850575 / 1.504120 (0.346455) \| 1.676554 / 1.541195 (0.135360) \| 1.765241 / 1.468490 (0.296751) \| 0.482131 / 4.584777 (-4.102646) \| 3.512739 / 3.745712 (-0.232973) \| 3.977042 / 5.269862 (-1.292820) \| 2.387568 / 4.565676 (-2.178109) \| 0.056657 / 0.424275 (-0.367618) \| 0.007283 / 0.007607 (-0.000324) \| 0.468193 / 0.226044 (0.242149) \| 4.704077 / 2.268929 (2.435149) \| 2.373467 / 55.444624 (-53.071157) \| 2.002470 / 6.876477 (-4.874007) \| 2.228280 / 2.142072 (0.086208) \| 0.576908 / 4.805227 (-4.228320) \| 0.132000 / 6.500664 (-6.368664) \| 0.060544 / 0.075469 (-0.014926) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.256168 / 1.841788 (-0.585619) \| 19.965458 / 8.074308 (11.891150) \| 14.521435 / 10.191392 (4.330043) \| 0.159156 / 0.680424 (-0.521268) \| 0.018170 / 0.534201 (-0.516031) \| 0.393019 / 0.579283 (-0.186264) \| 0.415002 / 0.434364 (-0.019362) \| 0.471810 / 0.540337 (-0.068528) \| 0.658907 / 1.386936 (-0.728029) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006836 / 0.011353 (-0.004517) \| 0.004067 / 0.011008 (-0.006942) \| 0.066242 / 0.038508 (0.027734) \| 0.078601 / 0.023109 (0.055491) \| 0.369371 / 0.275898 (0.093473) \| 0.402026 / 0.323480 (0.078546) \| 0.006097 / 0.007986 (-0.001889) \| 0.003337 / 0.004328 (-0.000991) \| 0.065854 / 0.004250 (0.061603) \| 0.057665 / 0.037052 (0.020612) \| 0.379709 / 0.258489 (0.121219) \| 0.406868 / 0.293841 (0.113027) \| 0.031946 / 0.128546 (-0.096600) \| 0.008691 / 0.075646 (-0.066955) \| 0.071430 / 0.419271 (-0.347841) \| 0.049518 / 0.043533 (0.005986) \| 0.370439 / 0.255139 (0.115300) \| 0.389235 / 0.283200 (0.106036) \| 0.023730 / 0.141683 (-0.117953) \| 1.509035 / 1.452155 (0.056880) \| 1.548890 / 1.492716 (0.056173) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.229264 / 0.018006 (0.211258) \| 0.445801 / 0.000490 (0.445312) \| 0.000363 / 0.000200 (0.000163) \| 0.000053 / 0.000054 (-0.000001) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032377 / 0.037411 (-0.005034) \| 0.091082 / 0.014526 (0.076556) \| 0.104816 / 0.176557 (-0.071740) \| 0.161040 / 0.737135 (-0.576095) \| 0.105165 / 0.296338 (-0.191173) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.411012 / 0.215209 (0.195803) \| 4.097256 / 2.077655 (2.019602) \| 2.088686 / 1.504120 (0.584566) \| 1.934429 / 1.541195 (0.393234) \| 2.027387 / 1.468490 (0.558896) \| 0.476262 / 4.584777 (-4.108515) \| 3.518416 / 3.745712 (-0.227296) \| 3.260919 / 5.269862 (-2.008943) \| 2.041441 / 4.565676 (-2.524235) \| 0.056302 / 0.424275 (-0.367973) \| 0.007750 / 0.007607 (0.000143) \| 0.489966 / 0.226044 (0.263922) \| 4.915844 / 2.268929 (2.646916) \| 2.617001 / 55.444624 (-52.827623) \| 2.333557 / 6.876477 (-4.542920) \| 2.484530 / 2.142072 (0.342458) \| 0.572009 / 4.805227 (-4.233219) \| 0.142557 / 6.500664 (-6.358107) \| 0.066711 / 0.075469 (-0.008758) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.359929 / 1.841788 (-0.481859) \| 20.332252 / 8.074308 (12.257943) \| 14.585842 / 10.191392 (4.394450) \| 0.170498 / 0.680424 (-0.509926) \| 0.018450 / 0.534201 (-0.515751) \| 0.395449 / 0.579283 (-0.183834) \| 0.409666 / 0.434364 (-0.024698) \| 0.467937 / 0.540337 (-0.072401) \| 0.616078 / 1.386936 (-0.770858) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a888bc94dc6bce7815e3061a28e718097f4b8b9e \"CML watermark\")\n" ]	2023-07-27T14:22:18	2023-07-28T11:09:54	2023-07-28T11:01:04	CONTRIBUTOR	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6081", "html_url": "https://github.com/huggingface/datasets/pull/6081", "diff_url": "https://github.com/huggingface/datasets/pull/6081.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6081.patch", "merged_at": "2023-07-28T11:01:04" }	Deprecate `Dataset.export` that generates a TFRecord file from a dataset as this method is undocumented, and the usage seems low. Users should use [TFRecordWriter](https://www.tensorflow.org/api_docs/python/tf/io/TFRecordWriter#write) or the official [TFRecord](https://www.tensorflow.org/tutorials/load_data/tfrecord) tutorial (on which this method is based) to write TFRecord files instead.	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6081/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6081/timeline	null	null	true
https://api.github.com/repos/huggingface/datasets/issues/6080	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6080/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6080/comments	https://api.github.com/repos/huggingface/datasets/issues/6080/events	https://github.com/huggingface/datasets/pull/6080	1,822,667,554	PR_kwDODunzps5WdL4K	6,080	Remove README link to deprecated Colab notebook	{ "login": "mariosasko", "id": 47462742, "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "gravatar_id": "", "url": "https://api.github.com/users/mariosasko", "html_url": "https://github.com/mariosasko", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "repos_url": "https://api.github.com/users/mariosasko/repos", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006458 / 0.011353 (-0.004894) \| 0.003895 / 0.011008 (-0.007114) \| 0.084280 / 0.038508 (0.045772) \| 0.071304 / 0.023109 (0.048195) \| 0.313910 / 0.275898 (0.038012) \| 0.344070 / 0.323480 (0.020590) \| 0.005413 / 0.007986 (-0.002573) \| 0.003308 / 0.004328 (-0.001021) \| 0.064570 / 0.004250 (0.060320) \| 0.056824 / 0.037052 (0.019771) \| 0.321102 / 0.258489 (0.062613) \| 0.355834 / 0.293841 (0.061993) \| 0.031252 / 0.128546 (-0.097294) \| 0.008427 / 0.075646 (-0.067219) \| 0.287348 / 0.419271 (-0.131924) \| 0.053261 / 0.043533 (0.009728) \| 0.324892 / 0.255139 (0.069753) \| 0.335847 / 0.283200 (0.052647) \| 0.023453 / 0.141683 (-0.118230) \| 1.485456 / 1.452155 (0.033301) \| 1.531329 / 1.492716 (0.038612) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.201924 / 0.018006 (0.183918) \| 0.447188 / 0.000490 (0.446698) \| 0.005543 / 0.000200 (0.005343) \| 0.000086 / 0.000054 (0.000031) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027586 / 0.037411 (-0.009825) \| 0.082412 / 0.014526 (0.067886) \| 0.094851 / 0.176557 (-0.081706) \| 0.151331 / 0.737135 (-0.585804) \| 0.094475 / 0.296338 (-0.201863) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.399004 / 0.215209 (0.183795) \| 3.974652 / 2.077655 (1.896997) \| 1.991909 / 1.504120 (0.487789) \| 1.811684 / 1.541195 (0.270489) \| 1.869774 / 1.468490 (0.401283) \| 0.487745 / 4.584777 (-4.097032) \| 3.558945 / 3.745712 (-0.186768) \| 5.530468 / 5.269862 (0.260606) \| 3.293147 / 4.565676 (-1.272529) \| 0.057531 / 0.424275 (-0.366744) \| 0.007212 / 0.007607 (-0.000395) \| 0.470325 / 0.226044 (0.244281) \| 4.701652 / 2.268929 (2.432723) \| 2.453020 / 55.444624 (-52.991605) \| 2.110152 / 6.876477 (-4.766325) \| 2.314669 / 2.142072 (0.172597) \| 0.615039 / 4.805227 (-4.190189) \| 0.133229 / 6.500664 (-6.367435) \| 0.060821 / 0.075469 (-0.014648) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.296708 / 1.841788 (-0.545079) \| 18.717251 / 8.074308 (10.642943) \| 14.325305 / 10.191392 (4.133913) \| 0.147680 / 0.680424 (-0.532744) \| 0.018312 / 0.534201 (-0.515889) \| 0.392766 / 0.579283 (-0.186517) \| 0.403319 / 0.434364 (-0.031045) \| 0.453696 / 0.540337 (-0.086641) \| 0.622564 / 1.386936 (-0.764372) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006483 / 0.011353 (-0.004870) \| 0.004018 / 0.011008 (-0.006991) \| 0.064436 / 0.038508 (0.025928) \| 0.072365 / 0.023109 (0.049256) \| 0.387532 / 0.275898 (0.111634) \| 0.418175 / 0.323480 (0.094695) \| 0.005453 / 0.007986 (-0.002533) \| 0.003368 / 0.004328 (-0.000961) \| 0.064896 / 0.004250 (0.060645) \| 0.057018 / 0.037052 (0.019966) \| 0.406596 / 0.258489 (0.148107) \| 0.431194 / 0.293841 (0.137353) \| 0.031788 / 0.128546 (-0.096759) \| 0.008532 / 0.075646 (-0.067114) \| 0.070605 / 0.419271 (-0.348666) \| 0.053317 / 0.043533 (0.009785) \| 0.391930 / 0.255139 (0.136791) \| 0.406071 / 0.283200 (0.122872) \| 0.028652 / 0.141683 (-0.113030) \| 1.487677 / 1.452155 (0.035522) \| 1.546071 / 1.492716 (0.053355) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.220063 / 0.018006 (0.202056) \| 0.441111 / 0.000490 (0.440621) \| 0.006066 / 0.000200 (0.005867) \| 0.000084 / 0.000054 (0.000030) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.035179 / 0.037411 (-0.002232) \| 0.096745 / 0.014526 (0.082219) \| 0.108171 / 0.176557 (-0.068386) \| 0.164590 / 0.737135 (-0.572545) \| 0.109425 / 0.296338 (-0.186913) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.408101 / 0.215209 (0.192892) \| 4.062961 / 2.077655 (1.985306) \| 2.101849 / 1.504120 (0.597730) \| 1.935919 / 1.541195 (0.394724) \| 1.993749 / 1.468490 (0.525259) \| 0.487788 / 4.584777 (-4.096989) \| 3.533972 / 3.745712 (-0.211740) \| 3.218448 / 5.269862 (-2.051414) \| 2.002322 / 4.565676 (-2.563355) \| 0.057371 / 0.424275 (-0.366904) \| 0.007704 / 0.007607 (0.000097) \| 0.491695 / 0.226044 (0.265650) \| 4.905009 / 2.268929 (2.636080) \| 2.597879 / 55.444624 (-52.846745) \| 2.252086 / 6.876477 (-4.624391) \| 2.434439 / 2.142072 (0.292367) \| 0.583071 / 4.805227 (-4.222156) \| 0.133765 / 6.500664 (-6.366899) \| 0.061276 / 0.075469 (-0.014193) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.403111 / 1.841788 (-0.438676) \| 19.218886 / 8.074308 (11.144578) \| 13.981775 / 10.191392 (3.790383) \| 0.167784 / 0.680424 (-0.512640) \| 0.018401 / 0.534201 (-0.515800) \| 0.392038 / 0.579283 (-0.187245) \| 0.414776 / 0.434364 (-0.019587) \| 0.476221 / 0.540337 (-0.064117) \| 0.632724 / 1.386936 (-0.754212) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#420dbd92c42840d6c91ecf5d3560c6799ee0cca1 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007595 / 0.011353 (-0.003758) \| 0.004540 / 0.011008 (-0.006468) \| 0.099350 / 0.038508 (0.060842) \| 0.087062 / 0.023109 (0.063953) \| 0.415980 / 0.275898 (0.140082) \| 0.466390 / 0.323480 (0.142910) \| 0.005958 / 0.007986 (-0.002027) \| 0.003671 / 0.004328 (-0.000657) \| 0.075714 / 0.004250 (0.071463) \| 0.066062 / 0.037052 (0.029010) \| 0.426527 / 0.258489 (0.168038) \| 0.473282 / 0.293841 (0.179441) \| 0.035669 / 0.128546 (-0.092878) \| 0.009729 / 0.075646 (-0.065918) \| 0.344035 / 0.419271 (-0.075237) \| 0.061153 / 0.043533 (0.017620) \| 0.428607 / 0.255139 (0.173468) \| 0.445951 / 0.283200 (0.162752) \| 0.026373 / 0.141683 (-0.115310) \| 1.788725 / 1.452155 (0.336570) \| 1.871055 / 1.492716 (0.378339) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.230606 / 0.018006 (0.212600) \| 0.489835 / 0.000490 (0.489345) \| 0.005669 / 0.000200 (0.005469) \| 0.000100 / 0.000054 (0.000045) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032197 / 0.037411 (-0.005214) \| 0.099571 / 0.014526 (0.085045) \| 0.112686 / 0.176557 (-0.063871) \| 0.179478 / 0.737135 (-0.557658) \| 0.112670 / 0.296338 (-0.183668) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.449606 / 0.215209 (0.234397) \| 4.503356 / 2.077655 (2.425701) \| 2.190480 / 1.504120 (0.686361) \| 1.986054 / 1.541195 (0.444860) \| 2.071594 / 1.468490 (0.603104) \| 0.566301 / 4.584777 (-4.018475) \| 4.088460 / 3.745712 (0.342748) \| 4.840100 / 5.269862 (-0.429761) \| 2.857697 / 4.565676 (-1.707980) \| 0.066718 / 0.424275 (-0.357557) \| 0.008642 / 0.007607 (0.001034) \| 0.539785 / 0.226044 (0.313740) \| 5.383252 / 2.268929 (3.114323) \| 2.878177 / 55.444624 (-52.566447) \| 2.374577 / 6.876477 (-4.501899) \| 2.590500 / 2.142072 (0.448428) \| 0.675196 / 4.805227 (-4.130031) \| 0.153544 / 6.500664 (-6.347120) \| 0.070958 / 0.075469 (-0.004511) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.490403 / 1.841788 (-0.351385) \| 22.085740 / 8.074308 (14.011432) \| 16.588093 / 10.191392 (6.396701) \| 0.188598 / 0.680424 (-0.491826) \| 0.021567 / 0.534201 (-0.512634) \| 0.472594 / 0.579283 (-0.106689) \| 0.472903 / 0.434364 (0.038539) \| 0.545305 / 0.540337 (0.004968) \| 0.736399 / 1.386936 (-0.650537) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007635 / 0.011353 (-0.003718) \| 0.004731 / 0.011008 (-0.006277) \| 0.076482 / 0.038508 (0.037974) \| 0.083666 / 0.023109 (0.060557) \| 0.469596 / 0.275898 (0.193698) \| 0.493068 / 0.323480 (0.169588) \| 0.006014 / 0.007986 (-0.001971) \| 0.003902 / 0.004328 (-0.000426) \| 0.077142 / 0.004250 (0.072891) \| 0.064355 / 0.037052 (0.027303) \| 0.468859 / 0.258489 (0.210370) \| 0.504002 / 0.293841 (0.210161) \| 0.037606 / 0.128546 (-0.090940) \| 0.010141 / 0.075646 (-0.065505) \| 0.083790 / 0.419271 (-0.335482) \| 0.060923 / 0.043533 (0.017390) \| 0.464752 / 0.255139 (0.209613) \| 0.500464 / 0.283200 (0.217264) \| 0.031183 / 0.141683 (-0.110499) \| 1.779294 / 1.452155 (0.327139) \| 1.870848 / 1.492716 (0.378131) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.246567 / 0.018006 (0.228560) \| 0.477182 / 0.000490 (0.476693) \| 0.000426 / 0.000200 (0.000226) \| 0.000067 / 0.000054 (0.000013) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.035377 / 0.037411 (-0.002034) \| 0.106042 / 0.014526 (0.091516) \| 0.119237 / 0.176557 (-0.057320) \| 0.182145 / 0.737135 (-0.554991) \| 0.119537 / 0.296338 (-0.176801) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.491352 / 0.215209 (0.276143) \| 4.824220 / 2.077655 (2.746565) \| 2.652039 / 1.504120 (1.147919) \| 2.535310 / 1.541195 (0.994116) \| 2.620009 / 1.468490 (1.151519) \| 0.567865 / 4.584777 (-4.016912) \| 4.158795 / 3.745712 (0.413082) \| 6.042582 / 5.269862 (0.772721) \| 3.957193 / 4.565676 (-0.608484) \| 0.066647 / 0.424275 (-0.357628) \| 0.008893 / 0.007607 (0.001285) \| 0.570137 / 0.226044 (0.344093) \| 5.687126 / 2.268929 (3.418198) \| 3.137605 / 55.444624 (-52.307019) \| 2.655979 / 6.876477 (-4.220498) \| 2.893338 / 2.142072 (0.751265) \| 0.698388 / 4.805227 (-4.106840) \| 0.154897 / 6.500664 (-6.345767) \| 0.071208 / 0.075469 (-0.004261) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.619346 / 1.841788 (-0.222441) \| 22.782510 / 8.074308 (14.708202) \| 16.317395 / 10.191392 (6.126003) \| 0.197630 / 0.680424 (-0.482794) \| 0.021795 / 0.534201 (-0.512406) \| 0.466982 / 0.579283 (-0.112302) \| 0.468609 / 0.434364 (0.034245) \| 0.574380 / 0.540337 (0.034043) \| 0.759827 / 1.386936 (-0.627109) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8c1c5d8268ae59a0dcaea47da825e87c3f9528b4 \"CML watermark\")\n" ]	2023-07-26T15:27:49	2023-07-26T16:24:43	2023-07-26T16:14:34	CONTRIBUTOR	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6080", "html_url": "https://github.com/huggingface/datasets/pull/6080", "diff_url": "https://github.com/huggingface/datasets/pull/6080.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6080.patch", "merged_at": "2023-07-26T16:14:34" }	null	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6080/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6080/timeline	null	null	true
https://api.github.com/repos/huggingface/datasets/issues/6079	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6079/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6079/comments	https://api.github.com/repos/huggingface/datasets/issues/6079/events	https://github.com/huggingface/datasets/issues/6079	1,822,597,471	I_kwDODunzps5soqFf	6,079	Iterating over DataLoader based on HF datasets is stuck forever	{ "login": "arindamsarkar93", "id": 5454868, "node_id": "MDQ6VXNlcjU0NTQ4Njg=", "avatar_url": "https://avatars.githubusercontent.com/u/5454868?v=4", "gravatar_id": "", "url": "https://api.github.com/users/arindamsarkar93", "html_url": "https://github.com/arindamsarkar93", "followers_url": "https://api.github.com/users/arindamsarkar93/followers", "following_url": "https://api.github.com/users/arindamsarkar93/following{/other_user}", "gists_url": "https://api.github.com/users/arindamsarkar93/gists{/gist_id}", "starred_url": "https://api.github.com/users/arindamsarkar93/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/arindamsarkar93/subscriptions", "organizations_url": "https://api.github.com/users/arindamsarkar93/orgs", "repos_url": "https://api.github.com/users/arindamsarkar93/repos", "events_url": "https://api.github.com/users/arindamsarkar93/events{/privacy}", "received_events_url": "https://api.github.com/users/arindamsarkar93/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "When the process starts to hang, can you interrupt it with CTRL + C and paste the error stack trace here? ", "Thanks @mariosasko for your prompt response, here's the stack trace:\r\n\r\n```\r\nKeyboardInterrupt Traceback (most recent call last)\r\nCell In[12], line 4\r\n 2 t = time.time()\r\n 3 iter_ = 0\r\n----> 4 for batch in train_dataloader:\r\n 5 #batch_proc = streaming_obj.collect_streaming_data_batch(batch)\r\n 6 iter_ += 1\r\n 8 if iter_ == 1:\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/torch/utils/data/dataloader.py:634, in _BaseDataLoaderIter.__next__(self)\r\n 631 if self._sampler_iter is None:\r\n 632 # TODO(https://github.com/pytorch/pytorch/issues/76750)\r\n 633 self._reset() # type: ignore[call-arg]\r\n--> 634 data = self._next_data()\r\n 635 self._num_yielded += 1\r\n 636 if self._dataset_kind == _DatasetKind.Iterable and \\\r\n 637 self._IterableDataset_len_called is not None and \\\r\n 638 self._num_yielded > self._IterableDataset_len_called:\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/torch/utils/data/dataloader.py:678, in _SingleProcessDataLoaderIter._next_data(self)\r\n 676 def _next_data(self):\r\n 677 index = self._next_index() # may raise StopIteration\r\n--> 678 data = self._dataset_fetcher.fetch(index) # may raise StopIteration\r\n 679 if self._pin_memory:\r\n 680 data = _utils.pin_memory.pin_memory(data, self._pin_memory_device)\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py:32, in _IterableDatasetFetcher.fetch(self, possibly_batched_index)\r\n 30 for _ in possibly_batched_index:\r\n 31 try:\r\n---> 32 data.append(next(self.dataset_iter))\r\n 33 except StopIteration:\r\n 34 self.ended = True\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/datasets/iterable_dataset.py:1353, in IterableDataset.__iter__(self)\r\n 1350 yield formatter.format_row(pa_table)\r\n 1351 return\r\n-> 1353 for key, example in ex_iterable:\r\n 1354 if self.features:\r\n 1355 # `IterableDataset` automatically fills missing columns with None.\r\n 1356 # This is done with `_apply_feature_types_on_example`.\r\n 1357 example = _apply_feature_types_on_example(\r\n 1358 example, self.features, token_per_repo_id=self._token_per_repo_id\r\n 1359 )\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/datasets/iterable_dataset.py:956, in BufferShuffledExamplesIterable.__iter__(self)\r\n 954 # this is the shuffle buffer that we keep in memory\r\n 955 mem_buffer = []\r\n--> 956 for x in self.ex_iterable:\r\n 957 if len(mem_buffer) == buffer_size: # if the buffer is full, pick and example from it\r\n 958 i = next(indices_iterator)\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/datasets/iterable_dataset.py:296, in ShuffledDataSourcesArrowExamplesIterable.__iter__(self)\r\n 294 for key, pa_table in self.generate_tables_fn(kwargs_with_shuffled_shards):\r\n 295 for pa_subtable in pa_table.to_reader(max_chunksize=config.ARROW_READER_BATCH_SIZE_IN_DATASET_ITER):\r\n--> 296 formatted_batch = formatter.format_batch(pa_subtable)\r\n 297 for example in _batch_to_examples(formatted_batch):\r\n 298 yield key, example\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/datasets/formatting/formatting.py:448, in PythonFormatter.format_batch(self, pa_table)\r\n 446 if self.lazy:\r\n 447 return LazyBatch(pa_table, self)\r\n--> 448 batch = self.python_arrow_extractor().extract_batch(pa_table)\r\n 449 batch = self.python_features_decoder.decode_batch(batch)\r\n 450 return batch\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/datasets/formatting/formatting.py:150, in PythonArrowExtractor.extract_batch(self, pa_table)\r\n 149 def extract_batch(self, pa_table: pa.Table) -> dict:\r\n--> 150 return pa_table.to_pydict()\r\n\r\nKeyboardInterrupt: \r\n```\r\n", "Update: If i let it run, it eventually fails with:\r\n\r\n```\r\nRuntimeError Traceback (most recent call last)\r\nCell In[16], line 4\r\n 2 t = time.time()\r\n 3 iter_ = 0\r\n----> 4 for batch in train_dataloader:\r\n 5 #batch_proc = streaming_obj.collect_streaming_data_batch(batch)\r\n 6 iter_ += 1\r\n 8 if iter_ == 1:\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/torch/utils/data/dataloader.py:634, in _BaseDataLoaderIter.__next__(self)\r\n 631 if self._sampler_iter is None:\r\n 632 # TODO(https://github.com/pytorch/pytorch/issues/76750)\r\n 633 self._reset() # type: ignore[call-arg]\r\n--> 634 data = self._next_data()\r\n 635 self._num_yielded += 1\r\n 636 if self._dataset_kind == _DatasetKind.Iterable and \\\r\n 637 self._IterableDataset_len_called is not None and \\\r\n 638 self._num_yielded > self._IterableDataset_len_called:\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/torch/utils/data/dataloader.py:678, in _SingleProcessDataLoaderIter._next_data(self)\r\n 676 def _next_data(self):\r\n 677 index = self._next_index() # may raise StopIteration\r\n--> 678 data = self._dataset_fetcher.fetch(index) # may raise StopIteration\r\n 679 if self._pin_memory:\r\n 680 data = _utils.pin_memory.pin_memory(data, self._pin_memory_device)\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py:32, in _IterableDatasetFetcher.fetch(self, possibly_batched_index)\r\n 30 for _ in possibly_batched_index:\r\n 31 try:\r\n---> 32 data.append(next(self.dataset_iter))\r\n 33 except StopIteration:\r\n 34 self.ended = True\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/datasets/iterable_dataset.py:1360, in IterableDataset.__iter__(self)\r\n 1354 if self.features:\r\n 1355 # `IterableDataset` automatically fills missing columns with None.\r\n 1356 # This is done with `_apply_feature_types_on_example`.\r\n 1357 example = _apply_feature_types_on_example(\r\n 1358 example, self.features, token_per_repo_id=self._token_per_repo_id\r\n 1359 )\r\n-> 1360 yield format_dict(example) if format_dict else example\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/datasets/formatting/torch_formatter.py:85, in TorchFormatter.recursive_tensorize(self, data_struct)\r\n 84 def recursive_tensorize(self, data_struct: dict):\r\n---> 85 return map_nested(self._recursive_tensorize, data_struct, map_list=False)\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/datasets/utils/py_utils.py:463, in map_nested(function, data_struct, dict_only, map_list, map_tuple, map_numpy, num_proc, parallel_min_length, types, disable_tqdm, desc)\r\n 461 num_proc = 1\r\n 462 if num_proc != -1 and num_proc <= 1 or len(iterable) < parallel_min_length:\r\n--> 463 mapped = [\r\n 464 _single_map_nested((function, obj, types, None, True, None))\r\n 465 for obj in logging.tqdm(iterable, disable=disable_tqdm, desc=desc)\r\n 466 ]\r\n 467 else:\r\n 468 mapped = parallel_map(function, iterable, num_proc, types, disable_tqdm, desc, _single_map_nested)\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/datasets/utils/py_utils.py:464, in <listcomp>(.0)\r\n 461 num_proc = 1\r\n 462 if num_proc != -1 and num_proc <= 1 or len(iterable) < parallel_min_length:\r\n 463 mapped = [\r\n--> 464 _single_map_nested((function, obj, types, None, True, None))\r\n 465 for obj in logging.tqdm(iterable, disable=disable_tqdm, desc=desc)\r\n 466 ]\r\n 467 else:\r\n 468 mapped = parallel_map(function, iterable, num_proc, types, disable_tqdm, desc, _single_map_nested)\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/datasets/utils/py_utils.py:366, in _single_map_nested(args)\r\n 364 # Singleton first to spare some computation\r\n 365 if not isinstance(data_struct, dict) and not isinstance(data_struct, types):\r\n--> 366 return function(data_struct)\r\n 368 # Reduce logging to keep things readable in multiprocessing with tqdm\r\n 369 if rank is not None and logging.get_verbosity() < logging.WARNING:\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/datasets/formatting/torch_formatter.py:82, in TorchFormatter._recursive_tensorize(self, data_struct)\r\n 80 elif isinstance(data_struct, (list, tuple)):\r\n 81 return self._consolidate([self.recursive_tensorize(substruct) for substruct in data_struct])\r\n---> 82 return self._tensorize(data_struct)\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/datasets/formatting/torch_formatter.py:68, in TorchFormatter._tensorize(self, value)\r\n 66 if isinstance(value, PIL.Image.Image):\r\n 67 value = np.asarray(value)\r\n---> 68 return torch.tensor(value, {default_dtype, self.torch_tensor_kwargs})\r\n\r\nRuntimeError: Could not infer dtype of decimal.Decimal\r\n```", "PyTorch tensors cannot store `Decimal` objects. Casting the column with decimals to `float` should fix the issue.", "I already have cast in collate_fn, in which I perform .astype(float) for each numerical field.\r\nOn the same instance, I installed a conda env with python 3.6, and this works well.\r\n\r\nSample:\r\n\r\n```\r\ndef streaming_data_collate_fn(batch):\r\n df = pd.DataFrame.from_dict(batch)\r\n feat_vals = torch.FloatTensor(np.nan_to_num(np.array(df[feats].astype(float))))\r\n\r\n```", "`collate_fn` is applied after the `torch` formatting step, so I think the only option when working with an `IterableDataset` is to remove the `with_format` call and perform the conversion from Python values to PyTorch tensors in `collate_fn`. The standard `Dataset` supports `with_format(\"numpy\")`, which should make this conversion faster.", "Thanks! \r\nPython 3.10 conda-env: After replacing with_format(\"torch\") with with_format(\"numpy\"), the error went away. However, it was still taking over 2 minutes to load a very small batch of 64 samples with num_workers set to 32. Once I removed with_format call altogether, it is finishing in 11 seconds.\r\n\r\nPython 3.6 based conda-env: When I switch the kernel , neither of the above work, and with_format(\"torch\") is the only thing that works, and executes in 1.6 seconds.\r\n\r\nI feel something else is also amiss here.", "Can you share the `datasets` and `torch` versions installed in these conda envs?\r\n\r\n> Once I removed with_format call altogether, it is finishing in 11 seconds.\r\n\r\nHmm, that's surprising. What are your dataset's `.features`?", "Python 3.6: \r\ndatasets.__version__ 2.4.0\r\ntorch.__version__ 1.10.1+cu102\r\n\r\nPython 3.10:\r\ndatasets.__version__ 2.14.0\r\ntorch.__version__ 2.0.0\r\n\r\nAnonymized features are of the form (subset shown here):\r\n{\r\n'string_feature_i': Value(dtype='string', id=None),\r\n'numerical_feature_i': Value(dtype='decimal128(38, 0)', id=None),\r\n'numerical_feature_series_i': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None),\r\n}\r\n\r\n\r\nThere is no output from .features in python 3.6 kernel BTW.", "One more thing, in python 3.10 based kernel, interestingly increasing num_workers seem to be increasing the runtime of iterating I was trying out. In python 3.10 kernel execution, I do not even see multiple CPU cores spiking unlike in 3.6.\r\n\r\n512 batch size on 32 workers executes in 2.4 seconds on python 3.6 kernel, while it takes ~118 seconds on 3.10!", "Update: It seems the latency part is more of a multiprocessing issue with torch and some host specific issue, and I had to scourge through relevant pytorch issues, when I stumbled across these threads:\r\n1. https://github.com/pytorch/pytorch/issues/102494\r\n2. https://github.com/pytorch/pytorch/issues/102269\r\n3. https://github.com/pytorch/pytorch/issues/99625\r\n\r\nOut of the suggested solutions, the one that worked in my case was:\r\n```\r\nos.environ['KMP_AFFINITY'] = \"disabled\"\r\n```\r\nIt is working for now, though I have no clue why, just I hope it does not get stuck when I do actual model training, will update by tomorrow.\r\n\r\n\r\n" ]	2023-07-26T14:52:37	2023-07-30T14:09:06	2023-07-30T14:09:06	NONE	null	null	null	### Describe the bug I am using Amazon Sagemaker notebook (Amazon Linux 2) with python 3.10 based Conda environment. I have a dataset in parquet format locally. When I try to iterate over it, the loader is stuck forever. Note that the same code is working for python 3.6 based conda environment seamlessly. What should be my next steps here? ### Steps to reproduce the bug ``` train_dataset = load_dataset( "parquet", data_files = {'train': tr_data_path + '*.parquet'}, split = 'train', collate_fn = streaming_data_collate_fn, streaming = True ).with_format('torch') train_dataloader = DataLoader(train_dataset, batch_size = 2, num_workers = 0) t = time.time() iter_ = 0 for batch in train_dataloader: iter_ += 1 if iter_ == 1000: break print (time.time() - t) ``` ### Expected behavior The snippet should work normally and load the next batch of data. ### Environment info datasets: '2.14.0' pyarrow: '12.0.0' torch: '2.0.0' Python: 3.10.10 \| packaged by conda-forge \| (main, Mar 24 2023, 20:08:06) [GCC 11.3.0] !uname -r 5.10.178-162.673.amzn2.x86_64	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6079/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6079/timeline	null	completed	false
https://api.github.com/repos/huggingface/datasets/issues/6078	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6078/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6078/comments	https://api.github.com/repos/huggingface/datasets/issues/6078/events	https://github.com/huggingface/datasets/issues/6078	1,822,501,472	I_kwDODunzps5soSpg	6,078	resume_download with streaming=True	{ "login": "NicolasMICAUX", "id": 72763959, "node_id": "MDQ6VXNlcjcyNzYzOTU5", "avatar_url": "https://avatars.githubusercontent.com/u/72763959?v=4", "gravatar_id": "", "url": "https://api.github.com/users/NicolasMICAUX", "html_url": "https://github.com/NicolasMICAUX", "followers_url": "https://api.github.com/users/NicolasMICAUX/followers", "following_url": "https://api.github.com/users/NicolasMICAUX/following{/other_user}", "gists_url": "https://api.github.com/users/NicolasMICAUX/gists{/gist_id}", "starred_url": "https://api.github.com/users/NicolasMICAUX/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/NicolasMICAUX/subscriptions", "organizations_url": "https://api.github.com/users/NicolasMICAUX/orgs", "repos_url": "https://api.github.com/users/NicolasMICAUX/repos", "events_url": "https://api.github.com/users/NicolasMICAUX/events{/privacy}", "received_events_url": "https://api.github.com/users/NicolasMICAUX/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "Currently, it's not possible to efficiently resume streaming after an error. Eventually, we plan to support this for Parquet (see https://github.com/huggingface/datasets/issues/5380). ", "Ok thank you for your answer", "I'm closing this as a duplicate of #5380" ]	2023-07-26T14:08:22	2023-07-28T11:05:03	2023-07-28T11:05:03	NONE	null	null	null	### Describe the bug I used: ``` dataset = load_dataset( "oscar-corpus/OSCAR-2201", token=True, language="fr", streaming=True, split="train" ) ``` Unfortunately, the server had a problem during the training process. I saved the step my training stopped at. But how can I resume download from step 1_000_´000 without re-streaming all the first 1 million docs of the dataset? `download_config=DownloadConfig(resume_download=True)` seems to not work with streaming=True. ### Steps to reproduce the bug ``` from datasets import load_dataset, DownloadConfig dataset = load_dataset( "oscar-corpus/OSCAR-2201", token=True, language="fr", streaming=True, # optional split="train", download_config=DownloadConfig(resume_download=True) ) # interupt the run and try to relaunch it => this restart from scratch ``` ### Expected behavior I would expect a parameter to start streaming from a given index in the dataset. ### Environment info - `datasets` version: 2.14.0 - Platform: Linux-5.19.0-45-generic-x86_64-with-glibc2.29 - Python version: 3.8.10 - Huggingface_hub version: 0.15.1 - PyArrow version: 12.0.1 - Pandas version: 2.0.0	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6078/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6078/timeline	null	completed	false
https://api.github.com/repos/huggingface/datasets/issues/6077	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6077/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6077/comments	https://api.github.com/repos/huggingface/datasets/issues/6077/events	https://github.com/huggingface/datasets/issues/6077	1,822,486,810	I_kwDODunzps5soPEa	6,077	Mapping gets stuck at 99%	{ "login": "Laurent2916", "id": 21087104, "node_id": "MDQ6VXNlcjIxMDg3MTA0", "avatar_url": "https://avatars.githubusercontent.com/u/21087104?v=4", "gravatar_id": "", "url": "https://api.github.com/users/Laurent2916", "html_url": "https://github.com/Laurent2916", "followers_url": "https://api.github.com/users/Laurent2916/followers", "following_url": "https://api.github.com/users/Laurent2916/following{/other_user}", "gists_url": "https://api.github.com/users/Laurent2916/gists{/gist_id}", "starred_url": "https://api.github.com/users/Laurent2916/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/Laurent2916/subscriptions", "organizations_url": "https://api.github.com/users/Laurent2916/orgs", "repos_url": "https://api.github.com/users/Laurent2916/repos", "events_url": "https://api.github.com/users/Laurent2916/events{/privacy}", "received_events_url": "https://api.github.com/users/Laurent2916/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	null	[ "The `MAX_MAP_BATCH_SIZE = 1_000_000_000` hack is bad as it loads the entire dataset into RAM when performing `.map`. Instead, it's best to use `.iter(batch_size)` to iterate over the data batches and compute `mean` for each column. (`stddev` can be computed in another pass).\r\n\r\nAlso, these arrays are big, so it makes sense to reduce `batch_size`/`writer_batch_size` to avoid RAM issues and slow IO.", "Hi @mariosasko !\r\n\r\nI agree, it's an ugly hack, but it was convenient since the resulting `mean_std` could be cached by the library. For my large dataset (which doesn't fit in RAM), I'm actually using something similar to what you suggested. I got rid of the first mapping in the above scripts and replaced it with an iterator, but the issue with the second mapping still persists.", "Have you tried to reduce `batch_size`/`writer_batch_size` in the 2nd `.map`? Also, can you interrupt the process when it gets stuck and share the error stack trace?", "I think `batch_size/writer_batch_size` is already at its lowest in the 2nd `.map` since `batched=False` implies `batch_size=1` and `len(ds) = 1000 = writer_batch_size`.\r\n\r\nHere is also a bunch of stack traces when I interrupted the process:\r\n\r\n<details>\r\n <summary>stack trace 1</summary>\r\n\r\n```python\r\n(pyg)[d623204@rosetta-bigviz01 stage-laurent-f]$ python src/random_scripts/uses_random_data.py \r\nFound cached dataset random_data (/local_scratch/lfainsin/.cache/huggingface/datasets/random_data/default/0.0.0/444e214e1d0e6298cfd3f2368323ec37073dc1439f618e19395b1f421c69b066)\r\nApplying mean/std: 97%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████ \| 967/1000 [00:01<00:00, 534.87 examples/s]Traceback (most recent call last): \r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 179, in __arrow_array__\r\n storage = to_pyarrow_listarray(data, pa_type)\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 1466, in to_pyarrow_listarray\r\n return pa.array(data, pa_type.storage_dtype)\r\n File \"pyarrow/array.pxi\", line 320, in pyarrow.lib.array\r\n File \"pyarrow/array.pxi\", line 39, in pyarrow.lib._sequence_to_array\r\n File \"pyarrow/error.pxi\", line 144, in pyarrow.lib.pyarrow_internal_check_status\r\n File \"pyarrow/error.pxi\", line 123, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowTypeError: Could not convert tensor([[-1.0273, -0.8037, -0.6860],\r\n [-0.5034, -1.2685, -0.0558],\r\n [-1.0908, -1.1820, -0.3178],\r\n ...,\r\n [-0.8171, 0.1781, -0.5903],\r\n [ 0.4370, 1.9305, 0.5899],\r\n [-0.1426, 0.9053, -1.7559]]) with type Tensor: was not a sequence or recognized null for conversion to list type\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_dataset.py\", line 3449, in _map_single\r\n writer.write(example)\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 490, in write\r\n self.write_examples_on_file()\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 448, in write_examples_on_file\r\n self.write_batch(batch_examples=batch_examples)\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 553, in write_batch\r\n arrays.append(pa.array(typed_sequence))\r\n File \"pyarrow/array.pxi\", line 236, in pyarrow.lib.array\r\n File \"pyarrow/array.pxi\", line 110, in pyarrow.lib._handle_arrow_array_protocol\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 223, in __arrow_array__\r\n return pa.array(cast_to_python_objects(data, only_1d_for_numpy=True))\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 446, in cast_to_python_objects\r\n return _cast_to_python_objects(\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 407, in _cast_to_python_objects\r\n [\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 408, in <listcomp>\r\n _cast_to_python_objects(\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 319, in _cast_to_python_objects\r\n [\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 320, in <listcomp>\r\n _cast_to_python_objects(\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 263, in _cast_to_python_objects\r\n def _cast_to_python_objects(obj: Any, only_1d_for_numpy: bool, optimize_list_casting: bool) -> Tuple[Any, bool]:\r\nKeyboardInterrupt\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 179, in __arrow_array__\r\n storage = to_pyarrow_listarray(data, pa_type)\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 1466, in to_pyarrow_listarray\r\n return pa.array(data, pa_type.storage_dtype)\r\n File \"pyarrow/array.pxi\", line 320, in pyarrow.lib.array\r\n File \"pyarrow/array.pxi\", line 39, in pyarrow.lib._sequence_to_array\r\n File \"pyarrow/error.pxi\", line 144, in pyarrow.lib.pyarrow_internal_check_status\r\n File \"pyarrow/error.pxi\", line 123, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowTypeError: Could not convert tensor([[-1.0273, -0.8037, -0.6860],\r\n [-0.5034, -1.2685, -0.0558],\r\n [-1.0908, -1.1820, -0.3178],\r\n ...,\r\n [-0.8171, 0.1781, -0.5903],\r\n [ 0.4370, 1.9305, 0.5899],\r\n [-0.1426, 0.9053, -1.7559]]) with type Tensor: was not a sequence or recognized null for conversion to list type\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"/gpfs_new/data/users/lfainsin/stage-laurent-f/src/random_scripts/uses_random_data.py\", line 62, in <module>\r\n ds_normalized = ds.map(\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_dataset.py\", line 580, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, args, kwargs)\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_dataset.py\", line 545, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, args, kwargs)\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_dataset.py\", line 3087, in map\r\n for rank, done, content in Dataset._map_single(dataset_kwargs):\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_dataset.py\", line 3492, in _map_single\r\n writer.finalize()\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 584, in finalize\r\n self.write_examples_on_file()\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 448, in write_examples_on_file\r\n self.write_batch(batch_examples=batch_examples)\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 553, in write_batch\r\n arrays.append(pa.array(typed_sequence))\r\n File \"pyarrow/array.pxi\", line 236, in pyarrow.lib.array\r\n File \"pyarrow/array.pxi\", line 110, in pyarrow.lib._handle_arrow_array_protocol\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 223, in __arrow_array__\r\n return pa.array(cast_to_python_objects(data, only_1d_for_numpy=True))\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 446, in cast_to_python_objects\r\n return _cast_to_python_objects(\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 407, in _cast_to_python_objects\r\n [\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 408, in <listcomp>\r\n _cast_to_python_objects(\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 319, in _cast_to_python_objects\r\n [\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 319, in <listcomp>\r\n [\r\nKeyboardInterrupt\r\n```\r\n\r\n</details>\r\n\r\n<details>\r\n <summary>stack trace 2</summary>\r\n\r\n```python\r\n(pyg)[d623204@rosetta-bigviz01 stage-laurent-f]$ python src/random_scripts/uses_random_data.py \r\nFound cached dataset random_data (/local_scratch/lfainsin/.cache/huggingface/datasets/random_data/default/0.0.0/444e214e1d0e6298cfd3f2368323ec37073dc1439f618e19395b1f421c69b066)\r\nApplying mean/std: 99%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏ \| 988/1000 [00:20<00:00, 526.19 examples/s]Applying mean/std: 100%\|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊\| 999/1000 [00:21<00:00, 9.66 examples/s]Traceback (most recent call last): \r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 179, in __arrow_array__\r\n storage = to_pyarrow_listarray(data, pa_type)\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 1466, in to_pyarrow_listarray\r\n return pa.array(data, pa_type.storage_dtype)\r\n File \"pyarrow/array.pxi\", line 320, in pyarrow.lib.array\r\n File \"pyarrow/array.pxi\", line 39, in pyarrow.lib._sequence_to_array\r\n File \"pyarrow/error.pxi\", line 144, in pyarrow.lib.pyarrow_internal_check_status\r\n File \"pyarrow/error.pxi\", line 123, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowTypeError: Could not convert tensor([[-1.0273, -0.8037, -0.6860],\r\n [-0.5034, -1.2685, -0.0558],\r\n [-1.0908, -1.1820, -0.3178],\r\n ...,\r\n [-0.8171, 0.1781, -0.5903],\r\n [ 0.4370, 1.9305, 0.5899],\r\n [-0.1426, 0.9053, -1.7559]]) with type Tensor: was not a sequence or recognized null for conversion to list type\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_dataset.py\", line 3449, in _map_single\r\n writer.write(example)\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 490, in write\r\n self.write_examples_on_file()\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 448, in write_examples_on_file\r\n self.write_batch(batch_examples=batch_examples)\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 553, in write_batch\r\n arrays.append(pa.array(typed_sequence))\r\n File \"pyarrow/array.pxi\", line 236, in pyarrow.lib.array\r\n File \"pyarrow/array.pxi\", line 110, in pyarrow.lib._handle_arrow_array_protocol\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 223, in __arrow_array__\r\n return pa.array(cast_to_python_objects(data, only_1d_for_numpy=True))\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 446, in cast_to_python_objects\r\n return _cast_to_python_objects(\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 407, in _cast_to_python_objects\r\n [\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 408, in <listcomp>\r\n _cast_to_python_objects(\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 319, in _cast_to_python_objects\r\n [\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 320, in <listcomp>\r\n _cast_to_python_objects(\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 263, in _cast_to_python_objects\r\n def _cast_to_python_objects(obj: Any, only_1d_for_numpy: bool, optimize_list_casting: bool) -> Tuple[Any, bool]:\r\nKeyboardInterrupt\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 179, in __arrow_array__\r\n storage = to_pyarrow_listarray(data, pa_type)\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 1466, in to_pyarrow_listarray\r\n return pa.array(data, pa_type.storage_dtype)\r\n File \"pyarrow/array.pxi\", line 320, in pyarrow.lib.array\r\n File \"pyarrow/array.pxi\", line 39, in pyarrow.lib._sequence_to_array\r\n File \"pyarrow/error.pxi\", line 144, in pyarrow.lib.pyarrow_internal_check_status\r\n File \"pyarrow/error.pxi\", line 123, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowTypeError: Could not convert tensor([[-1.0273, -0.8037, -0.6860],\r\n [-0.5034, -1.2685, -0.0558],\r\n [-1.0908, -1.1820, -0.3178],\r\n ...,\r\n [-0.8171, 0.1781, -0.5903],\r\n [ 0.4370, 1.9305, 0.5899],\r\n [-0.1426, 0.9053, -1.7559]]) with type Tensor: was not a sequence or recognized null for conversion to list type\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"/gpfs_new/data/users/lfainsin/stage-laurent-f/src/random_scripts/uses_random_data.py\", line 62, in <module>\r\n ds_normalized = ds.map(\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_dataset.py\", line 580, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, args, kwargs)\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_dataset.py\", line 545, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, args, kwargs)\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_dataset.py\", line 3087, in map\r\n for rank, done, content in Dataset._map_single(dataset_kwargs):\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_dataset.py\", line 3492, in _map_single\r\n writer.finalize()\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 584, in finalize\r\n self.write_examples_on_file()\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 448, in write_examples_on_file\r\n self.write_batch(batch_examples=batch_examples)\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 553, in write_batch\r\n arrays.append(pa.array(typed_sequence))\r\n File \"pyarrow/array.pxi\", line 236, in pyarrow.lib.array\r\n File \"pyarrow/array.pxi\", line 110, in pyarrow.lib._handle_arrow_array_protocol\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 223, in __arrow_array__\r\n return pa.array(cast_to_python_objects(data, only_1d_for_numpy=True))\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 446, in cast_to_python_objects\r\n return _cast_to_python_objects(\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 407, in _cast_to_python_objects\r\n [\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 408, in <listcomp>\r\n _cast_to_python_objects(\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 319, in _cast_to_python_objects\r\n [\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 320, in <listcomp>\r\n _cast_to_python_objects(\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 291, in _cast_to_python_objects\r\n if config.JAX_AVAILABLE and \"jax\" in sys.modules:\r\nKeyboardInterrupt\r\n```\r\n\r\n</details>\r\n\r\n<details>\r\n <summary>stack trace 3</summary>\r\n\r\n```python\r\n(pyg)[d623204@rosetta-bigviz01 stage-laurent-f]$ python src/random_scripts/uses_random_data.py \r\nFound cached dataset random_data (/local_scratch/lfainsin/.cache/huggingface/datasets/random_data/default/0.0.0/444e214e1d0e6298cfd3f2368323ec37073dc1439f618e19395b1f421c69b066)\r\nApplying mean/std: 99%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎ \| 989/1000 [00:01<00:00, 504.80 examples/s]Traceback (most recent call last): \r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 179, in __arrow_array__\r\n storage = to_pyarrow_listarray(data, pa_type)\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 1466, in to_pyarrow_listarray\r\n return pa.array(data, pa_type.storage_dtype)\r\n File \"pyarrow/array.pxi\", line 320, in pyarrow.lib.array\r\n File \"pyarrow/array.pxi\", line 39, in pyarrow.lib._sequence_to_array\r\n File \"pyarrow/error.pxi\", line 144, in pyarrow.lib.pyarrow_internal_check_status\r\n File \"pyarrow/error.pxi\", line 123, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowTypeError: Could not convert tensor([[-1.0273, -0.8037, -0.6860],\r\n [-0.5034, -1.2685, -0.0558],\r\n [-1.0908, -1.1820, -0.3178],\r\n ...,\r\n [-0.8171, 0.1781, -0.5903],\r\n [ 0.4370, 1.9305, 0.5899],\r\n [-0.1426, 0.9053, -1.7559]]) with type Tensor: was not a sequence or recognized null for conversion to list type\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_dataset.py\", line 3449, in _map_single\r\n writer.write(example)\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 490, in write\r\n self.write_examples_on_file()\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 448, in write_examples_on_file\r\n self.write_batch(batch_examples=batch_examples)\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 553, in write_batch\r\n arrays.append(pa.array(typed_sequence))\r\n File \"pyarrow/array.pxi\", line 236, in pyarrow.lib.array\r\n File \"pyarrow/array.pxi\", line 110, in pyarrow.lib._handle_arrow_array_protocol\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 223, in __arrow_array__\r\n return pa.array(cast_to_python_objects(data, only_1d_for_numpy=True))\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 446, in cast_to_python_objects\r\n return _cast_to_python_objects(\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 407, in _cast_to_python_objects\r\n [\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 408, in <listcomp>\r\n _cast_to_python_objects(\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 319, in _cast_to_python_objects\r\n [\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 320, in <listcomp>\r\n _cast_to_python_objects(\r\nKeyboardInterrupt\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 179, in __arrow_array__\r\n storage = to_pyarrow_listarray(data, pa_type)\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 1466, in to_pyarrow_listarray\r\n return pa.array(data, pa_type.storage_dtype)\r\n File \"pyarrow/array.pxi\", line 320, in pyarrow.lib.array\r\n File \"pyarrow/array.pxi\", line 39, in pyarrow.lib._sequence_to_array\r\n File \"pyarrow/error.pxi\", line 144, in pyarrow.lib.pyarrow_internal_check_status\r\n File \"pyarrow/error.pxi\", line 123, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowTypeError: Could not convert tensor([[-1.0273, -0.8037, -0.6860],\r\n [-0.5034, -1.2685, -0.0558],\r\n [-1.0908, -1.1820, -0.3178],\r\n ...,\r\n [-0.8171, 0.1781, -0.5903],\r\n [ 0.4370, 1.9305, 0.5899],\r\n [-0.1426, 0.9053, -1.7559]]) with type Tensor: was not a sequence or recognized null for conversion to list type\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"/gpfs_new/data/users/lfainsin/stage-laurent-f/src/random_scripts/uses_random_data.py\", line 62, in <module>\r\n ds_normalized = ds.map(\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_dataset.py\", line 580, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, args, kwargs)\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_dataset.py\", line 545, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, args, kwargs)\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_dataset.py\", line 3087, in map\r\n for rank, done, content in Dataset._map_single(dataset_kwargs):\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_dataset.py\", line 3492, in _map_single\r\n writer.finalize()\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 584, in finalize\r\n self.write_examples_on_file()\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 448, in write_examples_on_file\r\n self.write_batch(batch_examples=batch_examples)\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 553, in write_batch\r\n arrays.append(pa.array(typed_sequence))\r\n File \"pyarrow/array.pxi\", line 236, in pyarrow.lib.array\r\n File \"pyarrow/array.pxi\", line 110, in pyarrow.lib._handle_arrow_array_protocol\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 223, in __arrow_array__\r\n return pa.array(cast_to_python_objects(data, only_1d_for_numpy=True))\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 446, in cast_to_python_objects\r\n return _cast_to_python_objects(\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 407, in _cast_to_python_objects\r\n [\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 408, in <listcomp>\r\n _cast_to_python_objects(\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 319, in _cast_to_python_objects\r\n [\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 320, in <listcomp>\r\n _cast_to_python_objects(\r\n File \"/local_scratch/lfainsin/.conda/envs/pyg/lib/python3.10/site-packages/datasets/features/features.py\", line 298, in _cast_to_python_objects\r\n if obj.ndim == 0:\r\nKeyboardInterrupt\r\n```\r\n\r\n</details>\r\n" ]	2023-07-26T14:00:40	2023-07-28T09:21:07	null	CONTRIBUTOR	null	null	null	### Describe the bug Hi ! I'm currently working with a large (~150GB) unnormalized dataset at work. The dataset is available on a read-only filesystem internally, and I use a [loading script](https://huggingface.co/docs/datasets/dataset_script) to retreive it. I want to normalize the features of the dataset, meaning I need to compute the mean and standard deviation metric for each feature of the entire dataset. I cannot load the entire dataset to RAM as it is too big, so following [this discussion on the huggingface discourse](https://discuss.huggingface.co/t/copy-columns-in-a-dataset-and-compute-statistics-for-a-column/22157) I am using a [map operation](https://huggingface.co/docs/datasets/v2.14.0/en/package_reference/main_classes#datasets.Dataset.map) to first compute the metrics and a second map operation to apply them on the dataset. The problem lies in the second mapping, as it gets stuck at ~99%. By checking what the process does (using `htop` and `strace`) it seems to be doing a lot of I/O operations, and I'm not sure why. Obviously, I could always normalize the dataset externally and then load it using a loading script. However, since the internal dataset is updated fairly frequently, using the library to perform normalization automatically would make it much easier for me. ### Steps to reproduce the bug I'm able to reproduce the problem using the following scripts: ```python # random_data.py import datasets import torch _VERSION = "1.0.0" class RandomDataset(datasets.GeneratorBasedBuilder): def _info(self): return datasets.DatasetInfo( version=_VERSION, supervised_keys=None, features=datasets.Features( { "positions": datasets.Array2D( shape=(30000, 3), dtype="float32", ), "normals": datasets.Array2D( shape=(30000, 3), dtype="float32", ), "features": datasets.Array2D( shape=(30000, 6), dtype="float32", ), "scalars": datasets.Sequence( feature=datasets.Value("float32"), length=20, ), }, ), ) def _split_generators(self, dl_manager): return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, # type: ignore gen_kwargs={"nb_samples": 1000}, ), datasets.SplitGenerator( name=datasets.Split.TEST, # type: ignore gen_kwargs={"nb_samples": 100}, ), ] def _generate_examples(self, nb_samples: int): for idx in range(nb_samples): yield idx, { "positions": torch.randn(30000, 3), "normals": torch.randn(30000, 3), "features": torch.randn(30000, 6), "scalars": torch.randn(20), } ``` ```python # main.py import datasets import torch def apply_mean_std( dataset: datasets.Dataset, means: dict[str, torch.Tensor], stds: dict[str, torch.Tensor], ) -> dict[str, torch.Tensor]: """Normalize the dataset using the mean and standard deviation of each feature. Args: dataset (`Dataset`): A huggingface dataset. mean (`dict[str, Tensor]`): A dictionary containing the mean of each feature. std (`dict[str, Tensor]`): A dictionary containing the standard deviation of each feature. Returns: dict: A dictionary containing the normalized dataset. """ result = {} for key in means.keys(): # extract data from dataset data: torch.Tensor = dataset[key] # type: ignore # extract mean and std from dict mean = means[key] # type: ignore std = stds[key] # type: ignore # normalize data normalized_data = (data - mean) / std result[key] = normalized_data return result # get dataset ds = datasets.load_dataset( path="random_data.py", split="train", ).with_format("torch") # compute mean (along last axis) means = {key: torch.zeros(ds[key][0].shape[-1]) for key in ds.column_names} means_sq = {key: torch.zeros(ds[key][0].shape[-1]) for key in ds.column_names} for batch in ds.iter(batch_size=8): for key in ds.column_names: data = batch[key] batch_size = data.shape[0] data = data.reshape(-1, data.shape[-1]) means[key] += data.mean(dim=0) / len(ds) * batch_size means_sq[key] += (data*2).mean(dim=0) / len(ds) batch_size # compute std (along last axis) stds = {key: torch.sqrt(means_sq[key] - means[key] ** 2) for key in ds.column_names} # normalize each feature of the dataset ds_normalized = ds.map( desc="Applying mean/std", # type: ignore function=apply_mean_std, batched=False, fn_kwargs={ "means": means, "stds": stds, }, ) ``` ### Expected behavior Using the previous scripts, the `ds_normalized` mapping completes in ~5 minutes, but any subsequent use of `ds_normalized` is really really slow, for example reapplying `apply_mean_std` to `ds_normalized` takes forever. This is very strange, I'm sure I must be missing something, but I would still expect this to be faster. ### Environment info - `datasets` version: 2.13.1 - Platform: Linux-3.10.0-1160.66.1.el7.x86_64-x86_64-with-glibc2.17 - Python version: 3.10.12 - Huggingface_hub version: 0.15.1 - PyArrow version: 12.0.0 - Pandas version: 2.0.2	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6077/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6077/timeline	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6076	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6076/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6076/comments	https://api.github.com/repos/huggingface/datasets/issues/6076/events	https://github.com/huggingface/datasets/pull/6076	1,822,345,597	PR_kwDODunzps5WcGVR	6,076	No gzip encoding from github	{ "login": "lhoestq", "id": 42851186, "node_id": "MDQ6VXNlcjQyODUxMTg2", "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "gravatar_id": "", "url": "https://api.github.com/users/lhoestq", "html_url": "https://github.com/lhoestq", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "repos_url": "https://api.github.com/users/lhoestq/repos", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008191 / 0.011353 (-0.003162) \| 0.004669 / 0.011008 (-0.006339) \| 0.101315 / 0.038508 (0.062807) \| 0.090235 / 0.023109 (0.067126) \| 0.381265 / 0.275898 (0.105367) \| 0.418266 / 0.323480 (0.094786) \| 0.006292 / 0.007986 (-0.001693) \| 0.003979 / 0.004328 (-0.000349) \| 0.075946 / 0.004250 (0.071696) \| 0.070678 / 0.037052 (0.033625) \| 0.378006 / 0.258489 (0.119517) \| 0.425825 / 0.293841 (0.131984) \| 0.036325 / 0.128546 (-0.092221) \| 0.009814 / 0.075646 (-0.065832) \| 0.345687 / 0.419271 (-0.073584) \| 0.063846 / 0.043533 (0.020313) \| 0.386003 / 0.255139 (0.130864) \| 0.400875 / 0.283200 (0.117675) \| 0.027806 / 0.141683 (-0.113877) \| 1.814810 / 1.452155 (0.362655) \| 1.879897 / 1.492716 (0.387180) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.218684 / 0.018006 (0.200677) \| 0.501715 / 0.000490 (0.501225) \| 0.004808 / 0.000200 (0.004608) \| 0.000093 / 0.000054 (0.000039) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.035494 / 0.037411 (-0.001917) \| 0.100949 / 0.014526 (0.086423) \| 0.114639 / 0.176557 (-0.061917) \| 0.188908 / 0.737135 (-0.548227) \| 0.115794 / 0.296338 (-0.180545) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.462537 / 0.215209 (0.247328) \| 4.612469 / 2.077655 (2.534814) \| 2.298065 / 1.504120 (0.793945) \| 2.088738 / 1.541195 (0.547543) \| 2.188072 / 1.468490 (0.719582) \| 0.565412 / 4.584777 (-4.019364) \| 4.180394 / 3.745712 (0.434681) \| 3.848696 / 5.269862 (-1.421165) \| 2.391381 / 4.565676 (-2.174296) \| 0.067647 / 0.424275 (-0.356628) \| 0.008847 / 0.007607 (0.001240) \| 0.553288 / 0.226044 (0.327243) \| 5.517962 / 2.268929 (3.249033) \| 2.866622 / 55.444624 (-52.578002) \| 2.439025 / 6.876477 (-4.437452) \| 2.740156 / 2.142072 (0.598084) \| 0.694796 / 4.805227 (-4.110431) \| 0.159022 / 6.500664 (-6.341642) \| 0.074471 / 0.075469 (-0.000998) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.534979 / 1.841788 (-0.306808) \| 23.297273 / 8.074308 (15.222965) \| 16.859178 / 10.191392 (6.667786) \| 0.207594 / 0.680424 (-0.472830) \| 0.021990 / 0.534201 (-0.512211) \| 0.472059 / 0.579283 (-0.107224) \| 0.497632 / 0.434364 (0.063268) \| 0.565672 / 0.540337 (0.025335) \| 0.772485 / 1.386936 (-0.614451) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007777 / 0.011353 (-0.003576) \| 0.004679 / 0.011008 (-0.006329) \| 0.077317 / 0.038508 (0.038809) \| 0.087433 / 0.023109 (0.064324) \| 0.437389 / 0.275898 (0.161491) \| 0.479562 / 0.323480 (0.156082) \| 0.006137 / 0.007986 (-0.001849) \| 0.003938 / 0.004328 (-0.000390) \| 0.074769 / 0.004250 (0.070518) \| 0.066605 / 0.037052 (0.029553) \| 0.454865 / 0.258489 (0.196376) \| 0.485103 / 0.293841 (0.191262) \| 0.036540 / 0.128546 (-0.092006) \| 0.009983 / 0.075646 (-0.065664) \| 0.083566 / 0.419271 (-0.335706) \| 0.059527 / 0.043533 (0.015994) \| 0.449154 / 0.255139 (0.194015) \| 0.462542 / 0.283200 (0.179342) \| 0.027581 / 0.141683 (-0.114102) \| 1.776720 / 1.452155 (0.324565) \| 1.847920 / 1.492716 (0.355204) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.246792 / 0.018006 (0.228786) \| 0.494513 / 0.000490 (0.494024) \| 0.004376 / 0.000200 (0.004176) \| 0.000115 / 0.000054 (0.000061) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.037837 / 0.037411 (0.000426) \| 0.112752 / 0.014526 (0.098226) \| 0.121742 / 0.176557 (-0.054815) \| 0.189365 / 0.737135 (-0.547770) \| 0.124366 / 0.296338 (-0.171973) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.492890 / 0.215209 (0.277681) \| 4.920270 / 2.077655 (2.842615) \| 2.565350 / 1.504120 (1.061230) \| 2.378679 / 1.541195 (0.837484) \| 2.483794 / 1.468490 (1.015304) \| 0.579623 / 4.584777 (-4.005154) \| 4.195924 / 3.745712 (0.450212) \| 3.903382 / 5.269862 (-1.366479) \| 2.466884 / 4.565676 (-2.098793) \| 0.064145 / 0.424275 (-0.360130) \| 0.008695 / 0.007607 (0.001088) \| 0.579300 / 0.226044 (0.353256) \| 5.809064 / 2.268929 (3.540136) \| 3.145393 / 55.444624 (-52.299232) \| 2.832760 / 6.876477 (-4.043717) \| 3.020460 / 2.142072 (0.878388) \| 0.700235 / 4.805227 (-4.104992) \| 0.161262 / 6.500664 (-6.339402) \| 0.076484 / 0.075469 (0.001015) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.606504 / 1.841788 (-0.235284) \| 23.747863 / 8.074308 (15.673555) \| 17.281712 / 10.191392 (7.090320) \| 0.203874 / 0.680424 (-0.476550) \| 0.021839 / 0.534201 (-0.512362) \| 0.472365 / 0.579283 (-0.106918) \| 0.475150 / 0.434364 (0.040786) \| 0.571713 / 0.540337 (0.031376) \| 0.759210 / 1.386936 (-0.627726) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c3a7fc003b1d181d8e8ece24d5ebd442ec5d6519 \"CML watermark\")\n", "> Some questions: won't this have an impact on downloading time, once we do not longer compress the payload? What is the advantage of this approach over the one with block_size: 0?\r\n\r\nSurely, but this prevents random access which is needed at multiple places in the code (eg to check the compression type).\r\nGithub isn't a good place for big files anyway so we should be fine" ]	2023-07-26T12:46:07	2023-07-27T16:15:11	2023-07-27T16:14:40	MEMBER	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6076", "html_url": "https://github.com/huggingface/datasets/pull/6076", "diff_url": "https://github.com/huggingface/datasets/pull/6076.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6076.patch", "merged_at": "2023-07-27T16:14:40" }	Don't accept gzip encoding from github, otherwise some files are not streamable + seekable. fix https://huggingface.co/datasets/code_x_glue_cc_code_to_code_trans/discussions/2#64c0e0c1a04a514ba6303e84 and making sure https://github.com/huggingface/datasets/issues/2918 works as well	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6076/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6076/timeline	null	null	true
https://api.github.com/repos/huggingface/datasets/issues/6075	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6075/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6075/comments	https://api.github.com/repos/huggingface/datasets/issues/6075/events	https://github.com/huggingface/datasets/issues/6075	1,822,341,398	I_kwDODunzps5snrkW	6,075	Error loading music files using `load_dataset`	{ "login": "susnato", "id": 56069179, "node_id": "MDQ6VXNlcjU2MDY5MTc5", "avatar_url": "https://avatars.githubusercontent.com/u/56069179?v=4", "gravatar_id": "", "url": "https://api.github.com/users/susnato", "html_url": "https://github.com/susnato", "followers_url": "https://api.github.com/users/susnato/followers", "following_url": "https://api.github.com/users/susnato/following{/other_user}", "gists_url": "https://api.github.com/users/susnato/gists{/gist_id}", "starred_url": "https://api.github.com/users/susnato/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/susnato/subscriptions", "organizations_url": "https://api.github.com/users/susnato/orgs", "repos_url": "https://api.github.com/users/susnato/repos", "events_url": "https://api.github.com/users/susnato/events{/privacy}", "received_events_url": "https://api.github.com/users/susnato/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "This code behaves as expected on my local machine or in Colab. Which version of `soundfile` do you have installed? MP3 requires `soundfile>=0.12.1`.", "I upgraded the `soundfile` and it's working now! \r\nThanks @mariosasko for the help!" ]	2023-07-26T12:44:05	2023-07-26T13:08:08	2023-07-26T13:08:08	NONE	null	null	null	### Describe the bug I tried to load a music file using `datasets.load_dataset()` from the repository - https://huggingface.co/datasets/susnato/pop2piano_real_music_test I got the following error - ``` Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 2803, in __getitem__ return self._getitem(key) File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 2788, in _getitem formatted_output = format_table( File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/datasets/formatting/formatting.py", line 629, in format_table return formatter(pa_table, query_type=query_type) File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/datasets/formatting/formatting.py", line 398, in __call__ return self.format_column(pa_table) File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/datasets/formatting/formatting.py", line 442, in format_column column = self.python_features_decoder.decode_column(column, pa_table.column_names[0]) File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/datasets/formatting/formatting.py", line 218, in decode_column return self.features.decode_column(column, column_name) if self.features else column File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/datasets/features/features.py", line 1924, in decode_column [decode_nested_example(self[column_name], value) if value is not None else None for value in column] File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/datasets/features/features.py", line 1924, in <listcomp> [decode_nested_example(self[column_name], value) if value is not None else None for value in column] File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/datasets/features/features.py", line 1325, in decode_nested_example return schema.decode_example(obj, token_per_repo_id=token_per_repo_id) File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/datasets/features/audio.py", line 184, in decode_example array, sampling_rate = sf.read(f) File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/soundfile.py", line 372, in read with SoundFile(file, 'r', samplerate, channels, File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/soundfile.py", line 740, in __init__ self._file = self._open(file, mode_int, closefd) File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/soundfile.py", line 1264, in _open _error_check(_snd.sf_error(file_ptr), File "/home/susnato/anaconda3/envs/p2p/lib/python3.9/site-packages/soundfile.py", line 1455, in _error_check raise RuntimeError(prefix + _ffi.string(err_str).decode('utf-8', 'replace')) RuntimeError: Error opening <_io.BufferedReader name='/home/susnato/.cache/huggingface/datasets/downloads/d2b09cb974b967b13f91553297c40c0f02f3c0d4c8356350743598ff48d6f29e'>: Format not recognised. ``` ### Steps to reproduce the bug Code to reproduce the error - ```python from datasets import load_dataset ds = load_dataset("susnato/pop2piano_real_music_test", split="test") print(ds[0]) ``` ### Expected behavior I should be able to read the music file without any error. ### Environment info - `datasets` version: 2.14.0 - Platform: Linux-5.19.0-50-generic-x86_64-with-glibc2.35 - Python version: 3.9.16 - Huggingface_hub version: 0.15.1 - PyArrow version: 11.0.0 - Pandas version: 1.5.3	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6075/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6075/timeline	null	completed	false
https://api.github.com/repos/huggingface/datasets/issues/6074	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6074/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6074/comments	https://api.github.com/repos/huggingface/datasets/issues/6074/events	https://github.com/huggingface/datasets/pull/6074	1,822,299,128	PR_kwDODunzps5Wb8O_	6,074	Misc doc improvements	{ "login": "mariosasko", "id": 47462742, "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "gravatar_id": "", "url": "https://api.github.com/users/mariosasko", "html_url": "https://github.com/mariosasko", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "repos_url": "https://api.github.com/users/mariosasko/repos", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006616 / 0.011353 (-0.004737) \| 0.003915 / 0.011008 (-0.007093) \| 0.083271 / 0.038508 (0.044763) \| 0.072595 / 0.023109 (0.049485) \| 0.307224 / 0.275898 (0.031326) \| 0.337244 / 0.323480 (0.013764) \| 0.005296 / 0.007986 (-0.002690) \| 0.003325 / 0.004328 (-0.001003) \| 0.064589 / 0.004250 (0.060339) \| 0.056369 / 0.037052 (0.019316) \| 0.310829 / 0.258489 (0.052340) \| 0.345563 / 0.293841 (0.051722) \| 0.030551 / 0.128546 (-0.097995) \| 0.008519 / 0.075646 (-0.067127) \| 0.286368 / 0.419271 (-0.132903) \| 0.052498 / 0.043533 (0.008966) \| 0.308735 / 0.255139 (0.053596) \| 0.329234 / 0.283200 (0.046034) \| 0.022588 / 0.141683 (-0.119095) \| 1.453135 / 1.452155 (0.000981) \| 1.525956 / 1.492716 (0.033239) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.199417 / 0.018006 (0.181410) \| 0.454621 / 0.000490 (0.454131) \| 0.004928 / 0.000200 (0.004728) \| 0.000079 / 0.000054 (0.000025) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028436 / 0.037411 (-0.008975) \| 0.083722 / 0.014526 (0.069196) \| 0.095162 / 0.176557 (-0.081395) \| 0.153434 / 0.737135 (-0.583702) \| 0.099480 / 0.296338 (-0.196859) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.384647 / 0.215209 (0.169438) \| 3.838406 / 2.077655 (1.760751) \| 1.891267 / 1.504120 (0.387148) \| 1.751432 / 1.541195 (0.210238) \| 1.737443 / 1.468490 (0.268953) \| 0.487758 / 4.584777 (-4.097019) \| 3.635925 / 3.745712 (-0.109787) \| 5.208718 / 5.269862 (-0.061144) \| 3.029374 / 4.565676 (-1.536302) \| 0.057613 / 0.424275 (-0.366662) \| 0.007177 / 0.007607 (-0.000430) \| 0.455596 / 0.226044 (0.229552) \| 4.559969 / 2.268929 (2.291040) \| 2.325321 / 55.444624 (-53.119303) \| 2.034924 / 6.876477 (-4.841552) \| 2.163869 / 2.142072 (0.021796) \| 0.583477 / 4.805227 (-4.221750) \| 0.132870 / 6.500664 (-6.367795) \| 0.059618 / 0.075469 (-0.015851) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.263751 / 1.841788 (-0.578037) \| 19.740004 / 8.074308 (11.665696) \| 14.410980 / 10.191392 (4.219588) \| 0.170367 / 0.680424 (-0.510057) \| 0.018225 / 0.534201 (-0.515976) \| 0.390101 / 0.579283 (-0.189182) \| 0.404298 / 0.434364 (-0.030066) \| 0.455295 / 0.540337 (-0.085043) \| 0.621179 / 1.386936 (-0.765757) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006580 / 0.011353 (-0.004773) \| 0.004078 / 0.011008 (-0.006930) \| 0.065842 / 0.038508 (0.027334) \| 0.074494 / 0.023109 (0.051385) \| 0.403644 / 0.275898 (0.127746) \| 0.430204 / 0.323480 (0.106724) \| 0.005343 / 0.007986 (-0.002643) \| 0.003366 / 0.004328 (-0.000963) \| 0.064858 / 0.004250 (0.060607) \| 0.056252 / 0.037052 (0.019200) \| 0.412556 / 0.258489 (0.154067) \| 0.434099 / 0.293841 (0.140258) \| 0.031518 / 0.128546 (-0.097028) \| 0.008543 / 0.075646 (-0.067104) \| 0.071658 / 0.419271 (-0.347613) \| 0.049962 / 0.043533 (0.006430) \| 0.398511 / 0.255139 (0.143372) \| 0.415908 / 0.283200 (0.132708) \| 0.025011 / 0.141683 (-0.116672) \| 1.492350 / 1.452155 (0.040195) \| 1.552996 / 1.492716 (0.060280) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.204971 / 0.018006 (0.186964) \| 0.439965 / 0.000490 (0.439475) \| 0.002071 / 0.000200 (0.001872) \| 0.000084 / 0.000054 (0.000029) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031673 / 0.037411 (-0.005738) \| 0.087529 / 0.014526 (0.073004) \| 0.099882 / 0.176557 (-0.076675) \| 0.156994 / 0.737135 (-0.580141) \| 0.101421 / 0.296338 (-0.194918) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.407480 / 0.215209 (0.192271) \| 4.069123 / 2.077655 (1.991468) \| 2.081288 / 1.504120 (0.577169) \| 1.920367 / 1.541195 (0.379172) \| 1.981053 / 1.468490 (0.512563) \| 0.481995 / 4.584777 (-4.102782) \| 3.546486 / 3.745712 (-0.199226) \| 5.133150 / 5.269862 (-0.136712) \| 3.056444 / 4.565676 (-1.509232) \| 0.056650 / 0.424275 (-0.367625) \| 0.007746 / 0.007607 (0.000139) \| 0.490891 / 0.226044 (0.264847) \| 4.902160 / 2.268929 (2.633232) \| 2.564726 / 55.444624 (-52.879899) \| 2.234988 / 6.876477 (-4.641489) \| 2.387656 / 2.142072 (0.245583) \| 0.576315 / 4.805227 (-4.228912) \| 0.132065 / 6.500664 (-6.368599) \| 0.060728 / 0.075469 (-0.014741) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.370568 / 1.841788 (-0.471220) \| 19.883159 / 8.074308 (11.808851) \| 14.442066 / 10.191392 (4.250674) \| 0.150119 / 0.680424 (-0.530305) \| 0.018359 / 0.534201 (-0.515842) \| 0.394128 / 0.579283 (-0.185155) \| 0.411697 / 0.434364 (-0.022667) \| 0.460580 / 0.540337 (-0.079757) \| 0.608490 / 1.386936 (-0.778446) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#035d0cf842b82b14059999baa78e8d158dfbed12 \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "merging now if you don't mind - this way I can make a patch release" ]	2023-07-26T12:20:54	2023-07-27T16:16:28	2023-07-27T16:16:02	CONTRIBUTOR	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6074", "html_url": "https://github.com/huggingface/datasets/pull/6074", "diff_url": "https://github.com/huggingface/datasets/pull/6074.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6074.patch", "merged_at": "2023-07-27T16:16:02" }	Removes the warning about requiring to write a dataset loading script to define multiple configurations, as the README YAML can be used instead (for simple cases). Also, deletes the section about using the `BatchSampler` in `torch<=1.12.1` to speed up loading, as `torch 1.12.1` is over a year old (and `torch 2.0` has been out for a while).	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6074/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6074/timeline	null	null	true
https://api.github.com/repos/huggingface/datasets/issues/6073	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6073/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6073/comments	https://api.github.com/repos/huggingface/datasets/issues/6073/events	https://github.com/huggingface/datasets/issues/6073	1,822,167,804	I_kwDODunzps5snBL8	6,073	version2.3.2 load_dataset()data_files can't include .xxxx in path	{ "login": "BUAAChuanWang", "id": 45893496, "node_id": "MDQ6VXNlcjQ1ODkzNDk2", "avatar_url": "https://avatars.githubusercontent.com/u/45893496?v=4", "gravatar_id": "", "url": "https://api.github.com/users/BUAAChuanWang", "html_url": "https://github.com/BUAAChuanWang", "followers_url": "https://api.github.com/users/BUAAChuanWang/followers", "following_url": "https://api.github.com/users/BUAAChuanWang/following{/other_user}", "gists_url": "https://api.github.com/users/BUAAChuanWang/gists{/gist_id}", "starred_url": "https://api.github.com/users/BUAAChuanWang/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/BUAAChuanWang/subscriptions", "organizations_url": "https://api.github.com/users/BUAAChuanWang/orgs", "repos_url": "https://api.github.com/users/BUAAChuanWang/repos", "events_url": "https://api.github.com/users/BUAAChuanWang/events{/privacy}", "received_events_url": "https://api.github.com/users/BUAAChuanWang/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	null	[ "Version 2.3.2 is over one year old, so please use the latest release (2.14.0) to get the expected behavior. Version 2.3.2 does not contain some fixes we made to fix resolving hidden files/directories (starting with a dot)." ]	2023-07-26T11:09:31	2023-07-26T12:34:45	null	NONE	null	null	null	### Describe the bug First, I cd workdir. Then, I just use load_dataset("json", data_file={"train":"/a/b/c/.d/train/train.json", "test":"/a/b/c/.d/train/test.json"}) that couldn't work and <FileNotFoundError: Unable to find '/a/b/c/.d/train/train.jsonl' at /a/b/c/.d/> And I debug, it is fine in version2.1.2 So there maybe a bug in path join. Here is the whole bug report: /x/datasets/loa │ │ d.py:1656 in load_dataset │ │ │ │ 1653 │ ignore_verifications = ignore_verifications or save_infos │ │ 1654 │ │ │ 1655 │ # Create a dataset builder │ │ ❱ 1656 │ builder_instance = load_dataset_builder( │ │ 1657 │ │ path=path, │ │ 1658 │ │ name=name, │ │ 1659 │ │ data_dir=data_dir, │ │ │ │ x/datasets/loa │ │ d.py:1439 in load_dataset_builder │ │ │ │ 1436 │ if use_auth_token is not None: │ │ 1437 │ │ download_config = download_config.copy() if download_config e │ │ 1438 │ │ download_config.use_auth_token = use_auth_token │ │ ❱ 1439 │ dataset_module = dataset_module_factory( │ │ 1440 │ │ path, │ │ 1441 │ │ revision=revision, │ │ 1442 │ │ download_config=download_config, │ │ │ │ x/datasets/loa │ │ d.py:1097 in dataset_module_factory │ │ │ │ 1094 │ │ │ 1095 │ # Try packaged │ │ 1096 │ if path in _PACKAGED_DATASETS_MODULES: │ │ ❱ 1097 │ │ return PackagedDatasetModuleFactory( │ │ 1098 │ │ │ path, │ │ 1099 │ │ │ data_dir=data_dir, │ │ 1100 │ │ │ data_files=data_files, │ │ │ │x/datasets/loa │ │ d.py:743 in get_module │ │ │ │ 740 │ │ │ if self.data_dir is not None │ │ 741 │ │ │ else get_patterns_locally(str(Path().resolve())) │ │ 742 │ │ ) │ │ ❱ 743 │ │ data_files = DataFilesDict.from_local_or_remote( │ │ 744 │ │ │ patterns, │ │ 745 │ │ │ use_auth_token=self.download_config.use_auth_token, │ │ 746 │ │ │ base_path=str(Path(self.data_dir).resolve()) if self.data │ │ │ │ x/datasets/dat │ │ a_files.py:590 in from_local_or_remote │ │ │ │ 587 │ │ out = cls() │ │ 588 │ │ for key, patterns_for_key in patterns.items(): │ │ 589 │ │ │ out[key] = ( │ │ ❱ 590 │ │ │ │ DataFilesList.from_local_or_remote( │ │ 591 │ │ │ │ │ patterns_for_key, │ │ 592 │ │ │ │ │ base_path=base_path, │ │ 593 │ │ │ │ │ allowed_extensions=allowed_extensions, │ │ │ │ /x/datasets/dat │ │ a_files.py:558 in from_local_or_remote │ │ │ │ 555 │ │ use_auth_token: Optional[Union[bool, str]] = None, │ │ 556 │ ) -> "DataFilesList": │ │ 557 │ │ base_path = base_path if base_path is not None else str(Path() │ │ ❱ 558 │ │ data_files = resolve_patterns_locally_or_by_urls(base_path, pa │ │ 559 │ │ origin_metadata = _get_origin_metadata_locally_or_by_urls(data │ │ 560 │ │ return cls(data_files, origin_metadata) │ │ 561 │ │ │ │ /x/datasets/dat │ │ a_files.py:195 in resolve_patterns_locally_or_by_urls │ │ │ │ 192 │ │ if is_remote_url(pattern): │ │ 193 │ │ │ data_files.append(Url(pattern)) │ │ 194 │ │ else: │ │ ❱ 195 │ │ │ for path in _resolve_single_pattern_locally(base_path, pat │ │ 196 │ │ │ │ data_files.append(path) │ │ 197 │ │ │ 198 │ if not data_files: │ │ │ │ /x/datasets/dat │ │ a_files.py:145 in _resolve_single_pattern_locally │ │ │ │ 142 │ │ error_msg = f"Unable to find '{pattern}' at {Path(base_path).r │ │ 143 │ │ if allowed_extensions is not None: │ │ 144 │ │ │ error_msg += f" with any supported extension {list(allowed │ │ ❱ 145 │ │ raise FileNotFoundError(error_msg) │ │ 146 │ return sorted(out) │ │ 147 ### Steps to reproduce the bug 1. Version=2.3.2 2. In shell, cd workdir.(cd /a/b/c/.d/) 3. load_dataset("json", data_file={"train":"/a/b/c/.d/train/train.json", "test":"/a/b/c/.d/train/test.json"}) ### Expected behavior fix it please~ ### Environment info 2.3.2	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6073/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6073/timeline	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6072	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6072/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6072/comments	https://api.github.com/repos/huggingface/datasets/issues/6072/events	https://github.com/huggingface/datasets/pull/6072	1,822,123,560	PR_kwDODunzps5WbWFN	6,072	Fix fsspec storage_options from load_dataset	{ "login": "lhoestq", "id": 42851186, "node_id": "MDQ6VXNlcjQyODUxMTg2", "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "gravatar_id": "", "url": "https://api.github.com/users/lhoestq", "html_url": "https://github.com/lhoestq", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "repos_url": "https://api.github.com/users/lhoestq/repos", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007617 / 0.011353 (-0.003736) \| 0.004580 / 0.011008 (-0.006428) \| 0.100913 / 0.038508 (0.062405) \| 0.087703 / 0.023109 (0.064594) \| 0.424159 / 0.275898 (0.148261) \| 0.467195 / 0.323480 (0.143715) \| 0.006890 / 0.007986 (-0.001096) \| 0.003765 / 0.004328 (-0.000564) \| 0.077513 / 0.004250 (0.073262) \| 0.064889 / 0.037052 (0.027837) \| 0.422349 / 0.258489 (0.163860) \| 0.477391 / 0.293841 (0.183550) \| 0.036025 / 0.128546 (-0.092522) \| 0.009939 / 0.075646 (-0.065707) \| 0.342409 / 0.419271 (-0.076862) \| 0.061568 / 0.043533 (0.018035) \| 0.431070 / 0.255139 (0.175931) \| 0.462008 / 0.283200 (0.178809) \| 0.027480 / 0.141683 (-0.114203) \| 1.802271 / 1.452155 (0.350116) \| 1.861336 / 1.492716 (0.368620) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.255806 / 0.018006 (0.237800) \| 0.507969 / 0.000490 (0.507479) \| 0.010060 / 0.000200 (0.009860) \| 0.000112 / 0.000054 (0.000058) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032286 / 0.037411 (-0.005125) \| 0.104468 / 0.014526 (0.089942) \| 0.112707 / 0.176557 (-0.063850) \| 0.181285 / 0.737135 (-0.555850) \| 0.113180 / 0.296338 (-0.183158) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.449265 / 0.215209 (0.234056) \| 4.465941 / 2.077655 (2.388287) \| 2.177889 / 1.504120 (0.673769) \| 1.969864 / 1.541195 (0.428669) \| 2.077502 / 1.468490 (0.609011) \| 0.561607 / 4.584777 (-4.023170) \| 4.281873 / 3.745712 (0.536161) \| 4.975352 / 5.269862 (-0.294510) \| 2.907121 / 4.565676 (-1.658555) \| 0.070205 / 0.424275 (-0.354070) \| 0.009164 / 0.007607 (0.001557) \| 0.581921 / 0.226044 (0.355876) \| 5.538667 / 2.268929 (3.269739) \| 2.798853 / 55.444624 (-52.645771) \| 2.314015 / 6.876477 (-4.562462) \| 2.584836 / 2.142072 (0.442763) \| 0.672333 / 4.805227 (-4.132894) \| 0.153828 / 6.500664 (-6.346836) \| 0.069757 / 0.075469 (-0.005712) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.559670 / 1.841788 (-0.282118) \| 23.994639 / 8.074308 (15.920331) \| 16.856160 / 10.191392 (6.664768) \| 0.195555 / 0.680424 (-0.484869) \| 0.021586 / 0.534201 (-0.512615) \| 0.469295 / 0.579283 (-0.109989) \| 0.481582 / 0.434364 (0.047218) \| 0.588667 / 0.540337 (0.048329) \| 0.734347 / 1.386936 (-0.652589) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009614 / 0.011353 (-0.001739) \| 0.004616 / 0.011008 (-0.006392) \| 0.077223 / 0.038508 (0.038715) \| 0.103074 / 0.023109 (0.079965) \| 0.447834 / 0.275898 (0.171936) \| 0.524696 / 0.323480 (0.201216) \| 0.007120 / 0.007986 (-0.000866) \| 0.003890 / 0.004328 (-0.000438) \| 0.076406 / 0.004250 (0.072156) \| 0.073488 / 0.037052 (0.036436) \| 0.466221 / 0.258489 (0.207732) \| 0.532206 / 0.293841 (0.238365) \| 0.037596 / 0.128546 (-0.090950) \| 0.010029 / 0.075646 (-0.065617) \| 0.084313 / 0.419271 (-0.334959) \| 0.060088 / 0.043533 (0.016555) \| 0.437792 / 0.255139 (0.182653) \| 0.512850 / 0.283200 (0.229650) \| 0.032424 / 0.141683 (-0.109259) \| 1.762130 / 1.452155 (0.309975) \| 1.946097 / 1.492716 (0.453381) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.250774 / 0.018006 (0.232768) \| 0.506869 / 0.000490 (0.506379) \| 0.008232 / 0.000200 (0.008032) \| 0.000164 / 0.000054 (0.000110) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.037779 / 0.037411 (0.000368) \| 0.111933 / 0.014526 (0.097407) \| 0.122385 / 0.176557 (-0.054172) \| 0.190372 / 0.737135 (-0.546763) \| 0.122472 / 0.296338 (-0.173866) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.488502 / 0.215209 (0.273293) \| 4.878114 / 2.077655 (2.800459) \| 2.504144 / 1.504120 (1.000024) \| 2.321077 / 1.541195 (0.779883) \| 2.416797 / 1.468490 (0.948307) \| 0.583582 / 4.584777 (-4.001195) \| 4.277896 / 3.745712 (0.532184) \| 3.874780 / 5.269862 (-1.395082) \| 2.540099 / 4.565676 (-2.025577) \| 0.068734 / 0.424275 (-0.355541) \| 0.009158 / 0.007607 (0.001550) \| 0.578401 / 0.226044 (0.352357) \| 5.763354 / 2.268929 (3.494426) \| 3.167771 / 55.444624 (-52.276853) \| 2.675220 / 6.876477 (-4.201257) \| 2.920927 / 2.142072 (0.778855) \| 0.673948 / 4.805227 (-4.131280) \| 0.157908 / 6.500664 (-6.342756) \| 0.071672 / 0.075469 (-0.003797) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.635120 / 1.841788 (-0.206668) \| 24.853480 / 8.074308 (16.779172) \| 17.162978 / 10.191392 (6.971586) \| 0.209577 / 0.680424 (-0.470847) \| 0.030110 / 0.534201 (-0.504091) \| 0.546970 / 0.579283 (-0.032313) \| 0.581912 / 0.434364 (0.147548) \| 0.571460 / 0.540337 (0.031123) \| 0.823411 / 1.386936 (-0.563525) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#83b792dddd074ccd007c407f942f6870aac7ee84 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006674 / 0.011353 (-0.004679) \| 0.004198 / 0.011008 (-0.006810) \| 0.084859 / 0.038508 (0.046351) \| 0.076065 / 0.023109 (0.052955) \| 0.316065 / 0.275898 (0.040167) \| 0.352097 / 0.323480 (0.028617) \| 0.005610 / 0.007986 (-0.002376) \| 0.003600 / 0.004328 (-0.000729) \| 0.064921 / 0.004250 (0.060671) \| 0.054493 / 0.037052 (0.017441) \| 0.318125 / 0.258489 (0.059636) \| 0.370183 / 0.293841 (0.076342) \| 0.031141 / 0.128546 (-0.097405) \| 0.008755 / 0.075646 (-0.066891) \| 0.288241 / 0.419271 (-0.131030) \| 0.052379 / 0.043533 (0.008846) \| 0.328147 / 0.255139 (0.073008) \| 0.347548 / 0.283200 (0.064348) \| 0.024393 / 0.141683 (-0.117290) \| 1.480646 / 1.452155 (0.028492) \| 1.575867 / 1.492716 (0.083151) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.268978 / 0.018006 (0.250971) \| 0.586470 / 0.000490 (0.585980) \| 0.003190 / 0.000200 (0.002990) \| 0.000081 / 0.000054 (0.000026) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030595 / 0.037411 (-0.006816) \| 0.083037 / 0.014526 (0.068511) \| 0.103706 / 0.176557 (-0.072850) \| 0.164104 / 0.737135 (-0.573031) \| 0.104536 / 0.296338 (-0.191802) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.382274 / 0.215209 (0.167065) \| 3.811878 / 2.077655 (1.734223) \| 1.840098 / 1.504120 (0.335978) \| 1.670949 / 1.541195 (0.129754) \| 1.763755 / 1.468490 (0.295264) \| 0.479526 / 4.584777 (-4.105251) \| 3.544443 / 3.745712 (-0.201269) \| 3.263004 / 5.269862 (-2.006858) \| 2.092801 / 4.565676 (-2.472875) \| 0.057167 / 0.424275 (-0.367108) \| 0.007450 / 0.007607 (-0.000157) \| 0.463731 / 0.226044 (0.237686) \| 4.624630 / 2.268929 (2.355701) \| 2.327078 / 55.444624 (-53.117546) \| 1.977734 / 6.876477 (-4.898743) \| 2.237152 / 2.142072 (0.095079) \| 0.573210 / 4.805227 (-4.232018) \| 0.132095 / 6.500664 (-6.368569) \| 0.060283 / 0.075469 (-0.015186) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.243404 / 1.841788 (-0.598384) \| 20.306778 / 8.074308 (12.232470) \| 14.561660 / 10.191392 (4.370268) \| 0.170826 / 0.680424 (-0.509598) \| 0.018574 / 0.534201 (-0.515627) \| 0.392367 / 0.579283 (-0.186916) \| 0.402918 / 0.434364 (-0.031446) \| 0.476629 / 0.540337 (-0.063708) \| 0.653709 / 1.386936 (-0.733227) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006562 / 0.011353 (-0.004791) \| 0.004092 / 0.011008 (-0.006916) \| 0.065951 / 0.038508 (0.027443) \| 0.078090 / 0.023109 (0.054981) \| 0.369679 / 0.275898 (0.093781) \| 0.411442 / 0.323480 (0.087962) \| 0.005646 / 0.007986 (-0.002339) \| 0.003537 / 0.004328 (-0.000791) \| 0.066024 / 0.004250 (0.061773) \| 0.058947 / 0.037052 (0.021895) \| 0.389219 / 0.258489 (0.130730) \| 0.414200 / 0.293841 (0.120359) \| 0.030372 / 0.128546 (-0.098174) \| 0.008631 / 0.075646 (-0.067015) \| 0.071692 / 0.419271 (-0.347580) \| 0.048035 / 0.043533 (0.004502) \| 0.376960 / 0.255139 (0.121821) \| 0.389847 / 0.283200 (0.106648) \| 0.023940 / 0.141683 (-0.117743) \| 1.487633 / 1.452155 (0.035479) \| 1.561680 / 1.492716 (0.068964) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.301467 / 0.018006 (0.283461) \| 0.544159 / 0.000490 (0.543669) \| 0.000408 / 0.000200 (0.000208) \| 0.000055 / 0.000054 (0.000001) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030939 / 0.037411 (-0.006472) \| 0.087432 / 0.014526 (0.072906) \| 0.103263 / 0.176557 (-0.073293) \| 0.154551 / 0.737135 (-0.582585) \| 0.104631 / 0.296338 (-0.191707) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.422348 / 0.215209 (0.207139) \| 4.206003 / 2.077655 (2.128348) \| 2.212619 / 1.504120 (0.708499) \| 2.049616 / 1.541195 (0.508421) \| 2.139093 / 1.468490 (0.670603) \| 0.489647 / 4.584777 (-4.095130) \| 3.523291 / 3.745712 (-0.222422) \| 3.277657 / 5.269862 (-1.992205) \| 2.111353 / 4.565676 (-2.454324) \| 0.057597 / 0.424275 (-0.366679) \| 0.007675 / 0.007607 (0.000068) \| 0.493068 / 0.226044 (0.267023) \| 4.939493 / 2.268929 (2.670565) \| 2.695995 / 55.444624 (-52.748630) \| 2.374904 / 6.876477 (-4.501573) \| 2.600110 / 2.142072 (0.458038) \| 0.586306 / 4.805227 (-4.218921) \| 0.134137 / 6.500664 (-6.366527) \| 0.061897 / 0.075469 (-0.013572) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.330628 / 1.841788 (-0.511160) \| 20.557964 / 8.074308 (12.483656) \| 14.251632 / 10.191392 (4.060240) \| 0.148772 / 0.680424 (-0.531652) \| 0.018383 / 0.534201 (-0.515817) \| 0.392552 / 0.579283 (-0.186731) \| 0.403959 / 0.434364 (-0.030405) \| 0.462154 / 0.540337 (-0.078184) \| 0.608832 / 1.386936 (-0.778104) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7a291b2b659a356199dff0ab004ad3845459034b \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007659 / 0.011353 (-0.003694) \| 0.004500 / 0.011008 (-0.006508) \| 0.100379 / 0.038508 (0.061871) \| 0.079731 / 0.023109 (0.056622) \| 0.381788 / 0.275898 (0.105890) \| 0.416524 / 0.323480 (0.093044) \| 0.004446 / 0.007986 (-0.003539) \| 0.003752 / 0.004328 (-0.000577) \| 0.074956 / 0.004250 (0.070706) \| 0.062885 / 0.037052 (0.025832) \| 0.383849 / 0.258489 (0.125360) \| 0.433906 / 0.293841 (0.140065) \| 0.036079 / 0.128546 (-0.092468) \| 0.009927 / 0.075646 (-0.065719) \| 0.343879 / 0.419271 (-0.075393) \| 0.061055 / 0.043533 (0.017523) \| 0.376703 / 0.255139 (0.121564) \| 0.428111 / 0.283200 (0.144911) \| 0.028667 / 0.141683 (-0.113016) \| 1.777755 / 1.452155 (0.325600) \| 1.878283 / 1.492716 (0.385567) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.220829 / 0.018006 (0.202823) \| 0.506406 / 0.000490 (0.505916) \| 0.005550 / 0.000200 (0.005350) \| 0.000123 / 0.000054 (0.000069) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034928 / 0.037411 (-0.002483) \| 0.103873 / 0.014526 (0.089347) \| 0.114352 / 0.176557 (-0.062204) \| 0.188218 / 0.737135 (-0.548918) \| 0.117343 / 0.296338 (-0.178995) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.459148 / 0.215209 (0.243939) \| 4.582092 / 2.077655 (2.504437) \| 2.275603 / 1.504120 (0.771483) \| 2.058155 / 1.541195 (0.516960) \| 2.163886 / 1.468490 (0.695396) \| 0.573033 / 4.584777 (-4.011744) \| 4.414891 / 3.745712 (0.669178) \| 7.280433 / 5.269862 (2.010572) \| 4.119414 / 4.565676 (-0.446262) \| 0.067432 / 0.424275 (-0.356843) \| 0.008687 / 0.007607 (0.001080) \| 0.556029 / 0.226044 (0.329984) \| 5.557192 / 2.268929 (3.288264) \| 2.921596 / 55.444624 (-52.523028) \| 2.520249 / 6.876477 (-4.356228) \| 2.778965 / 2.142072 (0.636893) \| 0.684765 / 4.805227 (-4.120462) \| 0.159228 / 6.500664 (-6.341436) \| 0.074015 / 0.075469 (-0.001454) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.534470 / 1.841788 (-0.307318) \| 23.630693 / 8.074308 (15.556385) \| 17.058142 / 10.191392 (6.866750) \| 0.200909 / 0.680424 (-0.479515) \| 0.021637 / 0.534201 (-0.512564) \| 0.467417 / 0.579283 (-0.111866) \| 0.460456 / 0.434364 (0.026092) \| 0.541131 / 0.540337 (0.000793) \| 0.728560 / 1.386936 (-0.658376) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007625 / 0.011353 (-0.003727) \| 0.004495 / 0.011008 (-0.006513) \| 0.076373 / 0.038508 (0.037865) \| 0.085260 / 0.023109 (0.062151) \| 0.475778 / 0.275898 (0.199880) \| 0.504604 / 0.323480 (0.181124) \| 0.006733 / 0.007986 (-0.001253) \| 0.003751 / 0.004328 (-0.000578) \| 0.074993 / 0.004250 (0.070743) \| 0.064704 / 0.037052 (0.027652) \| 0.490072 / 0.258489 (0.231583) \| 0.507560 / 0.293841 (0.213719) \| 0.036765 / 0.128546 (-0.091781) \| 0.009955 / 0.075646 (-0.065692) \| 0.082452 / 0.419271 (-0.336820) \| 0.057131 / 0.043533 (0.013598) \| 0.467664 / 0.255139 (0.212525) \| 0.482143 / 0.283200 (0.198943) \| 0.025396 / 0.141683 (-0.116287) \| 1.807587 / 1.452155 (0.355433) \| 1.853355 / 1.492716 (0.360639) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.250543 / 0.018006 (0.232537) \| 0.495685 / 0.000490 (0.495196) \| 0.000415 / 0.000200 (0.000215) \| 0.000063 / 0.000054 (0.000008) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.035795 / 0.037411 (-0.001616) \| 0.105954 / 0.014526 (0.091428) \| 0.120158 / 0.176557 (-0.056399) \| 0.181714 / 0.737135 (-0.555422) \| 0.121242 / 0.296338 (-0.175097) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.488241 / 0.215209 (0.273032) \| 4.866916 / 2.077655 (2.789262) \| 2.531530 / 1.504120 (1.027410) \| 2.360642 / 1.541195 (0.819448) \| 2.457320 / 1.468490 (0.988830) \| 0.571224 / 4.584777 (-4.013553) \| 4.339042 / 3.745712 (0.593330) \| 3.672812 / 5.269862 (-1.597050) \| 2.364535 / 4.565676 (-2.201142) \| 0.067004 / 0.424275 (-0.357271) \| 0.009019 / 0.007607 (0.001412) \| 0.563751 / 0.226044 (0.337707) \| 5.664917 / 2.268929 (3.395989) \| 3.043316 / 55.444624 (-52.401308) \| 2.682722 / 6.876477 (-4.193755) \| 2.863482 / 2.142072 (0.721409) \| 0.666171 / 4.805227 (-4.139056) \| 0.151862 / 6.500664 (-6.348802) \| 0.071199 / 0.075469 (-0.004271) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.601880 / 1.841788 (-0.239907) \| 23.069073 / 8.074308 (14.994765) \| 16.918377 / 10.191392 (6.726985) \| 0.173614 / 0.680424 (-0.506810) \| 0.021843 / 0.534201 (-0.512358) \| 0.470531 / 0.579283 (-0.108753) \| 0.471152 / 0.434364 (0.036788) \| 0.550968 / 0.540337 (0.010631) \| 0.718869 / 1.386936 (-0.668067) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f9e6eea46fc9503765c125395e30e26c1ae2e084 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007530 / 0.011353 (-0.003823) \| 0.004151 / 0.011008 (-0.006858) \| 0.098490 / 0.038508 (0.059982) \| 0.086955 / 0.023109 (0.063846) \| 0.362133 / 0.275898 (0.086235) \| 0.391402 / 0.323480 (0.067922) \| 0.006274 / 0.007986 (-0.001712) \| 0.003711 / 0.004328 (-0.000618) \| 0.073519 / 0.004250 (0.069269) \| 0.066170 / 0.037052 (0.029118) \| 0.379057 / 0.258489 (0.120568) \| 0.398132 / 0.293841 (0.104291) \| 0.033936 / 0.128546 (-0.094610) \| 0.009977 / 0.075646 (-0.065670) \| 0.323766 / 0.419271 (-0.095505) \| 0.078615 / 0.043533 (0.035082) \| 0.352403 / 0.255139 (0.097264) \| 0.386607 / 0.283200 (0.103407) \| 0.036579 / 0.141683 (-0.105103) \| 1.691899 / 1.452155 (0.239745) \| 1.819396 / 1.492716 (0.326680) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.216888 / 0.018006 (0.198882) \| 0.465781 / 0.000490 (0.465291) \| 0.006197 / 0.000200 (0.005997) \| 0.000086 / 0.000054 (0.000031) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032870 / 0.037411 (-0.004542) \| 0.096026 / 0.014526 (0.081500) \| 0.111093 / 0.176557 (-0.065464) \| 0.185982 / 0.737135 (-0.551154) \| 0.106967 / 0.296338 (-0.189371) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.441567 / 0.215209 (0.226358) \| 4.353813 / 2.077655 (2.276158) \| 2.176034 / 1.504120 (0.671914) \| 1.969631 / 1.541195 (0.428437) \| 2.048821 / 1.468490 (0.580330) \| 0.549144 / 4.584777 (-4.035633) \| 4.016166 / 3.745712 (0.270453) \| 3.764249 / 5.269862 (-1.505613) \| 2.293995 / 4.565676 (-2.271681) \| 0.065227 / 0.424275 (-0.359048) \| 0.008303 / 0.007607 (0.000695) \| 0.513783 / 0.226044 (0.287738) \| 5.247617 / 2.268929 (2.978689) \| 2.782114 / 55.444624 (-52.662510) \| 2.342776 / 6.876477 (-4.533701) \| 2.621569 / 2.142072 (0.479497) \| 0.679336 / 4.805227 (-4.125891) \| 0.152061 / 6.500664 (-6.348603) \| 0.070294 / 0.075469 (-0.005175) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.471778 / 1.841788 (-0.370010) \| 22.714904 / 8.074308 (14.640596) \| 15.607991 / 10.191392 (5.416599) \| 0.172592 / 0.680424 (-0.507832) \| 0.021799 / 0.534201 (-0.512402) \| 0.462740 / 0.579283 (-0.116543) \| 0.490885 / 0.434364 (0.056521) \| 0.552997 / 0.540337 (0.012660) \| 0.763784 / 1.386936 (-0.623152) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007466 / 0.011353 (-0.003886) \| 0.004322 / 0.011008 (-0.006686) \| 0.074331 / 0.038508 (0.035823) \| 0.085315 / 0.023109 (0.062206) \| 0.409284 / 0.275898 (0.133386) \| 0.464584 / 0.323480 (0.141104) \| 0.005651 / 0.007986 (-0.002335) \| 0.003577 / 0.004328 (-0.000751) \| 0.070250 / 0.004250 (0.066000) \| 0.059780 / 0.037052 (0.022727) \| 0.419668 / 0.258489 (0.161179) \| 0.462984 / 0.293841 (0.169143) \| 0.034159 / 0.128546 (-0.094387) \| 0.008999 / 0.075646 (-0.066647) \| 0.076302 / 0.419271 (-0.342969) \| 0.052274 / 0.043533 (0.008741) \| 0.425938 / 0.255139 (0.170799) \| 0.430399 / 0.283200 (0.147200) \| 0.025017 / 0.141683 (-0.116666) \| 1.680697 / 1.452155 (0.228542) \| 1.774677 / 1.492716 (0.281960) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.291514 / 0.018006 (0.273508) \| 0.461175 / 0.000490 (0.460685) \| 0.023061 / 0.000200 (0.022861) \| 0.000120 / 0.000054 (0.000065) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033950 / 0.037411 (-0.003462) \| 0.100032 / 0.014526 (0.085506) \| 0.118308 / 0.176557 (-0.058249) \| 0.183601 / 0.737135 (-0.553535) \| 0.116936 / 0.296338 (-0.179402) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.478779 / 0.215209 (0.263570) \| 4.709505 / 2.077655 (2.631850) \| 2.457442 / 1.504120 (0.953322) \| 2.213737 / 1.541195 (0.672542) \| 2.340642 / 1.468490 (0.872152) \| 0.567187 / 4.584777 (-4.017590) \| 3.923061 / 3.745712 (0.177349) \| 3.752989 / 5.269862 (-1.516873) \| 2.324028 / 4.565676 (-2.241649) \| 0.064471 / 0.424275 (-0.359804) \| 0.008845 / 0.007607 (0.001238) \| 0.547447 / 0.226044 (0.321402) \| 5.599435 / 2.268929 (3.330506) \| 2.980547 / 55.444624 (-52.464077) \| 2.754908 / 6.876477 (-4.121569) \| 2.832978 / 2.142072 (0.690906) \| 0.635059 / 4.805227 (-4.170168) \| 0.153478 / 6.500664 (-6.347187) \| 0.067146 / 0.075469 (-0.008323) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.555588 / 1.841788 (-0.286200) \| 22.828906 / 8.074308 (14.754597) \| 16.211008 / 10.191392 (6.019616) \| 0.168009 / 0.680424 (-0.512415) \| 0.021966 / 0.534201 (-0.512235) \| 0.464872 / 0.579283 (-0.114411) \| 0.460429 / 0.434364 (0.026065) \| 0.530498 / 0.540337 (-0.009839) \| 0.705020 / 1.386936 (-0.681916) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#deb9e703237c8310c5a6db04f54d54368e951edd \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005964 / 0.011353 (-0.005389) \| 0.003644 / 0.011008 (-0.007364) \| 0.079607 / 0.038508 (0.041099) \| 0.058387 / 0.023109 (0.035278) \| 0.312226 / 0.275898 (0.036328) \| 0.349206 / 0.323480 (0.025726) \| 0.004715 / 0.007986 (-0.003271) \| 0.002869 / 0.004328 (-0.001460) \| 0.061668 / 0.004250 (0.057417) \| 0.045694 / 0.037052 (0.008642) \| 0.313516 / 0.258489 (0.055027) \| 0.357543 / 0.293841 (0.063702) \| 0.027179 / 0.128546 (-0.101367) \| 0.007961 / 0.075646 (-0.067686) \| 0.262473 / 0.419271 (-0.156798) \| 0.045588 / 0.043533 (0.002055) \| 0.313102 / 0.255139 (0.057963) \| 0.368686 / 0.283200 (0.085486) \| 0.020556 / 0.141683 (-0.121127) \| 1.447258 / 1.452155 (-0.004897) \| 1.527319 / 1.492716 (0.034602) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.199417 / 0.018006 (0.181411) \| 0.422155 / 0.000490 (0.421665) \| 0.004972 / 0.000200 (0.004772) \| 0.000073 / 0.000054 (0.000018) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023539 / 0.037411 (-0.013872) \| 0.073055 / 0.014526 (0.058529) \| 0.083631 / 0.176557 (-0.092926) \| 0.145923 / 0.737135 (-0.591212) \| 0.083820 / 0.296338 (-0.212518) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.396305 / 0.215209 (0.181096) \| 3.967065 / 2.077655 (1.889410) \| 2.101109 / 1.504120 (0.596989) \| 1.958817 / 1.541195 (0.417622) \| 2.037894 / 1.468490 (0.569404) \| 0.496955 / 4.584777 (-4.087822) \| 3.078948 / 3.745712 (-0.666764) \| 3.363655 / 5.269862 (-1.906207) \| 2.087659 / 4.565676 (-2.478018) \| 0.057171 / 0.424275 (-0.367104) \| 0.006410 / 0.007607 (-0.001197) \| 0.470535 / 0.226044 (0.244491) \| 4.715259 / 2.268929 (2.446330) \| 2.355510 / 55.444624 (-53.089114) \| 2.025270 / 6.876477 (-4.851207) \| 2.210401 / 2.142072 (0.068329) \| 0.580538 / 4.805227 (-4.224689) \| 0.125068 / 6.500664 (-6.375596) \| 0.059871 / 0.075469 (-0.015598) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.245468 / 1.841788 (-0.596320) \| 18.322042 / 8.074308 (10.247734) \| 13.609726 / 10.191392 (3.418334) \| 0.143623 / 0.680424 (-0.536801) \| 0.017068 / 0.534201 (-0.517133) \| 0.330758 / 0.579283 (-0.248525) \| 0.339946 / 0.434364 (-0.094418) \| 0.377861 / 0.540337 (-0.162476) \| 0.524593 / 1.386936 (-0.862343) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006049 / 0.011353 (-0.005304) \| 0.003737 / 0.011008 (-0.007271) \| 0.062816 / 0.038508 (0.024308) \| 0.063768 / 0.023109 (0.040658) \| 0.362001 / 0.275898 (0.086103) \| 0.395251 / 0.323480 (0.071772) \| 0.004823 / 0.007986 (-0.003163) \| 0.002881 / 0.004328 (-0.001447) \| 0.061987 / 0.004250 (0.057737) \| 0.049950 / 0.037052 (0.012898) \| 0.362442 / 0.258489 (0.103953) \| 0.399321 / 0.293841 (0.105480) \| 0.027616 / 0.128546 (-0.100930) \| 0.007965 / 0.075646 (-0.067681) \| 0.068584 / 0.419271 (-0.350687) \| 0.044700 / 0.043533 (0.001168) \| 0.361011 / 0.255139 (0.105872) \| 0.386007 / 0.283200 (0.102807) \| 0.024621 / 0.141683 (-0.117061) \| 1.441497 / 1.452155 (-0.010657) \| 1.533145 / 1.492716 (0.040429) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.223446 / 0.018006 (0.205440) \| 0.411147 / 0.000490 (0.410657) \| 0.001821 / 0.000200 (0.001621) \| 0.000081 / 0.000054 (0.000027) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025661 / 0.037411 (-0.011751) \| 0.077838 / 0.014526 (0.063313) \| 0.086148 / 0.176557 (-0.090408) \| 0.140386 / 0.737135 (-0.596750) \| 0.088793 / 0.296338 (-0.207546) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.425209 / 0.215209 (0.210000) \| 4.250723 / 2.077655 (2.173068) \| 2.403437 / 1.504120 (0.899317) \| 2.283584 / 1.541195 (0.742390) \| 2.326870 / 1.468490 (0.858380) \| 0.504781 / 4.584777 (-4.079996) \| 3.017042 / 3.745712 (-0.728670) \| 4.643068 / 5.269862 (-0.626794) \| 2.535710 / 4.565676 (-2.029967) \| 0.058520 / 0.424275 (-0.365755) \| 0.006766 / 0.007607 (-0.000841) \| 0.500664 / 0.226044 (0.274620) \| 5.017073 / 2.268929 (2.748145) \| 2.668661 / 55.444624 (-52.775963) \| 2.335486 / 6.876477 (-4.540991) \| 2.486518 / 2.142072 (0.344445) \| 0.598795 / 4.805227 (-4.206432) \| 0.126395 / 6.500664 (-6.374269) \| 0.063154 / 0.075469 (-0.012315) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.358059 / 1.841788 (-0.483728) \| 18.615724 / 8.074308 (10.541416) \| 13.670934 / 10.191392 (3.479542) \| 0.134650 / 0.680424 (-0.545774) \| 0.016941 / 0.534201 (-0.517260) \| 0.335215 / 0.579283 (-0.244068) \| 0.356118 / 0.434364 (-0.078246) \| 0.393109 / 0.540337 (-0.147228) \| 0.534165 / 1.386936 (-0.852771) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#da7d3b557665f34e84cd151ffe9d80b45a19fe33 \"CML watermark\")\n" ]	2023-07-26T10:44:23	2023-07-27T12:51:51	2023-07-27T12:42:57	MEMBER	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6072", "html_url": "https://github.com/huggingface/datasets/pull/6072", "diff_url": "https://github.com/huggingface/datasets/pull/6072.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6072.patch", "merged_at": "2023-07-27T12:42:57" }	close https://github.com/huggingface/datasets/issues/6071	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6072/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6072/timeline	null	null	true
https://api.github.com/repos/huggingface/datasets/issues/6071	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6071/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6071/comments	https://api.github.com/repos/huggingface/datasets/issues/6071/events	https://github.com/huggingface/datasets/issues/6071	1,821,990,749	I_kwDODunzps5smV9d	6,071	storage_options provided to load_dataset not fully piping through since datasets 2.14.0	{ "login": "exs-avianello", "id": 128361578, "node_id": "U_kgDOB6akag", "avatar_url": "https://avatars.githubusercontent.com/u/128361578?v=4", "gravatar_id": "", "url": "https://api.github.com/users/exs-avianello", "html_url": "https://github.com/exs-avianello", "followers_url": "https://api.github.com/users/exs-avianello/followers", "following_url": "https://api.github.com/users/exs-avianello/following{/other_user}", "gists_url": "https://api.github.com/users/exs-avianello/gists{/gist_id}", "starred_url": "https://api.github.com/users/exs-avianello/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/exs-avianello/subscriptions", "organizations_url": "https://api.github.com/users/exs-avianello/orgs", "repos_url": "https://api.github.com/users/exs-avianello/repos", "events_url": "https://api.github.com/users/exs-avianello/events{/privacy}", "received_events_url": "https://api.github.com/users/exs-avianello/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "Hi ! Thanks for reporting, I opened a PR to fix this\r\n\r\nWhat filesystem are you using ?", "Hi @lhoestq ! Thank you so much 🙌 \r\n\r\nIt's a bit of a custom setup, but in practice I am using a [pyarrow.fs.S3FileSystem](https://arrow.apache.org/docs/python/generated/pyarrow.fs.S3FileSystem.html) (wrapped in a `fsspec.implementations.arrow.ArrowFSWrapper` [to make it](https://arrow.apache.org/docs/python/filesystems.html#using-arrow-filesystems-with-fsspec) `fsspec` compatible). I also register it as an entrypoint with `fsspec` so that it's the one that gets automatically resolved when looking for filesystems for the `s3` protocol\r\n\r\nIn my case the `storage_option` that seemed not getting piped through was the filesystem's `endpoint_override` that I use in some tests to point at a mock S3 bucket" ]	2023-07-26T09:37:20	2023-07-27T12:42:58	2023-07-27T12:42:58	NONE	null	null	null	### Describe the bug Since the latest release of `datasets` (`2.14.0`), custom filesystem `storage_options` passed to `load_dataset()` do not seem to propagate through all the way - leading to problems if loading data files that need those options to be set. I think this is because of the new `_prepare_path_and_storage_options()` (https://github.com/huggingface/datasets/pull/6028), which returns the right `storage_options` to use given a path and a `DownloadConfig` - but which might not be taking into account the extra `storage_options` explicitly provided e.g. through `load_dataset()` ### Steps to reproduce the bug ```python import fsspec import pandas as pd import datasets # Generate mock parquet file data_files = "demo.parquet" pd.DataFrame({"A": [1, 2, 3], "B": [4, 5, 6]}).to_parquet(data_files) _storage_options = {"x": 1, "y": 2} fs = fsspec.filesystem("file", **_storage_options) dataset = datasets.load_dataset( "parquet", data_files=data_files, storage_options=fs.storage_options ) ``` Looking at the `storage_options` resolved here: https://github.com/huggingface/datasets/blob/b0177910b32712f28d147879395e511207e39958/src/datasets/data_files.py#L331 they end up being `{}`, instead of propagating through the `storage_options` that were provided to `load_dataset` (`fs.storage_options`). As these then get used for the filesystem operation a few lines below https://github.com/huggingface/datasets/blob/b0177910b32712f28d147879395e511207e39958/src/datasets/data_files.py#L339 the call will fail if the user-provided `storage_options` were needed. --- A temporary workaround that seemed to work locally to bypass the problem was to bundle a duplicate of the `storage_options` into the `download_config`, so that they make their way all the way to `_prepare_path_and_storage_options()` and get extracted correctly: ```python dataset = datasets.load_dataset( "parquet", data_files=data_files, storage_options=fs.storage_options, download_config=datasets.DownloadConfig(storage_options={fs.protocol: fs.storage_options}), ) ``` ### Expected behavior `storage_options` provided to `load_dataset` take effect in all backend filesystem operations. ### Environment info datasets==2.14.0	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6071/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6071/timeline	null	completed	false
https://api.github.com/repos/huggingface/datasets/issues/6070	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6070/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6070/comments	https://api.github.com/repos/huggingface/datasets/issues/6070/events	https://github.com/huggingface/datasets/pull/6070	1,820,836,330	PR_kwDODunzps5WXDLc	6,070	Fix Quickstart notebook link	{ "login": "mariosasko", "id": 47462742, "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "gravatar_id": "", "url": "https://api.github.com/users/mariosasko", "html_url": "https://github.com/mariosasko", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "repos_url": "https://api.github.com/users/mariosasko/repos", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008473 / 0.011353 (-0.002880) \| 0.004734 / 0.011008 (-0.006274) \| 0.103895 / 0.038508 (0.065387) \| 0.071838 / 0.023109 (0.048729) \| 0.379949 / 0.275898 (0.104051) \| 0.397375 / 0.323480 (0.073895) \| 0.006695 / 0.007986 (-0.001290) \| 0.004536 / 0.004328 (0.000207) \| 0.076151 / 0.004250 (0.071901) \| 0.058690 / 0.037052 (0.021638) \| 0.379937 / 0.258489 (0.121448) \| 0.411833 / 0.293841 (0.117992) \| 0.046805 / 0.128546 (-0.081741) \| 0.013689 / 0.075646 (-0.061958) \| 0.327896 / 0.419271 (-0.091375) \| 0.063873 / 0.043533 (0.020340) \| 0.378451 / 0.255139 (0.123312) \| 0.398725 / 0.283200 (0.115525) \| 0.034961 / 0.141683 (-0.106722) \| 1.604999 / 1.452155 (0.152845) \| 1.748370 / 1.492716 (0.255654) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.224634 / 0.018006 (0.206628) \| 0.548468 / 0.000490 (0.547979) \| 0.005049 / 0.000200 (0.004849) \| 0.000097 / 0.000054 (0.000043) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028144 / 0.037411 (-0.009267) \| 0.092184 / 0.014526 (0.077659) \| 0.102987 / 0.176557 (-0.073570) \| 0.176987 / 0.737135 (-0.560149) \| 0.103093 / 0.296338 (-0.193246) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.578410 / 0.215209 (0.363201) \| 5.664781 / 2.077655 (3.587126) \| 2.487763 / 1.504120 (0.983643) \| 2.254213 / 1.541195 (0.713018) \| 2.239693 / 1.468490 (0.771202) \| 0.810380 / 4.584777 (-3.774397) \| 5.036540 / 3.745712 (1.290828) \| 7.064695 / 5.269862 (1.794834) \| 4.215101 / 4.565676 (-0.350575) \| 0.089792 / 0.424275 (-0.334483) \| 0.008487 / 0.007607 (0.000879) \| 0.692292 / 0.226044 (0.466248) \| 6.780226 / 2.268929 (4.511297) \| 3.245510 / 55.444624 (-52.199114) \| 2.575984 / 6.876477 (-4.300493) \| 2.747546 / 2.142072 (0.605473) \| 0.956604 / 4.805227 (-3.848623) \| 0.198937 / 6.500664 (-6.301727) \| 0.070849 / 0.075469 (-0.004620) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.536469 / 1.841788 (-0.305319) \| 21.750583 / 8.074308 (13.676275) \| 20.559532 / 10.191392 (10.368140) \| 0.241244 / 0.680424 (-0.439180) \| 0.030078 / 0.534201 (-0.504123) \| 0.462204 / 0.579283 (-0.117079) \| 0.600103 / 0.434364 (0.165739) \| 0.535074 / 0.540337 (-0.005264) \| 0.764427 / 1.386936 (-0.622509) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009712 / 0.011353 (-0.001641) \| 0.005036 / 0.011008 (-0.005972) \| 0.073683 / 0.038508 (0.035175) \| 0.078684 / 0.023109 (0.055574) \| 0.445096 / 0.275898 (0.169198) \| 0.496233 / 0.323480 (0.172754) \| 0.006231 / 0.007986 (-0.001755) \| 0.004720 / 0.004328 (0.000392) \| 0.076444 / 0.004250 (0.072194) \| 0.060932 / 0.037052 (0.023880) \| 0.505727 / 0.258489 (0.247238) \| 0.498702 / 0.293841 (0.204861) \| 0.047115 / 0.128546 (-0.081431) \| 0.014028 / 0.075646 (-0.061618) \| 0.099292 / 0.419271 (-0.319980) \| 0.061571 / 0.043533 (0.018038) \| 0.468435 / 0.255139 (0.213296) \| 0.481747 / 0.283200 (0.198547) \| 0.033962 / 0.141683 (-0.107721) \| 1.665397 / 1.452155 (0.213242) \| 1.830488 / 1.492716 (0.337772) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.268217 / 0.018006 (0.250211) \| 0.555123 / 0.000490 (0.554633) \| 0.000451 / 0.000200 (0.000251) \| 0.000156 / 0.000054 (0.000101) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034262 / 0.037411 (-0.003150) \| 0.107807 / 0.014526 (0.093281) \| 0.115631 / 0.176557 (-0.060926) \| 0.175914 / 0.737135 (-0.561221) \| 0.118775 / 0.296338 (-0.177564) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.583260 / 0.215209 (0.368051) \| 5.934976 / 2.077655 (3.857321) \| 2.752304 / 1.504120 (1.248184) \| 2.382746 / 1.541195 (0.841551) \| 2.389402 / 1.468490 (0.920912) \| 0.794213 / 4.584777 (-3.790564) \| 5.215269 / 3.745712 (1.469557) \| 7.083595 / 5.269862 (1.813733) \| 3.776136 / 4.565676 (-0.789540) \| 0.091141 / 0.424275 (-0.333135) \| 0.008803 / 0.007607 (0.001196) \| 0.726510 / 0.226044 (0.500465) \| 6.926860 / 2.268929 (4.657931) \| 3.475612 / 55.444624 (-51.969012) \| 2.730237 / 6.876477 (-4.146240) \| 2.879145 / 2.142072 (0.737073) \| 0.959956 / 4.805227 (-3.845271) \| 0.189812 / 6.500664 (-6.310852) \| 0.071624 / 0.075469 (-0.003845) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.748184 / 1.841788 (-0.093603) \| 23.764520 / 8.074308 (15.690212) \| 19.502461 / 10.191392 (9.311069) \| 0.233987 / 0.680424 (-0.446437) \| 0.028116 / 0.534201 (-0.506085) \| 0.478838 / 0.579283 (-0.100445) \| 0.560952 / 0.434364 (0.126588) \| 0.529902 / 0.540337 (-0.010435) \| 0.735095 / 1.386936 (-0.651841) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#dda3e389212f44117a40b44bb0cdf358cfd9f71e \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006735 / 0.011353 (-0.004618) \| 0.004131 / 0.011008 (-0.006878) \| 0.085619 / 0.038508 (0.047111) \| 0.076973 / 0.023109 (0.053864) \| 0.315175 / 0.275898 (0.039277) \| 0.354703 / 0.323480 (0.031223) \| 0.005409 / 0.007986 (-0.002577) \| 0.003438 / 0.004328 (-0.000891) \| 0.064773 / 0.004250 (0.060523) \| 0.056117 / 0.037052 (0.019064) \| 0.313825 / 0.258489 (0.055336) \| 0.354654 / 0.293841 (0.060813) \| 0.031384 / 0.128546 (-0.097163) \| 0.008537 / 0.075646 (-0.067109) \| 0.288528 / 0.419271 (-0.130744) \| 0.053036 / 0.043533 (0.009504) \| 0.312213 / 0.255139 (0.057074) \| 0.335952 / 0.283200 (0.052752) \| 0.023165 / 0.141683 (-0.118518) \| 1.497559 / 1.452155 (0.045404) \| 1.561949 / 1.492716 (0.069233) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.212558 / 0.018006 (0.194552) \| 0.456555 / 0.000490 (0.456065) \| 0.000334 / 0.000200 (0.000134) \| 0.000052 / 0.000054 (-0.000002) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028571 / 0.037411 (-0.008840) \| 0.085154 / 0.014526 (0.070628) \| 0.095961 / 0.176557 (-0.080596) \| 0.153041 / 0.737135 (-0.584094) \| 0.099234 / 0.296338 (-0.197105) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.381796 / 0.215209 (0.166587) \| 3.806948 / 2.077655 (1.729294) \| 1.829597 / 1.504120 (0.325477) \| 1.659065 / 1.541195 (0.117870) \| 1.738524 / 1.468490 (0.270034) \| 0.483379 / 4.584777 (-4.101398) \| 3.540648 / 3.745712 (-0.205064) \| 3.269188 / 5.269862 (-2.000673) \| 2.042113 / 4.565676 (-2.523564) \| 0.056905 / 0.424275 (-0.367370) \| 0.007235 / 0.007607 (-0.000373) \| 0.460581 / 0.226044 (0.234537) \| 4.597451 / 2.268929 (2.328522) \| 2.334284 / 55.444624 (-53.110340) \| 1.960026 / 6.876477 (-4.916450) \| 2.172118 / 2.142072 (0.030045) \| 0.576758 / 4.805227 (-4.228470) \| 0.131196 / 6.500664 (-6.369468) \| 0.060053 / 0.075469 (-0.015417) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.289466 / 1.841788 (-0.552322) \| 19.713059 / 8.074308 (11.638750) \| 14.292390 / 10.191392 (4.100998) \| 0.146199 / 0.680424 (-0.534225) \| 0.018123 / 0.534201 (-0.516078) \| 0.392492 / 0.579283 (-0.186791) \| 0.416544 / 0.434364 (-0.017820) \| 0.457166 / 0.540337 (-0.083171) \| 0.645490 / 1.386936 (-0.741446) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006508 / 0.011353 (-0.004845) \| 0.004010 / 0.011008 (-0.006998) \| 0.065201 / 0.038508 (0.026693) \| 0.076322 / 0.023109 (0.053213) \| 0.364198 / 0.275898 (0.088300) \| 0.398251 / 0.323480 (0.074771) \| 0.005328 / 0.007986 (-0.002658) \| 0.003298 / 0.004328 (-0.001031) \| 0.064378 / 0.004250 (0.060128) \| 0.056053 / 0.037052 (0.019000) \| 0.365431 / 0.258489 (0.106942) \| 0.402777 / 0.293841 (0.108936) \| 0.031014 / 0.128546 (-0.097532) \| 0.008507 / 0.075646 (-0.067140) \| 0.071471 / 0.419271 (-0.347801) \| 0.048300 / 0.043533 (0.004768) \| 0.359700 / 0.255139 (0.104561) \| 0.382244 / 0.283200 (0.099044) \| 0.023783 / 0.141683 (-0.117900) \| 1.517518 / 1.452155 (0.065363) \| 1.569732 / 1.492716 (0.077015) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.257447 / 0.018006 (0.239440) \| 0.452598 / 0.000490 (0.452109) \| 0.015187 / 0.000200 (0.014987) \| 0.000164 / 0.000054 (0.000109) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030958 / 0.037411 (-0.006454) \| 0.090066 / 0.014526 (0.075540) \| 0.101120 / 0.176557 (-0.075437) \| 0.154295 / 0.737135 (-0.582840) \| 0.103582 / 0.296338 (-0.192756) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.415945 / 0.215209 (0.200736) \| 4.146464 / 2.077655 (2.068809) \| 2.121414 / 1.504120 (0.617294) \| 1.956885 / 1.541195 (0.415690) \| 2.047955 / 1.468490 (0.579465) \| 0.486334 / 4.584777 (-4.098443) \| 3.506263 / 3.745712 (-0.239449) \| 4.942274 / 5.269862 (-0.327587) \| 2.907836 / 4.565676 (-1.657841) \| 0.057344 / 0.424275 (-0.366931) \| 0.007813 / 0.007607 (0.000206) \| 0.497888 / 0.226044 (0.271844) \| 4.978017 / 2.268929 (2.709089) \| 2.600447 / 55.444624 (-52.844177) \| 2.335050 / 6.876477 (-4.541427) \| 2.480373 / 2.142072 (0.338301) \| 0.597954 / 4.805227 (-4.207274) \| 0.134794 / 6.500664 (-6.365870) \| 0.062605 / 0.075469 (-0.012864) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.344390 / 1.841788 (-0.497398) \| 20.020067 / 8.074308 (11.945759) \| 14.344626 / 10.191392 (4.153234) \| 0.172101 / 0.680424 (-0.508322) \| 0.018549 / 0.534201 (-0.515652) \| 0.393589 / 0.579283 (-0.185694) \| 0.438401 / 0.434364 (0.004037) \| 0.463800 / 0.540337 (-0.076537) \| 0.618269 / 1.386936 (-0.768667) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b0177910b32712f28d147879395e511207e39958 \"CML watermark\")\n" ]	2023-07-25T17:48:37	2023-07-25T18:19:01	2023-07-25T18:10:16	CONTRIBUTOR	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6070", "html_url": "https://github.com/huggingface/datasets/pull/6070", "diff_url": "https://github.com/huggingface/datasets/pull/6070.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6070.patch", "merged_at": "2023-07-25T18:10:16" }	Reported in https://github.com/huggingface/datasets/pull/5902#issuecomment-1649885621 (cc @alvarobartt)	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6070/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6070/timeline	null	null	true
https://api.github.com/repos/huggingface/datasets/issues/6069	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6069/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6069/comments	https://api.github.com/repos/huggingface/datasets/issues/6069/events	https://github.com/huggingface/datasets/issues/6069	1,820,831,535	I_kwDODunzps5sh68v	6,069	KeyError: dataset has no key "image"	{ "login": "etetteh", "id": 28512232, "node_id": "MDQ6VXNlcjI4NTEyMjMy", "avatar_url": "https://avatars.githubusercontent.com/u/28512232?v=4", "gravatar_id": "", "url": "https://api.github.com/users/etetteh", "html_url": "https://github.com/etetteh", "followers_url": "https://api.github.com/users/etetteh/followers", "following_url": "https://api.github.com/users/etetteh/following{/other_user}", "gists_url": "https://api.github.com/users/etetteh/gists{/gist_id}", "starred_url": "https://api.github.com/users/etetteh/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/etetteh/subscriptions", "organizations_url": "https://api.github.com/users/etetteh/orgs", "repos_url": "https://api.github.com/users/etetteh/repos", "events_url": "https://api.github.com/users/etetteh/events{/privacy}", "received_events_url": "https://api.github.com/users/etetteh/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "You can list the dataset's columns with `ds.column_names` before `.map` to check whether the dataset has an `image` column. If it doesn't, then this is a bug. Otherwise, please paste the line with the `.map` call.\r\n\r\n\r\n", "This is the piece of code I am running:\r\n```\r\ndata_transforms = utils.get_data_augmentation(args)\r\nimage_dataset = utils.load_image_dataset(args.dataset)\r\n\r\ndef resize(examples):\r\n examples[\"pixel_values\"] = [image.convert(\"RGB\").resize((300, 300)) for image in examples[\"image\"]]\r\n return examples\r\n\r\ndef preprocess_train(example_batch):\r\n print(f\"Example batch: \\n{example_batch}\")\r\n example_batch[\"pixel_values\"] = [\r\n data_transforms[\"train\"](image.convert(\"RGB\")) for image in example_batch[\"pixel_values\"]\r\n ]\r\n return example_batch\r\n\r\ndef preprocess_val(example_batch):\r\n example_batch[\"pixel_values\"] = [\r\n data_transforms[\"val\"](image.convert(\"RGB\")) for image in example_batch[\"pixel_values\"]\r\n ]\r\n return example_batch\r\n\r\nimage_dataset = image_dataset.map(resize, remove_columns=[\"image\"], batched=True)\r\n\r\nimage_dataset[\"train\"].set_transform(preprocess_train)\r\nimage_dataset[\"validation\"].set_transform(preprocess_val)\r\n```\r\n\r\nWhen I print ds.column_names I get the following\r\n`{'train': ['image', 'label'], 'validation': ['image', 'label'], 'test': ['image', 'label']}`\r\n\r\nThe `print(f\"Example batch: \\n{example_batch}\")` in the `preprocess_train` function outputs only labels without images:\r\n```\r\nExample batch: \r\n{'label': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]}\r\n```\r\n\r\nThe weird part of it all is that a sample code runs in a jupyter lab notebook without any bugs, but when I run my scripts from the terminal I get the bug. The same code.", "The `remove_columns=[\"image\"]` argument in the `.map` call removes the `image` column from the output, so drop this argument to preserve it.", "The problem is not with the removal of the image key. The bug is why only the labels are sent to be process, instead of all the featues or dictionary keys.\r\n\r\nP.S. I just dropped the removal argument as you've suggested, but that didn't solve the problem, because only the labels are being sent to be processed", "All the `image_dataset.column_names` after the `map` call should also be present in `preprocess_train `/`preprocess_val` unless (input) `columns` in `set_transform` are specified.\r\n\r\nIf that's not the case, we need a full reproducer (not snippets) with the environment info.", "I have resolved the error after including a collate function as indicated in the Quick Start session of the Datasets docs.:\r\n\r\nHere is what I did:\r\n```\r\ndata_transforms = utils.get_data_augmentation(args)\r\nimage_dataset = utils.load_image_dataset(args.dataset)\r\n\r\ndef preprocess_train(example_batch):\r\n example_batch[\"pixel_values\"] = [\r\n data_transforms[\"train\"](image.convert(\"RGB\")) for image in example_batch[\"image\"]\r\n ]\r\n return example_batch\r\n\r\ndef preprocess_val(example_batch):\r\n example_batch[\"pixel_values\"] = [\r\n data_transforms[\"val\"](image.convert(\"RGB\")) for image in example_batch[\"image\"]\r\n ]\r\n return example_batch\r\n\r\ndef collate_fn(examples):\r\n images = []\r\n labels = []\r\n for example in examples:\r\n images.append((example[\"pixel_values\"]))\r\n labels.append(example[\"label\"])\r\n\r\n pixel_values = torch.stack(images)\r\n labels = torch.tensor(labels)\r\n return {\"pixel_values\": pixel_values, \"label\": labels}\r\n\r\ntrain_dataset = image_dataset[\"train\"].with_transform(preprocess_train)\r\nval_dataset = image_dataset[\"validation\"].with_transform(preprocess_val)\r\n\r\nimage_datasets = {\r\n \"train\": train_dataset,\r\n \"val\": val_dataset\r\n}\r\n\r\nsamplers = {\r\n \"train\": data.RandomSampler(train_dataset),\r\n \"val\": data.SequentialSampler(val_dataset),\r\n}\r\n\r\ndataloaders = {\r\n x: data.DataLoader(\r\n image_datasets[x],\r\n collate_fn=collate_fn,\r\n batch_size=batch_size,\r\n sampler=samplers[x],\r\n num_workers=args.num_workers,\r\n worker_init_fn=utils.set_seed_for_worker,\r\n generator=g,\r\n pin_memory=True,\r\n )\r\n for x in [\"train\", \"val\"]\r\n}\r\n\r\ntrain_loader, val_loader = dataloaders[\"train\"], dataloaders[\"val\"]\r\n```\r\nEverything runs fine without any bug now. " ]	2023-07-25T17:45:50	2023-07-27T12:42:17	2023-07-27T12:42:17	NONE	null	null	null	### Describe the bug I've loaded a local image dataset with: `ds = laod_dataset("imagefolder", data_dir=path-to-data)` And defined a transform to process the data, following the Datasets docs. However, I get a keyError error, indicating there's no "image" key in my dataset. When I printed out the example_batch sent to the transformation function, it shows only the labels are being sent to the function. For some reason, the images are not in the example batches. ### Steps to reproduce the bug I'm using the latest stable version of datasets ### Expected behavior I expect the example_batches to contain both images and labels ### Environment info I'm using the latest stable version of datasets	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6069/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6069/timeline	null	completed	false
https://api.github.com/repos/huggingface/datasets/issues/6068	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6068/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6068/comments	https://api.github.com/repos/huggingface/datasets/issues/6068/events	https://github.com/huggingface/datasets/pull/6068	1,820,106,952	PR_kwDODunzps5WUkZi	6,068	fix tqdm lock deletion	{ "login": "lhoestq", "id": 42851186, "node_id": "MDQ6VXNlcjQyODUxMTg2", "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "gravatar_id": "", "url": "https://api.github.com/users/lhoestq", "html_url": "https://github.com/lhoestq", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "repos_url": "https://api.github.com/users/lhoestq/repos", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006573 / 0.011353 (-0.004780) \| 0.004014 / 0.011008 (-0.006994) \| 0.084999 / 0.038508 (0.046491) \| 0.074965 / 0.023109 (0.051855) \| 0.313294 / 0.275898 (0.037396) \| 0.349678 / 0.323480 (0.026198) \| 0.005401 / 0.007986 (-0.002585) \| 0.003401 / 0.004328 (-0.000927) \| 0.065363 / 0.004250 (0.061112) \| 0.057159 / 0.037052 (0.020107) \| 0.313260 / 0.258489 (0.054771) \| 0.354654 / 0.293841 (0.060813) \| 0.030895 / 0.128546 (-0.097651) \| 0.008605 / 0.075646 (-0.067042) \| 0.289190 / 0.419271 (-0.130081) \| 0.052474 / 0.043533 (0.008942) \| 0.316193 / 0.255139 (0.061054) \| 0.339966 / 0.283200 (0.056767) \| 0.024112 / 0.141683 (-0.117571) \| 1.515606 / 1.452155 (0.063452) \| 1.571428 / 1.492716 (0.078711) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.203284 / 0.018006 (0.185278) \| 0.452720 / 0.000490 (0.452230) \| 0.003891 / 0.000200 (0.003691) \| 0.000094 / 0.000054 (0.000040) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028992 / 0.037411 (-0.008419) \| 0.083170 / 0.014526 (0.068644) \| 0.097739 / 0.176557 (-0.078817) \| 0.153401 / 0.737135 (-0.583734) \| 0.098628 / 0.296338 (-0.197711) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.390190 / 0.215209 (0.174981) \| 3.901272 / 2.077655 (1.823617) \| 1.887194 / 1.504120 (0.383074) \| 1.723696 / 1.541195 (0.182501) \| 1.800537 / 1.468490 (0.332047) \| 0.481758 / 4.584777 (-4.103019) \| 3.605098 / 3.745712 (-0.140614) \| 3.304482 / 5.269862 (-1.965380) \| 2.053515 / 4.565676 (-2.512161) \| 0.056997 / 0.424275 (-0.367278) \| 0.007347 / 0.007607 (-0.000260) \| 0.461367 / 0.226044 (0.235323) \| 4.606152 / 2.268929 (2.337223) \| 2.470048 / 55.444624 (-52.974576) \| 2.060019 / 6.876477 (-4.816458) \| 2.320507 / 2.142072 (0.178435) \| 0.575050 / 4.805227 (-4.230178) \| 0.133030 / 6.500664 (-6.367634) \| 0.061508 / 0.075469 (-0.013962) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.275430 / 1.841788 (-0.566357) \| 19.725453 / 8.074308 (11.651145) \| 14.396360 / 10.191392 (4.204968) \| 0.157980 / 0.680424 (-0.522443) \| 0.018516 / 0.534201 (-0.515685) \| 0.394717 / 0.579283 (-0.184566) \| 0.404948 / 0.434364 (-0.029415) \| 0.474001 / 0.540337 (-0.066336) \| 0.668463 / 1.386936 (-0.718474) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006697 / 0.011353 (-0.004656) \| 0.004206 / 0.011008 (-0.006802) \| 0.065458 / 0.038508 (0.026950) \| 0.075845 / 0.023109 (0.052735) \| 0.365051 / 0.275898 (0.089153) \| 0.400919 / 0.323480 (0.077439) \| 0.005347 / 0.007986 (-0.002638) \| 0.003386 / 0.004328 (-0.000943) \| 0.065398 / 0.004250 (0.061148) \| 0.057320 / 0.037052 (0.020268) \| 0.379161 / 0.258489 (0.120672) \| 0.406892 / 0.293841 (0.113051) \| 0.031986 / 0.128546 (-0.096560) \| 0.008674 / 0.075646 (-0.066972) \| 0.071723 / 0.419271 (-0.347549) \| 0.049897 / 0.043533 (0.006364) \| 0.372034 / 0.255139 (0.116895) \| 0.394293 / 0.283200 (0.111094) \| 0.023681 / 0.141683 (-0.118002) \| 1.479793 / 1.452155 (0.027639) \| 1.553105 / 1.492716 (0.060389) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.233660 / 0.018006 (0.215654) \| 0.454412 / 0.000490 (0.453923) \| 0.004473 / 0.000200 (0.004273) \| 0.000085 / 0.000054 (0.000030) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031115 / 0.037411 (-0.006296) \| 0.090541 / 0.014526 (0.076015) \| 0.104363 / 0.176557 (-0.072193) \| 0.161022 / 0.737135 (-0.576114) \| 0.105114 / 0.296338 (-0.191225) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.427668 / 0.215209 (0.212459) \| 4.263145 / 2.077655 (2.185490) \| 2.247043 / 1.504120 (0.742923) \| 2.082554 / 1.541195 (0.541360) \| 2.170505 / 1.468490 (0.702015) \| 0.491802 / 4.584777 (-4.092975) \| 3.587295 / 3.745712 (-0.158417) \| 3.344697 / 5.269862 (-1.925165) \| 2.060529 / 4.565676 (-2.505148) \| 0.057829 / 0.424275 (-0.366446) \| 0.007780 / 0.007607 (0.000173) \| 0.503374 / 0.226044 (0.277330) \| 5.034742 / 2.268929 (2.765814) \| 2.701957 / 55.444624 (-52.742667) \| 2.479002 / 6.876477 (-4.397474) \| 2.622055 / 2.142072 (0.479982) \| 0.591363 / 4.805227 (-4.213864) \| 0.133834 / 6.500664 (-6.366830) \| 0.062276 / 0.075469 (-0.013193) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.338788 / 1.841788 (-0.503000) \| 20.333599 / 8.074308 (12.259291) \| 14.783196 / 10.191392 (4.591804) \| 0.168695 / 0.680424 (-0.511729) \| 0.018478 / 0.534201 (-0.515723) \| 0.397398 / 0.579283 (-0.181885) \| 0.409900 / 0.434364 (-0.024464) \| 0.475315 / 0.540337 (-0.065023) \| 0.644267 / 1.386936 (-0.742669) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#cb0b324e0bae4c93bb5509b2f0731bc346adb21b \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007315 / 0.011353 (-0.004038) \| 0.004294 / 0.011008 (-0.006714) \| 0.100300 / 0.038508 (0.061792) \| 0.077780 / 0.023109 (0.054670) \| 0.353728 / 0.275898 (0.077830) \| 0.400538 / 0.323480 (0.077058) \| 0.005807 / 0.007986 (-0.002178) \| 0.003649 / 0.004328 (-0.000680) \| 0.077548 / 0.004250 (0.073297) \| 0.058834 / 0.037052 (0.021781) \| 0.352064 / 0.258489 (0.093574) \| 0.399951 / 0.293841 (0.106110) \| 0.036472 / 0.128546 (-0.092074) \| 0.008653 / 0.075646 (-0.066994) \| 0.323089 / 0.419271 (-0.096182) \| 0.075127 / 0.043533 (0.031594) \| 0.334412 / 0.255139 (0.079273) \| 0.375718 / 0.283200 (0.092519) \| 0.027915 / 0.141683 (-0.113768) \| 1.698795 / 1.452155 (0.246640) \| 1.781447 / 1.492716 (0.288730) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.216111 / 0.018006 (0.198104) \| 0.507706 / 0.000490 (0.507216) \| 0.000851 / 0.000200 (0.000651) \| 0.000085 / 0.000054 (0.000030) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030451 / 0.037411 (-0.006960) \| 0.087488 / 0.014526 (0.072962) \| 0.105094 / 0.176557 (-0.071462) \| 0.168130 / 0.737135 (-0.569006) \| 0.106791 / 0.296338 (-0.189547) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.426291 / 0.215209 (0.211082) \| 4.281046 / 2.077655 (2.203391) \| 2.162268 / 1.504120 (0.658148) \| 1.909503 / 1.541195 (0.368309) \| 1.943165 / 1.468490 (0.474675) \| 0.516667 / 4.584777 (-4.068110) \| 4.113218 / 3.745712 (0.367506) \| 5.931372 / 5.269862 (0.661510) \| 3.563521 / 4.565676 (-1.002155) \| 0.062415 / 0.424275 (-0.361860) \| 0.007577 / 0.007607 (-0.000030) \| 0.534588 / 0.226044 (0.308543) \| 5.183490 / 2.268929 (2.914561) \| 2.790662 / 55.444624 (-52.653962) \| 2.258630 / 6.876477 (-4.617846) \| 2.499930 / 2.142072 (0.357857) \| 0.606154 / 4.805227 (-4.199073) \| 0.136093 / 6.500664 (-6.364571) \| 0.061151 / 0.075469 (-0.014318) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.398392 / 1.841788 (-0.443396) \| 21.482150 / 8.074308 (13.407842) \| 15.477336 / 10.191392 (5.285944) \| 0.192878 / 0.680424 (-0.487546) \| 0.021764 / 0.534201 (-0.512437) \| 0.437149 / 0.579283 (-0.142134) \| 0.439976 / 0.434364 (0.005612) \| 0.514498 / 0.540337 (-0.025840) \| 0.762642 / 1.386936 (-0.624294) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007504 / 0.011353 (-0.003849) \| 0.004526 / 0.011008 (-0.006482) \| 0.071008 / 0.038508 (0.032500) \| 0.078305 / 0.023109 (0.055195) \| 0.436160 / 0.275898 (0.160262) \| 0.439048 / 0.323480 (0.115568) \| 0.006061 / 0.007986 (-0.001925) \| 0.003681 / 0.004328 (-0.000648) \| 0.069445 / 0.004250 (0.065195) \| 0.059258 / 0.037052 (0.022206) \| 0.437745 / 0.258489 (0.179256) \| 0.464247 / 0.293841 (0.170406) \| 0.033286 / 0.128546 (-0.095260) \| 0.009846 / 0.075646 (-0.065800) \| 0.076330 / 0.419271 (-0.342941) \| 0.051919 / 0.043533 (0.008386) \| 0.432817 / 0.255139 (0.177678) \| 0.426295 / 0.283200 (0.143095) \| 0.029818 / 0.141683 (-0.111865) \| 1.747640 / 1.452155 (0.295485) \| 1.726653 / 1.492716 (0.233937) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.251253 / 0.018006 (0.233247) \| 0.483394 / 0.000490 (0.482904) \| 0.003992 / 0.000200 (0.003793) \| 0.000096 / 0.000054 (0.000041) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032180 / 0.037411 (-0.005231) \| 0.095425 / 0.014526 (0.080900) \| 0.105908 / 0.176557 (-0.070648) \| 0.164732 / 0.737135 (-0.572403) \| 0.115903 / 0.296338 (-0.180435) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.469467 / 0.215209 (0.254258) \| 4.633239 / 2.077655 (2.555584) \| 2.517557 / 1.504120 (1.013437) \| 2.352726 / 1.541195 (0.811531) \| 2.314618 / 1.468490 (0.846128) \| 0.548446 / 4.584777 (-4.036331) \| 3.908797 / 3.745712 (0.163085) \| 3.525941 / 5.269862 (-1.743921) \| 2.178858 / 4.565676 (-2.386819) \| 0.057614 / 0.424275 (-0.366661) \| 0.008604 / 0.007607 (0.000997) \| 0.554756 / 0.226044 (0.328711) \| 5.325635 / 2.268929 (3.056706) \| 3.014266 / 55.444624 (-52.430359) \| 2.844165 / 6.876477 (-4.032312) \| 2.903019 / 2.142072 (0.760947) \| 0.617750 / 4.805227 (-4.187478) \| 0.144259 / 6.500664 (-6.356405) \| 0.065944 / 0.075469 (-0.009525) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.504625 / 1.841788 (-0.337163) \| 22.400787 / 8.074308 (14.326479) \| 15.223702 / 10.191392 (5.032310) \| 0.213357 / 0.680424 (-0.467067) \| 0.019310 / 0.534201 (-0.514891) \| 0.456596 / 0.579283 (-0.122687) \| 0.473811 / 0.434364 (0.039447) \| 0.517800 / 0.540337 (-0.022537) \| 0.792468 / 1.386936 (-0.594468) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#03750f4a4c664125c7de910be004710b181dd354 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007420 / 0.011353 (-0.003933) \| 0.004502 / 0.011008 (-0.006506) \| 0.097882 / 0.038508 (0.059374) \| 0.079084 / 0.023109 (0.055975) \| 0.361797 / 0.275898 (0.085899) \| 0.416563 / 0.323480 (0.093083) \| 0.006106 / 0.007986 (-0.001879) \| 0.003803 / 0.004328 (-0.000526) \| 0.074669 / 0.004250 (0.070418) \| 0.062168 / 0.037052 (0.025116) \| 0.378844 / 0.258489 (0.120355) \| 0.426601 / 0.293841 (0.132760) \| 0.035619 / 0.128546 (-0.092927) \| 0.009686 / 0.075646 (-0.065960) \| 0.336481 / 0.419271 (-0.082790) \| 0.065553 / 0.043533 (0.022021) \| 0.362501 / 0.255139 (0.107362) \| 0.399752 / 0.283200 (0.116552) \| 0.028685 / 0.141683 (-0.112998) \| 1.683495 / 1.452155 (0.231340) \| 1.786105 / 1.492716 (0.293388) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.220792 / 0.018006 (0.202786) \| 0.501936 / 0.000490 (0.501447) \| 0.000389 / 0.000200 (0.000189) \| 0.000057 / 0.000054 (0.000003) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032180 / 0.037411 (-0.005232) \| 0.093079 / 0.014526 (0.078553) \| 0.107967 / 0.176557 (-0.068589) \| 0.171747 / 0.737135 (-0.565389) \| 0.107920 / 0.296338 (-0.188418) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.444431 / 0.215209 (0.229222) \| 4.454934 / 2.077655 (2.377279) \| 2.140265 / 1.504120 (0.636145) \| 1.960126 / 1.541195 (0.418931) \| 2.049649 / 1.468490 (0.581158) \| 0.557861 / 4.584777 (-4.026916) \| 4.046240 / 3.745712 (0.300528) \| 4.513748 / 5.269862 (-0.756114) \| 2.593643 / 4.565676 (-1.972034) \| 0.066795 / 0.424275 (-0.357480) \| 0.008302 / 0.007607 (0.000694) \| 0.535643 / 0.226044 (0.309599) \| 5.299429 / 2.268929 (3.030500) \| 2.656019 / 55.444624 (-52.788606) \| 2.281214 / 6.876477 (-4.595263) \| 2.302910 / 2.142072 (0.160837) \| 0.661696 / 4.805227 (-4.143532) \| 0.149787 / 6.500664 (-6.350877) \| 0.069609 / 0.075469 (-0.005860) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.509842 / 1.841788 (-0.331946) \| 21.717504 / 8.074308 (13.643196) \| 15.825102 / 10.191392 (5.633710) \| 0.168115 / 0.680424 (-0.512309) \| 0.021637 / 0.534201 (-0.512564) \| 0.454270 / 0.579283 (-0.125013) \| 0.458531 / 0.434364 (0.024167) \| 0.523052 / 0.540337 (-0.017285) \| 0.711219 / 1.386936 (-0.675717) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007189 / 0.011353 (-0.004164) \| 0.004437 / 0.011008 (-0.006571) \| 0.075111 / 0.038508 (0.036603) \| 0.079245 / 0.023109 (0.056136) \| 0.423169 / 0.275898 (0.147270) \| 0.455007 / 0.323480 (0.131527) \| 0.006076 / 0.007986 (-0.001909) \| 0.003819 / 0.004328 (-0.000509) \| 0.074976 / 0.004250 (0.070726) \| 0.062127 / 0.037052 (0.025075) \| 0.456809 / 0.258489 (0.198320) \| 0.474707 / 0.293841 (0.180867) \| 0.036221 / 0.128546 (-0.092325) \| 0.009428 / 0.075646 (-0.066218) \| 0.082842 / 0.419271 (-0.336429) \| 0.057086 / 0.043533 (0.013553) \| 0.436121 / 0.255139 (0.180982) \| 0.453934 / 0.283200 (0.170734) \| 0.026045 / 0.141683 (-0.115638) \| 1.789782 / 1.452155 (0.337627) \| 1.820934 / 1.492716 (0.328218) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.230790 / 0.018006 (0.212784) \| 0.497987 / 0.000490 (0.497497) \| 0.002775 / 0.000200 (0.002575) \| 0.000093 / 0.000054 (0.000038) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034418 / 0.037411 (-0.002994) \| 0.105567 / 0.014526 (0.091041) \| 0.113134 / 0.176557 (-0.063423) \| 0.173742 / 0.737135 (-0.563394) \| 0.115936 / 0.296338 (-0.180403) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.502259 / 0.215209 (0.287050) \| 4.969877 / 2.077655 (2.892222) \| 2.684860 / 1.504120 (1.180740) \| 2.484386 / 1.541195 (0.943192) \| 2.543061 / 1.468490 (1.074571) \| 0.545733 / 4.584777 (-4.039044) \| 4.029660 / 3.745712 (0.283948) \| 5.927883 / 5.269862 (0.658021) \| 3.528372 / 4.565676 (-1.037305) \| 0.065957 / 0.424275 (-0.358318) \| 0.008933 / 0.007607 (0.001326) \| 0.601630 / 0.226044 (0.375585) \| 5.825872 / 2.268929 (3.556944) \| 3.230721 / 55.444624 (-52.213904) \| 2.891308 / 6.876477 (-3.985169) \| 3.054994 / 2.142072 (0.912922) \| 0.665480 / 4.805227 (-4.139747) \| 0.154815 / 6.500664 (-6.345849) \| 0.072997 / 0.075469 (-0.002472) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.549892 / 1.841788 (-0.291896) \| 22.337484 / 8.074308 (14.263176) \| 16.308286 / 10.191392 (6.116894) \| 0.189594 / 0.680424 (-0.490830) \| 0.021844 / 0.534201 (-0.512357) \| 0.456958 / 0.579283 (-0.122325) \| 0.459957 / 0.434364 (0.025593) \| 0.529014 / 0.540337 (-0.011323) \| 0.700359 / 1.386936 (-0.686577) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#32e4df86b5fb0bc164433ce615af641ec3ba437e \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009050 / 0.011353 (-0.002303) \| 0.004968 / 0.011008 (-0.006040) \| 0.114315 / 0.038508 (0.075807) \| 0.084475 / 0.023109 (0.061366) \| 0.426325 / 0.275898 (0.150427) \| 0.457870 / 0.323480 (0.134390) \| 0.007076 / 0.007986 (-0.000910) \| 0.004635 / 0.004328 (0.000307) \| 0.082950 / 0.004250 (0.078700) \| 0.065414 / 0.037052 (0.028361) \| 0.441936 / 0.258489 (0.183447) \| 0.476983 / 0.293841 (0.183142) \| 0.048575 / 0.128546 (-0.079972) \| 0.013929 / 0.075646 (-0.061717) \| 0.377498 / 0.419271 (-0.041774) \| 0.081503 / 0.043533 (0.037970) \| 0.426706 / 0.255139 (0.171567) \| 0.460374 / 0.283200 (0.177175) \| 0.046052 / 0.141683 (-0.095631) \| 1.894896 / 1.452155 (0.442741) \| 1.998639 / 1.492716 (0.505923) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.313267 / 0.018006 (0.295261) \| 0.607501 / 0.000490 (0.607012) \| 0.003369 / 0.000200 (0.003169) \| 0.000102 / 0.000054 (0.000047) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032266 / 0.037411 (-0.005145) \| 0.120138 / 0.014526 (0.105613) \| 0.115044 / 0.176557 (-0.061513) \| 0.181374 / 0.737135 (-0.555761) \| 0.114681 / 0.296338 (-0.181657) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.648039 / 0.215209 (0.432830) \| 6.005048 / 2.077655 (3.927394) \| 2.674524 / 1.504120 (1.170404) \| 2.284831 / 1.541195 (0.743637) \| 2.360150 / 1.468490 (0.891660) \| 0.888021 / 4.584777 (-3.696756) \| 5.419840 / 3.745712 (1.674128) \| 4.825816 / 5.269862 (-0.444046) \| 3.140876 / 4.565676 (-1.424801) \| 0.099511 / 0.424275 (-0.324764) \| 0.009176 / 0.007607 (0.001569) \| 0.735646 / 0.226044 (0.509602) \| 7.224026 / 2.268929 (4.955097) \| 3.551146 / 55.444624 (-51.893478) \| 2.844374 / 6.876477 (-4.032103) \| 3.145307 / 2.142072 (1.003235) \| 1.077636 / 4.805227 (-3.727591) \| 0.217754 / 6.500664 (-6.282910) \| 0.081755 / 0.075469 (0.006286) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.670956 / 1.841788 (-0.170831) \| 25.524961 / 8.074308 (17.450653) \| 23.061596 / 10.191392 (12.870204) \| 0.247524 / 0.680424 (-0.432899) \| 0.031712 / 0.534201 (-0.502489) \| 0.513049 / 0.579283 (-0.066234) \| 0.614568 / 0.434364 (0.180204) \| 0.574669 / 0.540337 (0.034331) \| 0.816621 / 1.386936 (-0.570315) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009384 / 0.011353 (-0.001969) \| 0.004959 / 0.011008 (-0.006049) \| 0.084782 / 0.038508 (0.046274) \| 0.098086 / 0.023109 (0.074977) \| 0.544395 / 0.275898 (0.268497) \| 0.585157 / 0.323480 (0.261677) \| 0.006507 / 0.007986 (-0.001479) \| 0.004151 / 0.004328 (-0.000178) \| 0.088596 / 0.004250 (0.084345) \| 0.069149 / 0.037052 (0.032097) \| 0.533109 / 0.258489 (0.274620) \| 0.604117 / 0.293841 (0.310276) \| 0.047685 / 0.128546 (-0.080861) \| 0.013651 / 0.075646 (-0.061996) \| 0.096566 / 0.419271 (-0.322705) \| 0.062022 / 0.043533 (0.018489) \| 0.561897 / 0.255139 (0.306758) \| 0.617636 / 0.283200 (0.334436) \| 0.034636 / 0.141683 (-0.107047) \| 1.854667 / 1.452155 (0.402512) \| 1.908923 / 1.492716 (0.416207) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.260633 / 0.018006 (0.242627) \| 0.622268 / 0.000490 (0.621778) \| 0.002116 / 0.000200 (0.001916) \| 0.000101 / 0.000054 (0.000047) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.035161 / 0.037411 (-0.002250) \| 0.103707 / 0.014526 (0.089181) \| 0.115467 / 0.176557 (-0.061090) \| 0.180077 / 0.737135 (-0.557059) \| 0.118871 / 0.296338 (-0.177467) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.628481 / 0.215209 (0.413271) \| 6.304929 / 2.077655 (4.227275) \| 3.027775 / 1.504120 (1.523655) \| 2.753880 / 1.541195 (1.212686) \| 2.820442 / 1.468490 (1.351952) \| 0.851103 / 4.584777 (-3.733674) \| 5.427383 / 3.745712 (1.681670) \| 7.434310 / 5.269862 (2.164449) \| 4.418790 / 4.565676 (-0.146887) \| 0.101733 / 0.424275 (-0.322542) \| 0.009701 / 0.007607 (0.002094) \| 0.763033 / 0.226044 (0.536989) \| 7.497927 / 2.268929 (5.228998) \| 3.735335 / 55.444624 (-51.709290) \| 3.149200 / 6.876477 (-3.727277) \| 3.306214 / 2.142072 (1.164141) \| 1.085440 / 4.805227 (-3.719787) \| 0.207562 / 6.500664 (-6.293102) \| 0.078091 / 0.075469 (0.002622) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.820097 / 1.841788 (-0.021691) \| 25.525539 / 8.074308 (17.451231) \| 21.874219 / 10.191392 (11.682827) \| 0.228391 / 0.680424 (-0.452033) \| 0.029584 / 0.534201 (-0.504617) \| 0.511546 / 0.579283 (-0.067737) \| 0.602719 / 0.434364 (0.168355) \| 0.581874 / 0.540337 (0.041537) \| 0.802861 / 1.386936 (-0.584075) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6063ea2069c8b5641b983ba2c1d39b60afe7c00a \"CML watermark\")\n" ]	2023-07-25T11:17:25	2023-07-25T15:29:39	2023-07-25T15:17:50	MEMBER	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6068", "html_url": "https://github.com/huggingface/datasets/pull/6068", "diff_url": "https://github.com/huggingface/datasets/pull/6068.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6068.patch", "merged_at": "2023-07-25T15:17:50" }	related to https://github.com/huggingface/datasets/issues/6066	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6068/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6068/timeline	null	null	true
https://api.github.com/repos/huggingface/datasets/issues/6067	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6067/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6067/comments	https://api.github.com/repos/huggingface/datasets/issues/6067/events	https://github.com/huggingface/datasets/pull/6067	1,819,919,025	PR_kwDODunzps5WT7EQ	6,067	fix tqdm lock	{ "login": "lhoestq", "id": 42851186, "node_id": "MDQ6VXNlcjQyODUxMTg2", "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "gravatar_id": "", "url": "https://api.github.com/users/lhoestq", "html_url": "https://github.com/lhoestq", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "repos_url": "https://api.github.com/users/lhoestq/repos", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006578 / 0.011353 (-0.004775) \| 0.003953 / 0.011008 (-0.007055) \| 0.084417 / 0.038508 (0.045908) \| 0.076729 / 0.023109 (0.053620) \| 0.315369 / 0.275898 (0.039471) \| 0.347012 / 0.323480 (0.023533) \| 0.005299 / 0.007986 (-0.002686) \| 0.003321 / 0.004328 (-0.001007) \| 0.063954 / 0.004250 (0.059704) \| 0.055810 / 0.037052 (0.018758) \| 0.317651 / 0.258489 (0.059162) \| 0.352603 / 0.293841 (0.058762) \| 0.031355 / 0.128546 (-0.097192) \| 0.008493 / 0.075646 (-0.067153) \| 0.287295 / 0.419271 (-0.131977) \| 0.052716 / 0.043533 (0.009183) \| 0.316410 / 0.255139 (0.061271) \| 0.328893 / 0.283200 (0.045693) \| 0.024005 / 0.141683 (-0.117678) \| 1.520333 / 1.452155 (0.068178) \| 1.601268 / 1.492716 (0.108552) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.205144 / 0.018006 (0.187138) \| 0.459160 / 0.000490 (0.458670) \| 0.000321 / 0.000200 (0.000121) \| 0.000054 / 0.000054 (-0.000000) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027503 / 0.037411 (-0.009908) \| 0.081476 / 0.014526 (0.066950) \| 0.096759 / 0.176557 (-0.079798) \| 0.157888 / 0.737135 (-0.579247) \| 0.094592 / 0.296338 (-0.201746) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.384762 / 0.215209 (0.169553) \| 3.843503 / 2.077655 (1.765849) \| 1.921685 / 1.504120 (0.417565) \| 1.752441 / 1.541195 (0.211246) \| 1.822105 / 1.468490 (0.353615) \| 0.480243 / 4.584777 (-4.104534) \| 3.577220 / 3.745712 (-0.168492) \| 5.047560 / 5.269862 (-0.222302) \| 2.988008 / 4.565676 (-1.577669) \| 0.056430 / 0.424275 (-0.367845) \| 0.007180 / 0.007607 (-0.000427) \| 0.458113 / 0.226044 (0.232069) \| 4.584096 / 2.268929 (2.315168) \| 2.395307 / 55.444624 (-53.049317) \| 2.080530 / 6.876477 (-4.795947) \| 2.239000 / 2.142072 (0.096927) \| 0.575822 / 4.805227 (-4.229405) \| 0.133303 / 6.500664 (-6.367361) \| 0.059449 / 0.075469 (-0.016020) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.256496 / 1.841788 (-0.585291) \| 19.651614 / 8.074308 (11.577306) \| 14.232480 / 10.191392 (4.041088) \| 0.146461 / 0.680424 (-0.533963) \| 0.018632 / 0.534201 (-0.515569) \| 0.399844 / 0.579283 (-0.179439) \| 0.411225 / 0.434364 (-0.023139) \| 0.458203 / 0.540337 (-0.082135) \| 0.669916 / 1.386936 (-0.717020) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006463 / 0.011353 (-0.004890) \| 0.003898 / 0.011008 (-0.007110) \| 0.064037 / 0.038508 (0.025529) \| 0.071982 / 0.023109 (0.048873) \| 0.361936 / 0.275898 (0.086038) \| 0.393165 / 0.323480 (0.069685) \| 0.005207 / 0.007986 (-0.002779) \| 0.003231 / 0.004328 (-0.001098) \| 0.064318 / 0.004250 (0.060068) \| 0.055776 / 0.037052 (0.018724) \| 0.383087 / 0.258489 (0.124598) \| 0.402428 / 0.293841 (0.108587) \| 0.031587 / 0.128546 (-0.096959) \| 0.008527 / 0.075646 (-0.067119) \| 0.070495 / 0.419271 (-0.348777) \| 0.048806 / 0.043533 (0.005273) \| 0.369932 / 0.255139 (0.114793) \| 0.385268 / 0.283200 (0.102068) \| 0.023183 / 0.141683 (-0.118500) \| 1.491175 / 1.452155 (0.039020) \| 1.534191 / 1.492716 (0.041475) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.224526 / 0.018006 (0.206520) \| 0.445460 / 0.000490 (0.444970) \| 0.003612 / 0.000200 (0.003412) \| 0.000089 / 0.000054 (0.000034) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029829 / 0.037411 (-0.007583) \| 0.087951 / 0.014526 (0.073425) \| 0.100069 / 0.176557 (-0.076487) \| 0.154944 / 0.737135 (-0.582192) \| 0.101271 / 0.296338 (-0.195067) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.412385 / 0.215209 (0.197175) \| 4.108038 / 2.077655 (2.030384) \| 2.163578 / 1.504120 (0.659459) \| 2.031934 / 1.541195 (0.490740) \| 2.155857 / 1.468490 (0.687367) \| 0.481132 / 4.584777 (-4.103645) \| 3.620868 / 3.745712 (-0.124844) \| 5.222175 / 5.269862 (-0.047687) \| 3.115637 / 4.565676 (-1.450039) \| 0.056480 / 0.424275 (-0.367795) \| 0.007761 / 0.007607 (0.000154) \| 0.483553 / 0.226044 (0.257509) \| 4.830087 / 2.268929 (2.561159) \| 2.629919 / 55.444624 (-52.814705) \| 2.327551 / 6.876477 (-4.548926) \| 2.539934 / 2.142072 (0.397861) \| 0.587963 / 4.805227 (-4.217265) \| 0.131085 / 6.500664 (-6.369579) \| 0.060807 / 0.075469 (-0.014662) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.350003 / 1.841788 (-0.491785) \| 19.491713 / 8.074308 (11.417405) \| 14.030429 / 10.191392 (3.839037) \| 0.174762 / 0.680424 (-0.505662) \| 0.018523 / 0.534201 (-0.515678) \| 0.394946 / 0.579283 (-0.184337) \| 0.407652 / 0.434364 (-0.026712) \| 0.465806 / 0.540337 (-0.074531) \| 0.605417 / 1.386936 (-0.781519) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#cc85979df3a39657079fdf0844c7e64547507f1a \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006235 / 0.011353 (-0.005118) \| 0.003675 / 0.011008 (-0.007333) \| 0.080680 / 0.038508 (0.042171) \| 0.064378 / 0.023109 (0.041268) \| 0.394312 / 0.275898 (0.118414) \| 0.428143 / 0.323480 (0.104663) \| 0.004794 / 0.007986 (-0.003191) \| 0.002899 / 0.004328 (-0.001429) \| 0.062592 / 0.004250 (0.058342) \| 0.050957 / 0.037052 (0.013904) \| 0.396831 / 0.258489 (0.138342) \| 0.438280 / 0.293841 (0.144439) \| 0.027743 / 0.128546 (-0.100804) \| 0.008068 / 0.075646 (-0.067578) \| 0.262541 / 0.419271 (-0.156730) \| 0.060837 / 0.043533 (0.017304) \| 0.397941 / 0.255139 (0.142802) \| 0.417012 / 0.283200 (0.133813) \| 0.030153 / 0.141683 (-0.111530) \| 1.477115 / 1.452155 (0.024960) \| 1.516642 / 1.492716 (0.023926) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.178032 / 0.018006 (0.160026) \| 0.445775 / 0.000490 (0.445286) \| 0.004275 / 0.000200 (0.004075) \| 0.000080 / 0.000054 (0.000026) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025025 / 0.037411 (-0.012386) \| 0.074113 / 0.014526 (0.059587) \| 0.083814 / 0.176557 (-0.092743) \| 0.148860 / 0.737135 (-0.588275) \| 0.085408 / 0.296338 (-0.210931) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.393714 / 0.215209 (0.178505) \| 3.936589 / 2.077655 (1.858934) \| 1.910501 / 1.504120 (0.406381) \| 1.729670 / 1.541195 (0.188475) \| 1.777647 / 1.468490 (0.309156) \| 0.499532 / 4.584777 (-4.085245) \| 3.002385 / 3.745712 (-0.743327) \| 2.906916 / 5.269862 (-2.362945) \| 1.883321 / 4.565676 (-2.682356) \| 0.057546 / 0.424275 (-0.366730) \| 0.006492 / 0.007607 (-0.001115) \| 0.463605 / 0.226044 (0.237560) \| 4.620215 / 2.268929 (2.351287) \| 2.399021 / 55.444624 (-53.045603) \| 2.182962 / 6.876477 (-4.693514) \| 2.357344 / 2.142072 (0.215272) \| 0.583946 / 4.805227 (-4.221282) \| 0.124644 / 6.500664 (-6.376021) \| 0.060831 / 0.075469 (-0.014638) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.276412 / 1.841788 (-0.565375) \| 18.462522 / 8.074308 (10.388214) \| 13.877375 / 10.191392 (3.685983) \| 0.150584 / 0.680424 (-0.529840) \| 0.016675 / 0.534201 (-0.517526) \| 0.331711 / 0.579283 (-0.247573) \| 0.366659 / 0.434364 (-0.067705) \| 0.396400 / 0.540337 (-0.143938) \| 0.555418 / 1.386936 (-0.831518) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005995 / 0.011353 (-0.005358) \| 0.003610 / 0.011008 (-0.007399) \| 0.061802 / 0.038508 (0.023294) \| 0.059265 / 0.023109 (0.036156) \| 0.392628 / 0.275898 (0.116730) \| 0.413143 / 0.323480 (0.089663) \| 0.004687 / 0.007986 (-0.003299) \| 0.002843 / 0.004328 (-0.001486) \| 0.061932 / 0.004250 (0.057682) \| 0.049466 / 0.037052 (0.012413) \| 0.402718 / 0.258489 (0.144229) \| 0.415039 / 0.293841 (0.121198) \| 0.027352 / 0.128546 (-0.101194) \| 0.007965 / 0.075646 (-0.067682) \| 0.067456 / 0.419271 (-0.351815) \| 0.042336 / 0.043533 (-0.001196) \| 0.405543 / 0.255139 (0.150404) \| 0.403209 / 0.283200 (0.120010) \| 0.021459 / 0.141683 (-0.120224) \| 1.442861 / 1.452155 (-0.009293) \| 1.491213 / 1.492716 (-0.001503) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.248225 / 0.018006 (0.230219) \| 0.434174 / 0.000490 (0.433684) \| 0.001973 / 0.000200 (0.001773) \| 0.000070 / 0.000054 (0.000015) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025475 / 0.037411 (-0.011936) \| 0.077865 / 0.014526 (0.063339) \| 0.086980 / 0.176557 (-0.089577) \| 0.143682 / 0.737135 (-0.593453) \| 0.088634 / 0.296338 (-0.207705) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.417591 / 0.215209 (0.202382) \| 4.168700 / 2.077655 (2.091045) \| 2.335743 / 1.504120 (0.831623) \| 2.208174 / 1.541195 (0.666980) \| 2.256658 / 1.468490 (0.788168) \| 0.503164 / 4.584777 (-4.081613) \| 3.026667 / 3.745712 (-0.719045) \| 4.496675 / 5.269862 (-0.773187) \| 2.741049 / 4.565676 (-1.824628) \| 0.057781 / 0.424275 (-0.366494) \| 0.006810 / 0.007607 (-0.000797) \| 0.490803 / 0.226044 (0.264759) \| 4.914369 / 2.268929 (2.645441) \| 2.594250 / 55.444624 (-52.850375) \| 2.274552 / 6.876477 (-4.601925) \| 2.397529 / 2.142072 (0.255456) \| 0.593008 / 4.805227 (-4.212220) \| 0.126194 / 6.500664 (-6.374470) \| 0.062261 / 0.075469 (-0.013208) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.357561 / 1.841788 (-0.484227) \| 18.622995 / 8.074308 (10.548687) \| 14.142569 / 10.191392 (3.951177) \| 0.146527 / 0.680424 (-0.533897) \| 0.016863 / 0.534201 (-0.517338) \| 0.336219 / 0.579283 (-0.243064) \| 0.348650 / 0.434364 (-0.085714) \| 0.385958 / 0.540337 (-0.154380) \| 0.517958 / 1.386936 (-0.868978) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f3da7a5a7d0d0415476ecebb0458e7c60df24445 \"CML watermark\")\n" ]	2023-07-25T09:32:16	2023-07-25T10:02:43	2023-07-25T09:54:12	MEMBER	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6067", "html_url": "https://github.com/huggingface/datasets/pull/6067", "diff_url": "https://github.com/huggingface/datasets/pull/6067.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6067.patch", "merged_at": "2023-07-25T09:54:12" }	close https://github.com/huggingface/datasets/issues/6066	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6067/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6067/timeline	null	null	true
https://api.github.com/repos/huggingface/datasets/issues/6066	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6066/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6066/comments	https://api.github.com/repos/huggingface/datasets/issues/6066/events	https://github.com/huggingface/datasets/issues/6066	1,819,717,542	I_kwDODunzps5sdq-m	6,066	AttributeError: '_tqdm_cls' object has no attribute '_lock'	{ "login": "codingl2k1", "id": 138426806, "node_id": "U_kgDOCEA5tg", "avatar_url": "https://avatars.githubusercontent.com/u/138426806?v=4", "gravatar_id": "", "url": "https://api.github.com/users/codingl2k1", "html_url": "https://github.com/codingl2k1", "followers_url": "https://api.github.com/users/codingl2k1/followers", "following_url": "https://api.github.com/users/codingl2k1/following{/other_user}", "gists_url": "https://api.github.com/users/codingl2k1/gists{/gist_id}", "starred_url": "https://api.github.com/users/codingl2k1/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/codingl2k1/subscriptions", "organizations_url": "https://api.github.com/users/codingl2k1/orgs", "repos_url": "https://api.github.com/users/codingl2k1/repos", "events_url": "https://api.github.com/users/codingl2k1/events{/privacy}", "received_events_url": "https://api.github.com/users/codingl2k1/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "Hi ! I opened https://github.com/huggingface/datasets/pull/6067 to add the missing `_lock`\r\n\r\nWe'll do a patch release soon, but feel free to install `datasets` from source in the meantime", "I have tested the latest main, it does not work.\r\n\r\nI add more logs to reproduce this issue, it looks like a multi threading bug:\r\n\r\n```python\r\n@contextmanager\r\ndef ensure_lock(tqdm_class, lock_name=\"\"):\r\n \"\"\"get (create if necessary) and then restore `tqdm_class`'s lock\"\"\"\r\n import os\r\n import threading\r\n print(os.getpid(), threading.get_ident(), \"ensure_lock\", tqdm_class, lock_name)\r\n old_lock = getattr(tqdm_class, '_lock', None) # don't create a new lock\r\n lock = old_lock or tqdm_class.get_lock() # maybe create a new lock\r\n lock = getattr(lock, lock_name, lock) # maybe subtype\r\n tqdm_class.set_lock(lock)\r\n print(os.getpid(), threading.get_ident(), \"set_lock\")\r\n yield lock\r\n if old_lock is None:\r\n print(os.getpid(), threading.get_ident(), \"del tqdm_class\")\r\n del tqdm_class._lock\r\n else:\r\n tqdm_class.set_lock(old_lock)\r\n```\r\noutput\r\n```\r\n64943 8424758784 ensure_lock <datasets.utils.logging._tqdm_cls object at 0x2aa7fb250> \r\n64943 8424758784 set_lock\r\n64943 8424758784 del tqdm_class\r\n64943 8424758784 ensure_lock <datasets.utils.logging._tqdm_cls object at 0x2aa7fb250> \r\n64943 8424758784 set_lock\r\n64943 8424758784 del tqdm_class\r\n64943 11638370304 ensure_lock <datasets.utils.logging._tqdm_cls object at 0x2aa7fb250> \r\n64943 11638370304 set_lock\r\n64943 11568967680 ensure_lock <datasets.utils.logging._tqdm_cls object at 0x2aa7fb250> \r\n64943 11568967680 set_lock\r\n64943 11638370304 del tqdm_class\r\n64943 11638370304 ensure_lock <datasets.utils.logging._tqdm_cls object at 0x2aa7fb250> \r\n64943 11638370304 set_lock\r\n64943 11638370304 del tqdm_class\r\n64943 11568967680 del tqdm_class\r\n```\r\n\r\nThread `11638370304` del the _lock from tqdm_class first, then thread `11568967680` del _lock failed.", "Maybe it is a bug of tqdm? I think simply use `try ... except AttributeError ...` wraps `del tqdm_class._lock` should work.", "Yes it looks like a bug on their end indeed, do you want to open a PR on tqdm ?\r\n\r\nLet me see if I can find a workaround in the meantime", "I opened https://github.com/huggingface/datasets/pull/6068 if you want to try it out", "> I opened #6068 if you want to try it out\r\n\r\nThis fix works! Thanks.", "Awesome ! closing this then :)\r\nWe'll do a patch release today or tomorrow" ]	2023-07-25T07:24:36	2023-07-26T10:56:25	2023-07-26T10:56:24	NONE	null	null	null	### Describe the bug ```python File "/Users/codingl2k1/.pyenv/versions/3.11.4/lib/python3.11/site-packages/datasets/load.py", line 1034, in get_module data_files = DataFilesDict.from_patterns( ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/codingl2k1/.pyenv/versions/3.11.4/lib/python3.11/site-packages/datasets/data_files.py", line 671, in from_patterns DataFilesList.from_patterns( File "/Users/codingl2k1/.pyenv/versions/3.11.4/lib/python3.11/site-packages/datasets/data_files.py", line 586, in from_patterns origin_metadata = _get_origin_metadata(data_files, download_config=download_config) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/codingl2k1/.pyenv/versions/3.11.4/lib/python3.11/site-packages/datasets/data_files.py", line 502, in _get_origin_metadata return thread_map( ^^^^^^^^^^^ File "/Users/codingl2k1/.pyenv/versions/3.11.4/lib/python3.11/site-packages/tqdm/contrib/concurrent.py", line 70, in thread_map return _executor_map(ThreadPoolExecutor, fn, iterables, tqdm_kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/codingl2k1/.pyenv/versions/3.11.4/lib/python3.11/site-packages/tqdm/contrib/concurrent.py", line 48, in _executor_map with ensure_lock(tqdm_class, lock_name=lock_name) as lk: File "/Users/codingl2k1/.pyenv/versions/3.11.4/lib/python3.11/contextlib.py", line 144, in __exit__ next(self.gen) File "/Users/codingl2k1/.pyenv/versions/3.11.4/lib/python3.11/site-packages/tqdm/contrib/concurrent.py", line 25, in ensure_lock del tqdm_class._lock ^^^^^^^^^^^^^^^^ AttributeError: '_tqdm_cls' object has no attribute '_lock' ``` ### Steps to reproduce the bug Happens ocasionally. ### Expected behavior I added a print in tqdm `ensure_lock()`, got a `ensure_lock <datasets.utils.logging._tqdm_cls object at 0x16dddead0> ` print. According to the code in https://github.com/tqdm/tqdm/blob/master/tqdm/contrib/concurrent.py#L24 ```python @contextmanager def ensure_lock(tqdm_class, lock_name=""): """get (create if necessary) and then restore `tqdm_class`'s lock""" print("ensure_lock", tqdm_class, lock_name) old_lock = getattr(tqdm_class, '_lock', None) # don't create a new lock lock = old_lock or tqdm_class.get_lock() # maybe create a new lock lock = getattr(lock, lock_name, lock) # maybe subtype tqdm_class.set_lock(lock) yield lock if old_lock is None: del tqdm_class._lock # <-- It tries to del the `_lock` attribute from tqdm_class. else: tqdm_class.set_lock(old_lock) ``` But, huggingface datasets `datasets.utils.logging._tqdm_cls` does not have the field `_lock`: https://github.com/huggingface/datasets/blob/main/src/datasets/utils/logging.py#L205 ```python class _tqdm_cls: def __call__(self, args, disable=False, *kwargs): if _tqdm_active and not disable: return tqdm_lib.tqdm(args, *kwargs) else: return EmptyTqdm(args, *kwargs) def set_lock(self, args, *kwargs): self._lock = None if _tqdm_active: return tqdm_lib.tqdm.set_lock(args, **kwargs) def get_lock(self): if _tqdm_active: return tqdm_lib.tqdm.get_lock() ``` ### Environment info Python 3.11.4 tqdm '4.65.0' datasets master	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6066/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6066/timeline	null	completed	false
https://api.github.com/repos/huggingface/datasets/issues/6065	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6065/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6065/comments	https://api.github.com/repos/huggingface/datasets/issues/6065/events	https://github.com/huggingface/datasets/pull/6065	1,819,334,932	PR_kwDODunzps5WR8jI	6,065	Add column type guessing from map return function	{ "login": "piercefreeman", "id": 1712066, "node_id": "MDQ6VXNlcjE3MTIwNjY=", "avatar_url": "https://avatars.githubusercontent.com/u/1712066?v=4", "gravatar_id": "", "url": "https://api.github.com/users/piercefreeman", "html_url": "https://github.com/piercefreeman", "followers_url": "https://api.github.com/users/piercefreeman/followers", "following_url": "https://api.github.com/users/piercefreeman/following{/other_user}", "gists_url": "https://api.github.com/users/piercefreeman/gists{/gist_id}", "starred_url": "https://api.github.com/users/piercefreeman/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/piercefreeman/subscriptions", "organizations_url": "https://api.github.com/users/piercefreeman/orgs", "repos_url": "https://api.github.com/users/piercefreeman/repos", "events_url": "https://api.github.com/users/piercefreeman/events{/privacy}", "received_events_url": "https://api.github.com/users/piercefreeman/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "Thanks for working on this. However, having thought about this issue a bit more, supporting this doesn't seem like a good idea - it's better to be explicit than implicit, according to the Zen of Python 🙂. Also, I don't think many users would use this, so this raises the question of whether this is something we want to maintain.\r\n\r\ncc @lhoestq for the 2nd opinion", "@mariosasko I was going to quote the Zen of Python in the other direction :) To me, this actually is much more explicit than the current behavior of guessing pyarrow types based on the raw dictionary return values. Explicit typehinting is increasingly the de facto way to deal with this dynamic type serialization - plus it feels like a clearer fit to me than separating out the mapper function from the feature column definition in the call to the actual `.map()`. Another benefit is providing typehinting support for clients that use mypy or other static typecheckers to detect return mismatches.\r\n\r\nBut will leave it to you and @lhoestq to see if it's something you'd like in core versus a support package.", "I meant that explicitly specifying the target features (the `features` param) is cleaner (easier to track) than relying on type hints.", "Passing features= to `map()` is richer and more explicit. Also I don't think users would guess that such API exist.\r\n\r\nOther libraries like dask also infer the type from the output or requires the typing to be specified using the `meta` argument", "Point about discoverability is a fair one, would certainly need some docs around it. All good! Will close this out and keep in our extension utilities." ]	2023-07-25T00:34:17	2023-07-26T15:13:45	2023-07-26T15:13:44	NONE	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6065", "html_url": "https://github.com/huggingface/datasets/pull/6065", "diff_url": "https://github.com/huggingface/datasets/pull/6065.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6065.patch", "merged_at": null }	As discussed [here](https://github.com/huggingface/datasets/issues/5965), there are some cases where datasets is unable to automatically promote columns during mapping. The fix is to explicitly provide a `features` definition so pyarrow can configure itself with the right column types from the outset. This PR provides an alternative approach, which is functionally equivalent to specifying features but a bit cleaner within a larger mapping pipeline. It allows clients to typehint the return variable coming from the mapper function - if we find one of these type annotations specified, and no explicit features have been passed in, we'll try to convert it into a Features map. If the map function runs and casting is unable to succeed, it will raise a DatasetTransformationNotAllowedError that indicates the typehint may be to blame. It works for batched and non-batched mapping functions. Currently supported column types: - builtins primitives: string, int, float, bool - dictionaries, lists (nested and one-deep) - Optional types and None-Unions (synonymous with optional types) It's used like: ```python class DatasetTyped(TypedDict): texts: list[str] def dataset_typed_map(batch) -> DatasetTyped: return {"texts": [text.split() for text in batch["raw_text"]]} dataset = {"raw_text": ["", "This is a test", "This is another test"]} with Dataset.from_dict(dataset) as dset: new_dataset = dset.map( dataset_typed_map, batched=True, batch_size=1, num_proc=1, ) ``` Open questions: - Should logging indicate we have automatically guessed these types? Or proceed quietly until we hit an error (as is the current implementation).	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6065/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6065/timeline	null	null	true
https://api.github.com/repos/huggingface/datasets/issues/6064	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6064/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6064/comments	https://api.github.com/repos/huggingface/datasets/issues/6064/events	https://github.com/huggingface/datasets/pull/6064	1,818,703,725	PR_kwDODunzps5WPzAv	6,064	set dev version	{ "login": "lhoestq", "id": 42851186, "node_id": "MDQ6VXNlcjQyODUxMTg2", "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "gravatar_id": "", "url": "https://api.github.com/users/lhoestq", "html_url": "https://github.com/lhoestq", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "repos_url": "https://api.github.com/users/lhoestq/repos", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6064). All of your documentation changes will be reflected on that endpoint.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006704 / 0.011353 (-0.004649) \| 0.004208 / 0.011008 (-0.006800) \| 0.085895 / 0.038508 (0.047387) \| 0.079303 / 0.023109 (0.056193) \| 0.353430 / 0.275898 (0.077532) \| 0.390814 / 0.323480 (0.067334) \| 0.006565 / 0.007986 (-0.001420) \| 0.003588 / 0.004328 (-0.000740) \| 0.065249 / 0.004250 (0.060999) \| 0.059772 / 0.037052 (0.022720) \| 0.356315 / 0.258489 (0.097826) \| 0.404812 / 0.293841 (0.110971) \| 0.031127 / 0.128546 (-0.097419) \| 0.008656 / 0.075646 (-0.066991) \| 0.288734 / 0.419271 (-0.130537) \| 0.053157 / 0.043533 (0.009625) \| 0.354651 / 0.255139 (0.099512) \| 0.370590 / 0.283200 (0.087391) \| 0.024944 / 0.141683 (-0.116738) \| 1.472393 / 1.452155 (0.020238) \| 1.548946 / 1.492716 (0.056229) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.223430 / 0.018006 (0.205424) \| 0.567359 / 0.000490 (0.566870) \| 0.006744 / 0.000200 (0.006544) \| 0.000094 / 0.000054 (0.000040) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030174 / 0.037411 (-0.007237) \| 0.084865 / 0.014526 (0.070339) \| 0.098986 / 0.176557 (-0.077571) \| 0.161458 / 0.737135 (-0.575678) \| 0.099198 / 0.296338 (-0.197141) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.404324 / 0.215209 (0.189115) \| 4.043744 / 2.077655 (1.966090) \| 1.972834 / 1.504120 (0.468714) \| 1.801634 / 1.541195 (0.260439) \| 1.891198 / 1.468490 (0.422708) \| 0.488511 / 4.584777 (-4.096266) \| 3.566890 / 3.745712 (-0.178823) \| 3.369415 / 5.269862 (-1.900447) \| 2.054995 / 4.565676 (-2.510682) \| 0.057225 / 0.424275 (-0.367050) \| 0.007360 / 0.007607 (-0.000247) \| 0.471813 / 0.226044 (0.245769) \| 4.734397 / 2.268929 (2.465468) \| 2.526585 / 55.444624 (-52.918039) \| 2.230535 / 6.876477 (-4.645942) \| 2.434403 / 2.142072 (0.292330) \| 0.630090 / 4.805227 (-4.175137) \| 0.138544 / 6.500664 (-6.362120) \| 0.060099 / 0.075469 (-0.015370) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.260951 / 1.841788 (-0.580837) \| 20.051513 / 8.074308 (11.977204) \| 14.675938 / 10.191392 (4.484546) \| 0.169535 / 0.680424 (-0.510889) \| 0.018574 / 0.534201 (-0.515627) \| 0.394255 / 0.579283 (-0.185028) \| 0.412713 / 0.434364 (-0.021651) \| 0.475891 / 0.540337 (-0.064446) \| 0.658223 / 1.386936 (-0.728713) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006969 / 0.011353 (-0.004384) \| 0.004417 / 0.011008 (-0.006591) \| 0.064399 / 0.038508 (0.025891) \| 0.082928 / 0.023109 (0.059819) \| 0.402285 / 0.275898 (0.126387) \| 0.440032 / 0.323480 (0.116552) \| 0.005896 / 0.007986 (-0.002090) \| 0.003580 / 0.004328 (-0.000749) \| 0.065340 / 0.004250 (0.061090) \| 0.060363 / 0.037052 (0.023311) \| 0.417413 / 0.258489 (0.158924) \| 0.448527 / 0.293841 (0.154686) \| 0.032238 / 0.128546 (-0.096308) \| 0.008820 / 0.075646 (-0.066826) \| 0.071516 / 0.419271 (-0.347755) \| 0.050614 / 0.043533 (0.007081) \| 0.406565 / 0.255139 (0.151426) \| 0.422527 / 0.283200 (0.139328) \| 0.025866 / 0.141683 (-0.115817) \| 1.512256 / 1.452155 (0.060101) \| 1.568433 / 1.492716 (0.075717) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.266521 / 0.018006 (0.248515) \| 0.564524 / 0.000490 (0.564034) \| 0.005236 / 0.000200 (0.005036) \| 0.000085 / 0.000054 (0.000030) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031998 / 0.037411 (-0.005413) \| 0.090754 / 0.014526 (0.076229) \| 0.105954 / 0.176557 (-0.070602) \| 0.164506 / 0.737135 (-0.572629) \| 0.108792 / 0.296338 (-0.187546) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.422044 / 0.215209 (0.206835) \| 4.204449 / 2.077655 (2.126795) \| 2.232060 / 1.504120 (0.727940) \| 2.060389 / 1.541195 (0.519194) \| 2.152723 / 1.468490 (0.684233) \| 0.488456 / 4.584777 (-4.096321) \| 3.591102 / 3.745712 (-0.154611) \| 5.250401 / 5.269862 (-0.019461) \| 3.060259 / 4.565676 (-1.505417) \| 0.057558 / 0.424275 (-0.366717) \| 0.007881 / 0.007607 (0.000274) \| 0.508631 / 0.226044 (0.282587) \| 5.064857 / 2.268929 (2.795928) \| 2.719068 / 55.444624 (-52.725556) \| 2.389992 / 6.876477 (-4.486485) \| 2.595073 / 2.142072 (0.453000) \| 0.590179 / 4.805227 (-4.215048) \| 0.136149 / 6.500664 (-6.364515) \| 0.062546 / 0.075469 (-0.012923) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.369252 / 1.841788 (-0.472535) \| 20.637580 / 8.074308 (12.563272) \| 14.217129 / 10.191392 (4.025737) \| 0.195464 / 0.680424 (-0.484960) \| 0.018452 / 0.534201 (-0.515749) \| 0.397044 / 0.579283 (-0.182239) \| 0.401127 / 0.434364 (-0.033237) \| 0.465033 / 0.540337 (-0.075305) \| 0.613484 / 1.386936 (-0.773452) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d9f1651128e50e7887f5e8eaaf6b55fe4cd84fdc \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006793 / 0.011353 (-0.004559) \| 0.004374 / 0.011008 (-0.006635) \| 0.084958 / 0.038508 (0.046450) \| 0.080440 / 0.023109 (0.057331) \| 0.317951 / 0.275898 (0.042053) \| 0.376133 / 0.323480 (0.052653) \| 0.005775 / 0.007986 (-0.002211) \| 0.003644 / 0.004328 (-0.000684) \| 0.064823 / 0.004250 (0.060573) \| 0.059442 / 0.037052 (0.022390) \| 0.319636 / 0.258489 (0.061147) \| 0.389668 / 0.293841 (0.095827) \| 0.031181 / 0.128546 (-0.097365) \| 0.008725 / 0.075646 (-0.066921) \| 0.288514 / 0.419271 (-0.130757) \| 0.053466 / 0.043533 (0.009933) \| 0.323131 / 0.255139 (0.067992) \| 0.345276 / 0.283200 (0.062076) \| 0.025046 / 0.141683 (-0.116637) \| 1.491659 / 1.452155 (0.039504) \| 1.562105 / 1.492716 (0.069389) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.286325 / 0.018006 (0.268319) \| 0.578021 / 0.000490 (0.577531) \| 0.007240 / 0.000200 (0.007040) \| 0.000095 / 0.000054 (0.000040) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030163 / 0.037411 (-0.007248) \| 0.082100 / 0.014526 (0.067574) \| 0.098331 / 0.176557 (-0.078225) \| 0.160517 / 0.737135 (-0.576618) \| 0.098479 / 0.296338 (-0.197859) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.401782 / 0.215209 (0.186573) \| 4.006330 / 2.077655 (1.928675) \| 2.033841 / 1.504120 (0.529721) \| 1.853248 / 1.541195 (0.312053) \| 1.980046 / 1.468490 (0.511556) \| 0.480636 / 4.584777 (-4.104141) \| 3.684482 / 3.745712 (-0.061231) \| 5.601940 / 5.269862 (0.332079) \| 3.369683 / 4.565676 (-1.195993) \| 0.057105 / 0.424275 (-0.367170) \| 0.007462 / 0.007607 (-0.000145) \| 0.474860 / 0.226044 (0.248815) \| 4.749624 / 2.268929 (2.480695) \| 2.492084 / 55.444624 (-52.952540) \| 2.157985 / 6.876477 (-4.718491) \| 2.420997 / 2.142072 (0.278925) \| 0.574718 / 4.805227 (-4.230509) \| 0.134672 / 6.500664 (-6.365992) \| 0.061677 / 0.075469 (-0.013792) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.284151 / 1.841788 (-0.557637) \| 20.186823 / 8.074308 (12.112515) \| 14.247024 / 10.191392 (4.055632) \| 0.171606 / 0.680424 (-0.508818) \| 0.018619 / 0.534201 (-0.515582) \| 0.394156 / 0.579283 (-0.185127) \| 0.424684 / 0.434364 (-0.009679) \| 0.476056 / 0.540337 (-0.064281) \| 0.668751 / 1.386936 (-0.718185) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006807 / 0.011353 (-0.004546) \| 0.004142 / 0.011008 (-0.006867) \| 0.065503 / 0.038508 (0.026995) \| 0.083232 / 0.023109 (0.060122) \| 0.378278 / 0.275898 (0.102380) \| 0.410191 / 0.323480 (0.086711) \| 0.005660 / 0.007986 (-0.002326) \| 0.003486 / 0.004328 (-0.000842) \| 0.066109 / 0.004250 (0.061859) \| 0.059654 / 0.037052 (0.022601) \| 0.375965 / 0.258489 (0.117476) \| 0.420046 / 0.293841 (0.126205) \| 0.031587 / 0.128546 (-0.096959) \| 0.008693 / 0.075646 (-0.066953) \| 0.071121 / 0.419271 (-0.348151) \| 0.049468 / 0.043533 (0.005935) \| 0.373785 / 0.255139 (0.118646) \| 0.395577 / 0.283200 (0.112377) \| 0.024138 / 0.141683 (-0.117545) \| 1.465451 / 1.452155 (0.013297) \| 1.547565 / 1.492716 (0.054849) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.325241 / 0.018006 (0.307234) \| 0.532415 / 0.000490 (0.531925) \| 0.004755 / 0.000200 (0.004555) \| 0.000084 / 0.000054 (0.000030) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033472 / 0.037411 (-0.003939) \| 0.090574 / 0.014526 (0.076048) \| 0.106712 / 0.176557 (-0.069845) \| 0.164353 / 0.737135 (-0.572783) \| 0.109344 / 0.296338 (-0.186994) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.420161 / 0.215209 (0.204952) \| 4.192334 / 2.077655 (2.114679) \| 2.178181 / 1.504120 (0.674061) \| 2.017405 / 1.541195 (0.476211) \| 2.182783 / 1.468490 (0.714293) \| 0.484037 / 4.584777 (-4.100740) \| 3.641911 / 3.745712 (-0.103801) \| 5.543874 / 5.269862 (0.274013) \| 3.440084 / 4.565676 (-1.125593) \| 0.056662 / 0.424275 (-0.367614) \| 0.007773 / 0.007607 (0.000166) \| 0.498357 / 0.226044 (0.272313) \| 4.951315 / 2.268929 (2.682386) \| 2.656732 / 55.444624 (-52.787892) \| 2.370566 / 6.876477 (-4.505910) \| 2.682289 / 2.142072 (0.540217) \| 0.598479 / 4.805227 (-4.206749) \| 0.151546 / 6.500664 (-6.349118) \| 0.063278 / 0.075469 (-0.012191) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.385897 / 1.841788 (-0.455891) \| 20.961851 / 8.074308 (12.887543) \| 14.465688 / 10.191392 (4.274296) \| 0.166156 / 0.680424 (-0.514268) \| 0.018848 / 0.534201 (-0.515353) \| 0.401712 / 0.579283 (-0.177571) \| 0.416674 / 0.434364 (-0.017690) \| 0.471834 / 0.540337 (-0.068503) \| 0.622463 / 1.386936 (-0.764473) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7e3ab9bc6ae8cc42f7e7d01afbd2637d51c3faf6 \"CML watermark\")\n" ]	2023-07-24T15:56:00	2023-07-24T16:05:19	2023-07-24T15:56:10	MEMBER	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6064", "html_url": "https://github.com/huggingface/datasets/pull/6064", "diff_url": "https://github.com/huggingface/datasets/pull/6064.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6064.patch", "merged_at": "2023-07-24T15:56:10" }	null	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6064/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6064/timeline	null	null	true
https://api.github.com/repos/huggingface/datasets/issues/6063	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6063/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6063/comments	https://api.github.com/repos/huggingface/datasets/issues/6063/events	https://github.com/huggingface/datasets/pull/6063	1,818,679,485	PR_kwDODunzps5WPtxi	6,063	Release: 2.14.0	{ "login": "lhoestq", "id": 42851186, "node_id": "MDQ6VXNlcjQyODUxMTg2", "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "gravatar_id": "", "url": "https://api.github.com/users/lhoestq", "html_url": "https://github.com/lhoestq", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "repos_url": "https://api.github.com/users/lhoestq/repos", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007703 / 0.011353 (-0.003650) \| 0.004699 / 0.011008 (-0.006309) \| 0.090195 / 0.038508 (0.051687) \| 0.119165 / 0.023109 (0.096056) \| 0.361435 / 0.275898 (0.085537) \| 0.404429 / 0.323480 (0.080949) \| 0.006172 / 0.007986 (-0.001814) \| 0.003932 / 0.004328 (-0.000397) \| 0.068384 / 0.004250 (0.064133) \| 0.066730 / 0.037052 (0.029678) \| 0.360978 / 0.258489 (0.102489) \| 0.401301 / 0.293841 (0.107460) \| 0.032836 / 0.128546 (-0.095710) \| 0.010821 / 0.075646 (-0.064825) \| 0.294526 / 0.419271 (-0.124745) \| 0.068751 / 0.043533 (0.025218) \| 0.368427 / 0.255139 (0.113288) \| 0.376969 / 0.283200 (0.093770) \| 0.040538 / 0.141683 (-0.101145) \| 1.509966 / 1.452155 (0.057811) \| 1.564885 / 1.492716 (0.072169) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.292243 / 0.018006 (0.274237) \| 0.662067 / 0.000490 (0.661577) \| 0.004966 / 0.000200 (0.004766) \| 0.000103 / 0.000054 (0.000048) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029050 / 0.037411 (-0.008361) \| 0.099880 / 0.014526 (0.085354) \| 0.109277 / 0.176557 (-0.067280) \| 0.167877 / 0.737135 (-0.569258) \| 0.110770 / 0.296338 (-0.185569) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.395742 / 0.215209 (0.180533) \| 3.944152 / 2.077655 (1.866498) \| 1.875295 / 1.504120 (0.371175) \| 1.705088 / 1.541195 (0.163893) \| 1.884443 / 1.468490 (0.415953) \| 0.497243 / 4.584777 (-4.087534) \| 3.749287 / 3.745712 (0.003575) \| 4.418826 / 5.269862 (-0.851035) \| 2.481149 / 4.565676 (-2.084528) \| 0.058260 / 0.424275 (-0.366015) \| 0.007744 / 0.007607 (0.000137) \| 0.472531 / 0.226044 (0.246486) \| 4.716022 / 2.268929 (2.447094) \| 2.480446 / 55.444624 (-52.964179) \| 2.163098 / 6.876477 (-4.713379) \| 2.217555 / 2.142072 (0.075482) \| 0.601965 / 4.805227 (-4.203262) \| 0.139364 / 6.500664 (-6.361301) \| 0.067097 / 0.075469 (-0.008372) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.330537 / 1.841788 (-0.511251) \| 22.176270 / 8.074308 (14.101962) \| 16.224981 / 10.191392 (6.033589) \| 0.173708 / 0.680424 (-0.506715) \| 0.019402 / 0.534201 (-0.514799) \| 0.401994 / 0.579283 (-0.177289) \| 0.432597 / 0.434364 (-0.001767) \| 0.489933 / 0.540337 (-0.050404) \| 0.672334 / 1.386936 (-0.714602) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008622 / 0.011353 (-0.002731) \| 0.004609 / 0.011008 (-0.006399) \| 0.067791 / 0.038508 (0.029283) \| 0.112770 / 0.023109 (0.089661) \| 0.380939 / 0.275898 (0.105041) \| 0.416940 / 0.323480 (0.093460) \| 0.006170 / 0.007986 (-0.001815) \| 0.003876 / 0.004328 (-0.000452) \| 0.066227 / 0.004250 (0.061976) \| 0.073132 / 0.037052 (0.036080) \| 0.390120 / 0.258489 (0.131631) \| 0.420893 / 0.293841 (0.127052) \| 0.033235 / 0.128546 (-0.095311) \| 0.009659 / 0.075646 (-0.065987) \| 0.072668 / 0.419271 (-0.346604) \| 0.051333 / 0.043533 (0.007801) \| 0.393828 / 0.255139 (0.138689) \| 0.412376 / 0.283200 (0.129176) \| 0.027760 / 0.141683 (-0.113923) \| 1.494369 / 1.452155 (0.042214) \| 1.592862 / 1.492716 (0.100145) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.345376 / 0.018006 (0.327369) \| 0.609399 / 0.000490 (0.608909) \| 0.000546 / 0.000200 (0.000346) \| 0.000061 / 0.000054 (0.000007) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.035601 / 0.037411 (-0.001810) \| 0.106527 / 0.014526 (0.092001) \| 0.114388 / 0.176557 (-0.062168) \| 0.175607 / 0.737135 (-0.561529) \| 0.113009 / 0.296338 (-0.183329) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.417237 / 0.215209 (0.202028) \| 4.136329 / 2.077655 (2.058675) \| 2.147134 / 1.504120 (0.643014) \| 2.009501 / 1.541195 (0.468306) \| 2.139499 / 1.468490 (0.671009) \| 0.491593 / 4.584777 (-4.093184) \| 3.766734 / 3.745712 (0.021022) \| 5.652446 / 5.269862 (0.382585) \| 3.021654 / 4.565676 (-1.544022) \| 0.058458 / 0.424275 (-0.365817) \| 0.008271 / 0.007607 (0.000664) \| 0.488229 / 0.226044 (0.262184) \| 4.861343 / 2.268929 (2.592415) \| 2.694142 / 55.444624 (-52.750482) \| 2.489130 / 6.876477 (-4.387346) \| 2.679376 / 2.142072 (0.537304) \| 0.589959 / 4.805227 (-4.215268) \| 0.137939 / 6.500664 (-6.362725) \| 0.066833 / 0.075469 (-0.008636) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.444871 / 1.841788 (-0.396916) \| 22.874961 / 8.074308 (14.800653) \| 15.842130 / 10.191392 (5.650738) \| 0.175529 / 0.680424 (-0.504895) \| 0.019024 / 0.534201 (-0.515177) \| 0.406551 / 0.579283 (-0.172732) \| 0.430335 / 0.434364 (-0.004029) \| 0.475750 / 0.540337 (-0.064587) \| 0.624836 / 1.386936 (-0.762100) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#dabbb7467f49fd22ae1a43cc577eb43008d63ee8 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006068 / 0.011353 (-0.005285) \| 0.003694 / 0.011008 (-0.007315) \| 0.080321 / 0.038508 (0.041813) \| 0.061738 / 0.023109 (0.038629) \| 0.329675 / 0.275898 (0.053777) \| 0.364008 / 0.323480 (0.040528) \| 0.004722 / 0.007986 (-0.003263) \| 0.002857 / 0.004328 (-0.001471) \| 0.062447 / 0.004250 (0.058197) \| 0.047006 / 0.037052 (0.009953) \| 0.335730 / 0.258489 (0.077241) \| 0.373047 / 0.293841 (0.079206) \| 0.027273 / 0.128546 (-0.101274) \| 0.007979 / 0.075646 (-0.067667) \| 0.262693 / 0.419271 (-0.156579) \| 0.045416 / 0.043533 (0.001883) \| 0.340774 / 0.255139 (0.085635) \| 0.359667 / 0.283200 (0.076468) \| 0.020848 / 0.141683 (-0.120835) \| 1.450110 / 1.452155 (-0.002045) \| 1.489511 / 1.492716 (-0.003206) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.185090 / 0.018006 (0.167084) \| 0.429823 / 0.000490 (0.429334) \| 0.000703 / 0.000200 (0.000503) \| 0.000058 / 0.000054 (0.000003) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024398 / 0.037411 (-0.013013) \| 0.072983 / 0.014526 (0.058457) \| 0.084012 / 0.176557 (-0.092544) \| 0.146160 / 0.737135 (-0.590975) \| 0.084068 / 0.296338 (-0.212270) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.432204 / 0.215209 (0.216995) \| 4.320593 / 2.077655 (2.242939) \| 2.261260 / 1.504120 (0.757140) \| 2.087148 / 1.541195 (0.545954) \| 2.144520 / 1.468490 (0.676029) \| 0.501477 / 4.584777 (-4.083300) \| 3.119557 / 3.745712 (-0.626156) \| 3.572527 / 5.269862 (-1.697335) \| 2.208836 / 4.565676 (-2.356840) \| 0.057232 / 0.424275 (-0.367043) \| 0.006494 / 0.007607 (-0.001113) \| 0.508135 / 0.226044 (0.282091) \| 5.090416 / 2.268929 (2.821488) \| 2.739800 / 55.444624 (-52.704824) \| 2.416105 / 6.876477 (-4.460372) \| 2.616037 / 2.142072 (0.473965) \| 0.583730 / 4.805227 (-4.221497) \| 0.124312 / 6.500664 (-6.376352) \| 0.060760 / 0.075469 (-0.014709) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.256097 / 1.841788 (-0.585691) \| 18.326073 / 8.074308 (10.251765) \| 13.859173 / 10.191392 (3.667781) \| 0.143639 / 0.680424 (-0.536785) \| 0.016649 / 0.534201 (-0.517552) \| 0.331671 / 0.579283 (-0.247612) \| 0.365370 / 0.434364 (-0.068994) \| 0.392753 / 0.540337 (-0.147584) \| 0.549302 / 1.386936 (-0.837634) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006054 / 0.011353 (-0.005299) \| 0.003641 / 0.011008 (-0.007367) \| 0.063109 / 0.038508 (0.024601) \| 0.060482 / 0.023109 (0.037372) \| 0.404047 / 0.275898 (0.128149) \| 0.425436 / 0.323480 (0.101956) \| 0.004603 / 0.007986 (-0.003382) \| 0.002905 / 0.004328 (-0.001423) \| 0.063207 / 0.004250 (0.058956) \| 0.048248 / 0.037052 (0.011196) \| 0.404325 / 0.258489 (0.145836) \| 0.432652 / 0.293841 (0.138811) \| 0.027630 / 0.128546 (-0.100916) \| 0.008062 / 0.075646 (-0.067584) \| 0.068367 / 0.419271 (-0.350905) \| 0.042169 / 0.043533 (-0.001364) \| 0.384903 / 0.255139 (0.129764) \| 0.418617 / 0.283200 (0.135417) \| 0.020767 / 0.141683 (-0.120915) \| 1.463606 / 1.452155 (0.011451) \| 1.512081 / 1.492716 (0.019365) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.229601 / 0.018006 (0.211594) \| 0.417878 / 0.000490 (0.417388) \| 0.000373 / 0.000200 (0.000173) \| 0.000053 / 0.000054 (-0.000002) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026354 / 0.037411 (-0.011057) \| 0.078100 / 0.014526 (0.063574) \| 0.087122 / 0.176557 (-0.089434) \| 0.140017 / 0.737135 (-0.597118) \| 0.089923 / 0.296338 (-0.206415) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.422405 / 0.215209 (0.207196) \| 4.237383 / 2.077655 (2.159728) \| 2.161104 / 1.504120 (0.656984) \| 1.982337 / 1.541195 (0.441142) \| 2.050216 / 1.468490 (0.581726) \| 0.499281 / 4.584777 (-4.085496) \| 2.996953 / 3.745712 (-0.748759) \| 5.027069 / 5.269862 (-0.242792) \| 2.804703 / 4.565676 (-1.760974) \| 0.057707 / 0.424275 (-0.366568) \| 0.006809 / 0.007607 (-0.000798) \| 0.495196 / 0.226044 (0.269152) \| 4.946593 / 2.268929 (2.677665) \| 2.598965 / 55.444624 (-52.845660) \| 2.349871 / 6.876477 (-4.526606) \| 2.451665 / 2.142072 (0.309593) \| 0.592314 / 4.805227 (-4.212913) \| 0.125685 / 6.500664 (-6.374979) \| 0.063252 / 0.075469 (-0.012217) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.325422 / 1.841788 (-0.516366) \| 18.521059 / 8.074308 (10.446751) \| 14.046757 / 10.191392 (3.855365) \| 0.133009 / 0.680424 (-0.547415) \| 0.017097 / 0.534201 (-0.517104) \| 0.339804 / 0.579283 (-0.239479) \| 0.345464 / 0.434364 (-0.088900) \| 0.387623 / 0.540337 (-0.152714) \| 0.519880 / 1.386936 (-0.867056) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#88896a7b28610ace95e444b94f9a4bc332cc1ee3 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008671 / 0.011353 (-0.002682) \| 0.004681 / 0.011008 (-0.006327) \| 0.107517 / 0.038508 (0.069008) \| 0.078846 / 0.023109 (0.055737) \| 0.449745 / 0.275898 (0.173847) \| 0.504075 / 0.323480 (0.180596) \| 0.005837 / 0.007986 (-0.002148) \| 0.004031 / 0.004328 (-0.000297) \| 0.092021 / 0.004250 (0.087771) \| 0.065954 / 0.037052 (0.028902) \| 0.442082 / 0.258489 (0.183593) \| 0.529349 / 0.293841 (0.235508) \| 0.052527 / 0.128546 (-0.076019) \| 0.013854 / 0.075646 (-0.061792) \| 0.367315 / 0.419271 (-0.051956) \| 0.068731 / 0.043533 (0.025199) \| 0.494733 / 0.255139 (0.239594) \| 0.472801 / 0.283200 (0.189601) \| 0.036791 / 0.141683 (-0.104892) \| 1.877648 / 1.452155 (0.425493) \| 1.928399 / 1.492716 (0.435683) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.231910 / 0.018006 (0.213904) \| 0.553464 / 0.000490 (0.552974) \| 0.011915 / 0.000200 (0.011715) \| 0.000378 / 0.000054 (0.000324) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028232 / 0.037411 (-0.009179) \| 0.091441 / 0.014526 (0.076916) \| 0.110394 / 0.176557 (-0.066162) \| 0.187638 / 0.737135 (-0.549497) \| 0.111810 / 0.296338 (-0.184529) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.599987 / 0.215209 (0.384778) \| 6.008709 / 2.077655 (3.931054) \| 2.518769 / 1.504120 (1.014650) \| 2.197029 / 1.541195 (0.655834) \| 2.217165 / 1.468490 (0.748675) \| 0.894939 / 4.584777 (-3.689837) \| 5.001217 / 3.745712 (1.255505) \| 4.636482 / 5.269862 (-0.633379) \| 3.237613 / 4.565676 (-1.328063) \| 0.104227 / 0.424275 (-0.320048) \| 0.008504 / 0.007607 (0.000897) \| 0.750190 / 0.226044 (0.524145) \| 7.514571 / 2.268929 (5.245642) \| 3.358003 / 55.444624 (-52.086621) \| 2.585649 / 6.876477 (-4.290827) \| 2.731129 / 2.142072 (0.589056) \| 1.088828 / 4.805227 (-3.716400) \| 0.217308 / 6.500664 (-6.283356) \| 0.076410 / 0.075469 (0.000941) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.620087 / 1.841788 (-0.221701) \| 23.145743 / 8.074308 (15.071435) \| 20.583403 / 10.191392 (10.392011) \| 0.225467 / 0.680424 (-0.454956) \| 0.029063 / 0.534201 (-0.505138) \| 0.480563 / 0.579283 (-0.098720) \| 0.539083 / 0.434364 (0.104719) \| 0.563787 / 0.540337 (0.023449) \| 0.782902 / 1.386936 (-0.604034) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.010113 / 0.011353 (-0.001239) \| 0.004997 / 0.011008 (-0.006011) \| 0.082974 / 0.038508 (0.044466) \| 0.090375 / 0.023109 (0.067266) \| 0.440273 / 0.275898 (0.164375) \| 0.476939 / 0.323480 (0.153459) \| 0.005955 / 0.007986 (-0.002031) \| 0.004375 / 0.004328 (0.000046) \| 0.080459 / 0.004250 (0.076209) \| 0.061787 / 0.037052 (0.024734) \| 0.477211 / 0.258489 (0.218722) \| 0.487164 / 0.293841 (0.193323) \| 0.054198 / 0.128546 (-0.074348) \| 0.013945 / 0.075646 (-0.061701) \| 0.093006 / 0.419271 (-0.326266) \| 0.062685 / 0.043533 (0.019152) \| 0.461373 / 0.255139 (0.206234) \| 0.475766 / 0.283200 (0.192567) \| 0.032059 / 0.141683 (-0.109623) \| 1.857989 / 1.452155 (0.405834) \| 1.837993 / 1.492716 (0.345277) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.243048 / 0.018006 (0.225042) \| 0.535850 / 0.000490 (0.535360) \| 0.007204 / 0.000200 (0.007004) \| 0.000104 / 0.000054 (0.000049) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032584 / 0.037411 (-0.004827) \| 0.098151 / 0.014526 (0.083625) \| 0.109691 / 0.176557 (-0.066866) \| 0.172803 / 0.737135 (-0.564333) \| 0.110469 / 0.296338 (-0.185869) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.635086 / 0.215209 (0.419877) \| 6.500864 / 2.077655 (4.423210) \| 2.996727 / 1.504120 (1.492607) \| 2.537218 / 1.541195 (0.996023) \| 2.572310 / 1.468490 (1.103820) \| 0.870868 / 4.584777 (-3.713909) \| 4.989744 / 3.745712 (1.244032) \| 4.422174 / 5.269862 (-0.847687) \| 2.935874 / 4.565676 (-1.629803) \| 0.097118 / 0.424275 (-0.327157) \| 0.009360 / 0.007607 (0.001753) \| 0.790447 / 0.226044 (0.564403) \| 7.859519 / 2.268929 (5.590591) \| 3.975616 / 55.444624 (-51.469009) \| 3.018271 / 6.876477 (-3.858206) \| 3.111173 / 2.142072 (0.969101) \| 1.085577 / 4.805227 (-3.719651) \| 0.225719 / 6.500664 (-6.274945) \| 0.080576 / 0.075469 (0.005107) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.802284 / 1.841788 (-0.039504) \| 23.487921 / 8.074308 (15.413613) \| 20.595171 / 10.191392 (10.403779) \| 0.196610 / 0.680424 (-0.483814) \| 0.027483 / 0.534201 (-0.506718) \| 0.485840 / 0.579283 (-0.093443) \| 0.542661 / 0.434364 (0.108297) \| 0.580602 / 0.540337 (0.040265) \| 0.768195 / 1.386936 (-0.618741) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#88896a7b28610ace95e444b94f9a4bc332cc1ee3 \"CML watermark\")\n" ]	2023-07-24T15:41:19	2023-07-24T16:05:16	2023-07-24T15:47:51	MEMBER	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6063", "html_url": "https://github.com/huggingface/datasets/pull/6063", "diff_url": "https://github.com/huggingface/datasets/pull/6063.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6063.patch", "merged_at": "2023-07-24T15:47:51" }	null	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6063/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6063/timeline	null	null	true
https://api.github.com/repos/huggingface/datasets/issues/6062	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6062/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6062/comments	https://api.github.com/repos/huggingface/datasets/issues/6062/events	https://github.com/huggingface/datasets/pull/6062	1,818,341,584	PR_kwDODunzps5WOj62	6,062	Improve `Dataset.from_list` docstring	{ "login": "mariosasko", "id": 47462742, "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "gravatar_id": "", "url": "https://api.github.com/users/mariosasko", "html_url": "https://github.com/mariosasko", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "repos_url": "https://api.github.com/users/mariosasko/repos", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008340 / 0.011353 (-0.003013) \| 0.005053 / 0.011008 (-0.005955) \| 0.103294 / 0.038508 (0.064786) \| 0.069417 / 0.023109 (0.046308) \| 0.436922 / 0.275898 (0.161024) \| 0.461348 / 0.323480 (0.137868) \| 0.006030 / 0.007986 (-0.001955) \| 0.003727 / 0.004328 (-0.000601) \| 0.076384 / 0.004250 (0.072134) \| 0.056742 / 0.037052 (0.019689) \| 0.439996 / 0.258489 (0.181507) \| 0.469417 / 0.293841 (0.175577) \| 0.044343 / 0.128546 (-0.084203) \| 0.012634 / 0.075646 (-0.063013) \| 0.359746 / 0.419271 (-0.059525) \| 0.064842 / 0.043533 (0.021309) \| 0.425960 / 0.255139 (0.170821) \| 0.458568 / 0.283200 (0.175368) \| 0.039802 / 0.141683 (-0.101881) \| 1.687320 / 1.452155 (0.235165) \| 1.806212 / 1.492716 (0.313496) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.255484 / 0.018006 (0.237478) \| 0.563039 / 0.000490 (0.562549) \| 0.000445 / 0.000200 (0.000245) \| 0.000076 / 0.000054 (0.000022) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027511 / 0.037411 (-0.009900) \| 0.089185 / 0.014526 (0.074659) \| 0.098397 / 0.176557 (-0.078160) \| 0.163897 / 0.737135 (-0.573238) \| 0.099905 / 0.296338 (-0.196434) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.612737 / 0.215209 (0.397528) \| 6.209948 / 2.077655 (4.132294) \| 2.756060 / 1.504120 (1.251940) \| 2.402115 / 1.541195 (0.860920) \| 2.422665 / 1.468490 (0.954175) \| 0.834799 / 4.584777 (-3.749977) \| 5.251699 / 3.745712 (1.505986) \| 5.554141 / 5.269862 (0.284280) \| 3.254699 / 4.565676 (-1.310977) \| 0.095697 / 0.424275 (-0.328578) \| 0.009406 / 0.007607 (0.001799) \| 0.729025 / 0.226044 (0.502980) \| 7.195521 / 2.268929 (4.926593) \| 3.360264 / 55.444624 (-52.084361) \| 2.696764 / 6.876477 (-4.179713) \| 2.702796 / 2.142072 (0.560724) \| 0.974420 / 4.805227 (-3.830808) \| 0.195215 / 6.500664 (-6.305450) \| 0.069754 / 0.075469 (-0.005715) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.553458 / 1.841788 (-0.288330) \| 21.972436 / 8.074308 (13.898128) \| 20.027392 / 10.191392 (9.836000) \| 0.216950 / 0.680424 (-0.463474) \| 0.032196 / 0.534201 (-0.502005) \| 0.449884 / 0.579283 (-0.129399) \| 0.586213 / 0.434364 (0.151849) \| 0.537227 / 0.540337 (-0.003111) \| 0.751022 / 1.386936 (-0.635914) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007859 / 0.011353 (-0.003493) \| 0.004762 / 0.011008 (-0.006246) \| 0.086023 / 0.038508 (0.047515) \| 0.069218 / 0.023109 (0.046109) \| 0.449312 / 0.275898 (0.173414) \| 0.481687 / 0.323480 (0.158207) \| 0.006318 / 0.007986 (-0.001668) \| 0.004063 / 0.004328 (-0.000266) \| 0.076917 / 0.004250 (0.072667) \| 0.058034 / 0.037052 (0.020981) \| 0.474265 / 0.258489 (0.215775) \| 0.497736 / 0.293841 (0.203895) \| 0.044587 / 0.128546 (-0.083959) \| 0.013880 / 0.075646 (-0.061766) \| 0.089233 / 0.419271 (-0.330038) \| 0.058760 / 0.043533 (0.015227) \| 0.439515 / 0.255139 (0.184376) \| 0.473246 / 0.283200 (0.190047) \| 0.042968 / 0.141683 (-0.098715) \| 1.802647 / 1.452155 (0.350493) \| 1.778563 / 1.492716 (0.285847) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.343741 / 0.018006 (0.325735) \| 0.567409 / 0.000490 (0.566919) \| 0.029727 / 0.000200 (0.029527) \| 0.000147 / 0.000054 (0.000092) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031021 / 0.037411 (-0.006390) \| 0.096659 / 0.014526 (0.082133) \| 0.103341 / 0.176557 (-0.073215) \| 0.169893 / 0.737135 (-0.567242) \| 0.103280 / 0.296338 (-0.193058) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.584724 / 0.215209 (0.369515) \| 5.792596 / 2.077655 (3.714941) \| 2.683133 / 1.504120 (1.179013) \| 2.367837 / 1.541195 (0.826643) \| 2.378567 / 1.468490 (0.910076) \| 0.803427 / 4.584777 (-3.781350) \| 5.179017 / 3.745712 (1.433305) \| 4.446323 / 5.269862 (-0.823538) \| 2.771731 / 4.565676 (-1.793945) \| 0.100943 / 0.424275 (-0.323332) \| 0.009875 / 0.007607 (0.002268) \| 0.725260 / 0.226044 (0.499216) \| 7.149728 / 2.268929 (4.880800) \| 3.646438 / 55.444624 (-51.798187) \| 2.793858 / 6.876477 (-4.082618) \| 2.971966 / 2.142072 (0.829894) \| 0.998147 / 4.805227 (-3.807080) \| 0.198004 / 6.500664 (-6.302660) \| 0.072581 / 0.075469 (-0.002888) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.696737 / 1.841788 (-0.145051) \| 22.615193 / 8.074308 (14.540884) \| 20.272421 / 10.191392 (10.081029) \| 0.237459 / 0.680424 (-0.442965) \| 0.034774 / 0.534201 (-0.499427) \| 0.484649 / 0.579283 (-0.094634) \| 0.590263 / 0.434364 (0.155899) \| 0.547833 / 0.540337 (0.007495) \| 0.762109 / 1.386936 (-0.624827) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4bc3628b5a8f71ad7cfc014d8ba5e798f26becb7 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.011183 / 0.011353 (-0.000170) \| 0.005267 / 0.011008 (-0.005741) \| 0.108506 / 0.038508 (0.069997) \| 0.083541 / 0.023109 (0.060431) \| 0.452189 / 0.275898 (0.176291) \| 0.496229 / 0.323480 (0.172749) \| 0.004951 / 0.007986 (-0.003035) \| 0.004452 / 0.004328 (0.000124) \| 0.085133 / 0.004250 (0.080883) \| 0.061291 / 0.037052 (0.024239) \| 0.450453 / 0.258489 (0.191964) \| 0.506456 / 0.293841 (0.212616) \| 0.049784 / 0.128546 (-0.078762) \| 0.014738 / 0.075646 (-0.060908) \| 0.372603 / 0.419271 (-0.046669) \| 0.065223 / 0.043533 (0.021690) \| 0.467872 / 0.255139 (0.212733) \| 0.500062 / 0.283200 (0.216862) \| 0.040911 / 0.141683 (-0.100772) \| 1.852970 / 1.452155 (0.400816) \| 2.016996 / 1.492716 (0.524280) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.262620 / 0.018006 (0.244614) \| 0.593925 / 0.000490 (0.593435) \| 0.000413 / 0.000200 (0.000213) \| 0.000085 / 0.000054 (0.000030) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.035713 / 0.037411 (-0.001698) \| 0.111403 / 0.014526 (0.096878) \| 0.117259 / 0.176557 (-0.059298) \| 0.201545 / 0.737135 (-0.535590) \| 0.133111 / 0.296338 (-0.163228) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.597318 / 0.215209 (0.382109) \| 5.882691 / 2.077655 (3.805036) \| 2.572203 / 1.504120 (1.068083) \| 2.248016 / 1.541195 (0.706821) \| 2.359103 / 1.468490 (0.890613) \| 0.852023 / 4.584777 (-3.732754) \| 5.270831 / 3.745712 (1.525119) \| 4.712915 / 5.269862 (-0.556947) \| 3.124295 / 4.565676 (-1.441381) \| 0.092045 / 0.424275 (-0.332230) \| 0.007834 / 0.007607 (0.000227) \| 0.695711 / 0.226044 (0.469666) \| 7.011760 / 2.268929 (4.742831) \| 3.333300 / 55.444624 (-52.111325) \| 2.745889 / 6.876477 (-4.130587) \| 3.153458 / 2.142072 (1.011385) \| 1.011089 / 4.805227 (-3.794139) \| 0.207467 / 6.500664 (-6.293197) \| 0.079802 / 0.075469 (0.004333) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.703784 / 1.841788 (-0.138003) \| 24.414340 / 8.074308 (16.340032) \| 22.534528 / 10.191392 (12.343136) \| 0.276129 / 0.680424 (-0.404295) \| 0.027954 / 0.534201 (-0.506247) \| 0.484261 / 0.579283 (-0.095022) \| 0.605316 / 0.434364 (0.170952) \| 0.557219 / 0.540337 (0.016882) \| 0.802209 / 1.386936 (-0.584727) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009109 / 0.011353 (-0.002244) \| 0.005376 / 0.011008 (-0.005632) \| 0.085141 / 0.038508 (0.046633) \| 0.100560 / 0.023109 (0.077450) \| 0.482673 / 0.275898 (0.206775) \| 0.551582 / 0.323480 (0.228103) \| 0.006756 / 0.007986 (-0.001229) \| 0.004171 / 0.004328 (-0.000158) \| 0.084184 / 0.004250 (0.079933) \| 0.069283 / 0.037052 (0.032230) \| 0.517722 / 0.258489 (0.259233) \| 0.542641 / 0.293841 (0.248801) \| 0.047790 / 0.128546 (-0.080756) \| 0.014063 / 0.075646 (-0.061583) \| 0.110591 / 0.419271 (-0.308680) \| 0.064373 / 0.043533 (0.020840) \| 0.496636 / 0.255139 (0.241497) \| 0.551906 / 0.283200 (0.268707) \| 0.046187 / 0.141683 (-0.095496) \| 1.864836 / 1.452155 (0.412681) \| 1.923765 / 1.492716 (0.431049) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.286558 / 0.018006 (0.268552) \| 0.610353 / 0.000490 (0.609863) \| 0.012647 / 0.000200 (0.012447) \| 0.000162 / 0.000054 (0.000107) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.037099 / 0.037411 (-0.000313) \| 0.108608 / 0.014526 (0.094082) \| 0.120386 / 0.176557 (-0.056170) \| 0.183450 / 0.737135 (-0.553686) \| 0.124860 / 0.296338 (-0.171479) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.629006 / 0.215209 (0.413797) \| 6.309206 / 2.077655 (4.231551) \| 2.878558 / 1.504120 (1.374438) \| 2.616093 / 1.541195 (1.074898) \| 2.668096 / 1.468490 (1.199606) \| 0.865732 / 4.584777 (-3.719045) \| 5.312433 / 3.745712 (1.566721) \| 4.799352 / 5.269862 (-0.470509) \| 3.142207 / 4.565676 (-1.423469) \| 0.099591 / 0.424275 (-0.324684) \| 0.009159 / 0.007607 (0.001552) \| 0.730999 / 0.226044 (0.504954) \| 7.486442 / 2.268929 (5.217513) \| 3.657699 / 55.444624 (-51.786925) \| 3.080094 / 6.876477 (-3.796383) \| 3.320976 / 2.142072 (1.178904) \| 1.089324 / 4.805227 (-3.715904) \| 0.222831 / 6.500664 (-6.277833) \| 0.083976 / 0.075469 (0.008507) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.793181 / 1.841788 (-0.048607) \| 25.307444 / 8.074308 (17.233136) \| 21.321713 / 10.191392 (11.130321) \| 0.216326 / 0.680424 (-0.464098) \| 0.034298 / 0.534201 (-0.499903) \| 0.497173 / 0.579283 (-0.082110) \| 0.643550 / 0.434364 (0.209186) \| 0.581213 / 0.540337 (0.040876) \| 0.830973 / 1.386936 (-0.555963) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#24875bb8494c3a7803182b08c70747b1b1a6bf4d \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006886 / 0.011353 (-0.004467) \| 0.004267 / 0.011008 (-0.006741) \| 0.086182 / 0.038508 (0.047674) \| 0.083405 / 0.023109 (0.060296) \| 0.313717 / 0.275898 (0.037819) \| 0.351476 / 0.323480 (0.027996) \| 0.005702 / 0.007986 (-0.002284) \| 0.003802 / 0.004328 (-0.000526) \| 0.065759 / 0.004250 (0.061508) \| 0.060056 / 0.037052 (0.023003) \| 0.315871 / 0.258489 (0.057382) \| 0.364520 / 0.293841 (0.070679) \| 0.032067 / 0.128546 (-0.096479) \| 0.008679 / 0.075646 (-0.066967) \| 0.294968 / 0.419271 (-0.124303) \| 0.054684 / 0.043533 (0.011152) \| 0.314124 / 0.255139 (0.058985) \| 0.337312 / 0.283200 (0.054113) \| 0.025051 / 0.141683 (-0.116632) \| 1.505242 / 1.452155 (0.053087) \| 1.608263 / 1.492716 (0.115547) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.266562 / 0.018006 (0.248556) \| 0.579887 / 0.000490 (0.579397) \| 0.004161 / 0.000200 (0.003961) \| 0.000090 / 0.000054 (0.000035) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031153 / 0.037411 (-0.006258) \| 0.087703 / 0.014526 (0.073177) \| 0.103864 / 0.176557 (-0.072693) \| 0.159032 / 0.737135 (-0.578104) \| 0.102482 / 0.296338 (-0.193857) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.405805 / 0.215209 (0.190596) \| 4.050669 / 2.077655 (1.973014) \| 2.064384 / 1.504120 (0.560264) \| 1.892825 / 1.541195 (0.351630) \| 2.001083 / 1.468490 (0.532593) \| 0.478174 / 4.584777 (-4.106603) \| 3.542580 / 3.745712 (-0.203132) \| 3.319205 / 5.269862 (-1.950656) \| 2.075868 / 4.565676 (-2.489808) \| 0.057345 / 0.424275 (-0.366930) \| 0.007459 / 0.007607 (-0.000148) \| 0.483564 / 0.226044 (0.257520) \| 4.827746 / 2.268929 (2.558818) \| 2.579541 / 55.444624 (-52.865083) \| 2.205125 / 6.876477 (-4.671352) \| 2.489206 / 2.142072 (0.347133) \| 0.575843 / 4.805227 (-4.229384) \| 0.133010 / 6.500664 (-6.367654) \| 0.061082 / 0.075469 (-0.014387) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.286059 / 1.841788 (-0.555729) \| 20.575173 / 8.074308 (12.500865) \| 14.351692 / 10.191392 (4.160300) \| 0.150401 / 0.680424 (-0.530022) \| 0.018678 / 0.534201 (-0.515523) \| 0.397860 / 0.579283 (-0.181423) \| 0.419474 / 0.434364 (-0.014890) \| 0.474492 / 0.540337 (-0.065846) \| 0.659510 / 1.386936 (-0.727426) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006948 / 0.011353 (-0.004405) \| 0.004305 / 0.011008 (-0.006703) \| 0.064220 / 0.038508 (0.025712) \| 0.083251 / 0.023109 (0.060142) \| 0.388148 / 0.275898 (0.112250) \| 0.417834 / 0.323480 (0.094354) \| 0.005762 / 0.007986 (-0.002224) \| 0.003803 / 0.004328 (-0.000525) \| 0.066365 / 0.004250 (0.062114) \| 0.061808 / 0.037052 (0.024756) \| 0.390889 / 0.258489 (0.132400) \| 0.430619 / 0.293841 (0.136778) \| 0.031777 / 0.128546 (-0.096770) \| 0.008781 / 0.075646 (-0.066865) \| 0.070844 / 0.419271 (-0.348427) \| 0.050552 / 0.043533 (0.007019) \| 0.378420 / 0.255139 (0.123281) \| 0.403273 / 0.283200 (0.120074) \| 0.024578 / 0.141683 (-0.117105) \| 1.494790 / 1.452155 (0.042636) \| 1.549408 / 1.492716 (0.056692) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.302668 / 0.018006 (0.284662) \| 0.542235 / 0.000490 (0.541746) \| 0.001847 / 0.000200 (0.001647) \| 0.000092 / 0.000054 (0.000037) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031947 / 0.037411 (-0.005465) \| 0.092220 / 0.014526 (0.077694) \| 0.104525 / 0.176557 (-0.072031) \| 0.162000 / 0.737135 (-0.575135) \| 0.106795 / 0.296338 (-0.189543) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.412035 / 0.215209 (0.196826) \| 4.106527 / 2.077655 (2.028872) \| 2.111529 / 1.504120 (0.607409) \| 1.953201 / 1.541195 (0.412006) \| 2.079258 / 1.468490 (0.610768) \| 0.479562 / 4.584777 (-4.105215) \| 3.606256 / 3.745712 (-0.139456) \| 5.175250 / 5.269862 (-0.094612) \| 3.292465 / 4.565676 (-1.273212) \| 0.057726 / 0.424275 (-0.366549) \| 0.008247 / 0.007607 (0.000640) \| 0.486143 / 0.226044 (0.260098) \| 4.859051 / 2.268929 (2.590123) \| 2.675629 / 55.444624 (-52.768995) \| 2.267448 / 6.876477 (-4.609029) \| 2.567639 / 2.142072 (0.425567) \| 0.580822 / 4.805227 (-4.224406) \| 0.134942 / 6.500664 (-6.365722) \| 0.063825 / 0.075469 (-0.011644) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.334421 / 1.841788 (-0.507367) \| 20.481428 / 8.074308 (12.407120) \| 14.227943 / 10.191392 (4.036551) \| 0.170711 / 0.680424 (-0.509713) \| 0.018212 / 0.534201 (-0.515989) \| 0.397212 / 0.579283 (-0.182071) \| 0.411934 / 0.434364 (-0.022430) \| 0.478019 / 0.540337 (-0.062319) \| 0.645434 / 1.386936 (-0.741502) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ef3d3f10886e23a65cce3bfd939b8ec0d5a5c2c1 \"CML watermark\")\n" ]	2023-07-24T12:36:38	2023-07-24T14:43:48	2023-07-24T14:34:43	CONTRIBUTOR	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6062", "html_url": "https://github.com/huggingface/datasets/pull/6062", "diff_url": "https://github.com/huggingface/datasets/pull/6062.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6062.patch", "merged_at": "2023-07-24T14:34:43" }	null	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6062/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6062/timeline	null	null	true
https://api.github.com/repos/huggingface/datasets/issues/6061	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6061/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6061/comments	https://api.github.com/repos/huggingface/datasets/issues/6061/events	https://github.com/huggingface/datasets/pull/6061	1,818,337,136	PR_kwDODunzps5WOi79	6,061	Dill 3.7 support	{ "login": "mariosasko", "id": 47462742, "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "gravatar_id": "", "url": "https://api.github.com/users/mariosasko", "html_url": "https://github.com/mariosasko", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "repos_url": "https://api.github.com/users/mariosasko/repos", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007700 / 0.011353 (-0.003653) \| 0.004680 / 0.011008 (-0.006328) \| 0.098812 / 0.038508 (0.060304) \| 0.085062 / 0.023109 (0.061952) \| 0.371472 / 0.275898 (0.095574) \| 0.412552 / 0.323480 (0.089072) \| 0.004700 / 0.007986 (-0.003285) \| 0.003765 / 0.004328 (-0.000564) \| 0.074267 / 0.004250 (0.070017) \| 0.063003 / 0.037052 (0.025951) \| 0.391842 / 0.258489 (0.133353) \| 0.436955 / 0.293841 (0.143114) \| 0.035291 / 0.128546 (-0.093255) \| 0.009309 / 0.075646 (-0.066338) \| 0.313097 / 0.419271 (-0.106174) \| 0.060098 / 0.043533 (0.016565) \| 0.350726 / 0.255139 (0.095587) \| 0.402692 / 0.283200 (0.119493) \| 0.029321 / 0.141683 (-0.112361) \| 1.671806 / 1.452155 (0.219651) \| 1.743760 / 1.492716 (0.251044) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.242281 / 0.018006 (0.224275) \| 0.505054 / 0.000490 (0.504564) \| 0.006595 / 0.000200 (0.006395) \| 0.000091 / 0.000054 (0.000037) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032174 / 0.037411 (-0.005238) \| 0.094483 / 0.014526 (0.079957) \| 0.108527 / 0.176557 (-0.068030) \| 0.178983 / 0.737135 (-0.558152) \| 0.113766 / 0.296338 (-0.182572) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.419764 / 0.215209 (0.204555) \| 4.282650 / 2.077655 (2.204995) \| 2.075325 / 1.504120 (0.571205) \| 1.897668 / 1.541195 (0.356473) \| 2.027109 / 1.468490 (0.558619) \| 0.519983 / 4.584777 (-4.064794) \| 4.134603 / 3.745712 (0.388891) \| 6.586711 / 5.269862 (1.316849) \| 3.811726 / 4.565676 (-0.753951) \| 0.058628 / 0.424275 (-0.365647) \| 0.007586 / 0.007607 (-0.000021) \| 0.502180 / 0.226044 (0.276136) \| 5.101588 / 2.268929 (2.832660) \| 2.534295 / 55.444624 (-52.910330) \| 2.220170 / 6.876477 (-4.656307) \| 2.441110 / 2.142072 (0.299038) \| 0.644775 / 4.805227 (-4.160452) \| 0.144716 / 6.500664 (-6.355948) \| 0.067018 / 0.075469 (-0.008451) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.431279 / 1.841788 (-0.410508) \| 21.947814 / 8.074308 (13.873506) \| 15.548236 / 10.191392 (5.356844) \| 0.174774 / 0.680424 (-0.505650) \| 0.021182 / 0.534201 (-0.513019) \| 0.441320 / 0.579283 (-0.137963) \| 0.476685 / 0.434364 (0.042321) \| 0.506277 / 0.540337 (-0.034060) \| 0.809943 / 1.386936 (-0.576993) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007172 / 0.011353 (-0.004181) \| 0.004358 / 0.011008 (-0.006650) \| 0.068604 / 0.038508 (0.030096) \| 0.083956 / 0.023109 (0.060847) \| 0.402579 / 0.275898 (0.126681) \| 0.444714 / 0.323480 (0.121235) \| 0.005940 / 0.007986 (-0.002046) \| 0.003607 / 0.004328 (-0.000722) \| 0.073134 / 0.004250 (0.068883) \| 0.061722 / 0.037052 (0.024669) \| 0.410957 / 0.258489 (0.152468) \| 0.458819 / 0.293841 (0.164978) \| 0.033710 / 0.128546 (-0.094836) \| 0.010230 / 0.075646 (-0.065417) \| 0.084678 / 0.419271 (-0.334593) \| 0.058203 / 0.043533 (0.014670) \| 0.444972 / 0.255139 (0.189833) \| 0.470962 / 0.283200 (0.187763) \| 0.029222 / 0.141683 (-0.112461) \| 1.671460 / 1.452155 (0.219306) \| 1.759471 / 1.492716 (0.266754) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.238894 / 0.018006 (0.220888) \| 0.493605 / 0.000490 (0.493115) \| 0.001979 / 0.000200 (0.001780) \| 0.000084 / 0.000054 (0.000030) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.036498 / 0.037411 (-0.000913) \| 0.095245 / 0.014526 (0.080719) \| 0.112147 / 0.176557 (-0.064409) \| 0.171128 / 0.737135 (-0.566007) \| 0.115295 / 0.296338 (-0.181044) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.461067 / 0.215209 (0.245858) \| 4.723932 / 2.077655 (2.646277) \| 2.432697 / 1.504120 (0.928578) \| 2.237302 / 1.541195 (0.696107) \| 2.351320 / 1.468490 (0.882830) \| 0.509963 / 4.584777 (-4.074813) \| 4.194817 / 3.745712 (0.449105) \| 6.689529 / 5.269862 (1.419667) \| 3.351198 / 4.565676 (-1.214478) \| 0.064563 / 0.424275 (-0.359712) \| 0.008605 / 0.007607 (0.000998) \| 0.575590 / 0.226044 (0.349546) \| 5.644179 / 2.268929 (3.375250) \| 3.021375 / 55.444624 (-52.423249) \| 2.595305 / 6.876477 (-4.281172) \| 2.839228 / 2.142072 (0.697156) \| 0.657148 / 4.805227 (-4.148079) \| 0.144831 / 6.500664 (-6.355834) \| 0.067882 / 0.075469 (-0.007587) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.595580 / 1.841788 (-0.246208) \| 22.431609 / 8.074308 (14.357301) \| 15.700845 / 10.191392 (5.509453) \| 0.164675 / 0.680424 (-0.515749) \| 0.021322 / 0.534201 (-0.512879) \| 0.455270 / 0.579283 (-0.124013) \| 0.451547 / 0.434364 (0.017183) \| 0.520955 / 0.540337 (-0.019383) \| 0.687803 / 1.386936 (-0.699133) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7d19574e9f44bd3b59a3e47ca7c4ea66305a8e6b \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008171 / 0.011353 (-0.003182) \| 0.005563 / 0.011008 (-0.005445) \| 0.102265 / 0.038508 (0.063757) \| 0.074755 / 0.023109 (0.051646) \| 0.431317 / 0.275898 (0.155419) \| 0.472179 / 0.323480 (0.148699) \| 0.006153 / 0.007986 (-0.001833) \| 0.003832 / 0.004328 (-0.000496) \| 0.078480 / 0.004250 (0.074230) \| 0.056250 / 0.037052 (0.019197) \| 0.432938 / 0.258489 (0.174449) \| 0.480983 / 0.293841 (0.187142) \| 0.048861 / 0.128546 (-0.079685) \| 0.016252 / 0.075646 (-0.059394) \| 0.343508 / 0.419271 (-0.075763) \| 0.065057 / 0.043533 (0.021524) \| 0.468418 / 0.255139 (0.213279) \| 0.463692 / 0.283200 (0.180492) \| 0.032912 / 0.141683 (-0.108771) \| 1.795194 / 1.452155 (0.343039) \| 1.833047 / 1.492716 (0.340331) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.197980 / 0.018006 (0.179974) \| 0.500662 / 0.000490 (0.500172) \| 0.007380 / 0.000200 (0.007181) \| 0.000110 / 0.000054 (0.000055) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028323 / 0.037411 (-0.009089) \| 0.089817 / 0.014526 (0.075291) \| 0.102923 / 0.176557 (-0.073633) \| 0.173851 / 0.737135 (-0.563284) \| 0.104006 / 0.296338 (-0.192333) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.580277 / 0.215209 (0.365068) \| 5.878739 / 2.077655 (3.801085) \| 2.404673 / 1.504120 (0.900553) \| 2.071765 / 1.541195 (0.530571) \| 2.106024 / 1.468490 (0.637534) \| 0.855217 / 4.584777 (-3.729560) \| 4.918602 / 3.745712 (1.172890) \| 5.354984 / 5.269862 (0.085122) \| 3.141288 / 4.565676 (-1.424389) \| 0.099553 / 0.424275 (-0.324723) \| 0.008152 / 0.007607 (0.000545) \| 0.709857 / 0.226044 (0.483813) \| 7.144602 / 2.268929 (4.875673) \| 3.137637 / 55.444624 (-52.306987) \| 2.379851 / 6.876477 (-4.496626) \| 2.346426 / 2.142072 (0.204353) \| 1.033416 / 4.805227 (-3.771811) \| 0.213120 / 6.500664 (-6.287544) \| 0.076037 / 0.075469 (0.000568) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.597742 / 1.841788 (-0.244046) \| 21.745366 / 8.074308 (13.671058) \| 20.830698 / 10.191392 (10.639306) \| 0.238727 / 0.680424 (-0.441697) \| 0.027923 / 0.534201 (-0.506278) \| 0.466073 / 0.579283 (-0.113210) \| 0.548647 / 0.434364 (0.114283) \| 0.549245 / 0.540337 (0.008908) \| 0.977148 / 1.386936 (-0.409788) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008252 / 0.011353 (-0.003101) \| 0.004653 / 0.011008 (-0.006356) \| 0.084012 / 0.038508 (0.045504) \| 0.077418 / 0.023109 (0.054309) \| 0.440748 / 0.275898 (0.164850) \| 0.464279 / 0.323480 (0.140799) \| 0.005762 / 0.007986 (-0.002224) \| 0.004909 / 0.004328 (0.000581) \| 0.086441 / 0.004250 (0.082190) \| 0.057883 / 0.037052 (0.020831) \| 0.466655 / 0.258489 (0.208166) \| 0.479751 / 0.293841 (0.185910) \| 0.047166 / 0.128546 (-0.081380) \| 0.014480 / 0.075646 (-0.061166) \| 0.092599 / 0.419271 (-0.326672) \| 0.062454 / 0.043533 (0.018921) \| 0.449753 / 0.255139 (0.194614) \| 0.461876 / 0.283200 (0.178676) \| 0.034828 / 0.141683 (-0.106855) \| 1.752249 / 1.452155 (0.300095) \| 1.865449 / 1.492716 (0.372732) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.245028 / 0.018006 (0.227022) \| 0.509564 / 0.000490 (0.509074) \| 0.003930 / 0.000200 (0.003730) \| 0.000110 / 0.000054 (0.000056) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034746 / 0.037411 (-0.002665) \| 0.096563 / 0.014526 (0.082037) \| 0.107581 / 0.176557 (-0.068975) \| 0.184952 / 0.737135 (-0.552184) \| 0.108747 / 0.296338 (-0.187591) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.613091 / 0.215209 (0.397882) \| 5.994985 / 2.077655 (3.917330) \| 2.711276 / 1.504120 (1.207156) \| 2.415862 / 1.541195 (0.874668) \| 2.391055 / 1.468490 (0.922565) \| 0.868723 / 4.584777 (-3.716054) \| 4.953992 / 3.745712 (1.208280) \| 4.606542 / 5.269862 (-0.663319) \| 2.942162 / 4.565676 (-1.623515) \| 0.102737 / 0.424275 (-0.321538) \| 0.008634 / 0.007607 (0.001027) \| 0.722122 / 0.226044 (0.496078) \| 7.245097 / 2.268929 (4.976168) \| 3.428232 / 55.444624 (-52.016393) \| 2.709539 / 6.876477 (-4.166938) \| 2.857956 / 2.142072 (0.715884) \| 1.045594 / 4.805227 (-3.759634) \| 0.213344 / 6.500664 (-6.287320) \| 0.073601 / 0.075469 (-0.001868) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.651954 / 1.841788 (-0.189834) \| 22.458646 / 8.074308 (14.384338) \| 19.583203 / 10.191392 (9.391811) \| 0.246932 / 0.680424 (-0.433492) \| 0.025730 / 0.534201 (-0.508471) \| 0.473475 / 0.579283 (-0.105808) \| 0.521411 / 0.434364 (0.087047) \| 0.562038 / 0.540337 (0.021700) \| 0.767673 / 1.386936 (-0.619263) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3869d99628329c696f6975377f65e625dd8ef3e0 \"CML watermark\")\n", "The CI error is unrelated.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006649 / 0.011353 (-0.004703) \| 0.003963 / 0.011008 (-0.007045) \| 0.084564 / 0.038508 (0.046056) \| 0.075668 / 0.023109 (0.052559) \| 0.314233 / 0.275898 (0.038335) \| 0.343320 / 0.323480 (0.019841) \| 0.005405 / 0.007986 (-0.002581) \| 0.003356 / 0.004328 (-0.000973) \| 0.065094 / 0.004250 (0.060844) \| 0.058774 / 0.037052 (0.021722) \| 0.320772 / 0.258489 (0.062283) \| 0.353546 / 0.293841 (0.059705) \| 0.030921 / 0.128546 (-0.097625) \| 0.008463 / 0.075646 (-0.067184) \| 0.287490 / 0.419271 (-0.131781) \| 0.053188 / 0.043533 (0.009656) \| 0.324023 / 0.255139 (0.068884) \| 0.337828 / 0.283200 (0.054628) \| 0.024764 / 0.141683 (-0.116918) \| 1.458028 / 1.452155 (0.005873) \| 1.521615 / 1.492716 (0.028899) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.209360 / 0.018006 (0.191353) \| 0.461331 / 0.000490 (0.460841) \| 0.000386 / 0.000200 (0.000186) \| 0.000052 / 0.000054 (-0.000002) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028405 / 0.037411 (-0.009006) \| 0.081074 / 0.014526 (0.066548) \| 0.094868 / 0.176557 (-0.081689) \| 0.151050 / 0.737135 (-0.586085) \| 0.095854 / 0.296338 (-0.200484) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.393957 / 0.215209 (0.178748) \| 3.938649 / 2.077655 (1.860994) \| 1.938190 / 1.504120 (0.434070) \| 1.766458 / 1.541195 (0.225263) \| 1.818028 / 1.468490 (0.349538) \| 0.483926 / 4.584777 (-4.100851) \| 3.641957 / 3.745712 (-0.103755) \| 4.883845 / 5.269862 (-0.386016) \| 2.960300 / 4.565676 (-1.605377) \| 0.057227 / 0.424275 (-0.367048) \| 0.007285 / 0.007607 (-0.000322) \| 0.475928 / 0.226044 (0.249884) \| 4.756757 / 2.268929 (2.487828) \| 2.502659 / 55.444624 (-52.941966) \| 2.178067 / 6.876477 (-4.698410) \| 2.378298 / 2.142072 (0.236226) \| 0.578639 / 4.805227 (-4.226588) \| 0.132512 / 6.500664 (-6.368152) \| 0.059656 / 0.075469 (-0.015813) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.272673 / 1.841788 (-0.569115) \| 19.266884 / 8.074308 (11.192576) \| 14.272930 / 10.191392 (4.081538) \| 0.165897 / 0.680424 (-0.514527) \| 0.018436 / 0.534201 (-0.515765) \| 0.395177 / 0.579283 (-0.184107) \| 0.420134 / 0.434364 (-0.014229) \| 0.460781 / 0.540337 (-0.079557) \| 0.645376 / 1.386936 (-0.741560) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006504 / 0.011353 (-0.004849) \| 0.003942 / 0.011008 (-0.007066) \| 0.064936 / 0.038508 (0.026428) \| 0.075015 / 0.023109 (0.051905) \| 0.396871 / 0.275898 (0.120973) \| 0.423448 / 0.323480 (0.099968) \| 0.005239 / 0.007986 (-0.002747) \| 0.003265 / 0.004328 (-0.001063) \| 0.064910 / 0.004250 (0.060660) \| 0.055006 / 0.037052 (0.017953) \| 0.392818 / 0.258489 (0.134329) \| 0.429735 / 0.293841 (0.135894) \| 0.031847 / 0.128546 (-0.096699) \| 0.008626 / 0.075646 (-0.067021) \| 0.071591 / 0.419271 (-0.347681) \| 0.049006 / 0.043533 (0.005473) \| 0.384913 / 0.255139 (0.129774) \| 0.408969 / 0.283200 (0.125769) \| 0.023573 / 0.141683 (-0.118110) \| 1.490271 / 1.452155 (0.038117) \| 1.564620 / 1.492716 (0.071904) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.225917 / 0.018006 (0.207911) \| 0.450369 / 0.000490 (0.449880) \| 0.000375 / 0.000200 (0.000175) \| 0.000055 / 0.000054 (0.000000) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031196 / 0.037411 (-0.006215) \| 0.090486 / 0.014526 (0.075960) \| 0.102326 / 0.176557 (-0.074231) \| 0.157483 / 0.737135 (-0.579653) \| 0.103670 / 0.296338 (-0.192668) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.417577 / 0.215209 (0.202368) \| 4.170798 / 2.077655 (2.093143) \| 2.123689 / 1.504120 (0.619569) \| 1.948231 / 1.541195 (0.407037) \| 2.040277 / 1.468490 (0.571787) \| 0.497919 / 4.584777 (-4.086858) \| 3.633270 / 3.745712 (-0.112442) \| 4.851698 / 5.269862 (-0.418164) \| 2.691992 / 4.565676 (-1.873684) \| 0.058641 / 0.424275 (-0.365634) \| 0.007719 / 0.007607 (0.000112) \| 0.500652 / 0.226044 (0.274607) \| 4.988657 / 2.268929 (2.719728) \| 2.604488 / 55.444624 (-52.840136) \| 2.329829 / 6.876477 (-4.546648) \| 2.468239 / 2.142072 (0.326167) \| 0.598724 / 4.805227 (-4.206503) \| 0.135959 / 6.500664 (-6.364706) \| 0.061088 / 0.075469 (-0.014381) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.352107 / 1.841788 (-0.489681) \| 19.973976 / 8.074308 (11.899668) \| 14.292812 / 10.191392 (4.101420) \| 0.163855 / 0.680424 (-0.516568) \| 0.018402 / 0.534201 (-0.515799) \| 0.393128 / 0.579283 (-0.186155) \| 0.407379 / 0.434364 (-0.026985) \| 0.462324 / 0.540337 (-0.078013) \| 0.607501 / 1.386936 (-0.779435) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ae126ac974cad3050f90106e5909232140786811 \"CML watermark\")\n" ]	2023-07-24T12:33:58	2023-07-24T14:13:20	2023-07-24T14:04:36	CONTRIBUTOR	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6061", "html_url": "https://github.com/huggingface/datasets/pull/6061", "diff_url": "https://github.com/huggingface/datasets/pull/6061.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6061.patch", "merged_at": "2023-07-24T14:04:36" }	Adds support for dill 3.7.	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6061/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6061/timeline	null	null	true
https://api.github.com/repos/huggingface/datasets/issues/6060	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6060/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6060/comments	https://api.github.com/repos/huggingface/datasets/issues/6060/events	https://github.com/huggingface/datasets/issues/6060	1,816,614,120	I_kwDODunzps5sR1To	6,060	Dataset.map() execute twice when in PyTorch DDP mode	{ "login": "wanghaoyucn", "id": 39429965, "node_id": "MDQ6VXNlcjM5NDI5OTY1", "avatar_url": "https://avatars.githubusercontent.com/u/39429965?v=4", "gravatar_id": "", "url": "https://api.github.com/users/wanghaoyucn", "html_url": "https://github.com/wanghaoyucn", "followers_url": "https://api.github.com/users/wanghaoyucn/followers", "following_url": "https://api.github.com/users/wanghaoyucn/following{/other_user}", "gists_url": "https://api.github.com/users/wanghaoyucn/gists{/gist_id}", "starred_url": "https://api.github.com/users/wanghaoyucn/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/wanghaoyucn/subscriptions", "organizations_url": "https://api.github.com/users/wanghaoyucn/orgs", "repos_url": "https://api.github.com/users/wanghaoyucn/repos", "events_url": "https://api.github.com/users/wanghaoyucn/events{/privacy}", "received_events_url": "https://api.github.com/users/wanghaoyucn/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	null	[ "Sorry for asking a duplicate question about `num_proc`, I searched the forum and find the solution.\r\n\r\nBut I still can't make the trick with `torch.distributed.barrier()` to only map at the main process work. The [post on forum]( https://discuss.huggingface.co/t/slow-processing-with-map-when-using-deepspeed-or-fairscale/7229/7) didn't help.", "If it does the `map` twice then it means the hash of your map function is not some same between your two processes.\r\n\r\nCan you make sure your map functions have the same hash in different processes ?\r\n\r\n```python\r\nfrom datasets.fingerprint import Hasher\r\n\r\nprint(Hasher.hash(lambda x: cut_reorder_keys(x, num_stations_list=args.num_stations_list, is_pad=True, is_train=True)))\r\nprint(Hasher.hash(lambda x: random_shift(x, shift_range=(-160, 0), feature_scale=16)))\r\n```\r\n\r\nYou can also set the fingerprint used to reload the resulting dataset by passing `new_finegrprint=` in `map`, see https://huggingface.co/docs/datasets/v2.13.1/en/about_cache#the-cache. This will force the different processes to use the same fingerprint used to locate the resulting dataset in the cache.", "Thanks for help! I find the fingerprint between processes don't have same hash:\r\n```\r\nRank 0: Gpu 0 cut_reorder_keys fingerprint c7f47f40e9a67657\r\nRank 0: Gpu 0 random_shift fingerprint 240a0ce79831e7d4\r\n\r\nRank 1: Gpu 1 cut_reorder_keys fingerprint 20edd3d9cf284001\r\nRank 1: Gpu 1 random_shift fingerprint 819f7c1c18e7733f\r\n```\r\nBut my functions only process the example one by one and don't need rank or other arguments. After all it can work in the test for dataset and dataloader.\r\nI'll try to set `new_fingerprint` to see if it works and figure out the reason of different hash." ]	2023-07-22T05:06:43	2023-07-24T19:29:55	null	NONE	null	null	null	### Describe the bug I use `torchrun --standalone --nproc_per_node=2 train.py` to start training. And write the code following the [docs](https://huggingface.co/docs/datasets/process#distributed-usage). The trick about using `torch.distributed.barrier()` to only execute map at the main process doesn't always work. When I am training model, it will map twice. When I am running a test for dataset and dataloader (just print the batches), it can work. Their code about loading dataset are same. And on another server with 30 CPU cores, I use 2 GPUs and it can't work neither. I have tried to use `rank` and `local_rank` to check, they all didn't make sense. ### Steps to reproduce the bug use `torchrun --standalone --nproc_per_node=2 train.py` or `torchrun --standalone train.py` to run This is my code: ```python if args.distributed and world_size > 1: if args.local_rank > 0: print(f"Rank {args.rank}: Gpu {args.gpu} waiting for main process to perform the mapping", force=True) torch.distributed.barrier() print("Mapping dataset") dataset = dataset.map(lambda x: cut_reorder_keys(x, num_stations_list=args.num_stations_list, is_pad=True, is_train=True), num_proc=8, desc="cut_reorder_keys") dataset = dataset.map(lambda x: random_shift(x, shift_range=(-160, 0), feature_scale=16), num_proc=8, desc="random_shift") dataset_test = dataset_test.map(lambda x: cut_reorder_keys(x, num_stations_list=args.num_stations_list, is_pad=True, is_train=False), num_proc=8, desc="cut_reorder_keys") if args.local_rank == 0: print("Mapping finished, loading results from main process") torch.distributed.barrier() ``` ### Expected behavior Only the main process will execute `map`, while the sub process will load cache from disk. ### Environment info server with 64 CPU cores (AMD Ryzen Threadripper PRO 5995WX 64-Cores) and 2 RTX 4090 - `python==3.9.16` - `datasets==2.13.1` - `torch==2.0.1+cu117` - `22.04.1-Ubuntu` server with 30 CPU cores (Intel(R) Xeon(R) Platinum 8375C CPU @ 2.90GHz) and 2 RTX 4090 - `python==3.9.0` - `datasets==2.13.1` - `torch==2.0.1+cu117` - `Ubuntu 20.04`	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6060/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6060/timeline	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6059	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6059/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6059/comments	https://api.github.com/repos/huggingface/datasets/issues/6059/events	https://github.com/huggingface/datasets/issues/6059	1,816,537,176	I_kwDODunzps5sRihY	6,059	Provide ability to load label mappings from file	{ "login": "david-waterworth", "id": 5028974, "node_id": "MDQ6VXNlcjUwMjg5NzQ=", "avatar_url": "https://avatars.githubusercontent.com/u/5028974?v=4", "gravatar_id": "", "url": "https://api.github.com/users/david-waterworth", "html_url": "https://github.com/david-waterworth", "followers_url": "https://api.github.com/users/david-waterworth/followers", "following_url": "https://api.github.com/users/david-waterworth/following{/other_user}", "gists_url": "https://api.github.com/users/david-waterworth/gists{/gist_id}", "starred_url": "https://api.github.com/users/david-waterworth/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/david-waterworth/subscriptions", "organizations_url": "https://api.github.com/users/david-waterworth/orgs", "repos_url": "https://api.github.com/users/david-waterworth/repos", "events_url": "https://api.github.com/users/david-waterworth/events{/privacy}", "received_events_url": "https://api.github.com/users/david-waterworth/received_events", "type": "User", "site_admin": false }	[ { "id": 1935892871, "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement", "name": "enhancement", "color": "a2eeef", "default": true, "description": "New feature or request" } ]	open	false	null	[]	null	[]	2023-07-22T02:04:19	2023-07-22T02:04:19	null	NONE	null	null	null	### Feature request My task is classification of a dataset containing a large label set that includes a hierarchy. Even ignoring the hierarchy I'm not able to find an example using `datasets` where the label names aren't hard-coded. This works find for classification of a handful of labels but ideally there would be a way of loading the name/id mappings required for `datasets.features.ClassLabel` from a file. It is possible to pass a file to ClassLabel but I cannot see an easy way of using this with `GeneratorBasedBuilder` since `self._info` is called before the `dl_manager` is constructed so even if my dataset contains say `label_mappings.json` there's no way of loading it in order to construct the `datasets.DatasetInfo` I can see other uses to accessing the `download_manager` from `self._info` - i.e. if the files contain a schema (i.e. `arrow` or `parquet` files) the `datasets.DatasetInfo` could be inferred. The workaround that was suggested in the forum is to generate a `.py` file from the `label_mappings.json` and import it. ``` class TestDatasetBuilder(datasets.GeneratorBasedBuilder): VERSION = datasets.Version("1.0.0") def _info(self): return datasets.DatasetInfo( description=_DESCRIPTION, features=datasets.Features( { "text": datasets.Value("string"), "label": datasets.features.ClassLabel(names=["label_1", "label_2"]), } ), task_templates=[TextClassification(text_column="text", label_column="label")], ) def _split_generators(self, dl_manager): train_path = dl_manager.download_and_extract(_TRAIN_DOWNLOAD_URL) test_path = dl_manager.download_and_extract(_TEST_DOWNLOAD_URL) return [ datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}), datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": test_path}), ] def _generate_examples(self, filepath): """Generate AG News examples.""" with open(filepath, encoding="utf-8") as csv_file: csv_reader = csv.DictReader(csv_file) for id_, row in enumerate(csv_reader): yield id_, row ``` ### Motivation Allow `datasets.DatasetInfo` to be generated based on the contents of the dataset. ### Your contribution I'm willing to work on a PR with guidence.	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6059/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6059/timeline	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6058	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6058/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6058/comments	https://api.github.com/repos/huggingface/datasets/issues/6058/events	https://github.com/huggingface/datasets/issues/6058	1,815,131,397	I_kwDODunzps5sMLUF	6,058	laion-coco download error	{ "login": "yangyijune", "id": 54424110, "node_id": "MDQ6VXNlcjU0NDI0MTEw", "avatar_url": "https://avatars.githubusercontent.com/u/54424110?v=4", "gravatar_id": "", "url": "https://api.github.com/users/yangyijune", "html_url": "https://github.com/yangyijune", "followers_url": "https://api.github.com/users/yangyijune/followers", "following_url": "https://api.github.com/users/yangyijune/following{/other_user}", "gists_url": "https://api.github.com/users/yangyijune/gists{/gist_id}", "starred_url": "https://api.github.com/users/yangyijune/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/yangyijune/subscriptions", "organizations_url": "https://api.github.com/users/yangyijune/orgs", "repos_url": "https://api.github.com/users/yangyijune/repos", "events_url": "https://api.github.com/users/yangyijune/events{/privacy}", "received_events_url": "https://api.github.com/users/yangyijune/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "This can also mean one of the files was not downloaded correctly.\r\n\r\nWe log an erroneous file's name before raising the reader's error, so this is how you can find the problematic file. Then, you should delete it and call `load_dataset` again.\r\n\r\n(I checked all the uploaded files, and they seem to be valid Parquet files, so I don't think this is a bug on their side)\r\n" ]	2023-07-21T04:24:15	2023-07-22T01:42:06	2023-07-22T01:42:06	NONE	null	null	null	### Describe the bug The full trace: ``` /home/bian/anaconda3/envs/sd/lib/python3.10/site-packages/datasets/load.py:1744: FutureWarning: 'ignore_verifications' was de precated in favor of 'verification_mode' in version 2.9.1 and will be removed in 3.0.0. You can remove this warning by passing 'verification_mode=no_checks' instead. warnings.warn( Downloading and preparing dataset parquet/laion--laion-coco to /home/bian/.cache/huggingface/datasets/laion___parquet/laion-- laion-coco-cb4205d7f1863066/0.0.0/bcacc8bdaa0614a5d73d0344c813275e590940c6ea8bc569da462847103a1afd... Downloading data: 100%\|█\| 1.89G/1.89G [04:57<00:00, Downloading data files: 100%\|█\| 1/1 [04:59<00:00, 2 Extracting data files: 100%\|█\| 1/1 [00:00<00:00, 13 Generating train split: 0 examples [00:00, ? examples/s]<_io.BufferedReader name='/home/bian/.cache/huggingface/datasets/downlo ads/26d7a016d25bbd9443115cfa3092136e8eb2f1f5bcd4154 0cb9234572927f04c'> Traceback (most recent call last): File "/home/bian/data/ZOC/download_laion_coco.py", line 4, in <module> dataset = load_dataset("laion/laion-coco", ignore_verifications=True) File "/home/bian/anaconda3/envs/sd/lib/python3.10/site-packages/datasets/load.py", line 1791, in load_dataset builder_instance.download_and_prepare( File "/home/bian/anaconda3/envs/sd/lib/python3.10/site-packages/datasets/builder.py", line 891, in download_and_prepare self._download_and_prepare( File "/home/bian/anaconda3/envs/sd/lib/python3.10/site-packages/datasets/builder.py", line 986, in _download_and_prepare self._prepare_split(split_generator, prepare_split_kwargs) File "/home/bian/anaconda3/envs/sd/lib/python3.10/site-packages/datasets/builder.py", line 1748, in _prepare_split for job_id, done, content in self._prepare_split_single( File "/home/bian/anaconda3/envs/sd/lib/python3.10/site-packages/datasets/builder.py", line 1842, in _prepare_split_single generator = self._generate_tables(gen_kwargs) File "/home/bian/anaconda3/envs/sd/lib/python3.10/site-packages/datasets/packaged_modules/parquet/parquet.py", line 67, in _generate_tables parquet_file = pq.ParquetFile(f) File "/home/bian/anaconda3/envs/sd/lib/python3.10/site-packages/pyarrow/parquet/core.py", line 323, in __init__ self.reader.open( File "pyarrow/_parquet.pyx", line 1227, in pyarrow._parquet.ParquetReader.open File "pyarrow/error.pxi", line 100, in pyarrow.lib.check_status pyarrow.lib.ArrowInvalid: Parquet magic bytes not found in footer. Either the file is corrupted or this is not a parquet file . ``` I have carefully followed the instructions in #5264 but still get the same error. Other helpful information: ``` ds = load_dataset("parquet", data_files= ...: "https://huggingface.co/datasets/laion/l ...: aion-coco/resolve/d22869de3ccd39dfec1507 ...: f7ded32e4a518dad24/part-00000-2256f782-1 ...: 26f-4dc6-b9c6-e6757637749d-c000.snappy.p ...: arquet") Found cached dataset parquet (/home/bian/.cache/huggingface/datasets/parquet/default-a02eea00aeb08b0e/0.0.0/bb8ccf89d9ee38581ff5e51506d721a9b37f14df8090dc9b2d8fb4a40957833f) 100%\|██████████████\| 1/1 [00:00<00:00, 4.55it/s] ``` ### Steps to reproduce the bug ``` from datasets import load_dataset dataset = load_dataset("laion/laion-coco", ignore_verifications=True/False) ``` ### Expected behavior Properly load Laion-coco dataset ### Environment info datasets==2.11.0 torch==1.12.1 python 3.10	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6058/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6058/timeline	null	completed	false
https://api.github.com/repos/huggingface/datasets/issues/6057	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6057/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6057/comments	https://api.github.com/repos/huggingface/datasets/issues/6057/events	https://github.com/huggingface/datasets/issues/6057	1,815,100,151	I_kwDODunzps5sMDr3	6,057	Why is the speed difference of gen example so big?	{ "login": "pixeli99", "id": 46072190, "node_id": "MDQ6VXNlcjQ2MDcyMTkw", "avatar_url": "https://avatars.githubusercontent.com/u/46072190?v=4", "gravatar_id": "", "url": "https://api.github.com/users/pixeli99", "html_url": "https://github.com/pixeli99", "followers_url": "https://api.github.com/users/pixeli99/followers", "following_url": "https://api.github.com/users/pixeli99/following{/other_user}", "gists_url": "https://api.github.com/users/pixeli99/gists{/gist_id}", "starred_url": "https://api.github.com/users/pixeli99/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/pixeli99/subscriptions", "organizations_url": "https://api.github.com/users/pixeli99/orgs", "repos_url": "https://api.github.com/users/pixeli99/repos", "events_url": "https://api.github.com/users/pixeli99/events{/privacy}", "received_events_url": "https://api.github.com/users/pixeli99/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	null	[ "Hi!\r\n\r\nIt's hard to explain this behavior without more information. Can you profile the slower version with the following code\r\n```python\r\nimport cProfile, pstats\r\nfrom datasets import load_dataset\r\n\r\nwith cProfile.Profile() as profiler:\r\n ds = load_dataset(...)\r\n\r\nstats = pstats.Stats(profiler).sort_stats(\"cumtime\")\r\nstats.print_stats()\r\n```\r\nand share the output?" ]	2023-07-21T03:34:49	2023-07-21T16:41:09	null	NONE	null	null	null	```python def _generate_examples(self, metadata_path, images_dir, conditioning_images_dir): with open(metadata_path, 'r') as file: metadata = json.load(file) for idx, item in enumerate(metadata): image_path = item.get('image_path') text_content = item.get('text_content') image_data = open(image_path, "rb").read() yield idx, { "text": text_content, "image": { "path": image_path, "bytes": image_data, }, "conditioning_image": { "path": image_path, "bytes": image_data, }, } ``` Hello, I use the above function to deal with my local data set, but I am very surprised that the speed at which I generate example is very different. When I start a training task, sometimes 1000examples/s, sometimes only 10examples/s. ![image](https://github.com/huggingface/datasets/assets/46072190/cdc17661-8267-4fd8-b30c-b74d505efd9b) I'm not saying that speed is changing all the time. I mean, the reading speed is different in different training, which will cause me to start training over and over again until the speed of this generation of examples is normal.	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6057/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6057/timeline	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6056	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6056/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6056/comments	https://api.github.com/repos/huggingface/datasets/issues/6056/events	https://github.com/huggingface/datasets/pull/6056	1,815,086,963	PR_kwDODunzps5WD4RY	6,056	Implement proper checkpointing for dataset uploading with resume function that does not require remapping shards that have already been uploaded	{ "login": "AntreasAntoniou", "id": 10792502, "node_id": "MDQ6VXNlcjEwNzkyNTAy", "avatar_url": "https://avatars.githubusercontent.com/u/10792502?v=4", "gravatar_id": "", "url": "https://api.github.com/users/AntreasAntoniou", "html_url": "https://github.com/AntreasAntoniou", "followers_url": "https://api.github.com/users/AntreasAntoniou/followers", "following_url": "https://api.github.com/users/AntreasAntoniou/following{/other_user}", "gists_url": "https://api.github.com/users/AntreasAntoniou/gists{/gist_id}", "starred_url": "https://api.github.com/users/AntreasAntoniou/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/AntreasAntoniou/subscriptions", "organizations_url": "https://api.github.com/users/AntreasAntoniou/orgs", "repos_url": "https://api.github.com/users/AntreasAntoniou/repos", "events_url": "https://api.github.com/users/AntreasAntoniou/events{/privacy}", "received_events_url": "https://api.github.com/users/AntreasAntoniou/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6056). All of your documentation changes will be reflected on that endpoint.", "@lhoestq Reading the filenames is something I tried earlier, but I decided to use the yaml direction because:\r\n\r\n1. The yaml file name is constructed to retain information about the shard_size, and total number of shards, hence ensuring that the files uploaded are not just files that have the same name but actually represent a different configuration of shard_size, and total number of shards. \r\n2. Remembering the total file size is done easily in the yaml, whereas alternatively I am not sure how one could access the file size of the uploaded files without downloading them.\r\n3. I also had an issue earlier with the hashes not being consistent with which the yaml helped -- but this is no longer an issue as I found a way around it. \r\n\r\nIf 1 and 2 can be achieved without an additional yaml, then I would be willing to make those changes. Let me know of any ideas. 1. could be done by changing the data file names, but I'd rather not do that as to prevent breaking existing datasets that try to upload updates to their data. ", "If the file name depends on the shard's fingerprint before mapping then we can know if a shard has been uploaded before mapping and without requiring an extra YAML file. It should do the job imo\r\n\r\n> I also had an issue earlier with the hashes not being consistent with which the yaml helped -- but this is no longer an issue as I found a way around it.\r\n\r\nwhat was the issue ?" ]	2023-07-21T03:13:21	2023-07-24T15:17:28	null	NONE	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6056", "html_url": "https://github.com/huggingface/datasets/pull/6056", "diff_url": "https://github.com/huggingface/datasets/pull/6056.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6056.patch", "merged_at": null }	Context: issue #5990 In order to implement the checkpointing, I introduce a metadata folder that keeps one yaml file for each set that one is uploading. This yaml keeps track of what shards have already been uploaded, and which one the idx of the latest one was. Using this information I am then able to easily get the push_to_hub function to retrieve on demand past history of uploads and continue mapping and uploading from where it was left off.	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6056/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6056/timeline	null	null	true
https://api.github.com/repos/huggingface/datasets/issues/6055	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6055/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6055/comments	https://api.github.com/repos/huggingface/datasets/issues/6055/events	https://github.com/huggingface/datasets/issues/6055	1,813,524,145	I_kwDODunzps5sGC6x	6,055	Fix host URL in The Pile datasets	{ "login": "nickovchinnikov", "id": 7540752, "node_id": "MDQ6VXNlcjc1NDA3NTI=", "avatar_url": "https://avatars.githubusercontent.com/u/7540752?v=4", "gravatar_id": "", "url": "https://api.github.com/users/nickovchinnikov", "html_url": "https://github.com/nickovchinnikov", "followers_url": "https://api.github.com/users/nickovchinnikov/followers", "following_url": "https://api.github.com/users/nickovchinnikov/following{/other_user}", "gists_url": "https://api.github.com/users/nickovchinnikov/gists{/gist_id}", "starred_url": "https://api.github.com/users/nickovchinnikov/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/nickovchinnikov/subscriptions", "organizations_url": "https://api.github.com/users/nickovchinnikov/orgs", "repos_url": "https://api.github.com/users/nickovchinnikov/repos", "events_url": "https://api.github.com/users/nickovchinnikov/events{/privacy}", "received_events_url": "https://api.github.com/users/nickovchinnikov/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	null	[]	2023-07-20T09:08:52	2023-07-20T09:09:37	null	NONE	null	null	null	### Describe the bug In #3627 and #5543, you tried to fix the host URL in The Pile datasets. But both URLs are not working now: `HTTPError: 404 Client Error: Not Found for URL: https://the-eye.eu/public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst` And `ConnectTimeout: HTTPSConnectionPool(host='mystic.the-eye.eu', port=443): Max retries exceeded with url: /public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst (Caused by ConnectTimeoutError(, 'Connection to mystic.the-eye.eu timed out. (connect timeout=10.0)'))` ### Steps to reproduce the bug ``` from datasets import load_dataset # This takes a few minutes to run, so go grab a tea or coffee while you wait :) data_files = "https://mystic.the-eye.eu/public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst" pubmed_dataset = load_dataset("json", data_files=data_files, split="train") pubmed_dataset ``` Result: `ConnectTimeout: HTTPSConnectionPool(host='mystic.the-eye.eu', port=443): Max retries exceeded with url: /public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst (Caused by ConnectTimeoutError(, 'Connection to mystic.the-eye.eu timed out. (connect timeout=10.0)'))` And ``` from datasets import load_dataset # This takes a few minutes to run, so go grab a tea or coffee while you wait :) data_files = "https://the-eye.eu/public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst" pubmed_dataset = load_dataset("json", data_files=data_files, split="train") pubmed_dataset ``` Result: `HTTPError: 404 Client Error: Not Found for URL: https://the-eye.eu/public/AI/pile_preliminary_components/PUBMED_title_abstracts_2019_baseline.jsonl.zst` ### Expected behavior Downloading as normal. ### Environment info Environment info `datasets` version: 2.9.0 Platform: Windows Python version: 3.9.13	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6055/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6055/timeline	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6054	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6054/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6054/comments	https://api.github.com/repos/huggingface/datasets/issues/6054/events	https://github.com/huggingface/datasets/issues/6054	1,813,271,304	I_kwDODunzps5sFFMI	6,054	Multi-processed `Dataset.map` slows down a lot when `import torch`	{ "login": "ShinoharaHare", "id": 47121592, "node_id": "MDQ6VXNlcjQ3MTIxNTky", "avatar_url": "https://avatars.githubusercontent.com/u/47121592?v=4", "gravatar_id": "", "url": "https://api.github.com/users/ShinoharaHare", "html_url": "https://github.com/ShinoharaHare", "followers_url": "https://api.github.com/users/ShinoharaHare/followers", "following_url": "https://api.github.com/users/ShinoharaHare/following{/other_user}", "gists_url": "https://api.github.com/users/ShinoharaHare/gists{/gist_id}", "starred_url": "https://api.github.com/users/ShinoharaHare/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/ShinoharaHare/subscriptions", "organizations_url": "https://api.github.com/users/ShinoharaHare/orgs", "repos_url": "https://api.github.com/users/ShinoharaHare/repos", "events_url": "https://api.github.com/users/ShinoharaHare/events{/privacy}", "received_events_url": "https://api.github.com/users/ShinoharaHare/received_events", "type": "User", "site_admin": false }	[ { "id": 1935892865, "node_id": "MDU6TGFiZWwxOTM1ODkyODY1", "url": "https://api.github.com/repos/huggingface/datasets/labels/duplicate", "name": "duplicate", "color": "cfd3d7", "default": true, "description": "This issue or pull request already exists" } ]	closed	false	null	[]	null	[ "A duplicate of https://github.com/huggingface/datasets/issues/5929" ]	2023-07-20T06:36:14	2023-07-21T15:19:37	2023-07-21T15:19:37	NONE	null	null	null	### Describe the bug When using `Dataset.map` with `num_proc > 1`, the speed slows down much if I add `import torch` to the start of the script even though I don't use it. I'm not sure if it's `torch` only or if any other package that is "large" will also cause the same result. BTW, `import lightning` also slows it down. Below are the progress bars of `Dataset.map`, the only difference between them is with or without `import torch`, but the speed varies by 6-7 times. - without `import torch` ![image](https://github.com/huggingface/datasets/assets/47121592/0233055a-ced4-424a-9f0f-32a2afd802c2) - with `import torch` ![image](https://github.com/huggingface/datasets/assets/47121592/463eafb7-b81e-4eb9-91ca-fd7fe20f3d59) ### Steps to reproduce the bug Below is the code I used, but I don't think the dataset and the mapping function have much to do with the phenomenon. ```python3 from datasets import load_from_disk, disable_caching from transformers import AutoTokenizer # import torch # import lightning def rearrange_datapoints( batch, tokenizer, sequence_length, ): datapoints = [] input_ids = [] for x in batch['input_ids']: input_ids += x while len(input_ids) >= sequence_length: datapoint = input_ids[:sequence_length] datapoints.append(datapoint) input_ids[:sequence_length] = [] if input_ids: paddings = [-1] * (sequence_length - len(input_ids)) datapoint = paddings + input_ids if tokenizer.padding_side == 'left' else input_ids + paddings datapoints.append(datapoint) batch['input_ids'] = datapoints return batch if __name__ == '__main__': disable_caching() tokenizer = AutoTokenizer.from_pretrained('...', use_fast=False) dataset = load_from_disk('...') dataset = dataset.map( rearrange_datapoints, fn_kwargs=dict( tokenizer=tokenizer, sequence_length=2048, ), batched=True, num_proc=8, ) ``` ### Expected behavior The multi-processed `Dataset.map` function speed between with and without `import torch` should be the same. ### Environment info - `datasets` version: 2.13.1 - Platform: Linux-3.10.0-1127.el7.x86_64-x86_64-with-glibc2.31 - Python version: 3.10.11 - Huggingface_hub version: 0.14.1 - PyArrow version: 12.0.0 - Pandas version: 2.0.1	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6054/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6054/timeline	null	completed	false
https://api.github.com/repos/huggingface/datasets/issues/6053	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6053/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6053/comments	https://api.github.com/repos/huggingface/datasets/issues/6053/events	https://github.com/huggingface/datasets/issues/6053	1,812,635,902	I_kwDODunzps5sCqD-	6,053	Change package name from "datasets" to something less generic	{ "login": "geajack", "id": 2124157, "node_id": "MDQ6VXNlcjIxMjQxNTc=", "avatar_url": "https://avatars.githubusercontent.com/u/2124157?v=4", "gravatar_id": "", "url": "https://api.github.com/users/geajack", "html_url": "https://github.com/geajack", "followers_url": "https://api.github.com/users/geajack/followers", "following_url": "https://api.github.com/users/geajack/following{/other_user}", "gists_url": "https://api.github.com/users/geajack/gists{/gist_id}", "starred_url": "https://api.github.com/users/geajack/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/geajack/subscriptions", "organizations_url": "https://api.github.com/users/geajack/orgs", "repos_url": "https://api.github.com/users/geajack/repos", "events_url": "https://api.github.com/users/geajack/events{/privacy}", "received_events_url": "https://api.github.com/users/geajack/received_events", "type": "User", "site_admin": false }	[ { "id": 1935892871, "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement", "name": "enhancement", "color": "a2eeef", "default": true, "description": "New feature or request" } ]	open	false	null	[]	null	[]	2023-07-19T19:53:28	2023-07-19T19:55:04	null	NONE	null	null	null	### Feature request I'm repeatedly finding myself in situations where I want to have a package called `datasets.py` or `evaluate.py` in my code and can't because those names are being taken up by Huggingface packages. While I can understand how (even from the user's perspective) it's aesthetically pleasing to have nice terse library names, ultimately a library hogging simple names like this is something I find short-sighted, impractical and at my most irritable, frankly rude. My preference would be a pattern like what you get with all the other big libraries like numpy or pandas: ``` import huggingface as hf # hf.transformers, hf.datasets, hf.evaluate ``` or things like ``` import huggingface.transformers as tf # tf.load_model(), etc ``` If this isn't possible for some technical reason, at least just call the packages something like `hf_transformers` and so on. I realize this is a very big change that's probably been discussed internally already, but I'm making this issue and sister issues on each huggingface project just to start the conversation and begin tracking community feeling on the matter, since I suspect I'm not the only one who feels like this. Sorry if this has been requested already on this issue tracker, I couldn't find anything looking for terms like "package name". Sister issues: - [transformers](https://github.com/huggingface/transformers/issues/24934) - datasets - [evaluate](https://github.com/huggingface/evaluate/issues/476) ### Motivation Not taking up package names the user is likely to want to use. ### Your contribution No - more a matter of internal discussion among core library authors.	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6053/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6053/timeline	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6052	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6052/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6052/comments	https://api.github.com/repos/huggingface/datasets/issues/6052/events	https://github.com/huggingface/datasets/pull/6052	1,812,145,100	PR_kwDODunzps5V5yOi	6,052	Remove `HfFileSystem` and deprecate `S3FileSystem`	{ "login": "mariosasko", "id": 47462742, "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "gravatar_id": "", "url": "https://api.github.com/users/mariosasko", "html_url": "https://github.com/mariosasko", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "repos_url": "https://api.github.com/users/mariosasko/repos", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006658 / 0.011353 (-0.004695) \| 0.004347 / 0.011008 (-0.006661) \| 0.084179 / 0.038508 (0.045671) \| 0.080842 / 0.023109 (0.057733) \| 0.321642 / 0.275898 (0.045744) \| 0.348758 / 0.323480 (0.025278) \| 0.005624 / 0.007986 (-0.002362) \| 0.003479 / 0.004328 (-0.000850) \| 0.065125 / 0.004250 (0.060875) \| 0.057624 / 0.037052 (0.020572) \| 0.323643 / 0.258489 (0.065154) \| 0.360939 / 0.293841 (0.067098) \| 0.031005 / 0.128546 (-0.097541) \| 0.008618 / 0.075646 (-0.067028) \| 0.287443 / 0.419271 (-0.131828) \| 0.052640 / 0.043533 (0.009107) \| 0.316947 / 0.255139 (0.061808) \| 0.330292 / 0.283200 (0.047093) \| 0.024393 / 0.141683 (-0.117289) \| 1.476734 / 1.452155 (0.024579) \| 1.534505 / 1.492716 (0.041789) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.273808 / 0.018006 (0.255802) \| 0.591146 / 0.000490 (0.590656) \| 0.000322 / 0.000200 (0.000122) \| 0.000053 / 0.000054 (-0.000001) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029992 / 0.037411 (-0.007419) \| 0.086654 / 0.014526 (0.072129) \| 0.098590 / 0.176557 (-0.077967) \| 0.157225 / 0.737135 (-0.579910) \| 0.101816 / 0.296338 (-0.194522) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.382578 / 0.215209 (0.167368) \| 3.803576 / 2.077655 (1.725922) \| 1.875136 / 1.504120 (0.371016) \| 1.704207 / 1.541195 (0.163012) \| 1.765146 / 1.468490 (0.296656) \| 0.482802 / 4.584777 (-4.101975) \| 3.571772 / 3.745712 (-0.173940) \| 3.245626 / 5.269862 (-2.024235) \| 2.051612 / 4.565676 (-2.514064) \| 0.056539 / 0.424275 (-0.367736) \| 0.007199 / 0.007607 (-0.000408) \| 0.462445 / 0.226044 (0.236401) \| 4.623800 / 2.268929 (2.354872) \| 2.318948 / 55.444624 (-53.125677) \| 1.971442 / 6.876477 (-4.905035) \| 2.225444 / 2.142072 (0.083371) \| 0.575205 / 4.805227 (-4.230022) \| 0.129243 / 6.500664 (-6.371421) \| 0.059036 / 0.075469 (-0.016433) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.266827 / 1.841788 (-0.574960) \| 20.323419 / 8.074308 (12.249110) \| 14.577603 / 10.191392 (4.386210) \| 0.162131 / 0.680424 (-0.518293) \| 0.018529 / 0.534201 (-0.515672) \| 0.395046 / 0.579283 (-0.184237) \| 0.410870 / 0.434364 (-0.023494) \| 0.455782 / 0.540337 (-0.084556) \| 0.662851 / 1.386936 (-0.724085) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006867 / 0.011353 (-0.004486) \| 0.004197 / 0.011008 (-0.006811) \| 0.066060 / 0.038508 (0.027552) \| 0.084145 / 0.023109 (0.061036) \| 0.366740 / 0.275898 (0.090842) \| 0.402362 / 0.323480 (0.078882) \| 0.005785 / 0.007986 (-0.002200) \| 0.003551 / 0.004328 (-0.000778) \| 0.066177 / 0.004250 (0.061926) \| 0.061521 / 0.037052 (0.024468) \| 0.377807 / 0.258489 (0.119318) \| 0.413490 / 0.293841 (0.119649) \| 0.031918 / 0.128546 (-0.096628) \| 0.008767 / 0.075646 (-0.066879) \| 0.071437 / 0.419271 (-0.347835) \| 0.049237 / 0.043533 (0.005704) \| 0.365929 / 0.255139 (0.110790) \| 0.393545 / 0.283200 (0.110346) \| 0.024054 / 0.141683 (-0.117628) \| 1.524599 / 1.452155 (0.072445) \| 1.576592 / 1.492716 (0.083876) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.315181 / 0.018006 (0.297174) \| 0.535501 / 0.000490 (0.535011) \| 0.000410 / 0.000200 (0.000210) \| 0.000054 / 0.000054 (-0.000000) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032915 / 0.037411 (-0.004497) \| 0.089310 / 0.014526 (0.074784) \| 0.105136 / 0.176557 (-0.071421) \| 0.158572 / 0.737135 (-0.578563) \| 0.106850 / 0.296338 (-0.189489) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.419343 / 0.215209 (0.204134) \| 4.200166 / 2.077655 (2.122511) \| 2.180234 / 1.504120 (0.676114) \| 2.016885 / 1.541195 (0.475690) \| 2.131480 / 1.468490 (0.662990) \| 0.484681 / 4.584777 (-4.100096) \| 3.613535 / 3.745712 (-0.132177) \| 5.762111 / 5.269862 (0.492249) \| 3.190590 / 4.565676 (-1.375086) \| 0.057403 / 0.424275 (-0.366872) \| 0.007862 / 0.007607 (0.000255) \| 0.490857 / 0.226044 (0.264813) \| 4.911241 / 2.268929 (2.642313) \| 2.650787 / 55.444624 (-52.793838) \| 2.317060 / 6.876477 (-4.559416) \| 2.579677 / 2.142072 (0.437605) \| 0.587388 / 4.805227 (-4.217840) \| 0.148109 / 6.500664 (-6.352555) \| 0.061435 / 0.075469 (-0.014034) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.322181 / 1.841788 (-0.519606) \| 20.647184 / 8.074308 (12.572875) \| 14.907635 / 10.191392 (4.716243) \| 0.156330 / 0.680424 (-0.524094) \| 0.018719 / 0.534201 (-0.515482) \| 0.397636 / 0.579283 (-0.181647) \| 0.414107 / 0.434364 (-0.020257) \| 0.460812 / 0.540337 (-0.079526) \| 0.609568 / 1.386936 (-0.777368) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#74398c95b81a08a51457a2bef56efb7e608bded2 \"CML watermark\")\n", "This would mean when i update my examples to newer datasets version i need to make a change right? nothing backward breaking? ", "what would be the change i need to make? ", "@philschmid You just need to replace the occurrences of `datasets.filesystems.S3FileSystem` with `s3fs.S3FileSystem`. From the moment it was added until now, `datasets.filesystems.S3FileSystem` is a \"dummy\" subclass of `s3fs.S3FileSystem` that only changes its docstring.\r\n\r\n\r\n", "The CI is failing because I updated the YAML validation for https://github.com/huggingface/datasets/pull/6044.\r\nIt will be fixed once https://github.com/huggingface/datasets/pull/6044 is merged", "I just merged the other PR so you should be good now", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006303 / 0.011353 (-0.005049) \| 0.003746 / 0.011008 (-0.007262) \| 0.081083 / 0.038508 (0.042575) \| 0.067973 / 0.023109 (0.044864) \| 0.322221 / 0.275898 (0.046323) \| 0.359432 / 0.323480 (0.035952) \| 0.004891 / 0.007986 (-0.003095) \| 0.002988 / 0.004328 (-0.001341) \| 0.064068 / 0.004250 (0.059818) \| 0.052042 / 0.037052 (0.014990) \| 0.323387 / 0.258489 (0.064898) \| 0.390416 / 0.293841 (0.096575) \| 0.028090 / 0.128546 (-0.100457) \| 0.008009 / 0.075646 (-0.067638) \| 0.262288 / 0.419271 (-0.156984) \| 0.044986 / 0.043533 (0.001453) \| 0.322319 / 0.255139 (0.067180) \| 0.345323 / 0.283200 (0.062123) \| 0.021798 / 0.141683 (-0.119885) \| 1.417259 / 1.452155 (-0.034895) \| 1.490050 / 1.492716 (-0.002667) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.195902 / 0.018006 (0.177896) \| 0.490808 / 0.000490 (0.490318) \| 0.002969 / 0.000200 (0.002770) \| 0.000126 / 0.000054 (0.000072) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025221 / 0.037411 (-0.012190) \| 0.075341 / 0.014526 (0.060815) \| 0.086703 / 0.176557 (-0.089853) \| 0.146953 / 0.737135 (-0.590182) \| 0.086610 / 0.296338 (-0.209728) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.434890 / 0.215209 (0.219681) \| 4.352283 / 2.077655 (2.274629) \| 2.293098 / 1.504120 (0.788979) \| 2.123023 / 1.541195 (0.581829) \| 2.179722 / 1.468490 (0.711232) \| 0.503851 / 4.584777 (-4.080926) \| 3.087991 / 3.745712 (-0.657721) \| 2.898689 / 5.269862 (-2.371173) \| 1.902813 / 4.565676 (-2.662864) \| 0.058079 / 0.424275 (-0.366196) \| 0.006600 / 0.007607 (-0.001007) \| 0.509243 / 0.226044 (0.283199) \| 5.080204 / 2.268929 (2.811275) \| 2.753594 / 55.444624 (-52.691030) \| 2.417385 / 6.876477 (-4.459091) \| 2.635470 / 2.142072 (0.493398) \| 0.591059 / 4.805227 (-4.214168) \| 0.126360 / 6.500664 (-6.374304) \| 0.062108 / 0.075469 (-0.013361) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.254398 / 1.841788 (-0.587390) \| 18.866729 / 8.074308 (10.792420) \| 14.120008 / 10.191392 (3.928616) \| 0.152388 / 0.680424 (-0.528035) \| 0.016997 / 0.534201 (-0.517204) \| 0.336435 / 0.579283 (-0.242848) \| 0.364612 / 0.434364 (-0.069752) \| 0.391434 / 0.540337 (-0.148903) \| 0.567180 / 1.386936 (-0.819756) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006477 / 0.011353 (-0.004876) \| 0.003723 / 0.011008 (-0.007285) \| 0.062712 / 0.038508 (0.024204) \| 0.069380 / 0.023109 (0.046271) \| 0.393394 / 0.275898 (0.117496) \| 0.446903 / 0.323480 (0.123423) \| 0.004833 / 0.007986 (-0.003153) \| 0.002946 / 0.004328 (-0.001382) \| 0.062076 / 0.004250 (0.057826) \| 0.051589 / 0.037052 (0.014537) \| 0.388536 / 0.258489 (0.130047) \| 0.451406 / 0.293841 (0.157565) \| 0.027824 / 0.128546 (-0.100722) \| 0.008040 / 0.075646 (-0.067606) \| 0.067085 / 0.419271 (-0.352187) \| 0.042269 / 0.043533 (-0.001264) \| 0.363419 / 0.255139 (0.108280) \| 0.435201 / 0.283200 (0.152001) \| 0.021275 / 0.141683 (-0.120408) \| 1.439838 / 1.452155 (-0.012316) \| 1.477279 / 1.492716 (-0.015437) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.229667 / 0.018006 (0.211661) \| 0.434101 / 0.000490 (0.433611) \| 0.000652 / 0.000200 (0.000452) \| 0.000060 / 0.000054 (0.000005) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026141 / 0.037411 (-0.011271) \| 0.078950 / 0.014526 (0.064424) \| 0.090143 / 0.176557 (-0.086413) \| 0.143941 / 0.737135 (-0.593195) \| 0.090465 / 0.296338 (-0.205873) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.432042 / 0.215209 (0.216833) \| 4.322134 / 2.077655 (2.244479) \| 2.242897 / 1.504120 (0.738777) \| 2.076351 / 1.541195 (0.535157) \| 2.166739 / 1.468490 (0.698249) \| 0.500833 / 4.584777 (-4.083944) \| 3.140117 / 3.745712 (-0.605595) \| 4.383050 / 5.269862 (-0.886812) \| 2.548245 / 4.565676 (-2.017432) \| 0.057521 / 0.424275 (-0.366754) \| 0.006946 / 0.007607 (-0.000662) \| 0.509613 / 0.226044 (0.283569) \| 5.114052 / 2.268929 (2.845123) \| 2.682112 / 55.444624 (-52.762512) \| 2.362385 / 6.876477 (-4.514092) \| 2.531787 / 2.142072 (0.389715) \| 0.595085 / 4.805227 (-4.210142) \| 0.130198 / 6.500664 (-6.370466) \| 0.064057 / 0.075469 (-0.011412) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.346254 / 1.841788 (-0.495534) \| 19.036911 / 8.074308 (10.962603) \| 14.478689 / 10.191392 (4.287297) \| 0.147541 / 0.680424 (-0.532883) \| 0.016851 / 0.534201 (-0.517350) \| 0.333901 / 0.579283 (-0.245382) \| 0.380012 / 0.434364 (-0.054352) \| 0.396021 / 0.540337 (-0.144317) \| 0.540612 / 1.386936 (-0.846324) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#02dd4ccaf7971cd71d658ce9f62bc0c5cfc1e3ad \"CML watermark\")\n", "CI failure is unrelated. Merging.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009498 / 0.011353 (-0.001855) \| 0.005639 / 0.011008 (-0.005369) \| 0.108731 / 0.038508 (0.070223) \| 0.094052 / 0.023109 (0.070943) \| 0.454375 / 0.275898 (0.178477) \| 0.486852 / 0.323480 (0.163372) \| 0.006627 / 0.007986 (-0.001359) \| 0.004712 / 0.004328 (0.000383) \| 0.082006 / 0.004250 (0.077756) \| 0.079394 / 0.037052 (0.042342) \| 0.450982 / 0.258489 (0.192493) \| 0.502659 / 0.293841 (0.208818) \| 0.049741 / 0.128546 (-0.078806) \| 0.014482 / 0.075646 (-0.061164) \| 0.362661 / 0.419271 (-0.056611) \| 0.068225 / 0.043533 (0.024692) \| 0.456219 / 0.255139 (0.201080) \| 0.483919 / 0.283200 (0.200719) \| 0.044490 / 0.141683 (-0.097193) \| 1.809420 / 1.452155 (0.357265) \| 1.908859 / 1.492716 (0.416143) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.267350 / 0.018006 (0.249344) \| 0.600403 / 0.000490 (0.599913) \| 0.003665 / 0.000200 (0.003465) \| 0.000162 / 0.000054 (0.000107) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032499 / 0.037411 (-0.004912) \| 0.104829 / 0.014526 (0.090303) \| 0.115809 / 0.176557 (-0.060747) \| 0.191561 / 0.737135 (-0.545574) \| 0.113454 / 0.296338 (-0.182885) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.599165 / 0.215209 (0.383956) \| 5.802947 / 2.077655 (3.725292) \| 2.477330 / 1.504120 (0.973210) \| 2.231147 / 1.541195 (0.689952) \| 2.365688 / 1.468490 (0.897197) \| 0.853912 / 4.584777 (-3.730865) \| 5.529472 / 3.745712 (1.783760) \| 6.145286 / 5.269862 (0.875424) \| 3.415871 / 4.565676 (-1.149805) \| 0.099889 / 0.424275 (-0.324386) \| 0.008933 / 0.007607 (0.001325) \| 0.704643 / 0.226044 (0.478598) \| 7.178101 / 2.268929 (4.909173) \| 3.367120 / 55.444624 (-52.077504) \| 2.795177 / 6.876477 (-4.081300) \| 2.796798 / 2.142072 (0.654726) \| 1.039097 / 4.805227 (-3.766130) \| 0.232784 / 6.500664 (-6.267881) \| 0.083608 / 0.075469 (0.008138) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.646827 / 1.841788 (-0.194961) \| 25.003419 / 8.074308 (16.929111) \| 22.165746 / 10.191392 (11.974354) \| 0.246179 / 0.680424 (-0.434245) \| 0.029304 / 0.534201 (-0.504897) \| 0.500767 / 0.579283 (-0.078516) \| 0.606501 / 0.434364 (0.172137) \| 0.564092 / 0.540337 (0.023755) \| 0.857568 / 1.386936 (-0.529368) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009206 / 0.011353 (-0.002146) \| 0.005084 / 0.011008 (-0.005925) \| 0.081402 / 0.038508 (0.042894) \| 0.088028 / 0.023109 (0.064919) \| 0.539509 / 0.275898 (0.263611) \| 0.590759 / 0.323480 (0.267280) \| 0.006527 / 0.007986 (-0.001459) \| 0.004375 / 0.004328 (0.000047) \| 0.082327 / 0.004250 (0.078076) \| 0.065442 / 0.037052 (0.028390) \| 0.548254 / 0.258489 (0.289765) \| 0.598388 / 0.293841 (0.304547) \| 0.049409 / 0.128546 (-0.079137) \| 0.014366 / 0.075646 (-0.061280) \| 0.094568 / 0.419271 (-0.324703) \| 0.063685 / 0.043533 (0.020152) \| 0.545359 / 0.255139 (0.290220) \| 0.573358 / 0.283200 (0.290159) \| 0.036864 / 0.141683 (-0.104819) \| 1.817985 / 1.452155 (0.365830) \| 1.925188 / 1.492716 (0.432472) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.303205 / 0.018006 (0.285199) \| 0.620981 / 0.000490 (0.620491) \| 0.004910 / 0.000200 (0.004710) \| 0.000104 / 0.000054 (0.000050) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033791 / 0.037411 (-0.003620) \| 0.114974 / 0.014526 (0.100448) \| 0.117682 / 0.176557 (-0.058875) \| 0.177188 / 0.737135 (-0.559947) \| 0.126425 / 0.296338 (-0.169914) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.636932 / 0.215209 (0.421723) \| 6.289054 / 2.077655 (4.211399) \| 2.920772 / 1.504120 (1.416652) \| 2.672080 / 1.541195 (1.130885) \| 2.712271 / 1.468490 (1.243781) \| 0.889305 / 4.584777 (-3.695472) \| 5.536018 / 3.745712 (1.790306) \| 4.687564 / 5.269862 (-0.582298) \| 3.204239 / 4.565676 (-1.361437) \| 0.095546 / 0.424275 (-0.328729) \| 0.008838 / 0.007607 (0.001231) \| 0.714584 / 0.226044 (0.488540) \| 7.482663 / 2.268929 (5.213735) \| 3.621392 / 55.444624 (-51.823232) \| 2.987777 / 6.876477 (-3.888700) \| 3.312636 / 2.142072 (1.170564) \| 1.033721 / 4.805227 (-3.771506) \| 0.206292 / 6.500664 (-6.294372) \| 0.079423 / 0.075469 (0.003953) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.798645 / 1.841788 (-0.043143) \| 25.544329 / 8.074308 (17.470021) \| 23.041318 / 10.191392 (12.849926) \| 0.259067 / 0.680424 (-0.421357) \| 0.029839 / 0.534201 (-0.504362) \| 0.495583 / 0.579283 (-0.083700) \| 0.598755 / 0.434364 (0.164391) \| 0.574864 / 0.540337 (0.034527) \| 0.831160 / 1.386936 (-0.555776) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4200443045e694a045446950e8b235c7beb6239e \"CML watermark\")\n" ]	2023-07-19T15:00:01	2023-07-19T17:39:11	2023-07-19T17:27:17	CONTRIBUTOR	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6052", "html_url": "https://github.com/huggingface/datasets/pull/6052", "diff_url": "https://github.com/huggingface/datasets/pull/6052.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6052.patch", "merged_at": "2023-07-19T17:27:17" }	Remove the legacy `HfFileSystem` and deprecate `S3FileSystem` cc @philschmid for the SageMaker scripts/notebooks that still use `datasets`' `S3FileSystem`	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6052/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6052/timeline	null	null	true
https://api.github.com/repos/huggingface/datasets/issues/6051	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6051/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6051/comments	https://api.github.com/repos/huggingface/datasets/issues/6051/events	https://github.com/huggingface/datasets/issues/6051	1,811,549,650	I_kwDODunzps5r-g3S	6,051	Skipping shard in the remote repo and resume upload	{ "login": "rs9000", "id": 9029817, "node_id": "MDQ6VXNlcjkwMjk4MTc=", "avatar_url": "https://avatars.githubusercontent.com/u/9029817?v=4", "gravatar_id": "", "url": "https://api.github.com/users/rs9000", "html_url": "https://github.com/rs9000", "followers_url": "https://api.github.com/users/rs9000/followers", "following_url": "https://api.github.com/users/rs9000/following{/other_user}", "gists_url": "https://api.github.com/users/rs9000/gists{/gist_id}", "starred_url": "https://api.github.com/users/rs9000/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/rs9000/subscriptions", "organizations_url": "https://api.github.com/users/rs9000/orgs", "repos_url": "https://api.github.com/users/rs9000/repos", "events_url": "https://api.github.com/users/rs9000/events{/privacy}", "received_events_url": "https://api.github.com/users/rs9000/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "Hi! `_select_contiguous` fetches a (zero-copy) slice of the dataset's Arrow table to build a shard, so I don't think this part is the problem. To me, the issue seems to be the step where we embed external image files' bytes (a lot of file reads). You can use `.map` with multiprocessing to perform this step before `push_to_hub` in a faster manner and cache it to disk:\r\n```python\r\nfrom datasets.table import embed_table_storage\r\n# load_dataset(...)\r\nformat = dataset.format\r\ndataset = dataset.with_format(\"arrow\")\r\ndataset = dataset.map(embed_table_storage, batched=True)\r\ndataset = dataset.with_format(**format)\r\n# push_to_hub(...)\r\n```\r\n\r\n(In Datasets 3.0, these external bytes will be written to an Arrow file when generating a dataset to avoid this \"embed\" step)", "Hi, thanks, this solution saves some time.\r\nBut can't we avoid embedding all external image files bytes with each push, skipping the images that have already been pushed into the repo?\r\n\r\nEdit: Ok I missed the part of cache it manually on the disk the first time, this solves the problem. Thank you" ]	2023-07-19T09:25:26	2023-07-20T18:16:01	2023-07-20T18:16:00	NONE	null	null	null	### Describe the bug For some reason when I try to resume the upload of my dataset, it is very slow to reach the index of the shard from which to resume the uploading. From my understanding, the problem is in this part of the code: arrow_dataset.py ```python for index, shard in logging.tqdm( enumerate(itertools.chain([first_shard], shards_iter)), desc="Pushing dataset shards to the dataset hub", total=num_shards, disable=not logging.is_progress_bar_enabled(), ): shard_path_in_repo = path_in_repo(index, shard) # Upload a shard only if it doesn't already exist in the repository if shard_path_in_repo not in data_files: ``` In particular, iterating the generator is slow during the call: ```python self._select_contiguous(start, length, new_fingerprint=new_fingerprint) ``` I wonder if it is possible to avoid calling this function for shards that are already uploaded and just start from the correct shard index. ### Steps to reproduce the bug 1. Start the upload ```python dataset = load_dataset("imagefolder", data_dir=DATA_DIR, split="train", drop_labels=True) dataset.push_to_hub("repo/name") ``` 2. Stop and restart the upload after hundreds of shards ### Expected behavior Skip the uploaded shards faster. ### Environment info - `datasets` version: 2.5.1 - Platform: Linux-4.18.0-193.el8.x86_64-x86_64-with-glibc2.17 - Python version: 3.8.16 - PyArrow version: 12.0.1 - Pandas version: 2.0.2	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6051/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6051/timeline	null	completed	false
https://api.github.com/repos/huggingface/datasets/issues/6049	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6049/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6049/comments	https://api.github.com/repos/huggingface/datasets/issues/6049/events	https://github.com/huggingface/datasets/pull/6049	1,810,378,706	PR_kwDODunzps5Vz1pd	6,049	Update `ruff` version in pre-commit config	{ "login": "polinaeterna", "id": 16348744, "node_id": "MDQ6VXNlcjE2MzQ4NzQ0", "avatar_url": "https://avatars.githubusercontent.com/u/16348744?v=4", "gravatar_id": "", "url": "https://api.github.com/users/polinaeterna", "html_url": "https://github.com/polinaeterna", "followers_url": "https://api.github.com/users/polinaeterna/followers", "following_url": "https://api.github.com/users/polinaeterna/following{/other_user}", "gists_url": "https://api.github.com/users/polinaeterna/gists{/gist_id}", "starred_url": "https://api.github.com/users/polinaeterna/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/polinaeterna/subscriptions", "organizations_url": "https://api.github.com/users/polinaeterna/orgs", "repos_url": "https://api.github.com/users/polinaeterna/repos", "events_url": "https://api.github.com/users/polinaeterna/events{/privacy}", "received_events_url": "https://api.github.com/users/polinaeterna/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6049). All of your documentation changes will be reflected on that endpoint." ]	2023-07-18T17:13:50	2023-07-20T12:09:16	null	CONTRIBUTOR	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6049", "html_url": "https://github.com/huggingface/datasets/pull/6049", "diff_url": "https://github.com/huggingface/datasets/pull/6049.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6049.patch", "merged_at": null }	so that it corresponds to the one that is being run in CI	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6049/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6049/timeline	null	null	true
https://api.github.com/repos/huggingface/datasets/issues/6048	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6048/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6048/comments	https://api.github.com/repos/huggingface/datasets/issues/6048/events	https://github.com/huggingface/datasets/issues/6048	1,809,629,346	I_kwDODunzps5r3MCi	6,048	when i use datasets.load_dataset, i encounter the http connect error!	{ "login": "yangy1992", "id": 137855591, "node_id": "U_kgDOCDeCZw", "avatar_url": "https://avatars.githubusercontent.com/u/137855591?v=4", "gravatar_id": "", "url": "https://api.github.com/users/yangy1992", "html_url": "https://github.com/yangy1992", "followers_url": "https://api.github.com/users/yangy1992/followers", "following_url": "https://api.github.com/users/yangy1992/following{/other_user}", "gists_url": "https://api.github.com/users/yangy1992/gists{/gist_id}", "starred_url": "https://api.github.com/users/yangy1992/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/yangy1992/subscriptions", "organizations_url": "https://api.github.com/users/yangy1992/orgs", "repos_url": "https://api.github.com/users/yangy1992/repos", "events_url": "https://api.github.com/users/yangy1992/events{/privacy}", "received_events_url": "https://api.github.com/users/yangy1992/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "The `audiofolder` loader is not available in version `2.3.2`, hence the error. Please run the `pip install -U datasets` command to update the `datasets` installation to make `load_dataset(\"audiofolder\", ...)` work." ]	2023-07-18T10:16:34	2023-07-18T16:18:39	2023-07-18T16:18:39	NONE	null	null	null	### Describe the bug `common_voice_test = load_dataset("audiofolder", data_dir="./dataset/",cache_dir="./cache",split=datasets.Split.TEST)` when i run the code above, i got the error as below: -------------------------------------------- ConnectionError: Couldn't reach https://raw.githubusercontent.com/huggingface/datasets/2.3.2/datasets/audiofolder/audiofolder.py (ConnectionError(MaxRetryError("HTTPSConnectionPool(host='raw.githubusercontent.com', port=443): Max retries exceeded with url: /huggingface/datasets/2.3.2/datasets/audiofolder/audiofolder.py (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7f299ed082e0>: Failed to establish a new connection: [Errno 101] Network is unreachable'))"))) -------------------------------------------------- My all data is on local machine, why does it need to connect the internet? how can i fix it, because my machine cannot connect the internet. ### Steps to reproduce the bug 1 ### Expected behavior no error when i use the load_dataset func ### Environment info python=3.8.15	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6048/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6048/timeline	null	completed	false
https://api.github.com/repos/huggingface/datasets/issues/6047	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6047/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6047/comments	https://api.github.com/repos/huggingface/datasets/issues/6047/events	https://github.com/huggingface/datasets/pull/6047	1,809,627,947	PR_kwDODunzps5VxRLA	6,047	Bump dev version	{ "login": "lhoestq", "id": 42851186, "node_id": "MDQ6VXNlcjQyODUxMTg2", "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "gravatar_id": "", "url": "https://api.github.com/users/lhoestq", "html_url": "https://github.com/lhoestq", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "repos_url": "https://api.github.com/users/lhoestq/repos", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6047). All of your documentation changes will be reflected on that endpoint.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006384 / 0.011353 (-0.004969) \| 0.003872 / 0.011008 (-0.007136) \| 0.083454 / 0.038508 (0.044946) \| 0.069120 / 0.023109 (0.046011) \| 0.312573 / 0.275898 (0.036675) \| 0.345814 / 0.323480 (0.022334) \| 0.005729 / 0.007986 (-0.002257) \| 0.003225 / 0.004328 (-0.001103) \| 0.063950 / 0.004250 (0.059700) \| 0.053998 / 0.037052 (0.016946) \| 0.316492 / 0.258489 (0.058003) \| 0.350738 / 0.293841 (0.056897) \| 0.030770 / 0.128546 (-0.097776) \| 0.008474 / 0.075646 (-0.067173) \| 0.286989 / 0.419271 (-0.132282) \| 0.052473 / 0.043533 (0.008940) \| 0.314361 / 0.255139 (0.059222) \| 0.335170 / 0.283200 (0.051970) \| 0.022885 / 0.141683 (-0.118798) \| 1.465430 / 1.452155 (0.013275) \| 1.527799 / 1.492716 (0.035083) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.209377 / 0.018006 (0.191371) \| 0.455583 / 0.000490 (0.455094) \| 0.003352 / 0.000200 (0.003152) \| 0.000080 / 0.000054 (0.000025) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026284 / 0.037411 (-0.011127) \| 0.080710 / 0.014526 (0.066185) \| 0.091741 / 0.176557 (-0.084816) \| 0.147602 / 0.737135 (-0.589534) \| 0.091173 / 0.296338 (-0.205166) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.386592 / 0.215209 (0.171383) \| 3.856665 / 2.077655 (1.779011) \| 1.835745 / 1.504120 (0.331625) \| 1.671814 / 1.541195 (0.130619) \| 1.711224 / 1.468490 (0.242734) \| 0.484704 / 4.584777 (-4.100073) \| 3.649239 / 3.745712 (-0.096473) \| 3.784051 / 5.269862 (-1.485810) \| 2.241195 / 4.565676 (-2.324482) \| 0.056613 / 0.424275 (-0.367662) \| 0.007140 / 0.007607 (-0.000467) \| 0.464585 / 0.226044 (0.238540) \| 4.616537 / 2.268929 (2.347609) \| 2.371969 / 55.444624 (-53.072656) \| 1.977754 / 6.876477 (-4.898723) \| 2.083385 / 2.142072 (-0.058687) \| 0.582330 / 4.805227 (-4.222897) \| 0.132744 / 6.500664 (-6.367920) \| 0.059822 / 0.075469 (-0.015647) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.259566 / 1.841788 (-0.582221) \| 18.990166 / 8.074308 (10.915858) \| 13.992069 / 10.191392 (3.800677) \| 0.160001 / 0.680424 (-0.520423) \| 0.018622 / 0.534201 (-0.515579) \| 0.392921 / 0.579283 (-0.186362) \| 0.418225 / 0.434364 (-0.016139) \| 0.471252 / 0.540337 (-0.069086) \| 0.653227 / 1.386936 (-0.733709) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006641 / 0.011353 (-0.004712) \| 0.003738 / 0.011008 (-0.007271) \| 0.064053 / 0.038508 (0.025545) \| 0.069467 / 0.023109 (0.046357) \| 0.360625 / 0.275898 (0.084727) \| 0.394291 / 0.323480 (0.070811) \| 0.005236 / 0.007986 (-0.002750) \| 0.003304 / 0.004328 (-0.001024) \| 0.064078 / 0.004250 (0.059827) \| 0.054605 / 0.037052 (0.017552) \| 0.374567 / 0.258489 (0.116078) \| 0.411227 / 0.293841 (0.117386) \| 0.031614 / 0.128546 (-0.096933) \| 0.008323 / 0.075646 (-0.067324) \| 0.070616 / 0.419271 (-0.348656) \| 0.050077 / 0.043533 (0.006544) \| 0.362229 / 0.255139 (0.107090) \| 0.388310 / 0.283200 (0.105110) \| 0.024053 / 0.141683 (-0.117630) \| 1.508913 / 1.452155 (0.056759) \| 1.562140 / 1.492716 (0.069423) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.230172 / 0.018006 (0.212165) \| 0.449363 / 0.000490 (0.448873) \| 0.002374 / 0.000200 (0.002174) \| 0.000097 / 0.000054 (0.000043) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029813 / 0.037411 (-0.007598) \| 0.087298 / 0.014526 (0.072772) \| 0.096712 / 0.176557 (-0.079845) \| 0.152864 / 0.737135 (-0.584271) \| 0.098204 / 0.296338 (-0.198135) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.408664 / 0.215209 (0.193455) \| 4.075068 / 2.077655 (1.997414) \| 2.096365 / 1.504120 (0.592245) \| 1.936096 / 1.541195 (0.394901) \| 1.961872 / 1.468490 (0.493382) \| 0.483383 / 4.584777 (-4.101394) \| 3.686926 / 3.745712 (-0.058787) \| 4.798824 / 5.269862 (-0.471037) \| 2.652279 / 4.565676 (-1.913398) \| 0.056695 / 0.424275 (-0.367580) \| 0.007592 / 0.007607 (-0.000016) \| 0.484710 / 0.226044 (0.258665) \| 4.842153 / 2.268929 (2.573225) \| 2.636828 / 55.444624 (-52.807796) \| 2.243666 / 6.876477 (-4.632811) \| 2.375972 / 2.142072 (0.233899) \| 0.578544 / 4.805227 (-4.226683) \| 0.132579 / 6.500664 (-6.368085) \| 0.061287 / 0.075469 (-0.014182) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.360287 / 1.841788 (-0.481501) \| 19.464110 / 8.074308 (11.389802) \| 14.530875 / 10.191392 (4.339483) \| 0.149479 / 0.680424 (-0.530944) \| 0.018471 / 0.534201 (-0.515730) \| 0.395399 / 0.579283 (-0.183884) \| 0.412897 / 0.434364 (-0.021467) \| 0.465194 / 0.540337 (-0.075144) \| 0.611752 / 1.386936 (-0.775184) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#79a535de98b590da7bc223a6498c59790882f14a \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008986 / 0.011353 (-0.002367) \| 0.005104 / 0.011008 (-0.005905) \| 0.108371 / 0.038508 (0.069863) \| 0.091655 / 0.023109 (0.068546) \| 0.430183 / 0.275898 (0.154285) \| 0.481387 / 0.323480 (0.157907) \| 0.006662 / 0.007986 (-0.001324) \| 0.004681 / 0.004328 (0.000353) \| 0.089325 / 0.004250 (0.085075) \| 0.065096 / 0.037052 (0.028044) \| 0.435021 / 0.258489 (0.176532) \| 0.478635 / 0.293841 (0.184794) \| 0.047628 / 0.128546 (-0.080918) \| 0.013496 / 0.075646 (-0.062150) \| 0.389661 / 0.419271 (-0.029611) \| 0.082260 / 0.043533 (0.038727) \| 0.474165 / 0.255139 (0.219026) \| 0.464877 / 0.283200 (0.181677) \| 0.039784 / 0.141683 (-0.101899) \| 1.874694 / 1.452155 (0.422539) \| 1.980183 / 1.492716 (0.487467) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.254044 / 0.018006 (0.236038) \| 0.631495 / 0.000490 (0.631005) \| 0.000628 / 0.000200 (0.000428) \| 0.000086 / 0.000054 (0.000032) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.038773 / 0.037411 (0.001362) \| 0.103681 / 0.014526 (0.089156) \| 0.125081 / 0.176557 (-0.051476) \| 0.198345 / 0.737135 (-0.538790) \| 0.122217 / 0.296338 (-0.174121) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.611677 / 0.215209 (0.396468) \| 6.220790 / 2.077655 (4.143135) \| 2.729858 / 1.504120 (1.225739) \| 2.351944 / 1.541195 (0.810749) \| 2.449137 / 1.468490 (0.980647) \| 0.896842 / 4.584777 (-3.687935) \| 5.537491 / 3.745712 (1.791778) \| 8.480182 / 5.269862 (3.210320) \| 5.251404 / 4.565676 (0.685728) \| 0.100449 / 0.424275 (-0.323826) \| 0.009008 / 0.007607 (0.001401) \| 0.750060 / 0.226044 (0.524016) \| 7.390940 / 2.268929 (5.122011) \| 3.478256 / 55.444624 (-51.966369) \| 2.883597 / 6.876477 (-3.992880) \| 3.082256 / 2.142072 (0.940183) \| 1.114339 / 4.805227 (-3.690889) \| 0.225389 / 6.500664 (-6.275275) \| 0.083972 / 0.075469 (0.008503) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.741522 / 1.841788 (-0.100266) \| 25.674700 / 8.074308 (17.600392) \| 24.324412 / 10.191392 (14.133020) \| 0.257878 / 0.680424 (-0.422546) \| 0.038384 / 0.534201 (-0.495817) \| 0.508302 / 0.579283 (-0.070981) \| 0.612979 / 0.434364 (0.178615) \| 0.584366 / 0.540337 (0.044029) \| 0.881115 / 1.386936 (-0.505821) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009114 / 0.011353 (-0.002239) \| 0.005333 / 0.011008 (-0.005675) \| 0.094944 / 0.038508 (0.056436) \| 0.099178 / 0.023109 (0.076068) \| 0.529813 / 0.275898 (0.253915) \| 0.551282 / 0.323480 (0.227802) \| 0.006442 / 0.007986 (-0.001543) \| 0.004283 / 0.004328 (-0.000045) \| 0.084257 / 0.004250 (0.080007) \| 0.067557 / 0.037052 (0.030504) \| 0.514733 / 0.258489 (0.256244) \| 0.568200 / 0.293841 (0.274359) \| 0.050969 / 0.128546 (-0.077577) \| 0.014495 / 0.075646 (-0.061151) \| 0.097089 / 0.419271 (-0.322182) \| 0.063142 / 0.043533 (0.019609) \| 0.513327 / 0.255139 (0.258188) \| 0.520593 / 0.283200 (0.237394) \| 0.036824 / 0.141683 (-0.104859) \| 1.954875 / 1.452155 (0.502720) \| 1.976307 / 1.492716 (0.483591) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.304070 / 0.018006 (0.286063) \| 0.611073 / 0.000490 (0.610583) \| 0.005027 / 0.000200 (0.004827) \| 0.000113 / 0.000054 (0.000059) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.037993 / 0.037411 (0.000582) \| 0.115876 / 0.014526 (0.101350) \| 0.118087 / 0.176557 (-0.058469) \| 0.186437 / 0.737135 (-0.550699) \| 0.129883 / 0.296338 (-0.166456) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.658292 / 0.215209 (0.443083) \| 6.618257 / 2.077655 (4.540602) \| 3.203786 / 1.504120 (1.699667) \| 2.858714 / 1.541195 (1.317519) \| 2.940974 / 1.468490 (1.472484) \| 0.856238 / 4.584777 (-3.728538) \| 5.427708 / 3.745712 (1.681996) \| 4.810048 / 5.269862 (-0.459813) \| 3.120006 / 4.565676 (-1.445671) \| 0.098098 / 0.424275 (-0.326177) \| 0.010077 / 0.007607 (0.002470) \| 0.790890 / 0.226044 (0.564845) \| 7.956679 / 2.268929 (5.687750) \| 3.955710 / 55.444624 (-51.488914) \| 3.446419 / 6.876477 (-3.430057) \| 3.541228 / 2.142072 (1.399156) \| 1.013420 / 4.805227 (-3.791808) \| 0.213741 / 6.500664 (-6.286923) \| 0.080857 / 0.075469 (0.005388) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.813265 / 1.841788 (-0.028522) \| 25.965199 / 8.074308 (17.890891) \| 21.892761 / 10.191392 (11.701369) \| 0.257843 / 0.680424 (-0.422580) \| 0.029388 / 0.534201 (-0.504813) \| 0.510609 / 0.579283 (-0.068674) \| 0.626579 / 0.434364 (0.192215) \| 0.576865 / 0.540337 (0.036528) \| 0.826610 / 1.386936 (-0.560326) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a1a9c00249b330f97f66ceb86c2939261091f4fe \"CML watermark\")\n" ]	2023-07-18T10:15:39	2023-07-18T10:28:01	2023-07-18T10:15:52	MEMBER	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6047", "html_url": "https://github.com/huggingface/datasets/pull/6047", "diff_url": "https://github.com/huggingface/datasets/pull/6047.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6047.patch", "merged_at": "2023-07-18T10:15:52" }	workaround to fix an issue with transformers CI https://github.com/huggingface/transformers/pull/24867#discussion_r1266519626	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6047/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6047/timeline	null	null	true
https://api.github.com/repos/huggingface/datasets/issues/6046	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6046/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6046/comments	https://api.github.com/repos/huggingface/datasets/issues/6046/events	https://github.com/huggingface/datasets/issues/6046	1,808,154,414	I_kwDODunzps5rxj8u	6,046	Support proxy and user-agent in fsspec calls	{ "login": "lhoestq", "id": 42851186, "node_id": "MDQ6VXNlcjQyODUxMTg2", "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "gravatar_id": "", "url": "https://api.github.com/users/lhoestq", "html_url": "https://github.com/lhoestq", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "repos_url": "https://api.github.com/users/lhoestq/repos", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "type": "User", "site_admin": false }	[ { "id": 1935892871, "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement", "name": "enhancement", "color": "a2eeef", "default": true, "description": "New feature or request" }, { "id": 3761482852, "node_id": "LA_kwDODunzps7gM6xk", "url": "https://api.github.com/repos/huggingface/datasets/labels/good%20second%20issue", "name": "good second issue", "color": "BDE59C", "default": false, "description": "Issues a bit more difficult than \"Good First\" issues" } ]	open	false	null	[]	null	[]	2023-07-17T16:39:26	2023-07-17T16:40:37	null	MEMBER	null	null	null	Since we switched to the new HfFileSystem we no longer apply user's proxy and user-agent. Using the HTTP_PROXY and HTTPS_PROXY environment variables works though since we use aiohttp to call the HF Hub. This can be implemented in `_prepare_single_hop_path_and_storage_options`. Though ideally the `HfFileSystem` could support passing at least the proxies	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6046/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6046/timeline	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6045	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6045/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6045/comments	https://api.github.com/repos/huggingface/datasets/issues/6045/events	https://github.com/huggingface/datasets/pull/6045	1,808,072,270	PR_kwDODunzps5Vr-r1	6,045	Check if column names match in Parquet loader only when config `features` are specified	{ "login": "mariosasko", "id": 47462742, "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "gravatar_id": "", "url": "https://api.github.com/users/mariosasko", "html_url": "https://github.com/mariosasko", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "repos_url": "https://api.github.com/users/mariosasko/repos", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006557 / 0.011353 (-0.004796) \| 0.004096 / 0.011008 (-0.006913) \| 0.083577 / 0.038508 (0.045069) \| 0.072092 / 0.023109 (0.048983) \| 0.319192 / 0.275898 (0.043294) \| 0.351845 / 0.323480 (0.028365) \| 0.005475 / 0.007986 (-0.002511) \| 0.003419 / 0.004328 (-0.000910) \| 0.064562 / 0.004250 (0.060311) \| 0.057930 / 0.037052 (0.020878) \| 0.326085 / 0.258489 (0.067596) \| 0.368316 / 0.293841 (0.074475) \| 0.030502 / 0.128546 (-0.098044) \| 0.008504 / 0.075646 (-0.067142) \| 0.287217 / 0.419271 (-0.132054) \| 0.052337 / 0.043533 (0.008804) \| 0.319011 / 0.255139 (0.063872) \| 0.352711 / 0.283200 (0.069511) \| 0.023278 / 0.141683 (-0.118405) \| 1.482578 / 1.452155 (0.030423) \| 1.553391 / 1.492716 (0.060675) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.199628 / 0.018006 (0.181622) \| 0.464571 / 0.000490 (0.464081) \| 0.003512 / 0.000200 (0.003312) \| 0.000072 / 0.000054 (0.000018) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029109 / 0.037411 (-0.008302) \| 0.082203 / 0.014526 (0.067677) \| 0.096223 / 0.176557 (-0.080333) \| 0.155598 / 0.737135 (-0.581537) \| 0.097738 / 0.296338 (-0.198600) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.386135 / 0.215209 (0.170926) \| 3.837157 / 2.077655 (1.759502) \| 1.836869 / 1.504120 (0.332750) \| 1.680592 / 1.541195 (0.139398) \| 1.769456 / 1.468490 (0.300966) \| 0.493150 / 4.584777 (-4.091627) \| 3.589797 / 3.745712 (-0.155915) \| 3.330000 / 5.269862 (-1.939861) \| 2.059856 / 4.565676 (-2.505821) \| 0.057951 / 0.424275 (-0.366324) \| 0.007340 / 0.007607 (-0.000267) \| 0.463203 / 0.226044 (0.237159) \| 4.631514 / 2.268929 (2.362585) \| 2.329887 / 55.444624 (-53.114738) \| 2.008815 / 6.876477 (-4.867662) \| 2.199067 / 2.142072 (0.056995) \| 0.591417 / 4.805227 (-4.213810) \| 0.137154 / 6.500664 (-6.363510) \| 0.061326 / 0.075469 (-0.014143) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.269676 / 1.841788 (-0.572111) \| 19.375167 / 8.074308 (11.300858) \| 13.945419 / 10.191392 (3.754027) \| 0.146482 / 0.680424 (-0.533942) \| 0.018257 / 0.534201 (-0.515944) \| 0.391684 / 0.579283 (-0.187599) \| 0.411454 / 0.434364 (-0.022910) \| 0.466260 / 0.540337 (-0.074077) \| 0.655571 / 1.386936 (-0.731365) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006619 / 0.011353 (-0.004734) \| 0.004102 / 0.011008 (-0.006907) \| 0.064848 / 0.038508 (0.026340) \| 0.074822 / 0.023109 (0.051713) \| 0.366535 / 0.275898 (0.090637) \| 0.395873 / 0.323480 (0.072394) \| 0.005315 / 0.007986 (-0.002670) \| 0.003270 / 0.004328 (-0.001059) \| 0.064829 / 0.004250 (0.060578) \| 0.056094 / 0.037052 (0.019042) \| 0.370355 / 0.258489 (0.111866) \| 0.406837 / 0.293841 (0.112996) \| 0.031634 / 0.128546 (-0.096912) \| 0.008569 / 0.075646 (-0.067077) \| 0.071126 / 0.419271 (-0.348145) \| 0.048629 / 0.043533 (0.005096) \| 0.365175 / 0.255139 (0.110036) \| 0.385234 / 0.283200 (0.102034) \| 0.023295 / 0.141683 (-0.118388) \| 1.466907 / 1.452155 (0.014752) \| 1.523118 / 1.492716 (0.030401) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.227872 / 0.018006 (0.209866) \| 0.451573 / 0.000490 (0.451083) \| 0.000379 / 0.000200 (0.000179) \| 0.000055 / 0.000054 (0.000001) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029496 / 0.037411 (-0.007915) \| 0.086614 / 0.014526 (0.072088) \| 0.098165 / 0.176557 (-0.078392) \| 0.152218 / 0.737135 (-0.584917) \| 0.101215 / 0.296338 (-0.195123) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.407519 / 0.215209 (0.192310) \| 4.074704 / 2.077655 (1.997049) \| 2.113185 / 1.504120 (0.609065) \| 1.947461 / 1.541195 (0.406266) \| 1.998521 / 1.468490 (0.530031) \| 0.487463 / 4.584777 (-4.097313) \| 3.465423 / 3.745712 (-0.280289) \| 3.376498 / 5.269862 (-1.893363) \| 2.001533 / 4.565676 (-2.564144) \| 0.057052 / 0.424275 (-0.367223) \| 0.007325 / 0.007607 (-0.000283) \| 0.485648 / 0.226044 (0.259604) \| 4.860191 / 2.268929 (2.591262) \| 2.550340 / 55.444624 (-52.894284) \| 2.231136 / 6.876477 (-4.645341) \| 2.262539 / 2.142072 (0.120467) \| 0.591422 / 4.805227 (-4.213805) \| 0.132875 / 6.500664 (-6.367789) \| 0.062154 / 0.075469 (-0.013315) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.321834 / 1.841788 (-0.519954) \| 19.734750 / 8.074308 (11.660442) \| 14.681049 / 10.191392 (4.489657) \| 0.148894 / 0.680424 (-0.531530) \| 0.018414 / 0.534201 (-0.515787) \| 0.393377 / 0.579283 (-0.185906) \| 0.402795 / 0.434364 (-0.031569) \| 0.478624 / 0.540337 (-0.061714) \| 0.656767 / 1.386936 (-0.730169) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a5a84a1fa226a4cafb3bb4387dc4b212a46caf31 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007012 / 0.011353 (-0.004341) \| 0.004120 / 0.011008 (-0.006888) \| 0.083720 / 0.038508 (0.045212) \| 0.083105 / 0.023109 (0.059996) \| 0.323803 / 0.275898 (0.047905) \| 0.340345 / 0.323480 (0.016865) \| 0.005872 / 0.007986 (-0.002113) \| 0.003528 / 0.004328 (-0.000801) \| 0.065185 / 0.004250 (0.060935) \| 0.063092 / 0.037052 (0.026040) \| 0.314900 / 0.258489 (0.056411) \| 0.349251 / 0.293841 (0.055410) \| 0.031612 / 0.128546 (-0.096934) \| 0.008541 / 0.075646 (-0.067105) \| 0.289865 / 0.419271 (-0.129407) \| 0.055264 / 0.043533 (0.011731) \| 0.309152 / 0.255139 (0.054013) \| 0.332625 / 0.283200 (0.049425) \| 0.024306 / 0.141683 (-0.117377) \| 1.489191 / 1.452155 (0.037037) \| 1.562447 / 1.492716 (0.069731) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.236681 / 0.018006 (0.218675) \| 0.567767 / 0.000490 (0.567277) \| 0.003022 / 0.000200 (0.002822) \| 0.000218 / 0.000054 (0.000164) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028698 / 0.037411 (-0.008714) \| 0.081681 / 0.014526 (0.067155) \| 0.099109 / 0.176557 (-0.077447) \| 0.154381 / 0.737135 (-0.582754) \| 0.098691 / 0.296338 (-0.197648) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.397985 / 0.215209 (0.182776) \| 3.962499 / 2.077655 (1.884844) \| 1.936158 / 1.504120 (0.432038) \| 1.762339 / 1.541195 (0.221144) \| 1.837451 / 1.468490 (0.368961) \| 0.485655 / 4.584777 (-4.099122) \| 3.538341 / 3.745712 (-0.207371) \| 5.110095 / 5.269862 (-0.159767) \| 3.066152 / 4.565676 (-1.499524) \| 0.057505 / 0.424275 (-0.366770) \| 0.007334 / 0.007607 (-0.000273) \| 0.475622 / 0.226044 (0.249578) \| 4.754091 / 2.268929 (2.485162) \| 2.431379 / 55.444624 (-53.013246) \| 2.106178 / 6.876477 (-4.770298) \| 2.364305 / 2.142072 (0.222232) \| 0.614038 / 4.805227 (-4.191190) \| 0.148530 / 6.500664 (-6.352134) \| 0.061033 / 0.075469 (-0.014436) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.242345 / 1.841788 (-0.599443) \| 19.017266 / 8.074308 (10.942958) \| 13.477782 / 10.191392 (3.286390) \| 0.158513 / 0.680424 (-0.521911) \| 0.018757 / 0.534201 (-0.515444) \| 0.393773 / 0.579283 (-0.185510) \| 0.416933 / 0.434364 (-0.017431) \| 0.460012 / 0.540337 (-0.080326) \| 0.637010 / 1.386936 (-0.749926) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006689 / 0.011353 (-0.004664) \| 0.004168 / 0.011008 (-0.006840) \| 0.065009 / 0.038508 (0.026501) \| 0.073766 / 0.023109 (0.050657) \| 0.369585 / 0.275898 (0.093687) \| 0.407945 / 0.323480 (0.084465) \| 0.005583 / 0.007986 (-0.002403) \| 0.003494 / 0.004328 (-0.000835) \| 0.065032 / 0.004250 (0.060782) \| 0.057166 / 0.037052 (0.020114) \| 0.370656 / 0.258489 (0.112166) \| 0.428381 / 0.293841 (0.134540) \| 0.031653 / 0.128546 (-0.096893) \| 0.008731 / 0.075646 (-0.066915) \| 0.071624 / 0.419271 (-0.347648) \| 0.049364 / 0.043533 (0.005832) \| 0.361824 / 0.255139 (0.106685) \| 0.387615 / 0.283200 (0.104415) \| 0.023228 / 0.141683 (-0.118455) \| 1.476204 / 1.452155 (0.024049) \| 1.553522 / 1.492716 (0.060806) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.266955 / 0.018006 (0.248948) \| 0.556566 / 0.000490 (0.556076) \| 0.000399 / 0.000200 (0.000199) \| 0.000056 / 0.000054 (0.000001) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033104 / 0.037411 (-0.004307) \| 0.088067 / 0.014526 (0.073541) \| 0.103333 / 0.176557 (-0.073224) \| 0.157061 / 0.737135 (-0.580074) \| 0.105007 / 0.296338 (-0.191331) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.420826 / 0.215209 (0.205617) \| 4.201656 / 2.077655 (2.124001) \| 2.208336 / 1.504120 (0.704216) \| 2.043780 / 1.541195 (0.502585) \| 2.156215 / 1.468490 (0.687725) \| 0.490485 / 4.584777 (-4.094292) \| 3.611446 / 3.745712 (-0.134267) \| 5.293140 / 5.269862 (0.023279) \| 2.739778 / 4.565676 (-1.825899) \| 0.058175 / 0.424275 (-0.366100) \| 0.007633 / 0.007607 (0.000026) \| 0.500773 / 0.226044 (0.274729) \| 5.000900 / 2.268929 (2.731971) \| 2.721200 / 55.444624 (-52.723424) \| 2.349381 / 6.876477 (-4.527095) \| 2.386261 / 2.142072 (0.244188) \| 0.583174 / 4.805227 (-4.222053) \| 0.134558 / 6.500664 (-6.366106) \| 0.062157 / 0.075469 (-0.013312) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.351087 / 1.841788 (-0.490701) \| 20.305703 / 8.074308 (12.231395) \| 14.548518 / 10.191392 (4.357126) \| 0.173720 / 0.680424 (-0.506704) \| 0.018100 / 0.534201 (-0.516101) \| 0.395187 / 0.579283 (-0.184097) \| 0.414619 / 0.434364 (-0.019745) \| 0.462515 / 0.540337 (-0.077823) \| 0.617822 / 1.386936 (-0.769114) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#033d0a9de5c825fc9a6a9ce3c3d80eaab3493720 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006909 / 0.011353 (-0.004444) \| 0.003954 / 0.011008 (-0.007054) \| 0.084329 / 0.038508 (0.045821) \| 0.074919 / 0.023109 (0.051809) \| 0.319350 / 0.275898 (0.043451) \| 0.347264 / 0.323480 (0.023785) \| 0.005326 / 0.007986 (-0.002660) \| 0.003323 / 0.004328 (-0.001006) \| 0.064286 / 0.004250 (0.060036) \| 0.054748 / 0.037052 (0.017696) \| 0.324784 / 0.258489 (0.066295) \| 0.361445 / 0.293841 (0.067605) \| 0.031239 / 0.128546 (-0.097308) \| 0.008361 / 0.075646 (-0.067286) \| 0.287482 / 0.419271 (-0.131789) \| 0.052093 / 0.043533 (0.008560) \| 0.321454 / 0.255139 (0.066315) \| 0.337999 / 0.283200 (0.054800) \| 0.025807 / 0.141683 (-0.115876) \| 1.501838 / 1.452155 (0.049683) \| 1.574484 / 1.492716 (0.081767) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.193220 / 0.018006 (0.175214) \| 0.448105 / 0.000490 (0.447615) \| 0.002949 / 0.000200 (0.002749) \| 0.000071 / 0.000054 (0.000016) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028517 / 0.037411 (-0.008894) \| 0.087281 / 0.014526 (0.072755) \| 0.098295 / 0.176557 (-0.078262) \| 0.156972 / 0.737135 (-0.580163) \| 0.101250 / 0.296338 (-0.195088) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.383734 / 0.215209 (0.168525) \| 3.821293 / 2.077655 (1.743638) \| 1.866487 / 1.504120 (0.362367) \| 1.722195 / 1.541195 (0.181000) \| 1.843762 / 1.468490 (0.375272) \| 0.484813 / 4.584777 (-4.099964) \| 3.535381 / 3.745712 (-0.210331) \| 5.502338 / 5.269862 (0.232477) \| 3.256078 / 4.565676 (-1.309599) \| 0.057312 / 0.424275 (-0.366963) \| 0.007305 / 0.007607 (-0.000302) \| 0.461523 / 0.226044 (0.235479) \| 4.611828 / 2.268929 (2.342899) \| 2.337180 / 55.444624 (-53.107445) \| 2.040956 / 6.876477 (-4.835521) \| 2.241233 / 2.142072 (0.099160) \| 0.583727 / 4.805227 (-4.221500) \| 0.132427 / 6.500664 (-6.368237) \| 0.060306 / 0.075469 (-0.015163) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.282223 / 1.841788 (-0.559565) \| 19.439745 / 8.074308 (11.365437) \| 13.627657 / 10.191392 (3.436265) \| 0.158975 / 0.680424 (-0.521449) \| 0.018599 / 0.534201 (-0.515601) \| 0.391136 / 0.579283 (-0.188147) \| 0.410947 / 0.434364 (-0.023417) \| 0.453889 / 0.540337 (-0.086448) \| 0.620928 / 1.386936 (-0.766008) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006428 / 0.011353 (-0.004925) \| 0.003980 / 0.011008 (-0.007028) \| 0.065006 / 0.038508 (0.026498) \| 0.076541 / 0.023109 (0.053432) \| 0.358518 / 0.275898 (0.082620) \| 0.394397 / 0.323480 (0.070917) \| 0.005845 / 0.007986 (-0.002140) \| 0.003258 / 0.004328 (-0.001071) \| 0.064436 / 0.004250 (0.060186) \| 0.056691 / 0.037052 (0.019639) \| 0.367369 / 0.258489 (0.108880) \| 0.420345 / 0.293841 (0.126504) \| 0.031047 / 0.128546 (-0.097499) \| 0.008430 / 0.075646 (-0.067216) \| 0.071280 / 0.419271 (-0.347991) \| 0.048872 / 0.043533 (0.005339) \| 0.360073 / 0.255139 (0.104934) \| 0.384150 / 0.283200 (0.100951) \| 0.023189 / 0.141683 (-0.118494) \| 1.500251 / 1.452155 (0.048096) \| 1.545910 / 1.492716 (0.053194) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.224861 / 0.018006 (0.206855) \| 0.439901 / 0.000490 (0.439411) \| 0.000372 / 0.000200 (0.000172) \| 0.000054 / 0.000054 (-0.000001) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029914 / 0.037411 (-0.007497) \| 0.086916 / 0.014526 (0.072390) \| 0.099527 / 0.176557 (-0.077029) \| 0.153031 / 0.737135 (-0.584104) \| 0.100008 / 0.296338 (-0.196330) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.420305 / 0.215209 (0.205096) \| 4.198224 / 2.077655 (2.120569) \| 2.223807 / 1.504120 (0.719687) \| 2.058475 / 1.541195 (0.517280) \| 2.140405 / 1.468490 (0.671915) \| 0.481224 / 4.584777 (-4.103553) \| 3.593767 / 3.745712 (-0.151945) \| 5.536710 / 5.269862 (0.266849) \| 3.162048 / 4.565676 (-1.403629) \| 0.056662 / 0.424275 (-0.367614) \| 0.007301 / 0.007607 (-0.000306) \| 0.507494 / 0.226044 (0.281450) \| 5.047824 / 2.268929 (2.778896) \| 2.715167 / 55.444624 (-52.729458) \| 2.334916 / 6.876477 (-4.541560) \| 2.406615 / 2.142072 (0.264543) \| 0.572761 / 4.805227 (-4.232466) \| 0.131248 / 6.500664 (-6.369416) \| 0.062401 / 0.075469 (-0.013068) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.375896 / 1.841788 (-0.465892) \| 19.836638 / 8.074308 (11.762329) \| 14.246645 / 10.191392 (4.055253) \| 0.164975 / 0.680424 (-0.515449) \| 0.018293 / 0.534201 (-0.515908) \| 0.394196 / 0.579283 (-0.185087) \| 0.405895 / 0.434364 (-0.028469) \| 0.459221 / 0.540337 (-0.081116) \| 0.609898 / 1.386936 (-0.777038) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f89210ad839c2225b64822dfa248f68ab29ad46f \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008463 / 0.011353 (-0.002890) \| 0.004754 / 0.011008 (-0.006254) \| 0.103574 / 0.038508 (0.065066) \| 0.083541 / 0.023109 (0.060432) \| 0.402498 / 0.275898 (0.126600) \| 0.434944 / 0.323480 (0.111465) \| 0.005766 / 0.007986 (-0.002219) \| 0.003823 / 0.004328 (-0.000505) \| 0.078433 / 0.004250 (0.074183) \| 0.056948 / 0.037052 (0.019895) \| 0.392539 / 0.258489 (0.134050) \| 0.447226 / 0.293841 (0.153385) \| 0.045845 / 0.128546 (-0.082701) \| 0.014043 / 0.075646 (-0.061603) \| 0.355768 / 0.419271 (-0.063503) \| 0.065492 / 0.043533 (0.021960) \| 0.408047 / 0.255139 (0.152908) \| 0.468313 / 0.283200 (0.185113) \| 0.033779 / 0.141683 (-0.107904) \| 1.772198 / 1.452155 (0.320043) \| 1.889127 / 1.492716 (0.396411) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.207107 / 0.018006 (0.189101) \| 0.533261 / 0.000490 (0.532771) \| 0.000864 / 0.000200 (0.000664) \| 0.000105 / 0.000054 (0.000051) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032139 / 0.037411 (-0.005272) \| 0.102002 / 0.014526 (0.087476) \| 0.108780 / 0.176557 (-0.067777) \| 0.202857 / 0.737135 (-0.534278) \| 0.110378 / 0.296338 (-0.185960) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.582814 / 0.215209 (0.367605) \| 5.870683 / 2.077655 (3.793028) \| 2.510290 / 1.504120 (1.006171) \| 2.146337 / 1.541195 (0.605142) \| 2.239278 / 1.468490 (0.770788) \| 0.861205 / 4.584777 (-3.723572) \| 5.177394 / 3.745712 (1.431682) \| 8.550713 / 5.269862 (3.280852) \| 4.867715 / 4.565676 (0.302038) \| 0.096665 / 0.424275 (-0.327610) \| 0.008702 / 0.007607 (0.001095) \| 0.748908 / 0.226044 (0.522863) \| 7.302815 / 2.268929 (5.033887) \| 3.205045 / 55.444624 (-52.239580) \| 2.743914 / 6.876477 (-4.132562) \| 2.831240 / 2.142072 (0.689167) \| 1.103912 / 4.805227 (-3.701315) \| 0.246075 / 6.500664 (-6.254589) \| 0.092092 / 0.075469 (0.016623) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.591331 / 1.841788 (-0.250457) \| 23.085848 / 8.074308 (15.011540) \| 22.887963 / 10.191392 (12.696571) \| 0.212735 / 0.680424 (-0.467689) \| 0.027400 / 0.534201 (-0.506801) \| 0.493822 / 0.579283 (-0.085461) \| 0.574485 / 0.434364 (0.140121) \| 0.574873 / 0.540337 (0.034536) \| 0.826178 / 1.386936 (-0.560758) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009155 / 0.011353 (-0.002198) \| 0.004976 / 0.011008 (-0.006032) \| 0.079308 / 0.038508 (0.040799) \| 0.093959 / 0.023109 (0.070850) \| 0.449110 / 0.275898 (0.173212) \| 0.493356 / 0.323480 (0.169876) \| 0.006317 / 0.007986 (-0.001669) \| 0.004179 / 0.004328 (-0.000150) \| 0.076991 / 0.004250 (0.072740) \| 0.061977 / 0.037052 (0.024924) \| 0.493823 / 0.258489 (0.235333) \| 0.491609 / 0.293841 (0.197768) \| 0.049552 / 0.128546 (-0.078994) \| 0.015174 / 0.075646 (-0.060472) \| 0.090431 / 0.419271 (-0.328841) \| 0.061597 / 0.043533 (0.018064) \| 0.467672 / 0.255139 (0.212533) \| 0.490542 / 0.283200 (0.207342) \| 0.035048 / 0.141683 (-0.106635) \| 1.807939 / 1.452155 (0.355784) \| 1.854859 / 1.492716 (0.362142) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.236672 / 0.018006 (0.218666) \| 0.542236 / 0.000490 (0.541746) \| 0.016334 / 0.000200 (0.016134) \| 0.000220 / 0.000054 (0.000165) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032051 / 0.037411 (-0.005360) \| 0.115352 / 0.014526 (0.100826) \| 0.125115 / 0.176557 (-0.051441) \| 0.173670 / 0.737135 (-0.563466) \| 0.117832 / 0.296338 (-0.178507) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.631513 / 0.215209 (0.416304) \| 6.371688 / 2.077655 (4.294033) \| 2.867240 / 1.504120 (1.363120) \| 2.454907 / 1.541195 (0.913713) \| 2.518860 / 1.468490 (1.050370) \| 0.879973 / 4.584777 (-3.704804) \| 5.170263 / 3.745712 (1.424551) \| 7.986429 / 5.269862 (2.716567) \| 4.828095 / 4.565676 (0.262418) \| 0.097808 / 0.424275 (-0.326468) \| 0.010541 / 0.007607 (0.002934) \| 0.745601 / 0.226044 (0.519557) \| 7.631683 / 2.268929 (5.362755) \| 3.524255 / 55.444624 (-51.920369) \| 2.866199 / 6.876477 (-4.010278) \| 2.982483 / 2.142072 (0.840410) \| 1.148957 / 4.805227 (-3.656270) \| 0.217067 / 6.500664 (-6.283598) \| 0.074357 / 0.075469 (-0.001112) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.714917 / 1.841788 (-0.126871) \| 24.151348 / 8.074308 (16.077040) \| 21.993604 / 10.191392 (11.802212) \| 0.234883 / 0.680424 (-0.445541) \| 0.028182 / 0.534201 (-0.506019) \| 0.474050 / 0.579283 (-0.105233) \| 0.557012 / 0.434364 (0.122648) \| 0.537823 / 0.540337 (-0.002514) \| 0.741488 / 1.386936 (-0.645448) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c2e5a7a01a952a17d0424e93c3be2b4a5ffca7da \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007640 / 0.011353 (-0.003713) \| 0.004776 / 0.011008 (-0.006232) \| 0.101582 / 0.038508 (0.063074) \| 0.085113 / 0.023109 (0.062003) \| 0.376000 / 0.275898 (0.100102) \| 0.421117 / 0.323480 (0.097637) \| 0.006095 / 0.007986 (-0.001891) \| 0.003884 / 0.004328 (-0.000445) \| 0.077263 / 0.004250 (0.073013) \| 0.065262 / 0.037052 (0.028210) \| 0.384041 / 0.258489 (0.125552) \| 0.442229 / 0.293841 (0.148388) \| 0.035706 / 0.128546 (-0.092840) \| 0.009996 / 0.075646 (-0.065651) \| 0.344925 / 0.419271 (-0.074346) \| 0.062358 / 0.043533 (0.018825) \| 0.371738 / 0.255139 (0.116599) \| 0.407093 / 0.283200 (0.123894) \| 0.026996 / 0.141683 (-0.114687) \| 1.762705 / 1.452155 (0.310550) \| 1.846777 / 1.492716 (0.354061) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.219660 / 0.018006 (0.201653) \| 0.521795 / 0.000490 (0.521305) \| 0.005344 / 0.000200 (0.005145) \| 0.000098 / 0.000054 (0.000044) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.036027 / 0.037411 (-0.001385) \| 0.100309 / 0.014526 (0.085784) \| 0.113041 / 0.176557 (-0.063515) \| 0.190037 / 0.737135 (-0.547099) \| 0.114552 / 0.296338 (-0.181786) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.466364 / 0.215209 (0.251154) \| 4.638745 / 2.077655 (2.561090) \| 2.317875 / 1.504120 (0.813755) \| 2.099241 / 1.541195 (0.558046) \| 2.149827 / 1.468490 (0.681337) \| 0.578913 / 4.584777 (-4.005864) \| 4.281866 / 3.745712 (0.536154) \| 3.778453 / 5.269862 (-1.491408) \| 2.411704 / 4.565676 (-2.153972) \| 0.068556 / 0.424275 (-0.355719) \| 0.008779 / 0.007607 (0.001172) \| 0.553165 / 0.226044 (0.327121) \| 5.524520 / 2.268929 (3.255591) \| 2.848444 / 55.444624 (-52.596181) \| 2.468591 / 6.876477 (-4.407885) \| 2.652117 / 2.142072 (0.510045) \| 0.694124 / 4.805227 (-4.111103) \| 0.157087 / 6.500664 (-6.343577) \| 0.070706 / 0.075469 (-0.004763) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.492031 / 1.841788 (-0.349757) \| 23.086596 / 8.074308 (15.012288) \| 16.791351 / 10.191392 (6.599959) \| 0.203932 / 0.680424 (-0.476492) \| 0.021736 / 0.534201 (-0.512464) \| 0.468344 / 0.579283 (-0.110939) \| 0.493790 / 0.434364 (0.059426) \| 0.563226 / 0.540337 (0.022889) \| 0.780384 / 1.386936 (-0.606553) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007980 / 0.011353 (-0.003373) \| 0.004696 / 0.011008 (-0.006312) \| 0.076712 / 0.038508 (0.038204) \| 0.095915 / 0.023109 (0.072805) \| 0.433615 / 0.275898 (0.157717) \| 0.482477 / 0.323480 (0.158997) \| 0.007029 / 0.007986 (-0.000957) \| 0.003842 / 0.004328 (-0.000487) \| 0.076331 / 0.004250 (0.072081) \| 0.069755 / 0.037052 (0.032703) \| 0.458914 / 0.258489 (0.200425) \| 0.486155 / 0.293841 (0.192314) \| 0.036966 / 0.128546 (-0.091580) \| 0.010082 / 0.075646 (-0.065564) \| 0.083886 / 0.419271 (-0.335385) \| 0.059329 / 0.043533 (0.015796) \| 0.453782 / 0.255139 (0.198643) \| 0.459508 / 0.283200 (0.176308) \| 0.028400 / 0.141683 (-0.113283) \| 1.796406 / 1.452155 (0.344251) \| 1.881161 / 1.492716 (0.388445) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.235053 / 0.018006 (0.217047) \| 0.501907 / 0.000490 (0.501417) \| 0.005211 / 0.000200 (0.005011) \| 0.000101 / 0.000054 (0.000046) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.037752 / 0.037411 (0.000341) \| 0.107299 / 0.014526 (0.092773) \| 0.120307 / 0.176557 (-0.056250) \| 0.187542 / 0.737135 (-0.549593) \| 0.121805 / 0.296338 (-0.174533) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.490039 / 0.215209 (0.274830) \| 4.919169 / 2.077655 (2.841515) \| 2.520610 / 1.504120 (1.016490) \| 2.324473 / 1.541195 (0.783279) \| 2.421195 / 1.468490 (0.952705) \| 0.576314 / 4.584777 (-4.008463) \| 4.304752 / 3.745712 (0.559040) \| 3.881151 / 5.269862 (-1.388710) \| 2.409777 / 4.565676 (-2.155900) \| 0.067400 / 0.424275 (-0.356875) \| 0.009235 / 0.007607 (0.001627) \| 0.586601 / 0.226044 (0.360556) \| 5.850080 / 2.268929 (3.581152) \| 3.064859 / 55.444624 (-52.379766) \| 2.701734 / 6.876477 (-4.174743) \| 2.926190 / 2.142072 (0.784117) \| 0.698511 / 4.805227 (-4.106716) \| 0.158273 / 6.500664 (-6.342392) \| 0.074530 / 0.075469 (-0.000939) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.607113 / 1.841788 (-0.234674) \| 23.499279 / 8.074308 (15.424971) \| 17.049509 / 10.191392 (6.858117) \| 0.175689 / 0.680424 (-0.504735) \| 0.021762 / 0.534201 (-0.512439) \| 0.491450 / 0.579283 (-0.087833) \| 0.487557 / 0.434364 (0.053193) \| 0.570104 / 0.540337 (0.029766) \| 0.761527 / 1.386936 (-0.625409) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7096c59e6a8f4d5b16f3b906075f9e2ed83bbb25 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008725 / 0.011353 (-0.002628) \| 0.005156 / 0.011008 (-0.005852) \| 0.095147 / 0.038508 (0.056639) \| 0.084916 / 0.023109 (0.061807) \| 0.390769 / 0.275898 (0.114871) \| 0.434716 / 0.323480 (0.111237) \| 0.005982 / 0.007986 (-0.002004) \| 0.004323 / 0.004328 (-0.000006) \| 0.074712 / 0.004250 (0.070461) \| 0.058889 / 0.037052 (0.021837) \| 0.403997 / 0.258489 (0.145508) \| 0.443361 / 0.293841 (0.149520) \| 0.045908 / 0.128546 (-0.082639) \| 0.013562 / 0.075646 (-0.062085) \| 0.330683 / 0.419271 (-0.088588) \| 0.064821 / 0.043533 (0.021288) \| 0.407202 / 0.255139 (0.152063) \| 0.409930 / 0.283200 (0.126730) \| 0.032693 / 0.141683 (-0.108990) \| 1.630181 / 1.452155 (0.178026) \| 1.729680 / 1.492716 (0.236963) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.261240 / 0.018006 (0.243234) \| 0.581850 / 0.000490 (0.581360) \| 0.002997 / 0.000200 (0.002797) \| 0.000107 / 0.000054 (0.000053) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029279 / 0.037411 (-0.008133) \| 0.085004 / 0.014526 (0.070478) \| 0.127782 / 0.176557 (-0.048774) \| 0.168852 / 0.737135 (-0.568283) \| 0.098697 / 0.296338 (-0.197641) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.546417 / 0.215209 (0.331208) \| 5.602186 / 2.077655 (3.524531) \| 2.597049 / 1.504120 (1.092930) \| 2.384880 / 1.541195 (0.843685) \| 2.444516 / 1.468490 (0.976026) \| 0.796562 / 4.584777 (-3.788214) \| 5.239440 / 3.745712 (1.493727) \| 7.087768 / 5.269862 (1.817906) \| 4.308476 / 4.565676 (-0.257200) \| 0.091215 / 0.424275 (-0.333060) \| 0.007942 / 0.007607 (0.000335) \| 0.690059 / 0.226044 (0.464015) \| 6.727809 / 2.268929 (4.458880) \| 3.294522 / 55.444624 (-52.150103) \| 2.604088 / 6.876477 (-4.272389) \| 2.786970 / 2.142072 (0.644898) \| 0.918817 / 4.805227 (-3.886410) \| 0.191451 / 6.500664 (-6.309213) \| 0.069557 / 0.075469 (-0.005912) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.486377 / 1.841788 (-0.355411) \| 22.363470 / 8.074308 (14.289162) \| 19.963684 / 10.191392 (9.772292) \| 0.204161 / 0.680424 (-0.476263) \| 0.034570 / 0.534201 (-0.499631) \| 0.467937 / 0.579283 (-0.111346) \| 0.564870 / 0.434364 (0.130506) \| 0.511133 / 0.540337 (-0.029204) \| 0.777084 / 1.386936 (-0.609852) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008612 / 0.011353 (-0.002741) \| 0.004993 / 0.011008 (-0.006015) \| 0.080769 / 0.038508 (0.042261) \| 0.075923 / 0.023109 (0.052814) \| 0.442271 / 0.275898 (0.166373) \| 0.495625 / 0.323480 (0.172146) \| 0.006467 / 0.007986 (-0.001518) \| 0.004001 / 0.004328 (-0.000328) \| 0.077309 / 0.004250 (0.073059) \| 0.063466 / 0.037052 (0.026414) \| 0.452460 / 0.258489 (0.193971) \| 0.494063 / 0.293841 (0.200223) \| 0.045751 / 0.128546 (-0.082796) \| 0.013402 / 0.075646 (-0.062245) \| 0.085760 / 0.419271 (-0.333511) \| 0.056532 / 0.043533 (0.012999) \| 0.440596 / 0.255139 (0.185457) \| 0.459540 / 0.283200 (0.176340) \| 0.035897 / 0.141683 (-0.105786) \| 1.728264 / 1.452155 (0.276109) \| 1.808142 / 1.492716 (0.315426) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.285094 / 0.018006 (0.267088) \| 0.598440 / 0.000490 (0.597950) \| 0.003476 / 0.000200 (0.003276) \| 0.000103 / 0.000054 (0.000048) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.035106 / 0.037411 (-0.002305) \| 0.091724 / 0.014526 (0.077198) \| 0.122803 / 0.176557 (-0.053754) \| 0.182114 / 0.737135 (-0.555022) \| 0.116196 / 0.296338 (-0.180143) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.585420 / 0.215209 (0.370211) \| 5.790370 / 2.077655 (3.712715) \| 2.833247 / 1.504120 (1.329127) \| 2.627949 / 1.541195 (1.086755) \| 2.643050 / 1.468490 (1.174560) \| 0.792036 / 4.584777 (-3.792741) \| 5.145084 / 3.745712 (1.399372) \| 4.423679 / 5.269862 (-0.846182) \| 2.802778 / 4.565676 (-1.762898) \| 0.093983 / 0.424275 (-0.330292) \| 0.009260 / 0.007607 (0.001652) \| 0.720302 / 0.226044 (0.494258) \| 7.116959 / 2.268929 (4.848031) \| 3.574782 / 55.444624 (-51.869843) \| 3.009330 / 6.876477 (-3.867147) \| 3.126488 / 2.142072 (0.984415) \| 0.949144 / 4.805227 (-3.856083) \| 0.195143 / 6.500664 (-6.305521) \| 0.072490 / 0.075469 (-0.002979) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.626368 / 1.841788 (-0.215419) \| 23.683021 / 8.074308 (15.608713) \| 20.085297 / 10.191392 (9.893905) \| 0.267057 / 0.680424 (-0.413367) \| 0.028306 / 0.534201 (-0.505894) \| 0.478448 / 0.579283 (-0.100835) \| 0.597619 / 0.434364 (0.163256) \| 0.544737 / 0.540337 (0.004399) \| 0.761805 / 1.386936 (-0.625131) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7ac53b590916c8d859fabcc2ef23c12add7f22f7 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009359 / 0.011353 (-0.001994) \| 0.004848 / 0.011008 (-0.006160) \| 0.099471 / 0.038508 (0.060963) \| 0.079483 / 0.023109 (0.056373) \| 0.375281 / 0.275898 (0.099383) \| 0.415566 / 0.323480 (0.092086) \| 0.006317 / 0.007986 (-0.001669) \| 0.005145 / 0.004328 (0.000817) \| 0.080345 / 0.004250 (0.076094) \| 0.064540 / 0.037052 (0.027487) \| 0.385897 / 0.258489 (0.127408) \| 0.432576 / 0.293841 (0.138735) \| 0.055109 / 0.128546 (-0.073437) \| 0.014166 / 0.075646 (-0.061480) \| 0.350870 / 0.419271 (-0.068402) \| 0.087483 / 0.043533 (0.043950) \| 0.402288 / 0.255139 (0.147149) \| 0.391997 / 0.283200 (0.108798) \| 0.045233 / 0.141683 (-0.096450) \| 1.795002 / 1.452155 (0.342847) \| 1.839063 / 1.492716 (0.346347) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.220851 / 0.018006 (0.202845) \| 0.513391 / 0.000490 (0.512901) \| 0.003740 / 0.000200 (0.003540) \| 0.000107 / 0.000054 (0.000053) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.035287 / 0.037411 (-0.002124) \| 0.090670 / 0.014526 (0.076144) \| 0.115651 / 0.176557 (-0.060905) \| 0.180469 / 0.737135 (-0.556667) \| 0.106955 / 0.296338 (-0.189384) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.632381 / 0.215209 (0.417172) \| 6.185151 / 2.077655 (4.107497) \| 2.548263 / 1.504120 (1.044143) \| 2.194931 / 1.541195 (0.653737) \| 2.368685 / 1.468490 (0.900194) \| 0.956467 / 4.584777 (-3.628310) \| 5.280904 / 3.745712 (1.535192) \| 4.783057 / 5.269862 (-0.486805) \| 3.218493 / 4.565676 (-1.347184) \| 0.103545 / 0.424275 (-0.320730) \| 0.008424 / 0.007607 (0.000817) \| 0.736303 / 0.226044 (0.510259) \| 7.354305 / 2.268929 (5.085376) \| 3.280670 / 55.444624 (-52.163954) \| 2.478628 / 6.876477 (-4.397848) \| 2.623290 / 2.142072 (0.481217) \| 1.033064 / 4.805227 (-3.772163) \| 0.206496 / 6.500664 (-6.294168) \| 0.066449 / 0.075469 (-0.009020) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.508756 / 1.841788 (-0.333031) \| 21.866012 / 8.074308 (13.791704) \| 21.887761 / 10.191392 (11.696369) \| 0.231415 / 0.680424 (-0.449008) \| 0.028917 / 0.534201 (-0.505284) \| 0.468761 / 0.579283 (-0.110522) \| 0.568236 / 0.434364 (0.133872) \| 0.550156 / 0.540337 (0.009818) \| 0.783197 / 1.386936 (-0.603739) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009413 / 0.011353 (-0.001939) \| 0.004951 / 0.011008 (-0.006058) \| 0.071402 / 0.038508 (0.032893) \| 0.068455 / 0.023109 (0.045346) \| 0.425216 / 0.275898 (0.149318) \| 0.431928 / 0.323480 (0.108448) \| 0.006477 / 0.007986 (-0.001509) \| 0.003891 / 0.004328 (-0.000437) \| 0.076898 / 0.004250 (0.072647) \| 0.057522 / 0.037052 (0.020470) \| 0.449585 / 0.258489 (0.191096) \| 0.431356 / 0.293841 (0.137515) \| 0.049728 / 0.128546 (-0.078818) \| 0.014456 / 0.075646 (-0.061190) \| 0.084618 / 0.419271 (-0.334653) \| 0.064482 / 0.043533 (0.020949) \| 0.456377 / 0.255139 (0.201238) \| 0.433949 / 0.283200 (0.150749) \| 0.036577 / 0.141683 (-0.105106) \| 1.819742 / 1.452155 (0.367588) \| 1.694691 / 1.492716 (0.201975) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.224610 / 0.018006 (0.206604) \| 0.494586 / 0.000490 (0.494096) \| 0.004506 / 0.000200 (0.004307) \| 0.000119 / 0.000054 (0.000065) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033172 / 0.037411 (-0.004239) \| 0.100562 / 0.014526 (0.086036) \| 0.116499 / 0.176557 (-0.060058) \| 0.153717 / 0.737135 (-0.583418) \| 0.140047 / 0.296338 (-0.156291) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.635922 / 0.215209 (0.420713) \| 6.359792 / 2.077655 (4.282137) \| 2.689083 / 1.504120 (1.184963) \| 2.330574 / 1.541195 (0.789380) \| 2.583535 / 1.468490 (1.115044) \| 0.902737 / 4.584777 (-3.682040) \| 5.136586 / 3.745712 (1.390874) \| 4.570824 / 5.269862 (-0.699037) \| 3.029953 / 4.565676 (-1.535724) \| 0.103961 / 0.424275 (-0.320314) \| 0.007908 / 0.007607 (0.000301) \| 0.723290 / 0.226044 (0.497246) \| 7.678599 / 2.268929 (5.409671) \| 3.342522 / 55.444624 (-52.102102) \| 2.774659 / 6.876477 (-4.101817) \| 2.966496 / 2.142072 (0.824423) \| 1.025395 / 4.805227 (-3.779832) \| 0.222246 / 6.500664 (-6.278418) \| 0.072455 / 0.075469 (-0.003014) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.603637 / 1.841788 (-0.238151) \| 21.387722 / 8.074308 (13.313414) \| 22.855221 / 10.191392 (12.663829) \| 0.222147 / 0.680424 (-0.458277) \| 0.030763 / 0.534201 (-0.503438) \| 0.472586 / 0.579283 (-0.106697) \| 0.560161 / 0.434364 (0.125797) \| 0.551941 / 0.540337 (0.011604) \| 0.711254 / 1.386936 (-0.675682) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#85cf123e553ff282b43ad1d1877ba2c40d206d52 \"CML watermark\")\n" ]	2023-07-17T15:50:15	2023-07-24T14:45:56	2023-07-24T14:35:03	CONTRIBUTOR	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6045", "html_url": "https://github.com/huggingface/datasets/pull/6045", "diff_url": "https://github.com/huggingface/datasets/pull/6045.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6045.patch", "merged_at": "2023-07-24T14:35:03" }	Fix #6039	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6045/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6045/timeline	null	null	true
https://api.github.com/repos/huggingface/datasets/issues/6044	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6044/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6044/comments	https://api.github.com/repos/huggingface/datasets/issues/6044/events	https://github.com/huggingface/datasets/pull/6044	1,808,057,906	PR_kwDODunzps5Vr7jr	6,044	Rename "pattern" to "path" in YAML data_files configs	{ "login": "lhoestq", "id": 42851186, "node_id": "MDQ6VXNlcjQyODUxMTg2", "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "gravatar_id": "", "url": "https://api.github.com/users/lhoestq", "html_url": "https://github.com/lhoestq", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "repos_url": "https://api.github.com/users/lhoestq/repos", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006543 / 0.011353 (-0.004809) \| 0.004085 / 0.011008 (-0.006924) \| 0.083989 / 0.038508 (0.045481) \| 0.074733 / 0.023109 (0.051623) \| 0.310839 / 0.275898 (0.034941) \| 0.333540 / 0.323480 (0.010060) \| 0.005566 / 0.007986 (-0.002419) \| 0.003461 / 0.004328 (-0.000868) \| 0.065194 / 0.004250 (0.060943) \| 0.057007 / 0.037052 (0.019954) \| 0.325633 / 0.258489 (0.067144) \| 0.351665 / 0.293841 (0.057824) \| 0.030561 / 0.128546 (-0.097985) \| 0.008579 / 0.075646 (-0.067068) \| 0.287457 / 0.419271 (-0.131815) \| 0.063554 / 0.043533 (0.020021) \| 0.309182 / 0.255139 (0.054043) \| 0.327809 / 0.283200 (0.044609) \| 0.034470 / 0.141683 (-0.107213) \| 1.452098 / 1.452155 (-0.000057) \| 1.527130 / 1.492716 (0.034414) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.241736 / 0.018006 (0.223729) \| 0.552432 / 0.000490 (0.551943) \| 0.004085 / 0.000200 (0.003885) \| 0.000089 / 0.000054 (0.000035) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027290 / 0.037411 (-0.010121) \| 0.081250 / 0.014526 (0.066724) \| 0.094739 / 0.176557 (-0.081818) \| 0.150424 / 0.737135 (-0.586711) \| 0.095488 / 0.296338 (-0.200851) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.377245 / 0.215209 (0.162036) \| 3.781021 / 2.077655 (1.703366) \| 1.820092 / 1.504120 (0.315972) \| 1.654420 / 1.541195 (0.113225) \| 1.751256 / 1.468490 (0.282766) \| 0.475161 / 4.584777 (-4.109616) \| 3.603462 / 3.745712 (-0.142251) \| 5.437837 / 5.269862 (0.167975) \| 3.305598 / 4.565676 (-1.260079) \| 0.055856 / 0.424275 (-0.368419) \| 0.007259 / 0.007607 (-0.000348) \| 0.454205 / 0.226044 (0.228161) \| 4.544157 / 2.268929 (2.275229) \| 2.296776 / 55.444624 (-53.147848) \| 1.951017 / 6.876477 (-4.925459) \| 2.128759 / 2.142072 (-0.013313) \| 0.590354 / 4.805227 (-4.214873) \| 0.129974 / 6.500664 (-6.370690) \| 0.059506 / 0.075469 (-0.015963) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.285866 / 1.841788 (-0.555921) \| 19.419446 / 8.074308 (11.345138) \| 13.985108 / 10.191392 (3.793716) \| 0.146803 / 0.680424 (-0.533620) \| 0.018176 / 0.534201 (-0.516025) \| 0.392345 / 0.579283 (-0.186938) \| 0.405394 / 0.434364 (-0.028970) \| 0.454649 / 0.540337 (-0.085688) \| 0.633075 / 1.386936 (-0.753861) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006497 / 0.011353 (-0.004855) \| 0.004092 / 0.011008 (-0.006916) \| 0.064908 / 0.038508 (0.026400) \| 0.073494 / 0.023109 (0.050385) \| 0.382227 / 0.275898 (0.106329) \| 0.407320 / 0.323480 (0.083840) \| 0.005653 / 0.007986 (-0.002332) \| 0.003500 / 0.004328 (-0.000829) \| 0.064570 / 0.004250 (0.060320) \| 0.058733 / 0.037052 (0.021681) \| 0.385702 / 0.258489 (0.127213) \| 0.426463 / 0.293841 (0.132622) \| 0.031073 / 0.128546 (-0.097473) \| 0.008710 / 0.075646 (-0.066936) \| 0.071378 / 0.419271 (-0.347893) \| 0.050141 / 0.043533 (0.006608) \| 0.377769 / 0.255139 (0.122630) \| 0.395016 / 0.283200 (0.111816) \| 0.025158 / 0.141683 (-0.116525) \| 1.470503 / 1.452155 (0.018348) \| 1.532742 / 1.492716 (0.040026) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.214249 / 0.018006 (0.196243) \| 0.583580 / 0.000490 (0.583090) \| 0.004027 / 0.000200 (0.003828) \| 0.000104 / 0.000054 (0.000050) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030186 / 0.037411 (-0.007226) \| 0.086927 / 0.014526 (0.072401) \| 0.102060 / 0.176557 (-0.074497) \| 0.156281 / 0.737135 (-0.580855) \| 0.100825 / 0.296338 (-0.195514) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.419942 / 0.215209 (0.204733) \| 4.183797 / 2.077655 (2.106142) \| 2.205079 / 1.504120 (0.700959) \| 2.071219 / 1.541195 (0.530024) \| 2.194047 / 1.468490 (0.725557) \| 0.478768 / 4.584777 (-4.106009) \| 3.584864 / 3.745712 (-0.160848) \| 3.371635 / 5.269862 (-1.898227) \| 2.022134 / 4.565676 (-2.543542) \| 0.056553 / 0.424275 (-0.367722) \| 0.007231 / 0.007607 (-0.000376) \| 0.493158 / 0.226044 (0.267113) \| 4.934370 / 2.268929 (2.665441) \| 2.699593 / 55.444624 (-52.745031) \| 2.396371 / 6.876477 (-4.480105) \| 2.438052 / 2.142072 (0.295979) \| 0.589578 / 4.805227 (-4.215649) \| 0.147234 / 6.500664 (-6.353430) \| 0.062049 / 0.075469 (-0.013420) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.318246 / 1.841788 (-0.523542) \| 19.829025 / 8.074308 (11.754717) \| 14.314825 / 10.191392 (4.123433) \| 0.168309 / 0.680424 (-0.512115) \| 0.018596 / 0.534201 (-0.515605) \| 0.397540 / 0.579283 (-0.181743) \| 0.421280 / 0.434364 (-0.013084) \| 0.479917 / 0.540337 (-0.060421) \| 0.643494 / 1.386936 (-0.743442) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5be59becaa65f1fa08129091b8c778823e4a50ac \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008349 / 0.011353 (-0.003004) \| 0.005362 / 0.011008 (-0.005646) \| 0.100777 / 0.038508 (0.062269) \| 0.078719 / 0.023109 (0.055609) \| 0.398105 / 0.275898 (0.122207) \| 0.444189 / 0.323480 (0.120709) \| 0.006834 / 0.007986 (-0.001152) \| 0.004642 / 0.004328 (0.000314) \| 0.076284 / 0.004250 (0.072034) \| 0.062738 / 0.037052 (0.025685) \| 0.409532 / 0.258489 (0.151043) \| 0.447218 / 0.293841 (0.153377) \| 0.052996 / 0.128546 (-0.075550) \| 0.012977 / 0.075646 (-0.062669) \| 0.347687 / 0.419271 (-0.071585) \| 0.068076 / 0.043533 (0.024543) \| 0.394526 / 0.255139 (0.139387) \| 0.434110 / 0.283200 (0.150910) \| 0.041719 / 0.141683 (-0.099963) \| 1.759109 / 1.452155 (0.306955) \| 1.866049 / 1.492716 (0.373333) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.287633 / 0.018006 (0.269627) \| 0.611540 / 0.000490 (0.611051) \| 0.005388 / 0.000200 (0.005188) \| 0.000096 / 0.000054 (0.000042) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027394 / 0.037411 (-0.010017) \| 0.089796 / 0.014526 (0.075270) \| 0.106931 / 0.176557 (-0.069625) \| 0.173560 / 0.737135 (-0.563575) \| 0.106948 / 0.296338 (-0.189391) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.575156 / 0.215209 (0.359947) \| 5.674170 / 2.077655 (3.596516) \| 2.463090 / 1.504120 (0.958971) \| 2.128245 / 1.541195 (0.587050) \| 2.118982 / 1.468490 (0.650492) \| 0.876976 / 4.584777 (-3.707801) \| 5.238229 / 3.745712 (1.492517) \| 4.548788 / 5.269862 (-0.721074) \| 2.905243 / 4.565676 (-1.660433) \| 0.090750 / 0.424275 (-0.333525) \| 0.008266 / 0.007607 (0.000659) \| 0.693305 / 0.226044 (0.467260) \| 7.126970 / 2.268929 (4.858041) \| 3.152131 / 55.444624 (-52.292494) \| 2.532118 / 6.876477 (-4.344359) \| 2.678442 / 2.142072 (0.536369) \| 0.932745 / 4.805227 (-3.872483) \| 0.196290 / 6.500664 (-6.304374) \| 0.074082 / 0.075469 (-0.001387) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.599636 / 1.841788 (-0.242152) \| 23.271435 / 8.074308 (15.197127) \| 19.696709 / 10.191392 (9.505317) \| 0.222668 / 0.680424 (-0.457756) \| 0.029088 / 0.534201 (-0.505113) \| 0.492477 / 0.579283 (-0.086806) \| 0.580578 / 0.434364 (0.146214) \| 0.558852 / 0.540337 (0.018514) \| 0.762083 / 1.386936 (-0.624853) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009021 / 0.011353 (-0.002332) \| 0.005011 / 0.011008 (-0.005997) \| 0.076504 / 0.038508 (0.037996) \| 0.077303 / 0.023109 (0.054193) \| 0.480660 / 0.275898 (0.204762) \| 0.493944 / 0.323480 (0.170464) \| 0.006339 / 0.007986 (-0.001646) \| 0.004302 / 0.004328 (-0.000026) \| 0.076228 / 0.004250 (0.071978) \| 0.060805 / 0.037052 (0.023753) \| 0.477539 / 0.258489 (0.219050) \| 0.496799 / 0.293841 (0.202958) \| 0.049495 / 0.128546 (-0.079052) \| 0.013333 / 0.075646 (-0.062313) \| 0.087217 / 0.419271 (-0.332055) \| 0.061451 / 0.043533 (0.017918) \| 0.485169 / 0.255139 (0.230030) \| 0.487348 / 0.283200 (0.204149) \| 0.035874 / 0.141683 (-0.105809) \| 1.829137 / 1.452155 (0.376982) \| 1.906151 / 1.492716 (0.413435) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.304526 / 0.018006 (0.286520) \| 0.627499 / 0.000490 (0.627009) \| 0.003786 / 0.000200 (0.003586) \| 0.000098 / 0.000054 (0.000043) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.035512 / 0.037411 (-0.001899) \| 0.096684 / 0.014526 (0.082158) \| 0.111879 / 0.176557 (-0.064678) \| 0.171489 / 0.737135 (-0.565647) \| 0.112175 / 0.296338 (-0.184164) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.604791 / 0.215209 (0.389582) \| 6.089137 / 2.077655 (4.011482) \| 2.883237 / 1.504120 (1.379117) \| 2.561109 / 1.541195 (1.019914) \| 2.542400 / 1.468490 (1.073910) \| 0.852828 / 4.584777 (-3.731949) \| 5.236812 / 3.745712 (1.491100) \| 4.756429 / 5.269862 (-0.513432) \| 2.885660 / 4.565676 (-1.680016) \| 0.095643 / 0.424275 (-0.328632) \| 0.008403 / 0.007607 (0.000796) \| 0.727707 / 0.226044 (0.501663) \| 7.428002 / 2.268929 (5.159074) \| 3.816051 / 55.444624 (-51.628573) \| 2.971057 / 6.876477 (-3.905420) \| 2.915965 / 2.142072 (0.773893) \| 1.006553 / 4.805227 (-3.798674) \| 0.201840 / 6.500664 (-6.298824) \| 0.080795 / 0.075469 (0.005326) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.794951 / 1.841788 (-0.046837) \| 23.624556 / 8.074308 (15.550248) \| 21.856195 / 10.191392 (11.664802) \| 0.253043 / 0.680424 (-0.427381) \| 0.031201 / 0.534201 (-0.503000) \| 0.461641 / 0.579283 (-0.117642) \| 0.577789 / 0.434364 (0.143425) \| 0.569197 / 0.540337 (0.028860) \| 0.780111 / 1.386936 (-0.606825) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4904f14459c862f0ab525ec034a636177be5dee4 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007646 / 0.011353 (-0.003707) \| 0.004750 / 0.011008 (-0.006258) \| 0.097981 / 0.038508 (0.059473) \| 0.088989 / 0.023109 (0.065880) \| 0.377732 / 0.275898 (0.101834) \| 0.406805 / 0.323480 (0.083325) \| 0.006389 / 0.007986 (-0.001597) \| 0.003854 / 0.004328 (-0.000474) \| 0.073977 / 0.004250 (0.069727) \| 0.066497 / 0.037052 (0.029444) \| 0.371498 / 0.258489 (0.113009) \| 0.417352 / 0.293841 (0.123511) \| 0.036326 / 0.128546 (-0.092220) \| 0.009876 / 0.075646 (-0.065770) \| 0.330142 / 0.419271 (-0.089130) \| 0.062423 / 0.043533 (0.018890) \| 0.369375 / 0.255139 (0.114236) \| 0.406048 / 0.283200 (0.122848) \| 0.026564 / 0.141683 (-0.115119) \| 1.713295 / 1.452155 (0.261140) \| 1.797493 / 1.492716 (0.304777) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.231889 / 0.018006 (0.213882) \| 0.512497 / 0.000490 (0.512007) \| 0.000390 / 0.000200 (0.000190) \| 0.000069 / 0.000054 (0.000015) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033978 / 0.037411 (-0.003433) \| 0.100117 / 0.014526 (0.085592) \| 0.112460 / 0.176557 (-0.064097) \| 0.179936 / 0.737135 (-0.557200) \| 0.114277 / 0.296338 (-0.182061) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.461320 / 0.215209 (0.246111) \| 4.563180 / 2.077655 (2.485526) \| 2.249474 / 1.504120 (0.745354) \| 2.100450 / 1.541195 (0.559255) \| 2.231080 / 1.468490 (0.762590) \| 0.567907 / 4.584777 (-4.016870) \| 4.117233 / 3.745712 (0.371521) \| 4.943159 / 5.269862 (-0.326703) \| 3.112299 / 4.565676 (-1.453377) \| 0.065500 / 0.424275 (-0.358775) \| 0.008407 / 0.007607 (0.000800) \| 0.545928 / 0.226044 (0.319883) \| 5.508058 / 2.268929 (3.239129) \| 2.834645 / 55.444624 (-52.609980) \| 2.440328 / 6.876477 (-4.436148) \| 2.680483 / 2.142072 (0.538410) \| 0.697191 / 4.805227 (-4.108036) \| 0.176646 / 6.500664 (-6.324018) \| 0.073608 / 0.075469 (-0.001861) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.451865 / 1.841788 (-0.389922) \| 22.752595 / 8.074308 (14.678287) \| 15.543338 / 10.191392 (5.351946) \| 0.214644 / 0.680424 (-0.465780) \| 0.022050 / 0.534201 (-0.512151) \| 0.463898 / 0.579283 (-0.115385) \| 0.481691 / 0.434364 (0.047327) \| 0.549715 / 0.540337 (0.009378) \| 0.773595 / 1.386936 (-0.613341) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007541 / 0.011353 (-0.003812) \| 0.004715 / 0.011008 (-0.006293) \| 0.076782 / 0.038508 (0.038274) \| 0.086242 / 0.023109 (0.063133) \| 0.458053 / 0.275898 (0.182155) \| 0.503097 / 0.323480 (0.179617) \| 0.006262 / 0.007986 (-0.001724) \| 0.003882 / 0.004328 (-0.000447) \| 0.075669 / 0.004250 (0.071419) \| 0.066004 / 0.037052 (0.028952) \| 0.469439 / 0.258489 (0.210950) \| 0.529744 / 0.293841 (0.235903) \| 0.037228 / 0.128546 (-0.091319) \| 0.009794 / 0.075646 (-0.065852) \| 0.082464 / 0.419271 (-0.336808) \| 0.058797 / 0.043533 (0.015264) \| 0.452069 / 0.255139 (0.196930) \| 0.488246 / 0.283200 (0.205046) \| 0.029324 / 0.141683 (-0.112359) \| 1.742237 / 1.452155 (0.290082) \| 1.839676 / 1.492716 (0.346959) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.228106 / 0.018006 (0.210100) \| 0.491632 / 0.000490 (0.491142) \| 0.004993 / 0.000200 (0.004793) \| 0.000114 / 0.000054 (0.000060) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.035413 / 0.037411 (-0.001999) \| 0.104617 / 0.014526 (0.090091) \| 0.121948 / 0.176557 (-0.054609) \| 0.186233 / 0.737135 (-0.550902) \| 0.121574 / 0.296338 (-0.174764) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.473849 / 0.215209 (0.258640) \| 4.788312 / 2.077655 (2.710657) \| 2.470535 / 1.504120 (0.966415) \| 2.270393 / 1.541195 (0.729198) \| 2.361096 / 1.468490 (0.892606) \| 0.556184 / 4.584777 (-4.028593) \| 4.216852 / 3.745712 (0.471140) \| 3.901718 / 5.269862 (-1.368143) \| 2.355209 / 4.565676 (-2.210467) \| 0.066708 / 0.424275 (-0.357567) \| 0.008709 / 0.007607 (0.001102) \| 0.571714 / 0.226044 (0.345669) \| 5.663150 / 2.268929 (3.394221) \| 3.025769 / 55.444624 (-52.418855) \| 2.652554 / 6.876477 (-4.223923) \| 2.750555 / 2.142072 (0.608483) \| 0.681536 / 4.805227 (-4.123691) \| 0.157187 / 6.500664 (-6.343477) \| 0.073533 / 0.075469 (-0.001936) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.604630 / 1.841788 (-0.237158) \| 22.735629 / 8.074308 (14.661321) \| 16.762347 / 10.191392 (6.570955) \| 0.175514 / 0.680424 (-0.504910) \| 0.021497 / 0.534201 (-0.512704) \| 0.461438 / 0.579283 (-0.117845) \| 0.476184 / 0.434364 (0.041820) \| 0.571048 / 0.540337 (0.030710) \| 0.747086 / 1.386936 (-0.639850) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6ea38fc40ee2b10d3b5c6df09b09ad05e02a2cff \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006889 / 0.011353 (-0.004464) \| 0.004241 / 0.011008 (-0.006767) \| 0.084542 / 0.038508 (0.046034) \| 0.080484 / 0.023109 (0.057374) \| 0.309356 / 0.275898 (0.033458) \| 0.338548 / 0.323480 (0.015068) \| 0.004904 / 0.007986 (-0.003082) \| 0.005220 / 0.004328 (0.000892) \| 0.065501 / 0.004250 (0.061251) \| 0.062095 / 0.037052 (0.025043) \| 0.317332 / 0.258489 (0.058843) \| 0.364797 / 0.293841 (0.070956) \| 0.030492 / 0.128546 (-0.098054) \| 0.008991 / 0.075646 (-0.066656) \| 0.288274 / 0.419271 (-0.130998) \| 0.052582 / 0.043533 (0.009049) \| 0.310838 / 0.255139 (0.055699) \| 0.346304 / 0.283200 (0.063104) \| 0.027968 / 0.141683 (-0.113715) \| 1.509727 / 1.452155 (0.057573) \| 1.577410 / 1.492716 (0.084694) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.269725 / 0.018006 (0.251719) \| 0.627685 / 0.000490 (0.627195) \| 0.000419 / 0.000200 (0.000219) \| 0.000060 / 0.000054 (0.000006) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031022 / 0.037411 (-0.006389) \| 0.081858 / 0.014526 (0.067332) \| 0.099477 / 0.176557 (-0.077080) \| 0.162981 / 0.737135 (-0.574154) \| 0.101987 / 0.296338 (-0.194351) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.386297 / 0.215209 (0.171088) \| 3.845321 / 2.077655 (1.767666) \| 1.834446 / 1.504120 (0.330326) \| 1.699730 / 1.541195 (0.158536) \| 1.764342 / 1.468490 (0.295852) \| 0.486423 / 4.584777 (-4.098354) \| 3.527595 / 3.745712 (-0.218117) \| 4.137034 / 5.269862 (-1.132827) \| 2.590457 / 4.565676 (-1.975219) \| 0.057598 / 0.424275 (-0.366677) \| 0.007318 / 0.007607 (-0.000289) \| 0.460775 / 0.226044 (0.234730) \| 4.627576 / 2.268929 (2.358647) \| 2.402566 / 55.444624 (-53.042059) \| 2.011392 / 6.876477 (-4.865085) \| 2.223915 / 2.142072 (0.081842) \| 0.623217 / 4.805227 (-4.182011) \| 0.148875 / 6.500664 (-6.351789) \| 0.059799 / 0.075469 (-0.015671) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.290768 / 1.841788 (-0.551020) \| 20.455083 / 8.074308 (12.380775) \| 13.469846 / 10.191392 (3.278454) \| 0.170329 / 0.680424 (-0.510095) \| 0.018409 / 0.534201 (-0.515792) \| 0.394356 / 0.579283 (-0.184927) \| 0.422685 / 0.434364 (-0.011679) \| 0.476241 / 0.540337 (-0.064096) \| 0.662682 / 1.386936 (-0.724254) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006724 / 0.011353 (-0.004629) \| 0.004508 / 0.011008 (-0.006500) \| 0.065304 / 0.038508 (0.026796) \| 0.080243 / 0.023109 (0.057133) \| 0.384545 / 0.275898 (0.108647) \| 0.415234 / 0.323480 (0.091754) \| 0.006361 / 0.007986 (-0.001624) \| 0.004193 / 0.004328 (-0.000135) \| 0.065940 / 0.004250 (0.061689) \| 0.063633 / 0.037052 (0.026581) \| 0.392799 / 0.258489 (0.134310) \| 0.443618 / 0.293841 (0.149777) \| 0.031134 / 0.128546 (-0.097412) \| 0.009058 / 0.075646 (-0.066588) \| 0.071051 / 0.419271 (-0.348221) \| 0.049096 / 0.043533 (0.005563) \| 0.379526 / 0.255139 (0.124387) \| 0.403370 / 0.283200 (0.120171) \| 0.026378 / 0.141683 (-0.115305) \| 1.457879 / 1.452155 (0.005724) \| 1.562890 / 1.492716 (0.070174) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.304416 / 0.018006 (0.286410) \| 0.626046 / 0.000490 (0.625557) \| 0.000469 / 0.000200 (0.000269) \| 0.000057 / 0.000054 (0.000002) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032979 / 0.037411 (-0.004433) \| 0.086769 / 0.014526 (0.072243) \| 0.108188 / 0.176557 (-0.068369) \| 0.163077 / 0.737135 (-0.574058) \| 0.106276 / 0.296338 (-0.190062) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.406922 / 0.215209 (0.191713) \| 4.052828 / 2.077655 (1.975174) \| 2.084802 / 1.504120 (0.580682) \| 1.927263 / 1.541195 (0.386069) \| 1.956078 / 1.468490 (0.487587) \| 0.480110 / 4.584777 (-4.104667) \| 3.553022 / 3.745712 (-0.192691) \| 3.554450 / 5.269862 (-1.715411) \| 2.082681 / 4.565676 (-2.482995) \| 0.056711 / 0.424275 (-0.367564) \| 0.007374 / 0.007607 (-0.000234) \| 0.480555 / 0.226044 (0.254510) \| 4.795851 / 2.268929 (2.526923) \| 2.606675 / 55.444624 (-52.837949) \| 2.249964 / 6.876477 (-4.626512) \| 2.274234 / 2.142072 (0.132162) \| 0.571767 / 4.805227 (-4.233461) \| 0.133312 / 6.500664 (-6.367352) \| 0.061703 / 0.075469 (-0.013766) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.354308 / 1.841788 (-0.487479) \| 20.959352 / 8.074308 (12.885044) \| 14.158420 / 10.191392 (3.967028) \| 0.197959 / 0.680424 (-0.482465) \| 0.018412 / 0.534201 (-0.515789) \| 0.394307 / 0.579283 (-0.184976) \| 0.402455 / 0.434364 (-0.031909) \| 0.463314 / 0.540337 (-0.077024) \| 0.621050 / 1.386936 (-0.765886) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d7298d4d1b169442a8d0bc8c1667298bb89ca501 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007179 / 0.011353 (-0.004174) \| 0.004318 / 0.011008 (-0.006690) \| 0.085209 / 0.038508 (0.046701) \| 0.089989 / 0.023109 (0.066880) \| 0.328188 / 0.275898 (0.052290) \| 0.346027 / 0.323480 (0.022547) \| 0.005711 / 0.007986 (-0.002275) \| 0.003703 / 0.004328 (-0.000625) \| 0.065419 / 0.004250 (0.061169) \| 0.065354 / 0.037052 (0.028301) \| 0.314531 / 0.258489 (0.056042) \| 0.354357 / 0.293841 (0.060516) \| 0.030918 / 0.128546 (-0.097628) \| 0.008632 / 0.075646 (-0.067015) \| 0.286817 / 0.419271 (-0.132455) \| 0.065267 / 0.043533 (0.021735) \| 0.310918 / 0.255139 (0.055779) \| 0.330497 / 0.283200 (0.047298) \| 0.035695 / 0.141683 (-0.105988) \| 1.471101 / 1.452155 (0.018947) \| 1.538658 / 1.492716 (0.045942) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.254314 / 0.018006 (0.236308) \| 0.591413 / 0.000490 (0.590923) \| 0.006082 / 0.000200 (0.005882) \| 0.000091 / 0.000054 (0.000037) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031843 / 0.037411 (-0.005568) \| 0.089968 / 0.014526 (0.075442) \| 0.101838 / 0.176557 (-0.074718) \| 0.164401 / 0.737135 (-0.572734) \| 0.103785 / 0.296338 (-0.192554) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.380486 / 0.215209 (0.165277) \| 3.798868 / 2.077655 (1.721213) \| 1.824645 / 1.504120 (0.320525) \| 1.660804 / 1.541195 (0.119610) \| 1.784793 / 1.468490 (0.316303) \| 0.487222 / 4.584777 (-4.097555) \| 3.560580 / 3.745712 (-0.185132) \| 5.392662 / 5.269862 (0.122800) \| 3.295327 / 4.565676 (-1.270350) \| 0.057699 / 0.424275 (-0.366576) \| 0.007559 / 0.007607 (-0.000048) \| 0.459655 / 0.226044 (0.233611) \| 4.587583 / 2.268929 (2.318654) \| 2.304845 / 55.444624 (-53.139779) \| 1.966433 / 6.876477 (-4.910044) \| 2.254591 / 2.142072 (0.112519) \| 0.582978 / 4.805227 (-4.222250) \| 0.133455 / 6.500664 (-6.367210) \| 0.061924 / 0.075469 (-0.013546) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.275685 / 1.841788 (-0.566103) \| 20.814545 / 8.074308 (12.740237) \| 13.753567 / 10.191392 (3.562175) \| 0.164076 / 0.680424 (-0.516348) \| 0.018768 / 0.534201 (-0.515433) \| 0.390991 / 0.579283 (-0.188293) \| 0.404417 / 0.434364 (-0.029947) \| 0.457522 / 0.540337 (-0.082815) \| 0.624654 / 1.386936 (-0.762282) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007435 / 0.011353 (-0.003918) \| 0.004255 / 0.011008 (-0.006754) \| 0.066134 / 0.038508 (0.027626) \| 0.086035 / 0.023109 (0.062925) \| 0.364688 / 0.275898 (0.088790) \| 0.403895 / 0.323480 (0.080415) \| 0.005868 / 0.007986 (-0.002117) \| 0.003634 / 0.004328 (-0.000694) \| 0.065803 / 0.004250 (0.061553) \| 0.065113 / 0.037052 (0.028061) \| 0.370057 / 0.258489 (0.111568) \| 0.412634 / 0.293841 (0.118793) \| 0.031660 / 0.128546 (-0.096886) \| 0.008699 / 0.075646 (-0.066947) \| 0.070618 / 0.419271 (-0.348654) \| 0.050814 / 0.043533 (0.007281) \| 0.362320 / 0.255139 (0.107181) \| 0.383863 / 0.283200 (0.100663) \| 0.027980 / 0.141683 (-0.113703) \| 1.486389 / 1.452155 (0.034234) \| 1.595534 / 1.492716 (0.102817) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.300991 / 0.018006 (0.282985) \| 0.565265 / 0.000490 (0.564775) \| 0.000400 / 0.000200 (0.000200) \| 0.000053 / 0.000054 (-0.000001) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034942 / 0.037411 (-0.002470) \| 0.092498 / 0.014526 (0.077972) \| 0.106737 / 0.176557 (-0.069819) \| 0.165400 / 0.737135 (-0.571735) \| 0.107809 / 0.296338 (-0.188529) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.412156 / 0.215209 (0.196947) \| 4.116747 / 2.077655 (2.039092) \| 2.199612 / 1.504120 (0.695492) \| 2.049310 / 1.541195 (0.508115) \| 2.174342 / 1.468490 (0.705852) \| 0.482794 / 4.584777 (-4.101983) \| 3.561344 / 3.745712 (-0.184368) \| 3.465935 / 5.269862 (-1.803926) \| 2.076595 / 4.565676 (-2.489081) \| 0.056242 / 0.424275 (-0.368033) \| 0.007371 / 0.007607 (-0.000236) \| 0.489135 / 0.226044 (0.263091) \| 4.895691 / 2.268929 (2.626763) \| 2.626936 / 55.444624 (-52.817688) \| 2.306658 / 6.876477 (-4.569818) \| 2.421705 / 2.142072 (0.279633) \| 0.599547 / 4.805227 (-4.205680) \| 0.133627 / 6.500664 (-6.367037) \| 0.063830 / 0.075469 (-0.011639) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.383039 / 1.841788 (-0.458748) \| 21.005346 / 8.074308 (12.931038) \| 14.911083 / 10.191392 (4.719691) \| 0.190995 / 0.680424 (-0.489429) \| 0.018510 / 0.534201 (-0.515691) \| 0.396346 / 0.579283 (-0.182937) \| 0.411496 / 0.434364 (-0.022868) \| 0.470972 / 0.540337 (-0.069366) \| 0.615670 / 1.386936 (-0.771266) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d6d2ba47759d8acbf3d750b1cc4d89b195b1f9c9 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007249 / 0.011353 (-0.004104) \| 0.004261 / 0.011008 (-0.006747) \| 0.100645 / 0.038508 (0.062137) \| 0.078522 / 0.023109 (0.055413) \| 0.423526 / 0.275898 (0.147628) \| 0.439541 / 0.323480 (0.116061) \| 0.005812 / 0.007986 (-0.002173) \| 0.003615 / 0.004328 (-0.000713) \| 0.075908 / 0.004250 (0.071658) \| 0.062490 / 0.037052 (0.025437) \| 0.414941 / 0.258489 (0.156452) \| 0.447267 / 0.293841 (0.153426) \| 0.035127 / 0.128546 (-0.093419) \| 0.009642 / 0.075646 (-0.066004) \| 0.354093 / 0.419271 (-0.065179) \| 0.060970 / 0.043533 (0.017437) \| 0.418579 / 0.255139 (0.163440) \| 0.427972 / 0.283200 (0.144772) \| 0.025838 / 0.141683 (-0.115845) \| 1.778349 / 1.452155 (0.326194) \| 1.845965 / 1.492716 (0.353249) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.227304 / 0.018006 (0.209298) \| 0.571833 / 0.000490 (0.571343) \| 0.001328 / 0.000200 (0.001128) \| 0.000071 / 0.000054 (0.000017) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031343 / 0.037411 (-0.006068) \| 0.096400 / 0.014526 (0.081875) \| 0.106881 / 0.176557 (-0.069676) \| 0.175449 / 0.737135 (-0.561686) \| 0.108751 / 0.296338 (-0.187588) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.480204 / 0.215209 (0.264995) \| 4.622063 / 2.077655 (2.544408) \| 2.211505 / 1.504120 (0.707385) \| 2.065154 / 1.541195 (0.523959) \| 2.159446 / 1.468490 (0.690956) \| 0.584571 / 4.584777 (-4.000206) \| 4.392449 / 3.745712 (0.646737) \| 4.790166 / 5.269862 (-0.479695) \| 2.840615 / 4.565676 (-1.725062) \| 0.070845 / 0.424275 (-0.353430) \| 0.009112 / 0.007607 (0.001505) \| 0.580251 / 0.226044 (0.354207) \| 5.660311 / 2.268929 (3.391382) \| 2.836136 / 55.444624 (-52.608489) \| 2.412859 / 6.876477 (-4.463618) \| 2.556710 / 2.142072 (0.414637) \| 0.691946 / 4.805227 (-4.113282) \| 0.160123 / 6.500664 (-6.340541) \| 0.072593 / 0.075469 (-0.002876) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.547339 / 1.841788 (-0.294448) \| 21.724793 / 8.074308 (13.650485) \| 16.315304 / 10.191392 (6.123912) \| 0.188733 / 0.680424 (-0.491690) \| 0.022109 / 0.534201 (-0.512092) \| 0.481623 / 0.579283 (-0.097660) \| 0.464316 / 0.434364 (0.029952) \| 0.557953 / 0.540337 (0.017615) \| 0.756023 / 1.386936 (-0.630913) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008637 / 0.011353 (-0.002716) \| 0.005286 / 0.011008 (-0.005723) \| 0.091387 / 0.038508 (0.052879) \| 0.114092 / 0.023109 (0.090983) \| 0.457547 / 0.275898 (0.181649) \| 0.506878 / 0.323480 (0.183398) \| 0.006849 / 0.007986 (-0.001137) \| 0.004255 / 0.004328 (-0.000073) \| 0.079556 / 0.004250 (0.075306) \| 0.077729 / 0.037052 (0.040677) \| 0.454094 / 0.258489 (0.195605) \| 0.515812 / 0.293841 (0.221971) \| 0.038271 / 0.128546 (-0.090275) \| 0.010110 / 0.075646 (-0.065536) \| 0.094254 / 0.419271 (-0.325017) \| 0.065392 / 0.043533 (0.021860) \| 0.459749 / 0.255139 (0.204610) \| 0.489829 / 0.283200 (0.206629) \| 0.040393 / 0.141683 (-0.101290) \| 1.810414 / 1.452155 (0.358259) \| 1.913212 / 1.492716 (0.420496) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.236898 / 0.018006 (0.218891) \| 0.513118 / 0.000490 (0.512628) \| 0.004432 / 0.000200 (0.004232) \| 0.000115 / 0.000054 (0.000060) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.035074 / 0.037411 (-0.002337) \| 0.102384 / 0.014526 (0.087858) \| 0.117326 / 0.176557 (-0.059231) \| 0.182596 / 0.737135 (-0.554539) \| 0.116384 / 0.296338 (-0.179955) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.514544 / 0.215209 (0.299335) \| 5.152930 / 2.077655 (3.075275) \| 2.624477 / 1.504120 (1.120357) \| 2.363209 / 1.541195 (0.822014) \| 2.436060 / 1.468490 (0.967570) \| 0.592523 / 4.584777 (-3.992254) \| 4.209668 / 3.745712 (0.463956) \| 6.284372 / 5.269862 (1.014511) \| 3.667303 / 4.565676 (-0.898374) \| 0.067017 / 0.424275 (-0.357259) \| 0.008607 / 0.007607 (0.001000) \| 0.600840 / 0.226044 (0.374796) \| 5.992630 / 2.268929 (3.723701) \| 3.114532 / 55.444624 (-52.330093) \| 2.693242 / 6.876477 (-4.183235) \| 2.767187 / 2.142072 (0.625115) \| 0.687591 / 4.805227 (-4.117636) \| 0.158477 / 6.500664 (-6.342187) \| 0.075504 / 0.075469 (0.000034) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.605039 / 1.841788 (-0.236749) \| 21.524730 / 8.074308 (13.450422) \| 17.014643 / 10.191392 (6.823251) \| 0.201580 / 0.680424 (-0.478843) \| 0.023028 / 0.534201 (-0.511173) \| 0.483801 / 0.579283 (-0.095482) \| 0.490221 / 0.434364 (0.055857) \| 0.589292 / 0.540337 (0.048955) \| 0.758532 / 1.386936 (-0.628404) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8c9c24d1d90f0c2db043ae2bc39f7c292454a58c \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008080 / 0.011353 (-0.003273) \| 0.004859 / 0.011008 (-0.006149) \| 0.101895 / 0.038508 (0.063387) \| 0.091168 / 0.023109 (0.068059) \| 0.378914 / 0.275898 (0.103016) \| 0.417172 / 0.323480 (0.093692) \| 0.006314 / 0.007986 (-0.001672) \| 0.004069 / 0.004328 (-0.000259) \| 0.076566 / 0.004250 (0.072315) \| 0.070986 / 0.037052 (0.033934) \| 0.380935 / 0.258489 (0.122446) \| 0.417131 / 0.293841 (0.123290) \| 0.036343 / 0.128546 (-0.092203) \| 0.009996 / 0.075646 (-0.065650) \| 0.346386 / 0.419271 (-0.072886) \| 0.063162 / 0.043533 (0.019630) \| 0.372620 / 0.255139 (0.117481) \| 0.404902 / 0.283200 (0.121702) \| 0.028217 / 0.141683 (-0.113466) \| 1.793875 / 1.452155 (0.341721) \| 1.836284 / 1.492716 (0.343568) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.223830 / 0.018006 (0.205823) \| 0.503643 / 0.000490 (0.503153) \| 0.004957 / 0.000200 (0.004757) \| 0.000107 / 0.000054 (0.000053) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.035455 / 0.037411 (-0.001957) \| 0.108015 / 0.014526 (0.093489) \| 0.116887 / 0.176557 (-0.059669) \| 0.188174 / 0.737135 (-0.548961) \| 0.117217 / 0.296338 (-0.179121) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.471681 / 0.215209 (0.256472) \| 4.694509 / 2.077655 (2.616855) \| 2.369539 / 1.504120 (0.865419) \| 2.176839 / 1.541195 (0.635644) \| 2.300536 / 1.468490 (0.832045) \| 0.575689 / 4.584777 (-4.009088) \| 4.232765 / 3.745712 (0.487053) \| 4.766775 / 5.269862 (-0.503087) \| 2.864667 / 4.565676 (-1.701010) \| 0.069390 / 0.424275 (-0.354885) \| 0.008822 / 0.007607 (0.001214) \| 0.559620 / 0.226044 (0.333576) \| 5.580401 / 2.268929 (3.311472) \| 2.920293 / 55.444624 (-52.524331) \| 2.552166 / 6.876477 (-4.324311) \| 2.795890 / 2.142072 (0.653818) \| 0.687863 / 4.805227 (-4.117364) \| 0.159129 / 6.500664 (-6.341535) \| 0.073475 / 0.075469 (-0.001994) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.505892 / 1.841788 (-0.335896) \| 24.127650 / 8.074308 (16.053342) \| 16.758238 / 10.191392 (6.566846) \| 0.200555 / 0.680424 (-0.479869) \| 0.021596 / 0.534201 (-0.512605) \| 0.480668 / 0.579283 (-0.098615) \| 0.483528 / 0.434364 (0.049164) \| 0.571241 / 0.540337 (0.030903) \| 0.790547 / 1.386936 (-0.596390) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007997 / 0.011353 (-0.003356) \| 0.004842 / 0.011008 (-0.006166) \| 0.077190 / 0.038508 (0.038681) \| 0.092765 / 0.023109 (0.069656) \| 0.457475 / 0.275898 (0.181577) \| 0.523914 / 0.323480 (0.200434) \| 0.006349 / 0.007986 (-0.001637) \| 0.003902 / 0.004328 (-0.000427) \| 0.075860 / 0.004250 (0.071609) \| 0.069708 / 0.037052 (0.032656) \| 0.459612 / 0.258489 (0.201123) \| 0.555028 / 0.293841 (0.261187) \| 0.036854 / 0.128546 (-0.091692) \| 0.010078 / 0.075646 (-0.065568) \| 0.083871 / 0.419271 (-0.335400) \| 0.061221 / 0.043533 (0.017689) \| 0.435737 / 0.255139 (0.180598) \| 0.509700 / 0.283200 (0.226500) \| 0.038091 / 0.141683 (-0.103592) \| 1.777161 / 1.452155 (0.325006) \| 1.859603 / 1.492716 (0.366886) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.250020 / 0.018006 (0.232014) \| 0.486198 / 0.000490 (0.485708) \| 0.007080 / 0.000200 (0.006880) \| 0.000114 / 0.000054 (0.000060) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.038163 / 0.037411 (0.000751) \| 0.110812 / 0.014526 (0.096286) \| 0.122489 / 0.176557 (-0.054068) \| 0.188215 / 0.737135 (-0.548920) \| 0.122375 / 0.296338 (-0.173963) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.484534 / 0.215209 (0.269325) \| 4.828654 / 2.077655 (2.751000) \| 2.545102 / 1.504120 (1.040982) \| 2.368867 / 1.541195 (0.827672) \| 2.458042 / 1.468490 (0.989552) \| 0.576372 / 4.584777 (-4.008404) \| 4.814033 / 3.745712 (1.068321) \| 6.175972 / 5.269862 (0.906110) \| 4.033422 / 4.565676 (-0.532254) \| 0.068544 / 0.424275 (-0.355731) \| 0.008906 / 0.007607 (0.001299) \| 0.581767 / 0.226044 (0.355723) \| 5.808623 / 2.268929 (3.539695) \| 3.120312 / 55.444624 (-52.324313) \| 2.774834 / 6.876477 (-4.101642) \| 2.770413 / 2.142072 (0.628340) \| 0.692715 / 4.805227 (-4.112512) \| 0.158883 / 6.500664 (-6.341782) \| 0.075894 / 0.075469 (0.000425) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.631250 / 1.841788 (-0.210538) \| 24.693250 / 8.074308 (16.618942) \| 17.434790 / 10.191392 (7.243398) \| 0.196456 / 0.680424 (-0.483968) \| 0.022505 / 0.534201 (-0.511696) \| 0.474788 / 0.579283 (-0.104495) \| 0.500947 / 0.434364 (0.066583) \| 0.553596 / 0.540337 (0.013259) \| 0.737767 / 1.386936 (-0.649169) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f87d6e6394bf4b390ccc82235eb7667f874e5d43 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006629 / 0.011353 (-0.004724) \| 0.004115 / 0.011008 (-0.006894) \| 0.083934 / 0.038508 (0.045426) \| 0.074952 / 0.023109 (0.051843) \| 0.313069 / 0.275898 (0.037171) \| 0.345878 / 0.323480 (0.022398) \| 0.006034 / 0.007986 (-0.001952) \| 0.003413 / 0.004328 (-0.000916) \| 0.065130 / 0.004250 (0.060880) \| 0.057363 / 0.037052 (0.020310) \| 0.314483 / 0.258489 (0.055994) \| 0.352626 / 0.293841 (0.058785) \| 0.031325 / 0.128546 (-0.097221) \| 0.008577 / 0.075646 (-0.067069) \| 0.288137 / 0.419271 (-0.131135) \| 0.053651 / 0.043533 (0.010118) \| 0.313006 / 0.255139 (0.057867) \| 0.338668 / 0.283200 (0.055468) \| 0.023709 / 0.141683 (-0.117974) \| 1.481209 / 1.452155 (0.029054) \| 1.559801 / 1.492716 (0.067085) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.211543 / 0.018006 (0.193537) \| 0.452185 / 0.000490 (0.451696) \| 0.003177 / 0.000200 (0.002977) \| 0.000078 / 0.000054 (0.000024) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028821 / 0.037411 (-0.008591) \| 0.083290 / 0.014526 (0.068765) \| 0.097478 / 0.176557 (-0.079079) \| 0.153506 / 0.737135 (-0.583629) \| 0.097054 / 0.296338 (-0.199284) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.385847 / 0.215209 (0.170638) \| 3.835629 / 2.077655 (1.757974) \| 1.880938 / 1.504120 (0.376819) \| 1.711848 / 1.541195 (0.170653) \| 1.785099 / 1.468490 (0.316609) \| 0.486256 / 4.584777 (-4.098521) \| 3.629026 / 3.745712 (-0.116686) \| 3.321578 / 5.269862 (-1.948283) \| 2.024314 / 4.565676 (-2.541363) \| 0.058097 / 0.424275 (-0.366179) \| 0.007724 / 0.007607 (0.000117) \| 0.458293 / 0.226044 (0.232249) \| 4.581314 / 2.268929 (2.312386) \| 2.314379 / 55.444624 (-53.130246) \| 1.966089 / 6.876477 (-4.910387) \| 2.203824 / 2.142072 (0.061752) \| 0.611581 / 4.805227 (-4.193647) \| 0.149166 / 6.500664 (-6.351498) \| 0.059825 / 0.075469 (-0.015644) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.235546 / 1.841788 (-0.606242) \| 19.747439 / 8.074308 (11.673131) \| 14.628383 / 10.191392 (4.436991) \| 0.193074 / 0.680424 (-0.487350) \| 0.020327 / 0.534201 (-0.513874) \| 0.397051 / 0.579283 (-0.182232) \| 0.418491 / 0.434364 (-0.015873) \| 0.462055 / 0.540337 (-0.078282) \| 0.637524 / 1.386936 (-0.749412) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007069 / 0.011353 (-0.004284) \| 0.004106 / 0.011008 (-0.006902) \| 0.065818 / 0.038508 (0.027310) \| 0.077101 / 0.023109 (0.053991) \| 0.363323 / 0.275898 (0.087425) \| 0.399463 / 0.323480 (0.075983) \| 0.005540 / 0.007986 (-0.002446) \| 0.003480 / 0.004328 (-0.000849) \| 0.065176 / 0.004250 (0.060926) \| 0.060867 / 0.037052 (0.023815) \| 0.365763 / 0.258489 (0.107273) \| 0.407789 / 0.293841 (0.113949) \| 0.032018 / 0.128546 (-0.096528) \| 0.008550 / 0.075646 (-0.067096) \| 0.071750 / 0.419271 (-0.347521) \| 0.050625 / 0.043533 (0.007092) \| 0.361434 / 0.255139 (0.106295) \| 0.384799 / 0.283200 (0.101599) \| 0.026104 / 0.141683 (-0.115579) \| 1.496093 / 1.452155 (0.043938) \| 1.592909 / 1.492716 (0.100193) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.185794 / 0.018006 (0.167787) \| 0.453379 / 0.000490 (0.452890) \| 0.004365 / 0.000200 (0.004165) \| 0.000092 / 0.000054 (0.000038) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031666 / 0.037411 (-0.005746) \| 0.088323 / 0.014526 (0.073798) \| 0.104602 / 0.176557 (-0.071954) \| 0.159827 / 0.737135 (-0.577308) \| 0.103725 / 0.296338 (-0.192614) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.413509 / 0.215209 (0.198300) \| 4.126071 / 2.077655 (2.048416) \| 2.137088 / 1.504120 (0.632968) \| 1.981034 / 1.541195 (0.439839) \| 2.063660 / 1.468490 (0.595170) \| 0.478798 / 4.584777 (-4.105979) \| 3.642801 / 3.745712 (-0.102911) \| 3.428994 / 5.269862 (-1.840867) \| 2.031902 / 4.565676 (-2.533774) \| 0.056244 / 0.424275 (-0.368032) \| 0.007365 / 0.007607 (-0.000242) \| 0.484371 / 0.226044 (0.258327) \| 4.838537 / 2.268929 (2.569608) \| 2.559497 / 55.444624 (-52.885127) \| 2.251863 / 6.876477 (-4.624614) \| 2.339227 / 2.142072 (0.197155) \| 0.607228 / 4.805227 (-4.198000) \| 0.133877 / 6.500664 (-6.366787) \| 0.062049 / 0.075469 (-0.013420) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.350389 / 1.841788 (-0.491399) \| 20.060359 / 8.074308 (11.986051) \| 14.305675 / 10.191392 (4.114283) \| 0.165642 / 0.680424 (-0.514782) \| 0.018206 / 0.534201 (-0.515994) \| 0.396907 / 0.579283 (-0.182376) \| 0.431896 / 0.434364 (-0.002468) \| 0.475778 / 0.540337 (-0.064559) \| 0.644688 / 1.386936 (-0.742248) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8f6fa96ae5de873a49ef28739e8f64edf8b18cae \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009048 / 0.011353 (-0.002305) \| 0.005787 / 0.011008 (-0.005221) \| 0.111617 / 0.038508 (0.073109) \| 0.087603 / 0.023109 (0.064494) \| 0.446481 / 0.275898 (0.170583) \| 0.491726 / 0.323480 (0.168247) \| 0.007052 / 0.007986 (-0.000934) \| 0.004481 / 0.004328 (0.000152) \| 0.084331 / 0.004250 (0.080081) \| 0.072006 / 0.037052 (0.034953) \| 0.454238 / 0.258489 (0.195749) \| 0.496749 / 0.293841 (0.202908) \| 0.049027 / 0.128546 (-0.079520) \| 0.014005 / 0.075646 (-0.061641) \| 0.372550 / 0.419271 (-0.046722) \| 0.071414 / 0.043533 (0.027881) \| 0.459432 / 0.255139 (0.204293) \| 0.467332 / 0.283200 (0.184133) \| 0.037539 / 0.141683 (-0.104144) \| 1.869179 / 1.452155 (0.417024) \| 1.983641 / 1.492716 (0.490925) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.265426 / 0.018006 (0.247419) \| 0.672527 / 0.000490 (0.672037) \| 0.001152 / 0.000200 (0.000953) \| 0.000181 / 0.000054 (0.000127) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032967 / 0.037411 (-0.004445) \| 0.103023 / 0.014526 (0.088497) \| 0.115978 / 0.176557 (-0.060578) \| 0.191698 / 0.737135 (-0.545438) \| 0.117867 / 0.296338 (-0.178471) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.602208 / 0.215209 (0.386999) \| 6.147784 / 2.077655 (4.070129) \| 2.768933 / 1.504120 (1.264813) \| 2.415619 / 1.541195 (0.874424) \| 2.456159 / 1.468490 (0.987669) \| 0.836270 / 4.584777 (-3.748507) \| 5.447754 / 3.745712 (1.702042) \| 7.751825 / 5.269862 (2.481963) \| 4.591892 / 4.565676 (0.026215) \| 0.108269 / 0.424275 (-0.316006) \| 0.009626 / 0.007607 (0.002019) \| 0.719260 / 0.226044 (0.493216) \| 7.313442 / 2.268929 (5.044514) \| 3.490739 / 55.444624 (-51.953885) \| 2.743543 / 6.876477 (-4.132934) \| 3.035071 / 2.142072 (0.892999) \| 1.042791 / 4.805227 (-3.762436) \| 0.217080 / 6.500664 (-6.283584) \| 0.084286 / 0.075469 (0.008817) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.655427 / 1.841788 (-0.186361) \| 25.386536 / 8.074308 (17.312228) \| 21.740666 / 10.191392 (11.549274) \| 0.246388 / 0.680424 (-0.434036) \| 0.029723 / 0.534201 (-0.504478) \| 0.491537 / 0.579283 (-0.087746) \| 0.603495 / 0.434364 (0.169131) \| 0.573938 / 0.540337 (0.033600) \| 0.981875 / 1.386936 (-0.405061) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009664 / 0.011353 (-0.001689) \| 0.006446 / 0.011008 (-0.004562) \| 0.085113 / 0.038508 (0.046605) \| 0.094533 / 0.023109 (0.071424) \| 0.498388 / 0.275898 (0.222490) \| 0.540127 / 0.323480 (0.216647) \| 0.007316 / 0.007986 (-0.000670) \| 0.004252 / 0.004328 (-0.000077) \| 0.086292 / 0.004250 (0.082041) \| 0.067956 / 0.037052 (0.030903) \| 0.507664 / 0.258489 (0.249175) \| 0.554324 / 0.293841 (0.260483) \| 0.050107 / 0.128546 (-0.078439) \| 0.014277 / 0.075646 (-0.061370) \| 0.098838 / 0.419271 (-0.320433) \| 0.066053 / 0.043533 (0.022521) \| 0.491090 / 0.255139 (0.235951) \| 0.537432 / 0.283200 (0.254232) \| 0.035937 / 0.141683 (-0.105746) \| 1.820715 / 1.452155 (0.368561) \| 1.996268 / 1.492716 (0.503552) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.300859 / 0.018006 (0.282852) \| 0.610958 / 0.000490 (0.610468) \| 0.000474 / 0.000200 (0.000274) \| 0.000098 / 0.000054 (0.000044) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.036372 / 0.037411 (-0.001039) \| 0.109115 / 0.014526 (0.094589) \| 0.122802 / 0.176557 (-0.053755) \| 0.187092 / 0.737135 (-0.550044) \| 0.123432 / 0.296338 (-0.172906) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.646979 / 0.215209 (0.431770) \| 6.577713 / 2.077655 (4.500058) \| 3.004606 / 1.504120 (1.500486) \| 2.661183 / 1.541195 (1.119989) \| 2.726717 / 1.468490 (1.258227) \| 0.889497 / 4.584777 (-3.695280) \| 5.485055 / 3.745712 (1.739343) \| 4.852043 / 5.269862 (-0.417819) \| 3.177392 / 4.565676 (-1.388285) \| 0.099796 / 0.424275 (-0.324479) \| 0.009868 / 0.007607 (0.002261) \| 0.819919 / 0.226044 (0.593874) \| 7.911255 / 2.268929 (5.642326) \| 3.839877 / 55.444624 (-51.604747) \| 3.088663 / 6.876477 (-3.787813) \| 3.371184 / 2.142072 (1.229112) \| 1.072762 / 4.805227 (-3.732466) \| 0.224536 / 6.500664 (-6.276128) \| 0.083415 / 0.075469 (0.007946) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.754426 / 1.841788 (-0.087361) \| 25.546690 / 8.074308 (17.472382) \| 22.998252 / 10.191392 (12.806860) \| 0.258019 / 0.680424 (-0.422405) \| 0.030104 / 0.534201 (-0.504097) \| 0.518406 / 0.579283 (-0.060877) \| 0.605753 / 0.434364 (0.171389) \| 0.599630 / 0.540337 (0.059292) \| 0.819042 / 1.386936 (-0.567894) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#350f4fd6caabbdfacb5fbf9193ab255c3d0daa4c \"CML watermark\")\n" ]	2023-07-17T15:41:16	2023-07-19T16:59:55	2023-07-19T16:48:06	MEMBER	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6044", "html_url": "https://github.com/huggingface/datasets/pull/6044", "diff_url": "https://github.com/huggingface/datasets/pull/6044.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6044.patch", "merged_at": "2023-07-19T16:48:06" }	To make it easier to understand for users. They can use "path" to specify a single path, <s>or "paths" to use a list of paths.</s> Glob patterns are still supported though	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6044/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6044/timeline	null	null	true
https://api.github.com/repos/huggingface/datasets/issues/6043	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6043/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6043/comments	https://api.github.com/repos/huggingface/datasets/issues/6043/events	https://github.com/huggingface/datasets/issues/6043	1,807,771,750	I_kwDODunzps5rwGhm	6,043	Compression kwargs have no effect when saving datasets as csv	{ "login": "exs-avianello", "id": 128361578, "node_id": "U_kgDOB6akag", "avatar_url": "https://avatars.githubusercontent.com/u/128361578?v=4", "gravatar_id": "", "url": "https://api.github.com/users/exs-avianello", "html_url": "https://github.com/exs-avianello", "followers_url": "https://api.github.com/users/exs-avianello/followers", "following_url": "https://api.github.com/users/exs-avianello/following{/other_user}", "gists_url": "https://api.github.com/users/exs-avianello/gists{/gist_id}", "starred_url": "https://api.github.com/users/exs-avianello/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/exs-avianello/subscriptions", "organizations_url": "https://api.github.com/users/exs-avianello/orgs", "repos_url": "https://api.github.com/users/exs-avianello/repos", "events_url": "https://api.github.com/users/exs-avianello/events{/privacy}", "received_events_url": "https://api.github.com/users/exs-avianello/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	null	[ "Hello @exs-avianello, I have reproduced the bug successfully and have understood the problem. But I am confused regarding this part of the statement, \"`pandas.DataFrame.to_csv` is always called with a buf-like `path_or_buf`\".\r\n\r\nCan you please elaborate on it?\r\n\r\nThanks!", "Hi @aryanxk02 ! Sure, what I actually meant is that when passing a path-like `path_or_buf` here\r\n\r\nhttps://github.com/huggingface/datasets/blob/14f6edd9222e577dccb962ed5338b79b73502fa5/src/datasets/arrow_dataset.py#L4708-L4714 \r\n\r\nit gets converted to a file object behind the scenes here\r\n\r\nhttps://github.com/huggingface/datasets/blob/14f6edd9222e577dccb962ed5338b79b73502fa5/src/datasets/io/csv.py#L92-L94\r\n\r\nand the eventual pandas `.to_csv()` calls that write to it always get `path_or_buf=None`, making pandas ignore the `compression` kwarg in the `to_csv_kwargs`\r\n\r\nhttps://github.com/huggingface/datasets/blob/14f6edd9222e577dccb962ed5338b79b73502fa5/src/datasets/io/csv.py#L107-L109", "@exs-avianello When `path_or_buf` is set to None, the `to_csv()` method will return the CSV data as a string instead of saving it to a file. Hence the compression doesn't take place. I think setting `path_or_buf=self.path_or_buf` should work. What you say?" ]	2023-07-17T13:19:21	2023-07-22T17:34:18	null	NONE	null	null	null	### Describe the bug Attempting to save a dataset as a compressed csv file, the compression kwargs provided to `.to_csv()` that get piped to panda's `pandas.DataFrame.to_csv` do not have any effect - resulting in the dataset not getting compressed. A warning is raised if explicitly providing a `compression` kwarg, but no warnings are raised if relying on the defaults. This can lead to datasets secretly not getting compressed for users expecting the behaviour to match panda's `.to_csv()`, where the compression format is automatically inferred from the destination path suffix. ### Steps to reproduce the bug ```python # dataset is not compressed (but at least a warning is emitted) import datasets dataset = datasets.load_dataset("rotten_tomatoes", split="train") dataset.to_csv("uncompressed.csv") print(os.path.getsize("uncompressed.csv")) # 1008607 dataset.to_csv("compressed.csv.gz", compression={'method': 'gzip', 'compresslevel': 1, 'mtime': 1}) print(os.path.getsize("compressed.csv.gz")) # 1008607 ``` ```shell >>> RuntimeWarning: compression has no effect when passing a non-binary object as input. csv_str = batch.to_pandas().to_csv( ``` ```python # dataset is not compressed and no warnings are emitted dataset.to_csv("compressed.csv.gz") print(os.path.getsize("compressed.csv.gz")) # 1008607 # compare with dataset.to_pandas().to_csv("pandas.csv.gz") print(os.path.getsize("pandas.csv.gz")) # 418561 ``` --- I think that this is because behind the scenes `pandas.DataFrame.to_csv` is always called with a buf-like `path_or_buf`, but users that are providing a path-like to `datasets.Dataset.to_csv` are likely not to expect / know that - leading to a mismatch in their understanding of the expected behaviour of the `compression` kwarg. ### Expected behavior The dataset to be saved as a compressed csv file when providing a `compression` kwarg, or when relying on the default `compression='infer'` ### Environment info `datasets == 2.13.1`	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6043/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6043/timeline	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6042	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6042/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6042/comments	https://api.github.com/repos/huggingface/datasets/issues/6042/events	https://github.com/huggingface/datasets/pull/6042	1,807,516,762	PR_kwDODunzps5VqEyb	6,042	Fix unused DatasetInfosDict code in push_to_hub	{ "login": "lhoestq", "id": 42851186, "node_id": "MDQ6VXNlcjQyODUxMTg2", "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "gravatar_id": "", "url": "https://api.github.com/users/lhoestq", "html_url": "https://github.com/lhoestq", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "repos_url": "https://api.github.com/users/lhoestq/repos", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008634 / 0.011353 (-0.002719) \| 0.005147 / 0.011008 (-0.005861) \| 0.102865 / 0.038508 (0.064357) \| 0.080245 / 0.023109 (0.057136) \| 0.401288 / 0.275898 (0.125390) \| 0.419708 / 0.323480 (0.096228) \| 0.006342 / 0.007986 (-0.001644) \| 0.003998 / 0.004328 (-0.000330) \| 0.078880 / 0.004250 (0.074630) \| 0.068199 / 0.037052 (0.031147) \| 0.389573 / 0.258489 (0.131084) \| 0.417292 / 0.293841 (0.123451) \| 0.048856 / 0.128546 (-0.079691) \| 0.014165 / 0.075646 (-0.061481) \| 0.348063 / 0.419271 (-0.071209) \| 0.067547 / 0.043533 (0.024014) \| 0.402251 / 0.255139 (0.147112) \| 0.419478 / 0.283200 (0.136278) \| 0.034846 / 0.141683 (-0.106837) \| 1.773493 / 1.452155 (0.321338) \| 1.930546 / 1.492716 (0.437830) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.211835 / 0.018006 (0.193829) \| 0.545311 / 0.000490 (0.544821) \| 0.006766 / 0.000200 (0.006566) \| 0.000104 / 0.000054 (0.000050) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.035406 / 0.037411 (-0.002006) \| 0.100769 / 0.014526 (0.086243) \| 0.108667 / 0.176557 (-0.067890) \| 0.193099 / 0.737135 (-0.544036) \| 0.113539 / 0.296338 (-0.182799) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.586935 / 0.215209 (0.371726) \| 5.895245 / 2.077655 (3.817591) \| 2.528375 / 1.504120 (1.024255) \| 2.228617 / 1.541195 (0.687423) \| 2.295799 / 1.468490 (0.827309) \| 0.859272 / 4.584777 (-3.725505) \| 5.033434 / 3.745712 (1.287722) \| 7.546587 / 5.269862 (2.276726) \| 4.457137 / 4.565676 (-0.108539) \| 0.099626 / 0.424275 (-0.324649) \| 0.009296 / 0.007607 (0.001689) \| 0.713498 / 0.226044 (0.487454) \| 7.409385 / 2.268929 (5.140456) \| 3.361418 / 55.444624 (-52.083206) \| 2.681111 / 6.876477 (-4.195366) \| 2.849598 / 2.142072 (0.707526) \| 1.114863 / 4.805227 (-3.690364) \| 0.215494 / 6.500664 (-6.285170) \| 0.075807 / 0.075469 (0.000338) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.606458 / 1.841788 (-0.235330) \| 23.751096 / 8.074308 (15.676788) \| 21.279110 / 10.191392 (11.087718) \| 0.220785 / 0.680424 (-0.459639) \| 0.032688 / 0.534201 (-0.501513) \| 0.530948 / 0.579283 (-0.048335) \| 0.630056 / 0.434364 (0.195693) \| 0.572743 / 0.540337 (0.032405) \| 0.771853 / 1.386936 (-0.615083) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008693 / 0.011353 (-0.002660) \| 0.004750 / 0.011008 (-0.006259) \| 0.079764 / 0.038508 (0.041256) \| 0.082096 / 0.023109 (0.058987) \| 0.467198 / 0.275898 (0.191300) \| 0.532361 / 0.323480 (0.208881) \| 0.005836 / 0.007986 (-0.002149) \| 0.004333 / 0.004328 (0.000005) \| 0.080444 / 0.004250 (0.076194) \| 0.065883 / 0.037052 (0.028831) \| 0.464871 / 0.258489 (0.206382) \| 0.575026 / 0.293841 (0.281185) \| 0.057807 / 0.128546 (-0.070739) \| 0.017462 / 0.075646 (-0.058185) \| 0.093667 / 0.419271 (-0.325605) \| 0.071466 / 0.043533 (0.027933) \| 0.495846 / 0.255139 (0.240707) \| 0.526100 / 0.283200 (0.242900) \| 0.034852 / 0.141683 (-0.106831) \| 1.884152 / 1.452155 (0.431998) \| 1.922681 / 1.492716 (0.429965) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.250969 / 0.018006 (0.232963) \| 0.504979 / 0.000490 (0.504489) \| 0.000466 / 0.000200 (0.000266) \| 0.000083 / 0.000054 (0.000028) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032411 / 0.037411 (-0.005000) \| 0.093184 / 0.014526 (0.078658) \| 0.110798 / 0.176557 (-0.065759) \| 0.165741 / 0.737135 (-0.571394) \| 0.111022 / 0.296338 (-0.185317) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.661284 / 0.215209 (0.446075) \| 6.622388 / 2.077655 (4.544733) \| 3.095705 / 1.504120 (1.591585) \| 2.745698 / 1.541195 (1.204503) \| 2.694103 / 1.468490 (1.225612) \| 0.862154 / 4.584777 (-3.722623) \| 5.109985 / 3.745712 (1.364273) \| 5.040362 / 5.269862 (-0.229499) \| 3.072837 / 4.565676 (-1.492840) \| 0.110421 / 0.424275 (-0.313854) \| 0.008476 / 0.007607 (0.000869) \| 0.910020 / 0.226044 (0.683975) \| 8.123626 / 2.268929 (5.854698) \| 3.813811 / 55.444624 (-51.630813) \| 3.017244 / 6.876477 (-3.859232) \| 3.061222 / 2.142072 (0.919150) \| 1.073548 / 4.805227 (-3.731680) \| 0.216327 / 6.500664 (-6.284338) \| 0.072977 / 0.075469 (-0.002492) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.722482 / 1.841788 (-0.119305) \| 23.706716 / 8.074308 (15.632407) \| 23.192134 / 10.191392 (13.000742) \| 0.276733 / 0.680424 (-0.403691) \| 0.033538 / 0.534201 (-0.500663) \| 0.602083 / 0.579283 (0.022799) \| 0.578718 / 0.434364 (0.144354) \| 0.558311 / 0.540337 (0.017974) \| 0.740341 / 1.386936 (-0.646595) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7ac575b8ed57dac60d7ba33a616894f38601f84a \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006862 / 0.011353 (-0.004491) \| 0.004223 / 0.011008 (-0.006786) \| 0.085931 / 0.038508 (0.047423) \| 0.081437 / 0.023109 (0.058328) \| 0.349542 / 0.275898 (0.073644) \| 0.379881 / 0.323480 (0.056401) \| 0.005651 / 0.007986 (-0.002334) \| 0.003662 / 0.004328 (-0.000666) \| 0.065251 / 0.004250 (0.061001) \| 0.061599 / 0.037052 (0.024547) \| 0.359681 / 0.258489 (0.101192) \| 0.392502 / 0.293841 (0.098661) \| 0.031300 / 0.128546 (-0.097246) \| 0.008591 / 0.075646 (-0.067055) \| 0.288577 / 0.419271 (-0.130694) \| 0.062920 / 0.043533 (0.019388) \| 0.348989 / 0.255139 (0.093850) \| 0.362769 / 0.283200 (0.079569) \| 0.030087 / 0.141683 (-0.111596) \| 1.480748 / 1.452155 (0.028594) \| 1.580413 / 1.492716 (0.087697) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.205804 / 0.018006 (0.187798) \| 0.455386 / 0.000490 (0.454897) \| 0.003134 / 0.000200 (0.002934) \| 0.000077 / 0.000054 (0.000023) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030252 / 0.037411 (-0.007159) \| 0.087566 / 0.014526 (0.073041) \| 0.098209 / 0.176557 (-0.078347) \| 0.155816 / 0.737135 (-0.581319) \| 0.098938 / 0.296338 (-0.197401) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.386688 / 0.215209 (0.171479) \| 3.852777 / 2.077655 (1.775123) \| 1.938688 / 1.504120 (0.434568) \| 1.779234 / 1.541195 (0.238039) \| 1.864262 / 1.468490 (0.395772) \| 0.482472 / 4.584777 (-4.102305) \| 3.658060 / 3.745712 (-0.087652) \| 5.206489 / 5.269862 (-0.063373) \| 3.262498 / 4.565676 (-1.303179) \| 0.057523 / 0.424275 (-0.366752) \| 0.007365 / 0.007607 (-0.000242) \| 0.466886 / 0.226044 (0.240841) \| 4.671026 / 2.268929 (2.402097) \| 2.380357 / 55.444624 (-53.064268) \| 2.096590 / 6.876477 (-4.779887) \| 2.274415 / 2.142072 (0.132342) \| 0.579705 / 4.805227 (-4.225522) \| 0.134522 / 6.500664 (-6.366142) \| 0.062232 / 0.075469 (-0.013237) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.245965 / 1.841788 (-0.595823) \| 20.115180 / 8.074308 (12.040872) \| 14.602983 / 10.191392 (4.411591) \| 0.146890 / 0.680424 (-0.533533) \| 0.018424 / 0.534201 (-0.515777) \| 0.393941 / 0.579283 (-0.185342) \| 0.413785 / 0.434364 (-0.020579) \| 0.453344 / 0.540337 (-0.086993) \| 0.655446 / 1.386936 (-0.731490) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006807 / 0.011353 (-0.004546) \| 0.004083 / 0.011008 (-0.006925) \| 0.065389 / 0.038508 (0.026881) \| 0.081056 / 0.023109 (0.057947) \| 0.362823 / 0.275898 (0.086925) \| 0.401928 / 0.323480 (0.078448) \| 0.005452 / 0.007986 (-0.002533) \| 0.003413 / 0.004328 (-0.000915) \| 0.065238 / 0.004250 (0.060987) \| 0.057264 / 0.037052 (0.020211) \| 0.375713 / 0.258489 (0.117224) \| 0.407858 / 0.293841 (0.114017) \| 0.031580 / 0.128546 (-0.096966) \| 0.008643 / 0.075646 (-0.067003) \| 0.071693 / 0.419271 (-0.347578) \| 0.049392 / 0.043533 (0.005859) \| 0.370194 / 0.255139 (0.115055) \| 0.384647 / 0.283200 (0.101447) \| 0.024805 / 0.141683 (-0.116877) \| 1.509511 / 1.452155 (0.057356) \| 1.560193 / 1.492716 (0.067477) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.234442 / 0.018006 (0.216436) \| 0.458818 / 0.000490 (0.458329) \| 0.000407 / 0.000200 (0.000207) \| 0.000060 / 0.000054 (0.000006) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031661 / 0.037411 (-0.005750) \| 0.093143 / 0.014526 (0.078618) \| 0.102205 / 0.176557 (-0.074352) \| 0.155850 / 0.737135 (-0.581286) \| 0.104345 / 0.296338 (-0.191994) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.419641 / 0.215209 (0.204432) \| 4.200808 / 2.077655 (2.123153) \| 2.218227 / 1.504120 (0.714107) \| 2.052604 / 1.541195 (0.511409) \| 2.150611 / 1.468490 (0.682121) \| 0.482665 / 4.584777 (-4.102112) \| 3.606541 / 3.745712 (-0.139172) \| 3.310637 / 5.269862 (-1.959224) \| 2.070200 / 4.565676 (-2.495476) \| 0.056586 / 0.424275 (-0.367689) \| 0.007826 / 0.007607 (0.000218) \| 0.491037 / 0.226044 (0.264992) \| 4.901538 / 2.268929 (2.632610) \| 2.676402 / 55.444624 (-52.768223) \| 2.363935 / 6.876477 (-4.512542) \| 2.587813 / 2.142072 (0.445741) \| 0.579302 / 4.805227 (-4.225926) \| 0.132792 / 6.500664 (-6.367873) \| 0.061865 / 0.075469 (-0.013604) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.354315 / 1.841788 (-0.487473) \| 20.874516 / 8.074308 (12.800208) \| 14.863559 / 10.191392 (4.672167) \| 0.183635 / 0.680424 (-0.496789) \| 0.018636 / 0.534201 (-0.515565) \| 0.395317 / 0.579283 (-0.183966) \| 0.410598 / 0.434364 (-0.023766) \| 0.476485 / 0.540337 (-0.063853) \| 0.643246 / 1.386936 (-0.743690) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4472a8795c603a95eef7c8f15cb04f1290cc8d11 \"CML watermark\")\n" ]	2023-07-17T11:03:09	2023-07-18T16:17:52	2023-07-18T16:08:42	MEMBER	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6042", "html_url": "https://github.com/huggingface/datasets/pull/6042", "diff_url": "https://github.com/huggingface/datasets/pull/6042.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6042.patch", "merged_at": "2023-07-18T16:08:42" }	null	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6042/reactions", "total_count": 1, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 1, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6042/timeline	null	null	true
https://api.github.com/repos/huggingface/datasets/issues/6041	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6041/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6041/comments	https://api.github.com/repos/huggingface/datasets/issues/6041/events	https://github.com/huggingface/datasets/pull/6041	1,807,441,055	PR_kwDODunzps5Vp0GX	6,041	Flatten repository_structure docs on yaml	{ "login": "lhoestq", "id": 42851186, "node_id": "MDQ6VXNlcjQyODUxMTg2", "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "gravatar_id": "", "url": "https://api.github.com/users/lhoestq", "html_url": "https://github.com/lhoestq", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "repos_url": "https://api.github.com/users/lhoestq/repos", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6041). All of your documentation changes will be reflected on that endpoint.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007587 / 0.011353 (-0.003766) \| 0.004469 / 0.011008 (-0.006540) \| 0.098028 / 0.038508 (0.059520) \| 0.086378 / 0.023109 (0.063269) \| 0.412290 / 0.275898 (0.136392) \| 0.449912 / 0.323480 (0.126432) \| 0.004769 / 0.007986 (-0.003217) \| 0.003708 / 0.004328 (-0.000621) \| 0.075541 / 0.004250 (0.071290) \| 0.063821 / 0.037052 (0.026768) \| 0.417213 / 0.258489 (0.158724) \| 0.471954 / 0.293841 (0.178113) \| 0.036243 / 0.128546 (-0.092303) \| 0.009540 / 0.075646 (-0.066106) \| 0.339043 / 0.419271 (-0.080228) \| 0.061853 / 0.043533 (0.018320) \| 0.418510 / 0.255139 (0.163371) \| 0.462372 / 0.283200 (0.179173) \| 0.027328 / 0.141683 (-0.114355) \| 1.745114 / 1.452155 (0.292959) \| 1.879839 / 1.492716 (0.387123) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.211042 / 0.018006 (0.193035) \| 0.512865 / 0.000490 (0.512375) \| 0.008744 / 0.000200 (0.008544) \| 0.000121 / 0.000054 (0.000066) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032493 / 0.037411 (-0.004918) \| 0.096472 / 0.014526 (0.081946) \| 0.110340 / 0.176557 (-0.066216) \| 0.183195 / 0.737135 (-0.553940) \| 0.112829 / 0.296338 (-0.183510) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.478040 / 0.215209 (0.262830) \| 4.743776 / 2.077655 (2.666121) \| 2.389770 / 1.504120 (0.885650) \| 2.168468 / 1.541195 (0.627274) \| 2.238154 / 1.468490 (0.769663) \| 0.572308 / 4.584777 (-4.012469) \| 4.154783 / 3.745712 (0.409071) \| 3.771509 / 5.269862 (-1.498353) \| 2.384828 / 4.565676 (-2.180848) \| 0.068122 / 0.424275 (-0.356153) \| 0.008573 / 0.007607 (0.000965) \| 0.560300 / 0.226044 (0.334256) \| 5.591163 / 2.268929 (3.322235) \| 2.929660 / 55.444624 (-52.514965) \| 2.517721 / 6.876477 (-4.358756) \| 2.762285 / 2.142072 (0.620213) \| 0.687193 / 4.805227 (-4.118034) \| 0.157839 / 6.500664 (-6.342825) \| 0.071862 / 0.075469 (-0.003607) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.484788 / 1.841788 (-0.357000) \| 21.696071 / 8.074308 (13.621763) \| 15.476166 / 10.191392 (5.284774) \| 0.185034 / 0.680424 (-0.495390) \| 0.021181 / 0.534201 (-0.513020) \| 0.463324 / 0.579283 (-0.115959) \| 0.502455 / 0.434364 (0.068091) \| 0.559880 / 0.540337 (0.019543) \| 0.767281 / 1.386936 (-0.619655) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007572 / 0.011353 (-0.003781) \| 0.004331 / 0.011008 (-0.006677) \| 0.075023 / 0.038508 (0.036515) \| 0.085474 / 0.023109 (0.062365) \| 0.464900 / 0.275898 (0.189002) \| 0.503348 / 0.323480 (0.179868) \| 0.006885 / 0.007986 (-0.001101) \| 0.003647 / 0.004328 (-0.000681) \| 0.074874 / 0.004250 (0.070623) \| 0.071076 / 0.037052 (0.034024) \| 0.465495 / 0.258489 (0.207006) \| 0.506418 / 0.293841 (0.212577) \| 0.038900 / 0.128546 (-0.089647) \| 0.009467 / 0.075646 (-0.066180) \| 0.082547 / 0.419271 (-0.336724) \| 0.058457 / 0.043533 (0.014924) \| 0.459114 / 0.255139 (0.203975) \| 0.484872 / 0.283200 (0.201673) \| 0.027443 / 0.141683 (-0.114240) \| 1.713996 / 1.452155 (0.261841) \| 1.893639 / 1.492716 (0.400922) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.248693 / 0.018006 (0.230687) \| 0.488805 / 0.000490 (0.488315) \| 0.000421 / 0.000200 (0.000221) \| 0.000067 / 0.000054 (0.000012) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034886 / 0.037411 (-0.002525) \| 0.103215 / 0.014526 (0.088689) \| 0.116422 / 0.176557 (-0.060134) \| 0.182789 / 0.737135 (-0.554346) \| 0.117788 / 0.296338 (-0.178550) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.482782 / 0.215209 (0.267573) \| 4.802895 / 2.077655 (2.725241) \| 2.489823 / 1.504120 (0.985703) \| 2.324005 / 1.541195 (0.782810) \| 2.457674 / 1.468490 (0.989184) \| 0.566980 / 4.584777 (-4.017797) \| 4.117359 / 3.745712 (0.371647) \| 3.841180 / 5.269862 (-1.428681) \| 2.322410 / 4.565676 (-2.243266) \| 0.066367 / 0.424275 (-0.357908) \| 0.008501 / 0.007607 (0.000894) \| 0.561453 / 0.226044 (0.335408) \| 5.694861 / 2.268929 (3.425932) \| 3.129829 / 55.444624 (-52.314796) \| 2.647375 / 6.876477 (-4.229102) \| 2.673071 / 2.142072 (0.530998) \| 0.676120 / 4.805227 (-4.129108) \| 0.153483 / 6.500664 (-6.347181) \| 0.070797 / 0.075469 (-0.004672) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.575697 / 1.841788 (-0.266091) \| 22.447462 / 8.074308 (14.373154) \| 15.964906 / 10.191392 (5.773514) \| 0.218343 / 0.680424 (-0.462081) \| 0.021051 / 0.534201 (-0.513150) \| 0.466079 / 0.579283 (-0.113204) \| 0.493190 / 0.434364 (0.058826) \| 0.565929 / 0.540337 (0.025592) \| 0.768638 / 1.386936 (-0.618298) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#104bafffef7ddc775ec2d0b10b2b262466041eb7 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006268 / 0.011353 (-0.005085) \| 0.003715 / 0.011008 (-0.007293) \| 0.080628 / 0.038508 (0.042120) \| 0.070294 / 0.023109 (0.047185) \| 0.404749 / 0.275898 (0.128851) \| 0.434130 / 0.323480 (0.110650) \| 0.005533 / 0.007986 (-0.002452) \| 0.002980 / 0.004328 (-0.001349) \| 0.063016 / 0.004250 (0.058766) \| 0.051667 / 0.037052 (0.014615) \| 0.403859 / 0.258489 (0.145370) \| 0.437913 / 0.293841 (0.144073) \| 0.027518 / 0.128546 (-0.101029) \| 0.007991 / 0.075646 (-0.067655) \| 0.260723 / 0.419271 (-0.158548) \| 0.046580 / 0.043533 (0.003047) \| 0.405453 / 0.255139 (0.150314) \| 0.428390 / 0.283200 (0.145190) \| 0.022774 / 0.141683 (-0.118909) \| 1.488204 / 1.452155 (0.036049) \| 1.536557 / 1.492716 (0.043841) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.185864 / 0.018006 (0.167858) \| 0.431388 / 0.000490 (0.430898) \| 0.003743 / 0.000200 (0.003543) \| 0.000065 / 0.000054 (0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024062 / 0.037411 (-0.013350) \| 0.075749 / 0.014526 (0.061224) \| 0.083519 / 0.176557 (-0.093037) \| 0.147965 / 0.737135 (-0.589170) \| 0.085635 / 0.296338 (-0.210703) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.400455 / 0.215209 (0.185246) \| 4.084294 / 2.077655 (2.006640) \| 1.928795 / 1.504120 (0.424675) \| 1.743205 / 1.541195 (0.202010) \| 1.811233 / 1.468490 (0.342743) \| 0.504976 / 4.584777 (-4.079801) \| 3.073134 / 3.745712 (-0.672578) \| 2.816357 / 5.269862 (-2.453505) \| 1.857462 / 4.565676 (-2.708214) \| 0.058329 / 0.424275 (-0.365946) \| 0.006850 / 0.007607 (-0.000757) \| 0.466017 / 0.226044 (0.239973) \| 4.660158 / 2.268929 (2.391230) \| 2.396614 / 55.444624 (-53.048010) \| 2.007491 / 6.876477 (-4.868986) \| 2.206997 / 2.142072 (0.064925) \| 0.592233 / 4.805227 (-4.212994) \| 0.125364 / 6.500664 (-6.375300) \| 0.061166 / 0.075469 (-0.014303) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.290148 / 1.841788 (-0.551640) \| 18.317462 / 8.074308 (10.243154) \| 13.465142 / 10.191392 (3.273750) \| 0.149696 / 0.680424 (-0.530728) \| 0.017120 / 0.534201 (-0.517081) \| 0.334818 / 0.579283 (-0.244465) \| 0.363976 / 0.434364 (-0.070388) \| 0.388271 / 0.540337 (-0.152066) \| 0.542383 / 1.386936 (-0.844553) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006029 / 0.011353 (-0.005324) \| 0.003656 / 0.011008 (-0.007352) \| 0.063518 / 0.038508 (0.025010) \| 0.058214 / 0.023109 (0.035105) \| 0.435987 / 0.275898 (0.160089) \| 0.442769 / 0.323480 (0.119289) \| 0.004675 / 0.007986 (-0.003310) \| 0.002911 / 0.004328 (-0.001418) \| 0.063020 / 0.004250 (0.058769) \| 0.049422 / 0.037052 (0.012369) \| 0.435521 / 0.258489 (0.177032) \| 0.478251 / 0.293841 (0.184411) \| 0.027294 / 0.128546 (-0.101252) \| 0.008073 / 0.075646 (-0.067574) \| 0.068397 / 0.419271 (-0.350875) \| 0.044796 / 0.043533 (0.001263) \| 0.416646 / 0.255139 (0.161507) \| 0.435021 / 0.283200 (0.151821) \| 0.024686 / 0.141683 (-0.116997) \| 1.495650 / 1.452155 (0.043496) \| 1.495846 / 1.492716 (0.003130) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.211205 / 0.018006 (0.193199) \| 0.414497 / 0.000490 (0.414007) \| 0.001704 / 0.000200 (0.001504) \| 0.000073 / 0.000054 (0.000019) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025237 / 0.037411 (-0.012174) \| 0.077291 / 0.014526 (0.062765) \| 0.085736 / 0.176557 (-0.090821) \| 0.141059 / 0.737135 (-0.596076) \| 0.087620 / 0.296338 (-0.208719) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.421995 / 0.215209 (0.206786) \| 4.158503 / 2.077655 (2.080849) \| 2.313598 / 1.504120 (0.809479) \| 2.183553 / 1.541195 (0.642359) \| 2.279656 / 1.468490 (0.811166) \| 0.500146 / 4.584777 (-4.084631) \| 3.092654 / 3.745712 (-0.653059) \| 4.371616 / 5.269862 (-0.898245) \| 2.605096 / 4.565676 (-1.960581) \| 0.057658 / 0.424275 (-0.366617) \| 0.006574 / 0.007607 (-0.001033) \| 0.491455 / 0.226044 (0.265411) \| 4.926730 / 2.268929 (2.657801) \| 2.635749 / 55.444624 (-52.808875) \| 2.255780 / 6.876477 (-4.620697) \| 2.305547 / 2.142072 (0.163474) \| 0.589027 / 4.805227 (-4.216200) \| 0.126229 / 6.500664 (-6.374435) \| 0.063268 / 0.075469 (-0.012201) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.299102 / 1.841788 (-0.542686) \| 18.547417 / 8.074308 (10.473109) \| 13.860030 / 10.191392 (3.668638) \| 0.145482 / 0.680424 (-0.534942) \| 0.016543 / 0.534201 (-0.517658) \| 0.330788 / 0.579283 (-0.248496) \| 0.362020 / 0.434364 (-0.072344) \| 0.380635 / 0.540337 (-0.159703) \| 0.517375 / 1.386936 (-0.869561) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#bf602e0193baca21e283babbac9622ae36d1e6b6 \"CML watermark\")\n" ]	2023-07-17T10:15:10	2023-07-17T10:24:51	2023-07-17T10:16:22	MEMBER	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6041", "html_url": "https://github.com/huggingface/datasets/pull/6041", "diff_url": "https://github.com/huggingface/datasets/pull/6041.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6041.patch", "merged_at": "2023-07-17T10:16:22" }	To have Splits, Configurations and Builder parameters at the same doc level	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6041/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6041/timeline	null	null	true
https://api.github.com/repos/huggingface/datasets/issues/6040	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6040/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6040/comments	https://api.github.com/repos/huggingface/datasets/issues/6040/events	https://github.com/huggingface/datasets/pull/6040	1,807,410,238	PR_kwDODunzps5VptVf	6,040	Fix legacy_dataset_infos	{ "login": "lhoestq", "id": 42851186, "node_id": "MDQ6VXNlcjQyODUxMTg2", "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "gravatar_id": "", "url": "https://api.github.com/users/lhoestq", "html_url": "https://github.com/lhoestq", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "repos_url": "https://api.github.com/users/lhoestq/repos", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006087 / 0.011353 (-0.005265) \| 0.003567 / 0.011008 (-0.007442) \| 0.079668 / 0.038508 (0.041160) \| 0.063647 / 0.023109 (0.040538) \| 0.323082 / 0.275898 (0.047184) \| 0.348679 / 0.323480 (0.025199) \| 0.004726 / 0.007986 (-0.003259) \| 0.002955 / 0.004328 (-0.001373) \| 0.062724 / 0.004250 (0.058473) \| 0.050194 / 0.037052 (0.013142) \| 0.321407 / 0.258489 (0.062918) \| 0.355053 / 0.293841 (0.061212) \| 0.026992 / 0.128546 (-0.101554) \| 0.007994 / 0.075646 (-0.067653) \| 0.260562 / 0.419271 (-0.158710) \| 0.050933 / 0.043533 (0.007400) \| 0.316644 / 0.255139 (0.061505) \| 0.336759 / 0.283200 (0.053560) \| 0.022581 / 0.141683 (-0.119101) \| 1.481259 / 1.452155 (0.029104) \| 1.535191 / 1.492716 (0.042475) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.194111 / 0.018006 (0.176104) \| 0.448146 / 0.000490 (0.447656) \| 0.000321 / 0.000200 (0.000121) \| 0.000056 / 0.000054 (0.000001) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023908 / 0.037411 (-0.013503) \| 0.073316 / 0.014526 (0.058790) \| 0.085588 / 0.176557 (-0.090968) \| 0.145377 / 0.737135 (-0.591759) \| 0.084788 / 0.296338 (-0.211550) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.439327 / 0.215209 (0.224118) \| 4.384833 / 2.077655 (2.307179) \| 2.322943 / 1.504120 (0.818823) \| 2.147737 / 1.541195 (0.606542) \| 2.226725 / 1.468490 (0.758235) \| 0.502957 / 4.584777 (-4.081820) \| 3.098106 / 3.745712 (-0.647606) \| 4.194642 / 5.269862 (-1.075220) \| 2.598820 / 4.565676 (-1.966856) \| 0.057942 / 0.424275 (-0.366333) \| 0.006857 / 0.007607 (-0.000750) \| 0.511517 / 0.226044 (0.285472) \| 5.121797 / 2.268929 (2.852868) \| 2.756506 / 55.444624 (-52.688118) \| 2.424602 / 6.876477 (-4.451875) \| 2.608342 / 2.142072 (0.466270) \| 0.589498 / 4.805227 (-4.215729) \| 0.126065 / 6.500664 (-6.374600) \| 0.061456 / 0.075469 (-0.014013) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.239928 / 1.841788 (-0.601860) \| 18.423532 / 8.074308 (10.349224) \| 13.935148 / 10.191392 (3.743756) \| 0.129913 / 0.680424 (-0.550511) \| 0.016744 / 0.534201 (-0.517457) \| 0.333468 / 0.579283 (-0.245815) \| 0.359615 / 0.434364 (-0.074749) \| 0.383678 / 0.540337 (-0.156659) \| 0.533007 / 1.386936 (-0.853929) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005980 / 0.011353 (-0.005373) \| 0.003640 / 0.011008 (-0.007368) \| 0.062500 / 0.038508 (0.023992) \| 0.059843 / 0.023109 (0.036733) \| 0.360993 / 0.275898 (0.085095) \| 0.401981 / 0.323480 (0.078501) \| 0.005495 / 0.007986 (-0.002490) \| 0.002862 / 0.004328 (-0.001467) \| 0.062491 / 0.004250 (0.058240) \| 0.050778 / 0.037052 (0.013726) \| 0.371007 / 0.258489 (0.112518) \| 0.405154 / 0.293841 (0.111313) \| 0.027390 / 0.128546 (-0.101156) \| 0.008042 / 0.075646 (-0.067604) \| 0.067590 / 0.419271 (-0.351681) \| 0.042485 / 0.043533 (-0.001048) \| 0.361305 / 0.255139 (0.106166) \| 0.388669 / 0.283200 (0.105469) \| 0.024143 / 0.141683 (-0.117540) \| 1.451508 / 1.452155 (-0.000647) \| 1.490431 / 1.492716 (-0.002285) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.175976 / 0.018006 (0.157970) \| 0.428923 / 0.000490 (0.428434) \| 0.002099 / 0.000200 (0.001899) \| 0.000068 / 0.000054 (0.000014) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026346 / 0.037411 (-0.011065) \| 0.078084 / 0.014526 (0.063558) \| 0.087287 / 0.176557 (-0.089269) \| 0.144179 / 0.737135 (-0.592957) \| 0.088286 / 0.296338 (-0.208053) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.450436 / 0.215209 (0.235227) \| 4.488801 / 2.077655 (2.411146) \| 2.479303 / 1.504120 (0.975184) \| 2.305396 / 1.541195 (0.764201) \| 2.370370 / 1.468490 (0.901879) \| 0.502355 / 4.584777 (-4.082422) \| 3.094733 / 3.745712 (-0.650979) \| 4.062367 / 5.269862 (-1.207495) \| 2.587506 / 4.565676 (-1.978170) \| 0.058245 / 0.424275 (-0.366030) \| 0.006487 / 0.007607 (-0.001120) \| 0.524147 / 0.226044 (0.298102) \| 5.236876 / 2.268929 (2.967947) \| 2.897134 / 55.444624 (-52.547490) \| 2.574631 / 6.876477 (-4.301846) \| 2.620307 / 2.142072 (0.478235) \| 0.586963 / 4.805227 (-4.218265) \| 0.125761 / 6.500664 (-6.374903) \| 0.062264 / 0.075469 (-0.013205) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.299668 / 1.841788 (-0.542120) \| 19.004441 / 8.074308 (10.930133) \| 13.841867 / 10.191392 (3.650475) \| 0.159674 / 0.680424 (-0.520750) \| 0.016699 / 0.534201 (-0.517502) \| 0.331868 / 0.579283 (-0.247415) \| 0.344604 / 0.434364 (-0.089760) \| 0.379391 / 0.540337 (-0.160947) \| 0.514790 / 1.386936 (-0.872146) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#47a006a90e9711b33db70b0ef2d2cefaadfa2179 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005792 / 0.011353 (-0.005561) \| 0.003519 / 0.011008 (-0.007489) \| 0.079133 / 0.038508 (0.040625) \| 0.057858 / 0.023109 (0.034749) \| 0.314206 / 0.275898 (0.038308) \| 0.346939 / 0.323480 (0.023459) \| 0.004583 / 0.007986 (-0.003403) \| 0.002824 / 0.004328 (-0.001504) \| 0.061652 / 0.004250 (0.057402) \| 0.048520 / 0.037052 (0.011467) \| 0.318018 / 0.258489 (0.059529) \| 0.350350 / 0.293841 (0.056509) \| 0.026284 / 0.128546 (-0.102262) \| 0.007827 / 0.075646 (-0.067819) \| 0.259624 / 0.419271 (-0.159647) \| 0.052318 / 0.043533 (0.008786) \| 0.317400 / 0.255139 (0.062261) \| 0.340530 / 0.283200 (0.057331) \| 0.025181 / 0.141683 (-0.116501) \| 1.459208 / 1.452155 (0.007053) \| 1.529158 / 1.492716 (0.036442) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.169692 / 0.018006 (0.151686) \| 0.432638 / 0.000490 (0.432148) \| 0.003675 / 0.000200 (0.003475) \| 0.000071 / 0.000054 (0.000017) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022956 / 0.037411 (-0.014456) \| 0.071860 / 0.014526 (0.057334) \| 0.082159 / 0.176557 (-0.094398) \| 0.142560 / 0.737135 (-0.594576) \| 0.082333 / 0.296338 (-0.214006) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.397923 / 0.215209 (0.182714) \| 3.958757 / 2.077655 (1.881102) \| 1.925837 / 1.504120 (0.421717) \| 1.758114 / 1.541195 (0.216919) \| 1.808845 / 1.468490 (0.340354) \| 0.501116 / 4.584777 (-4.083661) \| 3.007739 / 3.745712 (-0.737973) \| 3.295755 / 5.269862 (-1.974106) \| 2.123843 / 4.565676 (-2.441833) \| 0.057174 / 0.424275 (-0.367101) \| 0.006426 / 0.007607 (-0.001182) \| 0.468196 / 0.226044 (0.242152) \| 4.677392 / 2.268929 (2.408464) \| 2.334179 / 55.444624 (-53.110446) \| 1.989283 / 6.876477 (-4.887194) \| 2.140091 / 2.142072 (-0.001981) \| 0.590700 / 4.805227 (-4.214527) \| 0.124066 / 6.500664 (-6.376598) \| 0.059931 / 0.075469 (-0.015538) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.224547 / 1.841788 (-0.617240) \| 17.866979 / 8.074308 (9.792671) \| 13.142009 / 10.191392 (2.950617) \| 0.147081 / 0.680424 (-0.533343) \| 0.016777 / 0.534201 (-0.517424) \| 0.327766 / 0.579283 (-0.251517) \| 0.343988 / 0.434364 (-0.090376) \| 0.383268 / 0.540337 (-0.157070) \| 0.528109 / 1.386936 (-0.858827) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006145 / 0.011353 (-0.005208) \| 0.003634 / 0.011008 (-0.007374) \| 0.062887 / 0.038508 (0.024379) \| 0.062659 / 0.023109 (0.039550) \| 0.362962 / 0.275898 (0.087064) \| 0.405149 / 0.323480 (0.081669) \| 0.004821 / 0.007986 (-0.003164) \| 0.002888 / 0.004328 (-0.001441) \| 0.062982 / 0.004250 (0.058732) \| 0.051929 / 0.037052 (0.014877) \| 0.366825 / 0.258489 (0.108336) \| 0.409830 / 0.293841 (0.115989) \| 0.027263 / 0.128546 (-0.101283) \| 0.007972 / 0.075646 (-0.067674) \| 0.067413 / 0.419271 (-0.351858) \| 0.044233 / 0.043533 (0.000700) \| 0.365087 / 0.255139 (0.109948) \| 0.393845 / 0.283200 (0.110646) \| 0.027740 / 0.141683 (-0.113943) \| 1.497896 / 1.452155 (0.045741) \| 1.549419 / 1.492716 (0.056703) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.225510 / 0.018006 (0.207503) \| 0.417054 / 0.000490 (0.416564) \| 0.002184 / 0.000200 (0.001984) \| 0.000075 / 0.000054 (0.000020) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025503 / 0.037411 (-0.011908) \| 0.076164 / 0.014526 (0.061638) \| 0.086110 / 0.176557 (-0.090446) \| 0.140387 / 0.737135 (-0.596748) \| 0.086956 / 0.296338 (-0.209382) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.469667 / 0.215209 (0.254458) \| 4.689915 / 2.077655 (2.612261) \| 2.685000 / 1.504120 (1.180880) \| 2.516160 / 1.541195 (0.974965) \| 2.531733 / 1.468490 (1.063243) \| 0.501675 / 4.584777 (-4.083102) \| 3.000579 / 3.745712 (-0.745133) \| 2.853376 / 5.269862 (-2.416486) \| 1.810677 / 4.565676 (-2.754999) \| 0.057632 / 0.424275 (-0.366643) \| 0.006390 / 0.007607 (-0.001217) \| 0.543986 / 0.226044 (0.317941) \| 5.432837 / 2.268929 (3.163908) \| 3.138797 / 55.444624 (-52.305827) \| 2.813141 / 6.876477 (-4.063336) \| 2.803681 / 2.142072 (0.661609) \| 0.588736 / 4.805227 (-4.216491) \| 0.125696 / 6.500664 (-6.374968) \| 0.062492 / 0.075469 (-0.012977) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.337163 / 1.841788 (-0.504624) \| 18.611715 / 8.074308 (10.537407) \| 13.953016 / 10.191392 (3.761624) \| 0.154670 / 0.680424 (-0.525754) \| 0.016523 / 0.534201 (-0.517678) \| 0.333898 / 0.579283 (-0.245385) \| 0.336520 / 0.434364 (-0.097844) \| 0.389032 / 0.540337 (-0.151305) \| 0.529202 / 1.386936 (-0.857734) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#01d4b3330f2cc243a3f3b0cd61ec5558466c40fd \"CML watermark\")\n" ]	2023-07-17T09:56:21	2023-07-17T10:24:34	2023-07-17T10:16:03	MEMBER	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6040", "html_url": "https://github.com/huggingface/datasets/pull/6040", "diff_url": "https://github.com/huggingface/datasets/pull/6040.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6040.patch", "merged_at": "2023-07-17T10:16:03" }	was causing transformers CI to fail https://circleci.com/gh/huggingface/transformers/855105	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6040/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6040/timeline	null	null	true
https://api.github.com/repos/huggingface/datasets/issues/6039	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6039/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6039/comments	https://api.github.com/repos/huggingface/datasets/issues/6039/events	https://github.com/huggingface/datasets/issues/6039	1,806,508,451	I_kwDODunzps5rrSGj	6,039	Loading column subset from parquet file produces error since version 2.13	{ "login": "kklemon", "id": 1430243, "node_id": "MDQ6VXNlcjE0MzAyNDM=", "avatar_url": "https://avatars.githubusercontent.com/u/1430243?v=4", "gravatar_id": "", "url": "https://api.github.com/users/kklemon", "html_url": "https://github.com/kklemon", "followers_url": "https://api.github.com/users/kklemon/followers", "following_url": "https://api.github.com/users/kklemon/following{/other_user}", "gists_url": "https://api.github.com/users/kklemon/gists{/gist_id}", "starred_url": "https://api.github.com/users/kklemon/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/kklemon/subscriptions", "organizations_url": "https://api.github.com/users/kklemon/orgs", "repos_url": "https://api.github.com/users/kklemon/repos", "events_url": "https://api.github.com/users/kklemon/events{/privacy}", "received_events_url": "https://api.github.com/users/kklemon/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[]	2023-07-16T09:13:07	2023-07-24T14:35:04	2023-07-24T14:35:04	NONE	null	null	null	### Describe the bug `load_dataset` allows loading a subset of columns from a parquet file with the `columns` argument. Since version 2.13, this produces the following error: ``` Traceback (most recent call last): File "/usr/lib/python3.10/site-packages/datasets/builder.py", line 1879, in _prepare_split_single for _, table in generator: File "/usr/lib/python3.10/site-packages/datasets/packaged_modules/parquet/parquet.py", line 68, in _generate_tables raise ValueError( ValueError: Tried to load parquet data with columns '['sepal_length']' with mismatching features '{'sepal_length': Value(dtype='float64', id=None), 'sepal_width': Value(dtype='float64', id=None), 'petal_length': Value(dtype='float64', id=None), 'petal_width': Value(dtype='float64', id=None), 'species': Value(dtype='string', id=None)}' ``` This seems to occur because `datasets` is checking whether the columns in the schema exactly match the provided list of columns, instead of whether they are a subset. ### Steps to reproduce the bug ```python # Prepare some sample data import pandas as pd iris = pd.read_csv('https://raw.githubusercontent.com/mwaskom/seaborn-data/master/iris.csv') iris.to_parquet('iris.parquet') # ['sepal_length', 'sepal_width', 'petal_length', 'petal_width', 'species'] print(iris.columns) # Load data with datasets from datasets import load_dataset # Load full parquet file dataset = load_dataset('parquet', data_files='iris.parquet') # Load column subset; throws error for datasets>=2.13 dataset = load_dataset('parquet', data_files='iris.parquet', columns=['sepal_length']) ``` ### Expected behavior No error should be thrown and the given column subset should be loaded. ### Environment info - `datasets` version: 2.13.0 - Platform: Linux-5.15.0-76-generic-x86_64-with-glibc2.35 - Python version: 3.10.9 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 1.5.3	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6039/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6039/timeline	null	completed	false
https://api.github.com/repos/huggingface/datasets/issues/6038	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6038/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6038/comments	https://api.github.com/repos/huggingface/datasets/issues/6038/events	https://github.com/huggingface/datasets/issues/6038	1,805,960,244	I_kwDODunzps5rpMQ0	6,038	File "/home/zhizhou/anaconda3/envs/pytorch/lib/python3.10/site-packages/datasets/builder.py", line 992, in _download_and_prepare if str(split_generator.split_info.name).lower() == "all": AttributeError: 'str' object has no attribute 'split_info'. Did you mean: 'splitlines'?	{ "login": "BaiMeiyingxue", "id": 53547009, "node_id": "MDQ6VXNlcjUzNTQ3MDA5", "avatar_url": "https://avatars.githubusercontent.com/u/53547009?v=4", "gravatar_id": "", "url": "https://api.github.com/users/BaiMeiyingxue", "html_url": "https://github.com/BaiMeiyingxue", "followers_url": "https://api.github.com/users/BaiMeiyingxue/followers", "following_url": "https://api.github.com/users/BaiMeiyingxue/following{/other_user}", "gists_url": "https://api.github.com/users/BaiMeiyingxue/gists{/gist_id}", "starred_url": "https://api.github.com/users/BaiMeiyingxue/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/BaiMeiyingxue/subscriptions", "organizations_url": "https://api.github.com/users/BaiMeiyingxue/orgs", "repos_url": "https://api.github.com/users/BaiMeiyingxue/repos", "events_url": "https://api.github.com/users/BaiMeiyingxue/events{/privacy}", "received_events_url": "https://api.github.com/users/BaiMeiyingxue/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "Instead of writing the loading script, you can use the built-in loader to [load JSON files](https://huggingface.co/docs/datasets/loading#json):\r\n```python\r\nfrom datasets import load_dataset\r\nds = load_dataset(\"json\", data_files={\"train\": os.path.join(data_dir[\"train\"]), \"dev\": os.path.join(data_dir[\"dev\"])})\r\n```" ]	2023-07-15T07:58:08	2023-07-24T11:54:15	2023-07-24T11:54:15	NONE	null	null	null	Hi, I use the code below to load local file ``` def _split_generators(self, dl_manager): # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files. # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive # urls = _URLS[self.config.name] data_dir = dl_manager.download_and_extract(_URLs) print(data_dir) return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, # These kwargs will be passed to _generate_examples gen_kwargs={ "filepath": os.path.join(data_dir["train"]), "split": "train", }, ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, # These kwargs will be passed to _generate_examples gen_kwargs={ "filepath": os.path.join(data_dir["dev"]), "split": "dev", }, ), ] ``` and error occured ``` Traceback (most recent call last): File "/home/zhizhou/data1/zhanghao/huggingface/FineTuning_Transformer/load_local_dataset.py", line 2, in <module> dataset = load_dataset("./QA_script.py",data_files='/home/zhizhou/.cache/huggingface/datasets/conversatiom_corps/part_file.json') File "/home/zhizhou/anaconda3/envs/pytorch/lib/python3.10/site-packages/datasets/load.py", line 1809, in load_dataset builder_instance.download_and_prepare( File "/home/zhizhou/anaconda3/envs/pytorch/lib/python3.10/site-packages/datasets/builder.py", line 909, in download_and_prepare self._download_and_prepare( File "/home/zhizhou/anaconda3/envs/pytorch/lib/python3.10/site-packages/datasets/builder.py", line 1670, in _download_and_prepare super()._download_and_prepare( File "/home/zhizhou/anaconda3/envs/pytorch/lib/python3.10/site-packages/datasets/builder.py", line 992, in _download_and_prepare if str(split_generator.split_info.name).lower() == "all": AttributeError: 'str' object has no attribute 'split_info'. Did you mean: 'splitlines'? ``` Could you help me?	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6038/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6038/timeline	null	completed	false
https://api.github.com/repos/huggingface/datasets/issues/6037	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6037/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6037/comments	https://api.github.com/repos/huggingface/datasets/issues/6037/events	https://github.com/huggingface/datasets/issues/6037	1,805,887,184	I_kwDODunzps5ro6bQ	6,037	Documentation links to examples are broken	{ "login": "david-waterworth", "id": 5028974, "node_id": "MDQ6VXNlcjUwMjg5NzQ=", "avatar_url": "https://avatars.githubusercontent.com/u/5028974?v=4", "gravatar_id": "", "url": "https://api.github.com/users/david-waterworth", "html_url": "https://github.com/david-waterworth", "followers_url": "https://api.github.com/users/david-waterworth/followers", "following_url": "https://api.github.com/users/david-waterworth/following{/other_user}", "gists_url": "https://api.github.com/users/david-waterworth/gists{/gist_id}", "starred_url": "https://api.github.com/users/david-waterworth/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/david-waterworth/subscriptions", "organizations_url": "https://api.github.com/users/david-waterworth/orgs", "repos_url": "https://api.github.com/users/david-waterworth/repos", "events_url": "https://api.github.com/users/david-waterworth/events{/privacy}", "received_events_url": "https://api.github.com/users/david-waterworth/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "These docs are outdated (version 1.2.1 is over two years old). Please refer to [this](https://huggingface.co/docs/datasets/dataset_script) version instead.\r\n\r\nInitially, we hosted datasets in this repo, but now you can find them [on the HF Hub](https://huggingface.co/datasets) (e.g. the [`ag_news`](https://huggingface.co/datasets/ag_news/blob/main/ag_news.py) script)", "Sorry I thought I'd selected the latest version." ]	2023-07-15T04:54:50	2023-07-17T22:35:14	2023-07-17T15:10:32	NONE	null	null	null	### Describe the bug The links at the bottom of [add_dataset](https://huggingface.co/docs/datasets/v1.2.1/add_dataset.html) to examples of specific datasets are all broken, for example - text classification: [ag_news](https://github.com/huggingface/datasets/blob/master/datasets/ag_news/ag_news.py) (original data are in csv files) ### Steps to reproduce the bug Click on links to examples from latest documentation ### Expected behavior Links should be up to date - it might be more stable to link to https://huggingface.co/datasets/ag_news/blob/main/ag_news.py ### Environment info dataset v1.2.1	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6037/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6037/timeline	null	completed	false
https://api.github.com/repos/huggingface/datasets/issues/6036	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6036/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6036/comments	https://api.github.com/repos/huggingface/datasets/issues/6036/events	https://github.com/huggingface/datasets/pull/6036	1,805,138,898	PR_kwDODunzps5ViKc4	6,036	Deprecate search API	{ "login": "mariosasko", "id": 47462742, "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "gravatar_id": "", "url": "https://api.github.com/users/mariosasko", "html_url": "https://github.com/mariosasko", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "repos_url": "https://api.github.com/users/mariosasko/repos", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	null	[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005746 / 0.011353 (-0.005607) \| 0.003461 / 0.011008 (-0.007548) \| 0.078672 / 0.038508 (0.040164) \| 0.056800 / 0.023109 (0.033691) \| 0.312853 / 0.275898 (0.036955) \| 0.346715 / 0.323480 (0.023235) \| 0.004516 / 0.007986 (-0.003469) \| 0.002872 / 0.004328 (-0.001457) \| 0.061264 / 0.004250 (0.057013) \| 0.046606 / 0.037052 (0.009553) \| 0.320080 / 0.258489 (0.061591) \| 0.350390 / 0.293841 (0.056550) \| 0.026445 / 0.128546 (-0.102101) \| 0.007710 / 0.075646 (-0.067936) \| 0.259519 / 0.419271 (-0.159752) \| 0.043935 / 0.043533 (0.000402) \| 0.320015 / 0.255139 (0.064876) \| 0.339799 / 0.283200 (0.056599) \| 0.018638 / 0.141683 (-0.123044) \| 1.463393 / 1.452155 (0.011239) \| 1.496977 / 1.492716 (0.004261) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.185175 / 0.018006 (0.167168) \| 0.420734 / 0.000490 (0.420245) \| 0.002569 / 0.000200 (0.002369) \| 0.000067 / 0.000054 (0.000012) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022335 / 0.037411 (-0.015077) \| 0.071686 / 0.014526 (0.057161) \| 0.079906 / 0.176557 (-0.096650) \| 0.140386 / 0.737135 (-0.596749) \| 0.079712 / 0.296338 (-0.216627) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.392643 / 0.215209 (0.177434) \| 3.917934 / 2.077655 (1.840279) \| 1.906808 / 1.504120 (0.402688) \| 1.729564 / 1.541195 (0.188369) \| 1.751533 / 1.468490 (0.283043) \| 0.496810 / 4.584777 (-4.087967) \| 3.047405 / 3.745712 (-0.698307) \| 4.361766 / 5.269862 (-0.908095) \| 2.660845 / 4.565676 (-1.904832) \| 0.056951 / 0.424275 (-0.367324) \| 0.006277 / 0.007607 (-0.001330) \| 0.466357 / 0.226044 (0.240312) \| 4.660457 / 2.268929 (2.391529) \| 2.328590 / 55.444624 (-53.116034) \| 1.986140 / 6.876477 (-4.890337) \| 2.096182 / 2.142072 (-0.045891) \| 0.581685 / 4.805227 (-4.223542) \| 0.123643 / 6.500664 (-6.377021) \| 0.060286 / 0.075469 (-0.015183) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.237024 / 1.841788 (-0.604763) \| 17.778533 / 8.074308 (9.704225) \| 13.202205 / 10.191392 (3.010813) \| 0.141301 / 0.680424 (-0.539123) \| 0.016453 / 0.534201 (-0.517748) \| 0.329173 / 0.579283 (-0.250110) \| 0.349945 / 0.434364 (-0.084419) \| 0.375319 / 0.540337 (-0.165018) \| 0.530394 / 1.386936 (-0.856542) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005863 / 0.011353 (-0.005489) \| 0.003578 / 0.011008 (-0.007430) \| 0.062719 / 0.038508 (0.024211) \| 0.056192 / 0.023109 (0.033082) \| 0.422812 / 0.275898 (0.146914) \| 0.454316 / 0.323480 (0.130836) \| 0.004446 / 0.007986 (-0.003540) \| 0.002808 / 0.004328 (-0.001521) \| 0.062819 / 0.004250 (0.058569) \| 0.046243 / 0.037052 (0.009190) \| 0.445858 / 0.258489 (0.187369) \| 0.463750 / 0.293841 (0.169909) \| 0.027504 / 0.128546 (-0.101042) \| 0.007897 / 0.075646 (-0.067749) \| 0.068248 / 0.419271 (-0.351024) \| 0.041921 / 0.043533 (-0.001612) \| 0.413314 / 0.255139 (0.158175) \| 0.441619 / 0.283200 (0.158419) \| 0.019246 / 0.141683 (-0.122437) \| 1.457069 / 1.452155 (0.004914) \| 1.524168 / 1.492716 (0.031452) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.237785 / 0.018006 (0.219779) \| 0.418455 / 0.000490 (0.417965) \| 0.002301 / 0.000200 (0.002101) \| 0.000065 / 0.000054 (0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025630 / 0.037411 (-0.011781) \| 0.076673 / 0.014526 (0.062147) \| 0.084877 / 0.176557 (-0.091680) \| 0.137528 / 0.737135 (-0.599607) \| 0.085261 / 0.296338 (-0.211077) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.419040 / 0.215209 (0.203831) \| 4.183022 / 2.077655 (2.105368) \| 2.157852 / 1.504120 (0.653732) \| 1.966177 / 1.541195 (0.424982) \| 2.019612 / 1.468490 (0.551122) \| 0.497415 / 4.584777 (-4.087362) \| 3.102873 / 3.745712 (-0.642839) \| 4.526336 / 5.269862 (-0.743525) \| 2.991503 / 4.565676 (-1.574174) \| 0.057235 / 0.424275 (-0.367040) \| 0.006735 / 0.007607 (-0.000872) \| 0.498255 / 0.226044 (0.272211) \| 4.957364 / 2.268929 (2.688435) \| 2.632643 / 55.444624 (-52.811981) \| 2.249788 / 6.876477 (-4.626688) \| 2.289134 / 2.142072 (0.147062) \| 0.583581 / 4.805227 (-4.221646) \| 0.126046 / 6.500664 (-6.374618) \| 0.062966 / 0.075469 (-0.012504) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.295215 / 1.841788 (-0.546573) \| 18.554020 / 8.074308 (10.479711) \| 13.683273 / 10.191392 (3.491881) \| 0.132266 / 0.680424 (-0.548158) \| 0.016376 / 0.534201 (-0.517825) \| 0.334495 / 0.579283 (-0.244788) \| 0.347106 / 0.434364 (-0.087258) \| 0.387531 / 0.540337 (-0.152806) \| 0.525745 / 1.386936 (-0.861191) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f22aa0bf179602e4bf3f44a9de5d180579bb377e \"CML watermark\")\n", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6036). All of your documentation changes will be reflected on that endpoint.", "I don't think `transformers` should have any dataset indexing code. So before deprecating I'd be in favor of finding a suitable replacement. Not sure about the stats of the RAG model that uses `datasets` indexing though", "The RAG downloads stats are decent (over 20k downloads last month).\r\n\r\nI think it's suboptimal to maintain an API that only a single model uses. One option is to put this code into a separate lib. However, `langchain` and `docarray` already provide a unified interface to vector stores, so I don't see this as an impactful project. Considering how specific this model is, I think we should go with the simplest solution and combine an index with a dataset in Transformers (this wouldn't require too much code).", "What about migrating to the [datasets-server](https://github.com/huggingface/datasets-server) search feature instead? Would make more sense from a product perspective ", "I don't think it's a good idea:\r\n- using datasets-server would require to upload the data and to not control the indexing, whereas the current feature is about using a local index that you control\r\n- faiss indexes are vector indexes that are not supported by datasets-server, and they are also very customised. For instance RAG uses DPR embeddings and cosine similarity\r\n- FTS is only done for the first 5GB of data for now in datasets-server\r\n\r\nI think a better option would be to integrate with open source search tools such as docarray.\r\nAnd if we want to make the datasets-server search available in python we can build an integration in docarray and/or in huggingface_hub.", "`llama_index` is another popular tool in this space.\r\n\r\n@lhoestq \r\n> I think a better option would be to integrate with open source search tools such as docarray.\r\nAnd if we want to make the datasets-server search available in python we can build an integration in docarray and/or in huggingface_hub.\r\n\r\nI don't think these integrations would be popular unless we integrate them with the Hub \"UI-wise\" (e.g., through a widget), so they can wait IMO. Also, FAISS supports `fsspec` already with the callback reader/writer, so this doesn't require a specific integration. ", "After discussing it a bit with @lhoestq, do we need to deprecate the search API? While I understand it's imperfect, it looks like this will result in significant work to update it everywhere, so I'd favor keeping it until there's an obviously better alternative; this way we can focus on different things in the meantime." ]	2023-07-14T16:22:09	2023-07-21T19:53:51	null	CONTRIBUTOR	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6036", "html_url": "https://github.com/huggingface/datasets/pull/6036", "diff_url": "https://github.com/huggingface/datasets/pull/6036.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6036.patch", "merged_at": null }	The Search API only supports Faiss and ElasticSearch as vector stores, is somewhat difficult to maintain (e.g., it still doesn't support ElasticSeach 8.0, difficult testing, ...), does not have the best design (adds a bunch of methods to the `Dataset` class that are only useful after creating an index), the usage doesn't seem to be significant and is not integrated with the Hub. Since we have no plans/bandwidth to improve it and better alternatives such as `langchain` and `docarray` exist, I think it should be deprecated (and eventually removed). If we decide to deprecate/remove it, the following usage instances need to be addressed: * [Course](https://github.com/huggingface/course/blob/0018bb434204d9750a03592cb0d4e846093218d8/chapters/en/chapter5/6.mdx#L342 ) and [Blog](https://github.com/huggingface/blog/blob/4897c6f73d4492a0955ade503281711d01840e09/image-search-datasets.md?plain=1#L252) - calling the FAISS API directly should be OK in these instances as it's pretty simple to use for basic scenarios. Alternatively, we can use `langchain`, but this adds an extra dependency * [Transformers](https://github.com/huggingface/transformers/blob/50726f9ea7afc6113da617f8f4ca1ab264a5e28a/src/transformers/models/rag/retrieval_rag.py#L183) - we can use the FAISS API directly and store the index as a separate attribute (and instead of building the `wiki_dpr` index each time the dataset is generated, we can generate it once and push it to the Hub repo, and then read it from there cc @huggingface/datasets @LysandreJik for the opinion	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6036/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6036/timeline	null	null	true
https://api.github.com/repos/huggingface/datasets/issues/6035	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6035/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6035/comments	https://api.github.com/repos/huggingface/datasets/issues/6035/events	https://github.com/huggingface/datasets/pull/6035	1,805,087,687	PR_kwDODunzps5Vh_QR	6,035	Dataset representation	{ "login": "Ganryuu", "id": 63643948, "node_id": "MDQ6VXNlcjYzNjQzOTQ4", "avatar_url": "https://avatars.githubusercontent.com/u/63643948?v=4", "gravatar_id": "", "url": "https://api.github.com/users/Ganryuu", "html_url": "https://github.com/Ganryuu", "followers_url": "https://api.github.com/users/Ganryuu/followers", "following_url": "https://api.github.com/users/Ganryuu/following{/other_user}", "gists_url": "https://api.github.com/users/Ganryuu/gists{/gist_id}", "starred_url": "https://api.github.com/users/Ganryuu/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/Ganryuu/subscriptions", "organizations_url": "https://api.github.com/users/Ganryuu/orgs", "repos_url": "https://api.github.com/users/Ganryuu/repos", "events_url": "https://api.github.com/users/Ganryuu/events{/privacy}", "received_events_url": "https://api.github.com/users/Ganryuu/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6035). All of your documentation changes will be reflected on that endpoint." ]	2023-07-14T15:42:37	2023-07-19T19:41:35	null	NONE	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6035", "html_url": "https://github.com/huggingface/datasets/pull/6035", "diff_url": "https://github.com/huggingface/datasets/pull/6035.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6035.patch", "merged_at": null }	__repr__ and _repr_html_ now both are similar to that of Polars	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6035/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6035/timeline	null	null	true
https://api.github.com/repos/huggingface/datasets/issues/6034	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6034/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6034/comments	https://api.github.com/repos/huggingface/datasets/issues/6034/events	https://github.com/huggingface/datasets/issues/6034	1,804,501,361	I_kwDODunzps5rjoFx	6,034	load_dataset hangs on WSL	{ "login": "Andy-Zhou2", "id": 20140522, "node_id": "MDQ6VXNlcjIwMTQwNTIy", "avatar_url": "https://avatars.githubusercontent.com/u/20140522?v=4", "gravatar_id": "", "url": "https://api.github.com/users/Andy-Zhou2", "html_url": "https://github.com/Andy-Zhou2", "followers_url": "https://api.github.com/users/Andy-Zhou2/followers", "following_url": "https://api.github.com/users/Andy-Zhou2/following{/other_user}", "gists_url": "https://api.github.com/users/Andy-Zhou2/gists{/gist_id}", "starred_url": "https://api.github.com/users/Andy-Zhou2/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/Andy-Zhou2/subscriptions", "organizations_url": "https://api.github.com/users/Andy-Zhou2/orgs", "repos_url": "https://api.github.com/users/Andy-Zhou2/repos", "events_url": "https://api.github.com/users/Andy-Zhou2/events{/privacy}", "received_events_url": "https://api.github.com/users/Andy-Zhou2/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "Even if a dataset is cached, we still make requests to check whether the cache is up-to-date. [This](https://huggingface.co/docs/datasets/v2.13.1/en/loading#offline) section in the docs explains how to avoid them and directly load the cached version.", "Thanks - that works! However it doesn't resolve the original issue (but I am not sure if it is a WSL problem)", "We use `requests` to make HTTP requests (and `aiohttp` in the streaming mode), so I don't think we can provide much help regarding the socket issue (it probably has something to do with WSL). " ]	2023-07-14T09:03:10	2023-07-14T14:48:29	2023-07-14T14:48:29	NONE	null	null	null	### Describe the bug load_dataset simply hangs. It happens once every ~5 times, and interestingly hangs for a multiple of 5 minutes (hangs for 5/10/15 minutes). Using the profiler in PyCharm shows that it spends the time at <method 'connect' of '_socket.socket' objects>. However, a local cache is available so I am not sure why socket is needed. ([profiler result](https://ibb.co/0Btbbp8)) It only happens on WSL for me. It works for native Windows and my MacBook. (cache quickly recognized and loaded within a second). ### Steps to reproduce the bug I am using Ubuntu 22.04.2 LTS (GNU/Linux 5.15.90.1-microsoft-standard-WSL2 x86_64) Python 3.10.10 (main, Mar 21 2023, 18:45:11) [GCC 11.2.0] on linux >>> import datasets >>> datasets.load_dataset('ai2_arc', 'ARC-Challenge') # hangs for 5/10/15 minutes ### Expected behavior cache quickly recognized and loaded within a second ### Environment info Please let me know if I should provide more environment information.	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6034/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6034/timeline	null	completed	false
https://api.github.com/repos/huggingface/datasets/issues/6033	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6033/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6033/comments	https://api.github.com/repos/huggingface/datasets/issues/6033/events	https://github.com/huggingface/datasets/issues/6033	1,804,482,051	I_kwDODunzps5rjjYD	6,033	`map` function doesn't fully utilize `input_columns`.	{ "login": "kwonmha", "id": 8953934, "node_id": "MDQ6VXNlcjg5NTM5MzQ=", "avatar_url": "https://avatars.githubusercontent.com/u/8953934?v=4", "gravatar_id": "", "url": "https://api.github.com/users/kwonmha", "html_url": "https://github.com/kwonmha", "followers_url": "https://api.github.com/users/kwonmha/followers", "following_url": "https://api.github.com/users/kwonmha/following{/other_user}", "gists_url": "https://api.github.com/users/kwonmha/gists{/gist_id}", "starred_url": "https://api.github.com/users/kwonmha/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/kwonmha/subscriptions", "organizations_url": "https://api.github.com/users/kwonmha/orgs", "repos_url": "https://api.github.com/users/kwonmha/repos", "events_url": "https://api.github.com/users/kwonmha/events{/privacy}", "received_events_url": "https://api.github.com/users/kwonmha/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[]	2023-07-14T08:49:28	2023-07-14T09:16:04	2023-07-14T09:16:04	NONE	null	null	null	### Describe the bug I wanted to select only some columns of data. And I thought that's why the argument `input_columns` exists. What I expected is like this: If there are ["a", "b", "c", "d"] columns, and if I set `input_columns=["a", "d"]`, the data will have only ["a", "d"] columns. But it doesn't select columns. It preserves existing columns. The main cause is `update` function of `dictionary` type `transformed_batch`. https://github.com/huggingface/datasets/blob/682d21e94ab1e64c11b583de39dc4c93f0101c5a/src/datasets/iterable_dataset.py#L687-L691 `transformed_batch` gets all the columns by `transformed_batch = dict(batch)`. Even `function_args` selects `input_columns`, `update` preserves columns other than `input_columns`. I think it should take a new dictionary with columns in `input_columns` like this: ``` # transformed_batch = dict(batch) # transformed_batch.update(self.function(function_args, self.fn_kwargs) # This is what I think correct. transformed_batch = self.function(function_args, **self.fn_kwargs) ``` Let me know how to use `input_columns`. ### Steps to reproduce the bug Described all above. ### Expected behavior Described all above. ### Environment info datasets: 2.12 python: 3.8	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6033/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6033/timeline	null	completed	false
https://api.github.com/repos/huggingface/datasets/issues/6032	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6032/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6032/comments	https://api.github.com/repos/huggingface/datasets/issues/6032/events	https://github.com/huggingface/datasets/issues/6032	1,804,358,679	I_kwDODunzps5rjFQX	6,032	DownloadConfig.proxies not work when load_dataset_builder calling HfApi.dataset_info	{ "login": "codingl2k1", "id": 138426806, "node_id": "U_kgDOCEA5tg", "avatar_url": "https://avatars.githubusercontent.com/u/138426806?v=4", "gravatar_id": "", "url": "https://api.github.com/users/codingl2k1", "html_url": "https://github.com/codingl2k1", "followers_url": "https://api.github.com/users/codingl2k1/followers", "following_url": "https://api.github.com/users/codingl2k1/following{/other_user}", "gists_url": "https://api.github.com/users/codingl2k1/gists{/gist_id}", "starred_url": "https://api.github.com/users/codingl2k1/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/codingl2k1/subscriptions", "organizations_url": "https://api.github.com/users/codingl2k1/orgs", "repos_url": "https://api.github.com/users/codingl2k1/repos", "events_url": "https://api.github.com/users/codingl2k1/events{/privacy}", "received_events_url": "https://api.github.com/users/codingl2k1/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	null	[ "`HfApi` comes from the `huggingface_hub` package. You can use [this](https://huggingface.co/docs/huggingface_hub/v0.16.3/en/package_reference/utilities#huggingface_hub.configure_http_backend) utility to change the `huggingface_hub`'s `Session` proxies (see the example).\r\n\r\nWe plan to implement https://github.com/huggingface/datasets/issues/5080 and make this behavior more consistent eventually.", "> this\r\n\r\nThanks. I will try `huggingface_hub.configure_http_backend` to change session's config." ]	2023-07-14T07:22:55	2023-07-17T04:12:45	null	NONE	null	null	null	### Describe the bug ```python download_config = DownloadConfig(proxies={'https': '<my proxy>'}) builder = load_dataset_builder(..., download_config=download_config) ``` But, when getting the dataset_info from HfApi, the http requests not using the proxies. ### Steps to reproduce the bug 1. Setup proxies in DownloadConfig. 2. Call `load_dataset_build` with download_config. 3. Inspect the call stack in HfApi.dataset_info. ![image](https://github.com/huggingface/datasets/assets/138426806/33e538a8-2e22-4e63-b634-343febe5324b) ### Expected behavior DownloadConfig.proxies works for getting dataset_info. ### Environment info https://github.com/huggingface/datasets/commit/406b2212263c0d33f267e35b917f410ff6b3bc00 Python 3.11.4	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6032/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6032/timeline	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6031	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6031/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6031/comments	https://api.github.com/repos/huggingface/datasets/issues/6031/events	https://github.com/huggingface/datasets/issues/6031	1,804,183,858	I_kwDODunzps5riaky	6,031	Argument type for map function changes when using `input_columns` for `IterableDataset`	{ "login": "kwonmha", "id": 8953934, "node_id": "MDQ6VXNlcjg5NTM5MzQ=", "avatar_url": "https://avatars.githubusercontent.com/u/8953934?v=4", "gravatar_id": "", "url": "https://api.github.com/users/kwonmha", "html_url": "https://github.com/kwonmha", "followers_url": "https://api.github.com/users/kwonmha/followers", "following_url": "https://api.github.com/users/kwonmha/following{/other_user}", "gists_url": "https://api.github.com/users/kwonmha/gists{/gist_id}", "starred_url": "https://api.github.com/users/kwonmha/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/kwonmha/subscriptions", "organizations_url": "https://api.github.com/users/kwonmha/orgs", "repos_url": "https://api.github.com/users/kwonmha/repos", "events_url": "https://api.github.com/users/kwonmha/events{/privacy}", "received_events_url": "https://api.github.com/users/kwonmha/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "Yes, this is intended." ]	2023-07-14T05:11:14	2023-07-14T14:44:15	2023-07-14T14:44:15	NONE	null	null	null	### Describe the bug I wrote `tokenize(examples)` function as an argument for `map` function for `IterableDataset`. It process dictionary type `examples` as a parameter. It is used in `train_dataset = train_dataset.map(tokenize, batched=True)` No error is raised. And then, I found some unnecessary keys and values in `examples` so I added `input_columns` argument to `map` function to select keys and values. It gives me an error saying ``` TypeError: tokenize() takes 1 positional argument but 3 were given. ``` The code below matters. https://github.com/huggingface/datasets/blob/406b2212263c0d33f267e35b917f410ff6b3bc00/src/datasets/iterable_dataset.py#L687 For example, `inputs = {"a":1, "b":2, "c":3}`. If `self.input_coluns` is `None`, `inputs` is a dictionary type variable and `function_args` becomes a `list` of a single `dict` variable. `function_args` becomes `[{"a":1, "b":2, "c":3}]` Otherwise, lets say `self.input_columns = ["a", "c"]` `[inputs[col] for col in self.input_columns]` results in `[1, 3]`. I think it should be `[{"a":1, "c":3}]`. I want to ask if the resulting format is intended. Maybe I can modify `tokenize()` to have 2 parameters in this case instead of having 1 dictionary. But this is confusing to me. Or it should be fixed as `[{col:inputs[col] for col in self.input_columns}]` ### Steps to reproduce the bug Run `map` function of `IterableDataset` with `input_columns` argument. ### Expected behavior `function_args` looks better to have same format. I think it should be `[{"a":1, "c":3}]`. ### Environment info dataset version: 2.12 python: 3.8	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6031/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6031/timeline	null	completed	false
https://api.github.com/repos/huggingface/datasets/issues/6030	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6030/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6030/comments	https://api.github.com/repos/huggingface/datasets/issues/6030/events	https://github.com/huggingface/datasets/pull/6030	1,803,864,744	PR_kwDODunzps5Vd0ZG	6,030	fixed typo in comment	{ "login": "NightMachinery", "id": 36224762, "node_id": "MDQ6VXNlcjM2MjI0NzYy", "avatar_url": "https://avatars.githubusercontent.com/u/36224762?v=4", "gravatar_id": "", "url": "https://api.github.com/users/NightMachinery", "html_url": "https://github.com/NightMachinery", "followers_url": "https://api.github.com/users/NightMachinery/followers", "following_url": "https://api.github.com/users/NightMachinery/following{/other_user}", "gists_url": "https://api.github.com/users/NightMachinery/gists{/gist_id}", "starred_url": "https://api.github.com/users/NightMachinery/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/NightMachinery/subscriptions", "organizations_url": "https://api.github.com/users/NightMachinery/orgs", "repos_url": "https://api.github.com/users/NightMachinery/repos", "events_url": "https://api.github.com/users/NightMachinery/events{/privacy}", "received_events_url": "https://api.github.com/users/NightMachinery/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005855 / 0.011353 (-0.005498) \| 0.003556 / 0.011008 (-0.007452) \| 0.079430 / 0.038508 (0.040922) \| 0.056754 / 0.023109 (0.033645) \| 0.311718 / 0.275898 (0.035820) \| 0.346731 / 0.323480 (0.023251) \| 0.004414 / 0.007986 (-0.003571) \| 0.002835 / 0.004328 (-0.001493) \| 0.062138 / 0.004250 (0.057888) \| 0.044259 / 0.037052 (0.007206) \| 0.314681 / 0.258489 (0.056192) \| 0.359802 / 0.293841 (0.065961) \| 0.026684 / 0.128546 (-0.101862) \| 0.008023 / 0.075646 (-0.067623) \| 0.260148 / 0.419271 (-0.159123) \| 0.043734 / 0.043533 (0.000202) \| 0.312081 / 0.255139 (0.056942) \| 0.340004 / 0.283200 (0.056805) \| 0.019559 / 0.141683 (-0.122124) \| 1.488758 / 1.452155 (0.036604) \| 1.510828 / 1.492716 (0.018111) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.181376 / 0.018006 (0.163370) \| 0.441726 / 0.000490 (0.441236) \| 0.001722 / 0.000200 (0.001522) \| 0.000066 / 0.000054 (0.000012) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023760 / 0.037411 (-0.013651) \| 0.071847 / 0.014526 (0.057321) \| 0.082642 / 0.176557 (-0.093915) \| 0.145555 / 0.737135 (-0.591580) \| 0.084554 / 0.296338 (-0.211784) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.401688 / 0.215209 (0.186479) \| 4.000994 / 2.077655 (1.923339) \| 2.047109 / 1.504120 (0.542989) \| 1.891874 / 1.541195 (0.350679) \| 1.970599 / 1.468490 (0.502109) \| 0.500646 / 4.584777 (-4.084131) \| 3.006623 / 3.745712 (-0.739089) \| 4.248359 / 5.269862 (-1.021503) \| 2.613946 / 4.565676 (-1.951730) \| 0.057921 / 0.424275 (-0.366354) \| 0.006407 / 0.007607 (-0.001200) \| 0.470676 / 0.226044 (0.244631) \| 4.722280 / 2.268929 (2.453352) \| 2.448530 / 55.444624 (-52.996095) \| 2.175841 / 6.876477 (-4.700635) \| 2.352287 / 2.142072 (0.210214) \| 0.589049 / 4.805227 (-4.216179) \| 0.125145 / 6.500664 (-6.375519) \| 0.060829 / 0.075469 (-0.014640) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.189225 / 1.841788 (-0.652563) \| 16.753085 / 8.074308 (8.678777) \| 13.086512 / 10.191392 (2.895120) \| 0.132371 / 0.680424 (-0.548052) \| 0.016933 / 0.534201 (-0.517268) \| 0.328258 / 0.579283 (-0.251025) \| 0.344074 / 0.434364 (-0.090290) \| 0.374042 / 0.540337 (-0.166296) \| 0.515307 / 1.386936 (-0.871629) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005963 / 0.011353 (-0.005390) \| 0.003484 / 0.011008 (-0.007525) \| 0.062618 / 0.038508 (0.024110) \| 0.057217 / 0.023109 (0.034108) \| 0.426760 / 0.275898 (0.150862) \| 0.464422 / 0.323480 (0.140942) \| 0.005276 / 0.007986 (-0.002709) \| 0.002872 / 0.004328 (-0.001456) \| 0.062636 / 0.004250 (0.058385) \| 0.045953 / 0.037052 (0.008900) \| 0.433221 / 0.258489 (0.174732) \| 0.475087 / 0.293841 (0.181246) \| 0.027217 / 0.128546 (-0.101329) \| 0.007965 / 0.075646 (-0.067681) \| 0.067749 / 0.419271 (-0.351522) \| 0.041235 / 0.043533 (-0.002298) \| 0.425424 / 0.255139 (0.170285) \| 0.453390 / 0.283200 (0.170190) \| 0.020217 / 0.141683 (-0.121466) \| 1.436354 / 1.452155 (-0.015801) \| 1.492372 / 1.492716 (-0.000345) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.226896 / 0.018006 (0.208889) \| 0.411935 / 0.000490 (0.411445) \| 0.000356 / 0.000200 (0.000156) \| 0.000054 / 0.000054 (-0.000000) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024705 / 0.037411 (-0.012706) \| 0.076232 / 0.014526 (0.061706) \| 0.086949 / 0.176557 (-0.089608) \| 0.141867 / 0.737135 (-0.595269) \| 0.088199 / 0.296338 (-0.208140) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.419748 / 0.215209 (0.204539) \| 4.198597 / 2.077655 (2.120942) \| 2.338477 / 1.504120 (0.834357) \| 2.195741 / 1.541195 (0.654547) \| 2.278145 / 1.468490 (0.809655) \| 0.502365 / 4.584777 (-4.082412) \| 2.987773 / 3.745712 (-0.757939) \| 2.896526 / 5.269862 (-2.373336) \| 1.841610 / 4.565676 (-2.724067) \| 0.058032 / 0.424275 (-0.366243) \| 0.006470 / 0.007607 (-0.001137) \| 0.496969 / 0.226044 (0.270925) \| 4.960984 / 2.268929 (2.692056) \| 2.648615 / 55.444624 (-52.796009) \| 2.286846 / 6.876477 (-4.589631) \| 2.320176 / 2.142072 (0.178104) \| 0.600550 / 4.805227 (-4.204678) \| 0.125652 / 6.500664 (-6.375012) \| 0.062177 / 0.075469 (-0.013292) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.293063 / 1.841788 (-0.548725) \| 18.294204 / 8.074308 (10.219896) \| 13.720502 / 10.191392 (3.529110) \| 0.146480 / 0.680424 (-0.533944) \| 0.016965 / 0.534201 (-0.517236) \| 0.330137 / 0.579283 (-0.249146) \| 0.352051 / 0.434364 (-0.082313) \| 0.381754 / 0.540337 (-0.158584) \| 0.517935 / 1.386936 (-0.869001) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#269fcd31a2e759c65ffd5952ecef13e6a0d92574 \"CML watermark\")\n" ]	2023-07-13T22:49:57	2023-07-14T14:21:58	2023-07-14T14:13:38	CONTRIBUTOR	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6030", "html_url": "https://github.com/huggingface/datasets/pull/6030", "diff_url": "https://github.com/huggingface/datasets/pull/6030.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6030.patch", "merged_at": "2023-07-14T14:13:38" }	This mistake was a bit confusing, so I thought it was worth sending a PR over.	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6030/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6030/timeline	null	null	true
https://api.github.com/repos/huggingface/datasets/issues/6029	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6029/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6029/comments	https://api.github.com/repos/huggingface/datasets/issues/6029/events	https://github.com/huggingface/datasets/pull/6029	1,803,460,046	PR_kwDODunzps5VcbPW	6,029	[docs] Fix link	{ "login": "stevhliu", "id": 59462357, "node_id": "MDQ6VXNlcjU5NDYyMzU3", "avatar_url": "https://avatars.githubusercontent.com/u/59462357?v=4", "gravatar_id": "", "url": "https://api.github.com/users/stevhliu", "html_url": "https://github.com/stevhliu", "followers_url": "https://api.github.com/users/stevhliu/followers", "following_url": "https://api.github.com/users/stevhliu/following{/other_user}", "gists_url": "https://api.github.com/users/stevhliu/gists{/gist_id}", "starred_url": "https://api.github.com/users/stevhliu/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/stevhliu/subscriptions", "organizations_url": "https://api.github.com/users/stevhliu/orgs", "repos_url": "https://api.github.com/users/stevhliu/repos", "events_url": "https://api.github.com/users/stevhliu/events{/privacy}", "received_events_url": "https://api.github.com/users/stevhliu/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007039 / 0.011353 (-0.004314) \| 0.004175 / 0.011008 (-0.006833) \| 0.085426 / 0.038508 (0.046918) \| 0.079818 / 0.023109 (0.056709) \| 0.321924 / 0.275898 (0.046026) \| 0.345482 / 0.323480 (0.022002) \| 0.005510 / 0.007986 (-0.002475) \| 0.003452 / 0.004328 (-0.000877) \| 0.065158 / 0.004250 (0.060907) \| 0.058843 / 0.037052 (0.021791) \| 0.316280 / 0.258489 (0.057791) \| 0.351666 / 0.293841 (0.057825) \| 0.031190 / 0.128546 (-0.097357) \| 0.008500 / 0.075646 (-0.067147) \| 0.289595 / 0.419271 (-0.129676) \| 0.053798 / 0.043533 (0.010265) \| 0.315804 / 0.255139 (0.060665) \| 0.334957 / 0.283200 (0.051757) \| 0.024350 / 0.141683 (-0.117332) \| 1.515753 / 1.452155 (0.063599) \| 1.556215 / 1.492716 (0.063499) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.210378 / 0.018006 (0.192372) \| 0.469309 / 0.000490 (0.468820) \| 0.002890 / 0.000200 (0.002690) \| 0.000086 / 0.000054 (0.000031) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030214 / 0.037411 (-0.007197) \| 0.088492 / 0.014526 (0.073966) \| 0.098684 / 0.176557 (-0.077873) \| 0.156077 / 0.737135 (-0.581058) \| 0.098814 / 0.296338 (-0.197525) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.404548 / 0.215209 (0.189339) \| 4.026173 / 2.077655 (1.948518) \| 2.043216 / 1.504120 (0.539096) \| 1.880997 / 1.541195 (0.339802) \| 1.975205 / 1.468490 (0.506715) \| 0.489395 / 4.584777 (-4.095382) \| 3.684097 / 3.745712 (-0.061615) \| 5.126934 / 5.269862 (-0.142928) \| 3.092153 / 4.565676 (-1.473524) \| 0.057668 / 0.424275 (-0.366607) \| 0.007372 / 0.007607 (-0.000235) \| 0.479647 / 0.226044 (0.253603) \| 4.780207 / 2.268929 (2.511278) \| 2.533457 / 55.444624 (-52.911168) \| 2.182126 / 6.876477 (-4.694351) \| 2.431834 / 2.142072 (0.289761) \| 0.591760 / 4.805227 (-4.213467) \| 0.135450 / 6.500664 (-6.365214) \| 0.063218 / 0.075469 (-0.012251) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.262053 / 1.841788 (-0.579734) \| 20.246992 / 8.074308 (12.172684) \| 14.638222 / 10.191392 (4.446830) \| 0.150021 / 0.680424 (-0.530403) \| 0.018680 / 0.534201 (-0.515521) \| 0.395215 / 0.579283 (-0.184068) \| 0.421270 / 0.434364 (-0.013094) \| 0.458845 / 0.540337 (-0.081492) \| 0.634488 / 1.386936 (-0.752448) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007080 / 0.011353 (-0.004273) \| 0.004112 / 0.011008 (-0.006896) \| 0.066426 / 0.038508 (0.027918) \| 0.090088 / 0.023109 (0.066978) \| 0.400191 / 0.275898 (0.124293) \| 0.429614 / 0.323480 (0.106134) \| 0.005428 / 0.007986 (-0.002558) \| 0.003501 / 0.004328 (-0.000827) \| 0.065056 / 0.004250 (0.060806) \| 0.061643 / 0.037052 (0.024590) \| 0.398619 / 0.258489 (0.140130) \| 0.445497 / 0.293841 (0.151657) \| 0.031703 / 0.128546 (-0.096843) \| 0.008708 / 0.075646 (-0.066938) \| 0.071561 / 0.419271 (-0.347711) \| 0.050684 / 0.043533 (0.007151) \| 0.385361 / 0.255139 (0.130222) \| 0.409349 / 0.283200 (0.126149) \| 0.027388 / 0.141683 (-0.114295) \| 1.473021 / 1.452155 (0.020866) \| 1.525246 / 1.492716 (0.032529) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.237710 / 0.018006 (0.219704) \| 0.468719 / 0.000490 (0.468230) \| 0.000385 / 0.000200 (0.000185) \| 0.000054 / 0.000054 (-0.000000) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032539 / 0.037411 (-0.004872) \| 0.095324 / 0.014526 (0.080798) \| 0.102248 / 0.176557 (-0.074308) \| 0.156096 / 0.737135 (-0.581039) \| 0.103458 / 0.296338 (-0.192881) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.416226 / 0.215209 (0.201017) \| 4.141044 / 2.077655 (2.063389) \| 2.143732 / 1.504120 (0.639612) \| 2.001020 / 1.541195 (0.459825) \| 2.091194 / 1.468490 (0.622704) \| 0.489977 / 4.584777 (-4.094800) \| 3.579615 / 3.745712 (-0.166097) \| 3.438082 / 5.269862 (-1.831780) \| 2.069031 / 4.565676 (-2.496645) \| 0.056994 / 0.424275 (-0.367281) \| 0.007362 / 0.007607 (-0.000245) \| 0.493077 / 0.226044 (0.267033) \| 4.922622 / 2.268929 (2.653694) \| 2.627083 / 55.444624 (-52.817541) \| 2.301141 / 6.876477 (-4.575336) \| 2.356794 / 2.142072 (0.214722) \| 0.583792 / 4.805227 (-4.221436) \| 0.133707 / 6.500664 (-6.366958) \| 0.062892 / 0.075469 (-0.012577) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.364908 / 1.841788 (-0.476880) \| 20.641219 / 8.074308 (12.566911) \| 14.848528 / 10.191392 (4.657136) \| 0.174207 / 0.680424 (-0.506217) \| 0.018206 / 0.534201 (-0.515995) \| 0.413742 / 0.579283 (-0.165541) \| 0.419940 / 0.434364 (-0.014424) \| 0.458543 / 0.540337 (-0.081794) \| 0.616518 / 1.386936 (-0.770418) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#18b2202c3e7cdde05920078f01864964556427da \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006875 / 0.011353 (-0.004478) \| 0.003489 / 0.011008 (-0.007519) \| 0.082077 / 0.038508 (0.043569) \| 0.103011 / 0.023109 (0.079902) \| 0.370572 / 0.275898 (0.094674) \| 0.416400 / 0.323480 (0.092920) \| 0.004048 / 0.007986 (-0.003938) \| 0.003563 / 0.004328 (-0.000765) \| 0.062666 / 0.004250 (0.058416) \| 0.063664 / 0.037052 (0.026612) \| 0.374206 / 0.258489 (0.115717) \| 0.425590 / 0.293841 (0.131749) \| 0.028174 / 0.128546 (-0.100373) \| 0.007906 / 0.075646 (-0.067741) \| 0.266251 / 0.419271 (-0.153020) \| 0.045923 / 0.043533 (0.002390) \| 0.376746 / 0.255139 (0.121607) \| 0.401950 / 0.283200 (0.118750) \| 0.024628 / 0.141683 (-0.117054) \| 1.441903 / 1.452155 (-0.010252) \| 1.537494 / 1.492716 (0.044777) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.214696 / 0.018006 (0.196690) \| 0.425626 / 0.000490 (0.425137) \| 0.003370 / 0.000200 (0.003170) \| 0.000071 / 0.000054 (0.000017) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023133 / 0.037411 (-0.014279) \| 0.072374 / 0.014526 (0.057848) \| 0.081255 / 0.176557 (-0.095301) \| 0.146960 / 0.737135 (-0.590175) \| 0.081748 / 0.296338 (-0.214590) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.390683 / 0.215209 (0.175473) \| 3.893166 / 2.077655 (1.815511) \| 1.884321 / 1.504120 (0.380201) \| 1.701899 / 1.541195 (0.160704) \| 1.737839 / 1.468490 (0.269349) \| 0.497008 / 4.584777 (-4.087769) \| 3.041211 / 3.745712 (-0.704501) \| 3.519947 / 5.269862 (-1.749915) \| 2.015085 / 4.565676 (-2.550592) \| 0.057685 / 0.424275 (-0.366590) \| 0.006415 / 0.007607 (-0.001192) \| 0.465565 / 0.226044 (0.239520) \| 4.635224 / 2.268929 (2.366295) \| 2.297941 / 55.444624 (-53.146683) \| 1.946670 / 6.876477 (-4.929807) \| 2.078527 / 2.142072 (-0.063546) \| 0.584101 / 4.805227 (-4.221126) \| 0.126488 / 6.500664 (-6.374176) \| 0.060819 / 0.075469 (-0.014650) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.223400 / 1.841788 (-0.618388) \| 17.960923 / 8.074308 (9.886615) \| 13.187683 / 10.191392 (2.996291) \| 0.129258 / 0.680424 (-0.551166) \| 0.016601 / 0.534201 (-0.517600) \| 0.330028 / 0.579283 (-0.249255) \| 0.353861 / 0.434364 (-0.080503) \| 0.376022 / 0.540337 (-0.164315) \| 0.518145 / 1.386936 (-0.868791) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006015 / 0.011353 (-0.005338) \| 0.003605 / 0.011008 (-0.007403) \| 0.062169 / 0.038508 (0.023661) \| 0.056094 / 0.023109 (0.032985) \| 0.353085 / 0.275898 (0.077187) \| 0.393744 / 0.323480 (0.070265) \| 0.004672 / 0.007986 (-0.003313) \| 0.002859 / 0.004328 (-0.001469) \| 0.062992 / 0.004250 (0.058742) \| 0.049767 / 0.037052 (0.012714) \| 0.356850 / 0.258489 (0.098361) \| 0.403731 / 0.293841 (0.109890) \| 0.026664 / 0.128546 (-0.101882) \| 0.008026 / 0.075646 (-0.067621) \| 0.067944 / 0.419271 (-0.351327) \| 0.042133 / 0.043533 (-0.001400) \| 0.353865 / 0.255139 (0.098726) \| 0.383461 / 0.283200 (0.100261) \| 0.021250 / 0.141683 (-0.120433) \| 1.428102 / 1.452155 (-0.024053) \| 1.481061 / 1.492716 (-0.011655) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.223552 / 0.018006 (0.205546) \| 0.402390 / 0.000490 (0.401900) \| 0.000721 / 0.000200 (0.000521) \| 0.000059 / 0.000054 (0.000005) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025065 / 0.037411 (-0.012347) \| 0.075537 / 0.014526 (0.061011) \| 0.083519 / 0.176557 (-0.093037) \| 0.137068 / 0.737135 (-0.600068) \| 0.084165 / 0.296338 (-0.212173) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.420176 / 0.215209 (0.204967) \| 4.206226 / 2.077655 (2.128571) \| 2.168089 / 1.504120 (0.663969) \| 1.987299 / 1.541195 (0.446104) \| 2.029489 / 1.468490 (0.560999) \| 0.495822 / 4.584777 (-4.088955) \| 3.106580 / 3.745712 (-0.639132) \| 3.833215 / 5.269862 (-1.436647) \| 2.450450 / 4.565676 (-2.115226) \| 0.056979 / 0.424275 (-0.367296) \| 0.006514 / 0.007607 (-0.001093) \| 0.503646 / 0.226044 (0.277601) \| 5.035035 / 2.268929 (2.766106) \| 2.608245 / 55.444624 (-52.836379) \| 2.245492 / 6.876477 (-4.630985) \| 2.262868 / 2.142072 (0.120795) \| 0.590736 / 4.805227 (-4.214491) \| 0.124637 / 6.500664 (-6.376027) \| 0.061442 / 0.075469 (-0.014027) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.316736 / 1.841788 (-0.525052) \| 17.948635 / 8.074308 (9.874327) \| 13.752442 / 10.191392 (3.561050) \| 0.144107 / 0.680424 (-0.536317) \| 0.017112 / 0.534201 (-0.517089) \| 0.336537 / 0.579283 (-0.242746) \| 0.347832 / 0.434364 (-0.086532) \| 0.392944 / 0.540337 (-0.147393) \| 0.534455 / 1.386936 (-0.852481) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#406b2212263c0d33f267e35b917f410ff6b3bc00 \"CML watermark\")\n" ]	2023-07-13T17:24:12	2023-07-13T17:47:41	2023-07-13T17:38:59	MEMBER	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6029", "html_url": "https://github.com/huggingface/datasets/pull/6029", "diff_url": "https://github.com/huggingface/datasets/pull/6029.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6029.patch", "merged_at": "2023-07-13T17:38:59" }	Fixes link to the builder classes :)	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6029/reactions", "total_count": 1, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 1, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6029/timeline	null	null	true
https://api.github.com/repos/huggingface/datasets/issues/6028	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6028/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6028/comments	https://api.github.com/repos/huggingface/datasets/issues/6028/events	https://github.com/huggingface/datasets/pull/6028	1,803,294,981	PR_kwDODunzps5Vb3LJ	6,028	Use new hffs	{ "login": "lhoestq", "id": 42851186, "node_id": "MDQ6VXNlcjQyODUxMTg2", "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "gravatar_id": "", "url": "https://api.github.com/users/lhoestq", "html_url": "https://github.com/lhoestq", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "repos_url": "https://api.github.com/users/lhoestq/repos", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006665 / 0.011353 (-0.004688) \| 0.004376 / 0.011008 (-0.006633) \| 0.085529 / 0.038508 (0.047021) \| 0.076372 / 0.023109 (0.053263) \| 0.310019 / 0.275898 (0.034121) \| 0.341404 / 0.323480 (0.017924) \| 0.005666 / 0.007986 (-0.002320) \| 0.003763 / 0.004328 (-0.000566) \| 0.064678 / 0.004250 (0.060427) \| 0.059283 / 0.037052 (0.022231) \| 0.316194 / 0.258489 (0.057704) \| 0.349397 / 0.293841 (0.055557) \| 0.031199 / 0.128546 (-0.097347) \| 0.008724 / 0.075646 (-0.066923) \| 0.300236 / 0.419271 (-0.119035) \| 0.068872 / 0.043533 (0.025339) \| 0.308521 / 0.255139 (0.053382) \| 0.331292 / 0.283200 (0.048092) \| 0.028236 / 0.141683 (-0.113447) \| 1.501365 / 1.452155 (0.049211) \| 1.554334 / 1.492716 (0.061618) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.238291 / 0.018006 (0.220285) \| 0.565069 / 0.000490 (0.564580) \| 0.001626 / 0.000200 (0.001426) \| 0.000070 / 0.000054 (0.000015) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029777 / 0.037411 (-0.007634) \| 0.082873 / 0.014526 (0.068347) \| 0.099619 / 0.176557 (-0.076937) \| 0.156572 / 0.737135 (-0.580563) \| 0.099887 / 0.296338 (-0.196452) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.401017 / 0.215209 (0.185808) \| 3.827192 / 2.077655 (1.749537) \| 1.861554 / 1.504120 (0.357434) \| 1.699869 / 1.541195 (0.158674) \| 1.720043 / 1.468490 (0.251553) \| 0.486757 / 4.584777 (-4.098020) \| 3.638125 / 3.745712 (-0.107587) \| 5.844959 / 5.269862 (0.575097) \| 3.454901 / 4.565676 (-1.110775) \| 0.057650 / 0.424275 (-0.366625) \| 0.007341 / 0.007607 (-0.000266) \| 0.462698 / 0.226044 (0.236654) \| 4.633472 / 2.268929 (2.364544) \| 2.287607 / 55.444624 (-53.157017) \| 2.057318 / 6.876477 (-4.819159) \| 2.203657 / 2.142072 (0.061584) \| 0.598136 / 4.805227 (-4.207091) \| 0.134012 / 6.500664 (-6.366653) \| 0.060824 / 0.075469 (-0.014645) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.277752 / 1.841788 (-0.564036) \| 20.013398 / 8.074308 (11.939089) \| 14.372993 / 10.191392 (4.181601) \| 0.169991 / 0.680424 (-0.510433) \| 0.018344 / 0.534201 (-0.515857) \| 0.396985 / 0.579283 (-0.182299) \| 0.416289 / 0.434364 (-0.018075) \| 0.458658 / 0.540337 (-0.081680) \| 0.692980 / 1.386936 (-0.693956) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006689 / 0.011353 (-0.004664) \| 0.004393 / 0.011008 (-0.006615) \| 0.064069 / 0.038508 (0.025561) \| 0.080717 / 0.023109 (0.057607) \| 0.370090 / 0.275898 (0.094191) \| 0.400432 / 0.323480 (0.076952) \| 0.005613 / 0.007986 (-0.002372) \| 0.003641 / 0.004328 (-0.000687) \| 0.064771 / 0.004250 (0.060520) \| 0.057555 / 0.037052 (0.020502) \| 0.392156 / 0.258489 (0.133667) \| 0.409842 / 0.293841 (0.116001) \| 0.031500 / 0.128546 (-0.097047) \| 0.008786 / 0.075646 (-0.066860) \| 0.070342 / 0.419271 (-0.348929) \| 0.048646 / 0.043533 (0.005113) \| 0.360914 / 0.255139 (0.105775) \| 0.387626 / 0.283200 (0.104426) \| 0.022787 / 0.141683 (-0.118896) \| 1.508915 / 1.452155 (0.056761) \| 1.539719 / 1.492716 (0.047002) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.257985 / 0.018006 (0.239979) \| 0.550990 / 0.000490 (0.550501) \| 0.000407 / 0.000200 (0.000207) \| 0.000057 / 0.000054 (0.000003) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030183 / 0.037411 (-0.007228) \| 0.086882 / 0.014526 (0.072356) \| 0.102382 / 0.176557 (-0.074175) \| 0.154745 / 0.737135 (-0.582390) \| 0.104008 / 0.296338 (-0.192331) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.426284 / 0.215209 (0.211075) \| 4.240812 / 2.077655 (2.163158) \| 2.261240 / 1.504120 (0.757120) \| 2.085905 / 1.541195 (0.544710) \| 2.160374 / 1.468490 (0.691883) \| 0.481126 / 4.584777 (-4.103651) \| 3.516234 / 3.745712 (-0.229478) \| 3.325322 / 5.269862 (-1.944539) \| 2.043307 / 4.565676 (-2.522369) \| 0.056663 / 0.424275 (-0.367612) \| 0.007786 / 0.007607 (0.000179) \| 0.497614 / 0.226044 (0.271570) \| 4.974529 / 2.268929 (2.705600) \| 2.700018 / 55.444624 (-52.744606) \| 2.393778 / 6.876477 (-4.482699) \| 2.628202 / 2.142072 (0.486130) \| 0.594316 / 4.805227 (-4.210911) \| 0.147092 / 6.500664 (-6.353572) \| 0.062207 / 0.075469 (-0.013262) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.315676 / 1.841788 (-0.526112) \| 20.749251 / 8.074308 (12.674943) \| 14.371553 / 10.191392 (4.180160) \| 0.170249 / 0.680424 (-0.510175) \| 0.018478 / 0.534201 (-0.515722) \| 0.395710 / 0.579283 (-0.183573) \| 0.409706 / 0.434364 (-0.024658) \| 0.463454 / 0.540337 (-0.076884) \| 0.615657 / 1.386936 (-0.771279) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c5a752d8e8ca0a6ed118b024ba03c1b4a2881177 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007224 / 0.011353 (-0.004129) \| 0.004506 / 0.011008 (-0.006503) \| 0.096729 / 0.038508 (0.058221) \| 0.082394 / 0.023109 (0.059284) \| 0.390954 / 0.275898 (0.115056) \| 0.416647 / 0.323480 (0.093167) \| 0.005894 / 0.007986 (-0.002092) \| 0.003756 / 0.004328 (-0.000572) \| 0.075800 / 0.004250 (0.071549) \| 0.062683 / 0.037052 (0.025631) \| 0.398959 / 0.258489 (0.140470) \| 0.436624 / 0.293841 (0.142783) \| 0.034650 / 0.128546 (-0.093896) \| 0.009655 / 0.075646 (-0.065991) \| 0.315761 / 0.419271 (-0.103511) \| 0.060957 / 0.043533 (0.017424) \| 0.385649 / 0.255139 (0.130510) \| 0.394022 / 0.283200 (0.110822) \| 0.024601 / 0.141683 (-0.117082) \| 1.729586 / 1.452155 (0.277431) \| 1.724153 / 1.492716 (0.231437) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.207070 / 0.018006 (0.189063) \| 0.466502 / 0.000490 (0.466012) \| 0.010739 / 0.000200 (0.010540) \| 0.000214 / 0.000054 (0.000160) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031633 / 0.037411 (-0.005779) \| 0.095345 / 0.014526 (0.080819) \| 0.105399 / 0.176557 (-0.071157) \| 0.174173 / 0.737135 (-0.562962) \| 0.104207 / 0.296338 (-0.192132) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.435312 / 0.215209 (0.220103) \| 4.265600 / 2.077655 (2.187946) \| 2.056500 / 1.504120 (0.552380) \| 1.848023 / 1.541195 (0.306828) \| 1.946156 / 1.468490 (0.477666) \| 0.557788 / 4.584777 (-4.026989) \| 4.070289 / 3.745712 (0.324577) \| 3.608027 / 5.269862 (-1.661835) \| 2.214556 / 4.565676 (-2.351121) \| 0.062623 / 0.424275 (-0.361652) \| 0.008083 / 0.007607 (0.000476) \| 0.491782 / 0.226044 (0.265738) \| 4.989963 / 2.268929 (2.721035) \| 2.575867 / 55.444624 (-52.868757) \| 2.208045 / 6.876477 (-4.668431) \| 2.364184 / 2.142072 (0.222112) \| 0.633925 / 4.805227 (-4.171302) \| 0.144323 / 6.500664 (-6.356341) \| 0.067505 / 0.075469 (-0.007965) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.467219 / 1.841788 (-0.374569) \| 22.334967 / 8.074308 (14.260659) \| 15.715747 / 10.191392 (5.524355) \| 0.175443 / 0.680424 (-0.504980) \| 0.026165 / 0.534201 (-0.508036) \| 0.490675 / 0.579283 (-0.088608) \| 0.509211 / 0.434364 (0.074847) \| 0.586303 / 0.540337 (0.045965) \| 0.785052 / 1.386936 (-0.601884) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007893 / 0.011353 (-0.003460) \| 0.004577 / 0.011008 (-0.006431) \| 0.075781 / 0.038508 (0.037273) \| 0.095492 / 0.023109 (0.072382) \| 0.433259 / 0.275898 (0.157361) \| 0.469386 / 0.323480 (0.145906) \| 0.006317 / 0.007986 (-0.001669) \| 0.003708 / 0.004328 (-0.000621) \| 0.074417 / 0.004250 (0.070167) \| 0.068605 / 0.037052 (0.031552) \| 0.448701 / 0.258489 (0.190212) \| 0.469131 / 0.293841 (0.175290) \| 0.036647 / 0.128546 (-0.091899) \| 0.010077 / 0.075646 (-0.065570) \| 0.082457 / 0.419271 (-0.336815) \| 0.063255 / 0.043533 (0.019722) \| 0.428144 / 0.255139 (0.173005) \| 0.451872 / 0.283200 (0.168672) \| 0.033953 / 0.141683 (-0.107730) \| 1.781752 / 1.452155 (0.329597) \| 1.869014 / 1.492716 (0.376297) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.223596 / 0.018006 (0.205590) \| 0.470307 / 0.000490 (0.469818) \| 0.005059 / 0.000200 (0.004859) \| 0.000104 / 0.000054 (0.000049) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.038804 / 0.037411 (0.001393) \| 0.117879 / 0.014526 (0.103353) \| 0.140701 / 0.176557 (-0.035855) \| 0.194672 / 0.737135 (-0.542463) \| 0.132806 / 0.296338 (-0.163533) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.510109 / 0.215209 (0.294900) \| 4.729457 / 2.077655 (2.651803) \| 2.512113 / 1.504120 (1.007993) \| 2.302553 / 1.541195 (0.761358) \| 2.420462 / 1.468490 (0.951972) \| 0.531682 / 4.584777 (-4.053095) \| 4.061208 / 3.745712 (0.315496) \| 3.588542 / 5.269862 (-1.681320) \| 2.203187 / 4.565676 (-2.362489) \| 0.065791 / 0.424275 (-0.358484) \| 0.008839 / 0.007607 (0.001232) \| 0.562041 / 0.226044 (0.335997) \| 5.702340 / 2.268929 (3.433412) \| 3.127609 / 55.444624 (-52.317015) \| 2.823060 / 6.876477 (-4.053417) \| 2.898675 / 2.142072 (0.756603) \| 0.659589 / 4.805227 (-4.145638) \| 0.148798 / 6.500664 (-6.351866) \| 0.070787 / 0.075469 (-0.004682) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.478317 / 1.841788 (-0.363471) \| 21.995400 / 8.074308 (13.921092) \| 16.770729 / 10.191392 (6.579337) \| 0.226333 / 0.680424 (-0.454091) \| 0.021835 / 0.534201 (-0.512366) \| 0.460373 / 0.579283 (-0.118910) \| 0.479494 / 0.434364 (0.045130) \| 0.529470 / 0.540337 (-0.010868) \| 0.718066 / 1.386936 (-0.668870) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9a717b8eb80b0e50b25818127f79a35e0866fb14 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007824 / 0.011353 (-0.003529) \| 0.004601 / 0.011008 (-0.006407) \| 0.100025 / 0.038508 (0.061517) \| 0.096046 / 0.023109 (0.072936) \| 0.376226 / 0.275898 (0.100328) \| 0.410905 / 0.323480 (0.087425) \| 0.006048 / 0.007986 (-0.001938) \| 0.003817 / 0.004328 (-0.000511) \| 0.076624 / 0.004250 (0.072374) \| 0.066390 / 0.037052 (0.029338) \| 0.380098 / 0.258489 (0.121609) \| 0.413603 / 0.293841 (0.119762) \| 0.036546 / 0.128546 (-0.092001) \| 0.009881 / 0.075646 (-0.065765) \| 0.344338 / 0.419271 (-0.074934) \| 0.061882 / 0.043533 (0.018350) \| 0.368568 / 0.255139 (0.113429) \| 0.397133 / 0.283200 (0.113934) \| 0.027255 / 0.141683 (-0.114428) \| 1.795099 / 1.452155 (0.342945) \| 1.852443 / 1.492716 (0.359727) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.247436 / 0.018006 (0.229430) \| 0.494119 / 0.000490 (0.493629) \| 0.004359 / 0.000200 (0.004159) \| 0.000089 / 0.000054 (0.000035) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034765 / 0.037411 (-0.002647) \| 0.104541 / 0.014526 (0.090015) \| 0.113898 / 0.176557 (-0.062659) \| 0.183634 / 0.737135 (-0.553501) \| 0.116423 / 0.296338 (-0.179916) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.458747 / 0.215209 (0.243538) \| 4.555740 / 2.077655 (2.478085) \| 2.217240 / 1.504120 (0.713121) \| 2.039879 / 1.541195 (0.498684) \| 2.088581 / 1.468490 (0.620091) \| 0.588063 / 4.584777 (-3.996714) \| 4.238226 / 3.745712 (0.492514) \| 4.768060 / 5.269862 (-0.501802) \| 2.857117 / 4.565676 (-1.708560) \| 0.068742 / 0.424275 (-0.355533) \| 0.008667 / 0.007607 (0.001059) \| 0.549294 / 0.226044 (0.323249) \| 5.464635 / 2.268929 (3.195706) \| 2.744435 / 55.444624 (-52.700189) \| 2.347660 / 6.876477 (-4.528816) \| 2.616816 / 2.142072 (0.474743) \| 0.703701 / 4.805227 (-4.101526) \| 0.159749 / 6.500664 (-6.340915) \| 0.071990 / 0.075469 (-0.003479) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.486599 / 1.841788 (-0.355188) \| 22.745438 / 8.074308 (14.671130) \| 16.822332 / 10.191392 (6.630940) \| 0.184730 / 0.680424 (-0.495694) \| 0.021267 / 0.534201 (-0.512934) \| 0.467108 / 0.579283 (-0.112176) \| 0.472674 / 0.434364 (0.038311) \| 0.548094 / 0.540337 (0.007756) \| 0.735885 / 1.386936 (-0.651051) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007746 / 0.011353 (-0.003607) \| 0.004585 / 0.011008 (-0.006423) \| 0.076943 / 0.038508 (0.038435) \| 0.087473 / 0.023109 (0.064363) \| 0.480099 / 0.275898 (0.204201) \| 0.495271 / 0.323480 (0.171791) \| 0.006348 / 0.007986 (-0.001638) \| 0.003902 / 0.004328 (-0.000426) \| 0.077586 / 0.004250 (0.073335) \| 0.066467 / 0.037052 (0.029415) \| 0.468741 / 0.258489 (0.210252) \| 0.506778 / 0.293841 (0.212937) \| 0.036877 / 0.128546 (-0.091669) \| 0.010102 / 0.075646 (-0.065545) \| 0.084419 / 0.419271 (-0.334852) \| 0.058721 / 0.043533 (0.015188) \| 0.453633 / 0.255139 (0.198494) \| 0.481171 / 0.283200 (0.197971) \| 0.028716 / 0.141683 (-0.112967) \| 1.853048 / 1.452155 (0.400893) \| 1.885847 / 1.492716 (0.393130) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.192136 / 0.018006 (0.174130) \| 0.484481 / 0.000490 (0.483991) \| 0.002951 / 0.000200 (0.002751) \| 0.000098 / 0.000054 (0.000044) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.037949 / 0.037411 (0.000538) \| 0.108364 / 0.014526 (0.093838) \| 0.119542 / 0.176557 (-0.057014) \| 0.188542 / 0.737135 (-0.548593) \| 0.122011 / 0.296338 (-0.174327) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.483135 / 0.215209 (0.267926) \| 4.849715 / 2.077655 (2.772060) \| 2.497736 / 1.504120 (0.993616) \| 2.314243 / 1.541195 (0.773048) \| 2.412739 / 1.468490 (0.944249) \| 0.564137 / 4.584777 (-4.020639) \| 4.242273 / 3.745712 (0.496561) \| 6.337843 / 5.269862 (1.067982) \| 3.923250 / 4.565676 (-0.642426) \| 0.066464 / 0.424275 (-0.357811) \| 0.009217 / 0.007607 (0.001610) \| 0.575667 / 0.226044 (0.349623) \| 5.746187 / 2.268929 (3.477258) \| 3.069655 / 55.444624 (-52.374969) \| 2.674798 / 6.876477 (-4.201679) \| 2.956535 / 2.142072 (0.814463) \| 0.701043 / 4.805227 (-4.104185) \| 0.157241 / 6.500664 (-6.343423) \| 0.073175 / 0.075469 (-0.002294) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.609943 / 1.841788 (-0.231844) \| 23.478594 / 8.074308 (15.404286) \| 17.454437 / 10.191392 (7.263045) \| 0.186422 / 0.680424 (-0.494002) \| 0.021703 / 0.534201 (-0.512498) \| 0.471704 / 0.579283 (-0.107579) \| 0.480553 / 0.434364 (0.046189) \| 0.552881 / 0.540337 (0.012544) \| 0.722515 / 1.386936 (-0.664421) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#84645f80049cd00d9e0d4908faf3c3203fdcf21d \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007542 / 0.011353 (-0.003811) \| 0.004692 / 0.011008 (-0.006316) \| 0.099155 / 0.038508 (0.060647) \| 0.089365 / 0.023109 (0.066256) \| 0.370870 / 0.275898 (0.094972) \| 0.422152 / 0.323480 (0.098673) \| 0.006223 / 0.007986 (-0.001763) \| 0.003852 / 0.004328 (-0.000476) \| 0.075438 / 0.004250 (0.071188) \| 0.065973 / 0.037052 (0.028921) \| 0.381513 / 0.258489 (0.123024) \| 0.416196 / 0.293841 (0.122355) \| 0.035483 / 0.128546 (-0.093063) \| 0.009884 / 0.075646 (-0.065762) \| 0.341290 / 0.419271 (-0.077982) \| 0.060546 / 0.043533 (0.017014) \| 0.365101 / 0.255139 (0.109962) \| 0.391058 / 0.283200 (0.107859) \| 0.026325 / 0.141683 (-0.115358) \| 1.815168 / 1.452155 (0.363013) \| 1.834711 / 1.492716 (0.341994) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.222177 / 0.018006 (0.204171) \| 0.501151 / 0.000490 (0.500662) \| 0.010202 / 0.000200 (0.010002) \| 0.000102 / 0.000054 (0.000048) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034043 / 0.037411 (-0.003368) \| 0.097884 / 0.014526 (0.083358) \| 0.114022 / 0.176557 (-0.062534) \| 0.186200 / 0.737135 (-0.550935) \| 0.115555 / 0.296338 (-0.180783) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.485857 / 0.215209 (0.270648) \| 4.959263 / 2.077655 (2.881608) \| 2.501085 / 1.504120 (0.996965) \| 2.234660 / 1.541195 (0.693465) \| 2.238585 / 1.468490 (0.770095) \| 0.645431 / 4.584777 (-3.939345) \| 4.434311 / 3.745712 (0.688599) \| 4.771491 / 5.269862 (-0.498371) \| 2.778963 / 4.565676 (-1.786714) \| 0.075615 / 0.424275 (-0.348660) \| 0.009502 / 0.007607 (0.001895) \| 0.546539 / 0.226044 (0.320495) \| 5.464242 / 2.268929 (3.195314) \| 2.894101 / 55.444624 (-52.550524) \| 2.513761 / 6.876477 (-4.362715) \| 2.719843 / 2.142072 (0.577770) \| 0.678828 / 4.805227 (-4.126399) \| 0.157839 / 6.500664 (-6.342825) \| 0.071305 / 0.075469 (-0.004164) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.496879 / 1.841788 (-0.344909) \| 22.214452 / 8.074308 (14.140144) \| 17.707541 / 10.191392 (7.516149) \| 0.197008 / 0.680424 (-0.483416) \| 0.024883 / 0.534201 (-0.509318) \| 0.493611 / 0.579283 (-0.085672) \| 0.500677 / 0.434364 (0.066313) \| 0.569381 / 0.540337 (0.029044) \| 0.773950 / 1.386936 (-0.612986) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007337 / 0.011353 (-0.004015) \| 0.004572 / 0.011008 (-0.006436) \| 0.091123 / 0.038508 (0.052615) \| 0.079762 / 0.023109 (0.056652) \| 0.450527 / 0.275898 (0.174629) \| 0.525097 / 0.323480 (0.201617) \| 0.005873 / 0.007986 (-0.002112) \| 0.003797 / 0.004328 (-0.000532) \| 0.076259 / 0.004250 (0.072009) \| 0.062745 / 0.037052 (0.025692) \| 0.465553 / 0.258489 (0.207064) \| 0.546026 / 0.293841 (0.252186) \| 0.035638 / 0.128546 (-0.092909) \| 0.010086 / 0.075646 (-0.065560) \| 0.109269 / 0.419271 (-0.310002) \| 0.056765 / 0.043533 (0.013233) \| 0.440887 / 0.255139 (0.185748) \| 0.513325 / 0.283200 (0.230125) \| 0.027206 / 0.141683 (-0.114476) \| 1.863564 / 1.452155 (0.411409) \| 1.918206 / 1.492716 (0.425490) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.266479 / 0.018006 (0.248473) \| 0.487971 / 0.000490 (0.487481) \| 0.012246 / 0.000200 (0.012046) \| 0.000119 / 0.000054 (0.000065) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.035281 / 0.037411 (-0.002130) \| 0.102991 / 0.014526 (0.088465) \| 0.114638 / 0.176557 (-0.061919) \| 0.184117 / 0.737135 (-0.553018) \| 0.117943 / 0.296338 (-0.178396) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.497897 / 0.215209 (0.282688) \| 4.973806 / 2.077655 (2.896151) \| 2.596146 / 1.504120 (1.092026) \| 2.419694 / 1.541195 (0.878499) \| 2.525784 / 1.468490 (1.057294) \| 0.568021 / 4.584777 (-4.016756) \| 4.296431 / 3.745712 (0.550719) \| 3.690682 / 5.269862 (-1.579179) \| 2.345965 / 4.565676 (-2.219712) \| 0.066859 / 0.424275 (-0.357416) \| 0.009093 / 0.007607 (0.001486) \| 0.582616 / 0.226044 (0.356571) \| 5.826528 / 2.268929 (3.557600) \| 3.253222 / 55.444624 (-52.191403) \| 2.798447 / 6.876477 (-4.078030) \| 3.054609 / 2.142072 (0.912537) \| 0.678816 / 4.805227 (-4.126411) \| 0.157966 / 6.500664 (-6.342698) \| 0.073797 / 0.075469 (-0.001672) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.599480 / 1.841788 (-0.242308) \| 23.249738 / 8.074308 (15.175430) \| 16.965406 / 10.191392 (6.774014) \| 0.171390 / 0.680424 (-0.509034) \| 0.021810 / 0.534201 (-0.512391) \| 0.483339 / 0.579283 (-0.095944) \| 0.496615 / 0.434364 (0.062251) \| 0.583786 / 0.540337 (0.043448) \| 0.741699 / 1.386936 (-0.645237) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7935cd2e564f5d1c66ed1acf731703724ba7a287 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006054 / 0.011353 (-0.005299) \| 0.003706 / 0.011008 (-0.007302) \| 0.080060 / 0.038508 (0.041552) \| 0.061479 / 0.023109 (0.038370) \| 0.327981 / 0.275898 (0.052083) \| 0.356930 / 0.323480 (0.033450) \| 0.004671 / 0.007986 (-0.003315) \| 0.002901 / 0.004328 (-0.001428) \| 0.062425 / 0.004250 (0.058174) \| 0.046310 / 0.037052 (0.009258) \| 0.323657 / 0.258489 (0.065168) \| 0.370130 / 0.293841 (0.076289) \| 0.027151 / 0.128546 (-0.101395) \| 0.007850 / 0.075646 (-0.067797) \| 0.262300 / 0.419271 (-0.156971) \| 0.045456 / 0.043533 (0.001923) \| 0.325569 / 0.255139 (0.070430) \| 0.352962 / 0.283200 (0.069762) \| 0.020156 / 0.141683 (-0.121527) \| 1.429404 / 1.452155 (-0.022750) \| 1.615032 / 1.492716 (0.122316) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.187309 / 0.018006 (0.169303) \| 0.428848 / 0.000490 (0.428358) \| 0.003599 / 0.000200 (0.003399) \| 0.000069 / 0.000054 (0.000015) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023260 / 0.037411 (-0.014151) \| 0.072467 / 0.014526 (0.057941) \| 0.082398 / 0.176557 (-0.094159) \| 0.142573 / 0.737135 (-0.594562) \| 0.082570 / 0.296338 (-0.213768) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.426503 / 0.215209 (0.211294) \| 4.267875 / 2.077655 (2.190220) \| 2.189762 / 1.504120 (0.685642) \| 2.027992 / 1.541195 (0.486798) \| 2.053211 / 1.468490 (0.584721) \| 0.503850 / 4.584777 (-4.080927) \| 3.086444 / 3.745712 (-0.659268) \| 3.319492 / 5.269862 (-1.950370) \| 2.070714 / 4.565676 (-2.494962) \| 0.057591 / 0.424275 (-0.366684) \| 0.006407 / 0.007607 (-0.001200) \| 0.501145 / 0.226044 (0.275100) \| 5.017753 / 2.268929 (2.748825) \| 2.643145 / 55.444624 (-52.801479) \| 2.327440 / 6.876477 (-4.549037) \| 2.460250 / 2.142072 (0.318178) \| 0.589397 / 4.805227 (-4.215830) \| 0.124948 / 6.500664 (-6.375716) \| 0.060450 / 0.075469 (-0.015020) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.279870 / 1.841788 (-0.561918) \| 18.115908 / 8.074308 (10.041600) \| 13.570032 / 10.191392 (3.378640) \| 0.132981 / 0.680424 (-0.547442) \| 0.016942 / 0.534201 (-0.517259) \| 0.333591 / 0.579283 (-0.245692) \| 0.358844 / 0.434364 (-0.075520) \| 0.395748 / 0.540337 (-0.144590) \| 0.546213 / 1.386936 (-0.840723) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006062 / 0.011353 (-0.005291) \| 0.003673 / 0.011008 (-0.007336) \| 0.064726 / 0.038508 (0.026218) \| 0.061854 / 0.023109 (0.038745) \| 0.385343 / 0.275898 (0.109445) \| 0.441284 / 0.323480 (0.117805) \| 0.004830 / 0.007986 (-0.003156) \| 0.002909 / 0.004328 (-0.001420) \| 0.063874 / 0.004250 (0.059624) \| 0.049331 / 0.037052 (0.012278) \| 0.418484 / 0.258489 (0.159995) \| 0.451397 / 0.293841 (0.157556) \| 0.027665 / 0.128546 (-0.100881) \| 0.008088 / 0.075646 (-0.067558) \| 0.069625 / 0.419271 (-0.349646) \| 0.043437 / 0.043533 (-0.000095) \| 0.359789 / 0.255139 (0.104650) \| 0.430206 / 0.283200 (0.147007) \| 0.022308 / 0.141683 (-0.119375) \| 1.461030 / 1.452155 (0.008875) \| 1.513683 / 1.492716 (0.020966) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.230958 / 0.018006 (0.212952) \| 0.417553 / 0.000490 (0.417063) \| 0.000802 / 0.000200 (0.000602) \| 0.000066 / 0.000054 (0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025421 / 0.037411 (-0.011991) \| 0.077156 / 0.014526 (0.062630) \| 0.087533 / 0.176557 (-0.089024) \| 0.138048 / 0.737135 (-0.599087) \| 0.089358 / 0.296338 (-0.206981) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.439172 / 0.215209 (0.223963) \| 4.409509 / 2.077655 (2.331854) \| 2.491270 / 1.504120 (0.987150) \| 2.308446 / 1.541195 (0.767252) \| 2.378440 / 1.468490 (0.909950) \| 0.499834 / 4.584777 (-4.084943) \| 3.083168 / 3.745712 (-0.662544) \| 2.867543 / 5.269862 (-2.402318) \| 1.876354 / 4.565676 (-2.689323) \| 0.057092 / 0.424275 (-0.367183) \| 0.006955 / 0.007607 (-0.000653) \| 0.513799 / 0.226044 (0.287754) \| 5.126660 / 2.268929 (2.857731) \| 2.917348 / 55.444624 (-52.527277) \| 2.508035 / 6.876477 (-4.368441) \| 2.698089 / 2.142072 (0.556016) \| 0.586828 / 4.805227 (-4.218399) \| 0.124740 / 6.500664 (-6.375924) \| 0.062276 / 0.075469 (-0.013193) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.291624 / 1.841788 (-0.550164) \| 18.199968 / 8.074308 (10.125660) \| 13.888139 / 10.191392 (3.696747) \| 0.162955 / 0.680424 (-0.517469) \| 0.017343 / 0.534201 (-0.516858) \| 0.334683 / 0.579283 (-0.244600) \| 0.352708 / 0.434364 (-0.081656) \| 0.400629 / 0.540337 (-0.139708) \| 0.539497 / 1.386936 (-0.847439) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e7976db7fe22c6b93a869488d07b8137ea6a0db4 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007500 / 0.011353 (-0.003853) \| 0.004498 / 0.011008 (-0.006510) \| 0.100239 / 0.038508 (0.061731) \| 0.083424 / 0.023109 (0.060315) \| 0.366664 / 0.275898 (0.090766) \| 0.406641 / 0.323480 (0.083161) \| 0.004577 / 0.007986 (-0.003409) \| 0.004809 / 0.004328 (0.000480) \| 0.076898 / 0.004250 (0.072647) \| 0.064021 / 0.037052 (0.026969) \| 0.375836 / 0.258489 (0.117347) \| 0.413008 / 0.293841 (0.119167) \| 0.036010 / 0.128546 (-0.092537) \| 0.009655 / 0.075646 (-0.065991) \| 0.342595 / 0.419271 (-0.076677) \| 0.061846 / 0.043533 (0.018313) \| 0.376543 / 0.255139 (0.121404) \| 0.395858 / 0.283200 (0.112659) \| 0.026792 / 0.141683 (-0.114891) \| 1.775569 / 1.452155 (0.323414) \| 1.865077 / 1.492716 (0.372360) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.221521 / 0.018006 (0.203514) \| 0.474604 / 0.000490 (0.474114) \| 0.004354 / 0.000200 (0.004154) \| 0.000090 / 0.000054 (0.000035) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032947 / 0.037411 (-0.004464) \| 0.100454 / 0.014526 (0.085928) \| 0.111955 / 0.176557 (-0.064602) \| 0.179752 / 0.737135 (-0.557383) \| 0.114282 / 0.296338 (-0.182056) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.458261 / 0.215209 (0.243052) \| 4.563536 / 2.077655 (2.485881) \| 2.231928 / 1.504120 (0.727808) \| 2.036751 / 1.541195 (0.495556) \| 2.170413 / 1.468490 (0.701923) \| 0.570825 / 4.584777 (-4.013952) \| 4.505762 / 3.745712 (0.760050) \| 5.033461 / 5.269862 (-0.236401) \| 2.704989 / 4.565676 (-1.860687) \| 0.067011 / 0.424275 (-0.357264) \| 0.008568 / 0.007607 (0.000961) \| 0.545151 / 0.226044 (0.319106) \| 5.438984 / 2.268929 (3.170055) \| 2.771818 / 55.444624 (-52.672806) \| 2.393082 / 6.876477 (-4.483395) \| 2.467173 / 2.142072 (0.325101) \| 0.678849 / 4.805227 (-4.126379) \| 0.160480 / 6.500664 (-6.340184) \| 0.073681 / 0.075469 (-0.001788) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.532272 / 1.841788 (-0.309516) \| 22.548741 / 8.074308 (14.474433) \| 17.091044 / 10.191392 (6.899652) \| 0.172100 / 0.680424 (-0.508324) \| 0.022220 / 0.534201 (-0.511981) \| 0.467871 / 0.579283 (-0.111412) \| 0.491135 / 0.434364 (0.056771) \| 0.548433 / 0.540337 (0.008096) \| 0.733340 / 1.386936 (-0.653596) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007593 / 0.011353 (-0.003760) \| 0.004656 / 0.011008 (-0.006352) \| 0.076940 / 0.038508 (0.038431) \| 0.085183 / 0.023109 (0.062073) \| 0.447178 / 0.275898 (0.171280) \| 0.469545 / 0.323480 (0.146065) \| 0.006023 / 0.007986 (-0.001962) \| 0.003808 / 0.004328 (-0.000520) \| 0.076767 / 0.004250 (0.072517) \| 0.065713 / 0.037052 (0.028661) \| 0.445573 / 0.258489 (0.187084) \| 0.481689 / 0.293841 (0.187848) \| 0.036893 / 0.128546 (-0.091654) \| 0.009976 / 0.075646 (-0.065670) \| 0.084443 / 0.419271 (-0.334829) \| 0.058829 / 0.043533 (0.015297) \| 0.429291 / 0.255139 (0.174152) \| 0.454016 / 0.283200 (0.170816) \| 0.027289 / 0.141683 (-0.114394) \| 1.806786 / 1.452155 (0.354632) \| 1.887680 / 1.492716 (0.394964) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.241012 / 0.018006 (0.223006) \| 0.470629 / 0.000490 (0.470139) \| 0.003213 / 0.000200 (0.003013) \| 0.000107 / 0.000054 (0.000052) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.036896 / 0.037411 (-0.000515) \| 0.106932 / 0.014526 (0.092406) \| 0.120333 / 0.176557 (-0.056223) \| 0.186271 / 0.737135 (-0.550865) \| 0.121581 / 0.296338 (-0.174758) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.507782 / 0.215209 (0.292573) \| 5.062932 / 2.077655 (2.985278) \| 2.689539 / 1.504120 (1.185419) \| 2.482978 / 1.541195 (0.941784) \| 2.561320 / 1.468490 (1.092830) \| 0.570664 / 4.584777 (-4.014113) \| 4.346051 / 3.745712 (0.600339) \| 6.479374 / 5.269862 (1.209513) \| 4.096483 / 4.565676 (-0.469194) \| 0.067564 / 0.424275 (-0.356711) \| 0.009147 / 0.007607 (0.001540) \| 0.596059 / 0.226044 (0.370015) \| 5.963223 / 2.268929 (3.694295) \| 3.201039 / 55.444624 (-52.243585) \| 2.816581 / 6.876477 (-4.059896) \| 3.047821 / 2.142072 (0.905748) \| 0.687749 / 4.805227 (-4.117478) \| 0.158174 / 6.500664 (-6.342490) \| 0.073329 / 0.075469 (-0.002140) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.601346 / 1.841788 (-0.240441) \| 23.712210 / 8.074308 (15.637902) \| 16.567272 / 10.191392 (6.375880) \| 0.224745 / 0.680424 (-0.455679) \| 0.021662 / 0.534201 (-0.512539) \| 0.471427 / 0.579283 (-0.107856) \| 0.498751 / 0.434364 (0.064387) \| 0.572047 / 0.540337 (0.031710) \| 0.821868 / 1.386936 (-0.565068) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#34d0c9027c750adc89f3d04a6bf2e9cb95915da4 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006371 / 0.011353 (-0.004981) \| 0.003749 / 0.011008 (-0.007259) \| 0.084155 / 0.038508 (0.045647) \| 0.072450 / 0.023109 (0.049340) \| 0.308002 / 0.275898 (0.032104) \| 0.340471 / 0.323480 (0.016991) \| 0.005054 / 0.007986 (-0.002931) \| 0.003176 / 0.004328 (-0.001152) \| 0.064867 / 0.004250 (0.060616) \| 0.054305 / 0.037052 (0.017252) \| 0.321047 / 0.258489 (0.062558) \| 0.345999 / 0.293841 (0.052158) \| 0.030507 / 0.128546 (-0.098039) \| 0.008299 / 0.075646 (-0.067347) \| 0.287682 / 0.419271 (-0.131590) \| 0.052048 / 0.043533 (0.008515) \| 0.308322 / 0.255139 (0.053183) \| 0.333220 / 0.283200 (0.050020) \| 0.022698 / 0.141683 (-0.118985) \| 1.474033 / 1.452155 (0.021879) \| 1.544790 / 1.492716 (0.052074) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.200612 / 0.018006 (0.182606) \| 0.450934 / 0.000490 (0.450445) \| 0.005383 / 0.000200 (0.005183) \| 0.000200 / 0.000054 (0.000145) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027759 / 0.037411 (-0.009652) \| 0.080935 / 0.014526 (0.066409) \| 0.093041 / 0.176557 (-0.083516) \| 0.148643 / 0.737135 (-0.588492) \| 0.093463 / 0.296338 (-0.202876) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.381653 / 0.215209 (0.166444) \| 3.810699 / 2.077655 (1.733044) \| 1.866858 / 1.504120 (0.362738) \| 1.716985 / 1.541195 (0.175790) \| 1.788071 / 1.468490 (0.319581) \| 0.481130 / 4.584777 (-4.103647) \| 3.529798 / 3.745712 (-0.215914) \| 3.982037 / 5.269862 (-1.287824) \| 2.324866 / 4.565676 (-2.240811) \| 0.056767 / 0.424275 (-0.367508) \| 0.007306 / 0.007607 (-0.000301) \| 0.459472 / 0.226044 (0.233428) \| 4.602808 / 2.268929 (2.333879) \| 2.332014 / 55.444624 (-53.112610) \| 2.044858 / 6.876477 (-4.831619) \| 2.204165 / 2.142072 (0.062093) \| 0.577946 / 4.805227 (-4.227281) \| 0.130900 / 6.500664 (-6.369764) \| 0.059054 / 0.075469 (-0.016415) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.245211 / 1.841788 (-0.596576) \| 19.176397 / 8.074308 (11.102089) \| 13.995280 / 10.191392 (3.803888) \| 0.171743 / 0.680424 (-0.508681) \| 0.018038 / 0.534201 (-0.516163) \| 0.392338 / 0.579283 (-0.186945) \| 0.419370 / 0.434364 (-0.014994) \| 0.477829 / 0.540337 (-0.062508) \| 0.677409 / 1.386936 (-0.709527) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006513 / 0.011353 (-0.004840) \| 0.003984 / 0.011008 (-0.007024) \| 0.064516 / 0.038508 (0.026008) \| 0.070504 / 0.023109 (0.047395) \| 0.384509 / 0.275898 (0.108611) \| 0.410564 / 0.323480 (0.087084) \| 0.005310 / 0.007986 (-0.002675) \| 0.003268 / 0.004328 (-0.001061) \| 0.064684 / 0.004250 (0.060433) \| 0.055367 / 0.037052 (0.018315) \| 0.399108 / 0.258489 (0.140619) \| 0.422740 / 0.293841 (0.128900) \| 0.031624 / 0.128546 (-0.096922) \| 0.008617 / 0.075646 (-0.067030) \| 0.070929 / 0.419271 (-0.348342) \| 0.049146 / 0.043533 (0.005613) \| 0.385492 / 0.255139 (0.130353) \| 0.407434 / 0.283200 (0.124234) \| 0.021972 / 0.141683 (-0.119711) \| 1.496135 / 1.452155 (0.043980) \| 1.533739 / 1.492716 (0.041023) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.226218 / 0.018006 (0.208211) \| 0.443176 / 0.000490 (0.442686) \| 0.000376 / 0.000200 (0.000176) \| 0.000055 / 0.000054 (0.000000) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030315 / 0.037411 (-0.007097) \| 0.086416 / 0.014526 (0.071890) \| 0.097725 / 0.176557 (-0.078831) \| 0.150407 / 0.737135 (-0.586728) \| 0.099914 / 0.296338 (-0.196424) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.409807 / 0.215209 (0.194598) \| 4.099086 / 2.077655 (2.021431) \| 2.103160 / 1.504120 (0.599040) \| 1.927927 / 1.541195 (0.386733) \| 1.977751 / 1.468490 (0.509261) \| 0.476995 / 4.584777 (-4.107781) \| 3.521835 / 3.745712 (-0.223877) \| 3.237695 / 5.269862 (-2.032167) \| 1.995953 / 4.565676 (-2.569724) \| 0.056208 / 0.424275 (-0.368068) \| 0.007660 / 0.007607 (0.000053) \| 0.483537 / 0.226044 (0.257492) \| 4.833974 / 2.268929 (2.565046) \| 2.589115 / 55.444624 (-52.855510) \| 2.228076 / 6.876477 (-4.648401) \| 2.395271 / 2.142072 (0.253198) \| 0.577534 / 4.805227 (-4.227694) \| 0.131432 / 6.500664 (-6.369232) \| 0.060999 / 0.075469 (-0.014471) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.356043 / 1.841788 (-0.485745) \| 19.470401 / 8.074308 (11.396093) \| 14.091266 / 10.191392 (3.899874) \| 0.166809 / 0.680424 (-0.513615) \| 0.018782 / 0.534201 (-0.515419) \| 0.394916 / 0.579283 (-0.184367) \| 0.411378 / 0.434364 (-0.022986) \| 0.466886 / 0.540337 (-0.073451) \| 0.617369 / 1.386936 (-0.769567) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#601ae6c7baff33a600fd10b12940966024fd2221 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007590 / 0.011353 (-0.003762) \| 0.004068 / 0.011008 (-0.006941) \| 0.105479 / 0.038508 (0.066971) \| 0.085614 / 0.023109 (0.062505) \| 0.384325 / 0.275898 (0.108427) \| 0.467867 / 0.323480 (0.144387) \| 0.004652 / 0.007986 (-0.003333) \| 0.005445 / 0.004328 (0.001117) \| 0.079604 / 0.004250 (0.075353) \| 0.066031 / 0.037052 (0.028978) \| 0.426184 / 0.258489 (0.167695) \| 0.480712 / 0.293841 (0.186871) \| 0.037837 / 0.128546 (-0.090709) \| 0.009765 / 0.075646 (-0.065882) \| 0.351316 / 0.419271 (-0.067955) \| 0.063634 / 0.043533 (0.020101) \| 0.420297 / 0.255139 (0.165158) \| 0.449169 / 0.283200 (0.165969) \| 0.030947 / 0.141683 (-0.110736) \| 1.840184 / 1.452155 (0.388029) \| 1.934074 / 1.492716 (0.441357) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.223483 / 0.018006 (0.205477) \| 0.521086 / 0.000490 (0.520596) \| 0.000379 / 0.000200 (0.000179) \| 0.000065 / 0.000054 (0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032011 / 0.037411 (-0.005400) \| 0.101474 / 0.014526 (0.086948) \| 0.108652 / 0.176557 (-0.067904) \| 0.173340 / 0.737135 (-0.563796) \| 0.114186 / 0.296338 (-0.182153) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.478020 / 0.215209 (0.262811) \| 4.645400 / 2.077655 (2.567746) \| 2.590763 / 1.504120 (1.086643) \| 2.383002 / 1.541195 (0.841807) \| 2.482550 / 1.468490 (1.014060) \| 0.572417 / 4.584777 (-4.012360) \| 4.233436 / 3.745712 (0.487724) \| 4.858823 / 5.269862 (-0.411038) \| 2.838913 / 4.565676 (-1.726764) \| 0.070010 / 0.424275 (-0.354265) \| 0.009602 / 0.007607 (0.001995) \| 0.538735 / 0.226044 (0.312691) \| 5.534340 / 2.268929 (3.265411) \| 2.915006 / 55.444624 (-52.529619) \| 2.625132 / 6.876477 (-4.251345) \| 2.537838 / 2.142072 (0.395766) \| 0.667870 / 4.805227 (-4.137357) \| 0.146330 / 6.500664 (-6.354334) \| 0.071631 / 0.075469 (-0.003838) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.594686 / 1.841788 (-0.247101) \| 22.311113 / 8.074308 (14.236804) \| 17.603983 / 10.191392 (7.412591) \| 0.195995 / 0.680424 (-0.484428) \| 0.022254 / 0.534201 (-0.511947) \| 0.479661 / 0.579283 (-0.099622) \| 0.463626 / 0.434364 (0.029262) \| 0.483465 / 0.540337 (-0.056873) \| 0.676141 / 1.386936 (-0.710795) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006146 / 0.011353 (-0.005207) \| 0.004856 / 0.011008 (-0.006152) \| 0.067506 / 0.038508 (0.028998) \| 0.073968 / 0.023109 (0.050859) \| 0.470013 / 0.275898 (0.194115) \| 0.479022 / 0.323480 (0.155542) \| 0.005972 / 0.007986 (-0.002014) \| 0.003846 / 0.004328 (-0.000483) \| 0.075141 / 0.004250 (0.070890) \| 0.058597 / 0.037052 (0.021544) \| 0.481454 / 0.258489 (0.222965) \| 0.515634 / 0.293841 (0.221793) \| 0.034979 / 0.128546 (-0.093567) \| 0.010385 / 0.075646 (-0.065261) \| 0.072649 / 0.419271 (-0.346622) \| 0.058183 / 0.043533 (0.014650) \| 0.462138 / 0.255139 (0.206999) \| 0.476093 / 0.283200 (0.192893) \| 0.032918 / 0.141683 (-0.108765) \| 1.820530 / 1.452155 (0.368375) \| 1.626360 / 1.492716 (0.133644) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.208970 / 0.018006 (0.190964) \| 0.492478 / 0.000490 (0.491988) \| 0.005487 / 0.000200 (0.005287) \| 0.000140 / 0.000054 (0.000086) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.037896 / 0.037411 (0.000484) \| 0.089752 / 0.014526 (0.075227) \| 0.107445 / 0.176557 (-0.069111) \| 0.181260 / 0.737135 (-0.555876) \| 0.105700 / 0.296338 (-0.190639) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.495031 / 0.215209 (0.279821) \| 4.806939 / 2.077655 (2.729284) \| 2.227928 / 1.504120 (0.723808) \| 2.067117 / 1.541195 (0.525922) \| 2.348982 / 1.468490 (0.880492) \| 0.567201 / 4.584777 (-4.017576) \| 4.166592 / 3.745712 (0.420880) \| 3.654329 / 5.269862 (-1.615533) \| 2.331092 / 4.565676 (-2.234584) \| 0.062212 / 0.424275 (-0.362063) \| 0.008775 / 0.007607 (0.001168) \| 0.515413 / 0.226044 (0.289369) \| 5.449300 / 2.268929 (3.180371) \| 3.206574 / 55.444624 (-52.238050) \| 2.600455 / 6.876477 (-4.276022) \| 3.041162 / 2.142072 (0.899089) \| 0.681899 / 4.805227 (-4.123328) \| 0.155400 / 6.500664 (-6.345265) \| 0.073933 / 0.075469 (-0.001537) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.572329 / 1.841788 (-0.269459) \| 23.638519 / 8.074308 (15.564211) \| 17.145663 / 10.191392 (6.954271) \| 0.232690 / 0.680424 (-0.447734) \| 0.028620 / 0.534201 (-0.505581) \| 0.488105 / 0.579283 (-0.091178) \| 0.490365 / 0.434364 (0.056001) \| 0.599501 / 0.540337 (0.059164) \| 0.708101 / 1.386936 (-0.678835) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4a761315900880a25b347ad19b78bd567cfce1f0 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005947 / 0.011353 (-0.005406) \| 0.003577 / 0.011008 (-0.007431) \| 0.081631 / 0.038508 (0.043122) \| 0.058651 / 0.023109 (0.035541) \| 0.342742 / 0.275898 (0.066843) \| 0.384130 / 0.323480 (0.060650) \| 0.004620 / 0.007986 (-0.003366) \| 0.002885 / 0.004328 (-0.001444) \| 0.063698 / 0.004250 (0.059448) \| 0.048953 / 0.037052 (0.011901) \| 0.367880 / 0.258489 (0.109391) \| 0.407050 / 0.293841 (0.113209) \| 0.027242 / 0.128546 (-0.101305) \| 0.007914 / 0.075646 (-0.067733) \| 0.262156 / 0.419271 (-0.157116) \| 0.044750 / 0.043533 (0.001218) \| 0.351613 / 0.255139 (0.096474) \| 0.380284 / 0.283200 (0.097084) \| 0.020080 / 0.141683 (-0.121603) \| 1.498101 / 1.452155 (0.045946) \| 1.543608 / 1.492716 (0.050892) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.180014 / 0.018006 (0.162008) \| 0.436172 / 0.000490 (0.435682) \| 0.003694 / 0.000200 (0.003494) \| 0.000071 / 0.000054 (0.000017) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024389 / 0.037411 (-0.013022) \| 0.072874 / 0.014526 (0.058348) \| 0.083469 / 0.176557 (-0.093088) \| 0.144600 / 0.737135 (-0.592536) \| 0.084229 / 0.296338 (-0.212110) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.391636 / 0.215209 (0.176427) \| 3.906941 / 2.077655 (1.829286) \| 1.901944 / 1.504120 (0.397825) \| 1.762702 / 1.541195 (0.221507) \| 1.817970 / 1.468490 (0.349480) \| 0.500345 / 4.584777 (-4.084432) \| 3.011351 / 3.745712 (-0.734361) \| 4.417763 / 5.269862 (-0.852098) \| 2.689744 / 4.565676 (-1.875933) \| 0.057765 / 0.424275 (-0.366511) \| 0.006412 / 0.007607 (-0.001195) \| 0.468156 / 0.226044 (0.242112) \| 4.664975 / 2.268929 (2.396047) \| 2.323355 / 55.444624 (-53.121270) \| 1.984280 / 6.876477 (-4.892197) \| 2.165215 / 2.142072 (0.023142) \| 0.586950 / 4.805227 (-4.218278) \| 0.124363 / 6.500664 (-6.376301) \| 0.060702 / 0.075469 (-0.014767) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.238870 / 1.841788 (-0.602917) \| 18.587360 / 8.074308 (10.513052) \| 13.831674 / 10.191392 (3.640282) \| 0.143542 / 0.680424 (-0.536882) \| 0.016913 / 0.534201 (-0.517288) \| 0.332314 / 0.579283 (-0.246969) \| 0.345419 / 0.434364 (-0.088945) \| 0.381257 / 0.540337 (-0.159081) \| 0.537844 / 1.386936 (-0.849092) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006294 / 0.011353 (-0.005059) \| 0.003714 / 0.011008 (-0.007294) \| 0.062684 / 0.038508 (0.024176) \| 0.063520 / 0.023109 (0.040411) \| 0.389591 / 0.275898 (0.113693) \| 0.444278 / 0.323480 (0.120798) \| 0.004825 / 0.007986 (-0.003160) \| 0.003010 / 0.004328 (-0.001318) \| 0.062767 / 0.004250 (0.058517) \| 0.051739 / 0.037052 (0.014686) \| 0.434299 / 0.258489 (0.175810) \| 0.452003 / 0.293841 (0.158162) \| 0.027375 / 0.128546 (-0.101171) \| 0.008135 / 0.075646 (-0.067511) \| 0.067401 / 0.419271 (-0.351871) \| 0.042752 / 0.043533 (-0.000780) \| 0.367633 / 0.255139 (0.112494) \| 0.433039 / 0.283200 (0.149840) \| 0.021086 / 0.141683 (-0.120597) \| 1.488024 / 1.452155 (0.035870) \| 1.507767 / 1.492716 (0.015050) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.230046 / 0.018006 (0.212040) \| 0.428085 / 0.000490 (0.427595) \| 0.002188 / 0.000200 (0.001988) \| 0.000070 / 0.000054 (0.000015) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026705 / 0.037411 (-0.010706) \| 0.082466 / 0.014526 (0.067940) \| 0.089378 / 0.176557 (-0.087179) \| 0.147287 / 0.737135 (-0.589849) \| 0.090426 / 0.296338 (-0.205913) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.430882 / 0.215209 (0.215672) \| 4.296224 / 2.077655 (2.218569) \| 2.229982 / 1.504120 (0.725862) \| 2.048506 / 1.541195 (0.507311) \| 2.129514 / 1.468490 (0.661024) \| 0.502964 / 4.584777 (-4.081813) \| 3.048125 / 3.745712 (-0.697587) \| 4.208636 / 5.269862 (-1.061226) \| 2.594015 / 4.565676 (-1.971661) \| 0.057967 / 0.424275 (-0.366308) \| 0.006875 / 0.007607 (-0.000732) \| 0.513872 / 0.226044 (0.287828) \| 5.126435 / 2.268929 (2.857506) \| 2.691278 / 55.444624 (-52.753346) \| 2.361723 / 6.876477 (-4.514754) \| 2.511213 / 2.142072 (0.369141) \| 0.593558 / 4.805227 (-4.211670) \| 0.129332 / 6.500664 (-6.371332) \| 0.064051 / 0.075469 (-0.011418) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.289049 / 1.841788 (-0.552739) \| 18.912363 / 8.074308 (10.838055) \| 14.226500 / 10.191392 (4.035108) \| 0.131392 / 0.680424 (-0.549032) \| 0.016750 / 0.534201 (-0.517451) \| 0.330078 / 0.579283 (-0.249205) \| 0.347588 / 0.434364 (-0.086776) \| 0.383234 / 0.540337 (-0.157103) \| 0.510967 / 1.386936 (-0.875969) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d7892beb30bab0633b84398c5ea43d7e69fe38cc \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005974 / 0.011353 (-0.005379) \| 0.003691 / 0.011008 (-0.007317) \| 0.079410 / 0.038508 (0.040902) \| 0.061769 / 0.023109 (0.038660) \| 0.323310 / 0.275898 (0.047412) \| 0.354325 / 0.323480 (0.030845) \| 0.004794 / 0.007986 (-0.003191) \| 0.002899 / 0.004328 (-0.001430) \| 0.062104 / 0.004250 (0.057854) \| 0.048973 / 0.037052 (0.011921) \| 0.326497 / 0.258489 (0.068008) \| 0.361347 / 0.293841 (0.067506) \| 0.026741 / 0.128546 (-0.101805) \| 0.007936 / 0.075646 (-0.067710) \| 0.259168 / 0.419271 (-0.160104) \| 0.044859 / 0.043533 (0.001327) \| 0.319342 / 0.255139 (0.064203) \| 0.343711 / 0.283200 (0.060511) \| 0.022298 / 0.141683 (-0.119384) \| 1.451595 / 1.452155 (-0.000560) \| 1.573730 / 1.492716 (0.081014) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.173086 / 0.018006 (0.155080) \| 0.432400 / 0.000490 (0.431910) \| 0.003739 / 0.000200 (0.003539) \| 0.000073 / 0.000054 (0.000019) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024477 / 0.037411 (-0.012934) \| 0.073463 / 0.014526 (0.058937) \| 0.083410 / 0.176557 (-0.093146) \| 0.144760 / 0.737135 (-0.592376) \| 0.084199 / 0.296338 (-0.212140) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.388251 / 0.215209 (0.173042) \| 3.875375 / 2.077655 (1.797720) \| 1.875515 / 1.504120 (0.371395) \| 1.729282 / 1.541195 (0.188087) \| 1.784732 / 1.468490 (0.316242) \| 0.496985 / 4.584777 (-4.087792) \| 3.030276 / 3.745712 (-0.715436) \| 2.813192 / 5.269862 (-2.456669) \| 1.868647 / 4.565676 (-2.697030) \| 0.057376 / 0.424275 (-0.366899) \| 0.006463 / 0.007607 (-0.001144) \| 0.462153 / 0.226044 (0.236108) \| 4.586583 / 2.268929 (2.317654) \| 2.287730 / 55.444624 (-53.156894) \| 1.972177 / 6.876477 (-4.904299) \| 2.151592 / 2.142072 (0.009520) \| 0.587169 / 4.805227 (-4.218058) \| 0.127063 / 6.500664 (-6.373601) \| 0.060297 / 0.075469 (-0.015172) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.267651 / 1.841788 (-0.574136) \| 18.426011 / 8.074308 (10.351703) \| 14.050470 / 10.191392 (3.859078) \| 0.148063 / 0.680424 (-0.532361) \| 0.017112 / 0.534201 (-0.517089) \| 0.330051 / 0.579283 (-0.249232) \| 0.358730 / 0.434364 (-0.075634) \| 0.392365 / 0.540337 (-0.147972) \| 0.534650 / 1.386936 (-0.852286) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005936 / 0.011353 (-0.005417) \| 0.003652 / 0.011008 (-0.007356) \| 0.063066 / 0.038508 (0.024558) \| 0.060617 / 0.023109 (0.037507) \| 0.388293 / 0.275898 (0.112395) \| 0.411422 / 0.323480 (0.087942) \| 0.004691 / 0.007986 (-0.003295) \| 0.002857 / 0.004328 (-0.001472) \| 0.064198 / 0.004250 (0.059947) \| 0.049124 / 0.037052 (0.012071) \| 0.403601 / 0.258489 (0.145112) \| 0.413619 / 0.293841 (0.119778) \| 0.027279 / 0.128546 (-0.101267) \| 0.008072 / 0.075646 (-0.067575) \| 0.067890 / 0.419271 (-0.351381) \| 0.041866 / 0.043533 (-0.001667) \| 0.393438 / 0.255139 (0.138299) \| 0.402865 / 0.283200 (0.119666) \| 0.023381 / 0.141683 (-0.118302) \| 1.496324 / 1.452155 (0.044170) \| 1.538080 / 1.492716 (0.045364) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.212065 / 0.018006 (0.194059) \| 0.410511 / 0.000490 (0.410021) \| 0.001236 / 0.000200 (0.001036) \| 0.000067 / 0.000054 (0.000012) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026012 / 0.037411 (-0.011399) \| 0.076592 / 0.014526 (0.062066) \| 0.085963 / 0.176557 (-0.090594) \| 0.137803 / 0.737135 (-0.599332) \| 0.087594 / 0.296338 (-0.208745) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.434283 / 0.215209 (0.219074) \| 4.345478 / 2.077655 (2.267824) \| 2.400954 / 1.504120 (0.896834) \| 2.282024 / 1.541195 (0.740829) \| 2.414247 / 1.468490 (0.945757) \| 0.501855 / 4.584777 (-4.082922) \| 3.059433 / 3.745712 (-0.686279) \| 2.811288 / 5.269862 (-2.458574) \| 1.856839 / 4.565676 (-2.708838) \| 0.058017 / 0.424275 (-0.366258) \| 0.006844 / 0.007607 (-0.000763) \| 0.515376 / 0.226044 (0.289332) \| 5.148775 / 2.268929 (2.879847) \| 2.930807 / 55.444624 (-52.513817) \| 2.520532 / 6.876477 (-4.355944) \| 2.746299 / 2.142072 (0.604227) \| 0.590102 / 4.805227 (-4.215125) \| 0.125747 / 6.500664 (-6.374917) \| 0.061873 / 0.075469 (-0.013597) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.306247 / 1.841788 (-0.535541) \| 18.366048 / 8.074308 (10.291740) \| 13.855617 / 10.191392 (3.664225) \| 0.150124 / 0.680424 (-0.530300) \| 0.017189 / 0.534201 (-0.517012) \| 0.336285 / 0.579283 (-0.242998) \| 0.344985 / 0.434364 (-0.089379) \| 0.397973 / 0.540337 (-0.142364) \| 0.536142 / 1.386936 (-0.850794) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1ae24cf12054b4a512f198979b1ca7707bb99d56 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006401 / 0.011353 (-0.004952) \| 0.003789 / 0.011008 (-0.007219) \| 0.079516 / 0.038508 (0.041008) \| 0.068279 / 0.023109 (0.045170) \| 0.295691 / 0.275898 (0.019793) \| 0.327208 / 0.323480 (0.003728) \| 0.005070 / 0.007986 (-0.002915) \| 0.003044 / 0.004328 (-0.001285) \| 0.061411 / 0.004250 (0.057161) \| 0.053227 / 0.037052 (0.016175) \| 0.297368 / 0.258489 (0.038879) \| 0.334740 / 0.293841 (0.040899) \| 0.029459 / 0.128546 (-0.099087) \| 0.008080 / 0.075646 (-0.067566) \| 0.267344 / 0.419271 (-0.151927) \| 0.049877 / 0.043533 (0.006344) \| 0.293853 / 0.255139 (0.038714) \| 0.319819 / 0.283200 (0.036620) \| 0.022593 / 0.141683 (-0.119089) \| 1.459054 / 1.452155 (0.006900) \| 1.471250 / 1.492716 (-0.021466) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.194326 / 0.018006 (0.176320) \| 0.443565 / 0.000490 (0.443075) \| 0.003745 / 0.000200 (0.003545) \| 0.000075 / 0.000054 (0.000021) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026640 / 0.037411 (-0.010772) \| 0.077630 / 0.014526 (0.063104) \| 0.089364 / 0.176557 (-0.087192) \| 0.147327 / 0.737135 (-0.589809) \| 0.089603 / 0.296338 (-0.206735) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.373758 / 0.215209 (0.158549) \| 3.746778 / 2.077655 (1.669123) \| 1.814991 / 1.504120 (0.310871) \| 1.645650 / 1.541195 (0.104455) \| 1.690752 / 1.468490 (0.222262) \| 0.472117 / 4.584777 (-4.112660) \| 3.457346 / 3.745712 (-0.288367) \| 3.138869 / 5.269862 (-2.130993) \| 1.934924 / 4.565676 (-2.630753) \| 0.055709 / 0.424275 (-0.368566) \| 0.006680 / 0.007607 (-0.000927) \| 0.446874 / 0.226044 (0.220829) \| 4.458409 / 2.268929 (2.189480) \| 2.253932 / 55.444624 (-53.190693) \| 2.007240 / 6.876477 (-4.869237) \| 2.081687 / 2.142072 (-0.060386) \| 0.563379 / 4.805227 (-4.241848) \| 0.128694 / 6.500664 (-6.371970) \| 0.057409 / 0.075469 (-0.018060) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.212231 / 1.841788 (-0.629556) \| 18.519121 / 8.074308 (10.444813) \| 13.582243 / 10.191392 (3.390851) \| 0.142488 / 0.680424 (-0.537936) \| 0.017421 / 0.534201 (-0.516780) \| 0.366864 / 0.579283 (-0.212419) \| 0.401467 / 0.434364 (-0.032897) \| 0.443659 / 0.540337 (-0.096679) \| 0.618854 / 1.386936 (-0.768082) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006121 / 0.011353 (-0.005232) \| 0.003690 / 0.011008 (-0.007318) \| 0.060340 / 0.038508 (0.021832) \| 0.067215 / 0.023109 (0.044106) \| 0.382846 / 0.275898 (0.106948) \| 0.415774 / 0.323480 (0.092294) \| 0.004868 / 0.007986 (-0.003118) \| 0.003108 / 0.004328 (-0.001221) \| 0.060572 / 0.004250 (0.056321) \| 0.050453 / 0.037052 (0.013401) \| 0.400494 / 0.258489 (0.142005) \| 0.424368 / 0.293841 (0.130527) \| 0.030279 / 0.128546 (-0.098267) \| 0.008151 / 0.075646 (-0.067495) \| 0.066707 / 0.419271 (-0.352564) \| 0.046118 / 0.043533 (0.002585) \| 0.386697 / 0.255139 (0.131558) \| 0.410156 / 0.283200 (0.126957) \| 0.020688 / 0.141683 (-0.120995) \| 1.418162 / 1.452155 (-0.033993) \| 1.463057 / 1.492716 (-0.029659) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.216081 / 0.018006 (0.198075) \| 0.440541 / 0.000490 (0.440051) \| 0.000371 / 0.000200 (0.000171) \| 0.000054 / 0.000054 (-0.000000) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027763 / 0.037411 (-0.009648) \| 0.082316 / 0.014526 (0.067791) \| 0.094086 / 0.176557 (-0.082471) \| 0.144738 / 0.737135 (-0.592398) \| 0.094837 / 0.296338 (-0.201501) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.396277 / 0.215209 (0.181068) \| 3.958791 / 2.077655 (1.881136) \| 2.021367 / 1.504120 (0.517247) \| 1.860112 / 1.541195 (0.318917) \| 1.886032 / 1.468490 (0.417541) \| 0.468536 / 4.584777 (-4.116241) \| 3.417950 / 3.745712 (-0.327762) \| 4.849991 / 5.269862 (-0.419871) \| 2.773935 / 4.565676 (-1.791742) \| 0.055813 / 0.424275 (-0.368462) \| 0.007053 / 0.007607 (-0.000554) \| 0.470167 / 0.226044 (0.244122) \| 4.702969 / 2.268929 (2.434041) \| 2.474161 / 55.444624 (-52.970464) \| 2.171256 / 6.876477 (-4.705220) \| 2.315373 / 2.142072 (0.173301) \| 0.589195 / 4.805227 (-4.216032) \| 0.128237 / 6.500664 (-6.372427) \| 0.058641 / 0.075469 (-0.016828) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.292947 / 1.841788 (-0.548841) \| 18.851300 / 8.074308 (10.776992) \| 14.089764 / 10.191392 (3.898372) \| 0.164853 / 0.680424 (-0.515571) \| 0.017281 / 0.534201 (-0.516920) \| 0.359112 / 0.579283 (-0.220171) \| 0.386696 / 0.434364 (-0.047668) \| 0.428222 / 0.540337 (-0.112115) \| 0.568659 / 1.386936 (-0.818277) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#563864ded894b468e2ba3f677ef79c5ab3fe65df \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006051 / 0.011353 (-0.005301) \| 0.003654 / 0.011008 (-0.007355) \| 0.080081 / 0.038508 (0.041572) \| 0.062925 / 0.023109 (0.039815) \| 0.358097 / 0.275898 (0.082199) \| 0.405728 / 0.323480 (0.082248) \| 0.005359 / 0.007986 (-0.002627) \| 0.002820 / 0.004328 (-0.001508) \| 0.063108 / 0.004250 (0.058858) \| 0.049627 / 0.037052 (0.012575) \| 0.397870 / 0.258489 (0.139381) \| 0.437157 / 0.293841 (0.143316) \| 0.027707 / 0.128546 (-0.100839) \| 0.007911 / 0.075646 (-0.067735) \| 0.260991 / 0.419271 (-0.158280) \| 0.044771 / 0.043533 (0.001238) \| 0.340230 / 0.255139 (0.085091) \| 0.384925 / 0.283200 (0.101725) \| 0.021369 / 0.141683 (-0.120314) \| 1.431439 / 1.452155 (-0.020715) \| 1.478794 / 1.492716 (-0.013922) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.182626 / 0.018006 (0.164620) \| 0.435551 / 0.000490 (0.435061) \| 0.003015 / 0.000200 (0.002815) \| 0.000064 / 0.000054 (0.000009) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024703 / 0.037411 (-0.012708) \| 0.073640 / 0.014526 (0.059114) \| 0.084598 / 0.176557 (-0.091959) \| 0.145810 / 0.737135 (-0.591325) \| 0.085125 / 0.296338 (-0.211213) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.394539 / 0.215209 (0.179330) \| 3.945882 / 2.077655 (1.868227) \| 1.947166 / 1.504120 (0.443046) \| 1.763305 / 1.541195 (0.222111) \| 1.816208 / 1.468490 (0.347718) \| 0.498880 / 4.584777 (-4.085897) \| 3.098283 / 3.745712 (-0.647429) \| 2.823474 / 5.269862 (-2.446388) \| 1.873993 / 4.565676 (-2.691684) \| 0.058097 / 0.424275 (-0.366179) \| 0.006488 / 0.007607 (-0.001119) \| 0.466711 / 0.226044 (0.240667) \| 4.671520 / 2.268929 (2.402592) \| 2.363381 / 55.444624 (-53.081243) \| 2.052092 / 6.876477 (-4.824385) \| 2.209212 / 2.142072 (0.067140) \| 0.594650 / 4.805227 (-4.210577) \| 0.125604 / 6.500664 (-6.375060) \| 0.061511 / 0.075469 (-0.013958) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.226564 / 1.841788 (-0.615224) \| 18.583605 / 8.074308 (10.509297) \| 13.993091 / 10.191392 (3.801699) \| 0.146185 / 0.680424 (-0.534239) \| 0.016839 / 0.534201 (-0.517362) \| 0.334116 / 0.579283 (-0.245167) \| 0.360780 / 0.434364 (-0.073584) \| 0.386008 / 0.540337 (-0.154329) \| 0.643278 / 1.386936 (-0.743658) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006174 / 0.011353 (-0.005179) \| 0.003658 / 0.011008 (-0.007350) \| 0.063250 / 0.038508 (0.024742) \| 0.063542 / 0.023109 (0.040433) \| 0.366845 / 0.275898 (0.090947) \| 0.409794 / 0.323480 (0.086314) \| 0.005678 / 0.007986 (-0.002308) \| 0.003061 / 0.004328 (-0.001268) \| 0.063561 / 0.004250 (0.059311) \| 0.052648 / 0.037052 (0.015596) \| 0.378096 / 0.258489 (0.119607) \| 0.410706 / 0.293841 (0.116865) \| 0.027668 / 0.128546 (-0.100878) \| 0.008045 / 0.075646 (-0.067601) \| 0.068290 / 0.419271 (-0.350981) \| 0.042602 / 0.043533 (-0.000930) \| 0.364976 / 0.255139 (0.109837) \| 0.395599 / 0.283200 (0.112400) \| 0.022733 / 0.141683 (-0.118950) \| 1.522473 / 1.452155 (0.070319) \| 1.515891 / 1.492716 (0.023175) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.232554 / 0.018006 (0.214547) \| 0.420702 / 0.000490 (0.420213) \| 0.002161 / 0.000200 (0.001961) \| 0.000064 / 0.000054 (0.000009) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026276 / 0.037411 (-0.011135) \| 0.078504 / 0.014526 (0.063978) \| 0.088989 / 0.176557 (-0.087567) \| 0.144044 / 0.737135 (-0.593091) \| 0.091074 / 0.296338 (-0.205265) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.420189 / 0.215209 (0.204980) \| 4.189596 / 2.077655 (2.111941) \| 2.316425 / 1.504120 (0.812305) \| 2.186877 / 1.541195 (0.645682) \| 2.259065 / 1.468490 (0.790575) \| 0.502827 / 4.584777 (-4.081950) \| 3.135266 / 3.745712 (-0.610446) \| 2.838808 / 5.269862 (-2.431053) \| 1.876519 / 4.565676 (-2.689158) \| 0.057802 / 0.424275 (-0.366473) \| 0.006824 / 0.007607 (-0.000784) \| 0.500213 / 0.226044 (0.274168) \| 4.999798 / 2.268929 (2.730869) \| 2.627713 / 55.444624 (-52.816911) \| 2.344263 / 6.876477 (-4.532214) \| 2.415449 / 2.142072 (0.273376) \| 0.593082 / 4.805227 (-4.212145) \| 0.125787 / 6.500664 (-6.374877) \| 0.062699 / 0.075469 (-0.012770) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.308219 / 1.841788 (-0.533569) \| 18.703099 / 8.074308 (10.628791) \| 13.976234 / 10.191392 (3.784842) \| 0.144037 / 0.680424 (-0.536387) \| 0.016592 / 0.534201 (-0.517609) \| 0.333078 / 0.579283 (-0.246206) \| 0.342317 / 0.434364 (-0.092047) \| 0.396837 / 0.540337 (-0.143500) \| 0.532641 / 1.386936 (-0.854295) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#14f6edd9222e577dccb962ed5338b79b73502fa5 \"CML watermark\")\n" ]	2023-07-13T15:41:44	2023-07-17T17:09:39	2023-07-17T17:01:00	MEMBER	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6028", "html_url": "https://github.com/huggingface/datasets/pull/6028", "diff_url": "https://github.com/huggingface/datasets/pull/6028.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6028.patch", "merged_at": "2023-07-17T17:01:00" }	Thanks to @janineguo 's work in https://github.com/huggingface/datasets/pull/5919 which was needed to support HfFileSystem. Switching to `HfFileSystem` will help implementing optimization in data files resolution ## Implementation details I replaced all the from_hf_repo and from_local_or_remote in data_files.py to only use a new `from_patterns` which works for any fsspec path, including hf:// paths, https:// URLs and local paths. This simplifies the codebase since there is no logic duplication anymore when it comes to data files resolution. I added `_prepare_path_and_storage_options` which returns the right storage_options to use given a path and a `DownloadConfig`. This is the only place where the logic depends on the filesystem type that must be used. I also removed the `get_metadata_data_files_list ` and `get_patterns_and_data_files` functions added recently, since data files resolution is now handled using a common interface. ## New features hf:// paths are now supported in data_files ## Breaking changes DataFilesList and DataFilesDict: - use `str` paths instead of `Union[Path, Url]` - require posix paths for windows paths close https://github.com/huggingface/datasets/issues/6017	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6028/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6028/timeline	null	null	true
https://api.github.com/repos/huggingface/datasets/issues/6027	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6027/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6027/comments	https://api.github.com/repos/huggingface/datasets/issues/6027/events	https://github.com/huggingface/datasets/pull/6027	1,803,008,486	PR_kwDODunzps5Va4g3	6,027	Delete `task_templates` in `IterableDataset` when they are no longer valid	{ "login": "mariosasko", "id": 47462742, "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "gravatar_id": "", "url": "https://api.github.com/users/mariosasko", "html_url": "https://github.com/mariosasko", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "repos_url": "https://api.github.com/users/mariosasko/repos", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008698 / 0.011353 (-0.002655) \| 0.005250 / 0.011008 (-0.005758) \| 0.104101 / 0.038508 (0.065593) \| 0.085021 / 0.023109 (0.061912) \| 0.426653 / 0.275898 (0.150755) \| 0.460449 / 0.323480 (0.136969) \| 0.005222 / 0.007986 (-0.002763) \| 0.006280 / 0.004328 (0.001951) \| 0.083458 / 0.004250 (0.079207) \| 0.066132 / 0.037052 (0.029079) \| 0.433416 / 0.258489 (0.174927) \| 0.482718 / 0.293841 (0.188877) \| 0.048872 / 0.128546 (-0.079675) \| 0.013699 / 0.075646 (-0.061948) \| 0.365660 / 0.419271 (-0.053611) \| 0.071008 / 0.043533 (0.027475) \| 0.428688 / 0.255139 (0.173549) \| 0.443554 / 0.283200 (0.160354) \| 0.035901 / 0.141683 (-0.105782) \| 1.829296 / 1.452155 (0.377141) \| 1.862351 / 1.492716 (0.369635) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.236284 / 0.018006 (0.218278) \| 0.584075 / 0.000490 (0.583585) \| 0.004634 / 0.000200 (0.004434) \| 0.000125 / 0.000054 (0.000070) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034723 / 0.037411 (-0.002688) \| 0.100989 / 0.014526 (0.086464) \| 0.113722 / 0.176557 (-0.062834) \| 0.187659 / 0.737135 (-0.549477) \| 0.113937 / 0.296338 (-0.182401) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.587500 / 0.215209 (0.372291) \| 5.847371 / 2.077655 (3.769716) \| 2.599691 / 1.504120 (1.095571) \| 2.246187 / 1.541195 (0.704992) \| 2.419126 / 1.468490 (0.950636) \| 0.847327 / 4.584777 (-3.737450) \| 5.230438 / 3.745712 (1.484726) \| 7.539021 / 5.269862 (2.269160) \| 4.617473 / 4.565676 (0.051797) \| 0.103620 / 0.424275 (-0.320655) \| 0.009195 / 0.007607 (0.001588) \| 0.714247 / 0.226044 (0.488203) \| 7.331621 / 2.268929 (5.062693) \| 3.416575 / 55.444624 (-52.028049) \| 2.649467 / 6.876477 (-4.227009) \| 2.928091 / 2.142072 (0.786018) \| 1.002155 / 4.805227 (-3.803072) \| 0.210790 / 6.500664 (-6.289874) \| 0.081303 / 0.075469 (0.005834) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.655431 / 1.841788 (-0.186357) \| 24.069595 / 8.074308 (15.995287) \| 20.923766 / 10.191392 (10.732374) \| 0.232021 / 0.680424 (-0.448403) \| 0.026355 / 0.534201 (-0.507846) \| 0.496830 / 0.579283 (-0.082453) \| 0.582620 / 0.434364 (0.148257) \| 0.551227 / 0.540337 (0.010890) \| 0.756389 / 1.386936 (-0.630547) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009329 / 0.011353 (-0.002024) \| 0.005045 / 0.011008 (-0.005964) \| 0.082116 / 0.038508 (0.043608) \| 0.082420 / 0.023109 (0.059311) \| 0.502513 / 0.275898 (0.226615) \| 0.526098 / 0.323480 (0.202618) \| 0.007468 / 0.007986 (-0.000517) \| 0.005477 / 0.004328 (0.001148) \| 0.082617 / 0.004250 (0.078367) \| 0.070292 / 0.037052 (0.033239) \| 0.503290 / 0.258489 (0.244801) \| 0.541631 / 0.293841 (0.247790) \| 0.050826 / 0.128546 (-0.077721) \| 0.014699 / 0.075646 (-0.060948) \| 0.094441 / 0.419271 (-0.324830) \| 0.065034 / 0.043533 (0.021501) \| 0.486778 / 0.255139 (0.231639) \| 0.516907 / 0.283200 (0.233707) \| 0.045140 / 0.141683 (-0.096543) \| 1.831676 / 1.452155 (0.379521) \| 1.910865 / 1.492716 (0.418149) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.286818 / 0.018006 (0.268812) \| 0.558621 / 0.000490 (0.558131) \| 0.002830 / 0.000200 (0.002630) \| 0.000148 / 0.000054 (0.000094) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.036716 / 0.037411 (-0.000696) \| 0.107830 / 0.014526 (0.093305) \| 0.116368 / 0.176557 (-0.060188) \| 0.178401 / 0.737135 (-0.558734) \| 0.124729 / 0.296338 (-0.171609) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.633557 / 0.215209 (0.418348) \| 6.423135 / 2.077655 (4.345480) \| 2.981883 / 1.504120 (1.477763) \| 2.755592 / 1.541195 (1.214398) \| 2.769337 / 1.468490 (1.300847) \| 0.836219 / 4.584777 (-3.748558) \| 5.302030 / 3.745712 (1.556318) \| 7.463960 / 5.269862 (2.194098) \| 4.427254 / 4.565676 (-0.138422) \| 0.095990 / 0.424275 (-0.328285) \| 0.009264 / 0.007607 (0.001657) \| 0.770642 / 0.226044 (0.544597) \| 7.779667 / 2.268929 (5.510739) \| 3.799115 / 55.444624 (-51.645509) \| 3.212560 / 6.876477 (-3.663917) \| 3.281657 / 2.142072 (1.139584) \| 1.044981 / 4.805227 (-3.760246) \| 0.210693 / 6.500664 (-6.289971) \| 0.079466 / 0.075469 (0.003997) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.793155 / 1.841788 (-0.048632) \| 24.691127 / 8.074308 (16.616819) \| 22.083150 / 10.191392 (11.891758) \| 0.242246 / 0.680424 (-0.438178) \| 0.028001 / 0.534201 (-0.506200) \| 0.494061 / 0.579283 (-0.085222) \| 0.599288 / 0.434364 (0.164924) \| 0.552101 / 0.540337 (0.011764) \| 0.784093 / 1.386936 (-0.602843) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#cd429c39604af34bc3a3ba1f463329b23fcbc1e3 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006658 / 0.011353 (-0.004695) \| 0.004044 / 0.011008 (-0.006965) \| 0.085844 / 0.038508 (0.047336) \| 0.077147 / 0.023109 (0.054038) \| 0.344387 / 0.275898 (0.068489) \| 0.376718 / 0.323480 (0.053238) \| 0.005537 / 0.007986 (-0.002448) \| 0.003452 / 0.004328 (-0.000876) \| 0.065326 / 0.004250 (0.061076) \| 0.057639 / 0.037052 (0.020587) \| 0.352363 / 0.258489 (0.093873) \| 0.378939 / 0.293841 (0.085098) \| 0.031259 / 0.128546 (-0.097287) \| 0.008464 / 0.075646 (-0.067183) \| 0.289076 / 0.419271 (-0.130195) \| 0.052991 / 0.043533 (0.009459) \| 0.346053 / 0.255139 (0.090914) \| 0.362761 / 0.283200 (0.079561) \| 0.023501 / 0.141683 (-0.118182) \| 1.478312 / 1.452155 (0.026157) \| 1.545437 / 1.492716 (0.052721) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.202964 / 0.018006 (0.184957) \| 0.534793 / 0.000490 (0.534303) \| 0.006025 / 0.000200 (0.005825) \| 0.000225 / 0.000054 (0.000171) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029418 / 0.037411 (-0.007993) \| 0.084297 / 0.014526 (0.069771) \| 0.096702 / 0.176557 (-0.079855) \| 0.157355 / 0.737135 (-0.579781) \| 0.097858 / 0.296338 (-0.198480) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.380728 / 0.215209 (0.165519) \| 3.787712 / 2.077655 (1.710057) \| 1.836393 / 1.504120 (0.332273) \| 1.678415 / 1.541195 (0.137220) \| 1.781800 / 1.468490 (0.313310) \| 0.478677 / 4.584777 (-4.106100) \| 3.614080 / 3.745712 (-0.131632) \| 3.255637 / 5.269862 (-2.014225) \| 2.063642 / 4.565676 (-2.502035) \| 0.056470 / 0.424275 (-0.367805) \| 0.007408 / 0.007607 (-0.000199) \| 0.459155 / 0.226044 (0.233111) \| 4.586679 / 2.268929 (2.317750) \| 2.305737 / 55.444624 (-53.138888) \| 1.954755 / 6.876477 (-4.921721) \| 2.190809 / 2.142072 (0.048737) \| 0.572426 / 4.805227 (-4.232802) \| 0.130349 / 6.500664 (-6.370315) \| 0.059346 / 0.075469 (-0.016124) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.253671 / 1.841788 (-0.588117) \| 19.509015 / 8.074308 (11.434706) \| 13.951349 / 10.191392 (3.759957) \| 0.171038 / 0.680424 (-0.509386) \| 0.018826 / 0.534201 (-0.515375) \| 0.394642 / 0.579283 (-0.184642) \| 0.419614 / 0.434364 (-0.014750) \| 0.470931 / 0.540337 (-0.069406) \| 0.643858 / 1.386936 (-0.743078) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006765 / 0.011353 (-0.004587) \| 0.003955 / 0.011008 (-0.007053) \| 0.064377 / 0.038508 (0.025869) \| 0.076980 / 0.023109 (0.053871) \| 0.368675 / 0.275898 (0.092777) \| 0.403746 / 0.323480 (0.080267) \| 0.005303 / 0.007986 (-0.002683) \| 0.003257 / 0.004328 (-0.001072) \| 0.064154 / 0.004250 (0.059903) \| 0.056975 / 0.037052 (0.019923) \| 0.376718 / 0.258489 (0.118229) \| 0.416291 / 0.293841 (0.122450) \| 0.031444 / 0.128546 (-0.097102) \| 0.008532 / 0.075646 (-0.067115) \| 0.070455 / 0.419271 (-0.348816) \| 0.049032 / 0.043533 (0.005499) \| 0.361413 / 0.255139 (0.106274) \| 0.384648 / 0.283200 (0.101448) \| 0.024050 / 0.141683 (-0.117633) \| 1.514330 / 1.452155 (0.062176) \| 1.585424 / 1.492716 (0.092708) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.214701 / 0.018006 (0.196695) \| 0.447706 / 0.000490 (0.447216) \| 0.000373 / 0.000200 (0.000173) \| 0.000058 / 0.000054 (0.000003) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031007 / 0.037411 (-0.006404) \| 0.090545 / 0.014526 (0.076019) \| 0.100611 / 0.176557 (-0.075945) \| 0.154847 / 0.737135 (-0.582289) \| 0.102864 / 0.296338 (-0.193475) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.427740 / 0.215209 (0.212531) \| 4.273143 / 2.077655 (2.195488) \| 2.294906 / 1.504120 (0.790786) \| 2.138460 / 1.541195 (0.597265) \| 2.274126 / 1.468490 (0.805636) \| 0.486559 / 4.584777 (-4.098218) \| 3.565554 / 3.745712 (-0.180158) \| 3.377659 / 5.269862 (-1.892202) \| 2.029883 / 4.565676 (-2.535793) \| 0.057303 / 0.424275 (-0.366972) \| 0.007314 / 0.007607 (-0.000293) \| 0.504263 / 0.226044 (0.278219) \| 5.041196 / 2.268929 (2.772268) \| 2.819273 / 55.444624 (-52.625351) \| 2.421479 / 6.876477 (-4.454998) \| 2.503063 / 2.142072 (0.360991) \| 0.581467 / 4.805227 (-4.223760) \| 0.133532 / 6.500664 (-6.367132) \| 0.062504 / 0.075469 (-0.012965) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.328765 / 1.841788 (-0.513022) \| 20.131672 / 8.074308 (12.057363) \| 14.312895 / 10.191392 (4.121503) \| 0.191199 / 0.680424 (-0.489225) \| 0.018522 / 0.534201 (-0.515679) \| 0.393121 / 0.579283 (-0.186162) \| 0.413122 / 0.434364 (-0.021242) \| 0.469312 / 0.540337 (-0.071026) \| 0.633140 / 1.386936 (-0.753796) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#dbf6c103f5844de40431478e7e4a64fbf2c2c067 \"CML watermark\")\n" ]	2023-07-13T13:16:17	2023-07-13T14:06:20	2023-07-13T13:57:35	CONTRIBUTOR	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6027", "html_url": "https://github.com/huggingface/datasets/pull/6027", "diff_url": "https://github.com/huggingface/datasets/pull/6027.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6027.patch", "merged_at": "2023-07-13T13:57:35" }	Fix #6025	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6027/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6027/timeline	null	null	true
https://api.github.com/repos/huggingface/datasets/issues/6026	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6026/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6026/comments	https://api.github.com/repos/huggingface/datasets/issues/6026/events	https://github.com/huggingface/datasets/pull/6026	1,802,929,222	PR_kwDODunzps5VanI8	6,026	Fix style with ruff 0.0.278	{ "login": "lhoestq", "id": 42851186, "node_id": "MDQ6VXNlcjQyODUxMTg2", "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "gravatar_id": "", "url": "https://api.github.com/users/lhoestq", "html_url": "https://github.com/lhoestq", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "repos_url": "https://api.github.com/users/lhoestq/repos", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6026). All of your documentation changes will be reflected on that endpoint.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006444 / 0.011353 (-0.004909) \| 0.003768 / 0.011008 (-0.007240) \| 0.079625 / 0.038508 (0.041117) \| 0.064490 / 0.023109 (0.041381) \| 0.313858 / 0.275898 (0.037960) \| 0.350810 / 0.323480 (0.027330) \| 0.004804 / 0.007986 (-0.003182) \| 0.002904 / 0.004328 (-0.001425) \| 0.061728 / 0.004250 (0.057477) \| 0.052265 / 0.037052 (0.015213) \| 0.321246 / 0.258489 (0.062757) \| 0.353873 / 0.293841 (0.060032) \| 0.027510 / 0.128546 (-0.101036) \| 0.007942 / 0.075646 (-0.067704) \| 0.260518 / 0.419271 (-0.158754) \| 0.045686 / 0.043533 (0.002153) \| 0.316821 / 0.255139 (0.061682) \| 0.337086 / 0.283200 (0.053886) \| 0.022188 / 0.141683 (-0.119495) \| 1.427345 / 1.452155 (-0.024810) \| 1.476059 / 1.492716 (-0.016657) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.189640 / 0.018006 (0.171634) \| 0.429724 / 0.000490 (0.429235) \| 0.005314 / 0.000200 (0.005114) \| 0.000076 / 0.000054 (0.000021) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024412 / 0.037411 (-0.013000) \| 0.073488 / 0.014526 (0.058962) \| 0.083843 / 0.176557 (-0.092714) \| 0.147849 / 0.737135 (-0.589286) \| 0.085465 / 0.296338 (-0.210873) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.405314 / 0.215209 (0.190105) \| 4.071471 / 2.077655 (1.993816) \| 1.916252 / 1.504120 (0.412132) \| 1.721616 / 1.541195 (0.180422) \| 1.807187 / 1.468490 (0.338697) \| 0.498045 / 4.584777 (-4.086732) \| 3.057526 / 3.745712 (-0.688187) \| 4.451424 / 5.269862 (-0.818437) \| 2.764020 / 4.565676 (-1.801656) \| 0.057665 / 0.424275 (-0.366610) \| 0.006679 / 0.007607 (-0.000928) \| 0.485733 / 0.226044 (0.259688) \| 4.844367 / 2.268929 (2.575438) \| 2.435359 / 55.444624 (-53.009265) \| 2.111478 / 6.876477 (-4.764999) \| 2.377448 / 2.142072 (0.235375) \| 0.587997 / 4.805227 (-4.217230) \| 0.125545 / 6.500664 (-6.375120) \| 0.061509 / 0.075469 (-0.013960) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.229210 / 1.841788 (-0.612577) \| 18.553994 / 8.074308 (10.479686) \| 14.037877 / 10.191392 (3.846485) \| 0.144230 / 0.680424 (-0.536194) \| 0.016891 / 0.534201 (-0.517310) \| 0.329039 / 0.579283 (-0.250244) \| 0.357269 / 0.434364 (-0.077095) \| 0.384222 / 0.540337 (-0.156115) \| 0.521292 / 1.386936 (-0.865644) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006359 / 0.011353 (-0.004994) \| 0.003721 / 0.011008 (-0.007287) \| 0.062047 / 0.038508 (0.023539) \| 0.065267 / 0.023109 (0.042158) \| 0.360164 / 0.275898 (0.084266) \| 0.402292 / 0.323480 (0.078812) \| 0.005603 / 0.007986 (-0.002382) \| 0.002966 / 0.004328 (-0.001363) \| 0.062580 / 0.004250 (0.058330) \| 0.053634 / 0.037052 (0.016582) \| 0.362210 / 0.258489 (0.103721) \| 0.404285 / 0.293841 (0.110444) \| 0.027567 / 0.128546 (-0.100979) \| 0.008119 / 0.075646 (-0.067528) \| 0.067577 / 0.419271 (-0.351694) \| 0.042867 / 0.043533 (-0.000666) \| 0.361576 / 0.255139 (0.106437) \| 0.389061 / 0.283200 (0.105862) \| 0.021923 / 0.141683 (-0.119760) \| 1.446259 / 1.452155 (-0.005895) \| 1.490724 / 1.492716 (-0.001992) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.206433 / 0.018006 (0.188427) \| 0.424178 / 0.000490 (0.423688) \| 0.002340 / 0.000200 (0.002140) \| 0.000069 / 0.000054 (0.000015) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024955 / 0.037411 (-0.012456) \| 0.077446 / 0.014526 (0.062920) \| 0.088540 / 0.176557 (-0.088017) \| 0.141225 / 0.737135 (-0.595910) \| 0.089747 / 0.296338 (-0.206592) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.443738 / 0.215209 (0.228529) \| 4.208887 / 2.077655 (2.131233) \| 2.155127 / 1.504120 (0.651007) \| 2.028178 / 1.541195 (0.486983) \| 2.084903 / 1.468490 (0.616413) \| 0.497530 / 4.584777 (-4.087247) \| 3.069012 / 3.745712 (-0.676700) \| 3.025184 / 5.269862 (-2.244678) \| 1.904687 / 4.565676 (-2.660990) \| 0.057526 / 0.424275 (-0.366749) \| 0.006482 / 0.007607 (-0.001125) \| 0.494692 / 0.226044 (0.268647) \| 4.944437 / 2.268929 (2.675508) \| 2.655989 / 55.444624 (-52.788635) \| 2.331677 / 6.876477 (-4.544800) \| 2.382396 / 2.142072 (0.240324) \| 0.582019 / 4.805227 (-4.223209) \| 0.125866 / 6.500664 (-6.374799) \| 0.062908 / 0.075469 (-0.012561) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.294612 / 1.841788 (-0.547176) \| 19.016152 / 8.074308 (10.941844) \| 14.088828 / 10.191392 (3.897436) \| 0.160842 / 0.680424 (-0.519582) \| 0.017054 / 0.534201 (-0.517146) \| 0.333647 / 0.579283 (-0.245636) \| 0.348094 / 0.434364 (-0.086270) \| 0.394970 / 0.540337 (-0.145367) \| 0.551141 / 1.386936 (-0.835795) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9e9cfe886792b30b5000808072a0f91ec8536749 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007442 / 0.011353 (-0.003911) \| 0.004302 / 0.011008 (-0.006707) \| 0.087159 / 0.038508 (0.048651) \| 0.095094 / 0.023109 (0.071985) \| 0.315422 / 0.275898 (0.039524) \| 0.346672 / 0.323480 (0.023192) \| 0.005811 / 0.007986 (-0.002174) \| 0.003597 / 0.004328 (-0.000731) \| 0.066400 / 0.004250 (0.062150) \| 0.065947 / 0.037052 (0.028894) \| 0.323269 / 0.258489 (0.064780) \| 0.353309 / 0.293841 (0.059468) \| 0.032268 / 0.128546 (-0.096278) \| 0.008696 / 0.075646 (-0.066950) \| 0.291486 / 0.419271 (-0.127786) \| 0.054609 / 0.043533 (0.011076) \| 0.321061 / 0.255139 (0.065922) \| 0.336907 / 0.283200 (0.053707) \| 0.027338 / 0.141683 (-0.114345) \| 1.496442 / 1.452155 (0.044287) \| 1.576946 / 1.492716 (0.084229) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.229140 / 0.018006 (0.211134) \| 0.487500 / 0.000490 (0.487010) \| 0.002425 / 0.000200 (0.002225) \| 0.000089 / 0.000054 (0.000034) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029351 / 0.037411 (-0.008060) \| 0.089610 / 0.014526 (0.075084) \| 0.097880 / 0.176557 (-0.078676) \| 0.155947 / 0.737135 (-0.581189) \| 0.098593 / 0.296338 (-0.197745) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.382911 / 0.215209 (0.167702) \| 3.820363 / 2.077655 (1.742708) \| 1.866385 / 1.504120 (0.362265) \| 1.712910 / 1.541195 (0.171716) \| 1.813863 / 1.468490 (0.345373) \| 0.484884 / 4.584777 (-4.099893) \| 3.678911 / 3.745712 (-0.066801) \| 5.249908 / 5.269862 (-0.019953) \| 3.099614 / 4.565676 (-1.466063) \| 0.057449 / 0.424275 (-0.366826) \| 0.007728 / 0.007607 (0.000120) \| 0.462123 / 0.226044 (0.236078) \| 4.603942 / 2.268929 (2.335014) \| 2.380957 / 55.444624 (-53.063668) \| 2.059621 / 6.876477 (-4.816856) \| 2.293764 / 2.142072 (0.151691) \| 0.636471 / 4.805227 (-4.168756) \| 0.150112 / 6.500664 (-6.350552) \| 0.063705 / 0.075469 (-0.011764) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.358099 / 1.841788 (-0.483689) \| 20.193750 / 8.074308 (12.119442) \| 14.297350 / 10.191392 (4.105958) \| 0.164477 / 0.680424 (-0.515947) \| 0.018259 / 0.534201 (-0.515942) \| 0.399010 / 0.579283 (-0.180273) \| 0.417306 / 0.434364 (-0.017058) \| 0.456961 / 0.540337 (-0.083377) \| 0.631068 / 1.386936 (-0.755868) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007324 / 0.011353 (-0.004028) \| 0.004463 / 0.011008 (-0.006545) \| 0.066148 / 0.038508 (0.027640) \| 0.093909 / 0.023109 (0.070799) \| 0.399122 / 0.275898 (0.123224) \| 0.430226 / 0.323480 (0.106746) \| 0.005505 / 0.007986 (-0.002481) \| 0.003579 / 0.004328 (-0.000749) \| 0.066529 / 0.004250 (0.062278) \| 0.063471 / 0.037052 (0.026418) \| 0.406351 / 0.258489 (0.147862) \| 0.439987 / 0.293841 (0.146146) \| 0.032640 / 0.128546 (-0.095906) \| 0.008770 / 0.075646 (-0.066877) \| 0.072592 / 0.419271 (-0.346680) \| 0.050429 / 0.043533 (0.006896) \| 0.390873 / 0.255139 (0.135734) \| 0.412438 / 0.283200 (0.129239) \| 0.027113 / 0.141683 (-0.114570) \| 1.458281 / 1.452155 (0.006126) \| 1.536819 / 1.492716 (0.044103) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.228309 / 0.018006 (0.210303) \| 0.454042 / 0.000490 (0.453552) \| 0.000387 / 0.000200 (0.000187) \| 0.000055 / 0.000054 (0.000001) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029573 / 0.037411 (-0.007838) \| 0.086433 / 0.014526 (0.071907) \| 0.097992 / 0.176557 (-0.078565) \| 0.152464 / 0.737135 (-0.584671) \| 0.099901 / 0.296338 (-0.196437) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.413807 / 0.215209 (0.198598) \| 4.126395 / 2.077655 (2.048740) \| 2.113544 / 1.504120 (0.609424) \| 1.967829 / 1.541195 (0.426635) \| 2.037123 / 1.468490 (0.568633) \| 0.489403 / 4.584777 (-4.095374) \| 3.689508 / 3.745712 (-0.056204) \| 3.503909 / 5.269862 (-1.765952) \| 2.113812 / 4.565676 (-2.451864) \| 0.057988 / 0.424275 (-0.366287) \| 0.007336 / 0.007607 (-0.000271) \| 0.490840 / 0.226044 (0.264795) \| 4.885040 / 2.268929 (2.616112) \| 2.627864 / 55.444624 (-52.816760) \| 2.231467 / 6.876477 (-4.645010) \| 2.251307 / 2.142072 (0.109235) \| 0.577370 / 4.805227 (-4.227857) \| 0.131770 / 6.500664 (-6.368895) \| 0.061313 / 0.075469 (-0.014156) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.362052 / 1.841788 (-0.479735) \| 21.332694 / 8.074308 (13.258386) \| 15.562019 / 10.191392 (5.370627) \| 0.170874 / 0.680424 (-0.509550) \| 0.019226 / 0.534201 (-0.514975) \| 0.400311 / 0.579283 (-0.178972) \| 0.423060 / 0.434364 (-0.011304) \| 0.469946 / 0.540337 (-0.070391) \| 0.647745 / 1.386936 (-0.739191) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#aec567c2f224f192e6e1f9799e3afc755eb517b2 \"CML watermark\")\n" ]	2023-07-13T12:34:24	2023-07-13T12:46:26	2023-07-13T12:37:01	MEMBER	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6026", "html_url": "https://github.com/huggingface/datasets/pull/6026", "diff_url": "https://github.com/huggingface/datasets/pull/6026.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6026.patch", "merged_at": "2023-07-13T12:37:01" }	null	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6026/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6026/timeline	null	null	true
https://api.github.com/repos/huggingface/datasets/issues/6025	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6025/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6025/comments	https://api.github.com/repos/huggingface/datasets/issues/6025/events	https://github.com/huggingface/datasets/issues/6025	1,801,852,601	I_kwDODunzps5rZha5	6,025	Using a dataset for a use other than it was intended for.	{ "login": "surya-narayanan", "id": 17240858, "node_id": "MDQ6VXNlcjE3MjQwODU4", "avatar_url": "https://avatars.githubusercontent.com/u/17240858?v=4", "gravatar_id": "", "url": "https://api.github.com/users/surya-narayanan", "html_url": "https://github.com/surya-narayanan", "followers_url": "https://api.github.com/users/surya-narayanan/followers", "following_url": "https://api.github.com/users/surya-narayanan/following{/other_user}", "gists_url": "https://api.github.com/users/surya-narayanan/gists{/gist_id}", "starred_url": "https://api.github.com/users/surya-narayanan/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/surya-narayanan/subscriptions", "organizations_url": "https://api.github.com/users/surya-narayanan/orgs", "repos_url": "https://api.github.com/users/surya-narayanan/repos", "events_url": "https://api.github.com/users/surya-narayanan/events{/privacy}", "received_events_url": "https://api.github.com/users/surya-narayanan/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "I've opened a PR with a fix. In the meantime, you can avoid the error by deleting `task_templates` with `dataset.info.task_templates = None` before the `interleave_datasets` call.\r\n` " ]	2023-07-12T22:33:17	2023-07-13T13:57:36	2023-07-13T13:57:36	NONE	null	null	null	### Describe the bug Hi, I want to use the rotten tomatoes dataset but for a task other than classification, but when I interleave the dataset, it throws ```'ValueError: Column label is not present in features.'```. It seems that the label_col must be there in the dataset for some reason? Here is the full stacktrace ``` File "/home/suryahari/Vornoi/tryage-handoff-other-datasets.py", line 276, in create_dataloaders dataset = interleave_datasets(dsfold, stopping_strategy="all_exhausted") File "/home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/combine.py", line 134, in interleave_datasets return _interleave_iterable_datasets( File "/home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/iterable_dataset.py", line 1833, in _interleave_iterable_datasets info = DatasetInfo.from_merge([d.info for d in datasets]) File "/home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/info.py", line 275, in from_merge dataset_infos = [dset_info.copy() for dset_info in dataset_infos if dset_info is not None] File "/home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/info.py", line 275, in <listcomp> dataset_infos = [dset_info.copy() for dset_info in dataset_infos if dset_info is not None] File "/home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/info.py", line 378, in copy return self.__class__(**{k: copy.deepcopy(v) for k, v in self.__dict__.items()}) File "<string>", line 20, in __init__ File "/home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/info.py", line 208, in __post_init__ self.task_templates = [ File "/home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/info.py", line 209, in <listcomp> template.align_with_features(self.features) for template in (self.task_templates) File "/home/suryahari/miniconda3/envs/vornoi/lib/python3.10/site-packages/datasets/tasks/text_classification.py", line 20, in align_with_features raise ValueError(f"Column {self.label_column} is not present in features.") ValueError: Column label is not present in features. ``` ### Steps to reproduce the bug Delete the column `labels` from the `rotten_tomatoes` dataset. Try to interleave it with other datasets. ### Expected behavior Should let me use the dataset with just the `text` field ### Environment info latest datasets library? I don't think this was an issue in earlier versions.	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6025/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6025/timeline	null	completed	false
https://api.github.com/repos/huggingface/datasets/issues/6024	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6024/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6024/comments	https://api.github.com/repos/huggingface/datasets/issues/6024/events	https://github.com/huggingface/datasets/pull/6024	1,801,708,808	PR_kwDODunzps5VWbGe	6,024	Don't reference self in Spark._validate_cache_dir	{ "login": "maddiedawson", "id": 106995444, "node_id": "U_kgDOBmCe9A", "avatar_url": "https://avatars.githubusercontent.com/u/106995444?v=4", "gravatar_id": "", "url": "https://api.github.com/users/maddiedawson", "html_url": "https://github.com/maddiedawson", "followers_url": "https://api.github.com/users/maddiedawson/followers", "following_url": "https://api.github.com/users/maddiedawson/following{/other_user}", "gists_url": "https://api.github.com/users/maddiedawson/gists{/gist_id}", "starred_url": "https://api.github.com/users/maddiedawson/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/maddiedawson/subscriptions", "organizations_url": "https://api.github.com/users/maddiedawson/orgs", "repos_url": "https://api.github.com/users/maddiedawson/repos", "events_url": "https://api.github.com/users/maddiedawson/events{/privacy}", "received_events_url": "https://api.github.com/users/maddiedawson/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "Ptal @lhoestq :) I tested this manually on a multi-node Databricks cluster", "Hm looks like the check_code_quality failures are unrelated to me change... https://github.com/huggingface/datasets/actions/runs/5536162850/jobs/10103451883?pr=6024", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005952 / 0.011353 (-0.005400) \| 0.003585 / 0.011008 (-0.007424) \| 0.079163 / 0.038508 (0.040655) \| 0.057926 / 0.023109 (0.034817) \| 0.326647 / 0.275898 (0.050749) \| 0.383485 / 0.323480 (0.060005) \| 0.004530 / 0.007986 (-0.003456) \| 0.002821 / 0.004328 (-0.001508) \| 0.062071 / 0.004250 (0.057820) \| 0.048023 / 0.037052 (0.010971) \| 0.329368 / 0.258489 (0.070879) \| 0.390877 / 0.293841 (0.097036) \| 0.026959 / 0.128546 (-0.101588) \| 0.007911 / 0.075646 (-0.067735) \| 0.259956 / 0.419271 (-0.159315) \| 0.044582 / 0.043533 (0.001049) \| 0.320537 / 0.255139 (0.065398) \| 0.373814 / 0.283200 (0.090614) \| 0.020275 / 0.141683 (-0.121408) \| 1.532128 / 1.452155 (0.079973) \| 1.595031 / 1.492716 (0.102315) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.186127 / 0.018006 (0.168120) \| 0.428586 / 0.000490 (0.428097) \| 0.005180 / 0.000200 (0.004980) \| 0.000069 / 0.000054 (0.000015) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024876 / 0.037411 (-0.012536) \| 0.072169 / 0.014526 (0.057643) \| 0.082015 / 0.176557 (-0.094542) \| 0.147467 / 0.737135 (-0.589668) \| 0.082769 / 0.296338 (-0.213570) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.410625 / 0.215209 (0.195416) \| 4.116742 / 2.077655 (2.039088) \| 2.172291 / 1.504120 (0.668171) \| 2.022462 / 1.541195 (0.481268) \| 2.048142 / 1.468490 (0.579651) \| 0.503152 / 4.584777 (-4.081625) \| 3.019135 / 3.745712 (-0.726577) \| 3.589451 / 5.269862 (-1.680410) \| 2.206876 / 4.565676 (-2.358801) \| 0.057687 / 0.424275 (-0.366588) \| 0.006560 / 0.007607 (-0.001047) \| 0.475585 / 0.226044 (0.249541) \| 4.784344 / 2.268929 (2.515416) \| 2.506322 / 55.444624 (-52.938302) \| 2.168251 / 6.876477 (-4.708225) \| 2.324453 / 2.142072 (0.182381) \| 0.590609 / 4.805227 (-4.214618) \| 0.124178 / 6.500664 (-6.376486) \| 0.059197 / 0.075469 (-0.016272) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.212359 / 1.841788 (-0.629429) \| 17.915843 / 8.074308 (9.841535) \| 13.128330 / 10.191392 (2.936938) \| 0.144805 / 0.680424 (-0.535618) \| 0.016889 / 0.534201 (-0.517312) \| 0.344056 / 0.579283 (-0.235227) \| 0.359370 / 0.434364 (-0.074994) \| 0.404199 / 0.540337 (-0.136138) \| 0.549117 / 1.386936 (-0.837819) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005914 / 0.011353 (-0.005439) \| 0.003565 / 0.011008 (-0.007443) \| 0.061575 / 0.038508 (0.023067) \| 0.057677 / 0.023109 (0.034568) \| 0.359753 / 0.275898 (0.083855) \| 0.394135 / 0.323480 (0.070655) \| 0.004648 / 0.007986 (-0.003338) \| 0.002795 / 0.004328 (-0.001534) \| 0.061877 / 0.004250 (0.057626) \| 0.049673 / 0.037052 (0.012621) \| 0.363120 / 0.258489 (0.104631) \| 0.402685 / 0.293841 (0.108844) \| 0.027021 / 0.128546 (-0.101525) \| 0.008006 / 0.075646 (-0.067641) \| 0.067398 / 0.419271 (-0.351874) \| 0.044442 / 0.043533 (0.000909) \| 0.364851 / 0.255139 (0.109712) \| 0.387219 / 0.283200 (0.104019) \| 0.027267 / 0.141683 (-0.114416) \| 1.466675 / 1.452155 (0.014520) \| 1.512607 / 1.492716 (0.019891) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.206156 / 0.018006 (0.188150) \| 0.410877 / 0.000490 (0.410387) \| 0.003061 / 0.000200 (0.002861) \| 0.000068 / 0.000054 (0.000013) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024869 / 0.037411 (-0.012542) \| 0.075736 / 0.014526 (0.061210) \| 0.083922 / 0.176557 (-0.092634) \| 0.139510 / 0.737135 (-0.597626) \| 0.087685 / 0.296338 (-0.208654) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.414473 / 0.215209 (0.199264) \| 4.150633 / 2.077655 (2.072979) \| 2.132892 / 1.504120 (0.628773) \| 1.964072 / 1.541195 (0.422878) \| 2.003353 / 1.468490 (0.534863) \| 0.498012 / 4.584777 (-4.086765) \| 3.010135 / 3.745712 (-0.735577) \| 2.841130 / 5.269862 (-2.428732) \| 1.826013 / 4.565676 (-2.739664) \| 0.057443 / 0.424275 (-0.366832) \| 0.006374 / 0.007607 (-0.001234) \| 0.490337 / 0.226044 (0.264292) \| 4.889628 / 2.268929 (2.620700) \| 2.575626 / 55.444624 (-52.868998) \| 2.246522 / 6.876477 (-4.629955) \| 2.276183 / 2.142072 (0.134110) \| 0.581465 / 4.805227 (-4.223763) \| 0.123877 / 6.500664 (-6.376787) \| 0.060339 / 0.075469 (-0.015130) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.333202 / 1.841788 (-0.508585) \| 18.363558 / 8.074308 (10.289250) \| 14.109356 / 10.191392 (3.917964) \| 0.147358 / 0.680424 (-0.533066) \| 0.016813 / 0.534201 (-0.517388) \| 0.334815 / 0.579283 (-0.244468) \| 0.366576 / 0.434364 (-0.067788) \| 0.397223 / 0.540337 (-0.143115) \| 0.547893 / 1.386936 (-0.839043) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#67ac60bcbebe9ddac70264951b1d584c93003cdf \"CML watermark\")\n" ]	2023-07-12T20:31:16	2023-07-13T16:58:32	2023-07-13T12:37:09	CONTRIBUTOR	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6024", "html_url": "https://github.com/huggingface/datasets/pull/6024", "diff_url": "https://github.com/huggingface/datasets/pull/6024.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6024.patch", "merged_at": "2023-07-13T12:37:09" }	Fix for https://github.com/huggingface/datasets/issues/5963	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6024/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6024/timeline	null	null	true
https://api.github.com/repos/huggingface/datasets/issues/6023	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6023/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6023/comments	https://api.github.com/repos/huggingface/datasets/issues/6023/events	https://github.com/huggingface/datasets/pull/6023	1,801,272,420	PR_kwDODunzps5VU7EG	6,023	Fix `ClassLabel` min max check for `None` values	{ "login": "mariosasko", "id": 47462742, "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "gravatar_id": "", "url": "https://api.github.com/users/mariosasko", "html_url": "https://github.com/mariosasko", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "repos_url": "https://api.github.com/users/mariosasko/repos", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007108 / 0.011353 (-0.004245) \| 0.004446 / 0.011008 (-0.006562) \| 0.084013 / 0.038508 (0.045505) \| 0.084271 / 0.023109 (0.061162) \| 0.324496 / 0.275898 (0.048598) \| 0.347783 / 0.323480 (0.024303) \| 0.004382 / 0.007986 (-0.003604) \| 0.005200 / 0.004328 (0.000872) \| 0.065117 / 0.004250 (0.060866) \| 0.063368 / 0.037052 (0.026316) \| 0.328731 / 0.258489 (0.070242) \| 0.356676 / 0.293841 (0.062835) \| 0.031155 / 0.128546 (-0.097392) \| 0.008672 / 0.075646 (-0.066975) \| 0.287573 / 0.419271 (-0.131698) \| 0.053692 / 0.043533 (0.010160) \| 0.308796 / 0.255139 (0.053657) \| 0.330521 / 0.283200 (0.047321) \| 0.025010 / 0.141683 (-0.116672) \| 1.498968 / 1.452155 (0.046813) \| 1.552096 / 1.492716 (0.059380) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.263580 / 0.018006 (0.245574) \| 0.559765 / 0.000490 (0.559275) \| 0.003450 / 0.000200 (0.003250) \| 0.000079 / 0.000054 (0.000024) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029403 / 0.037411 (-0.008008) \| 0.088154 / 0.014526 (0.073628) \| 0.100372 / 0.176557 (-0.076185) \| 0.157777 / 0.737135 (-0.579359) \| 0.102273 / 0.296338 (-0.194066) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.387027 / 0.215209 (0.171818) \| 3.854260 / 2.077655 (1.776605) \| 1.875159 / 1.504120 (0.371039) \| 1.703734 / 1.541195 (0.162539) \| 1.814305 / 1.468490 (0.345815) \| 0.482524 / 4.584777 (-4.102253) \| 3.463602 / 3.745712 (-0.282110) \| 4.004766 / 5.269862 (-1.265095) \| 2.406751 / 4.565676 (-2.158925) \| 0.057069 / 0.424275 (-0.367206) \| 0.007448 / 0.007607 (-0.000159) \| 0.465801 / 0.226044 (0.239757) \| 4.636700 / 2.268929 (2.367771) \| 2.329475 / 55.444624 (-53.115150) \| 1.998330 / 6.876477 (-4.878146) \| 2.264617 / 2.142072 (0.122544) \| 0.577998 / 4.805227 (-4.227230) \| 0.130846 / 6.500664 (-6.369818) \| 0.059713 / 0.075469 (-0.015756) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.275931 / 1.841788 (-0.565857) \| 20.396288 / 8.074308 (12.321980) \| 13.875242 / 10.191392 (3.683850) \| 0.164367 / 0.680424 (-0.516057) \| 0.018573 / 0.534201 (-0.515628) \| 0.397516 / 0.579283 (-0.181767) \| 0.398977 / 0.434364 (-0.035387) \| 0.462386 / 0.540337 (-0.077951) \| 0.610129 / 1.386936 (-0.776807) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006912 / 0.011353 (-0.004441) \| 0.004212 / 0.011008 (-0.006797) \| 0.065707 / 0.038508 (0.027199) \| 0.090435 / 0.023109 (0.067325) \| 0.380539 / 0.275898 (0.104641) \| 0.412692 / 0.323480 (0.089212) \| 0.005545 / 0.007986 (-0.002441) \| 0.003657 / 0.004328 (-0.000672) \| 0.065380 / 0.004250 (0.061130) \| 0.062901 / 0.037052 (0.025848) \| 0.385931 / 0.258489 (0.127442) \| 0.416272 / 0.293841 (0.122431) \| 0.031974 / 0.128546 (-0.096572) \| 0.008783 / 0.075646 (-0.066863) \| 0.071424 / 0.419271 (-0.347847) \| 0.049454 / 0.043533 (0.005921) \| 0.374231 / 0.255139 (0.119092) \| 0.386530 / 0.283200 (0.103331) \| 0.025404 / 0.141683 (-0.116279) \| 1.469869 / 1.452155 (0.017715) \| 1.548629 / 1.492716 (0.055913) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.218413 / 0.018006 (0.200406) \| 0.573863 / 0.000490 (0.573373) \| 0.004156 / 0.000200 (0.003956) \| 0.000097 / 0.000054 (0.000043) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032610 / 0.037411 (-0.004801) \| 0.088270 / 0.014526 (0.073744) \| 0.106821 / 0.176557 (-0.069735) \| 0.164498 / 0.737135 (-0.572638) \| 0.106881 / 0.296338 (-0.189457) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.433730 / 0.215209 (0.218520) \| 4.323902 / 2.077655 (2.246247) \| 2.308607 / 1.504120 (0.804487) \| 2.138888 / 1.541195 (0.597693) \| 2.246760 / 1.468490 (0.778269) \| 0.486863 / 4.584777 (-4.097914) \| 3.561826 / 3.745712 (-0.183886) \| 5.592685 / 5.269862 (0.322824) \| 3.318560 / 4.565676 (-1.247116) \| 0.057348 / 0.424275 (-0.366927) \| 0.007434 / 0.007607 (-0.000174) \| 0.506767 / 0.226044 (0.280723) \| 5.083097 / 2.268929 (2.814168) \| 2.780618 / 55.444624 (-52.664006) \| 2.456924 / 6.876477 (-4.419553) \| 2.564184 / 2.142072 (0.422112) \| 0.580693 / 4.805227 (-4.224534) \| 0.134471 / 6.500664 (-6.366194) \| 0.062883 / 0.075469 (-0.012586) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.346618 / 1.841788 (-0.495169) \| 20.547998 / 8.074308 (12.473690) \| 14.404159 / 10.191392 (4.212767) \| 0.176612 / 0.680424 (-0.503812) \| 0.018372 / 0.534201 (-0.515829) \| 0.395636 / 0.579283 (-0.183647) \| 0.410661 / 0.434364 (-0.023703) \| 0.468782 / 0.540337 (-0.071555) \| 0.637476 / 1.386936 (-0.749460) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0172d4dac0ca823e8bd293cfd4d28e78d92efe42 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009896 / 0.011353 (-0.001457) \| 0.004658 / 0.011008 (-0.006351) \| 0.101185 / 0.038508 (0.062677) \| 0.075480 / 0.023109 (0.052371) \| 0.410620 / 0.275898 (0.134722) \| 0.470639 / 0.323480 (0.147159) \| 0.007042 / 0.007986 (-0.000943) \| 0.003909 / 0.004328 (-0.000419) \| 0.079676 / 0.004250 (0.075425) \| 0.066921 / 0.037052 (0.029869) \| 0.423624 / 0.258489 (0.165135) \| 0.473008 / 0.293841 (0.179167) \| 0.048492 / 0.128546 (-0.080054) \| 0.012833 / 0.075646 (-0.062813) \| 0.335286 / 0.419271 (-0.083985) \| 0.083506 / 0.043533 (0.039973) \| 0.401918 / 0.255139 (0.146779) \| 0.467975 / 0.283200 (0.184775) \| 0.050025 / 0.141683 (-0.091658) \| 1.679392 / 1.452155 (0.227237) \| 1.852812 / 1.492716 (0.360095) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.248067 / 0.018006 (0.230061) \| 0.584818 / 0.000490 (0.584328) \| 0.021558 / 0.000200 (0.021358) \| 0.000104 / 0.000054 (0.000050) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028572 / 0.037411 (-0.008839) \| 0.097212 / 0.014526 (0.082686) \| 0.121675 / 0.176557 (-0.054881) \| 0.186597 / 0.737135 (-0.550538) \| 0.122285 / 0.296338 (-0.174053) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.586279 / 0.215209 (0.371070) \| 5.634402 / 2.077655 (3.556747) \| 2.560648 / 1.504120 (1.056528) \| 2.288796 / 1.541195 (0.747601) \| 2.402580 / 1.468490 (0.934090) \| 0.801453 / 4.584777 (-3.783324) \| 5.036654 / 3.745712 (1.290942) \| 8.319972 / 5.269862 (3.050110) \| 4.665620 / 4.565676 (0.099944) \| 0.107292 / 0.424275 (-0.316983) \| 0.009206 / 0.007607 (0.001599) \| 0.766505 / 0.226044 (0.540461) \| 7.333784 / 2.268929 (5.064856) \| 3.601875 / 55.444624 (-51.842749) \| 2.886388 / 6.876477 (-3.990089) \| 3.231797 / 2.142072 (1.089725) \| 1.179509 / 4.805227 (-3.625718) \| 0.224656 / 6.500664 (-6.276008) \| 0.084749 / 0.075469 (0.009280) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.772345 / 1.841788 (-0.069443) \| 24.138788 / 8.074308 (16.064480) \| 20.712416 / 10.191392 (10.521024) \| 0.254655 / 0.680424 (-0.425769) \| 0.028858 / 0.534201 (-0.505343) \| 0.499314 / 0.579283 (-0.079969) \| 0.605797 / 0.434364 (0.171433) \| 0.567628 / 0.540337 (0.027290) \| 0.752288 / 1.386936 (-0.634648) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.010134 / 0.011353 (-0.001219) \| 0.004630 / 0.011008 (-0.006378) \| 0.082282 / 0.038508 (0.043774) \| 0.081722 / 0.023109 (0.058613) \| 0.465018 / 0.275898 (0.189120) \| 0.516392 / 0.323480 (0.192912) \| 0.006618 / 0.007986 (-0.001368) \| 0.004310 / 0.004328 (-0.000018) \| 0.078990 / 0.004250 (0.074739) \| 0.077729 / 0.037052 (0.040677) \| 0.464892 / 0.258489 (0.206403) \| 0.510551 / 0.293841 (0.216710) \| 0.050750 / 0.128546 (-0.077796) \| 0.014402 / 0.075646 (-0.061244) \| 0.092587 / 0.419271 (-0.326685) \| 0.074769 / 0.043533 (0.031237) \| 0.468591 / 0.255139 (0.213452) \| 0.508138 / 0.283200 (0.224938) \| 0.047774 / 0.141683 (-0.093909) \| 1.798354 / 1.452155 (0.346199) \| 1.851431 / 1.492716 (0.358714) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.282528 / 0.018006 (0.264522) \| 0.588286 / 0.000490 (0.587797) \| 0.004892 / 0.000200 (0.004692) \| 0.000136 / 0.000054 (0.000082) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.037048 / 0.037411 (-0.000364) \| 0.101513 / 0.014526 (0.086987) \| 0.133238 / 0.176557 (-0.043319) \| 0.234799 / 0.737135 (-0.502336) \| 0.120636 / 0.296338 (-0.175703) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.615377 / 0.215209 (0.400168) \| 6.225717 / 2.077655 (4.148062) \| 2.974137 / 1.504120 (1.470018) \| 2.642168 / 1.541195 (1.100973) \| 2.706051 / 1.468490 (1.237561) \| 0.837171 / 4.584777 (-3.747606) \| 5.143368 / 3.745712 (1.397656) \| 4.560241 / 5.269862 (-0.709621) \| 2.838375 / 4.565676 (-1.727301) \| 0.092505 / 0.424275 (-0.331770) \| 0.008962 / 0.007607 (0.001355) \| 0.726361 / 0.226044 (0.500317) \| 7.323998 / 2.268929 (5.055070) \| 3.650531 / 55.444624 (-51.794094) \| 2.960886 / 6.876477 (-3.915591) \| 3.003889 / 2.142072 (0.861816) \| 0.979264 / 4.805227 (-3.825963) \| 0.204531 / 6.500664 (-6.296133) \| 0.078285 / 0.075469 (0.002816) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.774225 / 1.841788 (-0.067563) \| 26.399536 / 8.074308 (18.325228) \| 22.312890 / 10.191392 (12.121498) \| 0.244651 / 0.680424 (-0.435773) \| 0.026950 / 0.534201 (-0.507251) \| 0.493037 / 0.579283 (-0.086246) \| 0.620399 / 0.434364 (0.186036) \| 0.748985 / 0.540337 (0.208648) \| 0.799766 / 1.386936 (-0.587170) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a49ac2864177ec4fb34c43b59a6e49de1f21f973 \"CML watermark\")\n" ]	2023-07-12T15:46:12	2023-07-12T16:29:26	2023-07-12T16:18:04	CONTRIBUTOR	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6023", "html_url": "https://github.com/huggingface/datasets/pull/6023", "diff_url": "https://github.com/huggingface/datasets/pull/6023.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6023.patch", "merged_at": "2023-07-12T16:18:04" }	Fix #6022	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6023/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6023/timeline	null	null	true
https://api.github.com/repos/huggingface/datasets/issues/6022	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6022/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6022/comments	https://api.github.com/repos/huggingface/datasets/issues/6022/events	https://github.com/huggingface/datasets/issues/6022	1,800,092,589	I_kwDODunzps5rSzut	6,022	Batch map raises TypeError: '>=' not supported between instances of 'NoneType' and 'int'	{ "login": "codingl2k1", "id": 138426806, "node_id": "U_kgDOCEA5tg", "avatar_url": "https://avatars.githubusercontent.com/u/138426806?v=4", "gravatar_id": "", "url": "https://api.github.com/users/codingl2k1", "html_url": "https://github.com/codingl2k1", "followers_url": "https://api.github.com/users/codingl2k1/followers", "following_url": "https://api.github.com/users/codingl2k1/following{/other_user}", "gists_url": "https://api.github.com/users/codingl2k1/gists{/gist_id}", "starred_url": "https://api.github.com/users/codingl2k1/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/codingl2k1/subscriptions", "organizations_url": "https://api.github.com/users/codingl2k1/orgs", "repos_url": "https://api.github.com/users/codingl2k1/repos", "events_url": "https://api.github.com/users/codingl2k1/events{/privacy}", "received_events_url": "https://api.github.com/users/codingl2k1/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "Thanks for reporting! I've opened a PR with a fix." ]	2023-07-12T03:20:17	2023-07-12T16:18:06	2023-07-12T16:18:05	NONE	null	null	null	### Describe the bug When mapping some datasets with `batched=True`, datasets may raise an exeception: ```python Traceback (most recent call last): File "/Users/codingl2k1/Work/datasets/venv/lib/python3.11/site-packages/multiprocess/pool.py", line 125, in worker result = (True, func(args, kwds)) ^^^^^^^^^^^^^^^^^^^ File "/Users/codingl2k1/Work/datasets/src/datasets/utils/py_utils.py", line 1328, in _write_generator_to_queue for i, result in enumerate(func(kwargs)): File "/Users/codingl2k1/Work/datasets/src/datasets/arrow_dataset.py", line 3483, in _map_single writer.write_batch(batch) File "/Users/codingl2k1/Work/datasets/src/datasets/arrow_writer.py", line 549, in write_batch array = cast_array_to_feature(col_values, col_type) if col_type is not None else col_values ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/codingl2k1/Work/datasets/src/datasets/table.py", line 1831, in wrapper return pa.chunked_array([func(chunk, args, *kwargs) for chunk in array.chunks]) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/codingl2k1/Work/datasets/src/datasets/table.py", line 1831, in <listcomp> return pa.chunked_array([func(chunk, args, *kwargs) for chunk in array.chunks]) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/codingl2k1/Work/datasets/src/datasets/table.py", line 2063, in cast_array_to_feature return feature.cast_storage(array) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/codingl2k1/Work/datasets/src/datasets/features/features.py", line 1098, in cast_storage if min_max["max"] >= self.num_classes: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ TypeError: '>=' not supported between instances of 'NoneType' and 'int' The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/Users/codingl2k1/Work/datasets/t1.py", line 33, in <module> ds = ds.map(transforms, num_proc=14, batched=True, batch_size=5) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/codingl2k1/Work/datasets/src/datasets/dataset_dict.py", line 850, in map { File "/Users/codingl2k1/Work/datasets/src/datasets/dataset_dict.py", line 851, in <dictcomp> k: dataset.map( ^^^^^^^^^^^^ File "/Users/codingl2k1/Work/datasets/src/datasets/arrow_dataset.py", line 577, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, args, *kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/codingl2k1/Work/datasets/src/datasets/arrow_dataset.py", line 542, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/codingl2k1/Work/datasets/src/datasets/arrow_dataset.py", line 3179, in map for rank, done, content in iflatmap_unordered( File "/Users/codingl2k1/Work/datasets/src/datasets/utils/py_utils.py", line 1368, in iflatmap_unordered [async_result.get(timeout=0.05) for async_result in async_results] File "/Users/codingl2k1/Work/datasets/src/datasets/utils/py_utils.py", line 1368, in <listcomp> [async_result.get(timeout=0.05) for async_result in async_results] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/codingl2k1/Work/datasets/venv/lib/python3.11/site-packages/multiprocess/pool.py", line 774, in get raise self._value TypeError: '>=' not supported between instances of 'NoneType' and 'int' ``` ### Steps to reproduce the bug 1. Checkout the latest main of datasets. 2. Run the code: ```python from datasets import load_dataset def transforms(examples): # examples["pixel_values"] = [image.convert("RGB").resize((100, 100)) for image in examples["image"]] return examples ds = load_dataset("scene_parse_150") ds = ds.map(transforms, num_proc=14, batched=True, batch_size=5) print(ds) ``` ### Expected behavior map without exception. ### Environment info Datasets: https://github.com/huggingface/datasets/commit/b8067c0262073891180869f700ebef5ac3dc5cce Python: 3.11.4 System: Macos	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6022/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6022/timeline	null	completed	false
https://api.github.com/repos/huggingface/datasets/issues/6021	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6021/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6021/comments	https://api.github.com/repos/huggingface/datasets/issues/6021/events	https://github.com/huggingface/datasets/pull/6021	1,799,785,904	PR_kwDODunzps5VP11Q	6,021	[docs] Update return statement of index search	{ "login": "stevhliu", "id": 59462357, "node_id": "MDQ6VXNlcjU5NDYyMzU3", "avatar_url": "https://avatars.githubusercontent.com/u/59462357?v=4", "gravatar_id": "", "url": "https://api.github.com/users/stevhliu", "html_url": "https://github.com/stevhliu", "followers_url": "https://api.github.com/users/stevhliu/followers", "following_url": "https://api.github.com/users/stevhliu/following{/other_user}", "gists_url": "https://api.github.com/users/stevhliu/gists{/gist_id}", "starred_url": "https://api.github.com/users/stevhliu/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/stevhliu/subscriptions", "organizations_url": "https://api.github.com/users/stevhliu/orgs", "repos_url": "https://api.github.com/users/stevhliu/repos", "events_url": "https://api.github.com/users/stevhliu/events{/privacy}", "received_events_url": "https://api.github.com/users/stevhliu/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007697 / 0.011353 (-0.003656) \| 0.004233 / 0.011008 (-0.006776) \| 0.087890 / 0.038508 (0.049382) \| 0.065305 / 0.023109 (0.042196) \| 0.366919 / 0.275898 (0.091020) \| 0.399656 / 0.323480 (0.076176) \| 0.006753 / 0.007986 (-0.001232) \| 0.003428 / 0.004328 (-0.000900) \| 0.070180 / 0.004250 (0.065930) \| 0.054164 / 0.037052 (0.017112) \| 0.377130 / 0.258489 (0.118641) \| 0.403456 / 0.293841 (0.109615) \| 0.042639 / 0.128546 (-0.085907) \| 0.012396 / 0.075646 (-0.063250) \| 0.314235 / 0.419271 (-0.105036) \| 0.061976 / 0.043533 (0.018443) \| 0.376959 / 0.255139 (0.121820) \| 0.433313 / 0.283200 (0.150113) \| 0.031253 / 0.141683 (-0.110430) \| 1.555749 / 1.452155 (0.103594) \| 1.643905 / 1.492716 (0.151189) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.208630 / 0.018006 (0.190624) \| 0.519532 / 0.000490 (0.519042) \| 0.003719 / 0.000200 (0.003519) \| 0.000099 / 0.000054 (0.000045) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027403 / 0.037411 (-0.010008) \| 0.080990 / 0.014526 (0.066464) \| 0.090424 / 0.176557 (-0.086133) \| 0.153922 / 0.737135 (-0.583213) \| 0.098156 / 0.296338 (-0.198183) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.519453 / 0.215209 (0.304244) \| 5.100089 / 2.077655 (3.022434) \| 2.212165 / 1.504120 (0.708045) \| 1.894405 / 1.541195 (0.353210) \| 1.922914 / 1.468490 (0.454424) \| 0.762443 / 4.584777 (-3.822334) \| 4.669214 / 3.745712 (0.923502) \| 5.016066 / 5.269862 (-0.253796) \| 3.128821 / 4.565676 (-1.436856) \| 0.091541 / 0.424275 (-0.332734) \| 0.007582 / 0.007607 (-0.000026) \| 0.652753 / 0.226044 (0.426709) \| 6.601375 / 2.268929 (4.332446) \| 3.076948 / 55.444624 (-52.367677) \| 2.250544 / 6.876477 (-4.625933) \| 2.404059 / 2.142072 (0.261987) \| 0.994917 / 4.805227 (-3.810311) \| 0.200318 / 6.500664 (-6.300346) \| 0.069354 / 0.075469 (-0.006115) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.482559 / 1.841788 (-0.359229) \| 20.722092 / 8.074308 (12.647784) \| 17.703217 / 10.191392 (7.511825) \| 0.215370 / 0.680424 (-0.465053) \| 0.028208 / 0.534201 (-0.505993) \| 0.425992 / 0.579283 (-0.153291) \| 0.492785 / 0.434364 (0.058421) \| 0.474154 / 0.540337 (-0.066183) \| 0.644599 / 1.386936 (-0.742337) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008372 / 0.011353 (-0.002981) \| 0.004543 / 0.011008 (-0.006465) \| 0.070564 / 0.038508 (0.032056) \| 0.066855 / 0.023109 (0.043746) \| 0.386724 / 0.275898 (0.110826) \| 0.432184 / 0.323480 (0.108704) \| 0.005250 / 0.007986 (-0.002736) \| 0.003630 / 0.004328 (-0.000698) \| 0.069310 / 0.004250 (0.065060) \| 0.055759 / 0.037052 (0.018707) \| 0.375789 / 0.258489 (0.117299) \| 0.417335 / 0.293841 (0.123494) \| 0.043424 / 0.128546 (-0.085122) \| 0.013106 / 0.075646 (-0.062541) \| 0.087836 / 0.419271 (-0.331436) \| 0.057770 / 0.043533 (0.014237) \| 0.396694 / 0.255139 (0.141555) \| 0.439350 / 0.283200 (0.156150) \| 0.031660 / 0.141683 (-0.110023) \| 1.571339 / 1.452155 (0.119185) \| 1.667169 / 1.492716 (0.174452) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.180534 / 0.018006 (0.162528) \| 0.540027 / 0.000490 (0.539537) \| 0.003573 / 0.000200 (0.003373) \| 0.000141 / 0.000054 (0.000086) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031380 / 0.037411 (-0.006032) \| 0.083762 / 0.014526 (0.069236) \| 0.098166 / 0.176557 (-0.078390) \| 0.160761 / 0.737135 (-0.576374) \| 0.097683 / 0.296338 (-0.198656) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.568074 / 0.215209 (0.352865) \| 5.660544 / 2.077655 (3.582889) \| 2.416698 / 1.504120 (0.912578) \| 2.177096 / 1.541195 (0.635901) \| 2.206178 / 1.468490 (0.737688) \| 0.844864 / 4.584777 (-3.739912) \| 4.793636 / 3.745712 (1.047923) \| 7.062387 / 5.269862 (1.792525) \| 4.201228 / 4.565676 (-0.364449) \| 0.091997 / 0.424275 (-0.332279) \| 0.007881 / 0.007607 (0.000274) \| 0.679466 / 0.226044 (0.453422) \| 6.580268 / 2.268929 (4.311340) \| 3.229907 / 55.444624 (-52.214717) \| 2.524877 / 6.876477 (-4.351600) \| 2.463796 / 2.142072 (0.321723) \| 0.975627 / 4.805227 (-3.829600) \| 0.186670 / 6.500664 (-6.313994) \| 0.065307 / 0.075469 (-0.010163) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.501447 / 1.841788 (-0.340340) \| 21.231037 / 8.074308 (13.156729) \| 17.591671 / 10.191392 (7.400279) \| 0.212745 / 0.680424 (-0.467679) \| 0.026100 / 0.534201 (-0.508101) \| 0.428391 / 0.579283 (-0.150892) \| 0.535268 / 0.434364 (0.100904) \| 0.506733 / 0.540337 (-0.033604) \| 0.660832 / 1.386936 (-0.726104) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#962537d7ee9191438ef47a4185d0ba626b2ee949 \"CML watermark\")\n" ]	2023-07-11T21:33:32	2023-07-12T17:13:02	2023-07-12T17:03:00	MEMBER	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6021", "html_url": "https://github.com/huggingface/datasets/pull/6021", "diff_url": "https://github.com/huggingface/datasets/pull/6021.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6021.patch", "merged_at": "2023-07-12T17:03:00" }	Clarifies in the return statement of the docstring that the retrieval score is `IndexFlatL2` by default (see [PR](https://github.com/huggingface/transformers/issues/24739) and internal Slack [convo](https://huggingface.slack.com/archives/C01229B19EX/p1689105179711689)), and fixes the formatting because multiple return values are not supported.	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6021/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6021/timeline	null	null	true
https://api.github.com/repos/huggingface/datasets/issues/6020	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6020/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6020/comments	https://api.github.com/repos/huggingface/datasets/issues/6020/events	https://github.com/huggingface/datasets/issues/6020	1,799,720,536	I_kwDODunzps5rRY5Y	6,020	Inconsistent "The features can't be aligned" error when combining map, multiprocessing, and variable length outputs	{ "login": "kheyer", "id": 38166299, "node_id": "MDQ6VXNlcjM4MTY2Mjk5", "avatar_url": "https://avatars.githubusercontent.com/u/38166299?v=4", "gravatar_id": "", "url": "https://api.github.com/users/kheyer", "html_url": "https://github.com/kheyer", "followers_url": "https://api.github.com/users/kheyer/followers", "following_url": "https://api.github.com/users/kheyer/following{/other_user}", "gists_url": "https://api.github.com/users/kheyer/gists{/gist_id}", "starred_url": "https://api.github.com/users/kheyer/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/kheyer/subscriptions", "organizations_url": "https://api.github.com/users/kheyer/orgs", "repos_url": "https://api.github.com/users/kheyer/repos", "events_url": "https://api.github.com/users/kheyer/events{/privacy}", "received_events_url": "https://api.github.com/users/kheyer/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	null	[ "This scenario currently requires explicitly passing the target features (to avoid the error): \r\n```python\r\nimport datasets\r\n\r\n...\r\n\r\nfeatures = dataset.features\r\nfeatures[\"output\"] = = [{\"test\": datasets.Value(\"int64\")}]\r\ntest2 = dataset.map(lambda row, idx: test_func(row, idx), with_indices=True, num_proc=32, features=features)\r\n```" ]	2023-07-11T20:40:38	2023-07-12T15:58:24	null	NONE	null	null	null	### Describe the bug I'm using a dataset with map and multiprocessing to run a function that returned a variable length list of outputs. This output list may be empty. Normally this is handled fine, but there is an edge case that crops up when using multiprocessing. In some cases, an empty list result ends up in a dataset shard consisting of a single item. This results in a `The features can't be aligned` error that is difficult to debug because it depends on the number of processes/shards used. I've reproduced a minimal example below. My current workaround is to fill empty results with a dummy value that I filter after, but this was a weird error that took a while to track down. ### Steps to reproduce the bug ```python import datasets dataset = datasets.Dataset.from_list([{'idx':i} for i in range(60)]) def test_func(row, idx): if idx==58: return {'output': []} else: return {'output' : [{'test':1}, {'test':2}]} # this works fine test1 = dataset.map(lambda row, idx: test_func(row, idx), with_indices=True, num_proc=4) # this fails test2 = dataset.map(lambda row, idx: test_func(row, idx), with_indices=True, num_proc=32) >ValueError: The features can't be aligned because the key output of features {'idx': Value(dtype='int64', id=None), 'output': Sequence(feature=Value(dtype='null', id=None), length=-1, id=None)} has unexpected type - Sequence(feature=Value(dtype='null', id=None), length=-1, id=None) (expected either [{'test': Value(dtype='int64', id=None)}] or Value("null"). ``` The error occurs during the check ```python _check_if_features_can_be_aligned([dset.features for dset in dsets]) ``` When the multiprocessing splitting lines up just right with the empty return value, one of the `dset` in `dsets` will have a single item with an empty list value, causing the error. ### Expected behavior Expected behavior is the result would be the same regardless of the `num_proc` value used. ### Environment info Datasets version 2.11.0 Python 3.9.16	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6020/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6020/timeline	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6019	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6019/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6019/comments	https://api.github.com/repos/huggingface/datasets/issues/6019/events	https://github.com/huggingface/datasets/pull/6019	1,799,532,822	PR_kwDODunzps5VPAlD	6,019	Improve logging	{ "login": "mariosasko", "id": 47462742, "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "gravatar_id": "", "url": "https://api.github.com/users/mariosasko", "html_url": "https://github.com/mariosasko", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "repos_url": "https://api.github.com/users/mariosasko/repos", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007782 / 0.011353 (-0.003571) \| 0.004451 / 0.011008 (-0.006557) \| 0.099928 / 0.038508 (0.061420) \| 0.081534 / 0.023109 (0.058425) \| 0.379382 / 0.275898 (0.103484) \| 0.410652 / 0.323480 (0.087172) \| 0.005967 / 0.007986 (-0.002019) \| 0.003702 / 0.004328 (-0.000627) \| 0.076359 / 0.004250 (0.072109) \| 0.066721 / 0.037052 (0.029669) \| 0.383595 / 0.258489 (0.125106) \| 0.423854 / 0.293841 (0.130013) \| 0.032796 / 0.128546 (-0.095750) \| 0.009728 / 0.075646 (-0.065918) \| 0.344347 / 0.419271 (-0.074925) \| 0.056320 / 0.043533 (0.012788) \| 0.379974 / 0.255139 (0.124835) \| 0.401294 / 0.283200 (0.118094) \| 0.024110 / 0.141683 (-0.117572) \| 1.804194 / 1.452155 (0.352039) \| 1.860240 / 1.492716 (0.367523) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.233803 / 0.018006 (0.215797) \| 0.506893 / 0.000490 (0.506404) \| 0.003894 / 0.000200 (0.003694) \| 0.000090 / 0.000054 (0.000035) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033328 / 0.037411 (-0.004083) \| 0.098661 / 0.014526 (0.084136) \| 0.114971 / 0.176557 (-0.061586) \| 0.186815 / 0.737135 (-0.550321) \| 0.115490 / 0.296338 (-0.180848) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.422590 / 0.215209 (0.207381) \| 4.277189 / 2.077655 (2.199535) \| 2.095565 / 1.504120 (0.591445) \| 2.040825 / 1.541195 (0.499630) \| 2.162562 / 1.468490 (0.694072) \| 0.578602 / 4.584777 (-4.006175) \| 4.203474 / 3.745712 (0.457762) \| 6.674595 / 5.269862 (1.404734) \| 3.913251 / 4.565676 (-0.652426) \| 0.067777 / 0.424275 (-0.356498) \| 0.008716 / 0.007607 (0.001109) \| 0.548704 / 0.226044 (0.322660) \| 5.162120 / 2.268929 (2.893192) \| 2.600250 / 55.444624 (-52.844374) \| 2.232730 / 6.876477 (-4.643747) \| 2.485617 / 2.142072 (0.343544) \| 0.650872 / 4.805227 (-4.154355) \| 0.148022 / 6.500664 (-6.352642) \| 0.064795 / 0.075469 (-0.010674) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.399439 / 1.841788 (-0.442349) \| 22.438959 / 8.074308 (14.364651) \| 16.447831 / 10.191392 (6.256439) \| 0.202003 / 0.680424 (-0.478421) \| 0.026200 / 0.534201 (-0.508001) \| 0.472966 / 0.579283 (-0.106317) \| 0.491621 / 0.434364 (0.057257) \| 0.551580 / 0.540337 (0.011242) \| 0.751420 / 1.386936 (-0.635516) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007241 / 0.011353 (-0.004112) \| 0.004434 / 0.011008 (-0.006574) \| 0.075872 / 0.038508 (0.037364) \| 0.080094 / 0.023109 (0.056985) \| 0.459244 / 0.275898 (0.183346) \| 0.492482 / 0.323480 (0.169002) \| 0.005791 / 0.007986 (-0.002194) \| 0.003657 / 0.004328 (-0.000671) \| 0.075214 / 0.004250 (0.070964) \| 0.064208 / 0.037052 (0.027156) \| 0.464195 / 0.258489 (0.205706) \| 0.497809 / 0.293841 (0.203968) \| 0.036301 / 0.128546 (-0.092245) \| 0.009855 / 0.075646 (-0.065791) \| 0.080826 / 0.419271 (-0.338445) \| 0.056700 / 0.043533 (0.013167) \| 0.452850 / 0.255139 (0.197711) \| 0.490738 / 0.283200 (0.207538) \| 0.024145 / 0.141683 (-0.117538) \| 1.689911 / 1.452155 (0.237757) \| 1.789803 / 1.492716 (0.297087) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.247741 / 0.018006 (0.229735) \| 0.486769 / 0.000490 (0.486279) \| 0.000418 / 0.000200 (0.000218) \| 0.000060 / 0.000054 (0.000005) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.036317 / 0.037411 (-0.001094) \| 0.104943 / 0.014526 (0.090417) \| 0.120972 / 0.176557 (-0.055585) \| 0.188461 / 0.737135 (-0.548674) \| 0.120926 / 0.296338 (-0.175412) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.465788 / 0.215209 (0.250579) \| 4.662369 / 2.077655 (2.584714) \| 2.442241 / 1.504120 (0.938121) \| 2.266328 / 1.541195 (0.725133) \| 2.438998 / 1.468490 (0.970508) \| 0.531384 / 4.584777 (-4.053393) \| 4.125286 / 3.745712 (0.379574) \| 3.920912 / 5.269862 (-1.348950) \| 2.292149 / 4.565676 (-2.273528) \| 0.070146 / 0.424275 (-0.354129) \| 0.008887 / 0.007607 (0.001280) \| 0.598181 / 0.226044 (0.372137) \| 5.726454 / 2.268929 (3.457526) \| 3.081836 / 55.444624 (-52.362788) \| 2.683508 / 6.876477 (-4.192969) \| 2.587350 / 2.142072 (0.445278) \| 0.604736 / 4.805227 (-4.200491) \| 0.141303 / 6.500664 (-6.359362) \| 0.065020 / 0.075469 (-0.010449) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.481850 / 1.841788 (-0.359938) \| 22.259592 / 8.074308 (14.185284) \| 16.304290 / 10.191392 (6.112898) \| 0.173514 / 0.680424 (-0.506909) \| 0.021590 / 0.534201 (-0.512611) \| 0.471753 / 0.579283 (-0.107531) \| 0.472132 / 0.434364 (0.037768) \| 0.563344 / 0.540337 (0.023007) \| 0.738509 / 1.386936 (-0.648427) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1cb7ae56dbd814945a4982c63bf0e50859a7b93a \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005910 / 0.011353 (-0.005443) \| 0.004372 / 0.011008 (-0.006636) \| 0.081583 / 0.038508 (0.043075) \| 0.069598 / 0.023109 (0.046488) \| 0.346360 / 0.275898 (0.070462) \| 0.360733 / 0.323480 (0.037254) \| 0.004725 / 0.007986 (-0.003261) \| 0.003106 / 0.004328 (-0.001222) \| 0.059916 / 0.004250 (0.055666) \| 0.053242 / 0.037052 (0.016189) \| 0.353551 / 0.258489 (0.095062) \| 0.373052 / 0.293841 (0.079211) \| 0.029036 / 0.128546 (-0.099510) \| 0.007894 / 0.075646 (-0.067753) \| 0.284131 / 0.419271 (-0.135140) \| 0.049348 / 0.043533 (0.005815) \| 0.347409 / 0.255139 (0.092270) \| 0.355029 / 0.283200 (0.071830) \| 0.022511 / 0.141683 (-0.119171) \| 1.454495 / 1.452155 (0.002340) \| 1.439551 / 1.492716 (-0.053166) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.218889 / 0.018006 (0.200883) \| 0.478734 / 0.000490 (0.478244) \| 0.003758 / 0.000200 (0.003558) \| 0.000083 / 0.000054 (0.000029) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025759 / 0.037411 (-0.011653) \| 0.082511 / 0.014526 (0.067985) \| 0.087578 / 0.176557 (-0.088979) \| 0.137760 / 0.737135 (-0.599375) \| 0.093312 / 0.296338 (-0.203027) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.378963 / 0.215209 (0.163754) \| 3.645846 / 2.077655 (1.568191) \| 1.741135 / 1.504120 (0.237015) \| 1.599166 / 1.541195 (0.057972) \| 1.610817 / 1.468490 (0.142327) \| 0.459209 / 4.584777 (-4.125568) \| 3.484857 / 3.745712 (-0.260855) \| 3.928109 / 5.269862 (-1.341752) \| 2.419784 / 4.565676 (-2.145892) \| 0.051987 / 0.424275 (-0.372288) \| 0.006495 / 0.007607 (-0.001112) \| 0.427311 / 0.226044 (0.201267) \| 4.226378 / 2.268929 (1.957450) \| 2.212331 / 55.444624 (-53.232293) \| 1.916213 / 6.876477 (-4.960264) \| 1.978809 / 2.142072 (-0.163263) \| 0.547351 / 4.805227 (-4.257876) \| 0.121110 / 6.500664 (-6.379554) \| 0.054163 / 0.075469 (-0.021306) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.228594 / 1.841788 (-0.613193) \| 19.410901 / 8.074308 (11.336593) \| 13.014722 / 10.191392 (2.823330) \| 0.156449 / 0.680424 (-0.523975) \| 0.021032 / 0.534201 (-0.513169) \| 0.403976 / 0.579283 (-0.175307) \| 0.413885 / 0.434364 (-0.020479) \| 0.470465 / 0.540337 (-0.069873) \| 0.641322 / 1.386936 (-0.745614) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007210 / 0.011353 (-0.004143) \| 0.003824 / 0.011008 (-0.007185) \| 0.058227 / 0.038508 (0.019719) \| 0.076211 / 0.023109 (0.053102) \| 0.336626 / 0.275898 (0.060728) \| 0.420542 / 0.323480 (0.097062) \| 0.006178 / 0.007986 (-0.001808) \| 0.003332 / 0.004328 (-0.000997) \| 0.058073 / 0.004250 (0.053823) \| 0.062485 / 0.037052 (0.025432) \| 0.386175 / 0.258489 (0.127686) \| 0.415659 / 0.293841 (0.121818) \| 0.031264 / 0.128546 (-0.097282) \| 0.007502 / 0.075646 (-0.068144) \| 0.072079 / 0.419271 (-0.347192) \| 0.055860 / 0.043533 (0.012327) \| 0.343508 / 0.255139 (0.088369) \| 0.437844 / 0.283200 (0.154645) \| 0.032852 / 0.141683 (-0.108831) \| 1.409241 / 1.452155 (-0.042913) \| 1.623949 / 1.492716 (0.131233) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.207511 / 0.018006 (0.189504) \| 0.464149 / 0.000490 (0.463660) \| 0.003248 / 0.000200 (0.003048) \| 0.000226 / 0.000054 (0.000172) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030767 / 0.037411 (-0.006645) \| 0.079169 / 0.014526 (0.064643) \| 0.093111 / 0.176557 (-0.083445) \| 0.153369 / 0.737135 (-0.583767) \| 0.092939 / 0.296338 (-0.203400) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.375602 / 0.215209 (0.160392) \| 3.968612 / 2.077655 (1.890957) \| 2.081749 / 1.504120 (0.577629) \| 1.899772 / 1.541195 (0.358577) \| 1.847923 / 1.468490 (0.379433) \| 0.442867 / 4.584777 (-4.141910) \| 3.646664 / 3.745712 (-0.099048) \| 5.870600 / 5.269862 (0.600739) \| 3.356698 / 4.565676 (-1.208979) \| 0.051422 / 0.424275 (-0.372853) \| 0.006006 / 0.007607 (-0.001601) \| 0.442439 / 0.226044 (0.216395) \| 4.466256 / 2.268929 (2.197328) \| 2.483832 / 55.444624 (-52.960792) \| 2.105612 / 6.876477 (-4.770865) \| 2.060650 / 2.142072 (-0.081422) \| 0.531119 / 4.805227 (-4.274108) \| 0.123436 / 6.500664 (-6.377228) \| 0.059838 / 0.075469 (-0.015632) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.283042 / 1.841788 (-0.558746) \| 19.688251 / 8.074308 (11.613943) \| 13.346386 / 10.191392 (3.154994) \| 0.197463 / 0.680424 (-0.482961) \| 0.018484 / 0.534201 (-0.515717) \| 0.391727 / 0.579283 (-0.187556) \| 0.425061 / 0.434364 (-0.009303) \| 0.448177 / 0.540337 (-0.092160) \| 0.653694 / 1.386936 (-0.733242) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#01604752fe89d290479fa406b1a24ac1f346826e \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008966 / 0.011353 (-0.002387) \| 0.005195 / 0.011008 (-0.005813) \| 0.102879 / 0.038508 (0.064371) \| 0.090902 / 0.023109 (0.067792) \| 0.434397 / 0.275898 (0.158498) \| 0.454013 / 0.323480 (0.130534) \| 0.008507 / 0.007986 (0.000521) \| 0.005000 / 0.004328 (0.000671) \| 0.075789 / 0.004250 (0.071538) \| 0.067608 / 0.037052 (0.030555) \| 0.435091 / 0.258489 (0.176602) \| 0.469411 / 0.293841 (0.175570) \| 0.050859 / 0.128546 (-0.077687) \| 0.013560 / 0.075646 (-0.062086) \| 0.345473 / 0.419271 (-0.073799) \| 0.094974 / 0.043533 (0.051441) \| 0.429626 / 0.255139 (0.174487) \| 0.434290 / 0.283200 (0.151090) \| 0.052269 / 0.141683 (-0.089413) \| 1.700549 / 1.452155 (0.248395) \| 1.890693 / 1.492716 (0.397976) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.296618 / 0.018006 (0.278612) \| 0.613908 / 0.000490 (0.613419) \| 0.000484 / 0.000200 (0.000284) \| 0.000086 / 0.000054 (0.000032) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034346 / 0.037411 (-0.003065) \| 0.096836 / 0.014526 (0.082310) \| 0.113332 / 0.176557 (-0.063224) \| 0.194464 / 0.737135 (-0.542671) \| 0.111732 / 0.296338 (-0.184606) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.624954 / 0.215209 (0.409745) \| 6.442193 / 2.077655 (4.364538) \| 2.818331 / 1.504120 (1.314211) \| 2.529607 / 1.541195 (0.988413) \| 2.549026 / 1.468490 (1.080536) \| 0.967367 / 4.584777 (-3.617410) \| 5.446885 / 3.745712 (1.701173) \| 6.259099 / 5.269862 (0.989237) \| 3.652936 / 4.565676 (-0.912740) \| 0.106420 / 0.424275 (-0.317855) \| 0.011293 / 0.007607 (0.003686) \| 0.772026 / 0.226044 (0.545982) \| 7.823986 / 2.268929 (5.555057) \| 3.725328 / 55.444624 (-51.719297) \| 2.851489 / 6.876477 (-4.024988) \| 3.013722 / 2.142072 (0.871649) \| 1.045090 / 4.805227 (-3.760137) \| 0.213174 / 6.500664 (-6.287490) \| 0.077104 / 0.075469 (0.001635) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.657135 / 1.841788 (-0.184652) \| 24.547604 / 8.074308 (16.473296) \| 19.989533 / 10.191392 (9.798141) \| 0.257139 / 0.680424 (-0.423285) \| 0.028448 / 0.534201 (-0.505753) \| 0.490801 / 0.579283 (-0.088482) \| 0.628072 / 0.434364 (0.193708) \| 0.584873 / 0.540337 (0.044536) \| 0.825258 / 1.386936 (-0.561678) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009258 / 0.011353 (-0.002095) \| 0.005660 / 0.011008 (-0.005348) \| 0.080577 / 0.038508 (0.042069) \| 0.095786 / 0.023109 (0.072676) \| 0.473334 / 0.275898 (0.197436) \| 0.527962 / 0.323480 (0.204482) \| 0.006537 / 0.007986 (-0.001449) \| 0.004411 / 0.004328 (0.000083) \| 0.080702 / 0.004250 (0.076452) \| 0.077020 / 0.037052 (0.039968) \| 0.483205 / 0.258489 (0.224716) \| 0.556916 / 0.293841 (0.263076) \| 0.047670 / 0.128546 (-0.080877) \| 0.016647 / 0.075646 (-0.058999) \| 0.090653 / 0.419271 (-0.328619) \| 0.062122 / 0.043533 (0.018589) \| 0.498326 / 0.255139 (0.243187) \| 0.546572 / 0.283200 (0.263372) \| 0.037525 / 0.141683 (-0.104157) \| 1.869520 / 1.452155 (0.417365) \| 1.915335 / 1.492716 (0.422619) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.248287 / 0.018006 (0.230281) \| 0.611440 / 0.000490 (0.610950) \| 0.004102 / 0.000200 (0.003902) \| 0.000132 / 0.000054 (0.000078) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.038228 / 0.037411 (0.000817) \| 0.103510 / 0.014526 (0.088984) \| 0.114337 / 0.176557 (-0.062219) \| 0.189662 / 0.737135 (-0.547473) \| 0.119078 / 0.296338 (-0.177260) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.606622 / 0.215209 (0.391413) \| 6.053900 / 2.077655 (3.976246) \| 2.857972 / 1.504120 (1.353852) \| 2.549756 / 1.541195 (1.008561) \| 2.584557 / 1.468490 (1.116067) \| 0.930431 / 4.584777 (-3.654346) \| 5.524077 / 3.745712 (1.778365) \| 7.858406 / 5.269862 (2.588545) \| 4.890697 / 4.565676 (0.325020) \| 0.095356 / 0.424275 (-0.328919) \| 0.008614 / 0.007607 (0.001007) \| 0.774227 / 0.226044 (0.548182) \| 7.470215 / 2.268929 (5.201287) \| 3.784820 / 55.444624 (-51.659805) \| 3.199364 / 6.876477 (-3.677113) \| 3.212002 / 2.142072 (1.069929) \| 1.054104 / 4.805227 (-3.751123) \| 0.226044 / 6.500664 (-6.274620) \| 0.092237 / 0.075469 (0.016768) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.801054 / 1.841788 (-0.040734) \| 24.220404 / 8.074308 (16.146096) \| 21.652936 / 10.191392 (11.461544) \| 0.247004 / 0.680424 (-0.433420) \| 0.029651 / 0.534201 (-0.504550) \| 0.475702 / 0.579283 (-0.103581) \| 0.621121 / 0.434364 (0.186757) \| 0.570489 / 0.540337 (0.030151) \| 0.768840 / 1.386936 (-0.618096) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b2fc21eda345643fb57d1d1167ebed9043310911 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009223 / 0.011353 (-0.002130) \| 0.005750 / 0.011008 (-0.005258) \| 0.105264 / 0.038508 (0.066756) \| 0.088478 / 0.023109 (0.065369) \| 0.461119 / 0.275898 (0.185221) \| 0.481115 / 0.323480 (0.157636) \| 0.006366 / 0.007986 (-0.001619) \| 0.004515 / 0.004328 (0.000186) \| 0.079296 / 0.004250 (0.075045) \| 0.063483 / 0.037052 (0.026430) \| 0.444490 / 0.258489 (0.186001) \| 0.496474 / 0.293841 (0.202634) \| 0.048568 / 0.128546 (-0.079978) \| 0.013574 / 0.075646 (-0.062073) \| 0.379213 / 0.419271 (-0.040059) \| 0.086464 / 0.043533 (0.042932) \| 0.437526 / 0.255139 (0.182387) \| 0.447117 / 0.283200 (0.163917) \| 0.049502 / 0.141683 (-0.092180) \| 1.749146 / 1.452155 (0.296992) \| 1.831082 / 1.492716 (0.338365) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.268205 / 0.018006 (0.250199) \| 0.627406 / 0.000490 (0.626917) \| 0.005439 / 0.000200 (0.005239) \| 0.000128 / 0.000054 (0.000074) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030564 / 0.037411 (-0.006848) \| 0.096365 / 0.014526 (0.081840) \| 0.117484 / 0.176557 (-0.059072) \| 0.189104 / 0.737135 (-0.548032) \| 0.118073 / 0.296338 (-0.178266) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.618229 / 0.215209 (0.403019) \| 6.437853 / 2.077655 (4.360199) \| 2.789946 / 1.504120 (1.285826) \| 2.339245 / 1.541195 (0.798050) \| 2.588779 / 1.468490 (1.120289) \| 0.921008 / 4.584777 (-3.663769) \| 5.402940 / 3.745712 (1.657227) \| 4.818783 / 5.269862 (-0.451078) \| 3.162259 / 4.565676 (-1.403417) \| 0.108501 / 0.424275 (-0.315774) \| 0.009384 / 0.007607 (0.001777) \| 0.766811 / 0.226044 (0.540766) \| 7.624629 / 2.268929 (5.355701) \| 3.442420 / 55.444624 (-52.002204) \| 2.759967 / 6.876477 (-4.116510) \| 3.049644 / 2.142072 (0.907572) \| 1.113308 / 4.805227 (-3.691919) \| 0.223923 / 6.500664 (-6.276741) \| 0.079156 / 0.075469 (0.003687) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.683318 / 1.841788 (-0.158470) \| 25.062141 / 8.074308 (16.987833) \| 21.777131 / 10.191392 (11.585739) \| 0.266939 / 0.680424 (-0.413485) \| 0.029670 / 0.534201 (-0.504531) \| 0.476761 / 0.579283 (-0.102522) \| 0.622080 / 0.434364 (0.187716) \| 0.601781 / 0.540337 (0.061443) \| 0.785126 / 1.386936 (-0.601811) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.010198 / 0.011353 (-0.001155) \| 0.005777 / 0.011008 (-0.005231) \| 0.083003 / 0.038508 (0.044495) \| 0.093411 / 0.023109 (0.070302) \| 0.496178 / 0.275898 (0.220280) \| 0.554670 / 0.323480 (0.231190) \| 0.008351 / 0.007986 (0.000365) \| 0.004678 / 0.004328 (0.000350) \| 0.083631 / 0.004250 (0.079381) \| 0.075538 / 0.037052 (0.038485) \| 0.492410 / 0.258489 (0.233921) \| 0.545209 / 0.293841 (0.251368) \| 0.048365 / 0.128546 (-0.080181) \| 0.014219 / 0.075646 (-0.061427) \| 0.100749 / 0.419271 (-0.318523) \| 0.063431 / 0.043533 (0.019898) \| 0.511115 / 0.255139 (0.255976) \| 0.532965 / 0.283200 (0.249765) \| 0.037968 / 0.141683 (-0.103715) \| 1.940268 / 1.452155 (0.488113) \| 2.032934 / 1.492716 (0.540217) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.238179 / 0.018006 (0.220172) \| 0.605767 / 0.000490 (0.605277) \| 0.004033 / 0.000200 (0.003833) \| 0.000125 / 0.000054 (0.000071) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.036436 / 0.037411 (-0.000975) \| 0.108034 / 0.014526 (0.093509) \| 0.118624 / 0.176557 (-0.057933) \| 0.183079 / 0.737135 (-0.554056) \| 0.121739 / 0.296338 (-0.174600) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.630538 / 0.215209 (0.415329) \| 6.552184 / 2.077655 (4.474529) \| 3.003412 / 1.504120 (1.499292) \| 2.669026 / 1.541195 (1.127832) \| 2.791109 / 1.468490 (1.322619) \| 0.884003 / 4.584777 (-3.700774) \| 5.538660 / 3.745712 (1.792947) \| 5.126708 / 5.269862 (-0.143154) \| 3.120825 / 4.565676 (-1.444852) \| 0.101178 / 0.424275 (-0.323097) \| 0.009027 / 0.007607 (0.001420) \| 0.785914 / 0.226044 (0.559869) \| 7.994720 / 2.268929 (5.725792) \| 4.061996 / 55.444624 (-51.382629) \| 3.263230 / 6.876477 (-3.613247) \| 3.288622 / 2.142072 (1.146550) \| 1.141867 / 4.805227 (-3.663360) \| 0.255287 / 6.500664 (-6.245378) \| 0.100637 / 0.075469 (0.025168) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.769821 / 1.841788 (-0.071967) \| 24.994008 / 8.074308 (16.919700) \| 21.765971 / 10.191392 (11.574579) \| 0.268493 / 0.680424 (-0.411931) \| 0.028047 / 0.534201 (-0.506154) \| 0.489472 / 0.579283 (-0.089811) \| 0.594809 / 0.434364 (0.160445) \| 0.613578 / 0.540337 (0.073241) \| 0.879360 / 1.386936 (-0.507576) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b85b1154aef2a9ab4d558f60d91623f2cc1583c4 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006003 / 0.011353 (-0.005350) \| 0.003590 / 0.011008 (-0.007418) \| 0.084657 / 0.038508 (0.046149) \| 0.057884 / 0.023109 (0.034775) \| 0.318347 / 0.275898 (0.042449) \| 0.345976 / 0.323480 (0.022496) \| 0.004706 / 0.007986 (-0.003279) \| 0.002921 / 0.004328 (-0.001407) \| 0.061850 / 0.004250 (0.057600) \| 0.050558 / 0.037052 (0.013505) \| 0.320877 / 0.258489 (0.062388) \| 0.356062 / 0.293841 (0.062222) \| 0.027511 / 0.128546 (-0.101035) \| 0.007954 / 0.075646 (-0.067693) \| 0.260290 / 0.419271 (-0.158981) \| 0.051207 / 0.043533 (0.007674) \| 0.334423 / 0.255139 (0.079284) \| 0.338575 / 0.283200 (0.055375) \| 0.022330 / 0.141683 (-0.119353) \| 1.445446 / 1.452155 (-0.006709) \| 1.500626 / 1.492716 (0.007910) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.192440 / 0.018006 (0.174433) \| 0.428455 / 0.000490 (0.427965) \| 0.000318 / 0.000200 (0.000118) \| 0.000056 / 0.000054 (0.000002) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022933 / 0.037411 (-0.014478) \| 0.072795 / 0.014526 (0.058269) \| 0.081149 / 0.176557 (-0.095407) \| 0.142941 / 0.737135 (-0.594195) \| 0.082410 / 0.296338 (-0.213928) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.405220 / 0.215209 (0.190011) \| 4.048585 / 2.077655 (1.970931) \| 2.027908 / 1.504120 (0.523788) \| 1.887828 / 1.541195 (0.346633) \| 2.131780 / 1.468490 (0.663290) \| 0.502847 / 4.584777 (-4.081930) \| 3.069498 / 3.745712 (-0.676215) \| 4.094774 / 5.269862 (-1.175088) \| 2.544004 / 4.565676 (-2.021673) \| 0.059540 / 0.424275 (-0.364735) \| 0.006501 / 0.007607 (-0.001106) \| 0.477218 / 0.226044 (0.251173) \| 4.764961 / 2.268929 (2.496032) \| 2.434594 / 55.444624 (-53.010030) \| 2.104833 / 6.876477 (-4.771644) \| 2.263059 / 2.142072 (0.120987) \| 0.591755 / 4.805227 (-4.213472) \| 0.131167 / 6.500664 (-6.369497) \| 0.061808 / 0.075469 (-0.013661) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.345364 / 1.841788 (-0.496424) \| 18.122584 / 8.074308 (10.048276) \| 13.318689 / 10.191392 (3.127297) \| 0.144526 / 0.680424 (-0.535898) \| 0.016997 / 0.534201 (-0.517204) \| 0.336036 / 0.579283 (-0.243247) \| 0.359532 / 0.434364 (-0.074832) \| 0.386945 / 0.540337 (-0.153392) \| 0.538659 / 1.386936 (-0.848277) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006088 / 0.011353 (-0.005265) \| 0.003684 / 0.011008 (-0.007324) \| 0.062340 / 0.038508 (0.023832) \| 0.058461 / 0.023109 (0.035352) \| 0.360134 / 0.275898 (0.084236) \| 0.393298 / 0.323480 (0.069818) \| 0.004664 / 0.007986 (-0.003322) \| 0.002909 / 0.004328 (-0.001420) \| 0.062668 / 0.004250 (0.058418) \| 0.050145 / 0.037052 (0.013092) \| 0.361897 / 0.258489 (0.103408) \| 0.402008 / 0.293841 (0.108167) \| 0.027491 / 0.128546 (-0.101055) \| 0.008113 / 0.075646 (-0.067534) \| 0.068114 / 0.419271 (-0.351157) \| 0.043303 / 0.043533 (-0.000230) \| 0.360569 / 0.255139 (0.105430) \| 0.387144 / 0.283200 (0.103944) \| 0.020194 / 0.141683 (-0.121489) \| 1.418066 / 1.452155 (-0.034089) \| 1.475640 / 1.492716 (-0.017076) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.200291 / 0.018006 (0.182285) \| 0.432298 / 0.000490 (0.431809) \| 0.003303 / 0.000200 (0.003103) \| 0.000075 / 0.000054 (0.000020) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027749 / 0.037411 (-0.009662) \| 0.081890 / 0.014526 (0.067364) \| 0.094319 / 0.176557 (-0.082238) \| 0.148646 / 0.737135 (-0.588490) \| 0.091830 / 0.296338 (-0.204509) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.433546 / 0.215209 (0.218337) \| 4.326855 / 2.077655 (2.249200) \| 2.230186 / 1.504120 (0.726066) \| 2.052524 / 1.541195 (0.511329) \| 2.117270 / 1.468490 (0.648779) \| 0.500331 / 4.584777 (-4.084446) \| 3.113662 / 3.745712 (-0.632050) \| 2.931540 / 5.269862 (-2.338322) \| 1.853615 / 4.565676 (-2.712062) \| 0.058250 / 0.424275 (-0.366025) \| 0.006546 / 0.007607 (-0.001061) \| 0.508850 / 0.226044 (0.282806) \| 5.081809 / 2.268929 (2.812880) \| 2.687037 / 55.444624 (-52.757588) \| 2.369317 / 6.876477 (-4.507160) \| 2.383549 / 2.142072 (0.241477) \| 0.587039 / 4.805227 (-4.218188) \| 0.125858 / 6.500664 (-6.374806) \| 0.062522 / 0.075469 (-0.012947) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.294929 / 1.841788 (-0.546858) \| 18.056312 / 8.074308 (9.982004) \| 13.755117 / 10.191392 (3.563725) \| 0.132037 / 0.680424 (-0.548387) \| 0.016866 / 0.534201 (-0.517335) \| 0.339040 / 0.579283 (-0.240243) \| 0.364371 / 0.434364 (-0.069993) \| 0.399533 / 0.540337 (-0.140804) \| 0.564524 / 1.386936 (-0.822412) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#64b811c13a7982015d7e078e3d693ce5359a05a2 \"CML watermark\")\n", "@lhoestq This bar comes from: https://github.com/huggingface/datasets/blob/b8067c0262073891180869f700ebef5ac3dc5cce/src/datasets/builder.py#L1156-L1166\r\n\r\nDo you prefer not showing it or, e.g., having `desc=\"Generating splits\"`?", "No strong opinion. Since there is a \"Generating\" progress bar already, maybe it can be \"Preparing splits\" (ref to download_and_prepare)", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006348 / 0.011353 (-0.005005) \| 0.003721 / 0.011008 (-0.007287) \| 0.084039 / 0.038508 (0.045531) \| 0.067627 / 0.023109 (0.044517) \| 0.308372 / 0.275898 (0.032474) \| 0.335131 / 0.323480 (0.011652) \| 0.005157 / 0.007986 (-0.002829) \| 0.003266 / 0.004328 (-0.001062) \| 0.065374 / 0.004250 (0.061124) \| 0.055550 / 0.037052 (0.018498) \| 0.314001 / 0.258489 (0.055512) \| 0.350510 / 0.293841 (0.056669) \| 0.030859 / 0.128546 (-0.097688) \| 0.008286 / 0.075646 (-0.067361) \| 0.287122 / 0.419271 (-0.132149) \| 0.051494 / 0.043533 (0.007961) \| 0.309868 / 0.255139 (0.054729) \| 0.325845 / 0.283200 (0.042645) \| 0.022622 / 0.141683 (-0.119061) \| 1.468730 / 1.452155 (0.016575) \| 1.547871 / 1.492716 (0.055155) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.202763 / 0.018006 (0.184757) \| 0.456403 / 0.000490 (0.455914) \| 0.003116 / 0.000200 (0.002916) \| 0.000079 / 0.000054 (0.000024) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027297 / 0.037411 (-0.010114) \| 0.081204 / 0.014526 (0.066678) \| 0.094274 / 0.176557 (-0.082282) \| 0.154391 / 0.737135 (-0.582744) \| 0.094312 / 0.296338 (-0.202026) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.387382 / 0.215209 (0.172173) \| 3.865597 / 2.077655 (1.787943) \| 1.855959 / 1.504120 (0.351839) \| 1.685411 / 1.541195 (0.144216) \| 1.732127 / 1.468490 (0.263637) \| 0.482230 / 4.584777 (-4.102547) \| 3.664947 / 3.745712 (-0.080765) \| 5.114379 / 5.269862 (-0.155482) \| 3.102803 / 4.565676 (-1.462873) \| 0.056509 / 0.424275 (-0.367766) \| 0.007230 / 0.007607 (-0.000377) \| 0.456788 / 0.226044 (0.230744) \| 4.575831 / 2.268929 (2.306902) \| 2.335249 / 55.444624 (-53.109375) \| 2.003805 / 6.876477 (-4.872672) \| 2.141788 / 2.142072 (-0.000285) \| 0.577501 / 4.805227 (-4.227726) \| 0.130264 / 6.500664 (-6.370400) \| 0.058889 / 0.075469 (-0.016580) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.252673 / 1.841788 (-0.589115) \| 18.676897 / 8.074308 (10.602589) \| 13.988101 / 10.191392 (3.796709) \| 0.151376 / 0.680424 (-0.529048) \| 0.018104 / 0.534201 (-0.516097) \| 0.388413 / 0.579283 (-0.190870) \| 0.414841 / 0.434364 (-0.019523) \| 0.456078 / 0.540337 (-0.084259) \| 0.641715 / 1.386936 (-0.745221) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006315 / 0.011353 (-0.005038) \| 0.003847 / 0.011008 (-0.007162) \| 0.063989 / 0.038508 (0.025481) \| 0.068244 / 0.023109 (0.045135) \| 0.416201 / 0.275898 (0.140303) \| 0.438446 / 0.323480 (0.114966) \| 0.005820 / 0.007986 (-0.002166) \| 0.003165 / 0.004328 (-0.001163) \| 0.064143 / 0.004250 (0.059892) \| 0.056529 / 0.037052 (0.019477) \| 0.414916 / 0.258489 (0.156427) \| 0.450771 / 0.293841 (0.156930) \| 0.030611 / 0.128546 (-0.097935) \| 0.008289 / 0.075646 (-0.067357) \| 0.070725 / 0.419271 (-0.348546) \| 0.047998 / 0.043533 (0.004465) \| 0.405609 / 0.255139 (0.150470) \| 0.421895 / 0.283200 (0.138696) \| 0.022135 / 0.141683 (-0.119548) \| 1.444238 / 1.452155 (-0.007916) \| 1.515823 / 1.492716 (0.023107) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.227043 / 0.018006 (0.209037) \| 0.439732 / 0.000490 (0.439242) \| 0.001267 / 0.000200 (0.001067) \| 0.000070 / 0.000054 (0.000016) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029082 / 0.037411 (-0.008329) \| 0.086201 / 0.014526 (0.071675) \| 0.098653 / 0.176557 (-0.077903) \| 0.152574 / 0.737135 (-0.584561) \| 0.100696 / 0.296338 (-0.195642) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.411243 / 0.215209 (0.196034) \| 4.100170 / 2.077655 (2.022515) \| 2.118310 / 1.504120 (0.614190) \| 1.935646 / 1.541195 (0.394451) \| 1.970798 / 1.468490 (0.502307) \| 0.478635 / 4.584777 (-4.106142) \| 3.589396 / 3.745712 (-0.156316) \| 3.312462 / 5.269862 (-1.957399) \| 1.963081 / 4.565676 (-2.602595) \| 0.056392 / 0.424275 (-0.367883) \| 0.007134 / 0.007607 (-0.000473) \| 0.485131 / 0.226044 (0.259086) \| 4.838946 / 2.268929 (2.570017) \| 2.624550 / 55.444624 (-52.820075) \| 2.223046 / 6.876477 (-4.653431) \| 2.230642 / 2.142072 (0.088570) \| 0.594892 / 4.805227 (-4.210335) \| 0.130523 / 6.500664 (-6.370141) \| 0.059585 / 0.075469 (-0.015884) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.329941 / 1.841788 (-0.511847) \| 19.199057 / 8.074308 (11.124748) \| 14.166009 / 10.191392 (3.974617) \| 0.190595 / 0.680424 (-0.489829) \| 0.018419 / 0.534201 (-0.515782) \| 0.392031 / 0.579283 (-0.187252) \| 0.409395 / 0.434364 (-0.024969) \| 0.475930 / 0.540337 (-0.064408) \| 0.654412 / 1.386936 (-0.732524) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#42fdfbd567674d075c3a9148ec3c95221eb62cfe \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007500 / 0.011353 (-0.003853) \| 0.004328 / 0.011008 (-0.006681) \| 0.086718 / 0.038508 (0.048209) \| 0.098638 / 0.023109 (0.075529) \| 0.335308 / 0.275898 (0.059409) \| 0.369163 / 0.323480 (0.045683) \| 0.005733 / 0.007986 (-0.002253) \| 0.003738 / 0.004328 (-0.000590) \| 0.066452 / 0.004250 (0.062202) \| 0.066245 / 0.037052 (0.029192) \| 0.337609 / 0.258489 (0.079120) \| 0.388584 / 0.293841 (0.094744) \| 0.031742 / 0.128546 (-0.096804) \| 0.008721 / 0.075646 (-0.066925) \| 0.290820 / 0.419271 (-0.128452) \| 0.053323 / 0.043533 (0.009790) \| 0.329192 / 0.255139 (0.074053) \| 0.350560 / 0.283200 (0.067360) \| 0.025402 / 0.141683 (-0.116281) \| 1.476174 / 1.452155 (0.024020) \| 1.578194 / 1.492716 (0.085478) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.256160 / 0.018006 (0.238154) \| 0.560315 / 0.000490 (0.559825) \| 0.005287 / 0.000200 (0.005088) \| 0.000094 / 0.000054 (0.000040) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029164 / 0.037411 (-0.008247) \| 0.084881 / 0.014526 (0.070356) \| 0.100979 / 0.176557 (-0.075577) \| 0.156539 / 0.737135 (-0.580597) \| 0.101510 / 0.296338 (-0.194828) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.381138 / 0.215209 (0.165929) \| 3.791573 / 2.077655 (1.713918) \| 1.841954 / 1.504120 (0.337834) \| 1.672463 / 1.541195 (0.131268) \| 1.785769 / 1.468490 (0.317279) \| 0.483263 / 4.584777 (-4.101514) \| 3.617391 / 3.745712 (-0.128322) \| 5.607794 / 5.269862 (0.337933) \| 3.359530 / 4.565676 (-1.206147) \| 0.056826 / 0.424275 (-0.367449) \| 0.007375 / 0.007607 (-0.000232) \| 0.455853 / 0.226044 (0.229809) \| 4.548965 / 2.268929 (2.280037) \| 2.412716 / 55.444624 (-53.031908) \| 1.991456 / 6.876477 (-4.885021) \| 2.242851 / 2.142072 (0.100778) \| 0.573070 / 4.805227 (-4.232157) \| 0.134658 / 6.500664 (-6.366006) \| 0.061539 / 0.075469 (-0.013930) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.278306 / 1.841788 (-0.563481) \| 20.634317 / 8.074308 (12.560009) \| 15.164246 / 10.191392 (4.972854) \| 0.167487 / 0.680424 (-0.512937) \| 0.019006 / 0.534201 (-0.515195) \| 0.394617 / 0.579283 (-0.184666) \| 0.423385 / 0.434364 (-0.010979) \| 0.469968 / 0.540337 (-0.070370) \| 0.630058 / 1.386936 (-0.756878) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006793 / 0.011353 (-0.004559) \| 0.004260 / 0.011008 (-0.006748) \| 0.065398 / 0.038508 (0.026890) \| 0.077850 / 0.023109 (0.054741) \| 0.371754 / 0.275898 (0.095855) \| 0.400652 / 0.323480 (0.077172) \| 0.005729 / 0.007986 (-0.002256) \| 0.003660 / 0.004328 (-0.000669) \| 0.065119 / 0.004250 (0.060869) \| 0.060714 / 0.037052 (0.023661) \| 0.384592 / 0.258489 (0.126103) \| 0.412806 / 0.293841 (0.118965) \| 0.031865 / 0.128546 (-0.096681) \| 0.008807 / 0.075646 (-0.066839) \| 0.071156 / 0.419271 (-0.348115) \| 0.049571 / 0.043533 (0.006038) \| 0.367381 / 0.255139 (0.112242) \| 0.386713 / 0.283200 (0.103513) \| 0.024838 / 0.141683 (-0.116845) \| 1.492986 / 1.452155 (0.040831) \| 1.559243 / 1.492716 (0.066526) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.269737 / 0.018006 (0.251730) \| 0.565177 / 0.000490 (0.564687) \| 0.000404 / 0.000200 (0.000204) \| 0.000060 / 0.000054 (0.000006) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031631 / 0.037411 (-0.005780) \| 0.087289 / 0.014526 (0.072764) \| 0.102798 / 0.176557 (-0.073759) \| 0.158977 / 0.737135 (-0.578158) \| 0.105495 / 0.296338 (-0.190843) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.425067 / 0.215209 (0.209858) \| 4.243121 / 2.077655 (2.165466) \| 2.234567 / 1.504120 (0.730447) \| 2.070810 / 1.541195 (0.529615) \| 2.176802 / 1.468490 (0.708312) \| 0.484987 / 4.584777 (-4.099790) \| 3.647000 / 3.745712 (-0.098712) \| 3.574843 / 5.269862 (-1.695019) \| 2.092581 / 4.565676 (-2.473095) \| 0.057299 / 0.424275 (-0.366976) \| 0.007480 / 0.007607 (-0.000128) \| 0.507838 / 0.226044 (0.281794) \| 5.076594 / 2.268929 (2.807666) \| 2.718858 / 55.444624 (-52.725766) \| 2.362793 / 6.876477 (-4.513684) \| 2.451962 / 2.142072 (0.309890) \| 0.581355 / 4.805227 (-4.223872) \| 0.133723 / 6.500664 (-6.366941) \| 0.061896 / 0.075469 (-0.013573) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.325814 / 1.841788 (-0.515974) \| 20.614502 / 8.074308 (12.540194) \| 14.769422 / 10.191392 (4.578029) \| 0.193797 / 0.680424 (-0.486627) \| 0.018379 / 0.534201 (-0.515822) \| 0.394153 / 0.579283 (-0.185130) \| 0.409585 / 0.434364 (-0.024779) \| 0.479107 / 0.540337 (-0.061231) \| 0.668397 / 1.386936 (-0.718539) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b2d892237169bad5512c91cae453d257ebefc201 \"CML watermark\")\n", "In the end, I decided to remove the progress bar to avoid having it displayed when loading a cached dataset.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006673 / 0.011353 (-0.004680) \| 0.004162 / 0.011008 (-0.006846) \| 0.084017 / 0.038508 (0.045509) \| 0.079536 / 0.023109 (0.056426) \| 0.313594 / 0.275898 (0.037695) \| 0.349200 / 0.323480 (0.025720) \| 0.005544 / 0.007986 (-0.002441) \| 0.003472 / 0.004328 (-0.000857) \| 0.064742 / 0.004250 (0.060491) \| 0.056857 / 0.037052 (0.019805) \| 0.318635 / 0.258489 (0.060146) \| 0.354378 / 0.293841 (0.060537) \| 0.030856 / 0.128546 (-0.097690) \| 0.008759 / 0.075646 (-0.066887) \| 0.287760 / 0.419271 (-0.131511) \| 0.052307 / 0.043533 (0.008775) \| 0.316396 / 0.255139 (0.061257) \| 0.351408 / 0.283200 (0.068208) \| 0.024914 / 0.141683 (-0.116769) \| 1.484592 / 1.452155 (0.032437) \| 1.560662 / 1.492716 (0.067945) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.280938 / 0.018006 (0.262932) \| 0.580236 / 0.000490 (0.579747) \| 0.003369 / 0.000200 (0.003169) \| 0.000090 / 0.000054 (0.000036) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028736 / 0.037411 (-0.008675) \| 0.082916 / 0.014526 (0.068390) \| 0.097761 / 0.176557 (-0.078796) \| 0.153515 / 0.737135 (-0.583620) \| 0.099282 / 0.296338 (-0.197057) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.401244 / 0.215209 (0.186035) \| 4.019866 / 2.077655 (1.942211) \| 2.029642 / 1.504120 (0.525522) \| 1.849591 / 1.541195 (0.308396) \| 1.946829 / 1.468490 (0.478339) \| 0.479750 / 4.584777 (-4.105027) \| 3.482822 / 3.745712 (-0.262890) \| 3.955859 / 5.269862 (-1.314003) \| 2.370747 / 4.565676 (-2.194930) \| 0.056905 / 0.424275 (-0.367370) \| 0.007319 / 0.007607 (-0.000288) \| 0.485310 / 0.226044 (0.259266) \| 4.858228 / 2.268929 (2.589299) \| 2.500476 / 55.444624 (-52.944148) \| 2.171156 / 6.876477 (-4.705320) \| 2.427266 / 2.142072 (0.285194) \| 0.570199 / 4.805227 (-4.235029) \| 0.130855 / 6.500664 (-6.369809) \| 0.060269 / 0.075469 (-0.015200) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.258044 / 1.841788 (-0.583743) \| 20.218657 / 8.074308 (12.144349) \| 13.597970 / 10.191392 (3.406578) \| 0.167656 / 0.680424 (-0.512768) \| 0.018137 / 0.534201 (-0.516064) \| 0.395309 / 0.579283 (-0.183975) \| 0.406325 / 0.434364 (-0.028039) \| 0.467457 / 0.540337 (-0.072880) \| 0.613636 / 1.386936 (-0.773300) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006846 / 0.011353 (-0.004507) \| 0.004207 / 0.011008 (-0.006802) \| 0.064525 / 0.038508 (0.026017) \| 0.081329 / 0.023109 (0.058220) \| 0.399838 / 0.275898 (0.123940) \| 0.431305 / 0.323480 (0.107825) \| 0.005859 / 0.007986 (-0.002127) \| 0.003568 / 0.004328 (-0.000760) \| 0.065262 / 0.004250 (0.061011) \| 0.064796 / 0.037052 (0.027744) \| 0.406858 / 0.258489 (0.148369) \| 0.440971 / 0.293841 (0.147130) \| 0.031421 / 0.128546 (-0.097125) \| 0.008777 / 0.075646 (-0.066870) \| 0.071418 / 0.419271 (-0.347853) \| 0.049263 / 0.043533 (0.005730) \| 0.384279 / 0.255139 (0.129140) \| 0.410745 / 0.283200 (0.127546) \| 0.024467 / 0.141683 (-0.117216) \| 1.522379 / 1.452155 (0.070224) \| 1.581636 / 1.492716 (0.088920) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.276161 / 0.018006 (0.258155) \| 0.548842 / 0.000490 (0.548352) \| 0.004523 / 0.000200 (0.004324) \| 0.000098 / 0.000054 (0.000043) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030747 / 0.037411 (-0.006664) \| 0.087493 / 0.014526 (0.072967) \| 0.106563 / 0.176557 (-0.069993) \| 0.162949 / 0.737135 (-0.574186) \| 0.105303 / 0.296338 (-0.191036) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.425854 / 0.215209 (0.210645) \| 4.244797 / 2.077655 (2.167142) \| 2.269006 / 1.504120 (0.764886) \| 2.097428 / 1.541195 (0.556234) \| 2.181038 / 1.468490 (0.712548) \| 0.477286 / 4.584777 (-4.107491) \| 3.591452 / 3.745712 (-0.154260) \| 3.481281 / 5.269862 (-1.788580) \| 2.066895 / 4.565676 (-2.498782) \| 0.056576 / 0.424275 (-0.367699) \| 0.007409 / 0.007607 (-0.000199) \| 0.498411 / 0.226044 (0.272367) \| 4.994873 / 2.268929 (2.725945) \| 2.749148 / 55.444624 (-52.695476) \| 2.378544 / 6.876477 (-4.497932) \| 2.452859 / 2.142072 (0.310786) \| 0.571340 / 4.805227 (-4.233887) \| 0.132174 / 6.500664 (-6.368490) \| 0.061507 / 0.075469 (-0.013962) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.370773 / 1.841788 (-0.471015) \| 20.493342 / 8.074308 (12.419034) \| 14.809886 / 10.191392 (4.618494) \| 0.175730 / 0.680424 (-0.504693) \| 0.018617 / 0.534201 (-0.515583) \| 0.393808 / 0.579283 (-0.185476) \| 0.416419 / 0.434364 (-0.017945) \| 0.477183 / 0.540337 (-0.063155) \| 0.668060 / 1.386936 (-0.718876) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2de7a2a4af5d94b0f98a7a6db94e78984af40602 \"CML watermark\")\n", "Nice one :)" ]	2023-07-11T18:30:23	2023-07-12T19:34:14	2023-07-12T17:19:28	CONTRIBUTOR	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6019", "html_url": "https://github.com/huggingface/datasets/pull/6019", "diff_url": "https://github.com/huggingface/datasets/pull/6019.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6019.patch", "merged_at": "2023-07-12T17:19:28" }	Adds the StreamHandler (as `hfh` and `transformers` do) to the library's logger to log INFO messages and logs the messages about "loading a cached result" (and some other warnings) as INFO (Also removes the `leave=False` arg in the progress bars to be consistent with `hfh` and `transformers` - progress bars serve as an indicator that a result is not cached, so it makes more sense not to delete them) Fix #2832, fix https://github.com/huggingface/datasets/issues/1948, fix https://github.com/huggingface/datasets/issues/5444	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6019/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6019/timeline	null	null	true
https://api.github.com/repos/huggingface/datasets/issues/6018	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6018/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6018/comments	https://api.github.com/repos/huggingface/datasets/issues/6018/events	https://github.com/huggingface/datasets/pull/6018	1,799,411,999	PR_kwDODunzps5VOmKY	6,018	test1	{ "login": "ognjenovicj", "id": 139256323, "node_id": "U_kgDOCEziAw", "avatar_url": "https://avatars.githubusercontent.com/u/139256323?v=4", "gravatar_id": "", "url": "https://api.github.com/users/ognjenovicj", "html_url": "https://github.com/ognjenovicj", "followers_url": "https://api.github.com/users/ognjenovicj/followers", "following_url": "https://api.github.com/users/ognjenovicj/following{/other_user}", "gists_url": "https://api.github.com/users/ognjenovicj/gists{/gist_id}", "starred_url": "https://api.github.com/users/ognjenovicj/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/ognjenovicj/subscriptions", "organizations_url": "https://api.github.com/users/ognjenovicj/orgs", "repos_url": "https://api.github.com/users/ognjenovicj/repos", "events_url": "https://api.github.com/users/ognjenovicj/events{/privacy}", "received_events_url": "https://api.github.com/users/ognjenovicj/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "We no longer host datasets in this repo. You should use the HF Hub instead." ]	2023-07-11T17:25:49	2023-07-20T10:11:41	2023-07-20T10:11:41	NONE	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6018", "html_url": "https://github.com/huggingface/datasets/pull/6018", "diff_url": "https://github.com/huggingface/datasets/pull/6018.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6018.patch", "merged_at": null }	null	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6018/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6018/timeline	null	null	true
https://api.github.com/repos/huggingface/datasets/issues/6017	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6017/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6017/comments	https://api.github.com/repos/huggingface/datasets/issues/6017/events	https://github.com/huggingface/datasets/issues/6017	1,799,309,132	I_kwDODunzps5rP0dM	6,017	Switch to huggingface_hub's HfFileSystem	{ "login": "lhoestq", "id": 42851186, "node_id": "MDQ6VXNlcjQyODUxMTg2", "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "gravatar_id": "", "url": "https://api.github.com/users/lhoestq", "html_url": "https://github.com/lhoestq", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "repos_url": "https://api.github.com/users/lhoestq/repos", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "type": "User", "site_admin": false }	[ { "id": 1935892871, "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement", "name": "enhancement", "color": "a2eeef", "default": true, "description": "New feature or request" } ]	closed	false	{ "login": "lhoestq", "id": 42851186, "node_id": "MDQ6VXNlcjQyODUxMTg2", "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "gravatar_id": "", "url": "https://api.github.com/users/lhoestq", "html_url": "https://github.com/lhoestq", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "repos_url": "https://api.github.com/users/lhoestq/repos", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "type": "User", "site_admin": false }	[ { "login": "lhoestq", "id": 42851186, "node_id": "MDQ6VXNlcjQyODUxMTg2", "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "gravatar_id": "", "url": "https://api.github.com/users/lhoestq", "html_url": "https://github.com/lhoestq", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "repos_url": "https://api.github.com/users/lhoestq/repos", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "type": "User", "site_admin": false } ]	null	[]	2023-07-11T16:24:40	2023-07-17T17:01:01	2023-07-17T17:01:01	MEMBER	null	null	null	instead of the current datasets.filesystems.hffilesystem.HfFileSystem which can be slow in some cases related to https://github.com/huggingface/datasets/issues/5846 and https://github.com/huggingface/datasets/pull/5919	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6017/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6017/timeline	null	completed	false
https://api.github.com/repos/huggingface/datasets/issues/6016	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6016/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6016/comments	https://api.github.com/repos/huggingface/datasets/issues/6016/events	https://github.com/huggingface/datasets/pull/6016	1,798,968,033	PR_kwDODunzps5VNEvn	6,016	Dataset string representation enhancement	{ "login": "Ganryuu", "id": 63643948, "node_id": "MDQ6VXNlcjYzNjQzOTQ4", "avatar_url": "https://avatars.githubusercontent.com/u/63643948?v=4", "gravatar_id": "", "url": "https://api.github.com/users/Ganryuu", "html_url": "https://github.com/Ganryuu", "followers_url": "https://api.github.com/users/Ganryuu/followers", "following_url": "https://api.github.com/users/Ganryuu/following{/other_user}", "gists_url": "https://api.github.com/users/Ganryuu/gists{/gist_id}", "starred_url": "https://api.github.com/users/Ganryuu/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/Ganryuu/subscriptions", "organizations_url": "https://api.github.com/users/Ganryuu/orgs", "repos_url": "https://api.github.com/users/Ganryuu/repos", "events_url": "https://api.github.com/users/Ganryuu/events{/privacy}", "received_events_url": "https://api.github.com/users/Ganryuu/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6016). All of your documentation changes will be reflected on that endpoint.", "It we could have something similar to Polars, that would be great.\r\n\r\nThis is what Polars outputs: \r\n* `__repr__`/`__str__` :\r\n```\r\nshape: (67_349, 3)\r\n┌───────┬───────────────────────────────────┬───────┐\r\n│ idx ┆ sentence ┆ label │\r\n│ --- ┆ --- ┆ --- │\r\n│ i32 ┆ str ┆ i64 │\r\n╞═══════╪═══════════════════════════════════╪═══════╡\r\n│ 0 ┆ hide new secretions from the par… ┆ 0 │\r\n│ 1 ┆ contains no wit , only labored g… ┆ 0 │\r\n│ 2 ┆ that loves its characters and co… ┆ 1 │\r\n│ 3 ┆ remains utterly satisfied to rem… ┆ 0 │\r\n│ … ┆ … ┆ … │\r\n│ 67345 ┆ anguish , anger and frustration ┆ 0 │\r\n│ 67346 ┆ at achieving the modest , crowd-… ┆ 1 │\r\n│ 67347 ┆ a patient viewer ┆ 1 │\r\n│ 67348 ┆ this new jangle of noise , mayhe… ┆ 0 │\r\n└───────┴───────────────────────────────────┴───────┘\r\n```\r\n\r\n* `_repr_html_`:\r\n<img width=\"251\" alt=\"Screenshot 2023-07-12 at 18 25 58\" src=\"https://github.com/huggingface/datasets/assets/47462742/5d04519d-f302-4411-9fbc-7445bdf53b23\">\r\n\r\n" ]	2023-07-11T13:38:25	2023-07-16T10:26:18	null	NONE	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6016", "html_url": "https://github.com/huggingface/datasets/pull/6016", "diff_url": "https://github.com/huggingface/datasets/pull/6016.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6016.patch", "merged_at": null }	my attempt at #6010 not sure if this is the right way to go about it, I will wait for your feedback	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6016/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6016/timeline	null	null	true
https://api.github.com/repos/huggingface/datasets/issues/6015	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6015/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6015/comments	https://api.github.com/repos/huggingface/datasets/issues/6015/events	https://github.com/huggingface/datasets/pull/6015	1,798,807,893	PR_kwDODunzps5VMhgB	6,015	Add metadata ui screenshot in docs	{ "login": "lhoestq", "id": 42851186, "node_id": "MDQ6VXNlcjQyODUxMTg2", "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "gravatar_id": "", "url": "https://api.github.com/users/lhoestq", "html_url": "https://github.com/lhoestq", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "repos_url": "https://api.github.com/users/lhoestq/repos", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007633 / 0.011353 (-0.003720) \| 0.004666 / 0.011008 (-0.006343) \| 0.097768 / 0.038508 (0.059260) \| 0.085153 / 0.023109 (0.062044) \| 0.400315 / 0.275898 (0.124417) \| 0.452903 / 0.323480 (0.129423) \| 0.006227 / 0.007986 (-0.001759) \| 0.003814 / 0.004328 (-0.000515) \| 0.074586 / 0.004250 (0.070336) \| 0.064295 / 0.037052 (0.027242) \| 0.408082 / 0.258489 (0.149593) \| 0.446921 / 0.293841 (0.153080) \| 0.034593 / 0.128546 (-0.093953) \| 0.009191 / 0.075646 (-0.066456) \| 0.337099 / 0.419271 (-0.082173) \| 0.075320 / 0.043533 (0.031787) \| 0.403488 / 0.255139 (0.148349) \| 0.435309 / 0.283200 (0.152109) \| 0.035675 / 0.141683 (-0.106008) \| 1.732642 / 1.452155 (0.280487) \| 1.770238 / 1.492716 (0.277522) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.235879 / 0.018006 (0.217873) \| 0.500330 / 0.000490 (0.499841) \| 0.005221 / 0.000200 (0.005021) \| 0.000150 / 0.000054 (0.000096) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032479 / 0.037411 (-0.004933) \| 0.095873 / 0.014526 (0.081348) \| 0.107118 / 0.176557 (-0.069438) \| 0.173809 / 0.737135 (-0.563326) \| 0.109832 / 0.296338 (-0.186507) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.444342 / 0.215209 (0.229133) \| 4.459010 / 2.077655 (2.381355) \| 2.209687 / 1.504120 (0.705567) \| 2.007556 / 1.541195 (0.466362) \| 2.113683 / 1.468490 (0.645193) \| 0.544281 / 4.584777 (-4.040496) \| 4.037151 / 3.745712 (0.291439) \| 4.852644 / 5.269862 (-0.417217) \| 3.134126 / 4.565676 (-1.431550) \| 0.066815 / 0.424275 (-0.357460) \| 0.008836 / 0.007607 (0.001229) \| 0.560904 / 0.226044 (0.334859) \| 5.302760 / 2.268929 (3.033832) \| 2.750182 / 55.444624 (-52.694442) \| 2.322595 / 6.876477 (-4.553882) \| 2.547486 / 2.142072 (0.405414) \| 0.665766 / 4.805227 (-4.139461) \| 0.151613 / 6.500664 (-6.349051) \| 0.071155 / 0.075469 (-0.004314) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.473717 / 1.841788 (-0.368071) \| 22.584179 / 8.074308 (14.509871) \| 15.888001 / 10.191392 (5.696609) \| 0.181073 / 0.680424 (-0.499351) \| 0.021395 / 0.534201 (-0.512806) \| 0.452693 / 0.579283 (-0.126590) \| 0.447709 / 0.434364 (0.013345) \| 0.529599 / 0.540337 (-0.010738) \| 0.699241 / 1.386936 (-0.687695) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007917 / 0.011353 (-0.003436) \| 0.004544 / 0.011008 (-0.006464) \| 0.074566 / 0.038508 (0.036058) \| 0.087530 / 0.023109 (0.064421) \| 0.419753 / 0.275898 (0.143854) \| 0.452352 / 0.323480 (0.128872) \| 0.005882 / 0.007986 (-0.002104) \| 0.003904 / 0.004328 (-0.000425) \| 0.073539 / 0.004250 (0.069289) \| 0.071320 / 0.037052 (0.034267) \| 0.432899 / 0.258489 (0.174409) \| 0.470365 / 0.293841 (0.176524) \| 0.036198 / 0.128546 (-0.092348) \| 0.009342 / 0.075646 (-0.066304) \| 0.080970 / 0.419271 (-0.338301) \| 0.058769 / 0.043533 (0.015236) \| 0.413397 / 0.255139 (0.158258) \| 0.448362 / 0.283200 (0.165162) \| 0.034177 / 0.141683 (-0.107506) \| 1.706217 / 1.452155 (0.254063) \| 1.776743 / 1.492716 (0.284026) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.198779 / 0.018006 (0.180773) \| 0.499862 / 0.000490 (0.499372) \| 0.003891 / 0.000200 (0.003692) \| 0.000108 / 0.000054 (0.000053) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034671 / 0.037411 (-0.002740) \| 0.103165 / 0.014526 (0.088639) \| 0.115813 / 0.176557 (-0.060744) \| 0.177407 / 0.737135 (-0.559728) \| 0.117733 / 0.296338 (-0.178606) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.476859 / 0.215209 (0.261650) \| 4.823063 / 2.077655 (2.745409) \| 2.524133 / 1.504120 (1.020013) \| 2.374482 / 1.541195 (0.833288) \| 2.518047 / 1.468490 (1.049557) \| 0.559131 / 4.584777 (-4.025646) \| 4.126213 / 3.745712 (0.380501) \| 6.488570 / 5.269862 (1.218708) \| 3.816540 / 4.565676 (-0.749137) \| 0.064742 / 0.424275 (-0.359533) \| 0.008476 / 0.007607 (0.000869) \| 0.576432 / 0.226044 (0.350387) \| 5.835133 / 2.268929 (3.566205) \| 3.237833 / 55.444624 (-52.206791) \| 2.726596 / 6.876477 (-4.149880) \| 2.799212 / 2.142072 (0.657139) \| 0.661628 / 4.805227 (-4.143599) \| 0.153997 / 6.500664 (-6.346667) \| 0.070621 / 0.075469 (-0.004848) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.648505 / 1.841788 (-0.193282) \| 22.454019 / 8.074308 (14.379711) \| 16.077098 / 10.191392 (5.885706) \| 0.217875 / 0.680424 (-0.462549) \| 0.021285 / 0.534201 (-0.512916) \| 0.459837 / 0.579283 (-0.119446) \| 0.476211 / 0.434364 (0.041847) \| 0.525903 / 0.540337 (-0.014435) \| 0.717224 / 1.386936 (-0.669712) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b767e9c3ef30f9da30d47cfcaccf9a7ac2500c43 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008929 / 0.011353 (-0.002424) \| 0.004188 / 0.011008 (-0.006820) \| 0.097030 / 0.038508 (0.058522) \| 0.071363 / 0.023109 (0.048254) \| 0.333116 / 0.275898 (0.057218) \| 0.371272 / 0.323480 (0.047792) \| 0.006430 / 0.007986 (-0.001555) \| 0.003689 / 0.004328 (-0.000639) \| 0.068666 / 0.004250 (0.064416) \| 0.057562 / 0.037052 (0.020510) \| 0.347208 / 0.258489 (0.088719) \| 0.390514 / 0.293841 (0.096673) \| 0.050560 / 0.128546 (-0.077987) \| 0.013372 / 0.075646 (-0.062275) \| 0.311345 / 0.419271 (-0.107927) \| 0.068990 / 0.043533 (0.025457) \| 0.363026 / 0.255139 (0.107887) \| 0.379793 / 0.283200 (0.096593) \| 0.036891 / 0.141683 (-0.104792) \| 1.583481 / 1.452155 (0.131327) \| 1.688727 / 1.492716 (0.196011) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.209777 / 0.018006 (0.191771) \| 0.507267 / 0.000490 (0.506777) \| 0.003637 / 0.000200 (0.003438) \| 0.000105 / 0.000054 (0.000051) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029309 / 0.037411 (-0.008102) \| 0.088386 / 0.014526 (0.073861) \| 0.104974 / 0.176557 (-0.071582) \| 0.171999 / 0.737135 (-0.565137) \| 0.110797 / 0.296338 (-0.185542) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.543465 / 0.215209 (0.328256) \| 5.361491 / 2.077655 (3.283836) \| 2.348712 / 1.504120 (0.844592) \| 2.012527 / 1.541195 (0.471332) \| 2.069776 / 1.468490 (0.601286) \| 0.874262 / 4.584777 (-3.710515) \| 4.877317 / 3.745712 (1.131605) \| 5.327459 / 5.269862 (0.057597) \| 3.336823 / 4.565676 (-1.228854) \| 0.100456 / 0.424275 (-0.323819) \| 0.008503 / 0.007607 (0.000895) \| 0.692009 / 0.226044 (0.465965) \| 6.912731 / 2.268929 (4.643802) \| 3.110548 / 55.444624 (-52.334076) \| 2.443665 / 6.876477 (-4.432811) \| 2.528713 / 2.142072 (0.386641) \| 1.076358 / 4.805227 (-3.728869) \| 0.220352 / 6.500664 (-6.280312) \| 0.080293 / 0.075469 (0.004824) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.538444 / 1.841788 (-0.303344) \| 21.121221 / 8.074308 (13.046913) \| 19.810609 / 10.191392 (9.619216) \| 0.225406 / 0.680424 (-0.455018) \| 0.026652 / 0.534201 (-0.507549) \| 0.430372 / 0.579283 (-0.148911) \| 0.510722 / 0.434364 (0.076358) \| 0.514347 / 0.540337 (-0.025991) \| 0.686050 / 1.386936 (-0.700886) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007675 / 0.011353 (-0.003678) \| 0.004542 / 0.011008 (-0.006466) \| 0.069655 / 0.038508 (0.031147) \| 0.069338 / 0.023109 (0.046229) \| 0.436505 / 0.275898 (0.160607) \| 0.481806 / 0.323480 (0.158326) \| 0.005315 / 0.007986 (-0.002670) \| 0.004455 / 0.004328 (0.000127) \| 0.072674 / 0.004250 (0.068424) \| 0.058088 / 0.037052 (0.021035) \| 0.445825 / 0.258489 (0.187336) \| 0.501706 / 0.293841 (0.207865) \| 0.047123 / 0.128546 (-0.081424) \| 0.012943 / 0.075646 (-0.062703) \| 0.093491 / 0.419271 (-0.325780) \| 0.060169 / 0.043533 (0.016637) \| 0.436530 / 0.255139 (0.181391) \| 0.466873 / 0.283200 (0.183674) \| 0.040453 / 0.141683 (-0.101230) \| 1.586438 / 1.452155 (0.134283) \| 1.671081 / 1.492716 (0.178365) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.180607 / 0.018006 (0.162601) \| 0.520145 / 0.000490 (0.519655) \| 0.004824 / 0.000200 (0.004624) \| 0.000116 / 0.000054 (0.000061) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029308 / 0.037411 (-0.008103) \| 0.093652 / 0.014526 (0.079126) \| 0.102332 / 0.176557 (-0.074224) \| 0.162414 / 0.737135 (-0.574721) \| 0.098017 / 0.296338 (-0.198321) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.583949 / 0.215209 (0.368740) \| 6.035191 / 2.077655 (3.957536) \| 2.801274 / 1.504120 (1.297155) \| 2.566150 / 1.541195 (1.024955) \| 2.437122 / 1.468490 (0.968632) \| 0.865038 / 4.584777 (-3.719739) \| 4.841727 / 3.745712 (1.096015) \| 4.683919 / 5.269862 (-0.585943) \| 2.941240 / 4.565676 (-1.624437) \| 0.104888 / 0.424275 (-0.319387) \| 0.007747 / 0.007607 (0.000140) \| 0.780041 / 0.226044 (0.553997) \| 7.771314 / 2.268929 (5.502385) \| 3.680814 / 55.444624 (-51.763811) \| 2.938472 / 6.876477 (-3.938004) \| 2.981740 / 2.142072 (0.839668) \| 1.065411 / 4.805227 (-3.739816) \| 0.222265 / 6.500664 (-6.278399) \| 0.082428 / 0.075469 (0.006959) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.626774 / 1.841788 (-0.215014) \| 21.618284 / 8.074308 (13.543976) \| 20.596743 / 10.191392 (10.405351) \| 0.240969 / 0.680424 (-0.439454) \| 0.025630 / 0.534201 (-0.508570) \| 0.481981 / 0.579283 (-0.097302) \| 0.547914 / 0.434364 (0.113550) \| 0.522296 / 0.540337 (-0.018041) \| 0.729174 / 1.386936 (-0.657762) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b8067c0262073891180869f700ebef5ac3dc5cce \"CML watermark\")\n" ]	2023-07-11T12:16:29	2023-07-11T16:07:28	2023-07-11T15:56:46	MEMBER	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6015", "html_url": "https://github.com/huggingface/datasets/pull/6015", "diff_url": "https://github.com/huggingface/datasets/pull/6015.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6015.patch", "merged_at": "2023-07-11T15:56:46" }	null	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6015/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6015/timeline	null	null	true
https://api.github.com/repos/huggingface/datasets/issues/6014	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6014/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6014/comments	https://api.github.com/repos/huggingface/datasets/issues/6014/events	https://github.com/huggingface/datasets/issues/6014	1,798,213,816	I_kwDODunzps5rLpC4	6,014	Request to Share/Update Dataset Viewer Code	{ "login": "lilyorlilypad", "id": 105081034, "node_id": "U_kgDOBkNoyg", "avatar_url": "https://avatars.githubusercontent.com/u/105081034?v=4", "gravatar_id": "", "url": "https://api.github.com/users/lilyorlilypad", "html_url": "https://github.com/lilyorlilypad", "followers_url": "https://api.github.com/users/lilyorlilypad/followers", "following_url": "https://api.github.com/users/lilyorlilypad/following{/other_user}", "gists_url": "https://api.github.com/users/lilyorlilypad/gists{/gist_id}", "starred_url": "https://api.github.com/users/lilyorlilypad/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lilyorlilypad/subscriptions", "organizations_url": "https://api.github.com/users/lilyorlilypad/orgs", "repos_url": "https://api.github.com/users/lilyorlilypad/repos", "events_url": "https://api.github.com/users/lilyorlilypad/events{/privacy}", "received_events_url": "https://api.github.com/users/lilyorlilypad/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	null	[ "Hi ! The huggingface/dataset-viewer code was not maintained anymore because we switched to a new dataset viewer that is deployed available for each dataset the Hugging Face website.\r\n\r\nWhat are you using this old repository for ?", "I think these parts are outdated:\r\n\r\n* https://github.com/huggingface/datasets-viewer/blob/8efad8eae313a891f713469983bf4c744786f26e/run.py#L126-L131\r\n* https://github.com/huggingface/datasets-viewer/blob/8efad8eae313a891f713469983bf4c744786f26e/run.py#L145-L150\r\n\r\nTo make the viewer work, the first one should be replaced with the following:\r\n```python\r\ndataset_module = datasets.load.dataset_module_factory(path)\r\nbuilder_cls = datasets.load.import_main_class(dataset_module.module_path)\r\nconfs = builder_cls.BUILDER_CONFIGS\r\n```\r\nAnd the second one:\r\n```python\r\ndataset_module = datasets.load.dataset_module_factory(path)\r\nbuilder_cls = datasets.load.import_main_class(dataset_module.module_path)\r\nif conf:\r\n builder_instance = builder_cls(name=conf, cache_dir=path if path_to_datasets is not None else None)\r\nelse:\r\n builder_instance = builder_cls(cache_dir=path if path_to_datasets is not None else None)\r\n```\r\n\r\nBut as @lhoestq suggested, it's better to use the `datasets-server` API nowadays to [fetch the rows](https://huggingface.co/docs/datasets-server/rows).", "> The dataset viewer on the Hugging Face website is incredibly useful\r\n\r\n@mariosasko i think @lilyorlilypad wants to run the new dataset-viewer, not the old one", "> wants to run the new dataset-viewer, not the old one\r\n\r\nThanks for the clarification for me. I do want to run the new dataset-viewer. ", "It should be possible to run it locally using the HF datasets-server API (docs [here](https://huggingface.co/docs/datasets-server)) but the front end part is not open source (yet ?)\r\n\r\nThe back-end is open source though if you're interested: https://github.com/huggingface/datasets-server\r\nIt automatically converts datasets on HF to Parquet, which is the format we use to power the viewer.", "the new frontend would probably be hard to open source, as is, as it's quite intertwined with the Hub's code.\r\n\r\nHowever, at some point it would be amazing to have a community-driven open source implementation of a frontend to datasets-server! " ]	2023-07-11T06:36:09	2023-07-12T14:18:49	null	NONE	null	null	null	Overview: The repository (huggingface/datasets-viewer) was recently archived and when I tried to run the code, there was the error message "AttributeError: module 'datasets.load' has no attribute 'prepare_module'". I could not resolve the issue myself due to lack of documentation of that attribute. Request: I kindly request the sharing of the code responsible for the dataset preview functionality or help with resolving the error. The dataset viewer on the Hugging Face website is incredibly useful since it is compatible with different types of inputs. It allows users to find datasets that meet their needs more efficiently. If needed, I am willing to contribute to the project by testing, documenting, and providing feedback on the dataset viewer code. Thank you for considering this request, and I look forward to your response.	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6014/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6014/timeline	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6013	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6013/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6013/comments	https://api.github.com/repos/huggingface/datasets/issues/6013/events	https://github.com/huggingface/datasets/issues/6013	1,796,083,437	I_kwDODunzps5rDg7t	6,013	[FR] `map` should reuse unchanged columns from the previous dataset to avoid disk usage	{ "login": "NightMachinery", "id": 36224762, "node_id": "MDQ6VXNlcjM2MjI0NzYy", "avatar_url": "https://avatars.githubusercontent.com/u/36224762?v=4", "gravatar_id": "", "url": "https://api.github.com/users/NightMachinery", "html_url": "https://github.com/NightMachinery", "followers_url": "https://api.github.com/users/NightMachinery/followers", "following_url": "https://api.github.com/users/NightMachinery/following{/other_user}", "gists_url": "https://api.github.com/users/NightMachinery/gists{/gist_id}", "starred_url": "https://api.github.com/users/NightMachinery/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/NightMachinery/subscriptions", "organizations_url": "https://api.github.com/users/NightMachinery/orgs", "repos_url": "https://api.github.com/users/NightMachinery/repos", "events_url": "https://api.github.com/users/NightMachinery/events{/privacy}", "received_events_url": "https://api.github.com/users/NightMachinery/received_events", "type": "User", "site_admin": false }	[ { "id": 1935892871, "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement", "name": "enhancement", "color": "a2eeef", "default": true, "description": "New feature or request" }, { "id": 3761482852, "node_id": "LA_kwDODunzps7gM6xk", "url": "https://api.github.com/repos/huggingface/datasets/labels/good%20second%20issue", "name": "good second issue", "color": "BDE59C", "default": false, "description": "Issues a bit more difficult than \"Good First\" issues" } ]	open	false	null	[]	null	[ "You can use the `remove_columns` parameter in `map` to avoid duplicating the columns (and save disk space) and then concatenate the original dataset with the map result:\r\n```python\r\nfrom datasets import concatenate_datasets\r\n# dummy example\r\nds_new = ds.map(lambda x: {\"new_col\": x[\"col\"] + 2}, remove_columns=ds.column_names)\r\nds_combined = concatenate_datasets([ds, ds_new], axis=1)\r\n```\r\n\r\nDoing this automatically is hard to implement efficiently unless we know ahead of time which existing columns will be modified by a `map` transform. We have this info when `input_columns` are specified, so I think this is the only case we can optimize." ]	2023-07-10T06:42:20	2023-07-10T15:37:52	null	CONTRIBUTOR	null	null	null	### Feature request Currently adding a new column with `map` will cause all the data in the dataset to be duplicated and stored/cached on the disk again. It should reuse unchanged columns. ### Motivation This allows having datasets with different columns but sharing some basic columns. Currently, these datasets would become too expensive to store and one would need some kind of on-the-fly join; which also doesn't seem implemented. ### Your contribution _	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6013/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6013/timeline	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6012	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6012/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6012/comments	https://api.github.com/repos/huggingface/datasets/issues/6012/events	https://github.com/huggingface/datasets/issues/6012	1,795,575,432	I_kwDODunzps5rBk6I	6,012	[FR] Transform Chaining, Lazy Mapping	{ "login": "NightMachinery", "id": 36224762, "node_id": "MDQ6VXNlcjM2MjI0NzYy", "avatar_url": "https://avatars.githubusercontent.com/u/36224762?v=4", "gravatar_id": "", "url": "https://api.github.com/users/NightMachinery", "html_url": "https://github.com/NightMachinery", "followers_url": "https://api.github.com/users/NightMachinery/followers", "following_url": "https://api.github.com/users/NightMachinery/following{/other_user}", "gists_url": "https://api.github.com/users/NightMachinery/gists{/gist_id}", "starred_url": "https://api.github.com/users/NightMachinery/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/NightMachinery/subscriptions", "organizations_url": "https://api.github.com/users/NightMachinery/orgs", "repos_url": "https://api.github.com/users/NightMachinery/repos", "events_url": "https://api.github.com/users/NightMachinery/events{/privacy}", "received_events_url": "https://api.github.com/users/NightMachinery/received_events", "type": "User", "site_admin": false }	[ { "id": 1935892871, "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement", "name": "enhancement", "color": "a2eeef", "default": true, "description": "New feature or request" } ]	open	false	null	[]	null	[ "You can use `with_transform` to get a new dataset object.\r\n\r\nSupport for lazy `map` has already been discussed [here](https://github.com/huggingface/datasets/issues/3385) a little bit. Personally, I'm not a fan, as this would make `map` even more complex. ", "> You can use `with_transform` to get a new dataset object.\r\n> \r\n> Support for lazy `map` has already been discussed [here](https://github.com/huggingface/datasets/issues/3385) a little bit. Personally, I'm not a fan, as this would make `map` even more complex.\r\n\r\nI read about IterableDataset, and it seems to have lazy mapping. But I can't figure out how to convert an IterableDataset into a normal one when needed.\r\n\r\n`with_transform` still does not chain AFAIU.", "> I read about IterableDataset, and it seems to have lazy mapping. But I can't figure out how to convert an IterableDataset into a normal one when needed.\r\n\r\nYou must cache an `IterableDataset` to disk to load it as a `Dataset`. One way to do this is with `Dataset.from_generator`:\r\n```python\r\nfrom functools import partial\r\nfrom datasets import Dataset\r\n\r\ndef gen_from_iterable_dataset(iterable_ds)\r\n yield from iterable_ds\r\n\r\nds = Dataset.from_generator(partial(gen_from_iterable_dataset, iterable_ds), features=iterable_ds.features})\r\n```\r\n\r\n> with_transform still does not chain AFAIU.\r\n\r\nYes, not supported yet - the solution is to combine the transforms into a single one.", "I wonder if it would be beneficial to have a dedicated method to do that ? Maybe a `.save_to_disk()` so that the user can reload the resulting dataset later ?", "> ```python\r\n> from functools import partial\r\n> from datasets import Dataset\r\n> \r\n> def gen_from_iterable_dataset(iterable_ds)\r\n> yield from iterable_ds\r\n> \r\n> ds = Dataset.from_generator(partial(gen_from_iterable_dataset, iterable_ds), features=iterable_ds.features})\r\n> ```\r\n\r\n@mariosasko With these complex mapping functions, what hash will be used to cache this dataset?\r\n", "The params passed to `Dataset.from_generator` will be used to compute the hash (`partial` encapsulates the `iterable_ds` value, so changing it will also change the hash)" ]	2023-07-09T21:40:21	2023-07-14T13:12:40	null	CONTRIBUTOR	null	null	null	### Feature request Currently using a `map` call processes and duplicates the whole dataset, which takes both time and disk space. The solution is to allow lazy mapping, which is essentially a saved chain of transforms that are applied on the fly whenever a slice of the dataset is requested. The API should look like `map`, as `set_transform` changes the current dataset while `map` returns another dataset. ### Motivation Lazy processing allows lower disk usage and faster experimentation. ### Your contribution _	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6012/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6012/timeline	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6011	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6011/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6011/comments	https://api.github.com/repos/huggingface/datasets/issues/6011/events	https://github.com/huggingface/datasets/issues/6011	1,795,296,568	I_kwDODunzps5rAg04	6,011	Documentation: wiki_dpr Dataset has no metric_type for Faiss Index	{ "login": "YichiRockyZhang", "id": 29335344, "node_id": "MDQ6VXNlcjI5MzM1MzQ0", "avatar_url": "https://avatars.githubusercontent.com/u/29335344?v=4", "gravatar_id": "", "url": "https://api.github.com/users/YichiRockyZhang", "html_url": "https://github.com/YichiRockyZhang", "followers_url": "https://api.github.com/users/YichiRockyZhang/followers", "following_url": "https://api.github.com/users/YichiRockyZhang/following{/other_user}", "gists_url": "https://api.github.com/users/YichiRockyZhang/gists{/gist_id}", "starred_url": "https://api.github.com/users/YichiRockyZhang/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/YichiRockyZhang/subscriptions", "organizations_url": "https://api.github.com/users/YichiRockyZhang/orgs", "repos_url": "https://api.github.com/users/YichiRockyZhang/repos", "events_url": "https://api.github.com/users/YichiRockyZhang/events{/privacy}", "received_events_url": "https://api.github.com/users/YichiRockyZhang/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "Hi! You can do `ds.get_index(\"embeddings\").faiss_index.metric_type` to get the metric type and then match the result with the FAISS metric [enum](https://github.com/facebookresearch/faiss/blob/43d86e30736ede853c384b24667fc3ab897d6ba9/faiss/MetricType.h#L22-L36) (should be L2).", "Ah! Thank you for pointing this out. FYI: the enum indicates it's using the inner product. Using `torch.inner` or `torch.dot` still produces a discrepancy compared to the built-in score. I think this is because of the compression/quantization that occurs with the FAISS index." ]	2023-07-09T08:30:19	2023-07-11T03:02:36	2023-07-11T03:02:36	NONE	null	null	null	### Describe the bug After loading `wiki_dpr` using: ```py ds = load_dataset(path='wiki_dpr', name='psgs_w100.multiset.compressed', split='train') print(ds.get_index("embeddings").metric_type) # prints nothing because the value is None ``` the index does not have a defined `metric_type`. This is an issue because I do not know how the `scores` are being computed for `get_nearest_examples()`. ### Steps to reproduce the bug System: Python 3.9.16, Transformers 4.30.2, WSL After loading `wiki_dpr` using: ```py ds = load_dataset(path='wiki_dpr', name='psgs_w100.multiset.compressed', split='train') print(ds.get_index("embeddings").metric_type) # prints nothing because the value is None ``` the index does not have a defined `metric_type`. This is an issue because I do not know how the `scores` are being computed for `get_nearest_examples()`. ```py from transformers import DPRQuestionEncoder, DPRContextEncoder, DPRQuestionEncoderTokenizer, DPRContextEncoderTokenizer tokenizer = DPRQuestionEncoderTokenizer.from_pretrained("facebook/dpr-question_encoder-multiset-base") encoder = DPRQuestionEncoder.from_pretrained("facebook/dpr-question_encoder-multiset-base") def encode_question(query, tokenizer=tokenizer, encoder=encoder): inputs = tokenizer(query, return_tensors='pt') question_embedding = encoder(**inputs)[0].detach().numpy() return question_embedding def get_knn(query, k=5, tokenizer=tokenizer, encoder=encoder, verbose=False): enc_question = encode_question(query, tokenizer, encoder) topk_results = ds.get_nearest_examples(index_name='embeddings', query=enc_question, k=k) a = torch.tensor(enc_question[0]).reshape(768) b = torch.tensor(topk_results.examples['embeddings'][0]) print(a.shape, b.shape) print(torch.dot(a, b)) print((a-b).pow(2).sum()) return topk_results ``` The [FAISS documentation](https://github.com/facebookresearch/faiss/wiki/MetricType-and-distances) suggests the metric is usually L2 distance (without the square root) or the inner product. I compute both for the sample query: ```py query = """ it catapulted into popular culture along with a line of action figures and other toys by Bandai.[2] By 2001, the media franchise had generated over $6 billion in toy sales. Despite initial criticism that its action violence targeted child audiences, the franchise has been commercially successful.""" get_knn(query,k=5) ``` Here, I get dot product of 80.6020 and L2 distance of 77.6616 and ```py NearestExamplesResults(scores=array([76.20431 , 75.312416, 74.945404, 74.866394, 74.68506 ], dtype=float32), examples={'id': ['3081096', '2004811', '8908258', '9594124', '286575'], 'text': ['actors, resulting in the "Power Rangers" franchise which has continued since then into sequel TV series (with "Power Rangers Beast Morphers" set to premiere in 2019), comic books, video games, and three feature films, with a further cinematic universe planned. Following from the success of "Power Rangers", Saban acquired the rights to more of Toei\'s library, creating "VR Troopers" and "Big Bad Beetleborgs" from several Metal Hero Series shows and "Masked Rider" from Kamen Rider Series footage. DIC Entertainment joined this boom by acquiring the rights to "Gridman the Hyper Agent" and turning it into "Superhuman Samurai Syber-Squad". In 2002,', ``` Doing `k=1` indicates the higher the outputted number, the better the match, so the metric should not be L2 distance. However, my manually computed inner product (80.6) has a discrepancy with the reported (76.2). Perhaps, this has to do with me using the `compressed` embeddings? ### Expected behavior ```py ds = load_dataset(path='wiki_dpr', name='psgs_w100.multiset.compressed', split='train') print(ds.get_index("embeddings").metric_type) # METRIC_INNER_PRODUCT ``` ### Environment info - `datasets` version: 2.12.0 - Platform: Linux-4.18.0-477.13.1.el8_8.x86_64-x86_64-with-glibc2.28 - Python version: 3.9.16 - Huggingface_hub version: 0.14.1 - PyArrow version: 12.0.0 - Pandas version: 2.0.1	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6011/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6011/timeline	null	completed	false
https://api.github.com/repos/huggingface/datasets/issues/6010	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6010/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6010/comments	https://api.github.com/repos/huggingface/datasets/issues/6010/events	https://github.com/huggingface/datasets/issues/6010	1,793,838,152	I_kwDODunzps5q68xI	6,010	Improve `Dataset`'s string representation	{ "login": "mariosasko", "id": 47462742, "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "gravatar_id": "", "url": "https://api.github.com/users/mariosasko", "html_url": "https://github.com/mariosasko", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "repos_url": "https://api.github.com/users/mariosasko/repos", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "type": "User", "site_admin": false }	[ { "id": 1935892871, "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement", "name": "enhancement", "color": "a2eeef", "default": true, "description": "New feature or request" } ]	open	false	null	[]	null	[ "I want to take a shot at this if possible ", "Yes, feel free to work on this.\r\n\r\nYou can check the PyArrow Table `__repr__` and Polars DataFrame `__repr__`/`_repr_html_` implementations for some pointers/ideas." ]	2023-07-07T16:38:03	2023-07-16T13:00:18	null	CONTRIBUTOR	null	null	null	Currently, `Dataset.__repr__` outputs a dataset's column names and the number of rows. We could improve it by printing its features and the first few rows. We should also implement `_repr_html_` to have a rich HTML representation in notebooks/Streamlit.	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6010/reactions", "total_count": 1, "+1": 1, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6010/timeline	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6009	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6009/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6009/comments	https://api.github.com/repos/huggingface/datasets/issues/6009/events	https://github.com/huggingface/datasets/pull/6009	1,792,059,808	PR_kwDODunzps5U1mus	6,009	Fix cast for dictionaries with no keys	{ "login": "mariosasko", "id": 47462742, "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "gravatar_id": "", "url": "https://api.github.com/users/mariosasko", "html_url": "https://github.com/mariosasko", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "repos_url": "https://api.github.com/users/mariosasko/repos", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006961 / 0.011353 (-0.004392) \| 0.004390 / 0.011008 (-0.006618) \| 0.103249 / 0.038508 (0.064741) \| 0.048084 / 0.023109 (0.024975) \| 0.351213 / 0.275898 (0.075315) \| 0.416918 / 0.323480 (0.093439) \| 0.005539 / 0.007986 (-0.002446) \| 0.003555 / 0.004328 (-0.000774) \| 0.079306 / 0.004250 (0.075055) \| 0.066937 / 0.037052 (0.029884) \| 0.382601 / 0.258489 (0.124112) \| 0.406125 / 0.293841 (0.112284) \| 0.032269 / 0.128546 (-0.096277) \| 0.009133 / 0.075646 (-0.066514) \| 0.354449 / 0.419271 (-0.064822) \| 0.068978 / 0.043533 (0.025445) \| 0.352314 / 0.255139 (0.097175) \| 0.390398 / 0.283200 (0.107199) \| 0.025640 / 0.141683 (-0.116043) \| 1.553865 / 1.452155 (0.101710) \| 1.601292 / 1.492716 (0.108576) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.208310 / 0.018006 (0.190303) \| 0.440076 / 0.000490 (0.439586) \| 0.000363 / 0.000200 (0.000163) \| 0.000059 / 0.000054 (0.000004) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029173 / 0.037411 (-0.008238) \| 0.111323 / 0.014526 (0.096797) \| 0.123001 / 0.176557 (-0.053556) \| 0.180180 / 0.737135 (-0.556955) \| 0.125804 / 0.296338 (-0.170534) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.419919 / 0.215209 (0.204710) \| 4.194515 / 2.077655 (2.116860) \| 1.881234 / 1.504120 (0.377114) \| 1.672914 / 1.541195 (0.131720) \| 1.723102 / 1.468490 (0.254612) \| 0.543584 / 4.584777 (-4.041193) \| 3.822477 / 3.745712 (0.076765) \| 1.837946 / 5.269862 (-3.431915) \| 1.094975 / 4.565676 (-3.470701) \| 0.066788 / 0.424275 (-0.357487) \| 0.011689 / 0.007607 (0.004082) \| 0.520983 / 0.226044 (0.294938) \| 5.209245 / 2.268929 (2.940316) \| 2.392916 / 55.444624 (-53.051708) \| 2.060042 / 6.876477 (-4.816434) \| 2.162291 / 2.142072 (0.020219) \| 0.668472 / 4.805227 (-4.136755) \| 0.144373 / 6.500664 (-6.356291) \| 0.066152 / 0.075469 (-0.009318) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.251256 / 1.841788 (-0.590532) \| 15.161338 / 8.074308 (7.087030) \| 14.416133 / 10.191392 (4.224741) \| 0.166145 / 0.680424 (-0.514279) \| 0.018168 / 0.534201 (-0.516033) \| 0.433364 / 0.579283 (-0.145919) \| 0.417484 / 0.434364 (-0.016880) \| 0.502543 / 0.540337 (-0.037794) \| 0.602904 / 1.386936 (-0.784032) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006946 / 0.011353 (-0.004407) \| 0.004248 / 0.011008 (-0.006761) \| 0.079707 / 0.038508 (0.041199) \| 0.046226 / 0.023109 (0.023117) \| 0.375864 / 0.275898 (0.099966) \| 0.430740 / 0.323480 (0.107260) \| 0.006222 / 0.007986 (-0.001764) \| 0.003474 / 0.004328 (-0.000854) \| 0.079622 / 0.004250 (0.075372) \| 0.066666 / 0.037052 (0.029613) \| 0.379487 / 0.258489 (0.120998) \| 0.423002 / 0.293841 (0.129161) \| 0.032836 / 0.128546 (-0.095710) \| 0.008976 / 0.075646 (-0.066670) \| 0.086578 / 0.419271 (-0.332693) \| 0.055651 / 0.043533 (0.012118) \| 0.360787 / 0.255139 (0.105648) \| 0.384265 / 0.283200 (0.101065) \| 0.025350 / 0.141683 (-0.116333) \| 1.547880 / 1.452155 (0.095725) \| 1.605850 / 1.492716 (0.113134) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.184227 / 0.018006 (0.166220) \| 0.442071 / 0.000490 (0.441582) \| 0.002887 / 0.000200 (0.002687) \| 0.000088 / 0.000054 (0.000034) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031923 / 0.037411 (-0.005488) \| 0.119093 / 0.014526 (0.104568) \| 0.128704 / 0.176557 (-0.047853) \| 0.187065 / 0.737135 (-0.550070) \| 0.134135 / 0.296338 (-0.162204) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.455731 / 0.215209 (0.240522) \| 4.562911 / 2.077655 (2.485256) \| 2.247431 / 1.504120 (0.743311) \| 2.053346 / 1.541195 (0.512151) \| 2.049611 / 1.468490 (0.581121) \| 0.546069 / 4.584777 (-4.038708) \| 3.821852 / 3.745712 (0.076140) \| 3.358497 / 5.269862 (-1.911364) \| 1.667697 / 4.565676 (-2.897979) \| 0.067968 / 0.424275 (-0.356307) \| 0.012344 / 0.007607 (0.004737) \| 0.550864 / 0.226044 (0.324820) \| 5.496867 / 2.268929 (3.227939) \| 2.680031 / 55.444624 (-52.764594) \| 2.328673 / 6.876477 (-4.547804) \| 2.436754 / 2.142072 (0.294682) \| 0.681195 / 4.805227 (-4.124033) \| 0.148761 / 6.500664 (-6.351904) \| 0.067716 / 0.075469 (-0.007753) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.353798 / 1.841788 (-0.487990) \| 15.992965 / 8.074308 (7.918657) \| 14.051539 / 10.191392 (3.860147) \| 0.181087 / 0.680424 (-0.499337) \| 0.018653 / 0.534201 (-0.515548) \| 0.433499 / 0.579283 (-0.145784) \| 0.428845 / 0.434364 (-0.005519) \| 0.501100 / 0.540337 (-0.039238) \| 0.603666 / 1.386936 (-0.783270) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#10cfa871a2f387fe9c6360e1873ea74c6d69ff67 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.010983 / 0.011353 (-0.000370) \| 0.005630 / 0.011008 (-0.005378) \| 0.109967 / 0.038508 (0.071458) \| 0.101580 / 0.023109 (0.078471) \| 0.490205 / 0.275898 (0.214307) \| 0.534653 / 0.323480 (0.211173) \| 0.008365 / 0.007986 (0.000379) \| 0.004317 / 0.004328 (-0.000012) \| 0.082429 / 0.004250 (0.078179) \| 0.080556 / 0.037052 (0.043504) \| 0.494627 / 0.258489 (0.236138) \| 0.544189 / 0.293841 (0.250348) \| 0.049419 / 0.128546 (-0.079127) \| 0.014033 / 0.075646 (-0.061613) \| 0.370406 / 0.419271 (-0.048866) \| 0.083468 / 0.043533 (0.039935) \| 0.463829 / 0.255139 (0.208690) \| 0.507516 / 0.283200 (0.224316) \| 0.053266 / 0.141683 (-0.088417) \| 1.778680 / 1.452155 (0.326525) \| 1.916616 / 1.492716 (0.423900) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.267646 / 0.018006 (0.249640) \| 0.617824 / 0.000490 (0.617334) \| 0.007720 / 0.000200 (0.007520) \| 0.000139 / 0.000054 (0.000085) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034464 / 0.037411 (-0.002948) \| 0.113626 / 0.014526 (0.099100) \| 0.118911 / 0.176557 (-0.057646) \| 0.194701 / 0.737135 (-0.542434) \| 0.123431 / 0.296338 (-0.172907) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.606073 / 0.215209 (0.390863) \| 6.086393 / 2.077655 (4.008738) \| 2.568712 / 1.504120 (1.064593) \| 2.260801 / 1.541195 (0.719606) \| 2.411798 / 1.468490 (0.943307) \| 0.876433 / 4.584777 (-3.708344) \| 5.521280 / 3.745712 (1.775568) \| 5.969722 / 5.269862 (0.699861) \| 3.671028 / 4.565676 (-0.894649) \| 0.097082 / 0.424275 (-0.327193) \| 0.011354 / 0.007607 (0.003747) \| 0.713842 / 0.226044 (0.487798) \| 7.291172 / 2.268929 (5.022244) \| 3.315272 / 55.444624 (-52.129352) \| 2.777487 / 6.876477 (-4.098990) \| 3.025449 / 2.142072 (0.883377) \| 1.014115 / 4.805227 (-3.791112) \| 0.217928 / 6.500664 (-6.282736) \| 0.083097 / 0.075469 (0.007627) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.640060 / 1.841788 (-0.201728) \| 25.342172 / 8.074308 (17.267864) \| 22.776510 / 10.191392 (12.585118) \| 0.227300 / 0.680424 (-0.453124) \| 0.032233 / 0.534201 (-0.501968) \| 0.507547 / 0.579283 (-0.071736) \| 0.647044 / 0.434364 (0.212680) \| 0.607019 / 0.540337 (0.066682) \| 0.823548 / 1.386936 (-0.563388) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009576 / 0.011353 (-0.001777) \| 0.009322 / 0.011008 (-0.001687) \| 0.087184 / 0.038508 (0.048676) \| 0.100795 / 0.023109 (0.077685) \| 0.492138 / 0.275898 (0.216240) \| 0.528386 / 0.323480 (0.204906) \| 0.006689 / 0.007986 (-0.001296) \| 0.004735 / 0.004328 (0.000406) \| 0.085519 / 0.004250 (0.081269) \| 0.072648 / 0.037052 (0.035595) \| 0.496068 / 0.258489 (0.237579) \| 0.549634 / 0.293841 (0.255793) \| 0.049709 / 0.128546 (-0.078837) \| 0.015077 / 0.075646 (-0.060569) \| 0.099445 / 0.419271 (-0.319826) \| 0.068080 / 0.043533 (0.024547) \| 0.500426 / 0.255139 (0.245287) \| 0.531437 / 0.283200 (0.248238) \| 0.053176 / 0.141683 (-0.088507) \| 1.827942 / 1.452155 (0.375787) \| 1.914286 / 1.492716 (0.421570) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.247658 / 0.018006 (0.229652) \| 0.590805 / 0.000490 (0.590315) \| 0.005319 / 0.000200 (0.005119) \| 0.000165 / 0.000054 (0.000110) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.036993 / 0.037411 (-0.000418) \| 0.112944 / 0.014526 (0.098419) \| 0.118964 / 0.176557 (-0.057593) \| 0.194867 / 0.737135 (-0.542269) \| 0.120816 / 0.296338 (-0.175523) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.638062 / 0.215209 (0.422853) \| 6.246785 / 2.077655 (4.169130) \| 2.957779 / 1.504120 (1.453659) \| 2.739118 / 1.541195 (1.197924) \| 2.795362 / 1.468490 (1.326872) \| 0.890532 / 4.584777 (-3.694245) \| 5.508198 / 3.745712 (1.762486) \| 5.222315 / 5.269862 (-0.047547) \| 3.152731 / 4.565676 (-1.412946) \| 0.098344 / 0.424275 (-0.325931) \| 0.008800 / 0.007607 (0.001193) \| 0.757889 / 0.226044 (0.531845) \| 7.545715 / 2.268929 (5.276787) \| 3.694536 / 55.444624 (-51.750088) \| 3.112872 / 6.876477 (-3.763605) \| 3.182358 / 2.142072 (1.040285) \| 1.028171 / 4.805227 (-3.777056) \| 0.215223 / 6.500664 (-6.285441) \| 0.085856 / 0.075469 (0.010387) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.853138 / 1.841788 (0.011350) \| 25.939672 / 8.074308 (17.865364) \| 23.118029 / 10.191392 (12.926637) \| 0.250599 / 0.680424 (-0.429825) \| 0.029942 / 0.534201 (-0.504259) \| 0.508748 / 0.579283 (-0.070535) \| 0.593966 / 0.434364 (0.159602) \| 0.605499 / 0.540337 (0.065162) \| 0.863827 / 1.386936 (-0.523109) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5d15950d99677e9473cdcd31cfd83aa17e313e28 \"CML watermark\")\n" ]	2023-07-06T18:48:14	2023-07-07T14:13:00	2023-07-07T14:01:13	CONTRIBUTOR	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6009", "html_url": "https://github.com/huggingface/datasets/pull/6009", "diff_url": "https://github.com/huggingface/datasets/pull/6009.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6009.patch", "merged_at": "2023-07-07T14:01:13" }	Fix #5677	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6009/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6009/timeline	null	null	true
https://api.github.com/repos/huggingface/datasets/issues/6008	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6008/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6008/comments	https://api.github.com/repos/huggingface/datasets/issues/6008/events	https://github.com/huggingface/datasets/issues/6008	1,789,869,344	I_kwDODunzps5qrz0g	6,008	Dataset.from_generator consistently freezes at ~1000 rows	{ "login": "andreemic", "id": 27695722, "node_id": "MDQ6VXNlcjI3Njk1NzIy", "avatar_url": "https://avatars.githubusercontent.com/u/27695722?v=4", "gravatar_id": "", "url": "https://api.github.com/users/andreemic", "html_url": "https://github.com/andreemic", "followers_url": "https://api.github.com/users/andreemic/followers", "following_url": "https://api.github.com/users/andreemic/following{/other_user}", "gists_url": "https://api.github.com/users/andreemic/gists{/gist_id}", "starred_url": "https://api.github.com/users/andreemic/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/andreemic/subscriptions", "organizations_url": "https://api.github.com/users/andreemic/orgs", "repos_url": "https://api.github.com/users/andreemic/repos", "events_url": "https://api.github.com/users/andreemic/events{/privacy}", "received_events_url": "https://api.github.com/users/andreemic/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "By default, we write data to disk (so it can be memory-mapped) every 1000 rows/samples. You can control this with the `writer_batch_size` parameter. Also, when working with fixed-size arrays, the `ArrayXD` feature types yield better performance (e.g., in your case, `features=datasets.Features({\"i\": datasets.Array3D(shape=(512,512,3), dtype=\"float32\")})` should be faster).\r\n\r\nOur support for multi-dim arrays could be better, and we plan to improve it as part of https://github.com/huggingface/datasets/issues/5272.", "> By default, we write data to disk (so it can be memory-mapped) every 1000 rows/samples. You can control this with the `writer_batch_size` parameter. Also, when working with fixed-size arrays, the `ArrayXD` feature types yield better performance (e.g., in your case, `features=datasets.Features({\"i\": datasets.Array3D(shape=(512,512,3), dtype=\"float32\")})` should be faster).\r\n> \r\n> Our support for multi-dim arrays could be better, and we plan to improve it as part of #5272.\r\n\r\nThanks for the explanation! The Image array was just for demonstration, I use PIL Images in practice. Does that make a difference? What's the best approach for a dataset with PIL Images as rows?", "It's best to use the `datasets.Image()` feature type for PIL images (to save space) :)" ]	2023-07-05T16:06:48	2023-07-10T13:46:39	2023-07-10T13:46:39	NONE	null	null	null	### Describe the bug Whenever I try to create a dataset which contains images using `Dataset.from_generator`, it freezes around 996 rows. I suppose it has something to do with memory consumption, but there's more memory available. I Somehow it worked a few times but mostly this makes the datasets library much more cumbersome to work with because generators are the easiest way to turn an existing dataset into a Hugging Face dataset. I've let it run in the frozen state for way longer than it can possibly take to load the actual dataset. Let me know if you have ideas how to resolve it! ### Steps to reproduce the bug ```python from datasets import Dataset import numpy as np def gen(): for row in range(10000): yield {"i": np.random.rand(512, 512, 3)} Dataset.from_generator(gen) # -> 90% of the time gets stuck around 1000 rows ``` ### Expected behavior Should continue and go through all the examples yielded by the generator, or at least throw an error or somehow communicate what's going on. ### Environment info - `datasets` version: 2.8.0 - Platform: Linux-5.15.0-52-generic-x86_64-with-glibc2.29 - Python version: 3.8.10 - PyArrow version: 12.0.1 - Pandas version: 1.5.1	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6008/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6008/timeline	null	completed	false
https://api.github.com/repos/huggingface/datasets/issues/6007	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6007/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6007/comments	https://api.github.com/repos/huggingface/datasets/issues/6007/events	https://github.com/huggingface/datasets/issues/6007	1,789,782,693	I_kwDODunzps5qreql	6,007	Get an error "OverflowError: Python int too large to convert to C long" when loading a large dataset	{ "login": "silverriver", "id": 2529049, "node_id": "MDQ6VXNlcjI1MjkwNDk=", "avatar_url": "https://avatars.githubusercontent.com/u/2529049?v=4", "gravatar_id": "", "url": "https://api.github.com/users/silverriver", "html_url": "https://github.com/silverriver", "followers_url": "https://api.github.com/users/silverriver/followers", "following_url": "https://api.github.com/users/silverriver/following{/other_user}", "gists_url": "https://api.github.com/users/silverriver/gists{/gist_id}", "starred_url": "https://api.github.com/users/silverriver/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/silverriver/subscriptions", "organizations_url": "https://api.github.com/users/silverriver/orgs", "repos_url": "https://api.github.com/users/silverriver/repos", "events_url": "https://api.github.com/users/silverriver/events{/privacy}", "received_events_url": "https://api.github.com/users/silverriver/received_events", "type": "User", "site_admin": false }	[ { "id": 5705560427, "node_id": "LA_kwDODunzps8AAAABVBPxaw", "url": "https://api.github.com/repos/huggingface/datasets/labels/arrow", "name": "arrow", "color": "c2e0c6", "default": false, "description": "Related to Apache Arrow" } ]	open	false	null	[]	null	[ "This error means that one of the int32 (`Value(\"int32\")`) columns in the dataset has a value that is out of the valid (int32) range.\r\n\r\nI'll open a PR to print the name of a problematic column to make debugging such errors easier.", "I am afraid int32 is not the reason for this error.\r\n\r\nI have submitted a commit to use int64 for all ints in the dataset:\r\nhttps://huggingface.co/datasets/liwu/MNBVC/commit/857ac00d9eab96a6708ad6a82bd9001686042a9e\r\n\r\nand I have updated my env to the latest datasets release:\r\nCopy-and-paste the text below in your GitHub issue.\r\n\r\n- `datasets` version: 2.13.1\r\n- Platform: macOS-13.2.1-arm64-arm-64bit\r\n- Python version: 3.11.2\r\n- Huggingface_hub version: 0.13.4\r\n- PyArrow version: 11.0.0\r\n- Pandas version: 1.5.3\r\n\r\nBut the error still exist\r\n\r\n```\r\nDownloading and preparing dataset mnbvc/news_peoples_daily to /Users/silver/.cache/huggingface/datasets/liwu___mnbvc/news_peoples_daily/0.0.1/ee380f6309fe9b8b0d1fb14d77118f132444f22c8c4b28bf5c1645312688e051...\r\nDownloading data files: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 12/12 [00:00<00:00, 9070.40it/s]\r\nExtracting data files: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 12/12 [00:00<00:00, 2697.16it/s]\r\n---------------------------------------------------------------------------\r\nOverflowError Traceback (most recent call last)\r\nFile ~/git/venv/lib/python3.11/site-packages/datasets/builder.py:1647, in GeneratorBasedBuilder._prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, split_info, check_duplicate_keys, job_id)\r\n 1646 example = self.info.features.encode_example(record) if self.info.features is not None else record\r\n-> 1647 writer.write(example, key)\r\n 1648 num_examples_progress_update += 1\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/datasets/arrow_writer.py:490, in ArrowWriter.write(self, example, key, writer_batch_size)\r\n 488 self.hkey_record = []\r\n--> 490 self.write_examples_on_file()\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/datasets/arrow_writer.py:448, in ArrowWriter.write_examples_on_file(self)\r\n 444 batch_examples[col] = [\r\n 445 row[0][col].to_pylist()[0] if isinstance(row[0][col], (pa.Array, pa.ChunkedArray)) else row[0][col]\r\n 446 for row in self.current_examples\r\n 447 ]\r\n--> 448 self.write_batch(batch_examples=batch_examples)\r\n 449 self.current_examples = []\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/datasets/arrow_writer.py:553, in ArrowWriter.write_batch(self, batch_examples, writer_batch_size)\r\n 552 typed_sequence = OptimizedTypedSequence(col_values, type=col_type, try_type=col_try_type, col=col)\r\n--> 553 arrays.append(pa.array(typed_sequence))\r\n 554 inferred_features[col] = typed_sequence.get_inferred_type()\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/pyarrow/array.pxi:236, in pyarrow.lib.array()\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/pyarrow/array.pxi:110, in pyarrow.lib._handle_arrow_array_protocol()\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/datasets/arrow_writer.py:189, in TypedSequence.__arrow_array__(self, type)\r\n 188 trying_cast_to_python_objects = True\r\n--> 189 out = pa.array(cast_to_python_objects(data, only_1d_for_numpy=True))\r\n 190 # use smaller integer precisions if possible\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/pyarrow/array.pxi:320, in pyarrow.lib.array()\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/pyarrow/array.pxi:39, in pyarrow.lib._sequence_to_array()\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/pyarrow/error.pxi:144, in pyarrow.lib.pyarrow_internal_check_status()\r\n\r\nOverflowError: Python int too large to convert to C long\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nOverflowError Traceback (most recent call last)\r\nFile ~/git/venv/lib/python3.11/site-packages/datasets/builder.py:1656, in GeneratorBasedBuilder._prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, split_info, check_duplicate_keys, job_id)\r\n 1655 num_shards = shard_id + 1\r\n-> 1656 num_examples, num_bytes = writer.finalize()\r\n 1657 writer.close()\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/datasets/arrow_writer.py:584, in ArrowWriter.finalize(self, close_stream)\r\n 583 self.hkey_record = []\r\n--> 584 self.write_examples_on_file()\r\n 585 # If schema is known, infer features even if no examples were written\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/datasets/arrow_writer.py:448, in ArrowWriter.write_examples_on_file(self)\r\n 444 batch_examples[col] = [\r\n 445 row[0][col].to_pylist()[0] if isinstance(row[0][col], (pa.Array, pa.ChunkedArray)) else row[0][col]\r\n 446 for row in self.current_examples\r\n 447 ]\r\n--> 448 self.write_batch(batch_examples=batch_examples)\r\n 449 self.current_examples = []\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/datasets/arrow_writer.py:553, in ArrowWriter.write_batch(self, batch_examples, writer_batch_size)\r\n 552 typed_sequence = OptimizedTypedSequence(col_values, type=col_type, try_type=col_try_type, col=col)\r\n--> 553 arrays.append(pa.array(typed_sequence))\r\n 554 inferred_features[col] = typed_sequence.get_inferred_type()\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/pyarrow/array.pxi:236, in pyarrow.lib.array()\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/pyarrow/array.pxi:110, in pyarrow.lib._handle_arrow_array_protocol()\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/datasets/arrow_writer.py:189, in TypedSequence.__arrow_array__(self, type)\r\n 188 trying_cast_to_python_objects = True\r\n--> 189 out = pa.array(cast_to_python_objects(data, only_1d_for_numpy=True))\r\n 190 # use smaller integer precisions if possible\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/pyarrow/array.pxi:320, in pyarrow.lib.array()\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/pyarrow/array.pxi:39, in pyarrow.lib._sequence_to_array()\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/pyarrow/error.pxi:144, in pyarrow.lib.pyarrow_internal_check_status()\r\n\r\nOverflowError: Python int too large to convert to C long\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nDatasetGenerationError Traceback (most recent call last)\r\nCell In[2], line 1\r\n----> 1 dataset = load_dataset(\"liwu/MNBVC\", 'news_peoples_daily', split='train')\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/datasets/load.py:1809, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, num_proc, storage_options, config_kwargs)\r\n 1806 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES\r\n 1808 # Download and prepare data\r\n-> 1809 builder_instance.download_and_prepare(\r\n 1810 download_config=download_config,\r\n 1811 download_mode=download_mode,\r\n 1812 verification_mode=verification_mode,\r\n 1813 try_from_hf_gcs=try_from_hf_gcs,\r\n 1814 num_proc=num_proc,\r\n 1815 storage_options=storage_options,\r\n 1816 )\r\n 1818 # Build dataset for splits\r\n 1819 keep_in_memory = (\r\n 1820 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size)\r\n 1821 )\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/datasets/builder.py:909, in DatasetBuilder.download_and_prepare(self, output_dir, download_config, download_mode, verification_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, file_format, max_shard_size, num_proc, storage_options, download_and_prepare_kwargs)\r\n 907 if num_proc is not None:\r\n 908 prepare_split_kwargs[\"num_proc\"] = num_proc\r\n--> 909 self._download_and_prepare(\r\n 910 dl_manager=dl_manager,\r\n 911 verification_mode=verification_mode,\r\n 912 prepare_split_kwargs,\r\n 913 download_and_prepare_kwargs,\r\n 914 )\r\n 915 # Sync info\r\n 916 self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values())\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/datasets/builder.py:1670, in GeneratorBasedBuilder._download_and_prepare(self, dl_manager, verification_mode, prepare_splits_kwargs)\r\n 1669 def _download_and_prepare(self, dl_manager, verification_mode, prepare_splits_kwargs):\r\n-> 1670 super()._download_and_prepare(\r\n 1671 dl_manager,\r\n 1672 verification_mode,\r\n 1673 check_duplicate_keys=verification_mode == VerificationMode.BASIC_CHECKS\r\n 1674 or verification_mode == VerificationMode.ALL_CHECKS,\r\n 1675 prepare_splits_kwargs,\r\n 1676 )\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/datasets/builder.py:1004, in DatasetBuilder._download_and_prepare(self, dl_manager, verification_mode, prepare_split_kwargs)\r\n 1000 split_dict.add(split_generator.split_info)\r\n 1002 try:\r\n 1003 # Prepare split will record examples associated to the split\r\n-> 1004 self._prepare_split(split_generator, prepare_split_kwargs)\r\n 1005 except OSError as e:\r\n 1006 raise OSError(\r\n 1007 \"Cannot find data file. \"\r\n 1008 + (self.manual_download_instructions or \"\")\r\n 1009 + \"\\nOriginal error:\\n\"\r\n 1010 + str(e)\r\n 1011 ) from None\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/datasets/builder.py:1508, in GeneratorBasedBuilder._prepare_split(self, split_generator, check_duplicate_keys, file_format, num_proc, max_shard_size)\r\n 1506 job_id = 0\r\n 1507 with pbar:\r\n-> 1508 for job_id, done, content in self._prepare_split_single(\r\n 1509 gen_kwargs=gen_kwargs, job_id=job_id, _prepare_split_args\r\n 1510 ):\r\n 1511 if done:\r\n 1512 result = content\r\n\r\nFile ~/git/venv/lib/python3.11/site-packages/datasets/builder.py:1665, in GeneratorBasedBuilder._prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, split_info, check_duplicate_keys, job_id)\r\n 1663 if isinstance(e, SchemaInferenceError) and e.__context__ is not None:\r\n 1664 e = e.__context__\r\n-> 1665 raise DatasetGenerationError(\"An error occurred while generating the dataset\") from e\r\n 1667 yield job_id, True, (total_num_examples, total_num_bytes, writer._features, num_shards, shard_lengths)\r\n\r\nDatasetGenerationError: An error occurred while generating the dataset\r\n```\r\n\r\nBesides, it works fine when I am using streamed dataset.", "`simhash` is the problematic column - it has values such as `18329103420363166823` that are out of the int64 range. You can fix this by setting the feature type to `Value(\"string\")` (it's advised to use this type for hash values in general)\r\n\r\n> Besides, it works fine when I am using streamed dataset.\r\n\r\nStreaming yields Python dictionaries from the script without converting them to the Arrow representation, as this conversion step is not that cheap performance-wise.", "i am using uint64 for simhash\r\n\r\nuint64 ranges up to about 3.69E19.\r\n\r\n18329103420363166823 is less than this value.\r\n\r\nmoreover, our simhash algorithm use 64 bits. it should fit in uint64.\r\n\r\n\r\n\r\n", "You are right. I overlooked the feature type.\r\n\r\nThis is a reproducer:\r\n```python\r\nimport pyarrow as pa\r\nfrom datasets.arrow_writer import TypedSequence\r\n\r\npa.array(TypedSequence([18329103420363166823], type=Value(\"uint64\")))\r\n```\r\n\r\n`pa.array([18329103420363166823])` also fails with the same error, so it seems PyArrow does not always infer the correct type as NumPy does (`uint64` in this case).\r\n\r\nI'll report this issue in the Arrow repo.\r\n\r\n`pa.array([18329103420363166823], pa.uint64)` works, so maybe we can implement a temporary fix (supporting complex input such as `[{\"image\": pil_image, \"num\": uint64_value}]` would be hard though).\r\n\r\nIn the meantime, you should be able to bypass this error by returning the `simhash` values as NumPy scalars in the script:\r\n```python\r\ndef _generate_examples(self, ...):\r\n ...\r\n yield {..., \"simhash\": np.uint64(simhash), ...}\r\n```", "Thank you for checking this issue in detail.\r\n\r\nHowever, it seems that using `np.uint64(simhash)` does not work. The same issue still exists.\r\n\r\nhttps://huggingface.co/datasets/liwu/MNBVC/commit/1e44f1e400b7e61052647d44c99cdae3bae9c830\r\n\r\nAnyway, we decide to use string type for these simhash values. Hope pyarrow can fix their bug soon.", "Arrow issue: https://github.com/apache/arrow/issues/36520" ]	2023-07-05T15:16:50	2023-07-10T19:11:17	null	CONTRIBUTOR	null	null	null	### Describe the bug When load a large dataset with the following code ```python from datasets import load_dataset dataset = load_dataset("liwu/MNBVC", 'news_peoples_daily', split='train') ``` We encountered the error: "OverflowError: Python int too large to convert to C long" The error look something like: ``` OverflowError: Python int too large to convert to C long During handling of the above exception, another exception occurred: OverflowError Traceback (most recent call last) <ipython-input-7-0ed8700e662d> in <module> ----> 1 dataset = load_dataset("liwu/MNBVC", 'news_peoples_daily', split='train', cache_dir='/sfs/MNBVC/.cache/') /sfs/MNBVC/venv/lib64/python3.6/site-packages/datasets/load.py in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, config_kwargs) 1749 ignore_verifications=ignore_verifications, 1750 try_from_hf_gcs=try_from_hf_gcs, -> 1751 use_auth_token=use_auth_token, 1752 ) 1753 /sfs/MNBVC/venv/lib64/python3.6/site-packages/datasets/builder.py in download_and_prepare(self, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, download_and_prepare_kwargs) 703 if not downloaded_from_gcs: 704 self._download_and_prepare( --> 705 dl_manager=dl_manager, verify_infos=verify_infos, download_and_prepare_kwargs 706 ) 707 # Sync info /sfs/MNBVC/venv/lib64/python3.6/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos) 1225 1226 def _download_and_prepare(self, dl_manager, verify_infos): -> 1227 super()._download_and_prepare(dl_manager, verify_infos, check_duplicate_keys=verify_infos) 1228 1229 def _get_examples_iterable_for_split(self, split_generator: SplitGenerator) -> ExamplesIterable: /sfs/MNBVC/venv/lib64/python3.6/site-packages/datasets/builder.py in _download_and_prepare(self, dl_manager, verify_infos, prepare_split_kwargs) 791 try: 792 # Prepare split will record examples associated to the split --> 793 self._prepare_split(split_generator, **prepare_split_kwargs) 794 except OSError as e: 795 raise OSError( /sfs/MNBVC/venv/lib64/python3.6/site-packages/datasets/builder.py in _prepare_split(self, split_generator, check_duplicate_keys) 1219 writer.write(example, key) 1220 finally: -> 1221 num_examples, num_bytes = writer.finalize() 1222 1223 split_generator.split_info.num_examples = num_examples /sfs/MNBVC/venv/lib64/python3.6/site-packages/datasets/arrow_writer.py in finalize(self, close_stream) 536 # Re-intializing to empty list for next batch 537 self.hkey_record = [] --> 538 self.write_examples_on_file() 539 if self.pa_writer is None: 540 if self.schema: /sfs/MNBVC/venv/lib64/python3.6/site-packages/datasets/arrow_writer.py in write_examples_on_file(self) 407 # Since current_examples contains (example, key) tuples 408 batch_examples[col] = [row[0][col] for row in self.current_examples] --> 409 self.write_batch(batch_examples=batch_examples) 410 self.current_examples = [] 411 /sfs/MNBVC/venv/lib64/python3.6/site-packages/datasets/arrow_writer.py in write_batch(self, batch_examples, writer_batch_size) 506 col_try_type = try_features[col] if try_features is not None and col in try_features else None 507 typed_sequence = OptimizedTypedSequence(batch_examples[col], type=col_type, try_type=col_try_type, col=col) --> 508 arrays.append(pa.array(typed_sequence)) 509 inferred_features[col] = typed_sequence.get_inferred_type() 510 schema = inferred_features.arrow_schema if self.pa_writer is None else self.schema /sfs/MNBVC/venv/lib64/python3.6/site-packages/pyarrow/array.pxi in pyarrow.lib.array() /sfs/MNBVC/venv/lib64/python3.6/site-packages/pyarrow/array.pxi in pyarrow.lib._handle_arrow_array_protocol() /sfs/MNBVC/venv/lib64/python3.6/site-packages/datasets/arrow_writer.py in __arrow_array__(self, type) 180 else: 181 trying_cast_to_python_objects = True --> 182 out = pa.array(cast_to_python_objects(data, only_1d_for_numpy=True)) 183 # use smaller integer precisions if possible 184 if self.trying_int_optimization: /sfs/MNBVC/venv/lib64/python3.6/site-packages/pyarrow/array.pxi in pyarrow.lib.array() /sfs/MNBVC/venv/lib64/python3.6/site-packages/pyarrow/array.pxi in pyarrow.lib._sequence_to_array() /sfs/MNBVC/venv/lib64/python3.6/site-packages/pyarrow/error.pxi in pyarrow.lib.pyarrow_internal_check_status() OverflowError: Python int too large to convert to C long ``` However, that dataset can be loaded in a streaming manner: ```python from datasets import load_dataset dataset = load_dataset("liwu/MNBVC", 'news_peoples_daily', split='train', streaming=True) for i in dataset: pass # it work well ``` Another issue is reported in our dataset hub: https://huggingface.co/datasets/liwu/MNBVC/discussions/2 ### Steps to reproduce the bug from datasets import load_dataset dataset = load_dataset("liwu/MNBVC", 'news_peoples_daily', split='train') ### Expected behavior the dataset can be safely loaded ### Environment info - `datasets` version: 2.4.0 - Platform: Linux-3.10.0-1160.an7.x86_64-x86_64-with-centos-7.9 - Python version: 3.6.8 - PyArrow version: 6.0.1 - Pandas version: 1.1.5	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6007/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6007/timeline	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6006	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6006/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6006/comments	https://api.github.com/repos/huggingface/datasets/issues/6006/events	https://github.com/huggingface/datasets/issues/6006	1,788,855,582	I_kwDODunzps5qn8Ue	6,006	NotADirectoryError when loading gigawords	{ "login": "xipq", "id": 115634163, "node_id": "U_kgDOBuRv8w", "avatar_url": "https://avatars.githubusercontent.com/u/115634163?v=4", "gravatar_id": "", "url": "https://api.github.com/users/xipq", "html_url": "https://github.com/xipq", "followers_url": "https://api.github.com/users/xipq/followers", "following_url": "https://api.github.com/users/xipq/following{/other_user}", "gists_url": "https://api.github.com/users/xipq/gists{/gist_id}", "starred_url": "https://api.github.com/users/xipq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/xipq/subscriptions", "organizations_url": "https://api.github.com/users/xipq/orgs", "repos_url": "https://api.github.com/users/xipq/repos", "events_url": "https://api.github.com/users/xipq/events{/privacy}", "received_events_url": "https://api.github.com/users/xipq/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "issue due to corrupted download files. resolved after cleaning download cache. sorry for any inconvinence." ]	2023-07-05T06:23:41	2023-07-05T06:31:02	2023-07-05T06:31:01	NONE	null	null	null	### Describe the bug got `NotADirectoryError` whtn loading gigawords dataset ### Steps to reproduce the bug When running ``` import datasets datasets.load_dataset('gigaword') ``` Got the following exception: ```bash Traceback (most recent call last): [0/1862] File "/home/x/.conda/envs/dataproc/lib/python3.8/site-packages/datasets/builder.py", line 1629, in _prepare_split_single for key, record in generator: File "/home/x/.cache/huggingface/modules/datasets_modules/datasets/gigaword/ea83a8b819190acac5f2dae011fad51dccf269a0604ec5dd24795b 64efb424b6/gigaword.py", line 115, in _generate_examples with open(src_path, encoding="utf-8") as f_d, open(tgt_path, encoding="utf-8") as f_s: File "/home/x/.conda/envs/dataproc/lib/python3.8/site-packages/datasets/streaming.py", line 71, in wrapper return function(args, use_auth_token=use_auth_token, kwargs) File "/home/x/.conda/envs/dataproc/lib/python3.8/site-packages/datasets/download/streaming_download_manager.py", line 493, in xope n return open(main_hop, mode, args, kwargs) NotADirectoryError: [Errno 20] Not a directory: '/home/x/.cache/huggingface/datasets/downloads/6da52431bb5124d90cf51a0187d2dbee9046e 89780c4be7599794a4f559048ec/org_data/train.src.txt' The above exception was the direct cause of the following exception: Traceback (most recent call last): File "gigaword.py", line 38, in <module> main() File "gigaword.py", line 35, in main train, dev, test = dataset.generate_k_shot_data(k=32, seed=seed, path="../data/") File "/home/x/MICL/preprocess/fewshot_gym_dataset.py", line 199, in generate_k_shot_data dataset = self.load_dataset() File "gigaword.py", line 29, in load_dataset return datasets.load_dataset('gigaword') File "/home/x/.conda/envs/dataproc/lib/python3.8/site-packages/datasets/load.py", line 1809, in load_dataset builder_instance.download_and_prepare( File "/home/x/.conda/envs/dataproc/lib/python3.8/site-packages/datasets/builder.py", line 909, in download_and_prepare self._download_and_prepare( File "/home/x/.conda/envs/dataproc/lib/python3.8/site-packages/datasets/builder.py", line 1670, in _download_and_prepare super()._download_and_prepare( File "/home/x/.conda/envs/dataproc/lib/python3.8/site-packages/datasets/builder.py", line 1004, in _download_and_prepare self._prepare_split(split_generator, prepare_split_kwargs) File "/home/x/.conda/envs/dataproc/lib/python3.8/site-packages/datasets/builder.py", line 1508, in _prepare_split for job_id, done, content in self._prepare_split_single( File "/home/x/.conda/envs/dataproc/lib/python3.8/site-packages/datasets/builder.py", line 1665, in _prepare_split_single raise DatasetGenerationError("An error occurred while generating the dataset") from e datasets.builder.DatasetGenerationError: An error occurred while generating the dataset ``` ### Expected behavior Download and process the dataset successfully ### Environment info - `datasets` version: 2.13.1 - Platform: Linux-5.0.0-1032-azure-x86_64-with-glibc2.10 - Python version: 3.8.0 - Huggingface_hub version: 0.15.1 - PyArrow version: 12.0.1 - Pandas version: 2.0.3	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6006/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6006/timeline	null	completed	false
https://api.github.com/repos/huggingface/datasets/issues/6005	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6005/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6005/comments	https://api.github.com/repos/huggingface/datasets/issues/6005/events	https://github.com/huggingface/datasets/pull/6005	1,788,103,576	PR_kwDODunzps5UoJ91	6,005	Drop Python 3.7 support	{ "login": "mariosasko", "id": 47462742, "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "gravatar_id": "", "url": "https://api.github.com/users/mariosasko", "html_url": "https://github.com/mariosasko", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "repos_url": "https://api.github.com/users/mariosasko/repos", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006152 / 0.011353 (-0.005200) \| 0.003916 / 0.011008 (-0.007092) \| 0.097355 / 0.038508 (0.058847) \| 0.037228 / 0.023109 (0.014119) \| 0.315753 / 0.275898 (0.039855) \| 0.387949 / 0.323480 (0.064470) \| 0.004804 / 0.007986 (-0.003181) \| 0.002975 / 0.004328 (-0.001353) \| 0.076932 / 0.004250 (0.072682) \| 0.053497 / 0.037052 (0.016445) \| 0.331143 / 0.258489 (0.072654) \| 0.388347 / 0.293841 (0.094506) \| 0.027535 / 0.128546 (-0.101011) \| 0.008509 / 0.075646 (-0.067137) \| 0.312639 / 0.419271 (-0.106632) \| 0.047212 / 0.043533 (0.003679) \| 0.316875 / 0.255139 (0.061736) \| 0.352191 / 0.283200 (0.068992) \| 0.021380 / 0.141683 (-0.120303) \| 1.541401 / 1.452155 (0.089247) \| 1.519420 / 1.492716 (0.026704) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.206332 / 0.018006 (0.188326) \| 0.412252 / 0.000490 (0.411762) \| 0.005119 / 0.000200 (0.004919) \| 0.000077 / 0.000054 (0.000022) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023856 / 0.037411 (-0.013556) \| 0.098216 / 0.014526 (0.083691) \| 0.106553 / 0.176557 (-0.070003) \| 0.168767 / 0.737135 (-0.568369) \| 0.109244 / 0.296338 (-0.187094) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.457580 / 0.215209 (0.242371) \| 4.583246 / 2.077655 (2.505591) \| 2.296356 / 1.504120 (0.792236) \| 2.096216 / 1.541195 (0.555021) \| 2.159086 / 1.468490 (0.690596) \| 0.557905 / 4.584777 (-4.026872) \| 3.345910 / 3.745712 (-0.399802) \| 1.767436 / 5.269862 (-3.502426) \| 1.021583 / 4.565676 (-3.544094) \| 0.067265 / 0.424275 (-0.357011) \| 0.011411 / 0.007607 (0.003804) \| 0.559841 / 0.226044 (0.333797) \| 5.586892 / 2.268929 (3.317963) \| 2.735520 / 55.444624 (-52.709104) \| 2.429393 / 6.876477 (-4.447084) \| 2.544901 / 2.142072 (0.402829) \| 0.667603 / 4.805227 (-4.137625) \| 0.136244 / 6.500664 (-6.364421) \| 0.066961 / 0.075469 (-0.008508) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.206529 / 1.841788 (-0.635259) \| 13.988306 / 8.074308 (5.913998) \| 13.481813 / 10.191392 (3.290421) \| 0.161901 / 0.680424 (-0.518523) \| 0.016850 / 0.534201 (-0.517351) \| 0.367657 / 0.579283 (-0.211626) \| 0.393343 / 0.434364 (-0.041021) \| 0.465288 / 0.540337 (-0.075050) \| 0.559888 / 1.386936 (-0.827048) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005956 / 0.011353 (-0.005397) \| 0.003734 / 0.011008 (-0.007274) \| 0.077841 / 0.038508 (0.039333) \| 0.036532 / 0.023109 (0.013422) \| 0.438923 / 0.275898 (0.163025) \| 0.490133 / 0.323480 (0.166653) \| 0.004651 / 0.007986 (-0.003335) \| 0.002881 / 0.004328 (-0.001448) \| 0.077868 / 0.004250 (0.073618) \| 0.051700 / 0.037052 (0.014647) \| 0.448018 / 0.258489 (0.189529) \| 0.500304 / 0.293841 (0.206464) \| 0.029051 / 0.128546 (-0.099496) \| 0.008498 / 0.075646 (-0.067148) \| 0.082932 / 0.419271 (-0.336339) \| 0.043665 / 0.043533 (0.000132) \| 0.431613 / 0.255139 (0.176474) \| 0.458749 / 0.283200 (0.175549) \| 0.021951 / 0.141683 (-0.119731) \| 1.556043 / 1.452155 (0.103888) \| 1.588391 / 1.492716 (0.095675) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.220674 / 0.018006 (0.202667) \| 0.415408 / 0.000490 (0.414918) \| 0.002613 / 0.000200 (0.002413) \| 0.000075 / 0.000054 (0.000020) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025548 / 0.037411 (-0.011863) \| 0.103633 / 0.014526 (0.089107) \| 0.115193 / 0.176557 (-0.061364) \| 0.163971 / 0.737135 (-0.573164) \| 0.114754 / 0.296338 (-0.181585) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.456823 / 0.215209 (0.241614) \| 4.569950 / 2.077655 (2.492296) \| 2.196339 / 1.504120 (0.692219) \| 1.985822 / 1.541195 (0.444628) \| 2.044083 / 1.468490 (0.575593) \| 0.567919 / 4.584777 (-4.016858) \| 3.397515 / 3.745712 (-0.348197) \| 1.741087 / 5.269862 (-3.528775) \| 1.041237 / 4.565676 (-3.524440) \| 0.068963 / 0.424275 (-0.355313) \| 0.011677 / 0.007607 (0.004070) \| 0.565010 / 0.226044 (0.338966) \| 5.625886 / 2.268929 (3.356957) \| 2.670658 / 55.444624 (-52.773967) \| 2.300279 / 6.876477 (-4.576198) \| 2.392178 / 2.142072 (0.250106) \| 0.680226 / 4.805227 (-4.125001) \| 0.139119 / 6.500664 (-6.361545) \| 0.067953 / 0.075469 (-0.007516) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.303280 / 1.841788 (-0.538507) \| 14.458686 / 8.074308 (6.384378) \| 14.409369 / 10.191392 (4.217977) \| 0.144581 / 0.680424 (-0.535843) \| 0.016634 / 0.534201 (-0.517567) \| 0.364607 / 0.579283 (-0.214676) \| 0.394521 / 0.434364 (-0.039843) \| 0.433417 / 0.540337 (-0.106921) \| 0.527127 / 1.386936 (-0.859809) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#04a36f9546484dceadb84a133c1a460281d018f8 \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006245 / 0.011353 (-0.005108) \| 0.003871 / 0.011008 (-0.007138) \| 0.098823 / 0.038508 (0.060315) \| 0.039853 / 0.023109 (0.016744) \| 0.314989 / 0.275898 (0.039091) \| 0.376733 / 0.323480 (0.053254) \| 0.004754 / 0.007986 (-0.003232) \| 0.002971 / 0.004328 (-0.001357) \| 0.078451 / 0.004250 (0.074201) \| 0.053160 / 0.037052 (0.016107) \| 0.324443 / 0.258489 (0.065954) \| 0.361488 / 0.293841 (0.067647) \| 0.027942 / 0.128546 (-0.100604) \| 0.008535 / 0.075646 (-0.067111) \| 0.315526 / 0.419271 (-0.103745) \| 0.045706 / 0.043533 (0.002174) \| 0.329614 / 0.255139 (0.074475) \| 0.336339 / 0.283200 (0.053139) \| 0.021278 / 0.141683 (-0.120405) \| 1.529710 / 1.452155 (0.077555) \| 1.566833 / 1.492716 (0.074116) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.215263 / 0.018006 (0.197257) \| 0.440320 / 0.000490 (0.439830) \| 0.002627 / 0.000200 (0.002427) \| 0.000075 / 0.000054 (0.000021) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023971 / 0.037411 (-0.013441) \| 0.100549 / 0.014526 (0.086023) \| 0.106995 / 0.176557 (-0.069561) \| 0.169630 / 0.737135 (-0.567505) \| 0.111614 / 0.296338 (-0.184724) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.424911 / 0.215209 (0.209702) \| 4.246920 / 2.077655 (2.169266) \| 1.923321 / 1.504120 (0.419202) \| 1.714795 / 1.541195 (0.173600) \| 1.772906 / 1.468490 (0.304416) \| 0.554676 / 4.584777 (-4.030101) \| 3.478896 / 3.745712 (-0.266816) \| 2.800494 / 5.269862 (-2.469368) \| 1.382630 / 4.565676 (-3.183047) \| 0.067271 / 0.424275 (-0.357004) \| 0.010967 / 0.007607 (0.003360) \| 0.526769 / 0.226044 (0.300725) \| 5.288564 / 2.268929 (3.019636) \| 2.337459 / 55.444624 (-53.107165) \| 1.999975 / 6.876477 (-4.876502) \| 2.102680 / 2.142072 (-0.039392) \| 0.672181 / 4.805227 (-4.133046) \| 0.135097 / 6.500664 (-6.365567) \| 0.066950 / 0.075469 (-0.008519) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.264365 / 1.841788 (-0.577423) \| 14.282440 / 8.074308 (6.208132) \| 14.220200 / 10.191392 (4.028808) \| 0.139055 / 0.680424 (-0.541369) \| 0.016681 / 0.534201 (-0.517520) \| 0.367936 / 0.579283 (-0.211348) \| 0.393959 / 0.434364 (-0.040404) \| 0.424438 / 0.540337 (-0.115900) \| 0.508065 / 1.386936 (-0.878872) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006514 / 0.011353 (-0.004839) \| 0.003890 / 0.011008 (-0.007118) \| 0.078871 / 0.038508 (0.040363) \| 0.038080 / 0.023109 (0.014971) \| 0.358282 / 0.275898 (0.082384) \| 0.430654 / 0.323480 (0.107174) \| 0.005712 / 0.007986 (-0.002273) \| 0.003030 / 0.004328 (-0.001299) \| 0.078636 / 0.004250 (0.074386) \| 0.057771 / 0.037052 (0.020719) \| 0.368814 / 0.258489 (0.110325) \| 0.437047 / 0.293841 (0.143206) \| 0.029470 / 0.128546 (-0.099076) \| 0.008523 / 0.075646 (-0.067124) \| 0.083334 / 0.419271 (-0.335938) \| 0.044505 / 0.043533 (0.000972) \| 0.357484 / 0.255139 (0.102345) \| 0.393839 / 0.283200 (0.110639) \| 0.023340 / 0.141683 (-0.118343) \| 1.561033 / 1.452155 (0.108878) \| 1.595560 / 1.492716 (0.102844) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.204149 / 0.018006 (0.186143) \| 0.442747 / 0.000490 (0.442257) \| 0.003105 / 0.000200 (0.002905) \| 0.000085 / 0.000054 (0.000030) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027002 / 0.037411 (-0.010409) \| 0.105595 / 0.014526 (0.091070) \| 0.108695 / 0.176557 (-0.067861) \| 0.163182 / 0.737135 (-0.573953) \| 0.114999 / 0.296338 (-0.181339) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.483713 / 0.215209 (0.268504) \| 4.836063 / 2.077655 (2.758409) \| 2.488072 / 1.504120 (0.983952) \| 2.289556 / 1.541195 (0.748361) \| 2.342912 / 1.468490 (0.874422) \| 0.565937 / 4.584777 (-4.018840) \| 3.479085 / 3.745712 (-0.266627) \| 1.770922 / 5.269862 (-3.498940) \| 1.046084 / 4.565676 (-3.519592) \| 0.067857 / 0.424275 (-0.356418) \| 0.011283 / 0.007607 (0.003676) \| 0.592966 / 0.226044 (0.366921) \| 5.932842 / 2.268929 (3.663914) \| 2.956252 / 55.444624 (-52.488372) \| 2.602704 / 6.876477 (-4.273772) \| 2.715625 / 2.142072 (0.573552) \| 0.674299 / 4.805227 (-4.130929) \| 0.136039 / 6.500664 (-6.364625) \| 0.067629 / 0.075469 (-0.007840) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.333734 / 1.841788 (-0.508054) \| 14.561943 / 8.074308 (6.487634) \| 14.455385 / 10.191392 (4.263993) \| 0.132020 / 0.680424 (-0.548404) \| 0.016893 / 0.534201 (-0.517308) \| 0.367146 / 0.579283 (-0.212137) \| 0.399623 / 0.434364 (-0.034741) \| 0.432658 / 0.540337 (-0.107680) \| 0.530475 / 1.386936 (-0.856461) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#18da5adb22b2b403b8d8ae673192746d2ed7e9f9 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006045 / 0.011353 (-0.005308) \| 0.003906 / 0.011008 (-0.007103) \| 0.097558 / 0.038508 (0.059050) \| 0.038827 / 0.023109 (0.015718) \| 0.393564 / 0.275898 (0.117666) \| 0.442459 / 0.323480 (0.118980) \| 0.004792 / 0.007986 (-0.003194) \| 0.002984 / 0.004328 (-0.001345) \| 0.076419 / 0.004250 (0.072169) \| 0.053606 / 0.037052 (0.016554) \| 0.409743 / 0.258489 (0.151254) \| 0.445753 / 0.293841 (0.151912) \| 0.027753 / 0.128546 (-0.100793) \| 0.008428 / 0.075646 (-0.067219) \| 0.310267 / 0.419271 (-0.109004) \| 0.057582 / 0.043533 (0.014049) \| 0.396624 / 0.255139 (0.141485) \| 0.416288 / 0.283200 (0.133089) \| 0.029048 / 0.141683 (-0.112635) \| 1.495362 / 1.452155 (0.043207) \| 1.546331 / 1.492716 (0.053615) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.203832 / 0.018006 (0.185826) \| 0.423649 / 0.000490 (0.423160) \| 0.004533 / 0.000200 (0.004333) \| 0.000076 / 0.000054 (0.000022) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023084 / 0.037411 (-0.014328) \| 0.100503 / 0.014526 (0.085977) \| 0.105058 / 0.176557 (-0.071499) \| 0.168506 / 0.737135 (-0.568629) \| 0.112019 / 0.296338 (-0.184320) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.425877 / 0.215209 (0.210668) \| 4.251278 / 2.077655 (2.173624) \| 1.931339 / 1.504120 (0.427219) \| 1.730578 / 1.541195 (0.189383) \| 1.750637 / 1.468490 (0.282147) \| 0.559307 / 4.584777 (-4.025470) \| 3.461665 / 3.745712 (-0.284047) \| 2.826959 / 5.269862 (-2.442903) \| 1.418448 / 4.565676 (-3.147229) \| 0.067881 / 0.424275 (-0.356394) \| 0.011394 / 0.007607 (0.003787) \| 0.533226 / 0.226044 (0.307181) \| 5.341849 / 2.268929 (3.072921) \| 2.367832 / 55.444624 (-53.076792) \| 2.027240 / 6.876477 (-4.849236) \| 2.095852 / 2.142072 (-0.046220) \| 0.673790 / 4.805227 (-4.131437) \| 0.136044 / 6.500664 (-6.364620) \| 0.066350 / 0.075469 (-0.009119) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.203740 / 1.841788 (-0.638048) \| 13.720879 / 8.074308 (5.646571) \| 13.405939 / 10.191392 (3.214547) \| 0.146792 / 0.680424 (-0.533632) \| 0.016844 / 0.534201 (-0.517357) \| 0.373455 / 0.579283 (-0.205828) \| 0.394596 / 0.434364 (-0.039768) \| 0.464715 / 0.540337 (-0.075623) \| 0.558931 / 1.386936 (-0.828005) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006118 / 0.011353 (-0.005235) \| 0.003817 / 0.011008 (-0.007191) \| 0.077494 / 0.038508 (0.038985) \| 0.037507 / 0.023109 (0.014398) \| 0.387030 / 0.275898 (0.111132) \| 0.437352 / 0.323480 (0.113872) \| 0.004810 / 0.007986 (-0.003176) \| 0.002935 / 0.004328 (-0.001394) \| 0.077143 / 0.004250 (0.072892) \| 0.053986 / 0.037052 (0.016933) \| 0.393164 / 0.258489 (0.134675) \| 0.449603 / 0.293841 (0.155762) \| 0.029303 / 0.128546 (-0.099244) \| 0.008481 / 0.075646 (-0.067165) \| 0.083363 / 0.419271 (-0.335908) \| 0.043877 / 0.043533 (0.000344) \| 0.378175 / 0.255139 (0.123036) \| 0.403996 / 0.283200 (0.120797) \| 0.021688 / 0.141683 (-0.119995) \| 1.541606 / 1.452155 (0.089452) \| 1.552996 / 1.492716 (0.060280) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.236759 / 0.018006 (0.218752) \| 0.416221 / 0.000490 (0.415732) \| 0.000862 / 0.000200 (0.000662) \| 0.000070 / 0.000054 (0.000016) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025543 / 0.037411 (-0.011868) \| 0.101731 / 0.014526 (0.087206) \| 0.108482 / 0.176557 (-0.068075) \| 0.160290 / 0.737135 (-0.576845) \| 0.111392 / 0.296338 (-0.184946) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.457767 / 0.215209 (0.242558) \| 4.565976 / 2.077655 (2.488321) \| 2.245413 / 1.504120 (0.741294) \| 2.031458 / 1.541195 (0.490264) \| 2.073193 / 1.468490 (0.604702) \| 0.560461 / 4.584777 (-4.024316) \| 3.422536 / 3.745712 (-0.323176) \| 2.977017 / 5.269862 (-2.292845) \| 1.377021 / 4.565676 (-3.188655) \| 0.068444 / 0.424275 (-0.355831) \| 0.011036 / 0.007607 (0.003429) \| 0.571501 / 0.226044 (0.345456) \| 5.702652 / 2.268929 (3.433723) \| 2.727132 / 55.444624 (-52.717492) \| 2.399269 / 6.876477 (-4.477208) \| 2.574281 / 2.142072 (0.432208) \| 0.682600 / 4.805227 (-4.122627) \| 0.136943 / 6.500664 (-6.363722) \| 0.067126 / 0.075469 (-0.008343) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.322196 / 1.841788 (-0.519592) \| 14.239509 / 8.074308 (6.165201) \| 14.235779 / 10.191392 (4.044387) \| 0.148262 / 0.680424 (-0.532162) \| 0.016566 / 0.534201 (-0.517635) \| 0.364034 / 0.579283 (-0.215249) \| 0.399157 / 0.434364 (-0.035207) \| 0.426348 / 0.540337 (-0.113990) \| 0.520804 / 1.386936 (-0.866132) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8f57aae06bd325d76cb70cb774450f3a66f169cf \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007808 / 0.011353 (-0.003545) \| 0.004706 / 0.011008 (-0.006303) \| 0.100530 / 0.038508 (0.062022) \| 0.052052 / 0.023109 (0.028943) \| 0.419300 / 0.275898 (0.143402) \| 0.488451 / 0.323480 (0.164971) \| 0.006350 / 0.007986 (-0.001636) \| 0.003875 / 0.004328 (-0.000453) \| 0.076489 / 0.004250 (0.072238) \| 0.077554 / 0.037052 (0.040502) \| 0.435863 / 0.258489 (0.177373) \| 0.483241 / 0.293841 (0.189400) \| 0.037518 / 0.128546 (-0.091028) \| 0.009857 / 0.075646 (-0.065789) \| 0.340933 / 0.419271 (-0.078339) \| 0.087046 / 0.043533 (0.043514) \| 0.410721 / 0.255139 (0.155582) \| 0.428995 / 0.283200 (0.145795) \| 0.041701 / 0.141683 (-0.099982) \| 1.821017 / 1.452155 (0.368862) \| 1.837021 / 1.492716 (0.344305) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.228444 / 0.018006 (0.210438) \| 0.480446 / 0.000490 (0.479956) \| 0.004963 / 0.000200 (0.004763) \| 0.000101 / 0.000054 (0.000046) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032485 / 0.037411 (-0.004926) \| 0.096500 / 0.014526 (0.081974) \| 0.111547 / 0.176557 (-0.065010) \| 0.178842 / 0.737135 (-0.558294) \| 0.111099 / 0.296338 (-0.185240) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.467159 / 0.215209 (0.251950) \| 4.701676 / 2.077655 (2.624021) \| 2.390560 / 1.504120 (0.886440) \| 2.197722 / 1.541195 (0.656528) \| 2.264705 / 1.468490 (0.796215) \| 0.568667 / 4.584777 (-4.016110) \| 4.200724 / 3.745712 (0.455012) \| 3.777625 / 5.269862 (-1.492236) \| 2.372451 / 4.565676 (-2.193225) \| 0.067562 / 0.424275 (-0.356714) \| 0.008947 / 0.007607 (0.001340) \| 0.556910 / 0.226044 (0.330865) \| 5.528927 / 2.268929 (3.259998) \| 2.902780 / 55.444624 (-52.541844) \| 2.507933 / 6.876477 (-4.368544) \| 2.734627 / 2.142072 (0.592554) \| 0.683305 / 4.805227 (-4.121922) \| 0.158288 / 6.500664 (-6.342376) \| 0.071252 / 0.075469 (-0.004217) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.487502 / 1.841788 (-0.354286) \| 22.193341 / 8.074308 (14.119033) \| 15.922607 / 10.191392 (5.731215) \| 0.172189 / 0.680424 (-0.508235) \| 0.021502 / 0.534201 (-0.512699) \| 0.471198 / 0.579283 (-0.108085) \| 0.475979 / 0.434364 (0.041615) \| 0.544675 / 0.540337 (0.004338) \| 0.756102 / 1.386936 (-0.630834) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007635 / 0.011353 (-0.003717) \| 0.004614 / 0.011008 (-0.006394) \| 0.075852 / 0.038508 (0.037344) \| 0.049700 / 0.023109 (0.026591) \| 0.425957 / 0.275898 (0.150059) \| 0.512590 / 0.323480 (0.189110) \| 0.006921 / 0.007986 (-0.001065) \| 0.003714 / 0.004328 (-0.000615) \| 0.075536 / 0.004250 (0.071286) \| 0.070206 / 0.037052 (0.033153) \| 0.455706 / 0.258489 (0.197217) \| 0.512231 / 0.293841 (0.218390) \| 0.036685 / 0.128546 (-0.091861) \| 0.009793 / 0.075646 (-0.065853) \| 0.084208 / 0.419271 (-0.335064) \| 0.065262 / 0.043533 (0.021729) \| 0.423761 / 0.255139 (0.168622) \| 0.456791 / 0.283200 (0.173591) \| 0.044539 / 0.141683 (-0.097144) \| 1.797029 / 1.452155 (0.344874) \| 1.864124 / 1.492716 (0.371408) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.366840 / 0.018006 (0.348834) \| 0.479254 / 0.000490 (0.478765) \| 0.070383 / 0.000200 (0.070183) \| 0.000762 / 0.000054 (0.000707) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034233 / 0.037411 (-0.003178) \| 0.103140 / 0.014526 (0.088614) \| 0.117099 / 0.176557 (-0.059457) \| 0.178532 / 0.737135 (-0.558603) \| 0.120092 / 0.296338 (-0.176247) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.492993 / 0.215209 (0.277784) \| 4.878776 / 2.077655 (2.801121) \| 2.566666 / 1.504120 (1.062547) \| 2.356383 / 1.541195 (0.815188) \| 2.454723 / 1.468490 (0.986233) \| 0.571432 / 4.584777 (-4.013345) \| 4.240554 / 3.745712 (0.494842) \| 7.509259 / 5.269862 (2.239398) \| 4.040294 / 4.565676 (-0.525382) \| 0.067409 / 0.424275 (-0.356866) \| 0.008657 / 0.007607 (0.001050) \| 0.585751 / 0.226044 (0.359707) \| 5.967668 / 2.268929 (3.698739) \| 3.195573 / 55.444624 (-52.249052) \| 2.839772 / 6.876477 (-4.036704) \| 2.806319 / 2.142072 (0.664246) \| 0.681502 / 4.805227 (-4.123725) \| 0.158673 / 6.500664 (-6.341991) \| 0.073224 / 0.075469 (-0.002245) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.623335 / 1.841788 (-0.218453) \| 22.490806 / 8.074308 (14.416498) \| 16.762435 / 10.191392 (6.571043) \| 0.180961 / 0.680424 (-0.499463) \| 0.022716 / 0.534201 (-0.511485) \| 0.472910 / 0.579283 (-0.106373) \| 0.471616 / 0.434364 (0.037252) \| 0.548192 / 0.540337 (0.007854) \| 0.734357 / 1.386936 (-0.652579) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c0498b47a00153d4730352b6595fc51ab054fb95 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005858 / 0.011353 (-0.005495) \| 0.003512 / 0.011008 (-0.007497) \| 0.079739 / 0.038508 (0.041231) \| 0.057736 / 0.023109 (0.034627) \| 0.317640 / 0.275898 (0.041742) \| 0.354157 / 0.323480 (0.030677) \| 0.004772 / 0.007986 (-0.003214) \| 0.002824 / 0.004328 (-0.001504) \| 0.063288 / 0.004250 (0.059037) \| 0.049542 / 0.037052 (0.012489) \| 0.323974 / 0.258489 (0.065485) \| 0.372149 / 0.293841 (0.078308) \| 0.026841 / 0.128546 (-0.101705) \| 0.007846 / 0.075646 (-0.067800) \| 0.262546 / 0.419271 (-0.156725) \| 0.051952 / 0.043533 (0.008420) \| 0.319439 / 0.255139 (0.064300) \| 0.343862 / 0.283200 (0.060663) \| 0.027021 / 0.141683 (-0.114662) \| 1.445211 / 1.452155 (-0.006944) \| 1.485006 / 1.492716 (-0.007711) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.183174 / 0.018006 (0.165167) \| 0.422794 / 0.000490 (0.422304) \| 0.004148 / 0.000200 (0.003948) \| 0.000067 / 0.000054 (0.000012) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023037 / 0.037411 (-0.014374) \| 0.071300 / 0.014526 (0.056775) \| 0.083022 / 0.176557 (-0.093535) \| 0.146215 / 0.737135 (-0.590920) \| 0.082549 / 0.296338 (-0.213789) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.422846 / 0.215209 (0.207637) \| 4.215280 / 2.077655 (2.137626) \| 2.256802 / 1.504120 (0.752682) \| 2.056867 / 1.541195 (0.515673) \| 2.102478 / 1.468490 (0.633988) \| 0.497552 / 4.584777 (-4.087225) \| 3.049716 / 3.745712 (-0.695996) \| 4.209227 / 5.269862 (-1.060635) \| 2.599947 / 4.565676 (-1.965730) \| 0.059131 / 0.424275 (-0.365144) \| 0.006459 / 0.007607 (-0.001148) \| 0.495047 / 0.226044 (0.269003) \| 4.952332 / 2.268929 (2.683404) \| 2.675260 / 55.444624 (-52.769365) \| 2.333223 / 6.876477 (-4.543254) \| 2.449573 / 2.142072 (0.307500) \| 0.583420 / 4.805227 (-4.221807) \| 0.125140 / 6.500664 (-6.375524) \| 0.060209 / 0.075469 (-0.015260) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.215033 / 1.841788 (-0.626755) \| 18.101107 / 8.074308 (10.026799) \| 13.489222 / 10.191392 (3.297830) \| 0.147122 / 0.680424 (-0.533302) \| 0.016567 / 0.534201 (-0.517634) \| 0.329909 / 0.579283 (-0.249374) \| 0.340952 / 0.434364 (-0.093412) \| 0.379166 / 0.540337 (-0.161172) \| 0.510767 / 1.386936 (-0.876169) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005942 / 0.011353 (-0.005411) \| 0.003628 / 0.011008 (-0.007380) \| 0.061975 / 0.038508 (0.023467) \| 0.058331 / 0.023109 (0.035221) \| 0.393277 / 0.275898 (0.117379) \| 0.410740 / 0.323480 (0.087261) \| 0.004546 / 0.007986 (-0.003440) \| 0.002826 / 0.004328 (-0.001503) \| 0.062216 / 0.004250 (0.057966) \| 0.049801 / 0.037052 (0.012748) \| 0.394070 / 0.258489 (0.135581) \| 0.414407 / 0.293841 (0.120566) \| 0.027161 / 0.128546 (-0.101385) \| 0.007901 / 0.075646 (-0.067746) \| 0.066778 / 0.419271 (-0.352493) \| 0.041354 / 0.043533 (-0.002179) \| 0.379432 / 0.255139 (0.124293) \| 0.402966 / 0.283200 (0.119766) \| 0.020279 / 0.141683 (-0.121404) \| 1.416986 / 1.452155 (-0.035169) \| 1.474335 / 1.492716 (-0.018382) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.226147 / 0.018006 (0.208140) \| 0.404361 / 0.000490 (0.403871) \| 0.000358 / 0.000200 (0.000158) \| 0.000054 / 0.000054 (-0.000000) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025105 / 0.037411 (-0.012306) \| 0.075849 / 0.014526 (0.061323) \| 0.084781 / 0.176557 (-0.091775) \| 0.137415 / 0.737135 (-0.599720) \| 0.086288 / 0.296338 (-0.210051) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.445925 / 0.215209 (0.230716) \| 4.453478 / 2.077655 (2.375823) \| 2.419048 / 1.504120 (0.914928) \| 2.246363 / 1.541195 (0.705168) \| 2.304022 / 1.468490 (0.835532) \| 0.499132 / 4.584777 (-4.085645) \| 3.001336 / 3.745712 (-0.744376) \| 2.902593 / 5.269862 (-2.367269) \| 1.819843 / 4.565676 (-2.745834) \| 0.057210 / 0.424275 (-0.367065) \| 0.006338 / 0.007607 (-0.001269) \| 0.523280 / 0.226044 (0.297236) \| 5.235969 / 2.268929 (2.967040) \| 2.897585 / 55.444624 (-52.547039) \| 2.541586 / 6.876477 (-4.334891) \| 2.564233 / 2.142072 (0.422160) \| 0.584714 / 4.805227 (-4.220513) \| 0.124611 / 6.500664 (-6.376053) \| 0.061774 / 0.075469 (-0.013695) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.349799 / 1.841788 (-0.491988) \| 18.225076 / 8.074308 (10.150768) \| 13.781518 / 10.191392 (3.590126) \| 0.130562 / 0.680424 (-0.549862) \| 0.016434 / 0.534201 (-0.517767) \| 0.331607 / 0.579283 (-0.247676) \| 0.343456 / 0.434364 (-0.090908) \| 0.380437 / 0.540337 (-0.159900) \| 0.522793 / 1.386936 (-0.864143) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f0a3dbbd2e7ace162346d95ec27db674e80c1e23 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.013721 / 0.011353 (0.002368) \| 0.005715 / 0.011008 (-0.005293) \| 0.090116 / 0.038508 (0.051608) \| 0.087185 / 0.023109 (0.064075) \| 0.427813 / 0.275898 (0.151915) \| 0.390614 / 0.323480 (0.067135) \| 0.006976 / 0.007986 (-0.001009) \| 0.004231 / 0.004328 (-0.000098) \| 0.078320 / 0.004250 (0.074070) \| 0.066235 / 0.037052 (0.029183) \| 0.439904 / 0.258489 (0.181415) \| 0.424119 / 0.293841 (0.130278) \| 0.050362 / 0.128546 (-0.078184) \| 0.014992 / 0.075646 (-0.060654) \| 0.293519 / 0.419271 (-0.125753) \| 0.066906 / 0.043533 (0.023373) \| 0.449657 / 0.255139 (0.194518) \| 0.393800 / 0.283200 (0.110600) \| 0.032258 / 0.141683 (-0.109425) \| 1.539534 / 1.452155 (0.087379) \| 1.675292 / 1.492716 (0.182576) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.210515 / 0.018006 (0.192508) \| 0.506817 / 0.000490 (0.506327) \| 0.001938 / 0.000200 (0.001738) \| 0.000118 / 0.000054 (0.000064) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026019 / 0.037411 (-0.011393) \| 0.080635 / 0.014526 (0.066109) \| 0.103050 / 0.176557 (-0.073507) \| 0.160597 / 0.737135 (-0.576538) \| 0.095844 / 0.296338 (-0.200495) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.506359 / 0.215209 (0.291150) \| 5.041586 / 2.077655 (2.963931) \| 2.198288 / 1.504120 (0.694168) \| 1.987544 / 1.541195 (0.446349) \| 1.866790 / 1.468490 (0.398300) \| 0.681642 / 4.584777 (-3.903135) \| 4.719306 / 3.745712 (0.973593) \| 7.669869 / 5.269862 (2.400008) \| 4.466082 / 4.565676 (-0.099595) \| 0.092974 / 0.424275 (-0.331301) \| 0.008196 / 0.007607 (0.000589) \| 0.707656 / 0.226044 (0.481612) \| 6.974507 / 2.268929 (4.705579) \| 3.254206 / 55.444624 (-52.190418) \| 2.499019 / 6.876477 (-4.377457) \| 2.509089 / 2.142072 (0.367017) \| 0.915952 / 4.805227 (-3.889276) \| 0.192119 / 6.500664 (-6.308545) \| 0.065473 / 0.075469 (-0.009996) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.309078 / 1.841788 (-0.532710) \| 19.660348 / 8.074308 (11.586040) \| 16.659582 / 10.191392 (6.468190) \| 0.194315 / 0.680424 (-0.486109) \| 0.027773 / 0.534201 (-0.506428) \| 0.401241 / 0.579283 (-0.178042) \| 0.515799 / 0.434364 (0.081435) \| 0.488772 / 0.540337 (-0.051566) \| 0.604790 / 1.386936 (-0.782146) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006823 / 0.011353 (-0.004530) \| 0.003940 / 0.011008 (-0.007068) \| 0.061533 / 0.038508 (0.023025) \| 0.065241 / 0.023109 (0.042132) \| 0.411790 / 0.275898 (0.135892) \| 0.475720 / 0.323480 (0.152241) \| 0.005376 / 0.007986 (-0.002609) \| 0.003433 / 0.004328 (-0.000895) \| 0.065703 / 0.004250 (0.061452) \| 0.050736 / 0.037052 (0.013683) \| 0.435890 / 0.258489 (0.177401) \| 0.436698 / 0.293841 (0.142857) \| 0.040357 / 0.128546 (-0.088189) \| 0.011578 / 0.075646 (-0.064069) \| 0.072831 / 0.419271 (-0.346440) \| 0.055698 / 0.043533 (0.012165) \| 0.408225 / 0.255139 (0.153086) \| 0.439551 / 0.283200 (0.156352) \| 0.030469 / 0.141683 (-0.111214) \| 1.443866 / 1.452155 (-0.008289) \| 1.502022 / 1.492716 (0.009306) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.290338 / 0.018006 (0.272332) \| 0.540726 / 0.000490 (0.540236) \| 0.003244 / 0.000200 (0.003044) \| 0.000170 / 0.000054 (0.000116) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030865 / 0.037411 (-0.006547) \| 0.090866 / 0.014526 (0.076340) \| 0.106224 / 0.176557 (-0.070332) \| 0.166583 / 0.737135 (-0.570553) \| 0.104448 / 0.296338 (-0.191891) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.518025 / 0.215209 (0.302816) \| 6.027065 / 2.077655 (3.949410) \| 2.671840 / 1.504120 (1.167720) \| 2.273949 / 1.541195 (0.732754) \| 2.414892 / 1.468490 (0.946402) \| 0.774318 / 4.584777 (-3.810459) \| 5.020364 / 3.745712 (1.274652) \| 4.146927 / 5.269862 (-1.122934) \| 2.584598 / 4.565676 (-1.981078) \| 0.089519 / 0.424275 (-0.334756) \| 0.009181 / 0.007607 (0.001574) \| 0.654467 / 0.226044 (0.428423) \| 6.421595 / 2.268929 (4.152666) \| 3.091589 / 55.444624 (-52.353036) \| 2.554798 / 6.876477 (-4.321679) \| 2.441354 / 2.142072 (0.299282) \| 0.943386 / 4.805227 (-3.861841) \| 0.173641 / 6.500664 (-6.327023) \| 0.072209 / 0.075469 (-0.003260) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.557147 / 1.841788 (-0.284641) \| 19.980747 / 8.074308 (11.906439) \| 17.816813 / 10.191392 (7.625421) \| 0.212078 / 0.680424 (-0.468346) \| 0.025435 / 0.534201 (-0.508766) \| 0.396200 / 0.579283 (-0.183084) \| 0.546249 / 0.434364 (0.111885) \| 0.459632 / 0.540337 (-0.080705) \| 0.616548 / 1.386936 (-0.770388) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#535e972a70a3d4f8490a7e1a77ac43d5a4ab2655 \"CML watermark\")\n" ]	2023-07-04T15:02:37	2023-07-06T15:32:41	2023-07-06T15:22:43	CONTRIBUTOR	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6005", "html_url": "https://github.com/huggingface/datasets/pull/6005", "diff_url": "https://github.com/huggingface/datasets/pull/6005.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6005.patch", "merged_at": "2023-07-06T15:22:43" }	`hfh` and `transformers` have dropped Python 3.7 support, so we should do the same :). (Based on the stats, it seems less than 10% of the users use `datasets` with Python 3.7)	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6005/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6005/timeline	null	null	true
https://api.github.com/repos/huggingface/datasets/issues/6004	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6004/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6004/comments	https://api.github.com/repos/huggingface/datasets/issues/6004/events	https://github.com/huggingface/datasets/pull/6004	1,786,636,368	PR_kwDODunzps5UjN2h	6,004	Misc improvements	{ "login": "mariosasko", "id": 47462742, "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "gravatar_id": "", "url": "https://api.github.com/users/mariosasko", "html_url": "https://github.com/mariosasko", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "repos_url": "https://api.github.com/users/mariosasko/repos", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006897 / 0.011353 (-0.004456) \| 0.004207 / 0.011008 (-0.006802) \| 0.104828 / 0.038508 (0.066320) \| 0.048054 / 0.023109 (0.024945) \| 0.373991 / 0.275898 (0.098093) \| 0.426740 / 0.323480 (0.103260) \| 0.005540 / 0.007986 (-0.002446) \| 0.003531 / 0.004328 (-0.000797) \| 0.079304 / 0.004250 (0.075053) \| 0.066996 / 0.037052 (0.029944) \| 0.370675 / 0.258489 (0.112186) \| 0.414154 / 0.293841 (0.120313) \| 0.031567 / 0.128546 (-0.096979) \| 0.008843 / 0.075646 (-0.066803) \| 0.357426 / 0.419271 (-0.061845) \| 0.067040 / 0.043533 (0.023508) \| 0.362384 / 0.255139 (0.107245) \| 0.376056 / 0.283200 (0.092856) \| 0.032985 / 0.141683 (-0.108697) \| 1.560603 / 1.452155 (0.108448) \| 1.619024 / 1.492716 (0.126308) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.229059 / 0.018006 (0.211053) \| 0.440513 / 0.000490 (0.440023) \| 0.004647 / 0.000200 (0.004447) \| 0.000085 / 0.000054 (0.000030) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029517 / 0.037411 (-0.007894) \| 0.120974 / 0.014526 (0.106448) \| 0.125070 / 0.176557 (-0.051486) \| 0.184695 / 0.737135 (-0.552441) \| 0.130244 / 0.296338 (-0.166095) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.436930 / 0.215209 (0.221721) \| 4.356118 / 2.077655 (2.278463) \| 2.049169 / 1.504120 (0.545049) \| 1.842898 / 1.541195 (0.301703) \| 1.918948 / 1.468490 (0.450458) \| 0.553573 / 4.584777 (-4.031204) \| 3.883195 / 3.745712 (0.137483) \| 3.209780 / 5.269862 (-2.060081) \| 1.551707 / 4.565676 (-3.013970) \| 0.068181 / 0.424275 (-0.356094) \| 0.012370 / 0.007607 (0.004762) \| 0.539899 / 0.226044 (0.313854) \| 5.380008 / 2.268929 (3.111079) \| 2.518178 / 55.444624 (-52.926446) \| 2.174190 / 6.876477 (-4.702286) \| 2.317812 / 2.142072 (0.175740) \| 0.674154 / 4.805227 (-4.131073) \| 0.149313 / 6.500664 (-6.351351) \| 0.068297 / 0.075469 (-0.007172) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.261426 / 1.841788 (-0.580362) \| 15.316378 / 8.074308 (7.242070) \| 13.573512 / 10.191392 (3.382120) \| 0.190022 / 0.680424 (-0.490401) \| 0.018697 / 0.534201 (-0.515504) \| 0.448122 / 0.579283 (-0.131161) \| 0.435044 / 0.434364 (0.000681) \| 0.550065 / 0.540337 (0.009728) \| 0.653547 / 1.386936 (-0.733389) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007116 / 0.011353 (-0.004237) \| 0.004375 / 0.011008 (-0.006633) \| 0.081793 / 0.038508 (0.043285) \| 0.047980 / 0.023109 (0.024871) \| 0.392185 / 0.275898 (0.116287) \| 0.462263 / 0.323480 (0.138783) \| 0.005574 / 0.007986 (-0.002412) \| 0.003552 / 0.004328 (-0.000776) \| 0.080413 / 0.004250 (0.076162) \| 0.065539 / 0.037052 (0.028487) \| 0.413137 / 0.258489 (0.154648) \| 0.467377 / 0.293841 (0.173536) \| 0.034386 / 0.128546 (-0.094160) \| 0.009183 / 0.075646 (-0.066464) \| 0.087542 / 0.419271 (-0.331730) \| 0.053954 / 0.043533 (0.010421) \| 0.385096 / 0.255139 (0.129957) \| 0.404900 / 0.283200 (0.121701) \| 0.025908 / 0.141683 (-0.115775) \| 1.550159 / 1.452155 (0.098005) \| 1.598794 / 1.492716 (0.106078) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.246222 / 0.018006 (0.228216) \| 0.441095 / 0.000490 (0.440605) \| 0.006863 / 0.000200 (0.006663) \| 0.000109 / 0.000054 (0.000055) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032179 / 0.037411 (-0.005233) \| 0.120112 / 0.014526 (0.105586) \| 0.129326 / 0.176557 (-0.047230) \| 0.184542 / 0.737135 (-0.552593) \| 0.135038 / 0.296338 (-0.161300) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.459002 / 0.215209 (0.243793) \| 4.580258 / 2.077655 (2.502604) \| 2.296689 / 1.504120 (0.792569) \| 2.104338 / 1.541195 (0.563143) \| 2.182896 / 1.468490 (0.714406) \| 0.546447 / 4.584777 (-4.038330) \| 3.854047 / 3.745712 (0.108335) \| 1.873829 / 5.269862 (-3.396032) \| 1.116484 / 4.565676 (-3.449193) \| 0.067158 / 0.424275 (-0.357117) \| 0.012035 / 0.007607 (0.004428) \| 0.556642 / 0.226044 (0.330597) \| 5.574436 / 2.268929 (3.305508) \| 2.828223 / 55.444624 (-52.616402) \| 2.519851 / 6.876477 (-4.356626) \| 2.668594 / 2.142072 (0.526521) \| 0.675989 / 4.805227 (-4.129238) \| 0.146075 / 6.500664 (-6.354589) \| 0.067788 / 0.075469 (-0.007681) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.345958 / 1.841788 (-0.495830) \| 15.672748 / 8.074308 (7.598440) \| 14.937583 / 10.191392 (4.746191) \| 0.163479 / 0.680424 (-0.516945) \| 0.018364 / 0.534201 (-0.515837) \| 0.433296 / 0.579283 (-0.145987) \| 0.432463 / 0.434364 (-0.001901) \| 0.512000 / 0.540337 (-0.028338) \| 0.619397 / 1.386936 (-0.767539) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0832d48a07ed00b406271f4b4439e6d54ae38ebf \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.010097 / 0.011353 (-0.001256) \| 0.005070 / 0.011008 (-0.005939) \| 0.118638 / 0.038508 (0.080130) \| 0.043651 / 0.023109 (0.020542) \| 0.356074 / 0.275898 (0.080176) \| 0.414578 / 0.323480 (0.091098) \| 0.005939 / 0.007986 (-0.002046) \| 0.004927 / 0.004328 (0.000598) \| 0.089545 / 0.004250 (0.085294) \| 0.067533 / 0.037052 (0.030481) \| 0.371550 / 0.258489 (0.113061) \| 0.417808 / 0.293841 (0.123967) \| 0.045186 / 0.128546 (-0.083361) \| 0.015763 / 0.075646 (-0.059883) \| 0.393304 / 0.419271 (-0.025967) \| 0.065123 / 0.043533 (0.021591) \| 0.345057 / 0.255139 (0.089918) \| 0.378809 / 0.283200 (0.095610) \| 0.033243 / 0.141683 (-0.108440) \| 1.679956 / 1.452155 (0.227802) \| 1.775456 / 1.492716 (0.282739) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.229723 / 0.018006 (0.211717) \| 0.554630 / 0.000490 (0.554140) \| 0.008729 / 0.000200 (0.008529) \| 0.000183 / 0.000054 (0.000129) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027284 / 0.037411 (-0.010128) \| 0.114741 / 0.014526 (0.100215) \| 0.129188 / 0.176557 (-0.047369) \| 0.189270 / 0.737135 (-0.547866) \| 0.126000 / 0.296338 (-0.170339) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.580417 / 0.215209 (0.365208) \| 5.829337 / 2.077655 (3.751683) \| 2.421191 / 1.504120 (0.917071) \| 2.063673 / 1.541195 (0.522479) \| 2.133427 / 1.468490 (0.664937) \| 0.830964 / 4.584777 (-3.753813) \| 5.107139 / 3.745712 (1.361427) \| 4.599451 / 5.269862 (-0.670410) \| 2.406502 / 4.565676 (-2.159175) \| 0.100422 / 0.424275 (-0.323853) \| 0.011850 / 0.007607 (0.004243) \| 0.741881 / 0.226044 (0.515836) \| 7.425689 / 2.268929 (5.156760) \| 3.068948 / 55.444624 (-52.375676) \| 2.496292 / 6.876477 (-4.380184) \| 2.566420 / 2.142072 (0.424348) \| 1.093084 / 4.805227 (-3.712144) \| 0.224106 / 6.500664 (-6.276558) \| 0.084549 / 0.075469 (0.009080) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.416315 / 1.841788 (-0.425473) \| 16.306901 / 8.074308 (8.232593) \| 19.792419 / 10.191392 (9.601027) \| 0.224223 / 0.680424 (-0.456201) \| 0.026385 / 0.534201 (-0.507816) \| 0.463460 / 0.579283 (-0.115823) \| 0.598385 / 0.434364 (0.164021) \| 0.543981 / 0.540337 (0.003644) \| 0.647454 / 1.386936 (-0.739482) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009470 / 0.011353 (-0.001883) \| 0.004800 / 0.011008 (-0.006208) \| 0.094276 / 0.038508 (0.055768) \| 0.045157 / 0.023109 (0.022048) \| 0.397302 / 0.275898 (0.121404) \| 0.474213 / 0.323480 (0.150733) \| 0.005826 / 0.007986 (-0.002160) \| 0.003724 / 0.004328 (-0.000605) \| 0.090060 / 0.004250 (0.085809) \| 0.066671 / 0.037052 (0.029618) \| 0.439560 / 0.258489 (0.181071) \| 0.468598 / 0.293841 (0.174757) \| 0.044549 / 0.128546 (-0.083997) \| 0.014000 / 0.075646 (-0.061646) \| 0.110457 / 0.419271 (-0.308815) \| 0.065898 / 0.043533 (0.022365) \| 0.408101 / 0.255139 (0.152962) \| 0.433473 / 0.283200 (0.150273) \| 0.038438 / 0.141683 (-0.103245) \| 1.767781 / 1.452155 (0.315626) \| 1.791575 / 1.492716 (0.298859) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.230257 / 0.018006 (0.212251) \| 0.492280 / 0.000490 (0.491790) \| 0.005110 / 0.000200 (0.004910) \| 0.000119 / 0.000054 (0.000065) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028854 / 0.037411 (-0.008557) \| 0.111702 / 0.014526 (0.097176) \| 0.122040 / 0.176557 (-0.054517) \| 0.179103 / 0.737135 (-0.558032) \| 0.128869 / 0.296338 (-0.167470) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.634795 / 0.215209 (0.419586) \| 6.204760 / 2.077655 (4.127105) \| 2.692479 / 1.504120 (1.188359) \| 2.324260 / 1.541195 (0.783066) \| 2.380640 / 1.468490 (0.912149) \| 0.887827 / 4.584777 (-3.696950) \| 5.251648 / 3.745712 (1.505935) \| 2.632767 / 5.269862 (-2.637095) \| 1.745721 / 4.565676 (-2.819955) \| 0.108364 / 0.424275 (-0.315911) \| 0.013409 / 0.007607 (0.005802) \| 0.783427 / 0.226044 (0.557383) \| 7.765144 / 2.268929 (5.496216) \| 3.340686 / 55.444624 (-52.103938) \| 2.715340 / 6.876477 (-4.161137) \| 2.768604 / 2.142072 (0.626531) \| 1.119746 / 4.805227 (-3.685481) \| 0.210804 / 6.500664 (-6.289860) \| 0.072600 / 0.075469 (-0.002869) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.517334 / 1.841788 (-0.324454) \| 17.046837 / 8.074308 (8.972529) \| 19.371090 / 10.191392 (9.179698) \| 0.194275 / 0.680424 (-0.486148) \| 0.026712 / 0.534201 (-0.507488) \| 0.462731 / 0.579283 (-0.116552) \| 0.568958 / 0.434364 (0.134595) \| 0.555707 / 0.540337 (0.015370) \| 0.663654 / 1.386936 (-0.723283) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5d20476b1d4c8e11e0ffafc1570cbf4bd19011cf \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006423 / 0.011353 (-0.004930) \| 0.003882 / 0.011008 (-0.007126) \| 0.082976 / 0.038508 (0.044468) \| 0.071281 / 0.023109 (0.048171) \| 0.311367 / 0.275898 (0.035469) \| 0.348228 / 0.323480 (0.024748) \| 0.005315 / 0.007986 (-0.002671) \| 0.003326 / 0.004328 (-0.001003) \| 0.064641 / 0.004250 (0.060391) \| 0.056134 / 0.037052 (0.019081) \| 0.314071 / 0.258489 (0.055582) \| 0.360534 / 0.293841 (0.066693) \| 0.030642 / 0.128546 (-0.097904) \| 0.008301 / 0.075646 (-0.067345) \| 0.285820 / 0.419271 (-0.133451) \| 0.069241 / 0.043533 (0.025708) \| 0.313995 / 0.255139 (0.058856) \| 0.336656 / 0.283200 (0.053457) \| 0.031686 / 0.141683 (-0.109997) \| 1.467627 / 1.452155 (0.015472) \| 1.536493 / 1.492716 (0.043777) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.196518 / 0.018006 (0.178512) \| 0.458235 / 0.000490 (0.457745) \| 0.005599 / 0.000200 (0.005399) \| 0.000088 / 0.000054 (0.000034) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027371 / 0.037411 (-0.010040) \| 0.080986 / 0.014526 (0.066460) \| 0.093296 / 0.176557 (-0.083260) \| 0.150592 / 0.737135 (-0.586543) \| 0.094150 / 0.296338 (-0.202188) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.379412 / 0.215209 (0.164202) \| 3.797927 / 2.077655 (1.720272) \| 1.830654 / 1.504120 (0.326534) \| 1.669569 / 1.541195 (0.128374) \| 1.746738 / 1.468490 (0.278248) \| 0.479536 / 4.584777 (-4.105241) \| 3.592867 / 3.745712 (-0.152845) \| 5.468098 / 5.269862 (0.198237) \| 3.268013 / 4.565676 (-1.297663) \| 0.056635 / 0.424275 (-0.367640) \| 0.007224 / 0.007607 (-0.000383) \| 0.456681 / 0.226044 (0.230636) \| 4.566736 / 2.268929 (2.297807) \| 2.362831 / 55.444624 (-53.081793) \| 1.965141 / 6.876477 (-4.911336) \| 2.156905 / 2.142072 (0.014833) \| 0.572543 / 4.805227 (-4.232684) \| 0.132203 / 6.500664 (-6.368461) \| 0.059254 / 0.075469 (-0.016215) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.256134 / 1.841788 (-0.585654) \| 19.905438 / 8.074308 (11.831130) \| 14.179556 / 10.191392 (3.988164) \| 0.168043 / 0.680424 (-0.512381) \| 0.018215 / 0.534201 (-0.515986) \| 0.392740 / 0.579283 (-0.186543) \| 0.398397 / 0.434364 (-0.035967) \| 0.463806 / 0.540337 (-0.076531) \| 0.616248 / 1.386936 (-0.770688) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006564 / 0.011353 (-0.004789) \| 0.003923 / 0.011008 (-0.007085) \| 0.063929 / 0.038508 (0.025421) \| 0.073780 / 0.023109 (0.050671) \| 0.360242 / 0.275898 (0.084344) \| 0.395078 / 0.323480 (0.071598) \| 0.005265 / 0.007986 (-0.002720) \| 0.003229 / 0.004328 (-0.001100) \| 0.064094 / 0.004250 (0.059843) \| 0.057468 / 0.037052 (0.020416) \| 0.369530 / 0.258489 (0.111041) \| 0.411159 / 0.293841 (0.117318) \| 0.031278 / 0.128546 (-0.097268) \| 0.008424 / 0.075646 (-0.067222) \| 0.070411 / 0.419271 (-0.348860) \| 0.048714 / 0.043533 (0.005181) \| 0.361280 / 0.255139 (0.106141) \| 0.382468 / 0.283200 (0.099269) \| 0.023059 / 0.141683 (-0.118624) \| 1.452369 / 1.452155 (0.000215) \| 1.519192 / 1.492716 (0.026475) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.223745 / 0.018006 (0.205739) \| 0.442086 / 0.000490 (0.441596) \| 0.000379 / 0.000200 (0.000179) \| 0.000055 / 0.000054 (0.000001) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030919 / 0.037411 (-0.006493) \| 0.088483 / 0.014526 (0.073958) \| 0.101165 / 0.176557 (-0.075391) \| 0.154332 / 0.737135 (-0.582804) \| 0.103030 / 0.296338 (-0.193309) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.414520 / 0.215209 (0.199311) \| 4.126754 / 2.077655 (2.049099) \| 2.142677 / 1.504120 (0.638557) \| 1.995300 / 1.541195 (0.454106) \| 2.101678 / 1.468490 (0.633188) \| 0.481099 / 4.584777 (-4.103678) \| 3.562813 / 3.745712 (-0.182900) \| 3.392463 / 5.269862 (-1.877399) \| 1.983943 / 4.565676 (-2.581734) \| 0.056594 / 0.424275 (-0.367681) \| 0.007216 / 0.007607 (-0.000391) \| 0.495085 / 0.226044 (0.269041) \| 4.955640 / 2.268929 (2.686712) \| 2.629434 / 55.444624 (-52.815191) \| 2.269577 / 6.876477 (-4.606900) \| 2.357708 / 2.142072 (0.215635) \| 0.612370 / 4.805227 (-4.192857) \| 0.131169 / 6.500664 (-6.369495) \| 0.061029 / 0.075469 (-0.014440) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.339438 / 1.841788 (-0.502350) \| 19.757611 / 8.074308 (11.683303) \| 14.246254 / 10.191392 (4.054862) \| 0.170750 / 0.680424 (-0.509674) \| 0.018192 / 0.534201 (-0.516009) \| 0.395693 / 0.579283 (-0.183590) \| 0.411003 / 0.434364 (-0.023361) \| 0.478531 / 0.540337 (-0.061806) \| 0.650291 / 1.386936 (-0.736645) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3e34d06d746688dd5d26e4c85517b7e1a2f361ca \"CML watermark\")\n" ]	2023-07-03T18:29:14	2023-07-06T17:04:11	2023-07-06T16:55:25	CONTRIBUTOR	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6004", "html_url": "https://github.com/huggingface/datasets/pull/6004", "diff_url": "https://github.com/huggingface/datasets/pull/6004.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6004.patch", "merged_at": "2023-07-06T16:55:25" }	Contains the following improvements: * fixes a "share dataset" link in README and modifies the "hosting" part in the disclaimer section * updates `Makefile` to also run the style checks on `utils` and `setup.py` * deletes a test for GH-hosted datasets (no longer supported) * deletes `convert_dataset.sh` (outdated) * aligns `utils/release.py` with `transformers` (the current version is outdated)	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6004/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6004/timeline	null	null	true
https://api.github.com/repos/huggingface/datasets/issues/6003	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6003/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6003/comments	https://api.github.com/repos/huggingface/datasets/issues/6003/events	https://github.com/huggingface/datasets/issues/6003	1,786,554,110	I_kwDODunzps5qfKb-	6,003	interleave_datasets & DataCollatorForLanguageModeling having a conflict ?	{ "login": "PonteIneptique", "id": 1929830, "node_id": "MDQ6VXNlcjE5Mjk4MzA=", "avatar_url": "https://avatars.githubusercontent.com/u/1929830?v=4", "gravatar_id": "", "url": "https://api.github.com/users/PonteIneptique", "html_url": "https://github.com/PonteIneptique", "followers_url": "https://api.github.com/users/PonteIneptique/followers", "following_url": "https://api.github.com/users/PonteIneptique/following{/other_user}", "gists_url": "https://api.github.com/users/PonteIneptique/gists{/gist_id}", "starred_url": "https://api.github.com/users/PonteIneptique/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/PonteIneptique/subscriptions", "organizations_url": "https://api.github.com/users/PonteIneptique/orgs", "repos_url": "https://api.github.com/users/PonteIneptique/repos", "events_url": "https://api.github.com/users/PonteIneptique/events{/privacy}", "received_events_url": "https://api.github.com/users/PonteIneptique/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	null	[]	2023-07-03T17:15:31	2023-07-03T17:15:31	null	NONE	null	null	null	### Describe the bug Hi everyone :) I have two local & custom datasets (1 "sentence" per line) which I split along the 95/5 lines for pre-training a Bert model. I use a modified version of `run_mlm.py` in order to be able to make use of `interleave_dataset`: - `tokenize()` runs fine - `group_text()` runs fine Everytime, on step 19, I get ```pytb File "env/lib/python3.9/site-packages/transformers/data/data_collator.py", line 779, in torch_mask_tokens inputs[indices_random] = random_words[indices_random] RuntimeError: Index put requires the source and destination dtypes match, got Float for the destination and Long for the source. ``` I tried: - training without interleave on dataset 1, it runs - training without interleave on dataset 2, it runs - training without `.to_iterable_dataset()`, it hangs then crash - training without group_text() and padding to max_length seemed to fix the issue, but who knows if this was just because it was an issue that would come much later in terms of steps. I might have coded something wrong, but I don't get what ### Steps to reproduce the bug I have this function: ```py def build_dataset(path: str, percent: str): dataset = load_dataset( "text", data_files={"train": [path]}, split=f"train[{percent}]" ) dataset = dataset.map( lambda examples: tokenize(examples["text"]), batched=True, num_proc=num_proc, ) dataset = dataset.map( group_texts, batched=True, num_proc=num_proc, desc=f"Grouping texts in chunks of {tokenizer.max_seq_length}", remove_columns=["text"] ) print(len(dataset)) return dataset.to_iterable_dataset() ``` I hardcoded group_text: ```py def group_texts(examples): # Concatenate all texts. concatenated_examples = {k: list(chain(examples[k])) for k in examples.keys()} total_length = len(concatenated_examples[list(examples.keys())[0]]) # We drop the small remainder, and if the total_length < max_seq_length we exclude this batch and return an empty dict. # We could add padding if the model supported it instead of this drop, you can customize this part to your needs. total_length = (total_length // 512) 512 # Split by chunks of max_len. result = { k: [t[i: i + 512] for i in range(0, total_length, 512)] for k, t in concatenated_examples.items() } # result = {k: [el for el in elements if el] for k, elements in result.items()} return result ``` And then I build datasets using the following code: ```py train1 = build_dataset("d1.txt", ":95%") train2 = build_dataset("d2.txt", ":95%") dev1 = build_dataset("d1.txt", "95%:") dev2 = build_dataset("d2.txt", "95%:") ``` and finally I run ```py train_dataset = interleave_datasets( [train1, train2], probabilities=[0.8, 0.2], seed=42 ) eval_dataset = interleave_datasets( [dev1, dev2], probabilities=[0.8, 0.2], seed=42 ) ``` Then I run the training part which remains mostly untouched: > CUDA_VISIBLE_DEVICES=1 python custom_dataset.py --model_type bert --per_device_train_batch_size 32 --do_train --output_dir /var/mlm/training-bert/model --max_seq_length 512 --save_steps 10000 --save_total_limit 3 --auto_find_batch_size --logging_dir ./logs-bert --learning_rate 0.0001 --do_train --num_train_epochs 25 --warmup_steps 10000 --max_step 45000 --fp16 ### Expected behavior The model should then train normally, but fails every time at the same step (19). printing the variables at `inputs[indices_random] = random_words[indices_random]` shows a magnificient empty tensor (, 32) [if I remember well] ### Environment info transformers[torch] 4.30.2 Ubuntu A100 0 CUDA 12 Driver Version: 525.116.04	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6003/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6003/timeline	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6002	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6002/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6002/comments	https://api.github.com/repos/huggingface/datasets/issues/6002/events	https://github.com/huggingface/datasets/pull/6002	1,786,053,060	PR_kwDODunzps5UhP-Z	6,002	Add KLUE-MRC metrics	{ "login": "ingyuseong", "id": 37537248, "node_id": "MDQ6VXNlcjM3NTM3MjQ4", "avatar_url": "https://avatars.githubusercontent.com/u/37537248?v=4", "gravatar_id": "", "url": "https://api.github.com/users/ingyuseong", "html_url": "https://github.com/ingyuseong", "followers_url": "https://api.github.com/users/ingyuseong/followers", "following_url": "https://api.github.com/users/ingyuseong/following{/other_user}", "gists_url": "https://api.github.com/users/ingyuseong/gists{/gist_id}", "starred_url": "https://api.github.com/users/ingyuseong/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/ingyuseong/subscriptions", "organizations_url": "https://api.github.com/users/ingyuseong/orgs", "repos_url": "https://api.github.com/users/ingyuseong/repos", "events_url": "https://api.github.com/users/ingyuseong/events{/privacy}", "received_events_url": "https://api.github.com/users/ingyuseong/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "The metrics API in `datasets` is deprecated as of version 2.0, and `evaulate` is our new library for metrics. You can add a new metric to it by following [these steps](https://huggingface.co/docs/evaluate/creating_and_sharing)." ]	2023-07-03T12:11:10	2023-07-09T11:57:20	2023-07-09T11:57:20	NONE	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6002", "html_url": "https://github.com/huggingface/datasets/pull/6002", "diff_url": "https://github.com/huggingface/datasets/pull/6002.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6002.patch", "merged_at": null }	## Metrics for KLUE-MRC (Korean Language Understanding Evaluation — Machine Reading Comprehension) Adding metrics for [KLUE-MRC](https://huggingface.co/datasets/klue). KLUE-MRC is very similar to SQuAD 2.0 but has a slightly different format which is why I added metrics for KLUE-MRC. Specifically, in the case of [LM Eval Harness](https://github.com/EleutherAI/lm-evaluation-harness), it leverages the scoring script of SQuAD to evaluate SQuAD 2.0 and KorQuAD. But the script isn't suitable for KLUE-MRC because KLUE-MRC is a bit different from SQuAD 2.0. And this is why I added the scoring script for KLUE-MRC. - [x] All tests passed - [x] Added a metric card (referred the metric card of SQuAD 2.0) - [x] Compatibility test with [LM Eval Harness](https://github.com/EleutherAI/lm-evaluation-harness) passed ### References - [KLUE: Korean Language Understanding Evaluation](https://datasets-benchmarks-proceedings.neurips.cc/paper_files/paper/2021/file/98dce83da57b0395e163467c9dae521b-Paper-round2.pdf) - [KLUE on Hugging Face Datasets](https://huggingface.co/datasets/klue) - #2416	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6002/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6002/timeline	null	null	true
https://api.github.com/repos/huggingface/datasets/issues/6001	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6001/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6001/comments	https://api.github.com/repos/huggingface/datasets/issues/6001/events	https://github.com/huggingface/datasets/pull/6001	1,782,516,627	PR_kwDODunzps5UVMMh	6,001	Align `column_names` type check with type hint in `sort`	{ "login": "mariosasko", "id": 47462742, "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "gravatar_id": "", "url": "https://api.github.com/users/mariosasko", "html_url": "https://github.com/mariosasko", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "repos_url": "https://api.github.com/users/mariosasko/repos", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006038 / 0.011353 (-0.005315) \| 0.003797 / 0.011008 (-0.007211) \| 0.097686 / 0.038508 (0.059178) \| 0.035235 / 0.023109 (0.012126) \| 0.317294 / 0.275898 (0.041396) \| 0.377682 / 0.323480 (0.054202) \| 0.003485 / 0.007986 (-0.004501) \| 0.003603 / 0.004328 (-0.000725) \| 0.077268 / 0.004250 (0.073017) \| 0.054649 / 0.037052 (0.017597) \| 0.322293 / 0.258489 (0.063804) \| 0.372277 / 0.293841 (0.078436) \| 0.027927 / 0.128546 (-0.100619) \| 0.008495 / 0.075646 (-0.067151) \| 0.313078 / 0.419271 (-0.106193) \| 0.046974 / 0.043533 (0.003441) \| 0.313848 / 0.255139 (0.058709) \| 0.338454 / 0.283200 (0.055255) \| 0.020462 / 0.141683 (-0.121221) \| 1.473027 / 1.452155 (0.020873) \| 1.539468 / 1.492716 (0.046752) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.221429 / 0.018006 (0.203423) \| 0.412044 / 0.000490 (0.411555) \| 0.005866 / 0.000200 (0.005666) \| 0.000075 / 0.000054 (0.000021) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022870 / 0.037411 (-0.014541) \| 0.099129 / 0.014526 (0.084603) \| 0.103463 / 0.176557 (-0.073094) \| 0.164969 / 0.737135 (-0.572166) \| 0.110000 / 0.296338 (-0.186339) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.431311 / 0.215209 (0.216102) \| 4.293562 / 2.077655 (2.215907) \| 1.961209 / 1.504120 (0.457089) \| 1.733680 / 1.541195 (0.192485) \| 1.793171 / 1.468490 (0.324681) \| 0.568566 / 4.584777 (-4.016211) \| 3.401794 / 3.745712 (-0.343918) \| 1.827949 / 5.269862 (-3.441913) \| 1.055963 / 4.565676 (-3.509714) \| 0.068459 / 0.424275 (-0.355816) \| 0.011586 / 0.007607 (0.003979) \| 0.533936 / 0.226044 (0.307891) \| 5.347637 / 2.268929 (3.078708) \| 2.378056 / 55.444624 (-53.066569) \| 2.032159 / 6.876477 (-4.844318) \| 2.159064 / 2.142072 (0.016991) \| 0.674528 / 4.805227 (-4.130699) \| 0.136859 / 6.500664 (-6.363805) \| 0.066629 / 0.075469 (-0.008840) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.218084 / 1.841788 (-0.623704) \| 14.141710 / 8.074308 (6.067402) \| 13.588415 / 10.191392 (3.397023) \| 0.155104 / 0.680424 (-0.525320) \| 0.017160 / 0.534201 (-0.517041) \| 0.375558 / 0.579283 (-0.203725) \| 0.386293 / 0.434364 (-0.048071) \| 0.459476 / 0.540337 (-0.080862) \| 0.548561 / 1.386936 (-0.838375) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005878 / 0.011353 (-0.005475) \| 0.003750 / 0.011008 (-0.007259) \| 0.077720 / 0.038508 (0.039212) \| 0.034955 / 0.023109 (0.011846) \| 0.357480 / 0.275898 (0.081582) \| 0.418210 / 0.323480 (0.094730) \| 0.004566 / 0.007986 (-0.003419) \| 0.002918 / 0.004328 (-0.001410) \| 0.076517 / 0.004250 (0.072266) \| 0.050202 / 0.037052 (0.013150) \| 0.368166 / 0.258489 (0.109677) \| 0.415681 / 0.293841 (0.121840) \| 0.029496 / 0.128546 (-0.099050) \| 0.008547 / 0.075646 (-0.067099) \| 0.083037 / 0.419271 (-0.336234) \| 0.045001 / 0.043533 (0.001468) \| 0.356503 / 0.255139 (0.101364) \| 0.383747 / 0.283200 (0.100547) \| 0.025071 / 0.141683 (-0.116612) \| 1.541985 / 1.452155 (0.089830) \| 1.594710 / 1.492716 (0.101994) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.204491 / 0.018006 (0.186484) \| 0.408686 / 0.000490 (0.408196) \| 0.002505 / 0.000200 (0.002305) \| 0.000082 / 0.000054 (0.000027) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024446 / 0.037411 (-0.012965) \| 0.101432 / 0.014526 (0.086906) \| 0.108105 / 0.176557 (-0.068452) \| 0.161195 / 0.737135 (-0.575940) \| 0.112671 / 0.296338 (-0.183667) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.459697 / 0.215209 (0.244488) \| 4.570071 / 2.077655 (2.492416) \| 2.211547 / 1.504120 (0.707427) \| 1.996651 / 1.541195 (0.455457) \| 2.015621 / 1.468490 (0.547131) \| 0.567423 / 4.584777 (-4.017354) \| 3.408027 / 3.745712 (-0.337685) \| 2.913824 / 5.269862 (-2.356038) \| 1.423223 / 4.565676 (-3.142453) \| 0.068740 / 0.424275 (-0.355535) \| 0.010997 / 0.007607 (0.003390) \| 0.567340 / 0.226044 (0.341296) \| 5.666280 / 2.268929 (3.397351) \| 2.804934 / 55.444624 (-52.639690) \| 2.430761 / 6.876477 (-4.445716) \| 2.451820 / 2.142072 (0.309748) \| 0.681926 / 4.805227 (-4.123301) \| 0.137761 / 6.500664 (-6.362903) \| 0.067173 / 0.075469 (-0.008296) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.329853 / 1.841788 (-0.511934) \| 14.436232 / 8.074308 (6.361924) \| 14.398645 / 10.191392 (4.207253) \| 0.147421 / 0.680424 (-0.533002) \| 0.016743 / 0.534201 (-0.517458) \| 0.364964 / 0.579283 (-0.214319) \| 0.387072 / 0.434364 (-0.047292) \| 0.423892 / 0.540337 (-0.116445) \| 0.521304 / 1.386936 (-0.865632) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a62b6ce65f718e9ff4189da86d160ae4bb197fc2 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006463 / 0.011353 (-0.004889) \| 0.003923 / 0.011008 (-0.007086) \| 0.102096 / 0.038508 (0.063588) \| 0.040230 / 0.023109 (0.017121) \| 0.384688 / 0.275898 (0.108789) \| 0.445574 / 0.323480 (0.122094) \| 0.003590 / 0.007986 (-0.004395) \| 0.004023 / 0.004328 (-0.000306) \| 0.080125 / 0.004250 (0.075875) \| 0.057406 / 0.037052 (0.020354) \| 0.395049 / 0.258489 (0.136560) \| 0.438065 / 0.293841 (0.144224) \| 0.028963 / 0.128546 (-0.099583) \| 0.008693 / 0.075646 (-0.066954) \| 0.317158 / 0.419271 (-0.102114) \| 0.047930 / 0.043533 (0.004397) \| 0.382442 / 0.255139 (0.127303) \| 0.410665 / 0.283200 (0.127466) \| 0.020127 / 0.141683 (-0.121555) \| 1.558554 / 1.452155 (0.106400) \| 1.590959 / 1.492716 (0.098242) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.208826 / 0.018006 (0.190820) \| 0.432037 / 0.000490 (0.431547) \| 0.006509 / 0.000200 (0.006309) \| 0.000285 / 0.000054 (0.000230) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023460 / 0.037411 (-0.013951) \| 0.099070 / 0.014526 (0.084545) \| 0.105771 / 0.176557 (-0.070785) \| 0.166683 / 0.737135 (-0.570452) \| 0.108755 / 0.296338 (-0.187583) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.424324 / 0.215209 (0.209115) \| 4.225696 / 2.077655 (2.148042) \| 1.910955 / 1.504120 (0.406835) \| 1.704493 / 1.541195 (0.163298) \| 1.782784 / 1.468490 (0.314293) \| 0.562927 / 4.584777 (-4.021850) \| 3.380163 / 3.745712 (-0.365550) \| 1.779641 / 5.269862 (-3.490221) \| 1.029134 / 4.565676 (-3.536543) \| 0.068325 / 0.424275 (-0.355950) \| 0.011528 / 0.007607 (0.003921) \| 0.530141 / 0.226044 (0.304097) \| 5.323443 / 2.268929 (3.054514) \| 2.346956 / 55.444624 (-53.097668) \| 2.013335 / 6.876477 (-4.863142) \| 2.118531 / 2.142072 (-0.023541) \| 0.675206 / 4.805227 (-4.130021) \| 0.135473 / 6.500664 (-6.365191) \| 0.064804 / 0.075469 (-0.010665) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.240179 / 1.841788 (-0.601608) \| 14.692449 / 8.074308 (6.618141) \| 13.672223 / 10.191392 (3.480831) \| 0.147748 / 0.680424 (-0.532676) \| 0.017119 / 0.534201 (-0.517082) \| 0.369481 / 0.579283 (-0.209802) \| 0.390133 / 0.434364 (-0.044231) \| 0.458768 / 0.540337 (-0.081569) \| 0.548989 / 1.386936 (-0.837947) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006319 / 0.011353 (-0.005034) \| 0.003975 / 0.011008 (-0.007033) \| 0.077886 / 0.038508 (0.039378) \| 0.038322 / 0.023109 (0.015213) \| 0.379851 / 0.275898 (0.103953) \| 0.456749 / 0.323480 (0.133269) \| 0.005320 / 0.007986 (-0.002665) \| 0.003135 / 0.004328 (-0.001194) \| 0.078272 / 0.004250 (0.074022) \| 0.059919 / 0.037052 (0.022866) \| 0.430062 / 0.258489 (0.171573) \| 0.477432 / 0.293841 (0.183591) \| 0.029713 / 0.128546 (-0.098833) \| 0.008704 / 0.075646 (-0.066942) \| 0.082488 / 0.419271 (-0.336784) \| 0.044667 / 0.043533 (0.001134) \| 0.354910 / 0.255139 (0.099771) \| 0.434637 / 0.283200 (0.151438) \| 0.026402 / 0.141683 (-0.115281) \| 1.528825 / 1.452155 (0.076671) \| 1.548209 / 1.492716 (0.055493) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.237988 / 0.018006 (0.219982) \| 0.420402 / 0.000490 (0.419913) \| 0.003098 / 0.000200 (0.002898) \| 0.000077 / 0.000054 (0.000023) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026253 / 0.037411 (-0.011159) \| 0.106137 / 0.014526 (0.091611) \| 0.110273 / 0.176557 (-0.066284) \| 0.165316 / 0.737135 (-0.571819) \| 0.115720 / 0.296338 (-0.180619) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.454244 / 0.215209 (0.239035) \| 4.526018 / 2.077655 (2.448364) \| 2.395985 / 1.504120 (0.891865) \| 2.234822 / 1.541195 (0.693627) \| 2.370235 / 1.468490 (0.901745) \| 0.567607 / 4.584777 (-4.017169) \| 3.650156 / 3.745712 (-0.095556) \| 3.360094 / 5.269862 (-1.909768) \| 1.415252 / 4.565676 (-3.150424) \| 0.068012 / 0.424275 (-0.356263) \| 0.011135 / 0.007607 (0.003528) \| 0.561967 / 0.226044 (0.335923) \| 5.621819 / 2.268929 (3.352890) \| 2.676912 / 55.444624 (-52.767712) \| 2.338306 / 6.876477 (-4.538171) \| 2.430888 / 2.142072 (0.288815) \| 0.684576 / 4.805227 (-4.120651) \| 0.138923 / 6.500664 (-6.361741) \| 0.069933 / 0.075469 (-0.005536) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.313383 / 1.841788 (-0.528405) \| 15.125088 / 8.074308 (7.050780) \| 14.801501 / 10.191392 (4.610109) \| 0.134235 / 0.680424 (-0.546189) \| 0.017058 / 0.534201 (-0.517143) \| 0.365166 / 0.579283 (-0.214117) \| 0.395415 / 0.434364 (-0.038949) \| 0.419355 / 0.540337 (-0.120983) \| 0.513411 / 1.386936 (-0.873525) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8b9649b3cfb49342e44873ce7e29e0c75eaf3efa \"CML watermark\")\n" ]	2023-06-30T13:15:50	2023-06-30T14:18:32	2023-06-30T14:11:24	CONTRIBUTOR	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6001", "html_url": "https://github.com/huggingface/datasets/pull/6001", "diff_url": "https://github.com/huggingface/datasets/pull/6001.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6001.patch", "merged_at": "2023-06-30T14:11:24" }	Fix #5998	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6001/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6001/timeline	null	null	true
https://api.github.com/repos/huggingface/datasets/issues/6000	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6000/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6000/comments	https://api.github.com/repos/huggingface/datasets/issues/6000/events	https://github.com/huggingface/datasets/pull/6000	1,782,456,878	PR_kwDODunzps5UU_FB	6,000	Pin `joblib` to avoid `joblibspark` test failures	{ "login": "mariosasko", "id": 47462742, "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "gravatar_id": "", "url": "https://api.github.com/users/mariosasko", "html_url": "https://github.com/mariosasko", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "repos_url": "https://api.github.com/users/mariosasko/repos", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006722 / 0.011353 (-0.004631) \| 0.004425 / 0.011008 (-0.006583) \| 0.100850 / 0.038508 (0.062341) \| 0.040816 / 0.023109 (0.017707) \| 0.348823 / 0.275898 (0.072925) \| 0.446285 / 0.323480 (0.122805) \| 0.005738 / 0.007986 (-0.002247) \| 0.003517 / 0.004328 (-0.000811) \| 0.078824 / 0.004250 (0.074574) \| 0.064695 / 0.037052 (0.027643) \| 0.389894 / 0.258489 (0.131405) \| 0.416107 / 0.293841 (0.122266) \| 0.028850 / 0.128546 (-0.099696) \| 0.009011 / 0.075646 (-0.066635) \| 0.323117 / 0.419271 (-0.096154) \| 0.049162 / 0.043533 (0.005629) \| 0.340144 / 0.255139 (0.085005) \| 0.382072 / 0.283200 (0.098872) \| 0.023160 / 0.141683 (-0.118523) \| 1.549218 / 1.452155 (0.097063) \| 1.581266 / 1.492716 (0.088550) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.293360 / 0.018006 (0.275353) \| 0.602189 / 0.000490 (0.601700) \| 0.004608 / 0.000200 (0.004408) \| 0.000082 / 0.000054 (0.000028) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028144 / 0.037411 (-0.009267) \| 0.107088 / 0.014526 (0.092562) \| 0.112188 / 0.176557 (-0.064369) \| 0.174669 / 0.737135 (-0.562466) \| 0.116359 / 0.296338 (-0.179980) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.422911 / 0.215209 (0.207702) \| 4.231524 / 2.077655 (2.153869) \| 1.906711 / 1.504120 (0.402591) \| 1.706841 / 1.541195 (0.165646) \| 1.792066 / 1.468490 (0.323576) \| 0.559221 / 4.584777 (-4.025556) \| 3.434280 / 3.745712 (-0.311433) \| 1.918714 / 5.269862 (-3.351148) \| 1.073070 / 4.565676 (-3.492606) \| 0.067891 / 0.424275 (-0.356384) \| 0.011927 / 0.007607 (0.004320) \| 0.530843 / 0.226044 (0.304799) \| 5.309213 / 2.268929 (3.040285) \| 2.439246 / 55.444624 (-53.005378) \| 2.101245 / 6.876477 (-4.775231) \| 2.177436 / 2.142072 (0.035363) \| 0.672150 / 4.805227 (-4.133077) \| 0.137571 / 6.500664 (-6.363093) \| 0.068343 / 0.075469 (-0.007126) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.265262 / 1.841788 (-0.576525) \| 14.988021 / 8.074308 (6.913713) \| 13.611677 / 10.191392 (3.420285) \| 0.171389 / 0.680424 (-0.509035) \| 0.017681 / 0.534201 (-0.516520) \| 0.377542 / 0.579283 (-0.201741) \| 0.399475 / 0.434364 (-0.034889) \| 0.469553 / 0.540337 (-0.070785) \| 0.561888 / 1.386936 (-0.825048) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006782 / 0.011353 (-0.004571) \| 0.004412 / 0.011008 (-0.006597) \| 0.078594 / 0.038508 (0.040086) \| 0.039930 / 0.023109 (0.016820) \| 0.371879 / 0.275898 (0.095981) \| 0.444910 / 0.323480 (0.121430) \| 0.005707 / 0.007986 (-0.002279) \| 0.003901 / 0.004328 (-0.000427) \| 0.080125 / 0.004250 (0.075875) \| 0.063977 / 0.037052 (0.026925) \| 0.382781 / 0.258489 (0.124292) \| 0.441791 / 0.293841 (0.147950) \| 0.030428 / 0.128546 (-0.098118) \| 0.009008 / 0.075646 (-0.066638) \| 0.084447 / 0.419271 (-0.334824) \| 0.044432 / 0.043533 (0.000899) \| 0.365686 / 0.255139 (0.110547) \| 0.394312 / 0.283200 (0.111113) \| 0.024508 / 0.141683 (-0.117175) \| 1.577020 / 1.452155 (0.124865) \| 1.630259 / 1.492716 (0.137543) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.307960 / 0.018006 (0.289953) \| 0.591473 / 0.000490 (0.590983) \| 0.008098 / 0.000200 (0.007898) \| 0.000110 / 0.000054 (0.000056) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029567 / 0.037411 (-0.007845) \| 0.112773 / 0.014526 (0.098247) \| 0.117362 / 0.176557 (-0.059194) \| 0.174293 / 0.737135 (-0.562843) \| 0.123156 / 0.296338 (-0.173182) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.457475 / 0.215209 (0.242266) \| 4.599067 / 2.077655 (2.521412) \| 2.262638 / 1.504120 (0.758518) \| 2.124943 / 1.541195 (0.583748) \| 2.339912 / 1.468490 (0.871422) \| 0.566264 / 4.584777 (-4.018513) \| 3.489261 / 3.745712 (-0.256451) \| 1.925151 / 5.269862 (-3.344711) \| 1.099389 / 4.565676 (-3.466287) \| 0.068232 / 0.424275 (-0.356043) \| 0.011660 / 0.007607 (0.004052) \| 0.571227 / 0.226044 (0.345183) \| 5.702059 / 2.268929 (3.433130) \| 2.837701 / 55.444624 (-52.606924) \| 2.605468 / 6.876477 (-4.271008) \| 2.818396 / 2.142072 (0.676323) \| 0.681856 / 4.805227 (-4.123371) \| 0.141401 / 6.500664 (-6.359263) \| 0.069728 / 0.075469 (-0.005741) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.354935 / 1.841788 (-0.486853) \| 15.437404 / 8.074308 (7.363095) \| 15.415193 / 10.191392 (5.223801) \| 0.153459 / 0.680424 (-0.526964) \| 0.017190 / 0.534201 (-0.517011) \| 0.367256 / 0.579283 (-0.212027) \| 0.392709 / 0.434364 (-0.041655) \| 0.426125 / 0.540337 (-0.114213) \| 0.522612 / 1.386936 (-0.864324) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#25ac13d8ab23e7d99252ce083a45e8333b6bbcdc \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009183 / 0.011353 (-0.002170) \| 0.005232 / 0.011008 (-0.005776) \| 0.120349 / 0.038508 (0.081841) \| 0.044715 / 0.023109 (0.021606) \| 0.361519 / 0.275898 (0.085621) \| 0.463702 / 0.323480 (0.140223) \| 0.005842 / 0.007986 (-0.002144) \| 0.004041 / 0.004328 (-0.000288) \| 0.096953 / 0.004250 (0.092703) \| 0.070593 / 0.037052 (0.033540) \| 0.409790 / 0.258489 (0.151301) \| 0.477452 / 0.293841 (0.183611) \| 0.045827 / 0.128546 (-0.082719) \| 0.014038 / 0.075646 (-0.061608) \| 0.421317 / 0.419271 (0.002045) \| 0.065276 / 0.043533 (0.021743) \| 0.360074 / 0.255139 (0.104935) \| 0.409147 / 0.283200 (0.125947) \| 0.032444 / 0.141683 (-0.109238) \| 1.739257 / 1.452155 (0.287102) \| 1.831408 / 1.492716 (0.338692) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.274852 / 0.018006 (0.256846) \| 0.596320 / 0.000490 (0.595830) \| 0.006399 / 0.000200 (0.006199) \| 0.000133 / 0.000054 (0.000079) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031400 / 0.037411 (-0.006012) \| 0.127052 / 0.014526 (0.112526) \| 0.134269 / 0.176557 (-0.042288) \| 0.225998 / 0.737135 (-0.511137) \| 0.150019 / 0.296338 (-0.146319) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.654202 / 0.215209 (0.438993) \| 6.216735 / 2.077655 (4.139081) \| 2.440214 / 1.504120 (0.936094) \| 2.150575 / 1.541195 (0.609380) \| 2.124790 / 1.468490 (0.656300) \| 0.923514 / 4.584777 (-3.661263) \| 5.556924 / 3.745712 (1.811212) \| 2.843886 / 5.269862 (-2.425975) \| 1.834232 / 4.565676 (-2.731444) \| 0.111735 / 0.424275 (-0.312540) \| 0.014823 / 0.007607 (0.007216) \| 0.820503 / 0.226044 (0.594459) \| 7.887737 / 2.268929 (5.618809) \| 3.120307 / 55.444624 (-52.324317) \| 2.405856 / 6.876477 (-4.470621) \| 2.411239 / 2.142072 (0.269167) \| 1.071283 / 4.805227 (-3.733944) \| 0.227738 / 6.500664 (-6.272926) \| 0.073516 / 0.075469 (-0.001953) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.531806 / 1.841788 (-0.309982) \| 18.547661 / 8.074308 (10.473353) \| 21.083922 / 10.191392 (10.892530) \| 0.241706 / 0.680424 (-0.438718) \| 0.034169 / 0.534201 (-0.500032) \| 0.497514 / 0.579283 (-0.081769) \| 0.599801 / 0.434364 (0.165437) \| 0.576465 / 0.540337 (0.036127) \| 0.673509 / 1.386936 (-0.713427) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007558 / 0.011353 (-0.003795) \| 0.005001 / 0.011008 (-0.006008) \| 0.093809 / 0.038508 (0.055301) \| 0.039792 / 0.023109 (0.016683) \| 0.456869 / 0.275898 (0.180971) \| 0.493370 / 0.323480 (0.169891) \| 0.005561 / 0.007986 (-0.002424) \| 0.003982 / 0.004328 (-0.000346) \| 0.085421 / 0.004250 (0.081170) \| 0.059817 / 0.037052 (0.022765) \| 0.468040 / 0.258489 (0.209550) \| 0.514853 / 0.293841 (0.221012) \| 0.044267 / 0.128546 (-0.084279) \| 0.012674 / 0.075646 (-0.062972) \| 0.098324 / 0.419271 (-0.320948) \| 0.056604 / 0.043533 (0.013071) \| 0.432200 / 0.255139 (0.177061) \| 0.459812 / 0.283200 (0.176612) \| 0.033872 / 0.141683 (-0.107811) \| 1.618576 / 1.452155 (0.166421) \| 1.676562 / 1.492716 (0.183846) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.230625 / 0.018006 (0.212619) \| 0.600558 / 0.000490 (0.600068) \| 0.003419 / 0.000200 (0.003219) \| 0.000113 / 0.000054 (0.000059) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026916 / 0.037411 (-0.010496) \| 0.103003 / 0.014526 (0.088478) \| 0.117078 / 0.176557 (-0.059478) \| 0.169359 / 0.737135 (-0.567776) \| 0.120305 / 0.296338 (-0.176034) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.616877 / 0.215209 (0.401668) \| 6.157232 / 2.077655 (4.079577) \| 2.869219 / 1.504120 (1.365099) \| 2.381410 / 1.541195 (0.840216) \| 2.417357 / 1.468490 (0.948867) \| 0.914947 / 4.584777 (-3.669830) \| 5.718526 / 3.745712 (1.972814) \| 2.757253 / 5.269862 (-2.512609) \| 1.794122 / 4.565676 (-2.771554) \| 0.108423 / 0.424275 (-0.315852) \| 0.013378 / 0.007607 (0.005771) \| 0.831067 / 0.226044 (0.605023) \| 8.478946 / 2.268929 (6.210018) \| 3.685937 / 55.444624 (-51.758687) \| 2.867472 / 6.876477 (-4.009005) \| 2.895975 / 2.142072 (0.753903) \| 1.137547 / 4.805227 (-3.667681) \| 0.213891 / 6.500664 (-6.286773) \| 0.075825 / 0.075469 (0.000356) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.621193 / 1.841788 (-0.220594) \| 17.322110 / 8.074308 (9.247802) \| 21.804016 / 10.191392 (11.612624) \| 0.243692 / 0.680424 (-0.436732) \| 0.030331 / 0.534201 (-0.503870) \| 0.492186 / 0.579283 (-0.087097) \| 0.632583 / 0.434364 (0.198219) \| 0.576265 / 0.540337 (0.035927) \| 0.713165 / 1.386936 (-0.673771) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a293ceb5aa41c4ae265c0e2aa9ada2d544466121 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008916 / 0.011353 (-0.002437) \| 0.004737 / 0.011008 (-0.006271) \| 0.134271 / 0.038508 (0.095763) \| 0.054472 / 0.023109 (0.031363) \| 0.380942 / 0.275898 (0.105044) \| 0.474138 / 0.323480 (0.150658) \| 0.007917 / 0.007986 (-0.000068) \| 0.003748 / 0.004328 (-0.000580) \| 0.092765 / 0.004250 (0.088515) \| 0.077873 / 0.037052 (0.040821) \| 0.397533 / 0.258489 (0.139043) \| 0.454737 / 0.293841 (0.160896) \| 0.039901 / 0.128546 (-0.088645) \| 0.010188 / 0.075646 (-0.065458) \| 0.447312 / 0.419271 (0.028040) \| 0.068684 / 0.043533 (0.025151) \| 0.371554 / 0.255139 (0.116415) \| 0.459655 / 0.283200 (0.176455) \| 0.027157 / 0.141683 (-0.114526) \| 1.874643 / 1.452155 (0.422488) \| 2.014800 / 1.492716 (0.522083) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.227079 / 0.018006 (0.209073) \| 0.483241 / 0.000490 (0.482751) \| 0.012404 / 0.000200 (0.012204) \| 0.000409 / 0.000054 (0.000354) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033135 / 0.037411 (-0.004277) \| 0.137782 / 0.014526 (0.123257) \| 0.142951 / 0.176557 (-0.033605) \| 0.209825 / 0.737135 (-0.527311) \| 0.152438 / 0.296338 (-0.143900) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.513066 / 0.215209 (0.297857) \| 5.122776 / 2.077655 (3.045121) \| 2.399270 / 1.504120 (0.895150) \| 2.180143 / 1.541195 (0.638949) \| 2.286395 / 1.468490 (0.817905) \| 0.641866 / 4.584777 (-3.942911) \| 4.694922 / 3.745712 (0.949210) \| 2.543390 / 5.269862 (-2.726472) \| 1.398592 / 4.565676 (-3.167084) \| 0.088662 / 0.424275 (-0.335613) \| 0.015854 / 0.007607 (0.008247) \| 0.688891 / 0.226044 (0.462847) \| 6.370148 / 2.268929 (4.101220) \| 2.949974 / 55.444624 (-52.494650) \| 2.538049 / 6.876477 (-4.338428) \| 2.699380 / 2.142072 (0.557308) \| 0.792670 / 4.805227 (-4.012557) \| 0.169126 / 6.500664 (-6.331538) \| 0.078511 / 0.075469 (0.003042) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.609119 / 1.841788 (-0.232669) \| 18.785069 / 8.074308 (10.710761) \| 16.670783 / 10.191392 (6.479391) \| 0.213081 / 0.680424 (-0.467343) \| 0.023904 / 0.534201 (-0.510296) \| 0.567720 / 0.579283 (-0.011564) \| 0.505806 / 0.434364 (0.071442) \| 0.649466 / 0.540337 (0.109129) \| 0.773174 / 1.386936 (-0.613762) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008036 / 0.011353 (-0.003317) \| 0.004808 / 0.011008 (-0.006201) \| 0.094316 / 0.038508 (0.055808) \| 0.056174 / 0.023109 (0.033065) \| 0.481618 / 0.275898 (0.205720) \| 0.565300 / 0.323480 (0.241820) \| 0.006339 / 0.007986 (-0.001646) \| 0.003950 / 0.004328 (-0.000379) \| 0.093389 / 0.004250 (0.089139) \| 0.076163 / 0.037052 (0.039111) \| 0.489013 / 0.258489 (0.230524) \| 0.565451 / 0.293841 (0.271611) \| 0.039392 / 0.128546 (-0.089155) \| 0.010553 / 0.075646 (-0.065093) \| 0.101406 / 0.419271 (-0.317865) \| 0.062355 / 0.043533 (0.018822) \| 0.470461 / 0.255139 (0.215322) \| 0.502574 / 0.283200 (0.219375) \| 0.030196 / 0.141683 (-0.111486) \| 1.893926 / 1.452155 (0.441771) \| 1.958902 / 1.492716 (0.466185) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.198074 / 0.018006 (0.180068) \| 0.476828 / 0.000490 (0.476338) \| 0.003457 / 0.000200 (0.003257) \| 0.000105 / 0.000054 (0.000051) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.037576 / 0.037411 (0.000165) \| 0.146663 / 0.014526 (0.132138) \| 0.152969 / 0.176557 (-0.023588) \| 0.218683 / 0.737135 (-0.518452) \| 0.161552 / 0.296338 (-0.134786) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.525988 / 0.215209 (0.310779) \| 5.234673 / 2.077655 (3.157018) \| 2.571668 / 1.504120 (1.067548) \| 2.339760 / 1.541195 (0.798565) \| 2.422886 / 1.468490 (0.954395) \| 0.651537 / 4.584777 (-3.933240) \| 4.811148 / 3.745712 (1.065436) \| 4.451165 / 5.269862 (-0.818697) \| 2.016283 / 4.565676 (-2.549394) \| 0.096393 / 0.424275 (-0.327882) \| 0.015222 / 0.007607 (0.007615) \| 0.739132 / 0.226044 (0.513087) \| 6.813327 / 2.268929 (4.544399) \| 3.169018 / 55.444624 (-52.275606) \| 2.783120 / 6.876477 (-4.093356) \| 2.918979 / 2.142072 (0.776907) \| 0.797476 / 4.805227 (-4.007751) \| 0.171038 / 6.500664 (-6.329626) \| 0.079878 / 0.075469 (0.004409) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.595082 / 1.841788 (-0.246705) \| 19.685844 / 8.074308 (11.611536) \| 17.518989 / 10.191392 (7.327597) \| 0.220015 / 0.680424 (-0.460409) \| 0.026351 / 0.534201 (-0.507850) \| 0.578977 / 0.579283 (-0.000306) \| 0.549564 / 0.434364 (0.115200) \| 0.667564 / 0.540337 (0.127227) \| 0.802121 / 1.386936 (-0.584815) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e9aee64766aaddfda60a735cfc93345aed64bdcf \"CML watermark\")\n" ]	2023-06-30T12:36:54	2023-06-30T13:17:05	2023-06-30T13:08:27	CONTRIBUTOR	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6000", "html_url": "https://github.com/huggingface/datasets/pull/6000", "diff_url": "https://github.com/huggingface/datasets/pull/6000.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6000.patch", "merged_at": "2023-06-30T13:08:27" }	`joblibspark` doesn't support the latest `joblib` release. See https://github.com/huggingface/datasets/actions/runs/5401870932/jobs/9812337078 for the errors	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6000/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6000/timeline	null	null	true
https://api.github.com/repos/huggingface/datasets/issues/5999	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5999/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5999/comments	https://api.github.com/repos/huggingface/datasets/issues/5999/events	https://github.com/huggingface/datasets/issues/5999	1,781,851,513	I_kwDODunzps5qNOV5	5,999	Getting a 409 error while loading xglue dataset	{ "login": "Praful932", "id": 45713796, "node_id": "MDQ6VXNlcjQ1NzEzNzk2", "avatar_url": "https://avatars.githubusercontent.com/u/45713796?v=4", "gravatar_id": "", "url": "https://api.github.com/users/Praful932", "html_url": "https://github.com/Praful932", "followers_url": "https://api.github.com/users/Praful932/followers", "following_url": "https://api.github.com/users/Praful932/following{/other_user}", "gists_url": "https://api.github.com/users/Praful932/gists{/gist_id}", "starred_url": "https://api.github.com/users/Praful932/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/Praful932/subscriptions", "organizations_url": "https://api.github.com/users/Praful932/orgs", "repos_url": "https://api.github.com/users/Praful932/repos", "events_url": "https://api.github.com/users/Praful932/events{/privacy}", "received_events_url": "https://api.github.com/users/Praful932/received_events", "type": "User", "site_admin": false }	[]	closed	false	{ "login": "albertvillanova", "id": 8515462, "node_id": "MDQ6VXNlcjg1MTU0NjI=", "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "gravatar_id": "", "url": "https://api.github.com/users/albertvillanova", "html_url": "https://github.com/albertvillanova", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "repos_url": "https://api.github.com/users/albertvillanova/repos", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "type": "User", "site_admin": false }	[ { "login": "albertvillanova", "id": 8515462, "node_id": "MDQ6VXNlcjg1MTU0NjI=", "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "gravatar_id": "", "url": "https://api.github.com/users/albertvillanova", "html_url": "https://github.com/albertvillanova", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "repos_url": "https://api.github.com/users/albertvillanova/repos", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "type": "User", "site_admin": false } ]	null	[ "Thanks for reporting, @Praful932.\r\n\r\nLet's continue the conversation on the Hub: https://huggingface.co/datasets/xglue/discussions/5" ]	2023-06-30T04:13:54	2023-06-30T05:57:23	2023-06-30T05:57:22	NONE	null	null	null	### Describe the bug Unable to load xglue dataset ### Steps to reproduce the bug ```python import datasets dataset = datasets.load_dataset("xglue", "ntg") ``` > ConnectionError: Couldn't reach https://xglue.blob.core.windows.net/xglue/xglue_full_dataset.tar.gz (error 409) ### Expected behavior Expected the dataset to load ### Environment info - `datasets` version: 2.13.1 - Platform: Linux-5.15.107+-x86_64-with-glibc2.31 - Python version: 3.10.12 - Huggingface_hub version: 0.15.1 - PyArrow version: 9.0.0 - Pandas version: 1.5.3	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5999/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5999/timeline	null	completed	false
https://api.github.com/repos/huggingface/datasets/issues/5998	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5998/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5998/comments	https://api.github.com/repos/huggingface/datasets/issues/5998/events	https://github.com/huggingface/datasets/issues/5998	1,781,805,018	I_kwDODunzps5qNC_a	5,998	The current implementation has a potential bug in the sort method	{ "login": "wangyuxinwhy", "id": 22192665, "node_id": "MDQ6VXNlcjIyMTkyNjY1", "avatar_url": "https://avatars.githubusercontent.com/u/22192665?v=4", "gravatar_id": "", "url": "https://api.github.com/users/wangyuxinwhy", "html_url": "https://github.com/wangyuxinwhy", "followers_url": "https://api.github.com/users/wangyuxinwhy/followers", "following_url": "https://api.github.com/users/wangyuxinwhy/following{/other_user}", "gists_url": "https://api.github.com/users/wangyuxinwhy/gists{/gist_id}", "starred_url": "https://api.github.com/users/wangyuxinwhy/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/wangyuxinwhy/subscriptions", "organizations_url": "https://api.github.com/users/wangyuxinwhy/orgs", "repos_url": "https://api.github.com/users/wangyuxinwhy/repos", "events_url": "https://api.github.com/users/wangyuxinwhy/events{/privacy}", "received_events_url": "https://api.github.com/users/wangyuxinwhy/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "Thanks for reporting, @wangyuxinwhy. " ]	2023-06-30T03:16:57	2023-06-30T14:21:03	2023-06-30T14:11:25	NONE	null	null	null	### Describe the bug In the sort method，here's a piece of code ```python # column_names: Union[str, Sequence_[str]] # Check proper format of and for duplicates in column_names if not isinstance(column_names, list): column_names = [column_names] ``` I get an error when I pass in a tuple based on the column_names type annotation, it will raise an errror.As in the example below, while the type annotation implies that a tuple can be passed. ```python from datasets import load_dataset dataset = load_dataset('glue', 'ax')['test'] dataset.sort(column_names=('premise', 'hypothesis')) # Raise ValueError: Column '('premise', 'hypothesis')' not found in the dataset. ``` Of course, after I modified the tuple into a list, everything worked fine Change the code to the following so there will be no problem ```python # Check proper format of and for duplicates in column_names if not isinstance(column_names, list): if isinstance(column_names, str): column_names = [column_names] else: column_names = list(column_names) ``` ### Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset('glue', 'ax')['test'] dataset.sort(column_names=('premise', 'hypothesis')) # Raise ValueError: Column '('premise', 'hypothesis')' not found in the dataset. ``` ### Expected behavior Passing tuple into column_names should be equivalent to passing list ### Environment info - `datasets` version: 2.13.0 - Platform: macOS-13.1-arm64-arm-64bit - Python version: 3.10.11 - Huggingface_hub version: 0.15.1 - PyArrow version: 12.0.1 - Pandas version: 2.0.2	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5998/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5998/timeline	null	completed	false
https://api.github.com/repos/huggingface/datasets/issues/5997	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5997/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5997/comments	https://api.github.com/repos/huggingface/datasets/issues/5997/events	https://github.com/huggingface/datasets/issues/5997	1,781,582,818	I_kwDODunzps5qMMvi	5,997	extend the map function so it can wrap around long text that does not fit in the context window	{ "login": "siddhsql", "id": 127623723, "node_id": "U_kgDOB5tiKw", "avatar_url": "https://avatars.githubusercontent.com/u/127623723?v=4", "gravatar_id": "", "url": "https://api.github.com/users/siddhsql", "html_url": "https://github.com/siddhsql", "followers_url": "https://api.github.com/users/siddhsql/followers", "following_url": "https://api.github.com/users/siddhsql/following{/other_user}", "gists_url": "https://api.github.com/users/siddhsql/gists{/gist_id}", "starred_url": "https://api.github.com/users/siddhsql/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/siddhsql/subscriptions", "organizations_url": "https://api.github.com/users/siddhsql/orgs", "repos_url": "https://api.github.com/users/siddhsql/repos", "events_url": "https://api.github.com/users/siddhsql/events{/privacy}", "received_events_url": "https://api.github.com/users/siddhsql/received_events", "type": "User", "site_admin": false }	[ { "id": 1935892871, "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement", "name": "enhancement", "color": "a2eeef", "default": true, "description": "New feature or request" } ]	open	false	null	[]	null	[ "I just noticed the [docs](https://github.com/huggingface/datasets/blob/main/src/datasets/arrow_dataset.py#L2881C11-L2881C200) say:\r\n\r\n>If batched is `True` and `batch_size` is `n > 1`, then the function takes a batch of `n` examples as input and can return a batch with `n` examples, or with an arbitrary number of examples.\r\n\r\nso maybe this is a bug then.", "All the values in a batch must be of the same length. So one solution is dropping all the input columns:\r\n```python\r\ndata = data.map(lambda samples: tokenizer(samples[\"text\"], max_length=tokenizer.model_max_length, truncation=True, stride=4, return_overflowing_tokens=True), batched=True, remove_columns=data.column_names)\r\n```\r\n\r\nAnother is padding/transforming the input columns to the tokenizer output's length (447). " ]	2023-06-29T22:15:21	2023-07-03T17:58:52	null	NONE	null	null	null	### Feature request I understand `dataset` provides a [`map`](https://github.com/huggingface/datasets/blob/main/src/datasets/arrow_dataset.py#L2849) function. This function in turn takes in a callable that is used to tokenize the text on which a model is trained. Frequently this text will not fit within a models's context window. In this case it would be useful to wrap around the text into multiple rows with each row fitting the model's context window. I tried to do it using this code as example which in turn I have borrowed from [here](https://stackoverflow.com/a/76343993/147530): ``` data = data.map(lambda samples: tokenizer(samples["text"], max_length=tokenizer.model_max_length, truncation=True, stride=4, return_overflowing_tokens=True), batched=True) ``` but running the code gives me this error: ``` File "/llm/fine-tune.py", line 117, in <module> data = data.map(lambda samples: tokenizer(samples["text"], max_length=tokenizer.model_max_length, truncation=True, stride=4, return_overflowing_tokens=True), batched=True) File "/llm/.env/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 580, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, args, kwargs) File "/llm/.env/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 545, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, args, kwargs) File "/llm/.env/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 3087, in map for rank, done, content in Dataset._map_single(dataset_kwargs): File "/llm/.env/lib/python3.9/site-packages/datasets/arrow_dataset.py", line 3480, in _map_single writer.write_batch(batch) File "/llm/.env/lib/python3.9/site-packages/datasets/arrow_writer.py", line 556, in write_batch pa_table = pa.Table.from_arrays(arrays, schema=schema) File "pyarrow/table.pxi", line 3798, in pyarrow.lib.Table.from_arrays File "pyarrow/table.pxi", line 2962, in pyarrow.lib.Table.validate File "pyarrow/error.pxi", line 100, in pyarrow.lib.check_status pyarrow.lib.ArrowInvalid: Column 1 named input_ids expected length 394 but got length 447 ``` The lambda function I have provided is correctly chopping up long text so it wraps around (and because of this 394 samples become 447 after wrap around) but the dataset `map` function does not like it. ### Motivation please see above ### Your contribution I'm afraid I don't have much knowledge to help	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5997/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5997/timeline	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5996	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5996/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5996/comments	https://api.github.com/repos/huggingface/datasets/issues/5996/events	https://github.com/huggingface/datasets/pull/5996	1,779,294,374	PR_kwDODunzps5UKP0i	5,996	Deprecate `use_auth_token` in favor of `token`	{ "login": "mariosasko", "id": 47462742, "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "gravatar_id": "", "url": "https://api.github.com/users/mariosasko", "html_url": "https://github.com/mariosasko", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "repos_url": "https://api.github.com/users/mariosasko/repos", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006134 / 0.011353 (-0.005219) \| 0.003816 / 0.011008 (-0.007193) \| 0.098226 / 0.038508 (0.059718) \| 0.036830 / 0.023109 (0.013721) \| 0.314551 / 0.275898 (0.038653) \| 0.372251 / 0.323480 (0.048771) \| 0.004762 / 0.007986 (-0.003224) \| 0.003041 / 0.004328 (-0.001287) \| 0.077651 / 0.004250 (0.073401) \| 0.052445 / 0.037052 (0.015393) \| 0.324632 / 0.258489 (0.066143) \| 0.365724 / 0.293841 (0.071883) \| 0.028069 / 0.128546 (-0.100477) \| 0.008444 / 0.075646 (-0.067203) \| 0.312767 / 0.419271 (-0.106505) \| 0.047773 / 0.043533 (0.004240) \| 0.305317 / 0.255139 (0.050178) \| 0.332007 / 0.283200 (0.048807) \| 0.018985 / 0.141683 (-0.122698) \| 1.538022 / 1.452155 (0.085868) \| 1.575898 / 1.492716 (0.083182) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.204780 / 0.018006 (0.186774) \| 0.428125 / 0.000490 (0.427635) \| 0.003454 / 0.000200 (0.003254) \| 0.000078 / 0.000054 (0.000024) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025064 / 0.037411 (-0.012348) \| 0.099419 / 0.014526 (0.084893) \| 0.111068 / 0.176557 (-0.065489) \| 0.169775 / 0.737135 (-0.567361) \| 0.112067 / 0.296338 (-0.184271) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.429642 / 0.215209 (0.214433) \| 4.275556 / 2.077655 (2.197901) \| 1.914658 / 1.504120 (0.410539) \| 1.706556 / 1.541195 (0.165361) \| 1.754228 / 1.468490 (0.285738) \| 0.563669 / 4.584777 (-4.021108) \| 3.391501 / 3.745712 (-0.354211) \| 1.791517 / 5.269862 (-3.478345) \| 1.030704 / 4.565676 (-3.534973) \| 0.070882 / 0.424275 (-0.353393) \| 0.011351 / 0.007607 (0.003744) \| 0.529438 / 0.226044 (0.303394) \| 5.294316 / 2.268929 (3.025387) \| 2.344653 / 55.444624 (-53.099972) \| 1.997468 / 6.876477 (-4.879009) \| 2.108932 / 2.142072 (-0.033140) \| 0.676794 / 4.805227 (-4.128433) \| 0.135058 / 6.500664 (-6.365607) \| 0.065857 / 0.075469 (-0.009612) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.231864 / 1.841788 (-0.609924) \| 13.986694 / 8.074308 (5.912386) \| 13.306600 / 10.191392 (3.115208) \| 0.145520 / 0.680424 (-0.534904) \| 0.016717 / 0.534201 (-0.517484) \| 0.366303 / 0.579283 (-0.212980) \| 0.391637 / 0.434364 (-0.042727) \| 0.425445 / 0.540337 (-0.114892) \| 0.507719 / 1.386936 (-0.879217) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006236 / 0.011353 (-0.005116) \| 0.003766 / 0.011008 (-0.007242) \| 0.076794 / 0.038508 (0.038286) \| 0.037210 / 0.023109 (0.014101) \| 0.378387 / 0.275898 (0.102489) \| 0.425456 / 0.323480 (0.101977) \| 0.004694 / 0.007986 (-0.003291) \| 0.002921 / 0.004328 (-0.001407) \| 0.076985 / 0.004250 (0.072735) \| 0.052188 / 0.037052 (0.015136) \| 0.394385 / 0.258489 (0.135896) \| 0.432527 / 0.293841 (0.138686) \| 0.029091 / 0.128546 (-0.099455) \| 0.008364 / 0.075646 (-0.067282) \| 0.082583 / 0.419271 (-0.336689) \| 0.042928 / 0.043533 (-0.000605) \| 0.375321 / 0.255139 (0.120182) \| 0.391719 / 0.283200 (0.108519) \| 0.019388 / 0.141683 (-0.122295) \| 1.550644 / 1.452155 (0.098489) \| 1.604882 / 1.492716 (0.112166) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.236859 / 0.018006 (0.218853) \| 0.418528 / 0.000490 (0.418039) \| 0.000388 / 0.000200 (0.000188) \| 0.000059 / 0.000054 (0.000004) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025548 / 0.037411 (-0.011863) \| 0.100644 / 0.014526 (0.086118) \| 0.109102 / 0.176557 (-0.067455) \| 0.161694 / 0.737135 (-0.575441) \| 0.112088 / 0.296338 (-0.184250) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.484128 / 0.215209 (0.268919) \| 4.849952 / 2.077655 (2.772297) \| 2.512769 / 1.504120 (1.008649) \| 2.303295 / 1.541195 (0.762100) \| 2.356699 / 1.468490 (0.888209) \| 0.564181 / 4.584777 (-4.020596) \| 3.421393 / 3.745712 (-0.324319) \| 2.570875 / 5.269862 (-2.698987) \| 1.474307 / 4.565676 (-3.091370) \| 0.068035 / 0.424275 (-0.356240) \| 0.011300 / 0.007607 (0.003693) \| 0.587867 / 0.226044 (0.361823) \| 5.862447 / 2.268929 (3.593519) \| 3.004017 / 55.444624 (-52.440607) \| 2.664989 / 6.876477 (-4.211488) \| 2.740020 / 2.142072 (0.597948) \| 0.680840 / 4.805227 (-4.124387) \| 0.137001 / 6.500664 (-6.363663) \| 0.068098 / 0.075469 (-0.007371) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.297362 / 1.841788 (-0.544426) \| 14.207891 / 8.074308 (6.133583) \| 14.087562 / 10.191392 (3.896170) \| 0.149514 / 0.680424 (-0.530910) \| 0.016566 / 0.534201 (-0.517635) \| 0.367602 / 0.579283 (-0.211681) \| 0.400692 / 0.434364 (-0.033671) \| 0.432907 / 0.540337 (-0.107431) \| 0.525924 / 1.386936 (-0.861012) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1ec069feaaf6c28d4e4df76d344693b591a74c3f \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006223 / 0.011353 (-0.005130) \| 0.003672 / 0.011008 (-0.007336) \| 0.097451 / 0.038508 (0.058943) \| 0.036243 / 0.023109 (0.013133) \| 0.375650 / 0.275898 (0.099752) \| 0.431652 / 0.323480 (0.108172) \| 0.004758 / 0.007986 (-0.003227) \| 0.002941 / 0.004328 (-0.001387) \| 0.077383 / 0.004250 (0.073132) \| 0.055342 / 0.037052 (0.018289) \| 0.390335 / 0.258489 (0.131846) \| 0.427867 / 0.293841 (0.134026) \| 0.027619 / 0.128546 (-0.100927) \| 0.008244 / 0.075646 (-0.067402) \| 0.313499 / 0.419271 (-0.105773) \| 0.054987 / 0.043533 (0.011454) \| 0.394044 / 0.255139 (0.138905) \| 0.398784 / 0.283200 (0.115584) \| 0.026499 / 0.141683 (-0.115184) \| 1.496907 / 1.452155 (0.044753) \| 1.554465 / 1.492716 (0.061749) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.241197 / 0.018006 (0.223190) \| 0.427856 / 0.000490 (0.427366) \| 0.006264 / 0.000200 (0.006065) \| 0.000218 / 0.000054 (0.000164) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025550 / 0.037411 (-0.011862) \| 0.104426 / 0.014526 (0.089901) \| 0.110310 / 0.176557 (-0.066246) \| 0.173813 / 0.737135 (-0.563322) \| 0.112129 / 0.296338 (-0.184209) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.458806 / 0.215209 (0.243597) \| 4.576351 / 2.077655 (2.498697) \| 2.265670 / 1.504120 (0.761550) \| 2.073230 / 1.541195 (0.532035) \| 2.135283 / 1.468490 (0.666793) \| 0.562506 / 4.584777 (-4.022271) \| 3.375101 / 3.745712 (-0.370611) \| 1.734393 / 5.269862 (-3.535469) \| 1.026622 / 4.565676 (-3.539054) \| 0.068144 / 0.424275 (-0.356131) \| 0.011092 / 0.007607 (0.003485) \| 0.562779 / 0.226044 (0.336734) \| 5.608256 / 2.268929 (3.339328) \| 2.706468 / 55.444624 (-52.738157) \| 2.381607 / 6.876477 (-4.494869) \| 2.451027 / 2.142072 (0.308954) \| 0.671590 / 4.805227 (-4.133637) \| 0.135749 / 6.500664 (-6.364915) \| 0.065389 / 0.075469 (-0.010080) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.244806 / 1.841788 (-0.596981) \| 14.042150 / 8.074308 (5.967841) \| 14.246612 / 10.191392 (4.055220) \| 0.134309 / 0.680424 (-0.546114) \| 0.017082 / 0.534201 (-0.517119) \| 0.366043 / 0.579283 (-0.213240) \| 0.400748 / 0.434364 (-0.033616) \| 0.425695 / 0.540337 (-0.114643) \| 0.509355 / 1.386936 (-0.877581) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006134 / 0.011353 (-0.005219) \| 0.003980 / 0.011008 (-0.007028) \| 0.078353 / 0.038508 (0.039845) \| 0.038011 / 0.023109 (0.014902) \| 0.375784 / 0.275898 (0.099886) \| 0.433619 / 0.323480 (0.110139) \| 0.004897 / 0.007986 (-0.003088) \| 0.002981 / 0.004328 (-0.001347) \| 0.077362 / 0.004250 (0.073112) \| 0.056108 / 0.037052 (0.019056) \| 0.395984 / 0.258489 (0.137495) \| 0.427397 / 0.293841 (0.133556) \| 0.029325 / 0.128546 (-0.099221) \| 0.008498 / 0.075646 (-0.067148) \| 0.082478 / 0.419271 (-0.336794) \| 0.044085 / 0.043533 (0.000552) \| 0.389923 / 0.255139 (0.134784) \| 0.391180 / 0.283200 (0.107980) \| 0.022452 / 0.141683 (-0.119231) \| 1.507758 / 1.452155 (0.055603) \| 1.530459 / 1.492716 (0.037743) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.230928 / 0.018006 (0.212922) \| 0.408484 / 0.000490 (0.407995) \| 0.000806 / 0.000200 (0.000606) \| 0.000067 / 0.000054 (0.000012) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025183 / 0.037411 (-0.012228) \| 0.102292 / 0.014526 (0.087766) \| 0.108142 / 0.176557 (-0.068415) \| 0.161172 / 0.737135 (-0.575963) \| 0.114476 / 0.296338 (-0.181862) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.482978 / 0.215209 (0.267769) \| 4.816103 / 2.077655 (2.738448) \| 2.505567 / 1.504120 (1.001447) \| 2.302598 / 1.541195 (0.761404) \| 2.371238 / 1.468490 (0.902748) \| 0.567467 / 4.584777 (-4.017310) \| 3.363407 / 3.745712 (-0.382306) \| 1.746213 / 5.269862 (-3.523649) \| 1.035468 / 4.565676 (-3.530208) \| 0.068431 / 0.424275 (-0.355844) \| 0.011069 / 0.007607 (0.003462) \| 0.598241 / 0.226044 (0.372196) \| 5.953927 / 2.268929 (3.684999) \| 3.007493 / 55.444624 (-52.437132) \| 2.629399 / 6.876477 (-4.247078) \| 2.737201 / 2.142072 (0.595129) \| 0.682456 / 4.805227 (-4.122771) \| 0.137613 / 6.500664 (-6.363051) \| 0.067941 / 0.075469 (-0.007528) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.306015 / 1.841788 (-0.535772) \| 14.359240 / 8.074308 (6.284932) \| 14.187601 / 10.191392 (3.996209) \| 0.138612 / 0.680424 (-0.541812) \| 0.016708 / 0.534201 (-0.517493) \| 0.366365 / 0.579283 (-0.212918) \| 0.396982 / 0.434364 (-0.037382) \| 0.426939 / 0.540337 (-0.113398) \| 0.520064 / 1.386936 (-0.866872) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#21d0fd041a5eca02d3ee787396216ac613c662ac \"CML watermark\")\n", "They use `token` and emit a deprecation warning if `use_auth_token` is passed instead (see https://github.com/huggingface/transformers/blob/78a2b19fc84ed55c65f4bf20a901edb7ceb73c5f/src/transformers/modeling_utils.py#L1933). \r\n\r\nI think we can update the `examples` scripts after merging this PR.", "> I think we can update the examples scripts after merging this PR.\r\n\r\nWe should do a release before updated in the examples scripts no ? That's why it's an option to not have a deprecation warning until transformers and co are updated with the `token` arg", "> We should do a release before updated in the examples scripts no ? That's why it's an option to not have a deprecation warning until transformers and co are updated with the token arg\r\n\r\nThis would avoid the warning only for the latest `datasets` release. TBH, I don't think this is worth the hassle, considering how simple it is to remove it.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007644 / 0.011353 (-0.003709) \| 0.004667 / 0.011008 (-0.006341) \| 0.117347 / 0.038508 (0.078839) \| 0.050620 / 0.023109 (0.027510) \| 0.415402 / 0.275898 (0.139504) \| 0.485898 / 0.323480 (0.162418) \| 0.005848 / 0.007986 (-0.002138) \| 0.003736 / 0.004328 (-0.000592) \| 0.089798 / 0.004250 (0.085547) \| 0.069344 / 0.037052 (0.032292) \| 0.441684 / 0.258489 (0.183195) \| 0.468972 / 0.293841 (0.175131) \| 0.036637 / 0.128546 (-0.091909) \| 0.010219 / 0.075646 (-0.065427) \| 0.394293 / 0.419271 (-0.024978) \| 0.061462 / 0.043533 (0.017929) \| 0.409448 / 0.255139 (0.154309) \| 0.431557 / 0.283200 (0.148358) \| 0.027795 / 0.141683 (-0.113888) \| 1.837844 / 1.452155 (0.385690) \| 1.862683 / 1.492716 (0.369967) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.230500 / 0.018006 (0.212494) \| 0.483139 / 0.000490 (0.482649) \| 0.006517 / 0.000200 (0.006317) \| 0.000143 / 0.000054 (0.000088) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033152 / 0.037411 (-0.004259) \| 0.133673 / 0.014526 (0.119147) \| 0.143853 / 0.176557 (-0.032704) \| 0.215254 / 0.737135 (-0.521882) \| 0.150676 / 0.296338 (-0.145662) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.503796 / 0.215209 (0.288587) \| 5.049981 / 2.077655 (2.972326) \| 2.399427 / 1.504120 (0.895307) \| 2.167635 / 1.541195 (0.626441) \| 2.257448 / 1.468490 (0.788958) \| 0.641298 / 4.584777 (-3.943479) \| 4.828676 / 3.745712 (1.082964) \| 4.346069 / 5.269862 (-0.923793) \| 2.103890 / 4.565676 (-2.461786) \| 0.079115 / 0.424275 (-0.345160) \| 0.013377 / 0.007607 (0.005770) \| 0.621207 / 0.226044 (0.395162) \| 6.190939 / 2.268929 (3.922011) \| 2.920129 / 55.444624 (-52.524495) \| 2.549225 / 6.876477 (-4.327252) \| 2.719221 / 2.142072 (0.577149) \| 0.790949 / 4.805227 (-4.014278) \| 0.172032 / 6.500664 (-6.328632) \| 0.077779 / 0.075469 (0.002310) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.432572 / 1.841788 (-0.409216) \| 21.000031 / 8.074308 (12.925723) \| 17.555093 / 10.191392 (7.363701) \| 0.166646 / 0.680424 (-0.513778) \| 0.020451 / 0.534201 (-0.513750) \| 0.488767 / 0.579283 (-0.090516) \| 0.737036 / 0.434364 (0.302672) \| 0.621694 / 0.540337 (0.081356) \| 0.732074 / 1.386936 (-0.654862) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008198 / 0.011353 (-0.003155) \| 0.004987 / 0.011008 (-0.006021) \| 0.090714 / 0.038508 (0.052206) \| 0.053379 / 0.023109 (0.030270) \| 0.425199 / 0.275898 (0.149301) \| 0.514036 / 0.323480 (0.190556) \| 0.006043 / 0.007986 (-0.001943) \| 0.003888 / 0.004328 (-0.000441) \| 0.088294 / 0.004250 (0.084043) \| 0.073024 / 0.037052 (0.035971) \| 0.435983 / 0.258489 (0.177494) \| 0.514293 / 0.293841 (0.220452) \| 0.039451 / 0.128546 (-0.089095) \| 0.010439 / 0.075646 (-0.065207) \| 0.096885 / 0.419271 (-0.322387) \| 0.060165 / 0.043533 (0.016632) \| 0.421053 / 0.255139 (0.165914) \| 0.455545 / 0.283200 (0.172345) \| 0.027234 / 0.141683 (-0.114449) \| 1.768975 / 1.452155 (0.316820) \| 1.842853 / 1.492716 (0.350137) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.278940 / 0.018006 (0.260933) \| 0.480709 / 0.000490 (0.480219) \| 0.000436 / 0.000200 (0.000236) \| 0.000070 / 0.000054 (0.000016) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034900 / 0.037411 (-0.002511) \| 0.144893 / 0.014526 (0.130368) \| 0.149567 / 0.176557 (-0.026989) \| 0.213200 / 0.737135 (-0.523935) \| 0.156735 / 0.296338 (-0.139604) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.535897 / 0.215209 (0.320687) \| 5.336998 / 2.077655 (3.259343) \| 2.685854 / 1.504120 (1.181734) \| 2.470177 / 1.541195 (0.928983) \| 2.547495 / 1.468490 (1.079004) \| 0.642830 / 4.584777 (-3.941947) \| 4.595866 / 3.745712 (0.850154) \| 2.186696 / 5.269862 (-3.083165) \| 1.317969 / 4.565676 (-3.247708) \| 0.079268 / 0.424275 (-0.345007) \| 0.013792 / 0.007607 (0.006185) \| 0.662236 / 0.226044 (0.436192) \| 6.604775 / 2.268929 (4.335847) \| 3.355888 / 55.444624 (-52.088736) \| 2.968911 / 6.876477 (-3.907565) \| 3.121862 / 2.142072 (0.979790) \| 0.794752 / 4.805227 (-4.010475) \| 0.170800 / 6.500664 (-6.329864) \| 0.078393 / 0.075469 (0.002924) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.601605 / 1.841788 (-0.240183) \| 20.743553 / 8.074308 (12.669245) \| 17.543968 / 10.191392 (7.352576) \| 0.221884 / 0.680424 (-0.458540) \| 0.020779 / 0.534201 (-0.513422) \| 0.479677 / 0.579283 (-0.099606) \| 0.516207 / 0.434364 (0.081843) \| 0.564046 / 0.540337 (0.023709) \| 0.711336 / 1.386936 (-0.675600) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#819bb4346434912eb405ce3f3e9f21dc25a2fe85 \"CML watermark\")\n", "Yes, sounds great! Thanks", "yup" ]	2023-06-28T16:26:38	2023-07-05T15:22:20	2023-07-03T16:03:33	CONTRIBUTOR	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5996", "html_url": "https://github.com/huggingface/datasets/pull/5996", "diff_url": "https://github.com/huggingface/datasets/pull/5996.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5996.patch", "merged_at": "2023-07-03T16:03:33" }	... to be consistent with `transformers` and `huggingface_hub`.	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5996/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5996/timeline	null	null	true
https://api.github.com/repos/huggingface/datasets/issues/5995	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5995/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5995/comments	https://api.github.com/repos/huggingface/datasets/issues/5995/events	https://github.com/huggingface/datasets/pull/5995	1,777,088,925	PR_kwDODunzps5UCvYJ	5,995	Support returning dataframe in map transform	{ "login": "mariosasko", "id": 47462742, "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "gravatar_id": "", "url": "https://api.github.com/users/mariosasko", "html_url": "https://github.com/mariosasko", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "repos_url": "https://api.github.com/users/mariosasko/repos", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009725 / 0.011353 (-0.001628) \| 0.006014 / 0.011008 (-0.004994) \| 0.136039 / 0.038508 (0.097531) \| 0.049685 / 0.023109 (0.026576) \| 0.492967 / 0.275898 (0.217068) \| 0.553775 / 0.323480 (0.230295) \| 0.007421 / 0.007986 (-0.000564) \| 0.004686 / 0.004328 (0.000357) \| 0.106639 / 0.004250 (0.102389) \| 0.073483 / 0.037052 (0.036431) \| 0.507194 / 0.258489 (0.248705) \| 0.535760 / 0.293841 (0.241919) \| 0.049666 / 0.128546 (-0.078880) \| 0.014139 / 0.075646 (-0.061507) \| 0.435459 / 0.419271 (0.016188) \| 0.076026 / 0.043533 (0.032493) \| 0.454542 / 0.255139 (0.199403) \| 0.512724 / 0.283200 (0.229524) \| 0.034969 / 0.141683 (-0.106713) \| 1.881048 / 1.452155 (0.428893) \| 1.959915 / 1.492716 (0.467199) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.265322 / 0.018006 (0.247316) \| 0.573963 / 0.000490 (0.573474) \| 0.017493 / 0.000200 (0.017293) \| 0.000637 / 0.000054 (0.000582) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028712 / 0.037411 (-0.008699) \| 0.149554 / 0.014526 (0.135029) \| 0.130013 / 0.176557 (-0.046544) \| 0.203408 / 0.737135 (-0.533727) \| 0.144778 / 0.296338 (-0.151561) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.664198 / 0.215209 (0.448989) \| 6.418054 / 2.077655 (4.340399) \| 2.602338 / 1.504120 (1.098219) \| 2.212992 / 1.541195 (0.671797) \| 2.214309 / 1.468490 (0.745819) \| 0.914772 / 4.584777 (-3.670005) \| 5.824831 / 3.745712 (2.079119) \| 2.865381 / 5.269862 (-2.404481) \| 1.906020 / 4.565676 (-2.659657) \| 0.106947 / 0.424275 (-0.317328) \| 0.013467 / 0.007607 (0.005860) \| 0.834556 / 0.226044 (0.608512) \| 8.237078 / 2.268929 (5.968150) \| 3.380919 / 55.444624 (-52.063705) \| 2.656713 / 6.876477 (-4.219764) \| 2.834941 / 2.142072 (0.692869) \| 1.151241 / 4.805227 (-3.653986) \| 0.220860 / 6.500664 (-6.279804) \| 0.080781 / 0.075469 (0.005312) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.655128 / 1.841788 (-0.186660) \| 18.696108 / 8.074308 (10.621800) \| 22.882108 / 10.191392 (12.690716) \| 0.236041 / 0.680424 (-0.444383) \| 0.031073 / 0.534201 (-0.503128) \| 0.525263 / 0.579283 (-0.054021) \| 0.632933 / 0.434364 (0.198569) \| 0.707228 / 0.540337 (0.166890) \| 0.753508 / 1.386936 (-0.633428) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009875 / 0.011353 (-0.001478) \| 0.005135 / 0.011008 (-0.005873) \| 0.101307 / 0.038508 (0.062799) \| 0.044895 / 0.023109 (0.021786) \| 0.497824 / 0.275898 (0.221926) \| 0.573098 / 0.323480 (0.249618) \| 0.006669 / 0.007986 (-0.001317) \| 0.004289 / 0.004328 (-0.000039) \| 0.105824 / 0.004250 (0.101573) \| 0.061002 / 0.037052 (0.023950) \| 0.510127 / 0.258489 (0.251638) \| 0.581387 / 0.293841 (0.287546) \| 0.052843 / 0.128546 (-0.075703) \| 0.015506 / 0.075646 (-0.060140) \| 0.116057 / 0.419271 (-0.303215) \| 0.063444 / 0.043533 (0.019912) \| 0.479366 / 0.255139 (0.224227) \| 0.518419 / 0.283200 (0.235220) \| 0.034876 / 0.141683 (-0.106806) \| 2.018446 / 1.452155 (0.566292) \| 1.960755 / 1.492716 (0.468039) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.269077 / 0.018006 (0.251070) \| 0.606059 / 0.000490 (0.605569) \| 0.000488 / 0.000200 (0.000288) \| 0.000093 / 0.000054 (0.000038) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032465 / 0.037411 (-0.004946) \| 0.136517 / 0.014526 (0.121991) \| 0.147740 / 0.176557 (-0.028816) \| 0.193802 / 0.737135 (-0.543334) \| 0.151876 / 0.296338 (-0.144462) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.709866 / 0.215209 (0.494657) \| 6.848193 / 2.077655 (4.770538) \| 3.310853 / 1.504120 (1.806733) \| 2.940813 / 1.541195 (1.399619) \| 2.934934 / 1.468490 (1.466444) \| 0.927104 / 4.584777 (-3.657673) \| 5.921607 / 3.745712 (2.175895) \| 4.926558 / 5.269862 (-0.343303) \| 2.853269 / 4.565676 (-1.712407) \| 0.120278 / 0.424275 (-0.303998) \| 0.015468 / 0.007607 (0.007861) \| 0.820509 / 0.226044 (0.594464) \| 8.263136 / 2.268929 (5.994208) \| 3.780214 / 55.444624 (-51.664410) \| 3.108482 / 6.876477 (-3.767995) \| 3.101544 / 2.142072 (0.959471) \| 1.165539 / 4.805227 (-3.639688) \| 0.229215 / 6.500664 (-6.271449) \| 0.079862 / 0.075469 (0.004393) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.775071 / 1.841788 (-0.066717) \| 19.327621 / 8.074308 (11.253313) \| 23.057537 / 10.191392 (12.866145) \| 0.250649 / 0.680424 (-0.429775) \| 0.029767 / 0.534201 (-0.504434) \| 0.554774 / 0.579283 (-0.024509) \| 0.651919 / 0.434364 (0.217555) \| 0.651641 / 0.540337 (0.111304) \| 0.762386 / 1.386936 (-0.624550) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#fdc3ce7060366f480621e8640903c9ab476164e7 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005997 / 0.011353 (-0.005356) \| 0.003892 / 0.011008 (-0.007116) \| 0.098020 / 0.038508 (0.059512) \| 0.042584 / 0.023109 (0.019475) \| 0.317909 / 0.275898 (0.042011) \| 0.395042 / 0.323480 (0.071563) \| 0.005358 / 0.007986 (-0.002628) \| 0.003266 / 0.004328 (-0.001062) \| 0.076698 / 0.004250 (0.072447) \| 0.062331 / 0.037052 (0.025279) \| 0.334900 / 0.258489 (0.076411) \| 0.379355 / 0.293841 (0.085514) \| 0.030815 / 0.128546 (-0.097731) \| 0.008596 / 0.075646 (-0.067050) \| 0.327739 / 0.419271 (-0.091533) \| 0.054061 / 0.043533 (0.010528) \| 0.311044 / 0.255139 (0.055905) \| 0.336705 / 0.283200 (0.053506) \| 0.022785 / 0.141683 (-0.118898) \| 1.516793 / 1.452155 (0.064639) \| 1.590435 / 1.492716 (0.097719) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.289157 / 0.018006 (0.271151) \| 0.531074 / 0.000490 (0.530585) \| 0.004672 / 0.000200 (0.004472) \| 0.000095 / 0.000054 (0.000040) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026173 / 0.037411 (-0.011238) \| 0.105723 / 0.014526 (0.091197) \| 0.118010 / 0.176557 (-0.058547) \| 0.178062 / 0.737135 (-0.559073) \| 0.120059 / 0.296338 (-0.176279) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.410870 / 0.215209 (0.195661) \| 4.042183 / 2.077655 (1.964528) \| 1.830059 / 1.504120 (0.325939) \| 1.638996 / 1.541195 (0.097802) \| 1.701368 / 1.468490 (0.232878) \| 0.529915 / 4.584777 (-4.054861) \| 3.693308 / 3.745712 (-0.052404) \| 1.827875 / 5.269862 (-3.441986) \| 1.063237 / 4.565676 (-3.502440) \| 0.065368 / 0.424275 (-0.358907) \| 0.010986 / 0.007607 (0.003379) \| 0.509399 / 0.226044 (0.283354) \| 5.092739 / 2.268929 (2.823810) \| 2.293490 / 55.444624 (-53.151135) \| 1.958742 / 6.876477 (-4.917735) \| 2.024985 / 2.142072 (-0.117088) \| 0.646978 / 4.805227 (-4.158249) \| 0.138616 / 6.500664 (-6.362048) \| 0.062101 / 0.075469 (-0.013368) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.202016 / 1.841788 (-0.639772) \| 14.493204 / 8.074308 (6.418896) \| 12.992160 / 10.191392 (2.800768) \| 0.188922 / 0.680424 (-0.491502) \| 0.017594 / 0.534201 (-0.516606) \| 0.399917 / 0.579283 (-0.179367) \| 0.429760 / 0.434364 (-0.004604) \| 0.497906 / 0.540337 (-0.042431) \| 0.608745 / 1.386936 (-0.778191) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006164 / 0.011353 (-0.005189) \| 0.003980 / 0.011008 (-0.007028) \| 0.074676 / 0.038508 (0.036168) \| 0.041337 / 0.023109 (0.018228) \| 0.400981 / 0.275898 (0.125083) \| 0.448791 / 0.323480 (0.125312) \| 0.004063 / 0.007986 (-0.003923) \| 0.004443 / 0.004328 (0.000114) \| 0.075011 / 0.004250 (0.070760) \| 0.056494 / 0.037052 (0.019441) \| 0.402054 / 0.258489 (0.143565) \| 0.446122 / 0.293841 (0.152281) \| 0.031752 / 0.128546 (-0.096794) \| 0.008835 / 0.075646 (-0.066811) \| 0.081226 / 0.419271 (-0.338046) \| 0.051501 / 0.043533 (0.007969) \| 0.383674 / 0.255139 (0.128535) \| 0.405524 / 0.283200 (0.122325) \| 0.025929 / 0.141683 (-0.115754) \| 1.492985 / 1.452155 (0.040830) \| 1.541601 / 1.492716 (0.048885) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.305149 / 0.018006 (0.287142) \| 0.497259 / 0.000490 (0.496770) \| 0.000420 / 0.000200 (0.000220) \| 0.000056 / 0.000054 (0.000002) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027933 / 0.037411 (-0.009479) \| 0.111900 / 0.014526 (0.097374) \| 0.124879 / 0.176557 (-0.051678) \| 0.178952 / 0.737135 (-0.558184) \| 0.127698 / 0.296338 (-0.168640) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.448525 / 0.215209 (0.233316) \| 4.486791 / 2.077655 (2.409137) \| 2.256687 / 1.504120 (0.752567) \| 2.061078 / 1.541195 (0.519884) \| 2.078924 / 1.468490 (0.610434) \| 0.534412 / 4.584777 (-4.050365) \| 3.721098 / 3.745712 (-0.024614) \| 1.818735 / 5.269862 (-3.451127) \| 1.104198 / 4.565676 (-3.461479) \| 0.066277 / 0.424275 (-0.357998) \| 0.011441 / 0.007607 (0.003834) \| 0.550140 / 0.226044 (0.324095) \| 5.498079 / 2.268929 (3.229150) \| 2.717398 / 55.444624 (-52.727227) \| 2.410194 / 6.876477 (-4.466283) \| 2.405304 / 2.142072 (0.263231) \| 0.665432 / 4.805227 (-4.139796) \| 0.141488 / 6.500664 (-6.359177) \| 0.064051 / 0.075469 (-0.011419) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.272334 / 1.841788 (-0.569454) \| 14.901608 / 8.074308 (6.827300) \| 14.287857 / 10.191392 (4.096465) \| 0.165337 / 0.680424 (-0.515086) \| 0.017402 / 0.534201 (-0.516799) \| 0.398120 / 0.579283 (-0.181163) \| 0.416539 / 0.434364 (-0.017825) \| 0.463890 / 0.540337 (-0.076447) \| 0.567909 / 1.386936 (-0.819027) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#504ec0f2e00ee38e0993ed1e4f1e10f1eefaea0d \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009434 / 0.011353 (-0.001919) \| 0.005567 / 0.011008 (-0.005441) \| 0.122652 / 0.038508 (0.084144) \| 0.050177 / 0.023109 (0.027067) \| 0.384292 / 0.275898 (0.108394) \| 0.446608 / 0.323480 (0.123128) \| 0.006502 / 0.007986 (-0.001484) \| 0.004523 / 0.004328 (0.000194) \| 0.100581 / 0.004250 (0.096331) \| 0.073615 / 0.037052 (0.036563) \| 0.420179 / 0.258489 (0.161690) \| 0.474631 / 0.293841 (0.180790) \| 0.047942 / 0.128546 (-0.080604) \| 0.013864 / 0.075646 (-0.061783) \| 0.419384 / 0.419271 (0.000112) \| 0.088317 / 0.043533 (0.044784) \| 0.379620 / 0.255139 (0.124481) \| 0.412639 / 0.283200 (0.129440) \| 0.048947 / 0.141683 (-0.092736) \| 1.823498 / 1.452155 (0.371343) \| 1.966629 / 1.492716 (0.473913) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.300669 / 0.018006 (0.282663) \| 0.593499 / 0.000490 (0.593009) \| 0.007247 / 0.000200 (0.007047) \| 0.000114 / 0.000054 (0.000059) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030556 / 0.037411 (-0.006856) \| 0.119252 / 0.014526 (0.104726) \| 0.131403 / 0.176557 (-0.045153) \| 0.201845 / 0.737135 (-0.535291) \| 0.139350 / 0.296338 (-0.156989) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.652400 / 0.215209 (0.437191) \| 6.536540 / 2.077655 (4.458886) \| 2.644565 / 1.504120 (1.140445) \| 2.245181 / 1.541195 (0.703986) \| 2.316030 / 1.468490 (0.847540) \| 0.922535 / 4.584777 (-3.662242) \| 5.469065 / 3.745712 (1.723353) \| 2.800489 / 5.269862 (-2.469373) \| 1.749042 / 4.565676 (-2.816635) \| 0.108444 / 0.424275 (-0.315831) \| 0.015651 / 0.007607 (0.008044) \| 0.846085 / 0.226044 (0.620041) \| 8.018460 / 2.268929 (5.749531) \| 3.338710 / 55.444624 (-52.105914) \| 2.675998 / 6.876477 (-4.200479) \| 2.918550 / 2.142072 (0.776478) \| 1.135145 / 4.805227 (-3.670082) \| 0.215165 / 6.500664 (-6.285499) \| 0.082066 / 0.075469 (0.006597) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.561661 / 1.841788 (-0.280127) \| 18.519035 / 8.074308 (10.444727) \| 19.046300 / 10.191392 (8.854908) \| 0.236890 / 0.680424 (-0.443534) \| 0.027681 / 0.534201 (-0.506520) \| 0.511998 / 0.579283 (-0.067285) \| 0.591627 / 0.434364 (0.157264) \| 0.562021 / 0.540337 (0.021683) \| 0.679354 / 1.386936 (-0.707582) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009643 / 0.011353 (-0.001710) \| 0.005768 / 0.011008 (-0.005241) \| 0.104430 / 0.038508 (0.065922) \| 0.050044 / 0.023109 (0.026935) \| 0.464117 / 0.275898 (0.188219) \| 0.518439 / 0.323480 (0.194959) \| 0.006935 / 0.007986 (-0.001051) \| 0.004316 / 0.004328 (-0.000013) \| 0.094330 / 0.004250 (0.090080) \| 0.071451 / 0.037052 (0.034399) \| 0.492248 / 0.258489 (0.233759) \| 0.555740 / 0.293841 (0.261899) \| 0.047836 / 0.128546 (-0.080711) \| 0.014788 / 0.075646 (-0.060859) \| 0.107590 / 0.419271 (-0.311682) \| 0.064396 / 0.043533 (0.020863) \| 0.451529 / 0.255139 (0.196390) \| 0.475025 / 0.283200 (0.191826) \| 0.040006 / 0.141683 (-0.101677) \| 1.797107 / 1.452155 (0.344953) \| 1.879261 / 1.492716 (0.386545) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.298458 / 0.018006 (0.280451) \| 0.613022 / 0.000490 (0.612532) \| 0.003582 / 0.000200 (0.003382) \| 0.000106 / 0.000054 (0.000052) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030179 / 0.037411 (-0.007232) \| 0.123286 / 0.014526 (0.108760) \| 0.132070 / 0.176557 (-0.044486) \| 0.190883 / 0.737135 (-0.546252) \| 0.138526 / 0.296338 (-0.157812) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.666908 / 0.215209 (0.451699) \| 6.489035 / 2.077655 (4.411381) \| 2.897027 / 1.504120 (1.392907) \| 2.565150 / 1.541195 (1.023956) \| 2.504827 / 1.468490 (1.036336) \| 0.916112 / 4.584777 (-3.668665) \| 5.651751 / 3.745712 (1.906039) \| 2.743382 / 5.269862 (-2.526479) \| 1.773338 / 4.565676 (-2.792338) \| 0.128764 / 0.424275 (-0.295511) \| 0.013140 / 0.007607 (0.005533) \| 0.803281 / 0.226044 (0.577236) \| 8.258874 / 2.268929 (5.989945) \| 3.633260 / 55.444624 (-51.811364) \| 2.878827 / 6.876477 (-3.997649) \| 2.977178 / 2.142072 (0.835106) \| 1.130467 / 4.805227 (-3.674760) \| 0.226381 / 6.500664 (-6.274283) \| 0.081550 / 0.075469 (0.006081) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.842927 / 1.841788 (0.001139) \| 18.411520 / 8.074308 (10.337212) \| 21.118228 / 10.191392 (10.926836) \| 0.231526 / 0.680424 (-0.448898) \| 0.029300 / 0.534201 (-0.504901) \| 0.527450 / 0.579283 (-0.051834) \| 0.618873 / 0.434364 (0.184509) \| 0.593314 / 0.540337 (0.052976) \| 0.734430 / 1.386936 (-0.652506) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0d2b8854c265b4dc202e480427890f472b34ea15 \"CML watermark\")\n" ]	2023-06-27T14:15:08	2023-06-28T13:56:02	2023-06-28T13:46:33	CONTRIBUTOR	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5995", "html_url": "https://github.com/huggingface/datasets/pull/5995", "diff_url": "https://github.com/huggingface/datasets/pull/5995.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5995.patch", "merged_at": "2023-06-28T13:46:33" }	Allow returning Pandas DataFrames in `map` transforms. (Plus, raise an error in the non-batched mode if a returned PyArrow table/Pandas DataFrame has more than one row)	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5995/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5995/timeline	null	null	true
https://api.github.com/repos/huggingface/datasets/issues/5994	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5994/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5994/comments	https://api.github.com/repos/huggingface/datasets/issues/5994/events	https://github.com/huggingface/datasets/pull/5994	1,776,829,004	PR_kwDODunzps5UB1cA	5,994	Fix select_columns columns order	{ "login": "lhoestq", "id": 42851186, "node_id": "MDQ6VXNlcjQyODUxMTg2", "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "gravatar_id": "", "url": "https://api.github.com/users/lhoestq", "html_url": "https://github.com/lhoestq", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "repos_url": "https://api.github.com/users/lhoestq/repos", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005969 / 0.011353 (-0.005384) \| 0.003687 / 0.011008 (-0.007321) \| 0.100843 / 0.038508 (0.062335) \| 0.036912 / 0.023109 (0.013803) \| 0.312389 / 0.275898 (0.036491) \| 0.370335 / 0.323480 (0.046855) \| 0.003434 / 0.007986 (-0.004552) \| 0.003710 / 0.004328 (-0.000619) \| 0.076899 / 0.004250 (0.072648) \| 0.053647 / 0.037052 (0.016594) \| 0.324825 / 0.258489 (0.066336) \| 0.367711 / 0.293841 (0.073870) \| 0.028079 / 0.128546 (-0.100467) \| 0.008326 / 0.075646 (-0.067320) \| 0.312342 / 0.419271 (-0.106930) \| 0.047423 / 0.043533 (0.003890) \| 0.321063 / 0.255139 (0.065924) \| 0.336508 / 0.283200 (0.053308) \| 0.019973 / 0.141683 (-0.121710) \| 1.529334 / 1.452155 (0.077179) \| 1.573746 / 1.492716 (0.081030) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.210849 / 0.018006 (0.192843) \| 0.418798 / 0.000490 (0.418309) \| 0.007347 / 0.000200 (0.007147) \| 0.000070 / 0.000054 (0.000016) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022718 / 0.037411 (-0.014694) \| 0.098400 / 0.014526 (0.083874) \| 0.106590 / 0.176557 (-0.069967) \| 0.168460 / 0.737135 (-0.568675) \| 0.108401 / 0.296338 (-0.187938) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.443066 / 0.215209 (0.227857) \| 4.416658 / 2.077655 (2.339003) \| 2.088844 / 1.504120 (0.584724) \| 1.879564 / 1.541195 (0.338369) \| 1.933815 / 1.468490 (0.465325) \| 0.565085 / 4.584777 (-4.019692) \| 3.412440 / 3.745712 (-0.333273) \| 1.754686 / 5.269862 (-3.515175) \| 1.024576 / 4.565676 (-3.541100) \| 0.067909 / 0.424275 (-0.356366) \| 0.011054 / 0.007607 (0.003447) \| 0.534748 / 0.226044 (0.308703) \| 5.351457 / 2.268929 (3.082529) \| 2.517368 / 55.444624 (-52.927256) \| 2.182762 / 6.876477 (-4.693715) \| 2.238205 / 2.142072 (0.096133) \| 0.672962 / 4.805227 (-4.132265) \| 0.136098 / 6.500664 (-6.364566) \| 0.066534 / 0.075469 (-0.008935) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.281241 / 1.841788 (-0.560547) \| 13.872881 / 8.074308 (5.798573) \| 13.161023 / 10.191392 (2.969631) \| 0.130011 / 0.680424 (-0.550412) \| 0.016759 / 0.534201 (-0.517442) \| 0.359802 / 0.579283 (-0.219481) \| 0.392577 / 0.434364 (-0.041787) \| 0.427742 / 0.540337 (-0.112595) \| 0.522241 / 1.386936 (-0.864695) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005985 / 0.011353 (-0.005368) \| 0.003705 / 0.011008 (-0.007304) \| 0.077699 / 0.038508 (0.039191) \| 0.035686 / 0.023109 (0.012577) \| 0.420356 / 0.275898 (0.144458) \| 0.476753 / 0.323480 (0.153273) \| 0.003510 / 0.007986 (-0.004475) \| 0.002807 / 0.004328 (-0.001521) \| 0.077151 / 0.004250 (0.072901) \| 0.046420 / 0.037052 (0.009368) \| 0.391781 / 0.258489 (0.133292) \| 0.461128 / 0.293841 (0.167287) \| 0.027847 / 0.128546 (-0.100699) \| 0.008322 / 0.075646 (-0.067324) \| 0.082768 / 0.419271 (-0.336503) \| 0.042629 / 0.043533 (-0.000904) \| 0.405745 / 0.255139 (0.150606) \| 0.430797 / 0.283200 (0.147598) \| 0.019832 / 0.141683 (-0.121851) \| 1.556208 / 1.452155 (0.104054) \| 1.612166 / 1.492716 (0.119450) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.230633 / 0.018006 (0.212626) \| 0.401667 / 0.000490 (0.401178) \| 0.000776 / 0.000200 (0.000576) \| 0.000069 / 0.000054 (0.000014) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024959 / 0.037411 (-0.012452) \| 0.100560 / 0.014526 (0.086034) \| 0.109175 / 0.176557 (-0.067382) \| 0.159919 / 0.737135 (-0.577217) \| 0.112810 / 0.296338 (-0.183528) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.460601 / 0.215209 (0.245392) \| 4.620039 / 2.077655 (2.542385) \| 2.257900 / 1.504120 (0.753780) \| 2.039192 / 1.541195 (0.497997) \| 2.064451 / 1.468490 (0.595961) \| 0.557887 / 4.584777 (-4.026890) \| 3.356100 / 3.745712 (-0.389612) \| 1.703578 / 5.269862 (-3.566284) \| 1.024984 / 4.565676 (-3.540693) \| 0.067602 / 0.424275 (-0.356673) \| 0.011450 / 0.007607 (0.003842) \| 0.563230 / 0.226044 (0.337186) \| 5.632150 / 2.268929 (3.363221) \| 2.698701 / 55.444624 (-52.745924) \| 2.363218 / 6.876477 (-4.513259) \| 2.363997 / 2.142072 (0.221925) \| 0.671260 / 4.805227 (-4.133967) \| 0.136166 / 6.500664 (-6.364499) \| 0.067094 / 0.075469 (-0.008375) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.303030 / 1.841788 (-0.538757) \| 14.137277 / 8.074308 (6.062969) \| 13.937631 / 10.191392 (3.746239) \| 0.162626 / 0.680424 (-0.517798) \| 0.016687 / 0.534201 (-0.517514) \| 0.363657 / 0.579283 (-0.215626) \| 0.392021 / 0.434364 (-0.042343) \| 0.427275 / 0.540337 (-0.113062) \| 0.512192 / 1.386936 (-0.874744) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#42603528d9bd8c3ab287ed0eadc7fa3d1ef4cfd8 \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005974 / 0.011353 (-0.005378) \| 0.003947 / 0.011008 (-0.007061) \| 0.098604 / 0.038508 (0.060096) \| 0.036947 / 0.023109 (0.013838) \| 0.311844 / 0.275898 (0.035946) \| 0.375243 / 0.323480 (0.051763) \| 0.003453 / 0.007986 (-0.004533) \| 0.003834 / 0.004328 (-0.000495) \| 0.077943 / 0.004250 (0.073692) \| 0.052956 / 0.037052 (0.015904) \| 0.320812 / 0.258489 (0.062323) \| 0.373963 / 0.293841 (0.080122) \| 0.028382 / 0.128546 (-0.100164) \| 0.008525 / 0.075646 (-0.067121) \| 0.311306 / 0.419271 (-0.107965) \| 0.047029 / 0.043533 (0.003496) \| 0.309933 / 0.255139 (0.054794) \| 0.335114 / 0.283200 (0.051915) \| 0.019629 / 0.141683 (-0.122054) \| 1.569771 / 1.452155 (0.117617) \| 1.585899 / 1.492716 (0.093182) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.216565 / 0.018006 (0.198559) \| 0.426717 / 0.000490 (0.426228) \| 0.003609 / 0.000200 (0.003409) \| 0.000077 / 0.000054 (0.000023) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023079 / 0.037411 (-0.014332) \| 0.096954 / 0.014526 (0.082428) \| 0.105398 / 0.176557 (-0.071158) \| 0.165433 / 0.737135 (-0.571703) \| 0.109703 / 0.296338 (-0.186636) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.456227 / 0.215209 (0.241018) \| 4.529857 / 2.077655 (2.452202) \| 2.214054 / 1.504120 (0.709934) \| 2.029716 / 1.541195 (0.488521) \| 2.081175 / 1.468490 (0.612685) \| 0.563642 / 4.584777 (-4.021135) \| 3.355393 / 3.745712 (-0.390320) \| 1.765938 / 5.269862 (-3.503924) \| 1.039062 / 4.565676 (-3.526615) \| 0.067952 / 0.424275 (-0.356323) \| 0.011044 / 0.007607 (0.003437) \| 0.556935 / 0.226044 (0.330890) \| 5.588167 / 2.268929 (3.319239) \| 2.667217 / 55.444624 (-52.777407) \| 2.337383 / 6.876477 (-4.539094) \| 2.429590 / 2.142072 (0.287517) \| 0.676972 / 4.805227 (-4.128256) \| 0.135782 / 6.500664 (-6.364882) \| 0.066323 / 0.075469 (-0.009146) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.237358 / 1.841788 (-0.604429) \| 13.910492 / 8.074308 (5.836184) \| 13.227275 / 10.191392 (3.035883) \| 0.146857 / 0.680424 (-0.533567) \| 0.016991 / 0.534201 (-0.517210) \| 0.363637 / 0.579283 (-0.215646) \| 0.392462 / 0.434364 (-0.041902) \| 0.450009 / 0.540337 (-0.090329) \| 0.536077 / 1.386936 (-0.850859) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006067 / 0.011353 (-0.005286) \| 0.003851 / 0.011008 (-0.007158) \| 0.078462 / 0.038508 (0.039954) \| 0.036221 / 0.023109 (0.013112) \| 0.389195 / 0.275898 (0.113297) \| 0.428710 / 0.323480 (0.105230) \| 0.004645 / 0.007986 (-0.003341) \| 0.002973 / 0.004328 (-0.001355) \| 0.078299 / 0.004250 (0.074048) \| 0.047076 / 0.037052 (0.010024) \| 0.375673 / 0.258489 (0.117184) \| 0.432352 / 0.293841 (0.138511) \| 0.028212 / 0.128546 (-0.100334) \| 0.008475 / 0.075646 (-0.067172) \| 0.083902 / 0.419271 (-0.335369) \| 0.046699 / 0.043533 (0.003166) \| 0.364502 / 0.255139 (0.109363) \| 0.389792 / 0.283200 (0.106592) \| 0.025266 / 0.141683 (-0.116417) \| 1.517458 / 1.452155 (0.065303) \| 1.543634 / 1.492716 (0.050918) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.236479 / 0.018006 (0.218472) \| 0.411528 / 0.000490 (0.411038) \| 0.005213 / 0.000200 (0.005013) \| 0.000091 / 0.000054 (0.000036) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025764 / 0.037411 (-0.011647) \| 0.103174 / 0.014526 (0.088648) \| 0.110609 / 0.176557 (-0.065948) \| 0.164630 / 0.737135 (-0.572506) \| 0.114863 / 0.296338 (-0.181475) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.457155 / 0.215209 (0.241946) \| 4.550675 / 2.077655 (2.473021) \| 2.350473 / 1.504120 (0.846353) \| 2.204919 / 1.541195 (0.663724) \| 2.076724 / 1.468490 (0.608234) \| 0.563107 / 4.584777 (-4.021670) \| 3.390669 / 3.745712 (-0.355043) \| 1.741111 / 5.269862 (-3.528751) \| 1.033268 / 4.565676 (-3.532408) \| 0.068400 / 0.424275 (-0.355875) \| 0.011607 / 0.007607 (0.004000) \| 0.561944 / 0.226044 (0.335900) \| 5.620224 / 2.268929 (3.351296) \| 2.705241 / 55.444624 (-52.739384) \| 2.344520 / 6.876477 (-4.531957) \| 2.386119 / 2.142072 (0.244046) \| 0.681583 / 4.805227 (-4.123644) \| 0.137272 / 6.500664 (-6.363392) \| 0.069217 / 0.075469 (-0.006252) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.322690 / 1.841788 (-0.519098) \| 14.464953 / 8.074308 (6.390645) \| 14.269350 / 10.191392 (4.077958) \| 0.158879 / 0.680424 (-0.521545) \| 0.016722 / 0.534201 (-0.517479) \| 0.360299 / 0.579283 (-0.218984) \| 0.391609 / 0.434364 (-0.042755) \| 0.420507 / 0.540337 (-0.119831) \| 0.512822 / 1.386936 (-0.874114) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ca68191900d97b29abb3c2c4ba0502fe30d137d1 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007106 / 0.011353 (-0.004247) \| 0.005224 / 0.011008 (-0.005784) \| 0.127563 / 0.038508 (0.089055) \| 0.055067 / 0.023109 (0.031958) \| 0.418660 / 0.275898 (0.142761) \| 0.487891 / 0.323480 (0.164411) \| 0.005712 / 0.007986 (-0.002274) \| 0.004585 / 0.004328 (0.000256) \| 0.090994 / 0.004250 (0.086743) \| 0.071837 / 0.037052 (0.034784) \| 0.446957 / 0.258489 (0.188468) \| 0.475966 / 0.293841 (0.182125) \| 0.038062 / 0.128546 (-0.090484) \| 0.010056 / 0.075646 (-0.065590) \| 0.406796 / 0.419271 (-0.012475) \| 0.066542 / 0.043533 (0.023009) \| 0.413676 / 0.255139 (0.158537) \| 0.448624 / 0.283200 (0.165424) \| 0.030332 / 0.141683 (-0.111351) \| 1.895307 / 1.452155 (0.443152) \| 1.904411 / 1.492716 (0.411694) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.221246 / 0.018006 (0.203240) \| 0.461288 / 0.000490 (0.460799) \| 0.005957 / 0.000200 (0.005757) \| 0.000112 / 0.000054 (0.000058) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029255 / 0.037411 (-0.008156) \| 0.131299 / 0.014526 (0.116773) \| 0.135814 / 0.176557 (-0.040742) \| 0.201342 / 0.737135 (-0.535793) \| 0.141748 / 0.296338 (-0.154591) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.463936 / 0.215209 (0.248727) \| 4.709621 / 2.077655 (2.631966) \| 2.093844 / 1.504120 (0.589724) \| 1.897963 / 1.541195 (0.356768) \| 1.927865 / 1.468490 (0.459375) \| 0.610879 / 4.584777 (-3.973898) \| 4.481370 / 3.745712 (0.735658) \| 2.112235 / 5.269862 (-3.157627) \| 1.203349 / 4.565676 (-3.362327) \| 0.074828 / 0.424275 (-0.349447) \| 0.013121 / 0.007607 (0.005514) \| 0.580894 / 0.226044 (0.354849) \| 5.801872 / 2.268929 (3.532943) \| 2.579950 / 55.444624 (-52.864674) \| 2.251569 / 6.876477 (-4.624908) \| 2.421305 / 2.142072 (0.279232) \| 0.760938 / 4.805227 (-4.044289) \| 0.169554 / 6.500664 (-6.331110) \| 0.077499 / 0.075469 (0.002030) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.410419 / 1.841788 (-0.431368) \| 17.442331 / 8.074308 (9.368023) \| 15.782183 / 10.191392 (5.590791) \| 0.180649 / 0.680424 (-0.499775) \| 0.021790 / 0.534201 (-0.512411) \| 0.511040 / 0.579283 (-0.068243) \| 0.510472 / 0.434364 (0.076108) \| 0.607141 / 0.540337 (0.066804) \| 0.724794 / 1.386936 (-0.662142) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007280 / 0.011353 (-0.004073) \| 0.004712 / 0.011008 (-0.006296) \| 0.089225 / 0.038508 (0.050717) \| 0.053157 / 0.023109 (0.030048) \| 0.431949 / 0.275898 (0.156051) \| 0.478128 / 0.323480 (0.154648) \| 0.006181 / 0.007986 (-0.001804) \| 0.003387 / 0.004328 (-0.000941) \| 0.083741 / 0.004250 (0.079490) \| 0.071610 / 0.037052 (0.034557) \| 0.414698 / 0.258489 (0.156209) \| 0.484422 / 0.293841 (0.190581) \| 0.034988 / 0.128546 (-0.093558) \| 0.009831 / 0.075646 (-0.065816) \| 0.089644 / 0.419271 (-0.329628) \| 0.057053 / 0.043533 (0.013520) \| 0.413144 / 0.255139 (0.158005) \| 0.445464 / 0.283200 (0.162264) \| 0.026109 / 0.141683 (-0.115574) \| 1.842899 / 1.452155 (0.390745) \| 1.923774 / 1.492716 (0.431057) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.245051 / 0.018006 (0.227045) \| 0.460444 / 0.000490 (0.459954) \| 0.000444 / 0.000200 (0.000244) \| 0.000067 / 0.000054 (0.000012) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034835 / 0.037411 (-0.002577) \| 0.130078 / 0.014526 (0.115553) \| 0.147012 / 0.176557 (-0.029544) \| 0.203097 / 0.737135 (-0.534038) \| 0.149636 / 0.296338 (-0.146702) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.521664 / 0.215209 (0.306455) \| 5.283865 / 2.077655 (3.206210) \| 2.456701 / 1.504120 (0.952581) \| 2.266059 / 1.541195 (0.724864) \| 2.295387 / 1.468490 (0.826897) \| 0.613200 / 4.584777 (-3.971577) \| 4.526107 / 3.745712 (0.780394) \| 2.047327 / 5.269862 (-3.222535) \| 1.261063 / 4.565676 (-3.304614) \| 0.070402 / 0.424275 (-0.353873) \| 0.014128 / 0.007607 (0.006521) \| 0.620929 / 0.226044 (0.394884) \| 6.109127 / 2.268929 (3.840198) \| 3.081406 / 55.444624 (-52.363218) \| 2.658224 / 6.876477 (-4.218253) \| 2.671974 / 2.142072 (0.529902) \| 0.744081 / 4.805227 (-4.061146) \| 0.161498 / 6.500664 (-6.339166) \| 0.075148 / 0.075469 (-0.000321) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.585640 / 1.841788 (-0.256148) \| 17.884321 / 8.074308 (9.810013) \| 15.938937 / 10.191392 (5.747545) \| 0.220818 / 0.680424 (-0.459605) \| 0.021452 / 0.534201 (-0.512749) \| 0.499747 / 0.579283 (-0.079536) \| 0.512318 / 0.434364 (0.077954) \| 0.562853 / 0.540337 (0.022515) \| 0.678512 / 1.386936 (-0.708424) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#aa50937d82256827aee3dbd749c7a23555e05e38 \"CML watermark\")\n" ]	2023-06-27T12:32:46	2023-06-27T15:40:47	2023-06-27T15:32:43	MEMBER	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5994", "html_url": "https://github.com/huggingface/datasets/pull/5994", "diff_url": "https://github.com/huggingface/datasets/pull/5994.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5994.patch", "merged_at": "2023-06-27T15:32:43" }	Fix the order of the columns in dataset.features when the order changes with `dataset.select_columns()`. I also fixed the same issue for `dataset.flatten()` Close https://github.com/huggingface/datasets/issues/5993	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5994/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5994/timeline	null	null	true
https://api.github.com/repos/huggingface/datasets/issues/5993	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5993/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5993/comments	https://api.github.com/repos/huggingface/datasets/issues/5993/events	https://github.com/huggingface/datasets/issues/5993	1,776,643,555	I_kwDODunzps5p5W3j	5,993	ValueError: Table schema does not match schema used to create file	{ "login": "exs-avianello", "id": 128361578, "node_id": "U_kgDOB6akag", "avatar_url": "https://avatars.githubusercontent.com/u/128361578?v=4", "gravatar_id": "", "url": "https://api.github.com/users/exs-avianello", "html_url": "https://github.com/exs-avianello", "followers_url": "https://api.github.com/users/exs-avianello/followers", "following_url": "https://api.github.com/users/exs-avianello/following{/other_user}", "gists_url": "https://api.github.com/users/exs-avianello/gists{/gist_id}", "starred_url": "https://api.github.com/users/exs-avianello/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/exs-avianello/subscriptions", "organizations_url": "https://api.github.com/users/exs-avianello/orgs", "repos_url": "https://api.github.com/users/exs-avianello/repos", "events_url": "https://api.github.com/users/exs-avianello/events{/privacy}", "received_events_url": "https://api.github.com/users/exs-avianello/received_events", "type": "User", "site_admin": false }	[]	closed	false	{ "login": "lhoestq", "id": 42851186, "node_id": "MDQ6VXNlcjQyODUxMTg2", "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "gravatar_id": "", "url": "https://api.github.com/users/lhoestq", "html_url": "https://github.com/lhoestq", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "repos_url": "https://api.github.com/users/lhoestq/repos", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "type": "User", "site_admin": false }	[ { "login": "lhoestq", "id": 42851186, "node_id": "MDQ6VXNlcjQyODUxMTg2", "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "gravatar_id": "", "url": "https://api.github.com/users/lhoestq", "html_url": "https://github.com/lhoestq", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "repos_url": "https://api.github.com/users/lhoestq/repos", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "type": "User", "site_admin": false } ]	null	[ "We'll do a new release of `datasets` soon to make the fix available :)\r\n\r\nIn the meantime you can use `datasets` from source (main)", "Thank you very much @lhoestq ! 🚀 " ]	2023-06-27T10:54:07	2023-06-27T15:36:42	2023-06-27T15:32:44	NONE	null	null	null	### Describe the bug Saving a dataset as parquet fails with a `ValueError: Table schema does not match schema used to create file` if the dataset was obtained out of a `.select_columns()` call with columns selected out of order. ### Steps to reproduce the bug ```python import datasets dataset = datasets.Dataset.from_dict( { "x1": [1, 2, 3], "x2": [10, 11, 12], } ) ds = dataset.select_columns(["x2", "x1"]) ds.to_parquet("demo.parquet") ``` ```shell >>> ValueError: Table schema does not match schema used to create file: table: x2: int64 x1: int64 -- schema metadata -- huggingface: '{"info": {"features": {"x2": {"dtype": "int64", "_type": "V' + 53 vs. file: x1: int64 x2: int64 -- schema metadata -- huggingface: '{"info": {"features": {"x1": {"dtype": "int64", "_type": "V' + 53 ``` --- I think this is because after the `.select_columns()` call with out of order columns, the output dataset features' schema ends up being out of sync with the schema of the arrow table backing it. ```python ds.features.arrow_schema >>> x1: int64 x2: int64 -- schema metadata -- huggingface: '{"info": {"features": {"x1": {"dtype": "int64", "_type": "V' + 53 ds.data.schema >>> x2: int64 x1: int64 -- schema metadata -- huggingface: '{"info": {"features": {"x2": {"dtype": "int64", "_type": "V' + 53 ``` So when we call `.to_parquet()`, the call behind the scenes to `datasets.io.parquet.ParquetDatasetWriter(...).write()` which initialises the backend `pyarrow.parquet.ParquetWriter` with `schema = self.dataset.features.arrow_schema` triggers `pyarrow` on write when [it checks](https://github.com/apache/arrow/blob/11b140a734a516e436adaddaeb35d23f30dcce44/python/pyarrow/parquet/core.py#L1086-L1090) that the `ParquetWriter` schema matches the schema of the table being written 🙌 https://github.com/huggingface/datasets/blob/6ed837325cb539a5deb99129e5ad181d0269e050/src/datasets/io/parquet.py#L139-L141 ### Expected behavior The dataset gets successfully saved as parquet. *In the same way as it does if saving it as csv: ```python import datasets dataset = datasets.Dataset.from_dict( { "x1": [1, 2, 3], "x2": [10, 11, 12], } ) ds = dataset.select_columns(["x2", "x1"]) ds.to_csv("demo.csv") ``` ### Environment info `python==3.11` `datasets==2.13.1`	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5993/reactions", "total_count": 1, "+1": 1, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5993/timeline	null	completed	false
https://api.github.com/repos/huggingface/datasets/issues/5992	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5992/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5992/comments	https://api.github.com/repos/huggingface/datasets/issues/5992/events	https://github.com/huggingface/datasets/pull/5992	1,776,460,964	PR_kwDODunzps5UAk3C	5,992	speedup	{ "login": "qgallouedec", "id": 45557362, "node_id": "MDQ6VXNlcjQ1NTU3MzYy", "avatar_url": "https://avatars.githubusercontent.com/u/45557362?v=4", "gravatar_id": "", "url": "https://api.github.com/users/qgallouedec", "html_url": "https://github.com/qgallouedec", "followers_url": "https://api.github.com/users/qgallouedec/followers", "following_url": "https://api.github.com/users/qgallouedec/following{/other_user}", "gists_url": "https://api.github.com/users/qgallouedec/gists{/gist_id}", "starred_url": "https://api.github.com/users/qgallouedec/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/qgallouedec/subscriptions", "organizations_url": "https://api.github.com/users/qgallouedec/orgs", "repos_url": "https://api.github.com/users/qgallouedec/repos", "events_url": "https://api.github.com/users/qgallouedec/events{/privacy}", "received_events_url": "https://api.github.com/users/qgallouedec/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5992). All of your documentation changes will be reflected on that endpoint." ]	2023-06-27T09:17:58	2023-06-27T09:23:07	2023-06-27T09:18:04	CONTRIBUTOR	null	true	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/5992", "html_url": "https://github.com/huggingface/datasets/pull/5992", "diff_url": "https://github.com/huggingface/datasets/pull/5992.diff", "patch_url": "https://github.com/huggingface/datasets/pull/5992.patch", "merged_at": null }	null	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5992/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5992/timeline	null	null	true
https://api.github.com/repos/huggingface/datasets/issues/5991	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5991/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5991/comments	https://api.github.com/repos/huggingface/datasets/issues/5991/events	https://github.com/huggingface/datasets/issues/5991	1,774,456,518	I_kwDODunzps5pxA7G	5,991	`map` with any joblib backend	{ "login": "lhoestq", "id": 42851186, "node_id": "MDQ6VXNlcjQyODUxMTg2", "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "gravatar_id": "", "url": "https://api.github.com/users/lhoestq", "html_url": "https://github.com/lhoestq", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "repos_url": "https://api.github.com/users/lhoestq/repos", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "type": "User", "site_admin": false }	[ { "id": 1935892871, "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement", "name": "enhancement", "color": "a2eeef", "default": true, "description": "New feature or request" } ]	open	false	null	[]	null	[]	2023-06-26T10:33:42	2023-06-26T10:33:42	null	MEMBER	null	null	null	We recently enabled the (experimental) parallel backend switch for data download and extraction but not for `map` yet. Right now we're using our `iflatmap_unordered` implementation for multiprocessing that uses a shared Queue to gather progress updates from the subprocesses and show a progress bar in the main process. If a Queue implementation that would work on any joblib backend by leveraging the filesystem that is shared among workers, we can have `iflatmap_unordered` for joblib and therefore a `map` with any joblib backend with a progress bar ! Note that the Queue doesn't need to be that optimized though since we can choose a small frequency for progress updates (like 1 update per second).	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5991/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5991/timeline	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5989	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5989/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5989/comments	https://api.github.com/repos/huggingface/datasets/issues/5989/events	https://github.com/huggingface/datasets/issues/5989	1,774,134,091	I_kwDODunzps5pvyNL	5,989	Set a rule on the config and split names	{ "login": "severo", "id": 1676121, "node_id": "MDQ6VXNlcjE2NzYxMjE=", "avatar_url": "https://avatars.githubusercontent.com/u/1676121?v=4", "gravatar_id": "", "url": "https://api.github.com/users/severo", "html_url": "https://github.com/severo", "followers_url": "https://api.github.com/users/severo/followers", "following_url": "https://api.github.com/users/severo/following{/other_user}", "gists_url": "https://api.github.com/users/severo/gists{/gist_id}", "starred_url": "https://api.github.com/users/severo/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/severo/subscriptions", "organizations_url": "https://api.github.com/users/severo/orgs", "repos_url": "https://api.github.com/users/severo/repos", "events_url": "https://api.github.com/users/severo/events{/privacy}", "received_events_url": "https://api.github.com/users/severo/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	null	[ "in this case we need to decide what to do with the existing datasets with white space characters (there shouldn't be a lot of them I think)", "I imagine that we should stop supporting them, and help the user fix them?", "See a report where the datasets server fails: https://huggingface.co/datasets/poloclub/diffusiondb/discussions/2#6374ff55b93cbdf65675f564\r\n\r\nThe config name is `random_10k [2m]`!" ]	2023-06-26T07:34:14	2023-07-19T14:22:54	null	CONTRIBUTOR	null	null	null	> should we actually allow characters like spaces? maybe it's better to add validation for whitespace symbols and directly in datasets and raise https://github.com/huggingface/datasets-server/issues/853	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5989/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5989/timeline	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/5988	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5988/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5988/comments	https://api.github.com/repos/huggingface/datasets/issues/5988/events	https://github.com/huggingface/datasets/issues/5988	1,773,257,828	I_kwDODunzps5pscRk	5,988	ConnectionError: Couldn't reach dataset_infos.json	{ "login": "yulingao", "id": 20674868, "node_id": "MDQ6VXNlcjIwNjc0ODY4", "avatar_url": "https://avatars.githubusercontent.com/u/20674868?v=4", "gravatar_id": "", "url": "https://api.github.com/users/yulingao", "html_url": "https://github.com/yulingao", "followers_url": "https://api.github.com/users/yulingao/followers", "following_url": "https://api.github.com/users/yulingao/following{/other_user}", "gists_url": "https://api.github.com/users/yulingao/gists{/gist_id}", "starred_url": "https://api.github.com/users/yulingao/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/yulingao/subscriptions", "organizations_url": "https://api.github.com/users/yulingao/orgs", "repos_url": "https://api.github.com/users/yulingao/repos", "events_url": "https://api.github.com/users/yulingao/events{/privacy}", "received_events_url": "https://api.github.com/users/yulingao/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "Unfortunately, I can't reproduce the error. What does the following code return for you?\r\n```python\r\nimport requests\r\nfrom huggingface_hub import hf_hub_url\r\nr = requests.get(hf_hub_url(\"codeparrot/codeparrot-clean-train\", \"dataset_infos.json\", repo_type=\"dataset\"))\r\n```\r\n\r\nAlso, can you provide more info about your network (region, proxies, etc.)?" ]	2023-06-25T12:39:31	2023-07-07T13:20:57	2023-07-07T13:20:57	NONE	null	null	null	### Describe the bug I'm trying to load codeparrot/codeparrot-clean-train, but get the following error: ConnectionError: Couldn't reach https://huggingface.co/datasets/codeparrot/codeparrot-clean-train/resolve/main/dataset_infos.json (ConnectionError(ProtocolError('Connection aborted.', ConnectionResetError(104, 'Connection reset by peer')))) ### Steps to reproduce the bug train_data = load_dataset('codeparrot/codeparrot-clean-train', split='train') ### Expected behavior download the dataset ### Environment info centos7	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5988/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5988/timeline	null	completed	false
https://api.github.com/repos/huggingface/datasets/issues/5987	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5987/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5987/comments	https://api.github.com/repos/huggingface/datasets/issues/5987/events	https://github.com/huggingface/datasets/issues/5987	1,773,047,909	I_kwDODunzps5prpBl	5,987	Why max_shard_size is not supported in load_dataset and passed to download_and_prepare	{ "login": "npuichigo", "id": 11533479, "node_id": "MDQ6VXNlcjExNTMzNDc5", "avatar_url": "https://avatars.githubusercontent.com/u/11533479?v=4", "gravatar_id": "", "url": "https://api.github.com/users/npuichigo", "html_url": "https://github.com/npuichigo", "followers_url": "https://api.github.com/users/npuichigo/followers", "following_url": "https://api.github.com/users/npuichigo/following{/other_user}", "gists_url": "https://api.github.com/users/npuichigo/gists{/gist_id}", "starred_url": "https://api.github.com/users/npuichigo/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/npuichigo/subscriptions", "organizations_url": "https://api.github.com/users/npuichigo/orgs", "repos_url": "https://api.github.com/users/npuichigo/repos", "events_url": "https://api.github.com/users/npuichigo/events{/privacy}", "received_events_url": "https://api.github.com/users/npuichigo/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "Can you explain your use case for `max_shard_size`? \r\n\r\nOn some systems, there is a limit to the size of a memory-mapped file, so we could consider exposing this parameter in `load_dataset`.", "In my use case, users may choose a proper size to balance the cost and benefit of using large shard size. (On azure blob or hdfs which may automatically download the shard from background)", "But `load_dataset` doesn't support caching (and reading) Arrow datasets from remote storage. \r\n\r\n`load_datset_builder` + `download_and_prepare` is not equal to `load_dataset`. The latter has one more step, `builder.as_dataset`, that memory-maps Arrow files, which only works for local files.", "Thanks. So if I want to use `IterableDataset` and control the size of single arrow file, how should I organize the data loader? Maybe `load_dataset_build` + `download_and_prepare` + `builder.as_dataset` + `dataset.to_iterable_dataset`?", "Yes, this should work.\r\n\r\nI think we can expose `max_shard_size` in `load_dataset`, so feel free to open a PR." ]	2023-06-25T04:19:13	2023-06-29T16:06:08	2023-06-29T16:06:08	NONE	null	null	null	### Describe the bug https://github.com/huggingface/datasets/blob/a8a797cc92e860c8d0df71e0aa826f4d2690713e/src/datasets/load.py#L1809 What I can to is break the `load_dataset` and use `load_datset_builder` + `download_and_prepare` instead. ### Steps to reproduce the bug https://github.com/huggingface/datasets/blob/a8a797cc92e860c8d0df71e0aa826f4d2690713e/src/datasets/load.py#L1809 ### Expected behavior Users can define the max shard size. ### Environment info datasets==2.13.1	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/5987/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/5987/timeline	null	completed	false