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alex-atelo
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datasets-github-issues

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https://api.github.com/repos/huggingface/datasets/issues/6165	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6165/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6165/comments	https://api.github.com/repos/huggingface/datasets/issues/6165/events	https://github.com/huggingface/datasets/pull/6165	1,861,124,284	PR_kwDODunzps5YexBL	6,165	Fix multiprocessing with spawn in iterable datasets	{ "avatar_url": "https://avatars.githubusercontent.com/u/48770768?v=4", "events_url": "https://api.github.com/users/Hubert-Bonisseur/events{/privacy}", "followers_url": "https://api.github.com/users/Hubert-Bonisseur/followers", "following_url": "https://api.github.com/users/Hubert-Bonisseur/following{/other_user}", "gists_url": "https://api.github.com/users/Hubert-Bonisseur/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/Hubert-Bonisseur", "id": 48770768, "login": "Hubert-Bonisseur", "node_id": "MDQ6VXNlcjQ4NzcwNzY4", "organizations_url": "https://api.github.com/users/Hubert-Bonisseur/orgs", "received_events_url": "https://api.github.com/users/Hubert-Bonisseur/received_events", "repos_url": "https://api.github.com/users/Hubert-Bonisseur/repos", "site_admin": false, "starred_url": "https://api.github.com/users/Hubert-Bonisseur/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/Hubert-Bonisseur/subscriptions", "type": "User", "url": "https://api.github.com/users/Hubert-Bonisseur" }	[]	closed	false	null	[]	null	5	"2023-08-22T10:07:23Z"	"2023-08-29T13:27:14Z"	"2023-08-29T13:18:11Z"	CONTRIBUTOR	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6165.diff", "html_url": "https://github.com/huggingface/datasets/pull/6165", "merged_at": "2023-08-29T13:18:11Z", "patch_url": "https://github.com/huggingface/datasets/pull/6165.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6165" }	The "Spawn" method is preferred when multiprocessing on macOS or Windows systems, instead of the "Fork" method on linux systems. This causes some methods of Iterable Datasets to break when using a dataloader with more than 0 workers. I fixed the issue by replacing lambda and local methods which are not pickle-able. See the example below: ```python from datasets import load_dataset from torch.utils.data import DataLoader if __name__ == "__main__": dataset = load_dataset("lhoestq/demo1", split="train") dataset = dataset.to_iterable_dataset(num_shards=3) dataset = dataset.remove_columns(["package_name"]) dataset = dataset.rename_columns({ "review": "review1" }) dataset = dataset.rename_column("date", "date1") for sample in DataLoader(dataset, batch_size=None, num_workers=3): print(sample) ``` To notice the fix on a linux system, adding these lines should do the trick: ```python import multiprocessing multiprocessing.set_start_method('spawn') ``` I also removed what looks like code duplication between rename_colums and rename_column	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6165/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6165/timeline	null	null	600	true	[ "_The documentation is not available anymore as the PR was closed or merged._", "@lhoestq \r\nA test is failing, but I don't think it is due to my changes", "Good catch ! Could you add a test to make sure transformed IterableDataset objects are still picklable ?\r\n\r\nSomething like `test_pickle_after_many_transforms` in in `test_iterable_dataset.py` that does a bunch or rename, map, take on a dataset and checks that the dataset can be pickled at the end and the reloaded dataset returns the same elements", "@lhoestq \r\nI added the test and fixed one last method", "<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) \| 0.006537 / 0.011353 (-0.004816) \| 0.003960 / 0.011008 (-0.007048) \| 0.085135 / 0.038508 (0.046627) \| 0.079271 / 0.023109 (0.056162) \| 0.383743 / 0.275898 (0.107845) \| 0.414622 / 0.323480 (0.091143) \| 0.004202 / 0.007986 (-0.003784) \| 0.003537 / 0.004328 (-0.000791) \| 0.065758 / 0.004250 (0.061508) \| 0.054225 / 0.037052 (0.017173) \| 0.395715 / 0.258489 (0.137226) \| 0.438985 / 0.293841 (0.145144) \| 0.030590 / 0.128546 (-0.097956) \| 0.008754 / 0.075646 (-0.066892) \| 0.288415 / 0.419271 (-0.130857) \| 0.051863 / 0.043533 (0.008330) \| 0.382501 / 0.255139 (0.127363) \| 0.414428 / 0.283200 (0.131228) \| 0.024084 / 0.141683 (-0.117599) \| 1.478726 / 1.452155 (0.026572) \| 1.544763 / 1.492716 (0.052047) \|\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) \| 0.285143 / 0.018006 (0.267136) \| 0.603859 / 0.000490 (0.603369) \| 0.004330 / 0.000200 (0.004131) \| 0.000108 / 0.000054 (0.000054) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027856 / 0.037411 (-0.009555) \| 0.081963 / 0.014526 (0.067437) \| 0.104106 / 0.176557 (-0.072451) \| 0.151378 / 0.737135 (-0.585757) \| 0.096476 / 0.296338 (-0.199862) \|\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) \| 0.402938 / 0.215209 (0.187729) \| 4.042312 / 2.077655 (1.964657) \| 2.068421 / 1.504120 (0.564301) \| 1.877870 / 1.541195 (0.336675) \| 1.947643 / 1.468490 (0.479153) \| 0.482031 / 4.584777 (-4.102746) \| 3.554747 / 3.745712 (-0.190965) \| 3.307811 / 5.269862 (-1.962050) \| 2.082886 / 4.565676 (-2.482791) \| 0.056853 / 0.424275 (-0.367422) \| 0.007535 / 0.007607 (-0.000072) \| 0.483694 / 0.226044 (0.257649) \| 4.827906 / 2.268929 (2.558978) \| 2.567572 / 55.444624 (-52.877052) \| 2.167206 / 6.876477 (-4.709271) \| 2.414442 / 2.142072 (0.272369) \| 0.579472 / 4.805227 (-4.225755) \| 0.132976 / 6.500664 (-6.367688) \| 0.059315 / 0.075469 (-0.016154) \|\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) \| 1.260086 / 1.841788 (-0.581702) \| 19.438297 / 8.074308 (11.363989) \| 14.188161 / 10.191392 (3.996769) \| 0.168534 / 0.680424 (-0.511890) \| 0.018070 / 0.534201 (-0.516131) \| 0.394241 / 0.579283 (-0.185043) \| 0.411057 / 0.434364 (-0.023307) \| 0.461123 / 0.540337 (-0.079215) \| 0.626844 / 1.386936 (-0.760092) \|\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) \| 0.006896 / 0.011353 (-0.004457) \| 0.004207 / 0.011008 (-0.006801) \| 0.064981 / 0.038508 (0.026473) \| 0.080261 / 0.023109 (0.057152) \| 0.399403 / 0.275898 (0.123505) \| 0.433099 / 0.323480 (0.109619) \| 0.005697 / 0.007986 (-0.002288) \| 0.003601 / 0.004328 (-0.000728) \| 0.065924 / 0.004250 (0.061673) \| 0.058868 / 0.037052 (0.021815) \| 0.403705 / 0.258489 (0.145216) \| 0.439218 / 0.293841 (0.145377) \| 0.032789 / 0.128546 (-0.095757) \| 0.008675 / 0.075646 (-0.066971) \| 0.071217 / 0.419271 (-0.348055) \| 0.048487 / 0.043533 (0.004954) \| 0.399878 / 0.255139 (0.144739) \| 0.412816 / 0.283200 (0.129616) \| 0.023905 / 0.141683 (-0.117778) \| 1.541402 / 1.452155 (0.089247) \| 1.588080 / 1.492716 (0.095364) \|\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) \| 0.322863 / 0.018006 (0.304856) \| 0.530291 / 0.000490 (0.529802) \| 0.004862 / 0.000200 (0.004662) \| 0.000097 / 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) \| 0.032697 / 0.037411 (-0.004715) \| 0.092416 / 0.014526 (0.077891) \| 0.107355 / 0.176557 (-0.069201) \| 0.160217 / 0.737135 (-0.576918) \| 0.109286 / 0.296338 (-0.187052) \|\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) \| 0.437375 / 0.215209 (0.222166) \| 4.362644 / 2.077655 (2.284990) \| 2.335404 / 1.504120 (0.831284) \| 2.173215 / 1.541195 (0.632020) \| 2.254061 / 1.468490 (0.785571) \| 0.493906 / 4.584777 (-4.090871) \| 3.609025 / 3.745712 (-0.136687) \| 3.352380 / 5.269862 (-1.917481) \| 2.074185 / 4.565676 (-2.491492) \| 0.057863 / 0.424275 (-0.366412) \| 0.007297 / 0.007607 (-0.000310) \| 0.512464 / 0.226044 (0.286420) \| 5.135921 / 2.268929 (2.866993) \| 2.788889 / 55.444624 (-52.655736) \| 2.479097 / 6.876477 (-4.397379) \| 2.717848 / 2.142072 (0.575776) \| 0.590442 / 4.805227 (-4.214785) \| 0.133721 / 6.500664 (-6.366943) \| 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) \| 1.429564 / 1.841788 (-0.412224) \| 20.628733 / 8.074308 (12.554425) \| 15.299571 / 10.191392 (5.108179) \| 0.171032 / 0.680424 (-0.509392) \| 0.019995 / 0.534201 (-0.514206) \| 0.401283 / 0.579283 (-0.178000) \| 0.416504 / 0.434364 (-0.017860) \| 0.471219 / 0.540337 (-0.069118) \| 0.641299 / 1.386936 (-0.745637) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5503e7beb5a31926aec03c6c9b24813f9f83cd7b \"CML watermark\")\n" ]
https://api.github.com/repos/huggingface/datasets/issues/6164	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6164/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6164/comments	https://api.github.com/repos/huggingface/datasets/issues/6164/events	https://github.com/huggingface/datasets/pull/6164	1,859,560,007	PR_kwDODunzps5YZZAJ	6,164	Fix: Missing a MetadataConfigs init when the repo has a `datasets_info.json` but no README	{ "avatar_url": "https://avatars.githubusercontent.com/u/22726840?v=4", "events_url": "https://api.github.com/users/clefourrier/events{/privacy}", "followers_url": "https://api.github.com/users/clefourrier/followers", "following_url": "https://api.github.com/users/clefourrier/following{/other_user}", "gists_url": "https://api.github.com/users/clefourrier/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/clefourrier", "id": 22726840, "login": "clefourrier", "node_id": "MDQ6VXNlcjIyNzI2ODQw", "organizations_url": "https://api.github.com/users/clefourrier/orgs", "received_events_url": "https://api.github.com/users/clefourrier/received_events", "repos_url": "https://api.github.com/users/clefourrier/repos", "site_admin": false, "starred_url": "https://api.github.com/users/clefourrier/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/clefourrier/subscriptions", "type": "User", "url": "https://api.github.com/users/clefourrier" }	[]	closed	false	null	[]	null	3	"2023-08-21T14:57:54Z"	"2023-08-21T16:27:05Z"	"2023-08-21T16:18:26Z"	MEMBER	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6164.diff", "html_url": "https://github.com/huggingface/datasets/pull/6164", "merged_at": "2023-08-21T16:18:26Z", "patch_url": "https://github.com/huggingface/datasets/pull/6164.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6164" }	When I try to push to an arrow repo (can provide the link on Slack), it uploads the files but fails to update the metadata, with ``` File "app.py", line 123, in add_new_eval eval_results[level].push_to_hub(my_repo, token=TOKEN, split=SPLIT) File "blabla_my_env_path/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 5501, in push_to_hub if not metadata_configs: UnboundLocalError: local variable 'metadata_configs' referenced before assignment ``` This fixes it.	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6164/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6164/timeline	null	null	601	true	[ "_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) \| 0.006874 / 0.011353 (-0.004479) \| 0.004276 / 0.011008 (-0.006732) \| 0.085198 / 0.038508 (0.046690) \| 0.084281 / 0.023109 (0.061171) \| 0.344767 / 0.275898 (0.068869) \| 0.377798 / 0.323480 (0.054318) \| 0.005656 / 0.007986 (-0.002330) \| 0.003601 / 0.004328 (-0.000727) \| 0.065486 / 0.004250 (0.061235) \| 0.056191 / 0.037052 (0.019139) \| 0.351412 / 0.258489 (0.092923) \| 0.398591 / 0.293841 (0.104750) \| 0.031662 / 0.128546 (-0.096884) \| 0.008901 / 0.075646 (-0.066745) \| 0.290423 / 0.419271 (-0.128849) \| 0.053793 / 0.043533 (0.010260) \| 0.347968 / 0.255139 (0.092829) \| 0.376978 / 0.283200 (0.093778) \| 0.026745 / 0.141683 (-0.114938) \| 1.514119 / 1.452155 (0.061964) \| 1.580920 / 1.492716 (0.088203) \|\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) \| 0.273648 / 0.018006 (0.255642) \| 0.575176 / 0.000490 (0.574686) \| 0.003557 / 0.000200 (0.003357) \| 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) \| 0.031714 / 0.037411 (-0.005697) \| 0.089166 / 0.014526 (0.074640) \| 0.101525 / 0.176557 (-0.075032) \| 0.161855 / 0.737135 (-0.575281) \| 0.101391 / 0.296338 (-0.194947) \|\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) \| 0.380947 / 0.215209 (0.165738) \| 3.800527 / 2.077655 (1.722873) \| 1.820789 / 1.504120 (0.316669) \| 1.657327 / 1.541195 (0.116132) \| 1.776242 / 1.468490 (0.307752) \| 0.486954 / 4.584777 (-4.097823) \| 3.688340 / 3.745712 (-0.057372) \| 3.354453 / 5.269862 (-1.915409) \| 2.119995 / 4.565676 (-2.445682) \| 0.057446 / 0.424275 (-0.366829) \| 0.007752 / 0.007607 (0.000145) \| 0.461907 / 0.226044 (0.235862) \| 4.617870 / 2.268929 (2.348942) \| 2.337025 / 55.444624 (-53.107599) \| 1.964770 / 6.876477 (-4.911707) \| 2.252066 / 2.142072 (0.109993) \| 0.591585 / 4.805227 (-4.213642) \| 0.134655 / 6.500664 (-6.366009) \| 0.060646 / 0.075469 (-0.014823) \|\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) \| 1.263271 / 1.841788 (-0.578517) \| 20.822286 / 8.074308 (12.747978) \| 14.710256 / 10.191392 (4.518864) \| 0.167285 / 0.680424 (-0.513139) \| 0.018302 / 0.534201 (-0.515899) \| 0.401023 / 0.579283 (-0.178260) \| 0.428956 / 0.434364 (-0.005407) \| 0.466120 / 0.540337 (-0.074218) \| 0.637868 / 1.386936 (-0.749069) \|\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) \| 0.007174 / 0.011353 (-0.004179) \| 0.004418 / 0.011008 (-0.006590) \| 0.065731 / 0.038508 (0.027223) \| 0.090457 / 0.023109 (0.067348) \| 0.387306 / 0.275898 (0.111408) \| 0.427178 / 0.323480 (0.103698) \| 0.005699 / 0.007986 (-0.002286) \| 0.003662 / 0.004328 (-0.000666) \| 0.066190 / 0.004250 (0.061940) \| 0.062860 / 0.037052 (0.025808) \| 0.388855 / 0.258489 (0.130366) \| 0.427853 / 0.293841 (0.134012) \| 0.032770 / 0.128546 (-0.095776) \| 0.008780 / 0.075646 (-0.066866) \| 0.071156 / 0.419271 (-0.348116) \| 0.050174 / 0.043533 (0.006641) \| 0.385254 / 0.255139 (0.130115) \| 0.405069 / 0.283200 (0.121869) \| 0.025561 / 0.141683 (-0.116122) \| 1.506907 / 1.452155 (0.054752) \| 1.543270 / 1.492716 (0.050554) \|\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) \| 0.304651 / 0.018006 (0.286645) \| 0.577269 / 0.000490 (0.576780) \| 0.004479 / 0.000200 (0.004279) \| 0.000127 / 0.000054 (0.000073) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034070 / 0.037411 (-0.003341) \| 0.097664 / 0.014526 (0.083138) \| 0.106969 / 0.176557 (-0.069588) \| 0.163093 / 0.737135 (-0.574043) \| 0.109384 / 0.296338 (-0.186955) \|\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) \| 0.414823 / 0.215209 (0.199614) \| 4.148390 / 2.077655 (2.070735) \| 2.114038 / 1.504120 (0.609918) \| 1.959316 / 1.541195 (0.418121) \| 2.098138 / 1.468490 (0.629648) \| 0.486338 / 4.584777 (-4.098439) \| 3.642850 / 3.745712 (-0.102863) \| 3.458311 / 5.269862 (-1.811551) \| 2.185662 / 4.565676 (-2.380014) \| 0.057555 / 0.424275 (-0.366720) \| 0.007522 / 0.007607 (-0.000085) \| 0.497975 / 0.226044 (0.271931) \| 4.971528 / 2.268929 (2.702600) \| 2.614087 / 55.444624 (-52.830537) \| 2.288406 / 6.876477 (-4.588070) \| 2.564067 / 2.142072 (0.421995) \| 0.582248 / 4.805227 (-4.222979) \| 0.134931 / 6.500664 (-6.365733) \| 0.062689 / 0.075469 (-0.012780) \|\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) \| 1.343331 / 1.841788 (-0.498457) \| 21.398950 / 8.074308 (13.324642) \| 14.620971 / 10.191392 (4.429579) \| 0.169779 / 0.680424 (-0.510644) \| 0.018683 / 0.534201 (-0.515518) \| 0.396152 / 0.579283 (-0.183131) \| 0.409596 / 0.434364 (-0.024768) \| 0.482875 / 0.540337 (-0.057463) \| 0.659977 / 1.386936 (-0.726959) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1fd2234b8c802d47db5a5aa939148f98c9c49350 \"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) \| 0.006662 / 0.011353 (-0.004691) \| 0.003959 / 0.011008 (-0.007049) \| 0.084447 / 0.038508 (0.045939) \| 0.070267 / 0.023109 (0.047158) \| 0.310301 / 0.275898 (0.034403) \| 0.339866 / 0.323480 (0.016386) \| 0.004008 / 0.007986 (-0.003977) \| 0.003270 / 0.004328 (-0.001058) \| 0.064997 / 0.004250 (0.060746) \| 0.053151 / 0.037052 (0.016099) \| 0.327867 / 0.258489 (0.069378) \| 0.368560 / 0.293841 (0.074719) \| 0.031436 / 0.128546 (-0.097111) \| 0.008547 / 0.075646 (-0.067099) \| 0.288513 / 0.419271 (-0.130758) \| 0.051833 / 0.043533 (0.008300) \| 0.312660 / 0.255139 (0.057521) \| 0.347180 / 0.283200 (0.063980) \| 0.024982 / 0.141683 (-0.116701) \| 1.472487 / 1.452155 (0.020333) \| 1.550138 / 1.492716 (0.057422) \|\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) \| 0.208443 / 0.018006 (0.190437) \| 0.451927 / 0.000490 (0.451437) \| 0.004452 / 0.000200 (0.004252) \| 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) \| 0.029164 / 0.037411 (-0.008247) \| 0.085801 / 0.014526 (0.071275) \| 0.096229 / 0.176557 (-0.080327) \| 0.153063 / 0.737135 (-0.584072) \| 0.097712 / 0.296338 (-0.198626) \|\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) \| 0.383969 / 0.215209 (0.168760) \| 3.829216 / 2.077655 (1.751561) \| 1.854466 / 1.504120 (0.350346) \| 1.684149 / 1.541195 (0.142954) \| 1.759422 / 1.468490 (0.290932) \| 0.480229 / 4.584777 (-4.104548) \| 3.653363 / 3.745712 (-0.092349) \| 3.264456 / 5.269862 (-2.005406) \| 2.020579 / 4.565676 (-2.545097) \| 0.056920 / 0.424275 (-0.367355) \| 0.007625 / 0.007607 (0.000018) \| 0.458559 / 0.226044 (0.232515) \| 4.580288 / 2.268929 (2.311359) \| 2.353783 / 55.444624 (-53.090841) \| 1.967223 / 6.876477 (-4.909253) \| 2.182707 / 2.142072 (0.040634) \| 0.631341 / 4.805227 (-4.173886) \| 0.141656 / 6.500664 (-6.359008) \| 0.059918 / 0.075469 (-0.015551) \|\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) \| 1.279635 / 1.841788 (-0.562153) \| 19.725763 / 8.074308 (11.651455) \| 14.477946 / 10.191392 (4.286554) \| 0.164360 / 0.680424 (-0.516064) \| 0.018286 / 0.534201 (-0.515915) \| 0.394935 / 0.579283 (-0.184348) \| 0.419638 / 0.434364 (-0.014726) \| 0.460366 / 0.540337 (-0.079972) \| 0.636876 / 1.386936 (-0.750060) \|\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) \| 0.006568 / 0.011353 (-0.004785) \| 0.004270 / 0.011008 (-0.006738) \| 0.065522 / 0.038508 (0.027014) \| 0.071597 / 0.023109 (0.048487) \| 0.394929 / 0.275898 (0.119031) \| 0.427548 / 0.323480 (0.104068) \| 0.005320 / 0.007986 (-0.002665) \| 0.003366 / 0.004328 (-0.000962) \| 0.065780 / 0.004250 (0.061530) \| 0.055390 / 0.037052 (0.018338) \| 0.397950 / 0.258489 (0.139461) \| 0.435800 / 0.293841 (0.141959) \| 0.031816 / 0.128546 (-0.096730) \| 0.008555 / 0.075646 (-0.067091) \| 0.072110 / 0.419271 (-0.347161) \| 0.049077 / 0.043533 (0.005544) \| 0.390065 / 0.255139 (0.134926) \| 0.410294 / 0.283200 (0.127094) \| 0.023389 / 0.141683 (-0.118294) \| 1.491491 / 1.452155 (0.039336) \| 1.551057 / 1.492716 (0.058341) \|\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) \| 0.243869 / 0.018006 (0.225862) \| 0.451961 / 0.000490 (0.451471) \| 0.019834 / 0.000200 (0.019634) \| 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) \| 0.031031 / 0.037411 (-0.006380) \| 0.088189 / 0.014526 (0.073663) \| 0.101743 / 0.176557 (-0.074814) \| 0.155236 / 0.737135 (-0.581899) \| 0.101245 / 0.296338 (-0.195094) \|\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) \| 0.422178 / 0.215209 (0.206969) \| 4.199989 / 2.077655 (2.122334) \| 2.228816 / 1.504120 (0.724696) \| 2.057172 / 1.541195 (0.515978) \| 2.162651 / 1.468490 (0.694161) \| 0.491186 / 4.584777 (-4.093591) \| 3.666221 / 3.745712 (-0.079491) \| 3.289531 / 5.269862 (-1.980331) \| 2.050027 / 4.565676 (-2.515650) \| 0.057464 / 0.424275 (-0.366811) \| 0.007379 / 0.007607 (-0.000228) \| 0.506532 / 0.226044 (0.280487) \| 5.066385 / 2.268929 (2.797456) \| 2.694405 / 55.444624 (-52.750219) \| 2.372200 / 6.876477 (-4.504277) \| 2.562724 / 2.142072 (0.420652) \| 0.615474 / 4.805227 (-4.189753) \| 0.148284 / 6.500664 (-6.352380) \| 0.061380 / 0.075469 (-0.014089) \|\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) \| 1.332649 / 1.841788 (-0.509139) \| 20.591063 / 8.074308 (12.516755) \| 14.105253 / 10.191392 (3.913861) \| 0.151886 / 0.680424 (-0.528537) \| 0.018200 / 0.534201 (-0.516001) \| 0.395278 / 0.579283 (-0.184005) \| 0.407113 / 0.434364 (-0.027251) \| 0.473168 / 0.540337 (-0.067170) \| 0.660766 / 1.386936 (-0.726170) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8b8e6ee067eb74e7965ca2a6768f15f9398cb7c8 \"CML watermark\")\n" ]
https://api.github.com/repos/huggingface/datasets/issues/6163	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6163/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6163/comments	https://api.github.com/repos/huggingface/datasets/issues/6163/events	https://github.com/huggingface/datasets/issues/6163	1,857,682,241	I_kwDODunzps5uuftB	6,163	Error type: ArrowInvalid Details: Failed to parse string: '[254,254]' as a scalar of type int32	{ "avatar_url": "https://avatars.githubusercontent.com/u/90616801?v=4", "events_url": "https://api.github.com/users/shishirCTC/events{/privacy}", "followers_url": "https://api.github.com/users/shishirCTC/followers", "following_url": "https://api.github.com/users/shishirCTC/following{/other_user}", "gists_url": "https://api.github.com/users/shishirCTC/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/shishirCTC", "id": 90616801, "login": "shishirCTC", "node_id": "MDQ6VXNlcjkwNjE2ODAx", "organizations_url": "https://api.github.com/users/shishirCTC/orgs", "received_events_url": "https://api.github.com/users/shishirCTC/received_events", "repos_url": "https://api.github.com/users/shishirCTC/repos", "site_admin": false, "starred_url": "https://api.github.com/users/shishirCTC/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/shishirCTC/subscriptions", "type": "User", "url": "https://api.github.com/users/shishirCTC" }	[]	open	false	null	[]	null	1	"2023-08-19T11:34:40Z"	"2023-08-21T13:28:16Z"	null	NONE	null	null	null	### Describe the bug I am getting the following error while I am trying to upload the CSV sheet to train a model. My CSV sheet content is exactly same as shown in the example CSV file in the Auto Train page. Attaching screenshot of error for reference. I have also tried converting the index of the answer that are integer into string by placing inverted commas and also without inverted commas. Can anyone please help me out? FYI : I am using Chrome browser. Error type: ArrowInvalid Details: Failed to parse string: '[254,254]' as a scalar of type int32 ![Screenshot 2023-08-19 165827](https://github.com/huggingface/datasets/assets/90616801/95fad96e-7dce-4bb5-9f83-9f1659a32891) ### Steps to reproduce the bug Kindly let me know how to fix this? ### Expected behavior Kindly let me know how to fix this? ### Environment info Kindly let me know how to fix this?	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6163/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6163/timeline	null	null	602	false	[ "Answered on the forum [here](https://discuss.huggingface.co/t/error-type-arrowinvalid-details-failed-to-parse-string-254-254-as-a-scalar-of-type-int32/51323)." ]
https://api.github.com/repos/huggingface/datasets/issues/6162	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6162/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6162/comments	https://api.github.com/repos/huggingface/datasets/issues/6162/events	https://github.com/huggingface/datasets/issues/6162	1,856,198,342	I_kwDODunzps5uo1bG	6,162	load_dataset('json',...) from togethercomputer/RedPajama-Data-1T errors when jsonl rows contains different data fields	{ "avatar_url": "https://avatars.githubusercontent.com/u/82971690?v=4", "events_url": "https://api.github.com/users/rbrugaro/events{/privacy}", "followers_url": "https://api.github.com/users/rbrugaro/followers", "following_url": "https://api.github.com/users/rbrugaro/following{/other_user}", "gists_url": "https://api.github.com/users/rbrugaro/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/rbrugaro", "id": 82971690, "login": "rbrugaro", "node_id": "MDQ6VXNlcjgyOTcxNjkw", "organizations_url": "https://api.github.com/users/rbrugaro/orgs", "received_events_url": "https://api.github.com/users/rbrugaro/received_events", "repos_url": "https://api.github.com/users/rbrugaro/repos", "site_admin": false, "starred_url": "https://api.github.com/users/rbrugaro/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/rbrugaro/subscriptions", "type": "User", "url": "https://api.github.com/users/rbrugaro" }	[]	open	false	null	[]	null	4	"2023-08-18T07:19:39Z"	"2023-08-18T17:00:35Z"	null	NONE	null	null	null	### Describe the bug When loading some jsonl from redpajama-data-1T github source [togethercomputer/RedPajama-Data-1T](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T) fails due to one row of the file containing an extra field called symlink_target: string>. When deleting that line the loading is successful. We also tried loading this file with the discrepancy using this function and it is successful ```python os.environ["RED_PAJAMA_DATA_DIR"] ="/path_to_local_copy_of_RedPajama-Data-1T" ds = load_dataset('togethercomputer/RedPajama-Data-1T', 'github',cache_dir="/path_to_folder_with_jsonl",streaming=True)['train'] ``` ### Steps to reproduce the bug Steps to reproduce the behavior: 1. Load one jsonl from the redpajama-data-1T ```bash wget https://data.together.xyz/redpajama-data-1T/v1.0.0/github/filtered_27f05c041a1c401783f90b9415e40e4b.sampled.jsonl ``` 2.Load dataset will give error: ```python from datasets import load_dataset ds = load_dataset('json', data_files='/path_to/filtered_27f05c041a1c401783f90b9415e40e4b.sampled.jsonl') ``` _TypeError: Couldn't cast array of type Struct <content_hash: string, timestamp: string, source: string, line_count: int64, max_line_length: int64, avg_line_length: double, alnum_prop: double, repo_name: string, id: string, size: string, binary: bool, copies: string, ref: string, path: string, mode: string, license: string, language: list<item: struct<name: string, bytes: string>>, symlink_target: string> to {'content_hash': Value(dtype='string', id=None), 'timestamp': Value(dtype='string', id=None), 'source': Value(dtype='string', id=None), 'line_count': Value(dtype='int64', id=None), 'max_line_length': Value(dtype='int64', id=None), 'avg_line_length': Value(dtype='float64', id=None), 'alnum_prop': Value(dtype='float64', id=None), 'repo_name': Value(dtype='string', id=None), 'id': Value(dtype='string', id=None), 'size': Value(dtype='string', id=None), 'binary': Value(dtype='bool', id=None), 'copies': Value(dtype='string', id=None), 'ref': Value(dtype='string', id=None), 'path': Value(dtype='string', id=None), 'mode': Value(dtype='string', id=None), 'license': Value(dtype='string', id=None), 'language': [{'name': Value(dtype='string', id=None), 'bytes': Value(dtype='string', id=None)}]}_ 3. To remove the line causing the problem that includes the symlink_target: string> do: ```bash sed -i '112252d' filtered_27f05c041a1c401783f90b9415e40e4b.sampled.jsonl ``` 4. Rerun the loading function now is succesful: ```python from datasets import load_dataset ds = load_dataset('json', data_files='/path_to/filtered_27f05c041a1c401783f90b9415e40e4b.sampled.jsonl') ``` ### Expected behavior Have a clean dataset without discrepancies on the jsonl fields or have the load_dataset('json',...) method not error out. ### Environment info - `datasets` version: 2.14.1 - Platform: Linux-4.18.0-425.13.1.el8_7.x86_64-x86_64-with-glibc2.28 - Python version: 3.9.17 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 2.0.3	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6162/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6162/timeline	null	null	603	false	[ "Hi ! Feel free to open a discussion at https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T/discussions to ask the file to be fixed (or directly open a PR with the fixed file)\r\n\r\n`datasets` expects all the examples to have the same fields", "@lhoestq I think the problem is caused by the fact that hugging face datasets writes a copy of data to the local cache using pyarrow. And the data scheme is inferred from the first few data blocks as can be seen [here](https://github.com/huggingface/datasets/blob/main/src/datasets/arrow_writer.py#L570). Maybe setting `streaming=True` can workaround this problem. Would you agree with my statement? ", "> @lhoestq I think the problem is caused by the fact that hugging face datasets writes a copy of data to the local cache using pyarrow. And the data scheme is inferred from the first few data blocks as can be seen [here](https://github.com/huggingface/datasets/blob/main/src/datasets/arrow_writer.py#L570).\r\n\r\nCorrect. Therefore any example that doesn't follow the inferred schema will make the code fail.\r\n\r\n> Maybe setting streaming=True can workaround this problem. Would you agree with my statement?\r\n\r\nYou'll meet the same problem but later - when streaming and arriving at the problematic example", "@lhoestq I just run below test with streaming=True and is not failing at the problematic example\r\n```python\r\nds = load_dataset('json', data_files='/path_to_local_RedPajamaData/filtered_27f05c041a1c401783f90b9415e40e4b.sampled.jsonl', streaming=True)\r\ncount = 0\r\nfor i in ds['train']:\r\n count += 1\r\n print(count)\r\n```\r\n\r\nand completes the 262241 samples successfully. It does error our when streaming is not used " ]
https://api.github.com/repos/huggingface/datasets/issues/6161	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6161/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6161/comments	https://api.github.com/repos/huggingface/datasets/issues/6161/events	https://github.com/huggingface/datasets/pull/6161	1,855,794,354	PR_kwDODunzps5YM0g7	6,161	Fix protocol prefix for Beam	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "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}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko" }	[]	open	false	null	[]	null	4	"2023-08-17T22:40:37Z"	"2023-08-18T13:47:59Z"	null	CONTRIBUTOR	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6161.diff", "html_url": "https://github.com/huggingface/datasets/pull/6161", "merged_at": null, "patch_url": "https://github.com/huggingface/datasets/pull/6161.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6161" }	Fix #6147	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6161/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6161/timeline	null	null	604	true	[ "<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) \| 0.006736 / 0.011353 (-0.004617) \| 0.004099 / 0.011008 (-0.006909) \| 0.084339 / 0.038508 (0.045831) \| 0.073715 / 0.023109 (0.050605) \| 0.311962 / 0.275898 (0.036064) \| 0.356108 / 0.323480 (0.032628) \| 0.005321 / 0.007986 (-0.002665) \| 0.003390 / 0.004328 (-0.000939) \| 0.064622 / 0.004250 (0.060372) \| 0.053978 / 0.037052 (0.016926) \| 0.328967 / 0.258489 (0.070478) \| 0.370506 / 0.293841 (0.076665) \| 0.031123 / 0.128546 (-0.097423) \| 0.008465 / 0.075646 (-0.067181) \| 0.288136 / 0.419271 (-0.131136) \| 0.052909 / 0.043533 (0.009376) \| 0.325189 / 0.255139 (0.070050) \| 0.360112 / 0.283200 (0.076912) \| 0.023389 / 0.141683 (-0.118294) \| 1.492899 / 1.452155 (0.040744) \| 1.586449 / 1.492716 (0.093733) \|\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) \| 0.219708 / 0.018006 (0.201702) \| 0.469550 / 0.000490 (0.469060) \| 0.002776 / 0.000200 (0.002576) \| 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) \| 0.028985 / 0.037411 (-0.008427) \| 0.083487 / 0.014526 (0.068961) \| 0.096938 / 0.176557 (-0.079619) \| 0.152886 / 0.737135 (-0.584249) \| 0.096242 / 0.296338 (-0.200096) \|\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) \| 0.381959 / 0.215209 (0.166750) \| 3.800033 / 2.077655 (1.722378) \| 1.831903 / 1.504120 (0.327783) \| 1.663207 / 1.541195 (0.122012) \| 1.747282 / 1.468490 (0.278792) \| 0.481671 / 4.584777 (-4.103106) \| 3.653725 / 3.745712 (-0.091987) \| 3.253058 / 5.269862 (-2.016804) \| 2.022014 / 4.565676 (-2.543663) \| 0.056651 / 0.424275 (-0.367624) \| 0.007640 / 0.007607 (0.000033) \| 0.461795 / 0.226044 (0.235750) \| 4.625535 / 2.268929 (2.356606) \| 2.356341 / 55.444624 (-53.088283) \| 1.977437 / 6.876477 (-4.899040) \| 2.179672 / 2.142072 (0.037599) \| 0.582875 / 4.805227 (-4.222353) \| 0.132964 / 6.500664 (-6.367700) \| 0.060398 / 0.075469 (-0.015071) \|\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) \| 1.309567 / 1.841788 (-0.532220) \| 19.856306 / 8.074308 (11.781997) \| 14.074350 / 10.191392 (3.882958) \| 0.149615 / 0.680424 (-0.530809) \| 0.018487 / 0.534201 (-0.515714) \| 0.393995 / 0.579283 (-0.185288) \| 0.409057 / 0.434364 (-0.025307) \| 0.459551 / 0.540337 (-0.080787) \| 0.644594 / 1.386936 (-0.742342) \|\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) \| 0.006824 / 0.011353 (-0.004529) \| 0.004099 / 0.011008 (-0.006909) \| 0.064415 / 0.038508 (0.025907) \| 0.077983 / 0.023109 (0.054874) \| 0.359351 / 0.275898 (0.083453) \| 0.395168 / 0.323480 (0.071688) \| 0.005384 / 0.007986 (-0.002602) \| 0.003298 / 0.004328 (-0.001030) \| 0.065041 / 0.004250 (0.060791) \| 0.056717 / 0.037052 (0.019664) \| 0.366882 / 0.258489 (0.108393) \| 0.401337 / 0.293841 (0.107496) \| 0.032273 / 0.128546 (-0.096273) \| 0.008666 / 0.075646 (-0.066981) \| 0.071442 / 0.419271 (-0.347829) \| 0.049999 / 0.043533 (0.006466) \| 0.365001 / 0.255139 (0.109862) \| 0.379579 / 0.283200 (0.096379) \| 0.023357 / 0.141683 (-0.118326) \| 1.476839 / 1.452155 (0.024684) \| 1.541703 / 1.492716 (0.048987) \|\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) \| 0.239014 / 0.018006 (0.221008) \| 0.460678 / 0.000490 (0.460188) \| 0.003368 / 0.000200 (0.003168) \| 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) \| 0.030981 / 0.037411 (-0.006430) \| 0.088287 / 0.014526 (0.073761) \| 0.102459 / 0.176557 (-0.074098) \| 0.154695 / 0.737135 (-0.582441) \| 0.103479 / 0.296338 (-0.192860) \|\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) \| 0.416084 / 0.215209 (0.200874) \| 4.128365 / 2.077655 (2.050710) \| 2.113053 / 1.504120 (0.608934) \| 1.948993 / 1.541195 (0.407798) \| 2.035609 / 1.468490 (0.567119) \| 0.481705 / 4.584777 (-4.103072) \| 3.630366 / 3.745712 (-0.115346) \| 3.340837 / 5.269862 (-1.929024) \| 2.052573 / 4.565676 (-2.513104) \| 0.056805 / 0.424275 (-0.367470) \| 0.007294 / 0.007607 (-0.000313) \| 0.489597 / 0.226044 (0.263553) \| 4.892728 / 2.268929 (2.623799) \| 2.564692 / 55.444624 (-52.879932) \| 2.251964 / 6.876477 (-4.624513) \| 2.457912 / 2.142072 (0.315839) \| 0.588433 / 4.805227 (-4.216794) \| 0.133588 / 6.500664 (-6.367076) \| 0.062298 / 0.075469 (-0.013171) \|\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) \| 1.328566 / 1.841788 (-0.513222) \| 20.145568 / 8.074308 (12.071260) \| 14.231306 / 10.191392 (4.039914) \| 0.168356 / 0.680424 (-0.512067) \| 0.018333 / 0.534201 (-0.515868) \| 0.390901 / 0.579283 (-0.188382) \| 0.415005 / 0.434364 (-0.019359) \| 0.477282 / 0.540337 (-0.063055) \| 0.652085 / 1.386936 (-0.734851) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#341a41880a70b29f030caa0d36f1e297535ba5f9 \"CML watermark\")\n", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6161). 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) \| 0.006388 / 0.011353 (-0.004965) \| 0.003917 / 0.011008 (-0.007092) \| 0.087397 / 0.038508 (0.048889) \| 0.068522 / 0.023109 (0.045412) \| 0.313299 / 0.275898 (0.037401) \| 0.342884 / 0.323480 (0.019405) \| 0.005216 / 0.007986 (-0.002770) \| 0.003293 / 0.004328 (-0.001035) \| 0.067474 / 0.004250 (0.063224) \| 0.051122 / 0.037052 (0.014070) \| 0.326443 / 0.258489 (0.067954) \| 0.355744 / 0.293841 (0.061903) \| 0.031130 / 0.128546 (-0.097416) \| 0.008617 / 0.075646 (-0.067029) \| 0.291201 / 0.419271 (-0.128070) \| 0.052050 / 0.043533 (0.008517) \| 0.312135 / 0.255139 (0.056996) \| 0.347233 / 0.283200 (0.064034) \| 0.023775 / 0.141683 (-0.117907) \| 1.478807 / 1.452155 (0.026652) \| 1.581239 / 1.492716 (0.088522) \|\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) \| 0.208252 / 0.018006 (0.190246) \| 0.466314 / 0.000490 (0.465824) \| 0.004439 / 0.000200 (0.004239) \| 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) \| 0.027918 / 0.037411 (-0.009494) \| 0.082410 / 0.014526 (0.067884) \| 0.094231 / 0.176557 (-0.082326) \| 0.150189 / 0.737135 (-0.586946) \| 0.095404 / 0.296338 (-0.200935) \|\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) \| 0.382026 / 0.215209 (0.166817) \| 3.822213 / 2.077655 (1.744559) \| 1.833716 / 1.504120 (0.329596) \| 1.666250 / 1.541195 (0.125055) \| 1.703350 / 1.468490 (0.234860) \| 0.477918 / 4.584777 (-4.106859) \| 3.629304 / 3.745712 (-0.116408) \| 3.199672 / 5.269862 (-2.070190) \| 1.977855 / 4.565676 (-2.587821) \| 0.056275 / 0.424275 (-0.368000) \| 0.007538 / 0.007607 (-0.000070) \| 0.455995 / 0.226044 (0.229950) \| 4.559234 / 2.268929 (2.290305) \| 2.333819 / 55.444624 (-53.110805) \| 2.006851 / 6.876477 (-4.869625) \| 2.150683 / 2.142072 (0.008611) \| 0.576786 / 4.805227 (-4.228441) \| 0.132352 / 6.500664 (-6.368312) \| 0.059359 / 0.075469 (-0.016110) \|\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) \| 1.261525 / 1.841788 (-0.580262) \| 19.174957 / 8.074308 (11.100649) \| 14.286796 / 10.191392 (4.095404) \| 0.144610 / 0.680424 (-0.535813) \| 0.018213 / 0.534201 (-0.515988) \| 0.390404 / 0.579283 (-0.188879) \| 0.404678 / 0.434364 (-0.029686) \| 0.455636 / 0.540337 (-0.084701) \| 0.620801 / 1.386936 (-0.766135) \|\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) \| 0.006383 / 0.011353 (-0.004970) \| 0.003852 / 0.011008 (-0.007156) \| 0.064116 / 0.038508 (0.025607) \| 0.068920 / 0.023109 (0.045810) \| 0.359439 / 0.275898 (0.083541) \| 0.388904 / 0.323480 (0.065425) \| 0.005192 / 0.007986 (-0.002794) \| 0.003233 / 0.004328 (-0.001095) \| 0.064589 / 0.004250 (0.060339) \| 0.054496 / 0.037052 (0.017444) \| 0.368699 / 0.258489 (0.110210) \| 0.400420 / 0.293841 (0.106579) \| 0.030869 / 0.128546 (-0.097677) \| 0.008424 / 0.075646 (-0.067222) \| 0.071015 / 0.419271 (-0.348257) \| 0.048333 / 0.043533 (0.004801) \| 0.360652 / 0.255139 (0.105513) \| 0.393534 / 0.283200 (0.110334) \| 0.022685 / 0.141683 (-0.118998) \| 1.495565 / 1.452155 (0.043410) \| 1.537947 / 1.492716 (0.045230) \|\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) \| 0.232911 / 0.018006 (0.214905) \| 0.454191 / 0.000490 (0.453702) \| 0.005711 / 0.000200 (0.005511) \| 0.000117 / 0.000054 (0.000062) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029486 / 0.037411 (-0.007925) \| 0.087249 / 0.014526 (0.072724) \| 0.100104 / 0.176557 (-0.076453) \| 0.151556 / 0.737135 (-0.585580) \| 0.100853 / 0.296338 (-0.195485) \|\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) \| 0.415134 / 0.215209 (0.199925) \| 4.139068 / 2.077655 (2.061413) \| 2.121079 / 1.504120 (0.616959) \| 1.945616 / 1.541195 (0.404421) \| 1.988188 / 1.468490 (0.519698) \| 0.483994 / 4.584777 (-4.100783) \| 3.640366 / 3.745712 (-0.105347) \| 3.218896 / 5.269862 (-2.050966) \| 2.015527 / 4.565676 (-2.550149) \| 0.056946 / 0.424275 (-0.367329) \| 0.007262 / 0.007607 (-0.000345) \| 0.486075 / 0.226044 (0.260031) \| 4.864191 / 2.268929 (2.595262) \| 2.590853 / 55.444624 (-52.853772) \| 2.315359 / 6.876477 (-4.561118) \| 2.418733 / 2.142072 (0.276661) \| 0.582378 / 4.805227 (-4.222849) \| 0.134097 / 6.500664 (-6.366568) \| 0.060797 / 0.075469 (-0.014672) \|\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) \| 1.337021 / 1.841788 (-0.504766) \| 19.468907 / 8.074308 (11.394599) \| 14.348874 / 10.191392 (4.157482) \| 0.170408 / 0.680424 (-0.510016) \| 0.018414 / 0.534201 (-0.515787) \| 0.394551 / 0.579283 (-0.184732) \| 0.404750 / 0.434364 (-0.029613) \| 0.471972 / 0.540337 (-0.068365) \| 0.650607 / 1.386936 (-0.736329) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ab4d978e2d5c246dc91e2fed041b06a38190be3b \"CML watermark\")\n", "The CI errors are unrelated to the changes" ]
https://api.github.com/repos/huggingface/datasets/issues/6160	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6160/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6160/comments	https://api.github.com/repos/huggingface/datasets/issues/6160/events	https://github.com/huggingface/datasets/pull/6160	1,855,760,543	PR_kwDODunzps5YMtLQ	6,160	Fix Parquet loading with `columns`	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "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}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko" }	[]	closed	false	null	[]	null	4	"2023-08-17T21:58:24Z"	"2023-08-17T22:44:59Z"	"2023-08-17T22:36:04Z"	CONTRIBUTOR	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6160.diff", "html_url": "https://github.com/huggingface/datasets/pull/6160", "merged_at": "2023-08-17T22:36:04Z", "patch_url": "https://github.com/huggingface/datasets/pull/6160.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6160" }	Fix #6149	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6160/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6160/timeline	null	null	605	true	[ "_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) \| 0.008368 / 0.011353 (-0.002985) \| 0.004754 / 0.011008 (-0.006254) \| 0.096646 / 0.038508 (0.058138) \| 0.088980 / 0.023109 (0.065871) \| 0.374532 / 0.275898 (0.098633) \| 0.404840 / 0.323480 (0.081360) \| 0.006026 / 0.007986 (-0.001960) \| 0.005716 / 0.004328 (0.001387) \| 0.076297 / 0.004250 (0.072047) \| 0.072335 / 0.037052 (0.035283) \| 0.379435 / 0.258489 (0.120946) \| 0.423449 / 0.293841 (0.129608) \| 0.041344 / 0.128546 (-0.087202) \| 0.009758 / 0.075646 (-0.065889) \| 0.341550 / 0.419271 (-0.077721) \| 0.068559 / 0.043533 (0.025026) \| 0.368313 / 0.255139 (0.113174) \| 0.415147 / 0.283200 (0.131947) \| 0.028692 / 0.141683 (-0.112990) \| 1.816198 / 1.452155 (0.364044) \| 1.983351 / 1.492716 (0.490635) \|\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) \| 0.222712 / 0.018006 (0.204706) \| 0.517850 / 0.000490 (0.517360) \| 0.004436 / 0.000200 (0.004236) \| 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) \| 0.033168 / 0.037411 (-0.004243) \| 0.101353 / 0.014526 (0.086827) \| 0.113235 / 0.176557 (-0.063322) \| 0.180308 / 0.737135 (-0.556827) \| 0.114604 / 0.296338 (-0.181734) \|\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) \| 0.454415 / 0.215209 (0.239206) \| 4.500355 / 2.077655 (2.422701) \| 2.188223 / 1.504120 (0.684103) \| 1.974256 / 1.541195 (0.433061) \| 2.067331 / 1.468490 (0.598841) \| 0.572982 / 4.584777 (-4.011795) \| 4.239160 / 3.745712 (0.493448) \| 3.836812 / 5.269862 (-1.433049) \| 2.367022 / 4.565676 (-2.198655) \| 0.066886 / 0.424275 (-0.357389) \| 0.009111 / 0.007607 (0.001504) \| 0.539881 / 0.226044 (0.313837) \| 5.362247 / 2.268929 (3.093319) \| 2.784044 / 55.444624 (-52.660580) \| 2.320975 / 6.876477 (-4.555502) \| 2.543108 / 2.142072 (0.401036) \| 0.685751 / 4.805227 (-4.119477) \| 0.156840 / 6.500664 (-6.343824) \| 0.071764 / 0.075469 (-0.003705) \|\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) \| 1.549830 / 1.841788 (-0.291958) \| 22.799622 / 8.074308 (14.725314) \| 16.750692 / 10.191392 (6.559300) \| 0.196192 / 0.680424 (-0.484232) \| 0.024518 / 0.534201 (-0.509683) \| 0.479302 / 0.579283 (-0.099981) \| 0.522256 / 0.434364 (0.087892) \| 0.545809 / 0.540337 (0.005471) \| 0.748437 / 1.386936 (-0.638499) \|\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) \| 0.007789 / 0.011353 (-0.003564) \| 0.004563 / 0.011008 (-0.006445) \| 0.074631 / 0.038508 (0.036123) \| 0.086892 / 0.023109 (0.063783) \| 0.427014 / 0.275898 (0.151116) \| 0.463257 / 0.323480 (0.139777) \| 0.005987 / 0.007986 (-0.001999) \| 0.003803 / 0.004328 (-0.000526) \| 0.074799 / 0.004250 (0.070549) \| 0.063473 / 0.037052 (0.026420) \| 0.429905 / 0.258489 (0.171416) \| 0.468967 / 0.293841 (0.175127) \| 0.036768 / 0.128546 (-0.091778) \| 0.009675 / 0.075646 (-0.065971) \| 0.082546 / 0.419271 (-0.336725) \| 0.058027 / 0.043533 (0.014494) \| 0.429813 / 0.255139 (0.174674) \| 0.449200 / 0.283200 (0.166001) \| 0.026713 / 0.141683 (-0.114969) \| 1.812022 / 1.452155 (0.359867) \| 1.847305 / 1.492716 (0.354589) \|\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) \| 0.320383 / 0.018006 (0.302377) \| 0.485995 / 0.000490 (0.485505) \| 0.024365 / 0.000200 (0.024165) \| 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) \| 0.036341 / 0.037411 (-0.001071) \| 0.104635 / 0.014526 (0.090110) \| 0.119456 / 0.176557 (-0.057101) \| 0.182042 / 0.737135 (-0.555093) \| 0.118944 / 0.296338 (-0.177395) \|\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) \| 0.506410 / 0.215209 (0.291201) \| 5.061119 / 2.077655 (2.983465) \| 2.756557 / 1.504120 (1.252437) \| 2.546504 / 1.541195 (1.005309) \| 2.585509 / 1.468490 (1.117019) \| 0.564291 / 4.584777 (-4.020486) \| 4.281219 / 3.745712 (0.535507) \| 3.919439 / 5.269862 (-1.350423) \| 2.588788 / 4.565676 (-1.976889) \| 0.066900 / 0.424275 (-0.357375) \| 0.008680 / 0.007607 (0.001073) \| 0.598435 / 0.226044 (0.372390) \| 5.976054 / 2.268929 (3.707125) \| 3.260211 / 55.444624 (-52.184414) \| 2.874597 / 6.876477 (-4.001880) \| 3.105769 / 2.142072 (0.963697) \| 0.692938 / 4.805227 (-4.112289) \| 0.157777 / 6.500664 (-6.342887) \| 0.073128 / 0.075469 (-0.002341) \|\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) \| 1.559380 / 1.841788 (-0.282408) \| 22.986540 / 8.074308 (14.912232) \| 16.305564 / 10.191392 (6.114172) \| 0.174939 / 0.680424 (-0.505485) \| 0.021932 / 0.534201 (-0.512269) \| 0.468162 / 0.579283 (-0.111121) \| 0.472610 / 0.434364 (0.038246) \| 0.574574 / 0.540337 (0.034237) \| 0.783505 / 1.386936 (-0.603431) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#550923b5d6ae64eb20b8f66da843395e9fa404ac \"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) \| 0.012553 / 0.011353 (0.001201) \| 0.005358 / 0.011008 (-0.005650) \| 0.108338 / 0.038508 (0.069830) \| 0.101105 / 0.023109 (0.077995) \| 0.416808 / 0.275898 (0.140910) \| 0.454599 / 0.323480 (0.131119) \| 0.006665 / 0.007986 (-0.001321) \| 0.004186 / 0.004328 (-0.000143) \| 0.084900 / 0.004250 (0.080649) \| 0.062881 / 0.037052 (0.025829) \| 0.424423 / 0.258489 (0.165934) \| 0.482651 / 0.293841 (0.188810) \| 0.055740 / 0.128546 (-0.072807) \| 0.014469 / 0.075646 (-0.061177) \| 0.383267 / 0.419271 (-0.036005) \| 0.067487 / 0.043533 (0.023955) \| 0.414983 / 0.255139 (0.159844) \| 0.459437 / 0.283200 (0.176237) \| 0.038679 / 0.141683 (-0.103004) \| 1.828002 / 1.452155 (0.375847) \| 1.951946 / 1.492716 (0.459230) \|\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) \| 0.288033 / 0.018006 (0.270027) \| 0.603536 / 0.000490 (0.603046) \| 0.004874 / 0.000200 (0.004674) \| 0.000138 / 0.000054 (0.000084) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031988 / 0.037411 (-0.005423) \| 0.095807 / 0.014526 (0.081281) \| 0.113459 / 0.176557 (-0.063098) \| 0.182012 / 0.737135 (-0.555123) \| 0.113121 / 0.296338 (-0.183217) \|\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) \| 0.620709 / 0.215209 (0.405500) \| 6.096569 / 2.077655 (4.018915) \| 2.754612 / 1.504120 (1.250492) \| 2.449786 / 1.541195 (0.908591) \| 2.470694 / 1.468490 (1.002204) \| 0.837016 / 4.584777 (-3.747761) \| 5.237290 / 3.745712 (1.491578) \| 4.713220 / 5.269862 (-0.556642) \| 3.020934 / 4.565676 (-1.544743) \| 0.096892 / 0.424275 (-0.327383) \| 0.009423 / 0.007607 (0.001816) \| 0.720313 / 0.226044 (0.494269) \| 7.369673 / 2.268929 (5.100744) \| 3.550384 / 55.444624 (-51.894241) \| 2.868868 / 6.876477 (-4.007609) \| 3.081469 / 2.142072 (0.939397) \| 1.042968 / 4.805227 (-3.762259) \| 0.232530 / 6.500664 (-6.268134) \| 0.080805 / 0.075469 (0.005336) \|\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) \| 1.645777 / 1.841788 (-0.196011) \| 24.590862 / 8.074308 (16.516554) \| 21.315496 / 10.191392 (11.124104) \| 0.228796 / 0.680424 (-0.451628) \| 0.028479 / 0.534201 (-0.505722) \| 0.494413 / 0.579283 (-0.084870) \| 0.582773 / 0.434364 (0.148409) \| 0.552575 / 0.540337 (0.012238) \| 0.787217 / 1.386936 (-0.599719) \|\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) \| 0.008743 / 0.011353 (-0.002609) \| 0.005253 / 0.011008 (-0.005755) \| 0.083766 / 0.038508 (0.045257) \| 0.086305 / 0.023109 (0.063195) \| 0.520171 / 0.275898 (0.244273) \| 0.565812 / 0.323480 (0.242332) \| 0.006465 / 0.007986 (-0.001520) \| 0.004585 / 0.004328 (0.000257) \| 0.085344 / 0.004250 (0.081094) \| 0.063418 / 0.037052 (0.026366) \| 0.519759 / 0.258489 (0.261270) \| 0.552770 / 0.293841 (0.258929) \| 0.049439 / 0.128546 (-0.079107) \| 0.017564 / 0.075646 (-0.058082) \| 0.092713 / 0.419271 (-0.326559) \| 0.065837 / 0.043533 (0.022305) \| 0.516133 / 0.255139 (0.260994) \| 0.539813 / 0.283200 (0.256613) \| 0.036531 / 0.141683 (-0.105152) \| 1.919275 / 1.452155 (0.467121) \| 2.039987 / 1.492716 (0.547271) \|\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) \| 0.297978 / 0.018006 (0.279972) \| 0.608243 / 0.000490 (0.607753) \| 0.006611 / 0.000200 (0.006411) \| 0.000117 / 0.000054 (0.000062) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033909 / 0.037411 (-0.003503) \| 0.106370 / 0.014526 (0.091844) \| 0.119032 / 0.176557 (-0.057524) \| 0.180319 / 0.737135 (-0.556816) \| 0.122826 / 0.296338 (-0.173513) \|\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) \| 0.639265 / 0.215209 (0.424056) \| 6.248430 / 2.077655 (4.170775) \| 2.944760 / 1.504120 (1.440640) \| 2.654005 / 1.541195 (1.112811) \| 2.733625 / 1.468490 (1.265134) \| 0.837172 / 4.584777 (-3.747605) \| 5.245084 / 3.745712 (1.499372) \| 4.722614 / 5.269862 (-0.547248) \| 3.008286 / 4.565676 (-1.557391) \| 0.102340 / 0.424275 (-0.321935) \| 0.009433 / 0.007607 (0.001826) \| 0.762991 / 0.226044 (0.536946) \| 7.385020 / 2.268929 (5.116092) \| 3.787648 / 55.444624 (-51.656977) \| 3.234345 / 6.876477 (-3.642132) \| 3.394444 / 2.142072 (1.252371) \| 1.023472 / 4.805227 (-3.781756) \| 0.208199 / 6.500664 (-6.292465) \| 0.081513 / 0.075469 (0.006043) \|\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) \| 1.795864 / 1.841788 (-0.045923) \| 25.270852 / 8.074308 (17.196544) \| 23.356413 / 10.191392 (13.165021) \| 0.228002 / 0.680424 (-0.452422) \| 0.031851 / 0.534201 (-0.502350) \| 0.499424 / 0.579283 (-0.079859) \| 0.588027 / 0.434364 (0.153664) \| 0.581746 / 0.540337 (0.041408) \| 0.814183 / 1.386936 (-0.572753) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#33ee536876a667403ee44574bd685073261c4903 \"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) \| 0.006477 / 0.011353 (-0.004876) \| 0.003878 / 0.011008 (-0.007130) \| 0.084085 / 0.038508 (0.045577) \| 0.071297 / 0.023109 (0.048188) \| 0.309176 / 0.275898 (0.033278) \| 0.342830 / 0.323480 (0.019350) \| 0.005189 / 0.007986 (-0.002796) \| 0.003263 / 0.004328 (-0.001065) \| 0.063920 / 0.004250 (0.059670) \| 0.052233 / 0.037052 (0.015180) \| 0.324830 / 0.258489 (0.066341) \| 0.357956 / 0.293841 (0.064115) \| 0.030459 / 0.128546 (-0.098087) \| 0.008350 / 0.075646 (-0.067297) \| 0.287330 / 0.419271 (-0.131942) \| 0.051005 / 0.043533 (0.007473) \| 0.309227 / 0.255139 (0.054088) \| 0.346184 / 0.283200 (0.062984) \| 0.023961 / 0.141683 (-0.117722) \| 1.463983 / 1.452155 (0.011829) \| 1.573036 / 1.492716 (0.080319) \|\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) \| 0.205653 / 0.018006 (0.187647) \| 0.457336 / 0.000490 (0.456846) \| 0.005347 / 0.000200 (0.005147) \| 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) \| 0.028080 / 0.037411 (-0.009332) \| 0.081755 / 0.014526 (0.067229) \| 0.095716 / 0.176557 (-0.080841) \| 0.151340 / 0.737135 (-0.585795) \| 0.097174 / 0.296338 (-0.199164) \|\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) \| 0.390725 / 0.215209 (0.175516) \| 3.899114 / 2.077655 (1.821459) \| 1.895352 / 1.504120 (0.391232) \| 1.716072 / 1.541195 (0.174877) \| 1.784952 / 1.468490 (0.316462) \| 0.477247 / 4.584777 (-4.107530) \| 3.606641 / 3.745712 (-0.139071) \| 3.203337 / 5.269862 (-2.066524) \| 2.017003 / 4.565676 (-2.548674) \| 0.056182 / 0.424275 (-0.368094) \| 0.007508 / 0.007607 (-0.000099) \| 0.461965 / 0.226044 (0.235921) \| 4.605926 / 2.268929 (2.336997) \| 2.466695 / 55.444624 (-52.977929) \| 2.136376 / 6.876477 (-4.740100) \| 2.277334 / 2.142072 (0.135261) \| 0.576119 / 4.805227 (-4.229109) \| 0.131497 / 6.500664 (-6.369167) \| 0.060068 / 0.075469 (-0.015401) \|\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) \| 1.262681 / 1.841788 (-0.579107) \| 19.411572 / 8.074308 (11.337264) \| 14.383421 / 10.191392 (4.192029) \| 0.166115 / 0.680424 (-0.514308) \| 0.018366 / 0.534201 (-0.515835) \| 0.393903 / 0.579283 (-0.185380) \| 0.408788 / 0.434364 (-0.025576) \| 0.461796 / 0.540337 (-0.078541) \| 0.628460 / 1.386936 (-0.758476) \|\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) \| 0.006501 / 0.011353 (-0.004852) \| 0.003915 / 0.011008 (-0.007093) \| 0.065245 / 0.038508 (0.026737) \| 0.073146 / 0.023109 (0.050037) \| 0.363537 / 0.275898 (0.087639) \| 0.391571 / 0.323480 (0.068092) \| 0.005181 / 0.007986 (-0.002805) \| 0.003272 / 0.004328 (-0.001056) \| 0.065060 / 0.004250 (0.060810) \| 0.054302 / 0.037052 (0.017249) \| 0.361571 / 0.258489 (0.103082) \| 0.400221 / 0.293841 (0.106380) \| 0.030762 / 0.128546 (-0.097784) \| 0.008449 / 0.075646 (-0.067197) \| 0.071148 / 0.419271 (-0.348123) \| 0.048111 / 0.043533 (0.004578) \| 0.360327 / 0.255139 (0.105188) \| 0.379073 / 0.283200 (0.095874) \| 0.024367 / 0.141683 (-0.117316) \| 1.451080 / 1.452155 (-0.001074) \| 1.510818 / 1.492716 (0.018102) \|\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) \| 0.267078 / 0.018006 (0.249072) \| 0.454074 / 0.000490 (0.453584) \| 0.015055 / 0.000200 (0.014855) \| 0.000129 / 0.000054 (0.000075) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030916 / 0.037411 (-0.006496) \| 0.089212 / 0.014526 (0.074686) \| 0.100005 / 0.176557 (-0.076552) \| 0.155100 / 0.737135 (-0.582035) \| 0.101759 / 0.296338 (-0.194580) \|\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) \| 0.412826 / 0.215209 (0.197616) \| 4.122520 / 2.077655 (2.044865) \| 2.107870 / 1.504120 (0.603750) \| 1.911936 / 1.541195 (0.370741) \| 1.984936 / 1.468490 (0.516446) \| 0.483835 / 4.584777 (-4.100942) \| 3.641860 / 3.745712 (-0.103852) \| 3.220540 / 5.269862 (-2.049322) \| 2.015521 / 4.565676 (-2.550155) \| 0.056913 / 0.424275 (-0.367362) \| 0.007285 / 0.007607 (-0.000322) \| 0.484886 / 0.226044 (0.258842) \| 4.854734 / 2.268929 (2.585805) \| 2.593550 / 55.444624 (-52.851074) \| 2.233904 / 6.876477 (-4.642572) \| 2.438858 / 2.142072 (0.296785) \| 0.580880 / 4.805227 (-4.224347) \| 0.133891 / 6.500664 (-6.366773) \| 0.061678 / 0.075469 (-0.013791) \|\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) \| 1.336843 / 1.841788 (-0.504944) \| 19.731571 / 8.074308 (11.657263) \| 14.290228 / 10.191392 (4.098836) \| 0.167635 / 0.680424 (-0.512789) \| 0.018767 / 0.534201 (-0.515434) \| 0.394953 / 0.579283 (-0.184330) \| 0.407711 / 0.434364 (-0.026653) \| 0.472371 / 0.540337 (-0.067966) \| 0.655278 / 1.386936 (-0.731658) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#528b15f775a4724836bdefdc38d932c06484d702 \"CML watermark\")\n" ]
https://api.github.com/repos/huggingface/datasets/issues/6159	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6159/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6159/comments	https://api.github.com/repos/huggingface/datasets/issues/6159/events	https://github.com/huggingface/datasets/issues/6159	1,855,691,512	I_kwDODunzps5um5r4	6,159	Add `BoundingBox` feature	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "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}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko" }	[ { "color": "a2eeef", "default": true, "description": "New feature or request", "id": 1935892871, "name": "enhancement", "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement" } ]	open	false	null	[]	null	0	"2023-08-17T20:49:51Z"	"2023-08-17T20:49:51Z"	null	CONTRIBUTOR	null	null	null	... to make working with object detection datasets easier. Currently, `Sequence(int_or_float, length=4)` can be used to represent this feature optimally (in the storage backend), so I only see this feature being useful if we make it work with the viewer. Also, bounding boxes usually come in 4 different formats (explained [here](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/)), so we need to decide which one to support (or maybe all of them). cc @NielsRogge @severo	{ "+1": 1, "-1": 0, "confused": 0, "eyes": 0, "heart": 1, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 2, "url": "https://api.github.com/repos/huggingface/datasets/issues/6159/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6159/timeline	null	null	606	false	[]
https://api.github.com/repos/huggingface/datasets/issues/6158	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6158/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6158/comments	https://api.github.com/repos/huggingface/datasets/issues/6158/events	https://github.com/huggingface/datasets/pull/6158	1,855,374,220	PR_kwDODunzps5YLZBf	6,158	[docs] Complete `to_iterable_dataset`	{ "avatar_url": "https://avatars.githubusercontent.com/u/59462357?v=4", "events_url": "https://api.github.com/users/stevhliu/events{/privacy}", "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}", "gravatar_id": "", "html_url": "https://github.com/stevhliu", "id": 59462357, "login": "stevhliu", "node_id": "MDQ6VXNlcjU5NDYyMzU3", "organizations_url": "https://api.github.com/users/stevhliu/orgs", "received_events_url": "https://api.github.com/users/stevhliu/received_events", "repos_url": "https://api.github.com/users/stevhliu/repos", "site_admin": false, "starred_url": "https://api.github.com/users/stevhliu/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/stevhliu/subscriptions", "type": "User", "url": "https://api.github.com/users/stevhliu" }	[]	closed	false	null	[]	null	2	"2023-08-17T17:02:11Z"	"2023-08-17T19:24:20Z"	"2023-08-17T19:13:15Z"	MEMBER	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6158.diff", "html_url": "https://github.com/huggingface/datasets/pull/6158", "merged_at": "2023-08-17T19:13:15Z", "patch_url": "https://github.com/huggingface/datasets/pull/6158.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6158" }	Finishes the `to_iterable_dataset` documentation by adding it to the relevant sections in the tutorial and guide.	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6158/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6158/timeline	null	null	607	true	[ "_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) \| 0.008219 / 0.011353 (-0.003134) \| 0.005201 / 0.011008 (-0.005807) \| 0.108542 / 0.038508 (0.070034) \| 0.076427 / 0.023109 (0.053318) \| 0.441257 / 0.275898 (0.165358) \| 0.436477 / 0.323480 (0.112997) \| 0.006915 / 0.007986 (-0.001071) \| 0.004215 / 0.004328 (-0.000113) \| 0.072517 / 0.004250 (0.068267) \| 0.066906 / 0.037052 (0.029853) \| 0.431153 / 0.258489 (0.172664) \| 0.413359 / 0.293841 (0.119518) \| 0.051112 / 0.128546 (-0.077435) \| 0.014664 / 0.075646 (-0.060982) \| 0.358385 / 0.419271 (-0.060887) \| 0.069682 / 0.043533 (0.026149) \| 0.434810 / 0.255139 (0.179671) \| 0.484372 / 0.283200 (0.201172) \| 0.035731 / 0.141683 (-0.105952) \| 1.827648 / 1.452155 (0.375494) \| 2.039761 / 1.492716 (0.547045) \|\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) \| 0.277386 / 0.018006 (0.259379) \| 0.599771 / 0.000490 (0.599282) \| 0.005033 / 0.000200 (0.004833) \| 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) \| 0.030652 / 0.037411 (-0.006759) \| 0.103435 / 0.014526 (0.088909) \| 0.120072 / 0.176557 (-0.056485) \| 0.177886 / 0.737135 (-0.559249) \| 0.140636 / 0.296338 (-0.155702) \|\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) \| 0.603729 / 0.215209 (0.388520) \| 6.144213 / 2.077655 (4.066558) \| 2.785080 / 1.504120 (1.280960) \| 2.368958 / 1.541195 (0.827763) \| 2.409806 / 1.468490 (0.941316) \| 0.836531 / 4.584777 (-3.748246) \| 5.154035 / 3.745712 (1.408323) \| 4.620224 / 5.269862 (-0.649638) \| 2.879441 / 4.565676 (-1.686235) \| 0.087322 / 0.424275 (-0.336953) \| 0.007698 / 0.007607 (0.000090) \| 0.678443 / 0.226044 (0.452399) \| 7.431798 / 2.268929 (5.162869) \| 3.589905 / 55.444624 (-51.854719) \| 2.679349 / 6.876477 (-4.197127) \| 3.100569 / 2.142072 (0.958496) \| 1.021501 / 4.805227 (-3.783726) \| 0.203150 / 6.500664 (-6.297514) \| 0.073545 / 0.075469 (-0.001924) \|\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) \| 1.669981 / 1.841788 (-0.171806) \| 23.379274 / 8.074308 (15.304966) \| 19.811451 / 10.191392 (9.620059) \| 0.197705 / 0.680424 (-0.482719) \| 0.030112 / 0.534201 (-0.504089) \| 0.501720 / 0.579283 (-0.077563) \| 0.582413 / 0.434364 (0.148049) \| 0.513261 / 0.540337 (-0.027076) \| 0.729710 / 1.386936 (-0.657226) \|\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) \| 0.011493 / 0.011353 (0.000140) \| 0.005478 / 0.011008 (-0.005530) \| 0.070955 / 0.038508 (0.032447) \| 0.073877 / 0.023109 (0.050768) \| 0.425765 / 0.275898 (0.149867) \| 0.440869 / 0.323480 (0.117389) \| 0.008322 / 0.007986 (0.000337) \| 0.004004 / 0.004328 (-0.000325) \| 0.071968 / 0.004250 (0.067718) \| 0.060576 / 0.037052 (0.023524) \| 0.448731 / 0.258489 (0.190242) \| 0.517038 / 0.293841 (0.223197) \| 0.051542 / 0.128546 (-0.077005) \| 0.013219 / 0.075646 (-0.062427) \| 0.077933 / 0.419271 (-0.341339) \| 0.072879 / 0.043533 (0.029346) \| 0.436553 / 0.255139 (0.181414) \| 0.510050 / 0.283200 (0.226850) \| 0.037136 / 0.141683 (-0.104547) \| 1.535706 / 1.452155 (0.083552) \| 1.611909 / 1.492716 (0.119192) \|\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) \| 0.335648 / 0.018006 (0.317642) \| 0.612787 / 0.000490 (0.612297) \| 0.021934 / 0.000200 (0.021734) \| 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) \| 0.028164 / 0.037411 (-0.009247) \| 0.097686 / 0.014526 (0.083160) \| 0.093343 / 0.176557 (-0.083214) \| 0.156871 / 0.737135 (-0.580264) \| 0.102694 / 0.296338 (-0.193645) \|\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) \| 0.609348 / 0.215209 (0.394139) \| 5.835798 / 2.077655 (3.758144) \| 2.792700 / 1.504120 (1.288580) \| 2.539597 / 1.541195 (0.998403) \| 2.413003 / 1.468490 (0.944513) \| 0.882404 / 4.584777 (-3.702372) \| 5.170564 / 3.745712 (1.424852) \| 4.621663 / 5.269862 (-0.648199) \| 3.029683 / 4.565676 (-1.535993) \| 0.097061 / 0.424275 (-0.327214) \| 0.008940 / 0.007607 (0.001333) \| 0.723052 / 0.226044 (0.497007) \| 7.484947 / 2.268929 (5.216018) \| 3.833049 / 55.444624 (-51.611575) \| 3.019606 / 6.876477 (-3.856871) \| 3.270503 / 2.142072 (1.128430) \| 0.977870 / 4.805227 (-3.827357) \| 0.210090 / 6.500664 (-6.290574) \| 0.094723 / 0.075469 (0.019254) \|\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) \| 1.585278 / 1.841788 (-0.256510) \| 22.769727 / 8.074308 (14.695419) \| 19.503640 / 10.191392 (9.312248) \| 0.231996 / 0.680424 (-0.448428) \| 0.032641 / 0.534201 (-0.501560) \| 0.429833 / 0.579283 (-0.149451) \| 0.549606 / 0.434364 (0.115242) \| 0.527405 / 0.540337 (-0.012933) \| 0.713302 / 1.386936 (-0.673634) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#546c7bb5cbeff0f8673cf60c4432ea167283cc42 \"CML watermark\")\n" ]
https://api.github.com/repos/huggingface/datasets/issues/6157	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6157/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6157/comments	https://api.github.com/repos/huggingface/datasets/issues/6157/events	https://github.com/huggingface/datasets/issues/6157	1,855,265,663	I_kwDODunzps5ulRt_	6,157	DatasetInfo.__init__() got an unexpected keyword argument '_column_requires_decoding'	{ "avatar_url": "https://avatars.githubusercontent.com/u/51043929?v=4", "events_url": "https://api.github.com/users/aihao2000/events{/privacy}", "followers_url": "https://api.github.com/users/aihao2000/followers", "following_url": "https://api.github.com/users/aihao2000/following{/other_user}", "gists_url": "https://api.github.com/users/aihao2000/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/aihao2000", "id": 51043929, "login": "aihao2000", "node_id": "MDQ6VXNlcjUxMDQzOTI5", "organizations_url": "https://api.github.com/users/aihao2000/orgs", "received_events_url": "https://api.github.com/users/aihao2000/received_events", "repos_url": "https://api.github.com/users/aihao2000/repos", "site_admin": false, "starred_url": "https://api.github.com/users/aihao2000/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/aihao2000/subscriptions", "type": "User", "url": "https://api.github.com/users/aihao2000" }	[]	closed	false	null	[]	null	13	"2023-08-17T15:48:11Z"	"2023-09-27T17:36:14Z"	"2023-09-27T17:36:14Z"	NONE	null	null	null	### Describe the bug When I was in load_dataset, it said "DatasetInfo.__init__() got an unexpected keyword argument '_column_requires_decoding'". The second time I ran it, there was no error and the dataset object worked ```python --------------------------------------------------------------------------- TypeError Traceback (most recent call last) Cell In[3], line 1 ----> 1 dataset = load_dataset( 2 "/home/aihao/workspace/DeepLearningContent/datasets/manga", 3 data_dir="/home/aihao/workspace/DeepLearningContent/datasets/manga", 4 split="train", 5 ) File [~/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/load.py:2146](https://vscode-remote+ssh-002dremote-002bhome.vscode-resource.vscode-cdn.net/home/aihao/workspace/DeepLearningContent/datasets/~/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/load.py:2146), 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, token, use_auth_token, task, streaming, num_proc, storage_options, config_kwargs) 2142 # Build dataset for splits 2143 keep_in_memory = ( 2144 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size) 2145 ) -> 2146 ds = builder_instance.as_dataset(split=split, verification_mode=verification_mode, in_memory=keep_in_memory) 2147 # Rename and cast features to match task schema 2148 if task is not None: 2149 # To avoid issuing the same warning twice File [~/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/builder.py:1190](https://vscode-remote+ssh-002dremote-002bhome.vscode-resource.vscode-cdn.net/home/aihao/workspace/DeepLearningContent/datasets/~/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/builder.py:1190), in DatasetBuilder.as_dataset(self, split, run_post_process, verification_mode, ignore_verifications, in_memory) 1187 verification_mode = VerificationMode(verification_mode or VerificationMode.BASIC_CHECKS) 1189 # Create a dataset for each of the given splits -> 1190 datasets = map_nested( 1191 partial( 1192 self._build_single_dataset, ... File [~/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/info.py:379](https://vscode-remote+ssh-002dremote-002bhome.vscode-resource.vscode-cdn.net/home/aihao/workspace/DeepLearningContent/datasets/~/miniconda3/envs/torch/lib/python3.11/site-packages/datasets/info.py:379), in DatasetInfo.copy(self) 378 def copy(self) -> "DatasetInfo": --> 379 return self.__class__({k: copy.deepcopy(v) for k, v in self.__dict__.items()}) TypeError: DatasetInfo.__init__() got an unexpected keyword argument '_column_requires_decoding' ``` ### Steps to reproduce the bug /home/aihao/workspace/DeepLearningContent/datasets/images/images.py ```python from logging import config import datasets import os from PIL import Image import csv import json class ImagesConfig(datasets.BuilderConfig): def __init__(self, kwargs): super(ImagesConfig, self).__init__(kwargs) class Images(datasets.GeneratorBasedBuilder): def _split_generators(self, dl_manager: datasets.DownloadManager): return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={"split": datasets.Split.TRAIN}, ) ] BUILDER_CONFIGS = [ ImagesConfig( name="similar_pairs", description="simliar pair dataset,item is a pair of similar images", ), ImagesConfig( name="image_prompt_pairs", description="image prompt pairs", ), ] def _info(self): if self.config.name == "similar_pairs": return datasets.Features( { "image1": datasets.features.Image(), "image2": datasets.features.Image(), "similarity": datasets.Value("float32"), } ) elif self.config.name == "image_prompt_pairs": return datasets.Features( {"image": datasets.features.Image(), "prompt": datasets.Value("string")} ) def _generate_examples(self, split): data_path = os.path.join(self.config.data_dir, "data") if self.config.name == "similar_pairs": prompts = {} with open(os.path.join(data_path ,"prompts.json"), "r") as f: prompts = json.load(f) with open(os.path.join(data_path, "similar_pairs.csv"), "r") as f: reader = csv.reader(f) for row in reader: image1_path, image2_path, similarity = row yield image1_path + ":" + image2_path + ":", { "image1": Image.open(image1_path), "prompt1": prompts[image1_path], "image2": Image.open(image2_path), "prompt2": prompts[image2_path], "similarity": float(similarity), } ``` Code that indicates an error: ```python from datasets import load_dataset import json import csv import ast import torch data_dir = "/home/aihao/workspace/DeepLearningContent/datasets/images" dataset = load_dataset(data_dir, data_dir=data_dir, name="similar_pairs") ``` ### Expected behavior The first execution gives an error, but it works fine ### Environment info - `datasets` version: 2.14.3 - Platform: Linux-6.2.0-26-generic-x86_64-with-glibc2.35 - Python version: 3.11.4 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 2.0.3	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6157/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6157/timeline	null	completed	608	false	[ "Thanks for reporting, but we can only fix this issue if you can provide a reproducer that consistently reproduces it.", "@mariosasko Ok. What exactly does it mean to provide a reproducer", "To provide a code that reproduces the issue :)", "@mariosasko I complete the above code, is it enough?", "@mariosasko That's all the code, I'm using locally stored data", "Does this error occur even if you change the cache directory (the `cache_dir` parameter in `load_dataset`)?", "@mariosasko I didn't add any parameters for catch. Nor did any cache configuration change.", "@mariosasko And I changed the data file, but executing load_dataset is always the previous result. I had to change something in images.py to use the new results. Using 'cleanup_cache_files' is invalid! Help me.", "@mariosasko I added a comprehensive error message. Check that _column_requires_decoding is being passed where it shouldn't be. DatasetInfo.__init__() Whether this parameter is required", "I can see the issue now... \r\n\r\nYou can fix it by returning a `DatasetInfo` object in the `_info` method as follows:\r\n```python\r\n def _info(self):\r\n if self.config.name == \"similar_pairs\":\r\n features = datasets.Features(\r\n {\r\n \"image1\": datasets.features.Image(),\r\n \"prompt1\": datasets.Value(\"string\"),\r\n \"image2\": datasets.features.Image(),\r\n \"prompt2\": datasets.Value(\"string\"),\r\n \"similarity\": datasets.Value(\"float32\"),\r\n }\r\n )\r\n elif self.config.name == \"image_prompt_pairs\":\r\n features = datasets.Features(\r\n {\"image\": datasets.features.Image(), \"prompt\": datasets.Value(\"string\")}\r\n )\r\n return datasets.DatasetInfo(features=features)\r\n```", "@mariosasko Oh, that's the problem. Thank you very much. Returned the wrong object and it actually works? I've been training with it for a long time", "@mariosasko The original code can still see progress. emmm, I can't see how many examples is generated so far, so I don't know if we should wait", "The original issue has been addressed, so I'm closing it. \r\n\r\nPlease open a new issue if you encounter more errors." ]
https://api.github.com/repos/huggingface/datasets/issues/6156	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6156/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6156/comments	https://api.github.com/repos/huggingface/datasets/issues/6156/events	https://github.com/huggingface/datasets/issues/6156	1,854,768,618	I_kwDODunzps5ujYXq	6,156	Why not use self._epoch as seed to shuffle in distributed training with IterableDataset	{ "avatar_url": "https://avatars.githubusercontent.com/u/11533479?v=4", "events_url": "https://api.github.com/users/npuichigo/events{/privacy}", "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}", "gravatar_id": "", "html_url": "https://github.com/npuichigo", "id": 11533479, "login": "npuichigo", "node_id": "MDQ6VXNlcjExNTMzNDc5", "organizations_url": "https://api.github.com/users/npuichigo/orgs", "received_events_url": "https://api.github.com/users/npuichigo/received_events", "repos_url": "https://api.github.com/users/npuichigo/repos", "site_admin": false, "starred_url": "https://api.github.com/users/npuichigo/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/npuichigo/subscriptions", "type": "User", "url": "https://api.github.com/users/npuichigo" }	[]	closed	false	null	[]	null	3	"2023-08-17T10:58:20Z"	"2023-08-17T14:33:15Z"	"2023-08-17T14:33:14Z"	CONTRIBUTOR	null	null	null	### Describe the bug Currently, distributed training with `IterableDataset` needs to pass fixed seed to shuffle to keep each node use the same seed to avoid overlapping. https://github.com/huggingface/datasets/blob/a7f8d9019e7cb104eac4106bdc6ec0292f0dc61a/src/datasets/iterable_dataset.py#L1174-L1177 My question is why not directly use `self._epoch` which is set by `set_epoch` as seed? It's almost the same across nodes. https://github.com/huggingface/datasets/blob/a7f8d9019e7cb104eac4106bdc6ec0292f0dc61a/src/datasets/iterable_dataset.py#L1790-L1801 If not using `self._epoch` as shuffling seed, what does this method do to prepare an epoch seeded generator? https://github.com/huggingface/datasets/blob/a7f8d9019e7cb104eac4106bdc6ec0292f0dc61a/src/datasets/iterable_dataset.py#L1206 ### Steps to reproduce the bug As mentioned above. ### Expected behavior As mentioned above. ### Environment info Not related	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6156/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6156/timeline	null	completed	609	false	[ "@lhoestq ", "`_effective_generator` returns a RNG that takes into account `self._epoch` and the current dataset's base shuffling RNG (which can be set by specifying `seed=` in `.shuffle() for example`).\r\n\r\nTo fix your error you can pass `seed=` to `.shuffle()`. And the shuffling will depend on both this seed and `self._epoch`", "Thanks for the reply" ]
https://api.github.com/repos/huggingface/datasets/issues/6155	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6155/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6155/comments	https://api.github.com/repos/huggingface/datasets/issues/6155/events	https://github.com/huggingface/datasets/pull/6155	1,854,661,682	PR_kwDODunzps5YI8Pc	6,155	Raise FileNotFoundError when passing data_files that don't exist	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "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}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq" }	[]	closed	false	null	[]	null	5	"2023-08-17T09:49:48Z"	"2023-08-18T13:45:58Z"	"2023-08-18T13:35:13Z"	MEMBER	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6155.diff", "html_url": "https://github.com/huggingface/datasets/pull/6155", "merged_at": "2023-08-18T13:35:13Z", "patch_url": "https://github.com/huggingface/datasets/pull/6155.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6155" }	e.g. when running `load_dataset("parquet", data_files="doesnt_exist.parquet")`	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6155/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6155/timeline	null	null	610	true	[ "_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) \| 0.009288 / 0.011353 (-0.002065) \| 0.005950 / 0.011008 (-0.005058) \| 0.122376 / 0.038508 (0.083868) \| 0.093177 / 0.023109 (0.070068) \| 0.448517 / 0.275898 (0.172619) \| 0.474999 / 0.323480 (0.151520) \| 0.005133 / 0.007986 (-0.002853) \| 0.005123 / 0.004328 (0.000795) \| 0.085479 / 0.004250 (0.081229) \| 0.065613 / 0.037052 (0.028561) \| 0.451179 / 0.258489 (0.192690) \| 0.516876 / 0.293841 (0.223036) \| 0.047536 / 0.128546 (-0.081010) \| 0.013894 / 0.075646 (-0.061752) \| 0.382149 / 0.419271 (-0.037122) \| 0.067380 / 0.043533 (0.023848) \| 0.419282 / 0.255139 (0.164143) \| 0.482042 / 0.283200 (0.198842) \| 0.041230 / 0.141683 (-0.100452) \| 1.818127 / 1.452155 (0.365972) \| 1.938123 / 1.492716 (0.445406) \|\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) \| 0.271824 / 0.018006 (0.253817) \| 0.604933 / 0.000490 (0.604443) \| 0.004953 / 0.000200 (0.004753) \| 0.000173 / 0.000054 (0.000119) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.036682 / 0.037411 (-0.000729) \| 0.095604 / 0.014526 (0.081078) \| 0.116862 / 0.176557 (-0.059695) \| 0.191335 / 0.737135 (-0.545800) \| 0.116620 / 0.296338 (-0.179718) \|\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) \| 0.620735 / 0.215209 (0.405526) \| 6.157119 / 2.077655 (4.079465) \| 2.848548 / 1.504120 (1.344428) \| 2.493731 / 1.541195 (0.952536) \| 2.505801 / 1.468490 (1.037311) \| 0.837315 / 4.584777 (-3.747462) \| 5.360653 / 3.745712 (1.614941) \| 4.908863 / 5.269862 (-0.360999) \| 3.184672 / 4.565676 (-1.381004) \| 0.105687 / 0.424275 (-0.318588) \| 0.011350 / 0.007607 (0.003743) \| 0.745729 / 0.226044 (0.519684) \| 7.431584 / 2.268929 (5.162655) \| 3.644670 / 55.444624 (-51.799954) \| 2.910159 / 6.876477 (-3.966317) \| 3.257137 / 2.142072 (1.115065) \| 1.041377 / 4.805227 (-3.763851) \| 0.213289 / 6.500664 (-6.287375) \| 0.089208 / 0.075469 (0.013739) \|\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) \| 1.727274 / 1.841788 (-0.114513) \| 25.448436 / 8.074308 (17.374128) \| 23.016108 / 10.191392 (12.824716) \| 0.219454 / 0.680424 (-0.460970) \| 0.028531 / 0.534201 (-0.505670) \| 0.500231 / 0.579283 (-0.079052) \| 0.614631 / 0.434364 (0.180267) \| 0.557926 / 0.540337 (0.017588) \| 0.786261 / 1.386936 (-0.600675) \|\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) \| 0.008608 / 0.011353 (-0.002745) \| 0.006185 / 0.011008 (-0.004823) \| 0.089258 / 0.038508 (0.050750) \| 0.090109 / 0.023109 (0.067000) \| 0.522200 / 0.275898 (0.246302) \| 0.559218 / 0.323480 (0.235738) \| 0.008983 / 0.007986 (0.000997) \| 0.004488 / 0.004328 (0.000159) \| 0.083658 / 0.004250 (0.079408) \| 0.064962 / 0.037052 (0.027909) \| 0.519477 / 0.258489 (0.260988) \| 0.573842 / 0.293841 (0.280001) \| 0.053984 / 0.128546 (-0.074562) \| 0.014665 / 0.075646 (-0.060982) \| 0.089438 / 0.419271 (-0.329834) \| 0.065756 / 0.043533 (0.022223) \| 0.525131 / 0.255139 (0.269992) \| 0.568934 / 0.283200 (0.285734) \| 0.037308 / 0.141683 (-0.104375) \| 1.928790 / 1.452155 (0.476635) \| 2.027926 / 1.492716 (0.535209) \|\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) \| 0.309595 / 0.018006 (0.291588) \| 0.615675 / 0.000490 (0.615186) \| 0.004869 / 0.000200 (0.004669) \| 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) \| 0.033306 / 0.037411 (-0.004105) \| 0.104429 / 0.014526 (0.089904) \| 0.116989 / 0.176557 (-0.059568) \| 0.183638 / 0.737135 (-0.553497) \| 0.132624 / 0.296338 (-0.163714) \|\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) \| 0.644511 / 0.215209 (0.429302) \| 6.425544 / 2.077655 (4.347889) \| 3.079071 / 1.504120 (1.574951) \| 2.720963 / 1.541195 (1.179769) \| 2.835607 / 1.468490 (1.367117) \| 0.863561 / 4.584777 (-3.721216) \| 5.333462 / 3.745712 (1.587750) \| 4.843183 / 5.269862 (-0.426678) \| 3.106858 / 4.565676 (-1.458819) \| 0.106790 / 0.424275 (-0.317485) \| 0.008829 / 0.007607 (0.001222) \| 0.759003 / 0.226044 (0.532958) \| 7.771247 / 2.268929 (5.502318) \| 3.896844 / 55.444624 (-51.547780) \| 3.246671 / 6.876477 (-3.629806) \| 3.486167 / 2.142072 (1.344094) \| 1.071290 / 4.805227 (-3.733937) \| 0.217972 / 6.500664 (-6.282692) \| 0.089848 / 0.075469 (0.014379) \|\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) \| 1.816048 / 1.841788 (-0.025739) \| 25.625084 / 8.074308 (17.550776) \| 24.490882 / 10.191392 (14.299490) \| 0.242356 / 0.680424 (-0.438067) \| 0.027886 / 0.534201 (-0.506315) \| 0.496997 / 0.579283 (-0.082286) \| 0.613815 / 0.434364 (0.179451) \| 0.607132 / 0.540337 (0.066795) \| 0.833051 / 1.386936 (-0.553885) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0adfa9ada14c38fce5973b5e3f196a2c46dc9170 \"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) \| 0.011580 / 0.011353 (0.000227) \| 0.004199 / 0.011008 (-0.006809) \| 0.084055 / 0.038508 (0.045547) \| 0.096824 / 0.023109 (0.073715) \| 0.308755 / 0.275898 (0.032857) \| 0.341717 / 0.323480 (0.018237) \| 0.006018 / 0.007986 (-0.001968) \| 0.003597 / 0.004328 (-0.000731) \| 0.064953 / 0.004250 (0.060702) \| 0.059577 / 0.037052 (0.022525) \| 0.316292 / 0.258489 (0.057803) \| 0.358991 / 0.293841 (0.065150) \| 0.033925 / 0.128546 (-0.094621) \| 0.008828 / 0.075646 (-0.066818) \| 0.288673 / 0.419271 (-0.130599) \| 0.055494 / 0.043533 (0.011961) \| 0.311181 / 0.255139 (0.056042) \| 0.345220 / 0.283200 (0.062021) \| 0.024033 / 0.141683 (-0.117649) \| 1.504709 / 1.452155 (0.052554) \| 1.587920 / 1.492716 (0.095204) \|\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) \| 0.301099 / 0.018006 (0.283093) \| 0.594497 / 0.000490 (0.594007) \| 0.006244 / 0.000200 (0.006044) \| 0.000228 / 0.000054 (0.000174) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027663 / 0.037411 (-0.009748) \| 0.081767 / 0.014526 (0.067241) \| 0.097342 / 0.176557 (-0.079215) \| 0.153200 / 0.737135 (-0.583935) \| 0.097474 / 0.296338 (-0.198864) \|\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) \| 0.405929 / 0.215209 (0.190719) \| 4.045398 / 2.077655 (1.967743) \| 2.044669 / 1.504120 (0.540549) \| 1.872889 / 1.541195 (0.331694) \| 1.911901 / 1.468490 (0.443411) \| 0.480939 / 4.584777 (-4.103838) \| 3.652833 / 3.745712 (-0.092879) \| 3.281659 / 5.269862 (-1.988202) \| 2.038023 / 4.565676 (-2.527654) \| 0.056501 / 0.424275 (-0.367775) \| 0.007571 / 0.007607 (-0.000036) \| 0.481053 / 0.226044 (0.255009) \| 4.802048 / 2.268929 (2.533119) \| 2.560479 / 55.444624 (-52.884145) \| 2.164852 / 6.876477 (-4.711625) \| 2.374595 / 2.142072 (0.232523) \| 0.576309 / 4.805227 (-4.228918) \| 0.134831 / 6.500664 (-6.365833) \| 0.060649 / 0.075469 (-0.014820) \|\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) \| 1.254210 / 1.841788 (-0.587578) \| 19.826143 / 8.074308 (11.751835) \| 14.446391 / 10.191392 (4.254999) \| 0.165707 / 0.680424 (-0.514717) \| 0.018221 / 0.534201 (-0.515980) \| 0.395996 / 0.579283 (-0.183287) \| 0.424567 / 0.434364 (-0.009796) \| 0.459836 / 0.540337 (-0.080501) \| 0.635969 / 1.386936 (-0.750967) \|\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) \| 0.006696 / 0.011353 (-0.004657) \| 0.004131 / 0.011008 (-0.006877) \| 0.064587 / 0.038508 (0.026079) \| 0.079189 / 0.023109 (0.056080) \| 0.359977 / 0.275898 (0.084079) \| 0.389331 / 0.323480 (0.065851) \| 0.005502 / 0.007986 (-0.002483) \| 0.003492 / 0.004328 (-0.000837) \| 0.064967 / 0.004250 (0.060716) \| 0.055953 / 0.037052 (0.018901) \| 0.363997 / 0.258489 (0.105508) \| 0.398405 / 0.293841 (0.104564) \| 0.031292 / 0.128546 (-0.097254) \| 0.008693 / 0.075646 (-0.066953) \| 0.070451 / 0.419271 (-0.348820) \| 0.048965 / 0.043533 (0.005432) \| 0.358288 / 0.255139 (0.103149) \| 0.379136 / 0.283200 (0.095936) \| 0.024364 / 0.141683 (-0.117319) \| 1.478998 / 1.452155 (0.026843) \| 1.547282 / 1.492716 (0.054566) \|\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) \| 0.328188 / 0.018006 (0.310182) \| 0.525968 / 0.000490 (0.525478) \| 0.003782 / 0.000200 (0.003582) \| 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) \| 0.032528 / 0.037411 (-0.004883) \| 0.087685 / 0.014526 (0.073159) \| 0.100684 / 0.176557 (-0.075872) \| 0.155944 / 0.737135 (-0.581192) \| 0.101949 / 0.296338 (-0.194389) \|\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) \| 0.418591 / 0.215209 (0.203382) \| 4.199235 / 2.077655 (2.121580) \| 2.183880 / 1.504120 (0.679760) \| 2.024502 / 1.541195 (0.483307) \| 2.017435 / 1.468490 (0.548945) \| 0.488881 / 4.584777 (-4.095896) \| 3.635002 / 3.745712 (-0.110710) \| 3.359992 / 5.269862 (-1.909870) \| 2.089686 / 4.565676 (-2.475991) \| 0.057813 / 0.424275 (-0.366462) \| 0.007349 / 0.007607 (-0.000258) \| 0.490719 / 0.226044 (0.264674) \| 4.859950 / 2.268929 (2.591022) \| 2.616711 / 55.444624 (-52.827914) \| 2.238671 / 6.876477 (-4.637806) \| 2.442262 / 2.142072 (0.300190) \| 0.598368 / 4.805227 (-4.206859) \| 0.135281 / 6.500664 (-6.365383) \| 0.063072 / 0.075469 (-0.012397) \|\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) \| 1.356396 / 1.841788 (-0.485392) \| 20.075123 / 8.074308 (12.000815) \| 14.191317 / 10.191392 (3.999925) \| 0.167691 / 0.680424 (-0.512732) \| 0.018290 / 0.534201 (-0.515911) \| 0.392881 / 0.579283 (-0.186402) \| 0.413665 / 0.434364 (-0.020699) \| 0.480766 / 0.540337 (-0.059571) \| 0.655625 / 1.386936 (-0.731311) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a46ca9cc138754629be261522301e725c7d14152 \"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) \| 0.007834 / 0.011353 (-0.003519) \| 0.004744 / 0.011008 (-0.006264) \| 0.102061 / 0.038508 (0.063553) \| 0.089246 / 0.023109 (0.066137) \| 0.399936 / 0.275898 (0.124038) \| 0.436974 / 0.323480 (0.113494) \| 0.004791 / 0.007986 (-0.003195) \| 0.005976 / 0.004328 (0.001647) \| 0.079336 / 0.004250 (0.075086) \| 0.065947 / 0.037052 (0.028894) \| 0.403747 / 0.258489 (0.145258) \| 0.460249 / 0.293841 (0.166408) \| 0.038065 / 0.128546 (-0.090482) \| 0.010179 / 0.075646 (-0.065467) \| 0.403620 / 0.419271 (-0.015652) \| 0.066439 / 0.043533 (0.022906) \| 0.412123 / 0.255139 (0.156984) \| 0.452121 / 0.283200 (0.168921) \| 0.033533 / 0.141683 (-0.108150) \| 1.858650 / 1.452155 (0.406495) \| 1.916248 / 1.492716 (0.423532) \|\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) \| 0.237180 / 0.018006 (0.219174) \| 0.526844 / 0.000490 (0.526354) \| 0.004220 / 0.000200 (0.004020) \| 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) \| 0.033860 / 0.037411 (-0.003552) \| 0.105054 / 0.014526 (0.090528) \| 0.116494 / 0.176557 (-0.060063) \| 0.185990 / 0.737135 (-0.551145) \| 0.119072 / 0.296338 (-0.177266) \|\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) \| 0.488549 / 0.215209 (0.273340) \| 4.884950 / 2.077655 (2.807295) \| 2.521819 / 1.504120 (1.017699) \| 2.329382 / 1.541195 (0.788188) \| 2.413710 / 1.468490 (0.945220) \| 0.568325 / 4.584777 (-4.016452) \| 4.243505 / 3.745712 (0.497793) \| 3.785983 / 5.269862 (-1.483879) \| 2.387146 / 4.565676 (-2.178531) \| 0.067176 / 0.424275 (-0.357099) \| 0.009145 / 0.007607 (0.001538) \| 0.571482 / 0.226044 (0.345437) \| 5.688822 / 2.268929 (3.419894) \| 3.067346 / 55.444624 (-52.377278) \| 2.688723 / 6.876477 (-4.187754) \| 2.883785 / 2.142072 (0.741713) \| 0.679326 / 4.805227 (-4.125901) \| 0.156018 / 6.500664 (-6.344646) \| 0.070947 / 0.075469 (-0.004522) \|\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) \| 1.556611 / 1.841788 (-0.285177) \| 23.545074 / 8.074308 (15.470766) \| 17.125108 / 10.191392 (6.933716) \| 0.180180 / 0.680424 (-0.500244) \| 0.021420 / 0.534201 (-0.512781) \| 0.466888 / 0.579283 (-0.112395) \| 0.485746 / 0.434364 (0.051383) \| 0.606181 / 0.540337 (0.065843) \| 0.776691 / 1.386936 (-0.610245) \|\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) \| 0.007820 / 0.011353 (-0.003533) \| 0.004531 / 0.011008 (-0.006478) \| 0.076142 / 0.038508 (0.037634) \| 0.086367 / 0.023109 (0.063258) \| 0.456150 / 0.275898 (0.180252) \| 0.499712 / 0.323480 (0.176232) \| 0.006545 / 0.007986 (-0.001441) \| 0.003760 / 0.004328 (-0.000568) \| 0.076400 / 0.004250 (0.072150) \| 0.069689 / 0.037052 (0.032637) \| 0.459732 / 0.258489 (0.201243) \| 0.504217 / 0.293841 (0.210376) \| 0.037838 / 0.128546 (-0.090709) \| 0.009804 / 0.075646 (-0.065843) \| 0.084654 / 0.419271 (-0.334617) \| 0.060301 / 0.043533 (0.016768) \| 0.452984 / 0.255139 (0.197845) \| 0.479956 / 0.283200 (0.196757) \| 0.029674 / 0.141683 (-0.112009) \| 1.814059 / 1.452155 (0.361904) \| 1.878886 / 1.492716 (0.386170) \|\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) \| 0.326174 / 0.018006 (0.308168) \| 0.539722 / 0.000490 (0.539232) \| 0.025637 / 0.000200 (0.025437) \| 0.000209 / 0.000054 (0.000154) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.036328 / 0.037411 (-0.001084) \| 0.106369 / 0.014526 (0.091843) \| 0.118598 / 0.176557 (-0.057958) \| 0.182760 / 0.737135 (-0.554376) \| 0.120013 / 0.296338 (-0.176326) \|\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) \| 0.507328 / 0.215209 (0.292119) \| 5.092689 / 2.077655 (3.015034) \| 2.962334 / 1.504120 (1.458214) \| 2.507699 / 1.541195 (0.966504) \| 2.612245 / 1.468490 (1.143755) \| 0.568625 / 4.584777 (-4.016152) \| 4.296484 / 3.745712 (0.550772) \| 4.037788 / 5.269862 (-1.232073) \| 2.579826 / 4.565676 (-1.985850) \| 0.068558 / 0.424275 (-0.355717) \| 0.008916 / 0.007607 (0.001309) \| 0.601054 / 0.226044 (0.375010) \| 6.016061 / 2.268929 (3.747133) \| 3.311880 / 55.444624 (-52.132744) \| 2.912926 / 6.876477 (-3.963551) \| 3.101465 / 2.142072 (0.959393) \| 0.686848 / 4.805227 (-4.118380) \| 0.160243 / 6.500664 (-6.340421) \| 0.074084 / 0.075469 (-0.001385) \|\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) \| 1.754343 / 1.841788 (-0.087444) \| 24.215302 / 8.074308 (16.140994) \| 17.211007 / 10.191392 (7.019615) \| 0.188370 / 0.680424 (-0.492054) \| 0.028157 / 0.534201 (-0.506044) \| 0.490879 / 0.579283 (-0.088404) \| 0.501508 / 0.434364 (0.067144) \| 0.599719 / 0.540337 (0.059381) \| 0.852438 / 1.386936 (-0.534498) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d84cd1d6f51ca75ec5f5c3db3f372f093758cac9 \"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) \| 0.009736 / 0.011353 (-0.001617) \| 0.004761 / 0.011008 (-0.006247) \| 0.100069 / 0.038508 (0.061561) \| 0.077944 / 0.023109 (0.054835) \| 0.419944 / 0.275898 (0.144046) \| 0.459803 / 0.323480 (0.136323) \| 0.006296 / 0.007986 (-0.001689) \| 0.005375 / 0.004328 (0.001047) \| 0.089457 / 0.004250 (0.085207) \| 0.060585 / 0.037052 (0.023532) \| 0.437988 / 0.258489 (0.179499) \| 0.482676 / 0.293841 (0.188835) \| 0.049126 / 0.128546 (-0.079420) \| 0.015043 / 0.075646 (-0.060603) \| 0.342500 / 0.419271 (-0.076771) \| 0.067088 / 0.043533 (0.023555) \| 0.418364 / 0.255139 (0.163225) \| 0.458259 / 0.283200 (0.175059) \| 0.034091 / 0.141683 (-0.107592) \| 1.721589 / 1.452155 (0.269434) \| 1.823142 / 1.492716 (0.330426) \|\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) \| 0.212110 / 0.018006 (0.194103) \| 0.530957 / 0.000490 (0.530467) \| 0.003581 / 0.000200 (0.003382) \| 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) \| 0.030202 / 0.037411 (-0.007210) \| 0.100552 / 0.014526 (0.086026) \| 0.108150 / 0.176557 (-0.068407) \| 0.173203 / 0.737135 (-0.563932) \| 0.108624 / 0.296338 (-0.187715) \|\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) \| 0.577340 / 0.215209 (0.362131) \| 5.794197 / 2.077655 (3.716543) \| 2.396285 / 1.504120 (0.892165) \| 2.151972 / 1.541195 (0.610777) \| 2.109485 / 1.468490 (0.640995) \| 0.873906 / 4.584777 (-3.710871) \| 5.083302 / 3.745712 (1.337589) \| 4.600756 / 5.269862 (-0.669105) \| 2.891731 / 4.565676 (-1.673945) \| 0.096293 / 0.424275 (-0.327982) \| 0.008651 / 0.007607 (0.001044) \| 0.719095 / 0.226044 (0.493051) \| 7.193225 / 2.268929 (4.924297) \| 3.220145 / 55.444624 (-52.224479) \| 2.496715 / 6.876477 (-4.379762) \| 2.672972 / 2.142072 (0.530900) \| 1.031656 / 4.805227 (-3.773571) \| 0.207854 / 6.500664 (-6.292810) \| 0.074507 / 0.075469 (-0.000962) \|\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) \| 1.552821 / 1.841788 (-0.288967) \| 22.573015 / 8.074308 (14.498707) \| 21.074321 / 10.191392 (10.882929) \| 0.231911 / 0.680424 (-0.448513) \| 0.027761 / 0.534201 (-0.506440) \| 0.474644 / 0.579283 (-0.104639) \| 0.563780 / 0.434364 (0.129416) \| 0.527593 / 0.540337 (-0.012745) \| 0.732299 / 1.386936 (-0.654637) \|\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) \| 0.008675 / 0.011353 (-0.002678) \| 0.005268 / 0.011008 (-0.005741) \| 0.079078 / 0.038508 (0.040570) \| 0.073505 / 0.023109 (0.050395) \| 0.453982 / 0.275898 (0.178083) \| 0.487839 / 0.323480 (0.164359) \| 0.005950 / 0.007986 (-0.002035) \| 0.003848 / 0.004328 (-0.000481) \| 0.076004 / 0.004250 (0.071754) \| 0.058410 / 0.037052 (0.021358) \| 0.460099 / 0.258489 (0.201610) \| 0.514860 / 0.293841 (0.221019) \| 0.048843 / 0.128546 (-0.079703) \| 0.014275 / 0.075646 (-0.061371) \| 0.090243 / 0.419271 (-0.329029) \| 0.060092 / 0.043533 (0.016559) \| 0.455669 / 0.255139 (0.200530) \| 0.484738 / 0.283200 (0.201538) \| 0.033012 / 0.141683 (-0.108671) \| 1.738854 / 1.452155 (0.286699) \| 1.852552 / 1.492716 (0.359835) \|\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) \| 0.245453 / 0.018006 (0.227447) \| 0.519929 / 0.000490 (0.519439) \| 0.007262 / 0.000200 (0.007062) \| 0.000108 / 0.000054 (0.000054) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031446 / 0.037411 (-0.005965) \| 0.094236 / 0.014526 (0.079710) \| 0.114457 / 0.176557 (-0.062100) \| 0.167448 / 0.737135 (-0.569687) \| 0.108791 / 0.296338 (-0.187548) \|\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) \| 0.603331 / 0.215209 (0.388122) \| 6.051556 / 2.077655 (3.973902) \| 2.797110 / 1.504120 (1.292990) \| 2.500517 / 1.541195 (0.959322) \| 2.531421 / 1.468490 (1.062931) \| 0.852075 / 4.584777 (-3.732702) \| 5.034140 / 3.745712 (1.288427) \| 4.576573 / 5.269862 (-0.693289) \| 2.973541 / 4.565676 (-1.592135) \| 0.101303 / 0.424275 (-0.322972) \| 0.008467 / 0.007607 (0.000860) \| 0.707143 / 0.226044 (0.481098) \| 7.262803 / 2.268929 (4.993874) \| 3.548841 / 55.444624 (-51.895783) \| 2.895975 / 6.876477 (-3.980502) \| 3.063521 / 2.142072 (0.921449) \| 1.014961 / 4.805227 (-3.790266) \| 0.208527 / 6.500664 (-6.292137) \| 0.074939 / 0.075469 (-0.000530) \|\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) \| 1.670708 / 1.841788 (-0.171080) \| 22.685227 / 8.074308 (14.610919) \| 20.393017 / 10.191392 (10.201625) \| 0.239303 / 0.680424 (-0.441121) \| 0.027742 / 0.534201 (-0.506459) \| 0.467230 / 0.579283 (-0.112053) \| 0.564169 / 0.434364 (0.129805) \| 0.554859 / 0.540337 (0.014522) \| 0.767471 / 1.386936 (-0.619465) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#72a57356a46ded67f4d7a02741141a96061246a8 \"CML watermark\")\n" ]
https://api.github.com/repos/huggingface/datasets/issues/6154	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6154/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6154/comments	https://api.github.com/repos/huggingface/datasets/issues/6154/events	https://github.com/huggingface/datasets/pull/6154	1,854,595,943	PR_kwDODunzps5YItlH	6,154	Use yaml instead of get data patterns when possible	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "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}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq" }	[]	closed	false	null	[]	null	6	"2023-08-17T09:17:05Z"	"2023-08-17T20:46:25Z"	"2023-08-17T20:37:19Z"	MEMBER	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6154.diff", "html_url": "https://github.com/huggingface/datasets/pull/6154", "merged_at": "2023-08-17T20:37:19Z", "patch_url": "https://github.com/huggingface/datasets/pull/6154.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6154" }	This would make the data files resolution faster: no need to list all the data files to infer the dataset builder to use. fix https://github.com/huggingface/datasets/issues/6140	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6154/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6154/timeline	null	null	611	true	[ "_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) \| 0.006829 / 0.011353 (-0.004524) \| 0.004535 / 0.011008 (-0.006473) \| 0.085255 / 0.038508 (0.046747) \| 0.080861 / 0.023109 (0.057752) \| 0.366023 / 0.275898 (0.090125) \| 0.403095 / 0.323480 (0.079615) \| 0.005615 / 0.007986 (-0.002370) \| 0.003830 / 0.004328 (-0.000498) \| 0.064502 / 0.004250 (0.060251) \| 0.053916 / 0.037052 (0.016863) \| 0.366010 / 0.258489 (0.107521) \| 0.414565 / 0.293841 (0.120724) \| 0.031500 / 0.128546 (-0.097046) \| 0.009252 / 0.075646 (-0.066394) \| 0.289584 / 0.419271 (-0.129688) \| 0.052984 / 0.043533 (0.009451) \| 0.352626 / 0.255139 (0.097487) \| 0.390964 / 0.283200 (0.107764) \| 0.025118 / 0.141683 (-0.116565) \| 1.462316 / 1.452155 (0.010161) \| 1.565682 / 1.492716 (0.072966) \|\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) \| 0.294432 / 0.018006 (0.276426) \| 0.618366 / 0.000490 (0.617876) \| 0.003270 / 0.000200 (0.003071) \| 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) \| 0.031194 / 0.037411 (-0.006217) \| 0.088892 / 0.014526 (0.074366) \| 0.102580 / 0.176557 (-0.073977) \| 0.159449 / 0.737135 (-0.577686) \| 0.104434 / 0.296338 (-0.191905) \|\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) \| 0.385690 / 0.215209 (0.170481) \| 3.832782 / 2.077655 (1.755128) \| 1.862521 / 1.504120 (0.358401) \| 1.685674 / 1.541195 (0.144479) \| 1.724984 / 1.468490 (0.256494) \| 0.483700 / 4.584777 (-4.101077) \| 3.664154 / 3.745712 (-0.081558) \| 3.323023 / 5.269862 (-1.946839) \| 2.055958 / 4.565676 (-2.509718) \| 0.056990 / 0.424275 (-0.367285) \| 0.007674 / 0.007607 (0.000067) \| 0.460642 / 0.226044 (0.234598) \| 4.609964 / 2.268929 (2.341036) \| 2.434868 / 55.444624 (-53.009756) \| 2.003347 / 6.876477 (-4.873130) \| 2.209520 / 2.142072 (0.067448) \| 0.629363 / 4.805227 (-4.175864) \| 0.135434 / 6.500664 (-6.365230) \| 0.060498 / 0.075469 (-0.014971) \|\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) \| 1.253917 / 1.841788 (-0.587870) \| 19.988953 / 8.074308 (11.914645) \| 14.353739 / 10.191392 (4.162347) \| 0.165987 / 0.680424 (-0.514437) \| 0.018299 / 0.534201 (-0.515902) \| 0.395532 / 0.579283 (-0.183751) \| 0.418708 / 0.434364 (-0.015656) \| 0.460865 / 0.540337 (-0.079472) \| 0.633925 / 1.386936 (-0.753011) \|\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) \| 0.006631 / 0.011353 (-0.004722) \| 0.004109 / 0.011008 (-0.006899) \| 0.065003 / 0.038508 (0.026495) \| 0.080407 / 0.023109 (0.057297) \| 0.362966 / 0.275898 (0.087068) \| 0.389727 / 0.323480 (0.066247) \| 0.005588 / 0.007986 (-0.002397) \| 0.003517 / 0.004328 (-0.000812) \| 0.065821 / 0.004250 (0.061570) \| 0.057614 / 0.037052 (0.020561) \| 0.367422 / 0.258489 (0.108932) \| 0.400706 / 0.293841 (0.106865) \| 0.031560 / 0.128546 (-0.096986) \| 0.008659 / 0.075646 (-0.066987) \| 0.070756 / 0.419271 (-0.348516) \| 0.049821 / 0.043533 (0.006288) \| 0.360836 / 0.255139 (0.105697) \| 0.383981 / 0.283200 (0.100781) \| 0.023719 / 0.141683 (-0.117963) \| 1.485197 / 1.452155 (0.033043) \| 1.544899 / 1.492716 (0.052182) \|\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) \| 0.336480 / 0.018006 (0.318474) \| 0.532839 / 0.000490 (0.532349) \| 0.003767 / 0.000200 (0.003567) \| 0.000087 / 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) \| 0.034132 / 0.037411 (-0.003280) \| 0.090131 / 0.014526 (0.075605) \| 0.104086 / 0.176557 (-0.072471) \| 0.158385 / 0.737135 (-0.578751) \| 0.106417 / 0.296338 (-0.189922) \|\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) \| 0.416462 / 0.215209 (0.201253) \| 4.160409 / 2.077655 (2.082755) \| 2.195355 / 1.504120 (0.691235) \| 2.051234 / 1.541195 (0.510040) \| 2.012116 / 1.468490 (0.543626) \| 0.477414 / 4.584777 (-4.107363) \| 3.590326 / 3.745712 (-0.155386) \| 3.318490 / 5.269862 (-1.951371) \| 2.064124 / 4.565676 (-2.501553) \| 0.057040 / 0.424275 (-0.367235) \| 0.007283 / 0.007607 (-0.000324) \| 0.480490 / 0.226044 (0.254445) \| 4.804013 / 2.268929 (2.535084) \| 2.625940 / 55.444624 (-52.818685) \| 2.231537 / 6.876477 (-4.644939) \| 2.441649 / 2.142072 (0.299576) \| 0.573207 / 4.805227 (-4.232020) \| 0.131685 / 6.500664 (-6.368979) \| 0.060112 / 0.075469 (-0.015357) \|\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) \| 1.358587 / 1.841788 (-0.483200) \| 20.457562 / 8.074308 (12.383254) \| 14.236304 / 10.191392 (4.044912) \| 0.152860 / 0.680424 (-0.527563) \| 0.018466 / 0.534201 (-0.515735) \| 0.401391 / 0.579283 (-0.177893) \| 0.410252 / 0.434364 (-0.024111) \| 0.484335 / 0.540337 (-0.056002) \| 0.663818 / 1.386936 (-0.723118) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#acac88873abcb585892dc361eb9f6a70a1fd9a59 \"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) \| 0.007725 / 0.011353 (-0.003628) \| 0.004448 / 0.011008 (-0.006560) \| 0.098689 / 0.038508 (0.060180) \| 0.082919 / 0.023109 (0.059809) \| 0.380707 / 0.275898 (0.104809) \| 0.452977 / 0.323480 (0.129497) \| 0.004430 / 0.007986 (-0.003555) \| 0.003712 / 0.004328 (-0.000616) \| 0.076675 / 0.004250 (0.072425) \| 0.062281 / 0.037052 (0.025228) \| 0.403370 / 0.258489 (0.144881) \| 0.464557 / 0.293841 (0.170716) \| 0.035646 / 0.128546 (-0.092900) \| 0.009776 / 0.075646 (-0.065870) \| 0.341955 / 0.419271 (-0.077316) \| 0.059515 / 0.043533 (0.015983) \| 0.388421 / 0.255139 (0.133282) \| 0.439496 / 0.283200 (0.156296) \| 0.029090 / 0.141683 (-0.112593) \| 1.727473 / 1.452155 (0.275319) \| 1.810448 / 1.492716 (0.317732) \|\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) \| 0.221215 / 0.018006 (0.203208) \| 0.486660 / 0.000490 (0.486171) \| 0.005467 / 0.000200 (0.005267) \| 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) \| 0.032491 / 0.037411 (-0.004920) \| 0.094446 / 0.014526 (0.079920) \| 0.110339 / 0.176557 (-0.066217) \| 0.175004 / 0.737135 (-0.562131) \| 0.109209 / 0.296338 (-0.187129) \|\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) \| 0.453966 / 0.215209 (0.238757) \| 4.515842 / 2.077655 (2.438187) \| 2.240512 / 1.504120 (0.736392) \| 2.059911 / 1.541195 (0.518717) \| 2.150635 / 1.468490 (0.682145) \| 0.564509 / 4.584777 (-4.020268) \| 4.055208 / 3.745712 (0.309496) \| 3.614084 / 5.269862 (-1.655778) \| 2.295760 / 4.565676 (-2.269917) \| 0.066507 / 0.424275 (-0.357768) \| 0.008909 / 0.007607 (0.001302) \| 0.542604 / 0.226044 (0.316560) \| 5.412162 / 2.268929 (3.143233) \| 2.758757 / 55.444624 (-52.685867) \| 2.430693 / 6.876477 (-4.445784) \| 2.669866 / 2.142072 (0.527793) \| 0.681756 / 4.805227 (-4.123471) \| 0.156524 / 6.500664 (-6.344140) \| 0.069499 / 0.075469 (-0.005970) \|\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) \| 1.571591 / 1.841788 (-0.270197) \| 22.543437 / 8.074308 (14.469129) \| 16.068426 / 10.191392 (5.877034) \| 0.169860 / 0.680424 (-0.510564) \| 0.021216 / 0.534201 (-0.512985) \| 0.468745 / 0.579283 (-0.110538) \| 0.475924 / 0.434364 (0.041560) \| 0.535574 / 0.540337 (-0.004763) \| 0.733823 / 1.386936 (-0.653113) \|\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) \| 0.008038 / 0.011353 (-0.003315) \| 0.004565 / 0.011008 (-0.006443) \| 0.076892 / 0.038508 (0.038384) \| 0.089559 / 0.023109 (0.066450) \| 0.456752 / 0.275898 (0.180854) \| 0.497282 / 0.323480 (0.173802) \| 0.005991 / 0.007986 (-0.001995) \| 0.003784 / 0.004328 (-0.000545) \| 0.076339 / 0.004250 (0.072089) \| 0.066050 / 0.037052 (0.028998) \| 0.462708 / 0.258489 (0.204219) \| 0.503711 / 0.293841 (0.209870) \| 0.037098 / 0.128546 (-0.091448) \| 0.009869 / 0.075646 (-0.065777) \| 0.083678 / 0.419271 (-0.335594) \| 0.058166 / 0.043533 (0.014633) \| 0.461839 / 0.255139 (0.206700) \| 0.481546 / 0.283200 (0.198347) \| 0.027755 / 0.141683 (-0.113928) \| 1.738490 / 1.452155 (0.286335) \| 1.832276 / 1.492716 (0.339560) \|\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) \| 0.329935 / 0.018006 (0.311929) \| 0.497438 / 0.000490 (0.496949) \| 0.034644 / 0.000200 (0.034444) \| 0.000199 / 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) \| 0.035427 / 0.037411 (-0.001984) \| 0.105689 / 0.014526 (0.091163) \| 0.117706 / 0.176557 (-0.058850) \| 0.177862 / 0.737135 (-0.559273) \| 0.116791 / 0.296338 (-0.179547) \|\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) \| 0.484851 / 0.215209 (0.269642) \| 4.804346 / 2.077655 (2.726691) \| 2.494801 / 1.504120 (0.990681) \| 2.320185 / 1.541195 (0.778990) \| 2.374090 / 1.468490 (0.905600) \| 0.567397 / 4.584777 (-4.017380) \| 4.087402 / 3.745712 (0.341690) \| 3.794245 / 5.269862 (-1.475616) \| 2.378481 / 4.565676 (-2.187195) \| 0.068228 / 0.424275 (-0.356047) \| 0.008740 / 0.007607 (0.001133) \| 0.574876 / 0.226044 (0.348832) \| 5.742644 / 2.268929 (3.473716) \| 3.047661 / 55.444624 (-52.396963) \| 2.729742 / 6.876477 (-4.146735) \| 2.852510 / 2.142072 (0.710438) \| 0.679450 / 4.805227 (-4.125777) \| 0.156162 / 6.500664 (-6.344502) \| 0.074051 / 0.075469 (-0.001418) \|\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) \| 1.576182 / 1.841788 (-0.265605) \| 23.298147 / 8.074308 (15.223839) \| 16.344621 / 10.191392 (6.153229) \| 0.167571 / 0.680424 (-0.512852) \| 0.021423 / 0.534201 (-0.512778) \| 0.464511 / 0.579283 (-0.114772) \| 0.453257 / 0.434364 (0.018893) \| 0.563439 / 0.540337 (0.023102) \| 0.764759 / 1.386936 (-0.622177) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e8dc4b32b0d91bdb0971f8203ee37e6588c7770e \"CML watermark\")\n", "This should also fix https://github.com/huggingface/datasets/issues/6140, so please link it with this PR before merging.", "Done !", "<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) \| 0.006719 / 0.011353 (-0.004634) \| 0.004299 / 0.011008 (-0.006709) \| 0.085296 / 0.038508 (0.046788) \| 0.085144 / 0.023109 (0.062035) \| 0.361703 / 0.275898 (0.085805) \| 0.397721 / 0.323480 (0.074241) \| 0.005920 / 0.007986 (-0.002065) \| 0.003853 / 0.004328 (-0.000476) \| 0.065633 / 0.004250 (0.061383) \| 0.057000 / 0.037052 (0.019947) \| 0.379981 / 0.258489 (0.121492) \| 0.419041 / 0.293841 (0.125200) \| 0.031225 / 0.128546 (-0.097322) \| 0.008868 / 0.075646 (-0.066779) \| 0.288808 / 0.419271 (-0.130463) \| 0.052391 / 0.043533 (0.008859) \| 0.362349 / 0.255139 (0.107210) \| 0.399858 / 0.283200 (0.116658) \| 0.025843 / 0.141683 (-0.115840) \| 1.498988 / 1.452155 (0.046834) \| 1.547290 / 1.492716 (0.054574) \|\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) \| 0.278091 / 0.018006 (0.260085) \| 0.621794 / 0.000490 (0.621305) \| 0.003770 / 0.000200 (0.003570) \| 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) \| 0.029128 / 0.037411 (-0.008283) \| 0.082061 / 0.014526 (0.067536) \| 0.101758 / 0.176557 (-0.074799) \| 0.155724 / 0.737135 (-0.581411) \| 0.102173 / 0.296338 (-0.194165) \|\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) \| 0.387145 / 0.215209 (0.171935) \| 3.868262 / 2.077655 (1.790607) \| 1.886440 / 1.504120 (0.382320) \| 1.723305 / 1.541195 (0.182111) \| 1.805411 / 1.468490 (0.336921) \| 0.485024 / 4.584777 (-4.099753) \| 3.637859 / 3.745712 (-0.107853) \| 3.319593 / 5.269862 (-1.950269) \| 2.087860 / 4.565676 (-2.477817) \| 0.056992 / 0.424275 (-0.367283) \| 0.007623 / 0.007607 (0.000016) \| 0.468182 / 0.226044 (0.242138) \| 4.681112 / 2.268929 (2.412183) \| 2.407010 / 55.444624 (-53.037614) \| 2.026604 / 6.876477 (-4.849872) \| 2.298158 / 2.142072 (0.156086) \| 0.581839 / 4.805227 (-4.223388) \| 0.132101 / 6.500664 (-6.368563) \| 0.060472 / 0.075469 (-0.014997) \|\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) \| 1.236422 / 1.841788 (-0.605365) \| 20.505168 / 8.074308 (12.430860) \| 14.356081 / 10.191392 (4.164689) \| 0.148808 / 0.680424 (-0.531616) \| 0.018433 / 0.534201 (-0.515768) \| 0.391323 / 0.579283 (-0.187960) \| 0.413142 / 0.434364 (-0.021222) \| 0.453484 / 0.540337 (-0.086853) \| 0.620771 / 1.386936 (-0.766165) \|\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) \| 0.007030 / 0.011353 (-0.004323) \| 0.004430 / 0.011008 (-0.006578) \| 0.065578 / 0.038508 (0.027070) \| 0.090751 / 0.023109 (0.067642) \| 0.389121 / 0.275898 (0.113223) \| 0.424657 / 0.323480 (0.101177) \| 0.006575 / 0.007986 (-0.001410) \| 0.003855 / 0.004328 (-0.000473) \| 0.066175 / 0.004250 (0.061925) \| 0.063255 / 0.037052 (0.026202) \| 0.397161 / 0.258489 (0.138672) \| 0.435291 / 0.293841 (0.141450) \| 0.031622 / 0.128546 (-0.096925) \| 0.008900 / 0.075646 (-0.066747) \| 0.071694 / 0.419271 (-0.347577) \| 0.049161 / 0.043533 (0.005628) \| 0.386214 / 0.255139 (0.131075) \| 0.404571 / 0.283200 (0.121372) \| 0.024821 / 0.141683 (-0.116862) \| 1.489514 / 1.452155 (0.037359) \| 1.576139 / 1.492716 (0.083423) \|\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) \| 0.289884 / 0.018006 (0.271878) \| 0.629342 / 0.000490 (0.628852) \| 0.004799 / 0.000200 (0.004599) \| 0.000160 / 0.000054 (0.000106) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032081 / 0.037411 (-0.005331) \| 0.088152 / 0.014526 (0.073626) \| 0.107289 / 0.176557 (-0.069267) \| 0.164598 / 0.737135 (-0.572537) \| 0.108395 / 0.296338 (-0.187944) \|\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) \| 0.426723 / 0.215209 (0.211514) \| 4.267719 / 2.077655 (2.190064) \| 2.289657 / 1.504120 (0.785537) \| 2.117435 / 1.541195 (0.576240) \| 2.187292 / 1.468490 (0.718802) \| 0.478387 / 4.584777 (-4.106390) \| 3.625096 / 3.745712 (-0.120616) \| 3.408036 / 5.269862 (-1.861826) \| 2.124117 / 4.565676 (-2.441559) \| 0.056537 / 0.424275 (-0.367738) \| 0.007489 / 0.007607 (-0.000118) \| 0.502434 / 0.226044 (0.276389) \| 5.025357 / 2.268929 (2.756428) \| 2.740554 / 55.444624 (-52.704070) \| 2.418841 / 6.876477 (-4.457635) \| 2.730764 / 2.142072 (0.588691) \| 0.600013 / 4.805227 (-4.205214) \| 0.133039 / 6.500664 (-6.367625) \| 0.061466 / 0.075469 (-0.014003) \|\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) \| 1.330211 / 1.841788 (-0.511577) \| 21.092100 / 8.074308 (13.017792) \| 14.463054 / 10.191392 (4.271662) \| 0.154149 / 0.680424 (-0.526274) \| 0.018891 / 0.534201 (-0.515310) \| 0.393078 / 0.579283 (-0.186205) \| 0.415279 / 0.434364 (-0.019085) \| 0.479469 / 0.540337 (-0.060868) \| 0.659953 / 1.386936 (-0.726983) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5ca2ba050340829b4dd44791afc15db0d82a3276 \"CML watermark\")\n" ]
https://api.github.com/repos/huggingface/datasets/issues/6152	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6152/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6152/comments	https://api.github.com/repos/huggingface/datasets/issues/6152/events	https://github.com/huggingface/datasets/issues/6152	1,852,494,646	I_kwDODunzps5uatM2	6,152	FolderBase Dataset automatically resolves under current directory when data_dir is not specified	{ "avatar_url": "https://avatars.githubusercontent.com/u/11533479?v=4", "events_url": "https://api.github.com/users/npuichigo/events{/privacy}", "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}", "gravatar_id": "", "html_url": "https://github.com/npuichigo", "id": 11533479, "login": "npuichigo", "node_id": "MDQ6VXNlcjExNTMzNDc5", "organizations_url": "https://api.github.com/users/npuichigo/orgs", "received_events_url": "https://api.github.com/users/npuichigo/received_events", "repos_url": "https://api.github.com/users/npuichigo/repos", "site_admin": false, "starred_url": "https://api.github.com/users/npuichigo/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/npuichigo/subscriptions", "type": "User", "url": "https://api.github.com/users/npuichigo" }	[ { "color": "7057ff", "default": true, "description": "Good for newcomers", "id": 1935892877, "name": "good first issue", "node_id": "MDU6TGFiZWwxOTM1ODkyODc3", "url": "https://api.github.com/repos/huggingface/datasets/labels/good%20first%20issue" } ]	open	false	{ "avatar_url": "https://avatars.githubusercontent.com/u/126772439?v=4", "events_url": "https://api.github.com/users/debrupf2946/events{/privacy}", "followers_url": "https://api.github.com/users/debrupf2946/followers", "following_url": "https://api.github.com/users/debrupf2946/following{/other_user}", "gists_url": "https://api.github.com/users/debrupf2946/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/debrupf2946", "id": 126772439, "login": "debrupf2946", "node_id": "U_kgDOB45k1w", "organizations_url": "https://api.github.com/users/debrupf2946/orgs", "received_events_url": "https://api.github.com/users/debrupf2946/received_events", "repos_url": "https://api.github.com/users/debrupf2946/repos", "site_admin": false, "starred_url": "https://api.github.com/users/debrupf2946/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/debrupf2946/subscriptions", "type": "User", "url": "https://api.github.com/users/debrupf2946" }	[ { "avatar_url": "https://avatars.githubusercontent.com/u/126772439?v=4", "events_url": "https://api.github.com/users/debrupf2946/events{/privacy}", "followers_url": "https://api.github.com/users/debrupf2946/followers", "following_url": "https://api.github.com/users/debrupf2946/following{/other_user}", "gists_url": "https://api.github.com/users/debrupf2946/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/debrupf2946", "id": 126772439, "login": "debrupf2946", "node_id": "U_kgDOB45k1w", "organizations_url": "https://api.github.com/users/debrupf2946/orgs", "received_events_url": "https://api.github.com/users/debrupf2946/received_events", "repos_url": "https://api.github.com/users/debrupf2946/repos", "site_admin": false, "starred_url": "https://api.github.com/users/debrupf2946/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/debrupf2946/subscriptions", "type": "User", "url": "https://api.github.com/users/debrupf2946" } ]	null	10	"2023-08-16T04:38:09Z"	"2024-01-22T15:04:51Z"	null	CONTRIBUTOR	null	null	null	### Describe the bug FolderBase Dataset automatically resolves under current directory when data_dir is not specified. For example: ``` load_dataset("audiofolder") ``` takes long time to resolve and collect data_files from current directory. But I think it should reach out to this line for error handling https://github.com/huggingface/datasets/blob/cb8c5de5145c7e7eee65391cb7f4d92f0d565d62/src/datasets/packaged_modules/folder_based_builder/folder_based_builder.py#L58-L59 ### Steps to reproduce the bug ``` load_dataset("audiofolder") ``` ### Expected behavior Error report ### Environment info - `datasets` version: 2.14.4 - Platform: Linux-5.15.0-78-generic-x86_64-with-glibc2.17 - Python version: 3.8.15 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 1.5.3	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6152/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6152/timeline	null	null	612	false	[ "@lhoestq ", "Makes sense, I guess this can be fixed in the load_dataset_builder method.\r\nIt concerns every packaged builder I think (see values in `_PACKAGED_DATASETS_MODULES`)", "I think the behavior is related to these lines, which short circuited the error handling.\r\nhttps://github.com/huggingface/datasets/blob/664a1cb72ea1e6ef7c47e671e2686ca4a35e8d63/src/datasets/load.py#L946-L952\r\n\r\nSo should data_dir be checked here or still delegating to actual `DatasetModule`? In that case, how to properly set `data_files` here.", "This is location in PackagedDatasetModuleFactory.get_module seems the be the right place to check if at least data_dir or data_files are passed", "@mariosasko can you please assign this issue to me,I want to work on this", "#self-assign", "@mariosasko is this issue still open? i would love to kickstart my journey to open source with this issue!\r\nRegards\r\nzutarich", "@zutarich It is unless @debrupf2946 is working on it.", "#self-assign", "I am working and will open a pull request soon @Etelis \r\n" ]
https://api.github.com/repos/huggingface/datasets/issues/6151	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6151/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6151/comments	https://api.github.com/repos/huggingface/datasets/issues/6151/events	https://github.com/huggingface/datasets/issues/6151	1,851,497,818	I_kwDODunzps5uW51a	6,151	Faster sorting for single key items	{ "avatar_url": "https://avatars.githubusercontent.com/u/47942453?v=4", "events_url": "https://api.github.com/users/jackapbutler/events{/privacy}", "followers_url": "https://api.github.com/users/jackapbutler/followers", "following_url": "https://api.github.com/users/jackapbutler/following{/other_user}", "gists_url": "https://api.github.com/users/jackapbutler/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/jackapbutler", "id": 47942453, "login": "jackapbutler", "node_id": "MDQ6VXNlcjQ3OTQyNDUz", "organizations_url": "https://api.github.com/users/jackapbutler/orgs", "received_events_url": "https://api.github.com/users/jackapbutler/received_events", "repos_url": "https://api.github.com/users/jackapbutler/repos", "site_admin": false, "starred_url": "https://api.github.com/users/jackapbutler/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/jackapbutler/subscriptions", "type": "User", "url": "https://api.github.com/users/jackapbutler" }	[ { "color": "a2eeef", "default": true, "description": "New feature or request", "id": 1935892871, "name": "enhancement", "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement" } ]	closed	false	null	[]	null	2	"2023-08-15T14:02:31Z"	"2023-08-21T14:38:26Z"	"2023-08-21T14:38:25Z"	NONE	null	null	null	### Feature request A faster way to sort a dataset which contains a large number of rows. ### Motivation The current sorting implementations took significantly longer than expected when I was running on a dataset trying to sort by timestamps. Code snippet: ```python ds = datasets.load_dataset( "json", **{"data_files": {"train": "path-to-jsonlines"}, "split": "train"}, num_proc=os.cpu_count(), keep_in_memory=True) sorted_ds = ds.sort("pubDate", keep_in_memory=True) ``` However, once I switched to a different method which 1. unpacked to a list of tuples 2. sorted tuples by key 3. run `.select` with the sorted list of indices It was significantly faster (orders of magnitude, especially with M's of rows) ### Your contribution I'd be happy to implement a crude single key sorting algorithm so that other users can benefit from this trick. Broadly, this would take a `Dataset` and perform; ```python # ds is a Dataset object # key_name is the sorting key class Dataset: ... def _sort(key_name: str) -> Dataset: index_keys = [(i,x) for i,x in enumerate(self[key_name])] sorted_rows = sorted(row_pubdate, key=lambda x: x[1]) sorted_indicies = [x[0] for x in sorted_rows] return self.select(sorted_indicies) ```	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6151/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6151/timeline	null	completed	613	false	[ "`Dataset.sort` essentially does the same thing except it uses `pyarrow.compute.sort_indices` which doesn't involve copying the data into python objects (saving memory)\r\n\r\n```python\r\nsort_keys = [(col, \"ascending\") for col in column_names]\r\nindices = pc.sort_indices(self.data, sort_keys=sort_keys)\r\nreturn self.select(indices)\r\n```", "Ok interesting, I'll continue debugging to see what is going wrong on my end." ]
https://api.github.com/repos/huggingface/datasets/issues/6150	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6150/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6150/comments	https://api.github.com/repos/huggingface/datasets/issues/6150/events	https://github.com/huggingface/datasets/issues/6150	1,850,740,456	I_kwDODunzps5uUA7o	6,150	Allow dataset implement .take	{ "avatar_url": "https://avatars.githubusercontent.com/u/1855278?v=4", "events_url": "https://api.github.com/users/brando90/events{/privacy}", "followers_url": "https://api.github.com/users/brando90/followers", "following_url": "https://api.github.com/users/brando90/following{/other_user}", "gists_url": "https://api.github.com/users/brando90/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/brando90", "id": 1855278, "login": "brando90", "node_id": "MDQ6VXNlcjE4NTUyNzg=", "organizations_url": "https://api.github.com/users/brando90/orgs", "received_events_url": "https://api.github.com/users/brando90/received_events", "repos_url": "https://api.github.com/users/brando90/repos", "site_admin": false, "starred_url": "https://api.github.com/users/brando90/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/brando90/subscriptions", "type": "User", "url": "https://api.github.com/users/brando90" }	[ { "color": "a2eeef", "default": true, "description": "New feature or request", "id": 1935892871, "name": "enhancement", "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement" } ]	open	false	null	[]	null	4	"2023-08-15T00:17:51Z"	"2023-08-17T13:49:37Z"	null	NONE	null	null	null	### Feature request I want to do: ``` dataset.take(512) ``` but it only works with streaming = True ### Motivation uniform interface to data sets. Really surprising the above only works with streaming = True. ### Your contribution Should be trivial to copy paste the IterableDataset .take to use the local path in the data (when streaming = False)	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6150/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6150/timeline	null	null	614	false	[ "```\r\n dataset = IterableDataset(dataset) if type(dataset) != IterableDataset else dataset # to force dataset.take(batch_size) to work in non-streaming mode\r\n ```\r\n", "hf discuss: https://discuss.huggingface.co/t/how-does-one-make-dataset-take-512-work-with-streaming-false-with-hugging-face-data-set/50770", "so: https://stackoverflow.com/questions/76902824/how-does-one-make-dataset-take512-work-with-streaming-false-with-hugging-fac", "Feel free to work on this. In addition, `IterableDataset` supports `skip`, so we should also add this method to `Dataset`." ]
https://api.github.com/repos/huggingface/datasets/issues/6149	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6149/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6149/comments	https://api.github.com/repos/huggingface/datasets/issues/6149/events	https://github.com/huggingface/datasets/issues/6149	1,850,700,624	I_kwDODunzps5uT3NQ	6,149	Dataset.from_parquet cannot load subset of columns	{ "avatar_url": "https://avatars.githubusercontent.com/u/2512762?v=4", "events_url": "https://api.github.com/users/dwyatte/events{/privacy}", "followers_url": "https://api.github.com/users/dwyatte/followers", "following_url": "https://api.github.com/users/dwyatte/following{/other_user}", "gists_url": "https://api.github.com/users/dwyatte/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/dwyatte", "id": 2512762, "login": "dwyatte", "node_id": "MDQ6VXNlcjI1MTI3NjI=", "organizations_url": "https://api.github.com/users/dwyatte/orgs", "received_events_url": "https://api.github.com/users/dwyatte/received_events", "repos_url": "https://api.github.com/users/dwyatte/repos", "site_admin": false, "starred_url": "https://api.github.com/users/dwyatte/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/dwyatte/subscriptions", "type": "User", "url": "https://api.github.com/users/dwyatte" }	[]	closed	false	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "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}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko" }	[ { "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "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}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko" } ]	null	1	"2023-08-14T23:28:22Z"	"2023-08-17T22:36:05Z"	"2023-08-17T22:36:05Z"	CONTRIBUTOR	null	null	null	### Describe the bug When using `Dataset.from_parquet(path_or_paths, columns=[...])` and a subset of columns, loading fails with a variant of the following ``` ValueError: Couldn't cast a: int64 -- schema metadata -- pandas: '{"index_columns": [], "column_indexes": [], "columns": [{"name":' + 273 to {'a': Value(dtype='int64', id=None), 'b': Value(dtype='int64', id=None)} because column names don't match The above exception was the direct cause of the following exception: ``` Looks to be triggered by https://github.com/huggingface/datasets/blob/c02a44715c036b5261686669727394b1308a3a4b/src/datasets/table.py#L2285-L2286 ### Steps to reproduce the bug ``` import pandas as pd from datasets import Dataset pd.DataFrame([{"a": 1, "b": 2}]).to_parquet("test.pq") Dataset.from_parquet("test.pq", columns=["a"]) ``` ### Expected behavior A subset of columns should be loaded without error ### Environment info - `datasets` version: 2.14.4 - Platform: Linux-5.10.0-23-cloud-amd64-x86_64-with-glibc2.2.5 - Python version: 3.8.16 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 2.0.3	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6149/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6149/timeline	null	completed	615	false	[ "Looks like this regression was introduced in `datasets==2.13.0` (`2.12.0` could load a subset of columns)\r\n\r\nThis does not appear to be fixed by https://github.com/huggingface/datasets/pull/6045 (bug still exists on `main`)" ]
https://api.github.com/repos/huggingface/datasets/issues/6148	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6148/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6148/comments	https://api.github.com/repos/huggingface/datasets/issues/6148/events	https://github.com/huggingface/datasets/pull/6148	1,849,524,683	PR_kwDODunzps5X3oqv	6,148	Ignore parallel warning in map_nested	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "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}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq" }	[]	closed	false	null	[]	null	3	"2023-08-14T10:43:41Z"	"2023-08-17T08:54:06Z"	"2023-08-17T08:43:58Z"	MEMBER	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6148.diff", "html_url": "https://github.com/huggingface/datasets/pull/6148", "merged_at": "2023-08-17T08:43:58Z", "patch_url": "https://github.com/huggingface/datasets/pull/6148.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6148" }	This warning message was shown every time you pass num_proc to `load_dataset` because of `map_nested` ``` parallel_map is experimental and might be subject to breaking changes in the future ``` This PR removes it for `map_nested`. If someone uses another parallel backend they're already warned when `parallel_backend` is called anyway	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6148/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6148/timeline	null	null	616	true	[ "_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) \| 0.006818 / 0.011353 (-0.004534) \| 0.004166 / 0.011008 (-0.006842) \| 0.086756 / 0.038508 (0.048248) \| 0.084444 / 0.023109 (0.061335) \| 0.319249 / 0.275898 (0.043351) \| 0.358689 / 0.323480 (0.035209) \| 0.004344 / 0.007986 (-0.003641) \| 0.003564 / 0.004328 (-0.000765) \| 0.065021 / 0.004250 (0.060771) \| 0.055991 / 0.037052 (0.018939) \| 0.319573 / 0.258489 (0.061084) \| 0.373239 / 0.293841 (0.079398) \| 0.031431 / 0.128546 (-0.097115) \| 0.008671 / 0.075646 (-0.066975) \| 0.288484 / 0.419271 (-0.130788) \| 0.053501 / 0.043533 (0.009968) \| 0.316934 / 0.255139 (0.061795) \| 0.354233 / 0.283200 (0.071034) \| 0.028088 / 0.141683 (-0.113595) \| 1.510905 / 1.452155 (0.058750) \| 1.568614 / 1.492716 (0.075898) \|\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) \| 0.292343 / 0.018006 (0.274337) \| 0.592309 / 0.000490 (0.591819) \| 0.003850 / 0.000200 (0.003650) \| 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) \| 0.033510 / 0.037411 (-0.003901) \| 0.089546 / 0.014526 (0.075020) \| 0.104909 / 0.176557 (-0.071648) \| 0.162219 / 0.737135 (-0.574916) \| 0.104137 / 0.296338 (-0.192202) \|\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) \| 0.407993 / 0.215209 (0.192784) \| 4.063423 / 2.077655 (1.985768) \| 2.050237 / 1.504120 (0.546117) \| 1.888939 / 1.541195 (0.347744) \| 2.015195 / 1.468490 (0.546704) \| 0.492617 / 4.584777 (-4.092160) \| 3.595871 / 3.745712 (-0.149841) \| 3.320467 / 5.269862 (-1.949395) \| 2.099987 / 4.565676 (-2.465690) \| 0.058513 / 0.424275 (-0.365762) \| 0.007709 / 0.007607 (0.000102) \| 0.479277 / 0.226044 (0.253233) \| 4.790712 / 2.268929 (2.521783) \| 2.517292 / 55.444624 (-52.927332) \| 2.167461 / 6.876477 (-4.709016) \| 2.432011 / 2.142072 (0.289939) \| 0.600537 / 4.805227 (-4.204690) \| 0.133538 / 6.500664 (-6.367126) \| 0.059621 / 0.075469 (-0.015848) \|\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) \| 1.280375 / 1.841788 (-0.561413) \| 20.777971 / 8.074308 (12.703663) \| 14.869539 / 10.191392 (4.678147) \| 0.159372 / 0.680424 (-0.521052) \| 0.018096 / 0.534201 (-0.516105) \| 0.393945 / 0.579283 (-0.185338) \| 0.409598 / 0.434364 (-0.024766) \| 0.459202 / 0.540337 (-0.081136) \| 0.632298 / 1.386936 (-0.754638) \|\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) \| 0.006694 / 0.011353 (-0.004659) \| 0.004299 / 0.011008 (-0.006709) \| 0.064880 / 0.038508 (0.026372) \| 0.083233 / 0.023109 (0.060124) \| 0.366488 / 0.275898 (0.090590) \| 0.405049 / 0.323480 (0.081569) \| 0.005602 / 0.007986 (-0.002384) \| 0.003623 / 0.004328 (-0.000705) \| 0.064410 / 0.004250 (0.060160) \| 0.057962 / 0.037052 (0.020910) \| 0.365318 / 0.258489 (0.106829) \| 0.403151 / 0.293841 (0.109310) \| 0.031285 / 0.128546 (-0.097261) \| 0.008867 / 0.075646 (-0.066780) \| 0.071137 / 0.419271 (-0.348135) \| 0.048398 / 0.043533 (0.004865) \| 0.360187 / 0.255139 (0.105048) \| 0.383872 / 0.283200 (0.100673) \| 0.023232 / 0.141683 (-0.118451) \| 1.526980 / 1.452155 (0.074826) \| 1.587265 / 1.492716 (0.094549) \|\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) \| 0.362603 / 0.018006 (0.344596) \| 0.557034 / 0.000490 (0.556544) \| 0.025303 / 0.000200 (0.025103) \| 0.000562 / 0.000054 (0.000508) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030636 / 0.037411 (-0.006775) \| 0.088085 / 0.014526 (0.073559) \| 0.103238 / 0.176557 (-0.073318) \| 0.155208 / 0.737135 (-0.581928) \| 0.106661 / 0.296338 (-0.189678) \|\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) \| 0.413660 / 0.215209 (0.198451) \| 4.122717 / 2.077655 (2.045063) \| 2.097656 / 1.504120 (0.593536) \| 1.931995 / 1.541195 (0.390801) \| 2.071497 / 1.468490 (0.603007) \| 0.490257 / 4.584777 (-4.094520) \| 3.588076 / 3.745712 (-0.157636) \| 3.423087 / 5.269862 (-1.846774) \| 2.147974 / 4.565676 (-2.417703) \| 0.058783 / 0.424275 (-0.365492) \| 0.007456 / 0.007607 (-0.000151) \| 0.492350 / 0.226044 (0.266305) \| 4.935935 / 2.268929 (2.667006) \| 2.604217 / 55.444624 (-52.840407) \| 2.333723 / 6.876477 (-4.542754) \| 2.585293 / 2.142072 (0.443220) \| 0.608800 / 4.805227 (-4.196427) \| 0.135806 / 6.500664 (-6.364858) \| 0.062716 / 0.075469 (-0.012753) \|\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) \| 1.347359 / 1.841788 (-0.494429) \| 21.420505 / 8.074308 (13.346197) \| 14.325914 / 10.191392 (4.134522) \| 0.159617 / 0.680424 (-0.520806) \| 0.018769 / 0.534201 (-0.515432) \| 0.399677 / 0.579283 (-0.179606) \| 0.402992 / 0.434364 (-0.031372) \| 0.484629 / 0.540337 (-0.055709) \| 0.656007 / 1.386936 (-0.730929) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ac94bb10d5c00ce8fdaf461eb1ff4b8572cfe956 \"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) \| 0.007291 / 0.011353 (-0.004062) \| 0.004501 / 0.011008 (-0.006508) \| 0.097529 / 0.038508 (0.059021) \| 0.079257 / 0.023109 (0.056147) \| 0.356390 / 0.275898 (0.080492) \| 0.390065 / 0.323480 (0.066585) \| 0.006071 / 0.007986 (-0.001914) \| 0.003783 / 0.004328 (-0.000546) \| 0.074598 / 0.004250 (0.070348) \| 0.059626 / 0.037052 (0.022574) \| 0.395344 / 0.258489 (0.136855) \| 0.418564 / 0.293841 (0.124723) \| 0.041843 / 0.128546 (-0.086704) \| 0.009293 / 0.075646 (-0.066354) \| 0.332668 / 0.419271 (-0.086604) \| 0.065753 / 0.043533 (0.022220) \| 0.357285 / 0.255139 (0.102146) \| 0.402974 / 0.283200 (0.119775) \| 0.028714 / 0.141683 (-0.112968) \| 1.733913 / 1.452155 (0.281759) \| 1.802574 / 1.492716 (0.309858) \|\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) \| 0.253114 / 0.018006 (0.235108) \| 0.606338 / 0.000490 (0.605848) \| 0.006871 / 0.000200 (0.006671) \| 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) \| 0.031850 / 0.037411 (-0.005562) \| 0.095148 / 0.014526 (0.080622) \| 0.111499 / 0.176557 (-0.065057) \| 0.174653 / 0.737135 (-0.562483) \| 0.109396 / 0.296338 (-0.186943) \|\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) \| 0.440442 / 0.215209 (0.225233) \| 4.408792 / 2.077655 (2.331137) \| 2.149778 / 1.504120 (0.645658) \| 1.922430 / 1.541195 (0.381235) \| 2.029281 / 1.468490 (0.560791) \| 0.611586 / 4.584777 (-3.973191) \| 4.204571 / 3.745712 (0.458859) \| 3.638194 / 5.269862 (-1.631668) \| 2.336146 / 4.565676 (-2.229531) \| 0.065383 / 0.424275 (-0.358892) \| 0.008441 / 0.007607 (0.000834) \| 0.527357 / 0.226044 (0.301313) \| 5.247892 / 2.268929 (2.978963) \| 2.654005 / 55.444624 (-52.790620) \| 2.256596 / 6.876477 (-4.619881) \| 2.432191 / 2.142072 (0.290119) \| 0.672759 / 4.805227 (-4.132469) \| 0.148494 / 6.500664 (-6.352170) \| 0.068248 / 0.075469 (-0.007221) \|\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) \| 1.544250 / 1.841788 (-0.297538) \| 21.882016 / 8.074308 (13.807708) \| 16.470182 / 10.191392 (6.278790) \| 0.166107 / 0.680424 (-0.514317) \| 0.021305 / 0.534201 (-0.512896) \| 0.445069 / 0.579283 (-0.134214) \| 0.500631 / 0.434364 (0.066267) \| 0.525801 / 0.540337 (-0.014536) \| 0.806534 / 1.386936 (-0.580402) \|\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) \| 0.007322 / 0.011353 (-0.004030) \| 0.004206 / 0.011008 (-0.006802) \| 0.074827 / 0.038508 (0.036319) \| 0.084759 / 0.023109 (0.061650) \| 0.421204 / 0.275898 (0.145306) \| 0.464442 / 0.323480 (0.140962) \| 0.006523 / 0.007986 (-0.001463) \| 0.003613 / 0.004328 (-0.000716) \| 0.073796 / 0.004250 (0.069545) \| 0.066609 / 0.037052 (0.029557) \| 0.430108 / 0.258489 (0.171619) \| 0.463165 / 0.293841 (0.169324) \| 0.036015 / 0.128546 (-0.092532) \| 0.009696 / 0.075646 (-0.065951) \| 0.083326 / 0.419271 (-0.335946) \| 0.056804 / 0.043533 (0.013271) \| 0.423333 / 0.255139 (0.168194) \| 0.450538 / 0.283200 (0.167338) \| 0.027067 / 0.141683 (-0.114616) \| 1.700563 / 1.452155 (0.248408) \| 1.748738 / 1.492716 (0.256021) \|\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) \| 0.395682 / 0.018006 (0.377675) \| 0.540192 / 0.000490 (0.539702) \| 0.140049 / 0.000200 (0.139849) \| 0.000694 / 0.000054 (0.000639) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.036643 / 0.037411 (-0.000769) \| 0.104422 / 0.014526 (0.089896) \| 0.113072 / 0.176557 (-0.063484) \| 0.179561 / 0.737135 (-0.557575) \| 0.118620 / 0.296338 (-0.177718) \|\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) \| 0.476547 / 0.215209 (0.261338) \| 4.716009 / 2.077655 (2.638354) \| 2.412111 / 1.504120 (0.907991) \| 2.246389 / 1.541195 (0.705194) \| 2.307058 / 1.468490 (0.838568) \| 0.552759 / 4.584777 (-4.032018) \| 4.172484 / 3.745712 (0.426771) \| 3.848419 / 5.269862 (-1.421443) \| 2.310338 / 4.565676 (-2.255339) \| 0.071757 / 0.424275 (-0.352518) \| 0.011206 / 0.007607 (0.003599) \| 0.609526 / 0.226044 (0.383482) \| 5.583065 / 2.268929 (3.314136) \| 3.081227 / 55.444624 (-52.363397) \| 2.637782 / 6.876477 (-4.238695) \| 2.887561 / 2.142072 (0.745489) \| 0.667227 / 4.805227 (-4.138000) \| 0.154421 / 6.500664 (-6.346243) \| 0.070772 / 0.075469 (-0.004697) \|\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) \| 1.605500 / 1.841788 (-0.236288) \| 22.872717 / 8.074308 (14.798409) \| 15.865333 / 10.191392 (5.673941) \| 0.170353 / 0.680424 (-0.510071) \| 0.021854 / 0.534201 (-0.512347) \| 0.461467 / 0.579283 (-0.117816) \| 0.477743 / 0.434364 (0.043379) \| 0.597234 / 0.540337 (0.056896) \| 0.800416 / 1.386936 (-0.586520) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a7f8d9019e7cb104eac4106bdc6ec0292f0dc61a \"CML watermark\")\n" ]
https://api.github.com/repos/huggingface/datasets/issues/6147	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6147/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6147/comments	https://api.github.com/repos/huggingface/datasets/issues/6147/events	https://github.com/huggingface/datasets/issues/6147	1,848,914,830	I_kwDODunzps5uNDOO	6,147	ValueError when running BeamBasedBuilder with GCS path in cache_dir	{ "avatar_url": "https://avatars.githubusercontent.com/u/13844767?v=4", "events_url": "https://api.github.com/users/ktrk115/events{/privacy}", "followers_url": "https://api.github.com/users/ktrk115/followers", "following_url": "https://api.github.com/users/ktrk115/following{/other_user}", "gists_url": "https://api.github.com/users/ktrk115/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/ktrk115", "id": 13844767, "login": "ktrk115", "node_id": "MDQ6VXNlcjEzODQ0NzY3", "organizations_url": "https://api.github.com/users/ktrk115/orgs", "received_events_url": "https://api.github.com/users/ktrk115/received_events", "repos_url": "https://api.github.com/users/ktrk115/repos", "site_admin": false, "starred_url": "https://api.github.com/users/ktrk115/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/ktrk115/subscriptions", "type": "User", "url": "https://api.github.com/users/ktrk115" }	[]	open	false	null	[]	null	1	"2023-08-14T03:11:34Z"	"2023-08-14T03:19:43Z"	null	NONE	null	null	null	### Describe the bug When running the BeamBasedBuilder with a GCS path specified in the cache_dir, the following ValueError occurs: ``` ValueError: Unable to get filesystem from specified path, please use the correct path or ensure the required dependency is installed, e.g., pip install apache-beam[gcp]. Path specified: gcs://my-bucket/huggingface_datasets/my_beam_dataset/default/0.0.0/my_beam_dataset-train [while running 'train/Save to parquet/Write/WriteImpl/InitializeWrite'] ``` Same error occurs after running `pip install apache-beam[gcp]` as instructed. ### Steps to reproduce the bug Put `my_beam_dataset.py`: ```python import datasets class MyBeamDataset(datasets.BeamBasedBuilder): def _info(self): features = datasets.Features({"value": datasets.Value("int64")}) return datasets.DatasetInfo(features=features) def _split_generators(self, dl_manager, pipeline): return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={})] def _build_pcollection(self, pipeline): import apache_beam as beam return pipeline \| beam.Create([{"value": i} for i in range(10)]) ``` Run: ```bash datasets-cli run_beam my_beam_dataset.py --cache_dir=gs://my-bucket/huggingface_datasets/ --beam_pipeline_options="runner=DirectRunner" ``` ### Expected behavior Running the BeamBasedBuilder with a GCS cache path without any errors. ### Environment info - `datasets` version: 2.14.4 - Platform: macOS-13.4-arm64-arm-64bit - Python version: 3.9.17 - Huggingface_hub version: 0.16.4 - PyArrow version: 9.0.0 - Pandas version: 2.0.3	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6147/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6147/timeline	null	null	617	false	[ "The cause of the error seems to be that `datasets` adds \"gcs://\" as a schema, while `beam` checks only \"gs://\".\r\n\r\ndatasets: https://github.com/huggingface/datasets/blob/c02a44715c036b5261686669727394b1308a3a4b/src/datasets/builder.py#L822\r\n\r\nbeam: [link](https://github.com/apache/beam/blob/25e1a64641b1c8a3c0a6c75c6e86031b87307f22/sdks/python/apache_beam/io/filesystems.py#L98-L101)\r\n```\r\n systems = [\r\n fs for fs in FileSystem.get_all_subclasses()\r\n if fs.scheme() == path_scheme\r\n ]\r\n```" ]
https://api.github.com/repos/huggingface/datasets/issues/6146	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6146/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6146/comments	https://api.github.com/repos/huggingface/datasets/issues/6146/events	https://github.com/huggingface/datasets/issues/6146	1,848,417,366	I_kwDODunzps5uLJxW	6,146	DatasetGenerationError when load glue benchmark datasets from `load_dataset`	{ "avatar_url": "https://avatars.githubusercontent.com/u/78742415?v=4", "events_url": "https://api.github.com/users/yusx-swapp/events{/privacy}", "followers_url": "https://api.github.com/users/yusx-swapp/followers", "following_url": "https://api.github.com/users/yusx-swapp/following{/other_user}", "gists_url": "https://api.github.com/users/yusx-swapp/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/yusx-swapp", "id": 78742415, "login": "yusx-swapp", "node_id": "MDQ6VXNlcjc4NzQyNDE1", "organizations_url": "https://api.github.com/users/yusx-swapp/orgs", "received_events_url": "https://api.github.com/users/yusx-swapp/received_events", "repos_url": "https://api.github.com/users/yusx-swapp/repos", "site_admin": false, "starred_url": "https://api.github.com/users/yusx-swapp/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/yusx-swapp/subscriptions", "type": "User", "url": "https://api.github.com/users/yusx-swapp" }	[]	closed	false	null	[]	null	4	"2023-08-13T05:17:56Z"	"2023-08-26T22:09:09Z"	"2023-08-26T22:09:09Z"	NONE	null	null	null	### Describe the bug Package version: datasets-2.14.4 When I run the codes: ``` from datasets import load_dataset dataset = load_dataset("glue", "ax") ``` I got the following errors: --------------------------------------------------------------------------- SchemaInferenceError Traceback (most recent call last) File ~/anaconda3/envs/python3/lib/python3.10/site-packages/datasets/builder.py:1949, in ArrowBasedBuilder._prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id) 1948 num_shards = shard_id + 1 -> 1949 num_examples, num_bytes = writer.finalize() 1950 writer.close() File ~/anaconda3/envs/python3/lib/python3.10/site-packages/datasets/arrow_writer.py:598, in ArrowWriter.finalize(self, close_stream) 597 self.stream.close() --> 598 raise SchemaInferenceError("Please pass `features` or at least one example when writing data") 599 logger.debug( 600 f"Done writing {self._num_examples} {self.unit} in {self._num_bytes} bytes {self._path if self._path else ''}." 601 ) SchemaInferenceError: Please pass `features` or at least one example when writing data The above exception was the direct cause of the following exception: DatasetGenerationError Traceback (most recent call last) Cell In[5], line 3 1 from datasets import load_dataset ----> 3 dataset = load_dataset("glue", "ax") File ~/anaconda3/envs/python3/lib/python3.10/site-packages/datasets/load.py:2136, 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, token, use_auth_token, task, streaming, num_proc, storage_options, config_kwargs) 2133 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES 2135 # Download and prepare data -> 2136 builder_instance.download_and_prepare( 2137 download_config=download_config, 2138 download_mode=download_mode, 2139 verification_mode=verification_mode, 2140 try_from_hf_gcs=try_from_hf_gcs, 2141 num_proc=num_proc, 2142 storage_options=storage_options, 2143 ) 2145 # Build dataset for splits 2146 keep_in_memory = ( 2147 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size) 2148 ) File ~/anaconda3/envs/python3/lib/python3.10/site-packages/datasets/builder.py:954, 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) 952 if num_proc is not None: 953 prepare_split_kwargs["num_proc"] = num_proc --> 954 self._download_and_prepare( 955 dl_manager=dl_manager, 956 verification_mode=verification_mode, 957 prepare_split_kwargs, 958 download_and_prepare_kwargs, 959 ) 960 # Sync info 961 self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values()) File ~/anaconda3/envs/python3/lib/python3.10/site-packages/datasets/builder.py:1049, in DatasetBuilder._download_and_prepare(self, dl_manager, verification_mode, prepare_split_kwargs) 1045 split_dict.add(split_generator.split_info) 1047 try: 1048 # Prepare split will record examples associated to the split -> 1049 self._prepare_split(split_generator, prepare_split_kwargs) 1050 except OSError as e: 1051 raise OSError( 1052 "Cannot find data file. " 1053 + (self.manual_download_instructions or "") 1054 + "\nOriginal error:\n" 1055 + str(e) 1056 ) from None File ~/anaconda3/envs/python3/lib/python3.10/site-packages/datasets/builder.py:1813, in ArrowBasedBuilder._prepare_split(self, split_generator, file_format, num_proc, max_shard_size) 1811 job_id = 0 1812 with pbar: -> 1813 for job_id, done, content in self._prepare_split_single( 1814 gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args 1815 ): 1816 if done: 1817 result = content File ~/anaconda3/envs/python3/lib/python3.10/site-packages/datasets/builder.py:1958, in ArrowBasedBuilder._prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id) 1956 if isinstance(e, SchemaInferenceError) and e.__context__ is not None: 1957 e = e.__context__ -> 1958 raise DatasetGenerationError("An error occurred while generating the dataset") from e 1960 yield job_id, True, (total_num_examples, total_num_bytes, writer._features, num_shards, shard_lengths) DatasetGenerationError: An error occurred while generating the dataset ### Steps to reproduce the bug from datasets import load_dataset dataset = load_dataset("glue", "ax") ### Expected behavior When generating the train split: Generating train split: 0/0 [00:00<?, ? examples/s] It raise the error: DatasetGenerationError: An error occurred while generating the dataset ### Environment info datasets-2.14.4. Python 3.10	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6146/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6146/timeline	null	completed	618	false	[ "I've tried clear the .cache file, doesn't work.", "This issue happens on AWS sagemaker", "This issue can happen if there is a directory named \"glue\" relative to the Python script with the `load_dataset` call (similar issue to this one: https://github.com/huggingface/datasets/issues/5228). Is this the case?", "> This issue can happen if there is a directory named \"glue\" relative to the Python script with the `load_dataset` call (similar issue to this one: #5228). Is this the case?\r\n\r\nThats correct!\r\nSorry for my late response." ]
https://api.github.com/repos/huggingface/datasets/issues/6153	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6153/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6153/comments	https://api.github.com/repos/huggingface/datasets/issues/6153/events	https://github.com/huggingface/datasets/issues/6153	1,852,630,074	I_kwDODunzps5ubOQ6	6,153	custom load dataset to hub	{ "avatar_url": "https://avatars.githubusercontent.com/u/20493493?v=4", "events_url": "https://api.github.com/users/andysingal/events{/privacy}", "followers_url": "https://api.github.com/users/andysingal/followers", "following_url": "https://api.github.com/users/andysingal/following{/other_user}", "gists_url": "https://api.github.com/users/andysingal/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/andysingal", "id": 20493493, "login": "andysingal", "node_id": "MDQ6VXNlcjIwNDkzNDkz", "organizations_url": "https://api.github.com/users/andysingal/orgs", "received_events_url": "https://api.github.com/users/andysingal/received_events", "repos_url": "https://api.github.com/users/andysingal/repos", "site_admin": false, "starred_url": "https://api.github.com/users/andysingal/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/andysingal/subscriptions", "type": "User", "url": "https://api.github.com/users/andysingal" }	[]	closed	false	null	[]	null	5	"2023-08-13T04:42:22Z"	"2023-11-21T11:50:28Z"	"2023-10-08T17:04:16Z"	NONE	null	null	null	### System Info kaggle notebook i transformed dataset: ``` dataset = load_dataset("Dahoas/first-instruct-human-assistant-prompt") ``` to formatted_dataset: ``` Dataset({ features: ['message_tree_id', 'message_tree_text'], num_rows: 33143 }) ``` but would like to know how to upload to hub ### Who can help? @ArthurZucker @younesbelkada ### Information - [ ] The official example scripts - [ ] My own modified scripts ### Tasks - [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...) - [ ] My own task or dataset (give details below) ### Reproduction shared above ### Expected behavior load dataset to hub	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6153/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6153/timeline	null	completed	619	false	[ "This is an issue for the [Datasets repo](https://github.com/huggingface/datasets).", "> This is an issue for the [Datasets repo](https://github.com/huggingface/datasets).\r\n\r\nThanks @sgugger , I guess I will wait for them to address the issue . Looking forward to hearing from them ", "You can use `.push_to_hub(\"<username>/<repo>\")` to push a `Dataset` to the Hub.", "> You can use `.push_to_hub(\"<username>/<repo>\")` to push a `Dataset` to the Hub.\r\n\r\nhow about subset? like `.load_dataset(\"<username>/<repo>\", \"<subset>\")`, how can I upload multi-dataset in one repo? thanks a lot ! ", "> > You can use `.push_to_hub(\"<username>/<repo>\")` to push a `Dataset` to the Hub.\r\n> \r\n> how about subset? like `.load_dataset(\"<username>/<repo>\", \"<subset>\")`, how can I upload multi-dataset in one repo? thanks a lot !\r\n\r\nI solved it by upgrading `Datasets` version to 2.15.0" ]
https://api.github.com/repos/huggingface/datasets/issues/6145	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6145/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6145/comments	https://api.github.com/repos/huggingface/datasets/issues/6145/events	https://github.com/huggingface/datasets/pull/6145	1,847,811,310	PR_kwDODunzps5Xx5If	6,145	Export to_iterable_dataset to document	{ "avatar_url": "https://avatars.githubusercontent.com/u/11533479?v=4", "events_url": "https://api.github.com/users/npuichigo/events{/privacy}", "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}", "gravatar_id": "", "html_url": "https://github.com/npuichigo", "id": 11533479, "login": "npuichigo", "node_id": "MDQ6VXNlcjExNTMzNDc5", "organizations_url": "https://api.github.com/users/npuichigo/orgs", "received_events_url": "https://api.github.com/users/npuichigo/received_events", "repos_url": "https://api.github.com/users/npuichigo/repos", "site_admin": false, "starred_url": "https://api.github.com/users/npuichigo/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/npuichigo/subscriptions", "type": "User", "url": "https://api.github.com/users/npuichigo" }	[]	closed	false	null	[]	null	2	"2023-08-12T07:00:14Z"	"2023-08-15T17:04:01Z"	"2023-08-15T16:55:24Z"	CONTRIBUTOR	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6145.diff", "html_url": "https://github.com/huggingface/datasets/pull/6145", "merged_at": "2023-08-15T16:55:24Z", "patch_url": "https://github.com/huggingface/datasets/pull/6145.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6145" }	Fix the export of a missing method of `Dataset`	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6145/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6145/timeline	null	null	620	true	[ "_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) \| 0.006076 / 0.011353 (-0.005277) \| 0.003730 / 0.011008 (-0.007279) \| 0.080778 / 0.038508 (0.042270) \| 0.062970 / 0.023109 (0.039860) \| 0.395864 / 0.275898 (0.119966) \| 0.430024 / 0.323480 (0.106544) \| 0.004823 / 0.007986 (-0.003162) \| 0.002949 / 0.004328 (-0.001379) \| 0.062423 / 0.004250 (0.058172) \| 0.047343 / 0.037052 (0.010291) \| 0.403153 / 0.258489 (0.144664) \| 0.443666 / 0.293841 (0.149825) \| 0.027798 / 0.128546 (-0.100748) \| 0.008056 / 0.075646 (-0.067590) \| 0.262260 / 0.419271 (-0.157011) \| 0.045958 / 0.043533 (0.002425) \| 0.391349 / 0.255139 (0.136210) \| 0.421831 / 0.283200 (0.138632) \| 0.021837 / 0.141683 (-0.119846) \| 1.485509 / 1.452155 (0.033355) \| 1.542940 / 1.492716 (0.050224) \|\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) \| 0.196831 / 0.018006 (0.178825) \| 0.435774 / 0.000490 (0.435285) \| 0.003647 / 0.000200 (0.003447) \| 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) \| 0.023756 / 0.037411 (-0.013655) \| 0.075737 / 0.014526 (0.061211) \| 0.303703 / 0.176557 (0.127146) \| 0.164862 / 0.737135 (-0.572273) \| 0.198483 / 0.296338 (-0.097855) \|\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) \| 4.065983 / 2.077655 (1.988328) \| 2.043001 / 1.504120 (0.538881) \| 1.853318 / 1.541195 (0.312123) \| 1.977452 / 1.468490 (0.508962) \| 0.500897 / 4.584777 (-4.083880) \| 3.065756 / 3.745712 (-0.679956) \| 2.924096 / 5.269862 (-2.345765) \| 1.876194 / 4.565676 (-2.689482) \| 0.057774 / 0.424275 (-0.366501) \| 0.006809 / 0.007607 (-0.000798) \| 0.470979 / 0.226044 (0.244934) \| 4.719546 / 2.268929 (2.450618) \| 2.449651 / 55.444624 (-52.994973) \| 2.211817 / 6.876477 (-4.664660) \| 2.398760 / 2.142072 (0.256687) \| 0.590608 / 4.805227 (-4.214619) \| 0.125836 / 6.500664 (-6.374829) \| 0.060759 / 0.075469 (-0.014710) \|\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) \| 1.243609 / 1.841788 (-0.598179) \| 18.836193 / 8.074308 (10.761885) \| 13.835053 / 10.191392 (3.643661) \| 0.129708 / 0.680424 (-0.550716) \| 0.016708 / 0.534201 (-0.517493) \| 0.337219 / 0.579283 (-0.242065) \| 0.359045 / 0.434364 (-0.075319) \| 0.383329 / 0.540337 (-0.157009) \| 0.539629 / 1.386936 (-0.847307) \|\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) \| 0.006073 / 0.011353 (-0.005280) \| 0.003713 / 0.011008 (-0.007295) \| 0.062642 / 0.038508 (0.024134) \| 0.062618 / 0.023109 (0.039508) \| 0.362029 / 0.275898 (0.086130) \| 0.401924 / 0.323480 (0.078445) \| 0.004689 / 0.007986 (-0.003297) \| 0.002945 / 0.004328 (-0.001384) \| 0.062720 / 0.004250 (0.058470) \| 0.048901 / 0.037052 (0.011848) \| 0.363780 / 0.258489 (0.105291) \| 0.405111 / 0.293841 (0.111270) \| 0.027738 / 0.128546 (-0.100808) \| 0.008046 / 0.075646 (-0.067600) \| 0.067752 / 0.419271 (-0.351519) \| 0.041955 / 0.043533 (-0.001577) \| 0.361615 / 0.255139 (0.106476) \| 0.388762 / 0.283200 (0.105562) \| 0.021302 / 0.141683 (-0.120380) \| 1.473527 / 1.452155 (0.021372) \| 1.529753 / 1.492716 (0.037037) \|\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) \| 0.300446 / 0.018006 (0.282440) \| 0.425844 / 0.000490 (0.425354) \| 0.054507 / 0.000200 (0.054307) \| 0.000282 / 0.000054 (0.000228) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025478 / 0.037411 (-0.011933) \| 0.078298 / 0.014526 (0.063772) \| 0.087647 / 0.176557 (-0.088909) \| 0.138978 / 0.737135 (-0.598157) \| 0.088396 / 0.296338 (-0.207942) \|\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) \| 0.421345 / 0.215209 (0.206136) \| 4.209188 / 2.077655 (2.131533) \| 2.260731 / 1.504120 (0.756611) \| 2.072329 / 1.541195 (0.531134) \| 2.086778 / 1.468490 (0.618288) \| 0.495425 / 4.584777 (-4.089352) \| 2.987519 / 3.745712 (-0.758194) \| 2.895106 / 5.269862 (-2.374756) \| 1.874637 / 4.565676 (-2.691039) \| 0.057080 / 0.424275 (-0.367195) \| 0.006402 / 0.007607 (-0.001205) \| 0.498233 / 0.226044 (0.272188) \| 4.974385 / 2.268929 (2.705457) \| 2.671755 / 55.444624 (-52.772870) \| 2.356120 / 6.876477 (-4.520357) \| 2.531374 / 2.142072 (0.389301) \| 0.581955 / 4.805227 (-4.223272) \| 0.125491 / 6.500664 (-6.375173) \| 0.062267 / 0.075469 (-0.013202) \|\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) \| 1.307233 / 1.841788 (-0.534555) \| 18.929740 / 8.074308 (10.855431) \| 14.029693 / 10.191392 (3.838301) \| 0.161992 / 0.680424 (-0.518431) \| 0.017127 / 0.534201 (-0.517074) \| 0.336644 / 0.579283 (-0.242639) \| 0.336550 / 0.434364 (-0.097814) \| 0.400554 / 0.540337 (-0.139783) \| 0.560725 / 1.386936 (-0.826211) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#cb8c5de5145c7e7eee65391cb7f4d92f0d565d62 \"CML watermark\")\n" ]
https://api.github.com/repos/huggingface/datasets/issues/6144	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6144/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6144/comments	https://api.github.com/repos/huggingface/datasets/issues/6144/events	https://github.com/huggingface/datasets/issues/6144	1,847,296,711	I_kwDODunzps5uG4LH	6,144	NIH exporter file not found	{ "avatar_url": "https://avatars.githubusercontent.com/u/1855278?v=4", "events_url": "https://api.github.com/users/brando90/events{/privacy}", "followers_url": "https://api.github.com/users/brando90/followers", "following_url": "https://api.github.com/users/brando90/following{/other_user}", "gists_url": "https://api.github.com/users/brando90/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/brando90", "id": 1855278, "login": "brando90", "node_id": "MDQ6VXNlcjE4NTUyNzg=", "organizations_url": "https://api.github.com/users/brando90/orgs", "received_events_url": "https://api.github.com/users/brando90/received_events", "repos_url": "https://api.github.com/users/brando90/repos", "site_admin": false, "starred_url": "https://api.github.com/users/brando90/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/brando90/subscriptions", "type": "User", "url": "https://api.github.com/users/brando90" }	[]	open	false	null	[]	null	6	"2023-08-11T19:05:25Z"	"2023-08-14T23:28:38Z"	null	NONE	null	null	null	### Describe the bug can't use or download the nih exporter pile data. ``` 15 experiment_compute_diveristy_coeff_single_dataset_then_combined_datasets_with_domain_weights() 16 File "/lfs/ampere1/0/brando9/beyond-scale-language-data-diversity/src/diversity/div_coeff.py", line 474, in experiment_compute_diveristy_coeff_single_dataset_then_combined_datasets_with_domain_weights 17 column_names = next(iter(dataset)).keys() 18 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/datasets/iterable_dataset.py", line 1353, in __iter__ 19 for key, example in ex_iterable: 20 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/datasets/iterable_dataset.py", line 207, in __iter__ 21 yield from self.generate_examples_fn(*self.kwargs) 22 File "/lfs/ampere1/0/brando9/.cache/huggingface/modules/datasets_modules/datasets/EleutherAI--pile/ebea56d358e91cf4d37b0fde361d563bed1472fbd8221a21b38fc8bb4ba554fb/pile.py", line 236, in _generate_examples 23 with zstd.open(open(files[subset], "rb"), "rt", encoding="utf-8") as f: 24 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/datasets/streaming.py", line 74, in wrapper 25 return function(args, download_config=download_config, *kwargs) 26 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/datasets/download/streaming_download_manager.py", line 496, in xopen 27 file_obj = fsspec.open(file, mode=mode, args, kwargs).open() 28 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/core.py", line 134, in open 29 return self.__enter__() 30 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/core.py", line 102, in __enter__ 31 f = self.fs.open(self.path, mode=mode) 32 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/spec.py", line 1241, in open 33 f = self._open( 34 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/implementations/http.py", line 356, in _open 35 size = size or self.info(path, kwargs)["size"] 36 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/asyn.py", line 121, in wrapper 37 return sync(self.loop, func, args, *kwargs) 38 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/asyn.py", line 106, in sync 39 raise return_result 40 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/asyn.py", line 61, in _runner 41 result[0] = await coro 42 File "/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/implementations/http.py", line 430, in _info 43 raise FileNotFoundError(url) from exc 44 FileNotFoundError: https://the-eye.eu/public/AI/pile_preliminary_components/NIH_ExPORTER_awarded_grant_text.jsonl.zst ``` ### Steps to reproduce the bug run this: ``` from datasets import load_dataset path, name = 'EleutherAI/pile', 'nih_exporter' # -- Get data set dataset = load_dataset(path, name, streaming=True, split="train").with_format("torch") batch = dataset.take(512) print(f'{batch=}') ``` ### Expected behavior print the batch ### Environment info ``` (beyond_scale) brando9@ampere1:~/beyond-scale-language-data-diversity$ datasets-cli env Copy-and-paste the text below in your GitHub issue. - `datasets` version: 2.14.4 - Platform: Linux-5.4.0-122-generic-x86_64-with-glibc2.31 - Python version: 3.10.11 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 2.0.3 ```	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6144/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6144/timeline	null	null	621	false	[ "related: https://github.com/huggingface/datasets/issues/3504", "another file not found:\r\n```\r\nTraceback (most recent call last):\r\n File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/implementations/http.py\", line 417, in _info\r\n await _file_info(\r\n File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/implementations/http.py\", line 837, in _file_info\r\n r.raise_for_status()\r\n File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/aiohttp/client_reqrep.py\", line 1005, in raise_for_status\r\n raise ClientResponseError(\r\naiohttp.client_exceptions.ClientResponseError: 404, message='Not Found', url=URL('https://the-eye.eu/public/AI/pile_preliminary_components/pile_uspto.tar')\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/runpy.py\", line 196, in _run_module_as_main\r\n return _run_code(code, main_globals, None,\r\n File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/runpy.py\", line 86, in _run_code\r\n exec(code, run_globals)\r\n File \"/lfs/ampere1/0/brando9/.vscode-server-insiders/extensions/ms-python.python-2023.14.0/pythonFiles/lib/python/debugpy/adapter/../../debugpy/launcher/../../debugpy/__main__.py\", line 39, in <module>\r\n cli.main()\r\n File \"/lfs/ampere1/0/brando9/.vscode-server-insiders/extensions/ms-python.python-2023.14.0/pythonFiles/lib/python/debugpy/adapter/../../debugpy/launcher/../../debugpy/../debugpy/server/cli.py\", line 430, in main\r\n run()\r\n File \"/lfs/ampere1/0/brando9/.vscode-server-insiders/extensions/ms-python.python-2023.14.0/pythonFiles/lib/python/debugpy/adapter/../../debugpy/launcher/../../debugpy/../debugpy/server/cli.py\", line 284, in run_file\r\n runpy.run_path(target, run_name=\"__main__\")\r\n File \"/lfs/ampere1/0/brando9/.vscode-server-insiders/extensions/ms-python.python-2023.14.0/pythonFiles/lib/python/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_runpy.py\", line 321, in run_path\r\n return _run_module_code(code, init_globals, run_name,\r\n File \"/lfs/ampere1/0/brando9/.vscode-server-insiders/extensions/ms-python.python-2023.14.0/pythonFiles/lib/python/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_runpy.py\", line 135, in _run_module_code\r\n _run_code(code, mod_globals, init_globals,\r\n File \"/lfs/ampere1/0/brando9/.vscode-server-insiders/extensions/ms-python.python-2023.14.0/pythonFiles/lib/python/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_runpy.py\", line 124, in _run_code\r\n exec(code, run_globals)\r\n File \"/lfs/ampere1/0/brando9/beyond-scale-language-data-diversity/src/diversity/div_coeff.py\", line 526, in <module>\r\n experiment_compute_diveristy_coeff_single_dataset_then_combined_datasets_with_domain_weights()\r\n File \"/lfs/ampere1/0/brando9/beyond-scale-language-data-diversity/src/diversity/div_coeff.py\", line 475, in experiment_compute_diveristy_coeff_single_dataset_then_combined_datasets_with_domain_weights\r\n column_names = next(iter(dataset)).keys()\r\n File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/datasets/iterable_dataset.py\", line 1353, in __iter__\r\n for key, example in ex_iterable:\r\n File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/datasets/iterable_dataset.py\", line 207, in __iter__\r\n yield from self.generate_examples_fn(*self.kwargs)\r\n File \"/lfs/ampere1/0/brando9/.cache/huggingface/modules/datasets_modules/datasets/EleutherAI--pile/ebea56d358e91cf4d37b0fde361d563bed1472fbd8221a21b38fc8bb4ba554fb/pile.py\", line 257, in _generate_examples\r\n for path, file in files[subset]:\r\n File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/datasets/download/streaming_download_manager.py\", line 840, in __iter__\r\n yield from self.generator(self.args, *self.kwargs)\r\n File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/datasets/download/streaming_download_manager.py\", line 891, in _iter_from_urlpath\r\n with xopen(urlpath, \"rb\", download_config=download_config) as f:\r\n File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/datasets/download/streaming_download_manager.py\", line 496, in xopen\r\n file_obj = fsspec.open(file, mode=mode, args, kwargs).open()\r\n File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/core.py\", line 134, in open\r\n return self.__enter__()\r\n File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/core.py\", line 102, in __enter__\r\n f = self.fs.open(self.path, mode=mode)\r\n File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/spec.py\", line 1241, in open\r\n f = self._open(\r\n File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/implementations/http.py\", line 356, in _open\r\n size = size or self.info(path, kwargs)[\"size\"]\r\n File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/asyn.py\", line 121, in wrapper\r\n return sync(self.loop, func, args, *kwargs)\r\n File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/asyn.py\", line 106, in sync\r\n raise return_result\r\n File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/asyn.py\", line 61, in _runner\r\n result[0] = await coro\r\n File \"/lfs/ampere1/0/brando9/miniconda/envs/beyond_scale/lib/python3.10/site-packages/fsspec/implementations/http.py\", line 430, in _info\r\n raise FileNotFoundError(url) from exc\r\nFileNotFoundError: https://the-eye.eu/public/AI/pile_preliminary_components/pile_uspto.tar\r\n```", "```\r\nFileNotFoundError: https://the-eye.eu/public/AI/pile_preliminary_components/pile_uspto.tar\r\n```\r\nmost relevant line I think.", "link to tweet: https://twitter.com/BrandoHablando/status/1690081313519489024?s=20 about issue", "so: https://stackoverflow.com/questions/76891189/how-to-download-data-from-hugging-face-that-is-visible-on-the-data-viewer-but-th", "this seems to work but it's rather annoying.\r\n\r\nSummary of how to make it work:\r\n1. get urls to parquet files into a list\r\n2. load list to load_dataset via `load_dataset('parquet', data_files=urls)` (note api names to hf are really confusing sometimes)\r\n3. then it should work, print a batch of text.\r\n\r\npresudo code\r\n```python\r\nurls_hacker_news = [\r\n \"https://huggingface.co/datasets/EleutherAI/pile/resolve/refs%2Fconvert%2Fparquet/hacker_news/pile-train-00000-of-00004.parquet\",\r\n \"https://huggingface.co/datasets/EleutherAI/pile/resolve/refs%2Fconvert%2Fparquet/hacker_news/pile-train-00001-of-00004.parquet\",\r\n \"https://huggingface.co/datasets/EleutherAI/pile/resolve/refs%2Fconvert%2Fparquet/hacker_news/pile-train-00002-of-00004.parquet\",\r\n \"https://huggingface.co/datasets/EleutherAI/pile/resolve/refs%2Fconvert%2Fparquet/hacker_news/pile-train-00003-of-00004.parquet\"\r\n]\r\n\r\n...\r\n\r\n\r\n # streaming = False\r\n from diversity.pile_subset_urls import urls_hacker_news\r\n path, name, data_files = 'parquet', 'hacker_news', urls_hacker_news\r\n # not changing\r\n batch_size = 512\r\n today = datetime.datetime.now().strftime('%Y-m%m-d%d-t%Hh_%Mm_%Ss')\r\n run_name = f'{path} div_coeff_{num_batches=} ({today=} ({name=}) {data_mixture_name=} {probabilities=})'\r\n print(f'{run_name=}')\r\n\r\n # - Init wandb\r\n debug: bool = mode == 'dryrun'\r\n run = wandb.init(mode=mode, project=\"beyond-scale\", name=run_name, save_code=True)\r\n wandb.config.update({\"num_batches\": num_batches, \"path\": path, \"name\": name, \"today\": today, 'probabilities': probabilities, 'batch_size': batch_size, 'debug': debug, 'data_mixture_name': data_mixture_name, 'streaming': streaming, 'data_files': data_files})\r\n # run.notify_on_failure() # https://community.wandb.ai/t/how-do-i-set-the-wandb-alert-programatically-for-my-current-run/4891\r\n print(f'{debug=}')\r\n print(f'{wandb.config=}')\r\n\r\n # -- Get probe network\r\n from datasets import load_dataset\r\n import torch\r\n from transformers import GPT2Tokenizer, GPT2LMHeadModel\r\n\r\n tokenizer = GPT2Tokenizer.from_pretrained(\"gpt2\")\r\n if tokenizer.pad_token_id is None:\r\n tokenizer.pad_token = tokenizer.eos_token\r\n probe_network = GPT2LMHeadModel.from_pretrained(\"gpt2\")\r\n device = torch.device(f\"cuda:{0}\" if torch.cuda.is_available() else \"cpu\")\r\n probe_network = probe_network.to(device)\r\n\r\n # -- Get data set\r\n def my_load_dataset(path, name):\r\n print(f'{path=} {name=} {streaming=}')\r\n if path == 'json' or path == 'bin' or path == 'csv':\r\n print(f'{data_files_prefix+name=}')\r\n return load_dataset(path, data_files=data_files_prefix+name, streaming=streaming, split=\"train\").with_format(\"torch\")\r\n elif path == 'parquet':\r\n print(f'{data_files=}')\r\n return load_dataset(path, data_files=data_files, streaming=streaming, split=\"train\").with_format(\"torch\")\r\n else:\r\n return load_dataset(path, name, streaming=streaming, split=\"train\").with_format(\"torch\")\r\n # - get data set for real now\r\n if isinstance(path, str):\r\n dataset = my_load_dataset(path, name)\r\n else:\r\n print('-- interleaving datasets')\r\n datasets = [my_load_dataset(path, name).with_format(\"torch\") for path, name in zip(path, name)]\r\n [print(f'{dataset.description=}') for dataset in datasets]\r\n dataset = interleave_datasets(datasets, probabilities)\r\n print(f'{dataset=}')\r\n batch = dataset.take(batch_size)\r\n print(f'{next(iter(batch))=}')\r\n column_names = next(iter(batch)).keys()\r\n print(f'{column_names=}')\r\n\r\n # - Prepare functions to tokenize batch\r\n def preprocess(examples):\r\n return tokenizer(examples[\"text\"], padding=\"max_length\", max_length=128, truncation=True, return_tensors=\"pt\")\r\n remove_columns = column_names # remove all keys that are not tensors to avoid bugs in collate function in task2vec's pytorch data loader\r\n def map(batch):\r\n return batch.map(preprocess, batched=True, remove_columns=remove_columns)\r\n tokenized_batch = map(batch)\r\n print(f'{next(iter(tokenized_batch))=}')\r\n```\r\n\r\nhttps://stackoverflow.com/questions/76891189/how-to-download-data-from-hugging-face-that-is-visible-on-the-data-viewer-but-th/76902681#76902681\r\n\r\nhttps://discuss.huggingface.co/t/how-to-download-data-from-hugging-face-that-is-visible-on-the-data-viewer-but-the-files-are-not-available/50555/5?u=severo" ]
https://api.github.com/repos/huggingface/datasets/issues/6142	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6142/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6142/comments	https://api.github.com/repos/huggingface/datasets/issues/6142/events	https://github.com/huggingface/datasets/issues/6142	1,846,205,216	I_kwDODunzps5uCtsg	6,142	the-stack-dedup fails to generate	{ "avatar_url": "https://avatars.githubusercontent.com/u/45830328?v=4", "events_url": "https://api.github.com/users/michaelroyzen/events{/privacy}", "followers_url": "https://api.github.com/users/michaelroyzen/followers", "following_url": "https://api.github.com/users/michaelroyzen/following{/other_user}", "gists_url": "https://api.github.com/users/michaelroyzen/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/michaelroyzen", "id": 45830328, "login": "michaelroyzen", "node_id": "MDQ6VXNlcjQ1ODMwMzI4", "organizations_url": "https://api.github.com/users/michaelroyzen/orgs", "received_events_url": "https://api.github.com/users/michaelroyzen/received_events", "repos_url": "https://api.github.com/users/michaelroyzen/repos", "site_admin": false, "starred_url": "https://api.github.com/users/michaelroyzen/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/michaelroyzen/subscriptions", "type": "User", "url": "https://api.github.com/users/michaelroyzen" }	[]	closed	false	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "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}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq" }	[ { "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "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}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq" } ]	null	4	"2023-08-11T05:10:49Z"	"2023-08-17T09:26:13Z"	"2023-08-17T09:26:13Z"	NONE	null	null	null	### Describe the bug I'm getting an error generating the-stack-dedup with datasets 2.13.1, and with 2.14.4 nothing happens. ### Steps to reproduce the bug My code: ``` import os import datasets as ds MY_CACHE_DIR = "/home/ubuntu/the-stack-dedup-local" MY_TOKEN="my-token" the_stack_ds = ds.load_dataset("bigcode/the-stack-dedup", split="train", download_mode="reuse_cache_if_exists", cache_dir=MY_CACHE_DIR, use_auth_token=MY_TOKEN, num_proc=64) ``` The exception: ``` Generating train split: 233248251 examples [54:31, 57280.00 examples/s] multiprocess.pool.RemoteTraceback: """ Traceback (most recent call last): File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/build er.py", line 1879, in _prepare_split_single for _, table in generator: File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/packa ged_modules/parquet/parquet.py", line 82, in _generate_tables yield f"{file_idx}_{batch_idx}", self._cast_table(pa_table) File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/packa ged_modules/parquet/parquet.py", line 61, in _cast_table pa_table = table_cast(pa_table, self.info.features.arrow_schema) File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/table .py", line 2324, in table_cast return cast_table_to_schema(table, schema) File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/table .py", line 2282, in cast_table_to_schema raise ValueError(f"Couldn't cast\n{table.schema}\nto\n{features}\nb ecause column names don't match") ValueError: Couldn't cast hexsha: string size: int64 ext: string lang: string max_stars_repo_path: string max_stars_repo_name: string max_stars_repo_head_hexsha: string max_stars_repo_licenses: list<item: string> child 0, item: string max_stars_count: int64 max_stars_repo_stars_event_min_datetime: string max_stars_repo_stars_event_max_datetime: string max_issues_repo_path: string max_issues_repo_name: string max_issues_repo_head_hexsha: string max_issues_repo_licenses: list<item: string> child 0, item: string max_issues_count: int64 max_issues_repo_issues_event_min_datetime: string max_issues_repo_issues_event_max_datetime: string max_forks_repo_path: string max_forks_repo_name: string max_forks_repo_head_hexsha: string max_forks_repo_licenses: list<item: string> child 0, item: string max_forks_count: int64 max_forks_repo_forks_event_min_datetime: string max_forks_repo_forks_event_max_datetime: string content: string avg_line_length: double max_line_length: int64 alphanum_fraction: double __id__: int64 -- schema metadata -- huggingface: '{"info": {"features": {"hexsha": {"dtype": "string", "_type' + 1979 to {'hexsha': Value(dtype='string', id=None), 'size': Value(dtype='int64', id=None), 'ext': Value(dtype='string', id=None), 'lang': Value(dtype='string', id=None), 'max_stars_repo_path': Value(dtype='string', id=None), 'max_stars_repo_name': Value(dtype='string', id=None), 'max_stars_repo_head_hexsha': Value(dtype='string', id=None), 'max_stars_repo_licenses': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'max_stars_count': Value(dtype='int64', id=None), 'max_stars_repo_stars_event_min_datetime': Value(dtype='string', id=None), 'max_stars_repo_stars_event_max_datetime': Value(dtype='string', id=None), 'max_issues_repo_path': Value(dtype='string', id=None), 'max_issues_repo_name': Value(dtype='string', id=None), 'max_issues_repo_head_hexsha': Value(dtype='string', id=None), 'max_issues_repo_licenses': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'max_issues_count': Value(dtype='int64', id=None), 'max_issues_repo_issues_event_min_datetime': Value(dtype='string', id=None), 'max_issues_repo_issues_event_max_datetime': Value(dtype='string', id=None), 'max_forks_repo_path': Value(dtype='string', id=None), 'max_forks_repo_name': Value(dtype='string', id=None), 'max_forks_repo_head_hexsha': Value(dtype='string', id=None), 'max_forks_repo_licenses': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'max_forks_count': Value(dtype='int64', id=None), 'max_forks_repo_forks_event_min_datetime': Value(dtype='string', id=None), 'max_forks_repo_forks_event_max_datetime': Value(dtype='string', id=None), 'content': Value(dtype='string', id=None), 'avg_line_length': Value(dtype='float64', id=None), 'max_line_length': Value(dtype='int64', id=None), 'alphanum_fraction': Value(dtype='float64', id=None)} because column names don't match The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/home/ubuntu/.local/lib/python3.10/site-packages/multiprocess/p ool.py", line 125, in worker result = (True, func(args, kwds)) File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/utils /py_utils.py", line 1328, in _write_generator_to_queue for i, result in enumerate(func(kwargs)): File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/build er.py", line 1912, in _prepare_split_single raise DatasetGenerationError("An error occurred while generating th e dataset") from e datasets.builder.DatasetGenerationError: An error occurred while genera ting the dataset """ The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/home/ubuntu/download_the_stack.py", line 7, in <module> the_stack_ds = ds.load_dataset("bigcode/the-stack-dedup", split="tr ain", download_mode="reuse_cache_if_exists", cache_dir=MY_CACHE_DIR, us e_auth_token=MY_TOKEN, num_proc=64) File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/load. py", line 1809, in load_dataset builder_instance.download_and_prepare( File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/build er.py", line 909, in download_and_prepare self._download_and_prepare( File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/build er.py", line 1004, in _download_and_prepare self._prepare_split(split_generator, *prepare_split_kwargs) File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/build er.py", line 1796, in _prepare_split for job_id, done, content in iflatmap_unordered( File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/utils /py_utils.py", line 1354, in iflatmap_unordered [async_result.get(timeout=0.05) for async_result in async_results] File "/home/ubuntu/.local/lib/python3.10/site-packages/datasets/utils /py_utils.py", line 1354, in <listcomp> [async_result.get(timeout=0.05) for async_result in async_results] File "/home/ubuntu/.local/lib/python3.10/site-packages/multiprocess/p ool.py", line 774, in get raise self._value datasets.builder.DatasetGenerationError: An error occurred while generating the dataset ``` ### Expected behavior The dataset downloads properly. @lhoestq @loub ### Environment info Datasets 2.13.1, large VM with 2TB RAM, Ubuntu 20.04	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6142/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6142/timeline	null	completed	622	false	[ "@severo ", "It seems that some parquet files have additional columns.\r\n\r\nI ran a scan and found that two files have the additional `__id__` column:\r\n\r\n1. `hf://datasets/bigcode/the-stack-dedup/data/numpy/data-00000-of-00001.parquet`\r\n2. `hf://datasets/bigcode/the-stack-dedup/data/omgrofl/data-00000-of-00001.parquet`\r\n\r\nWe should open a PR to fix those two files", "I opened https://huggingface.co/datasets/bigcode/the-stack-dedup/discussions/31", "The files have been fixed ! I'm closing this one but feel free to re-open if you still have the issue" ]
https://api.github.com/repos/huggingface/datasets/issues/6141	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6141/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6141/comments	https://api.github.com/repos/huggingface/datasets/issues/6141/events	https://github.com/huggingface/datasets/issues/6141	1,846,117,729	I_kwDODunzps5uCYVh	6,141	TypeError: ClientSession._request() got an unexpected keyword argument 'https'	{ "avatar_url": "https://avatars.githubusercontent.com/u/35994018?v=4", "events_url": "https://api.github.com/users/q935970314/events{/privacy}", "followers_url": "https://api.github.com/users/q935970314/followers", "following_url": "https://api.github.com/users/q935970314/following{/other_user}", "gists_url": "https://api.github.com/users/q935970314/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/q935970314", "id": 35994018, "login": "q935970314", "node_id": "MDQ6VXNlcjM1OTk0MDE4", "organizations_url": "https://api.github.com/users/q935970314/orgs", "received_events_url": "https://api.github.com/users/q935970314/received_events", "repos_url": "https://api.github.com/users/q935970314/repos", "site_admin": false, "starred_url": "https://api.github.com/users/q935970314/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/q935970314/subscriptions", "type": "User", "url": "https://api.github.com/users/q935970314" }	[]	closed	false	null	[]	null	1	"2023-08-11T02:40:32Z"	"2023-08-30T13:51:33Z"	"2023-08-30T13:51:33Z"	NONE	null	null	null	### Describe the bug Hello, when I ran the [code snippet](https://huggingface.co/docs/datasets/v2.14.4/en/loading#json) on the document, I encountered the following problem: ``` Python 3.10.9 (main, Mar 1 2023, 18:23:06) [GCC 11.2.0] on linux Type "help", "copyright", "credits" or "license" for more information. >>> from datasets import load_dataset >>> base_url = "https://rajpurkar.github.io/SQuAD-explorer/dataset/" >>> dataset = load_dataset("json", data_files={"train": base_url + "train-v1.1.json", "validation": base_url + "dev-v1.1.json"}, field="data") Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/home/liushuai/anaconda3/lib/python3.10/site-packages/datasets/load.py", line 2112, in load_dataset builder_instance = load_dataset_builder( File "/home/liushuai/anaconda3/lib/python3.10/site-packages/datasets/load.py", line 1798, in load_dataset_builder dataset_module = dataset_module_factory( File "/home/liushuai/anaconda3/lib/python3.10/site-packages/datasets/load.py", line 1413, in dataset_module_factory ).get_module() File "/home/liushuai/anaconda3/lib/python3.10/site-packages/datasets/load.py", line 949, in get_module data_files = DataFilesDict.from_patterns( File "/home/liushuai/anaconda3/lib/python3.10/site-packages/datasets/data_files.py", line 672, in from_patterns DataFilesList.from_patterns( File "/home/liushuai/anaconda3/lib/python3.10/site-packages/datasets/data_files.py", line 578, in from_patterns resolve_pattern( File "/home/liushuai/anaconda3/lib/python3.10/site-packages/datasets/data_files.py", line 340, in resolve_pattern for filepath, info in fs.glob(pattern, detail=True).items() File "/home/liushuai/anaconda3/lib/python3.10/site-packages/fsspec/asyn.py", line 113, in wrapper return sync(self.loop, func, args, kwargs) File "/home/liushuai/anaconda3/lib/python3.10/site-packages/fsspec/asyn.py", line 98, in sync raise return_result File "/home/liushuai/anaconda3/lib/python3.10/site-packages/fsspec/asyn.py", line 53, in _runner result[0] = await coro File "/home/liushuai/anaconda3/lib/python3.10/site-packages/fsspec/implementations/http.py", line 449, in _glob elif await self._exists(path): File "/home/liushuai/anaconda3/lib/python3.10/site-packages/fsspec/implementations/http.py", line 306, in _exists r = await session.get(self.encode_url(path), kw) File "/home/liushuai/anaconda3/lib/python3.10/site-packages/aiohttp/client.py", line 922, in get self._request(hdrs.METH_GET, url, allow_redirects=allow_redirects, *kwargs) TypeError: ClientSession._request() got an unexpected keyword argument 'https' ``` ### Steps to reproduce the bug ``` from datasets import load_dataset base_url = "https://rajpurkar.github.io/SQuAD-explorer/dataset/" dataset = load_dataset("json", data_files={"train": base_url + "train-v1.1.json", "validation": base_url + "dev-v1.1.json"}, field="data") ``` ### Expected behavior able to load normally ### Environment info - `datasets` version: 2.14.4 - Platform: Linux-5.4.54-2-x86_64-with-glibc2.27 - Python version: 3.10.9 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 1.5.3	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6141/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6141/timeline	null	completed	623	false	[ "Hi! I cannot reproduce this error on my machine or in Colab. Which version of `fsspec` do you have installed?" ]
https://api.github.com/repos/huggingface/datasets/issues/6140	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6140/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6140/comments	https://api.github.com/repos/huggingface/datasets/issues/6140/events	https://github.com/huggingface/datasets/issues/6140	1,845,384,712	I_kwDODunzps5t_lYI	6,140	Misalignment between file format specified in configs metadata YAML and the inferred builder	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "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}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova" }	[ { "color": "d73a4a", "default": true, "description": "Something isn't working", "id": 1935892857, "name": "bug", "node_id": "MDU6TGFiZWwxOTM1ODkyODU3", "url": "https://api.github.com/repos/huggingface/datasets/labels/bug" } ]	closed	false	null	[]	null	0	"2023-08-10T15:07:34Z"	"2023-08-17T20:37:20Z"	"2023-08-17T20:37:20Z"	MEMBER	null	null	null	There is a misalignment between the format of the `data_files` specified in the configs metadata YAML (CSV): ```yaml configs: - config_name: default data_files: - split: train path: data.csv ``` and the inferred builder (JSON). Note there are multiple JSON files in the repo, but they do not appear in the configs metadata YAML. See: https://huggingface.co/datasets/freddyaboulton/chatinterface_with_image_csv/discussions/1 CC: @freddyaboulton @polinaeterna	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6140/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6140/timeline	null	completed	624	false	[]
https://api.github.com/repos/huggingface/datasets/issues/6139	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6139/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6139/comments	https://api.github.com/repos/huggingface/datasets/issues/6139/events	https://github.com/huggingface/datasets/issues/6139	1,844,991,583	I_kwDODunzps5t-FZf	6,139	Offline dataset viewer	{ "avatar_url": "https://avatars.githubusercontent.com/u/57996478?v=4", "events_url": "https://api.github.com/users/yuvalkirstain/events{/privacy}", "followers_url": "https://api.github.com/users/yuvalkirstain/followers", "following_url": "https://api.github.com/users/yuvalkirstain/following{/other_user}", "gists_url": "https://api.github.com/users/yuvalkirstain/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/yuvalkirstain", "id": 57996478, "login": "yuvalkirstain", "node_id": "MDQ6VXNlcjU3OTk2NDc4", "organizations_url": "https://api.github.com/users/yuvalkirstain/orgs", "received_events_url": "https://api.github.com/users/yuvalkirstain/received_events", "repos_url": "https://api.github.com/users/yuvalkirstain/repos", "site_admin": false, "starred_url": "https://api.github.com/users/yuvalkirstain/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/yuvalkirstain/subscriptions", "type": "User", "url": "https://api.github.com/users/yuvalkirstain" }	[ { "color": "a2eeef", "default": true, "description": "New feature or request", "id": 1935892871, "name": "enhancement", "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement" }, { "color": "E5583E", "default": false, "description": "Related to the dataset viewer on huggingface.co", "id": 3470211881, "name": "dataset-viewer", "node_id": "LA_kwDODunzps7O1zsp", "url": "https://api.github.com/repos/huggingface/datasets/labels/dataset-viewer" } ]	closed	false	null	[]	null	6	"2023-08-10T11:30:00Z"	"2024-02-13T12:22:55Z"	"2023-09-29T13:10:22Z"	NONE	null	null	null	### Feature request The dataset viewer feature is very nice. It enables to the user to easily view the dataset. However, when working for private companies we cannot always upload the dataset to the hub. Is there a way to create dataset viewer offline? I.e. to run a code that will open some kind of html or something that makes it easy to view the dataset. ### Motivation I want to easily view my dataset even when it is hosted locally. ### Your contribution N.A.	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6139/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6139/timeline	null	completed	625	false	[ "Hi, thanks for the suggestion. It's not possible at the moment. The viewer is part of the Hub codebase and only works on public datasets. Also, it relies on [Datasets Server](https://github.com/huggingface/datasets-server/), which prepares the data and provides an API to access the rows, size, etc.\r\n\r\nIf you're interested in hosting your data as a private dataset on the Hub, you might want to look at https://github.com/huggingface/datasets-server/issues/39.", "Hi, we are building an offline dataset viewer: https://github.com/Renumics/spotlight\r\nIt supports many HF datasets, but currently you have to use it via Pandas:\r\ndf=ds.to_pandas()\r\nspotlight.show(df)\r\n\r\nWould love to hear from you if that works for your use case. If not, feel free to open an issue on the repo: https://github.com/Renumics/spotlight/issues", "@ssuwelack thank you! I will definitely try it out.", "Related issues:\r\n- https://github.com/huggingface/datasets-server/issues/213\r\n- https://github.com/huggingface/datasets-server/issues/441\r\n- https://github.com/huggingface/datasets/issues/6014", "Closing for now, as developing and maintaining an offline viewer is not planned.", "@yuvalkirstain the dataset viewer is now available on private datasets for [PRO users](https://huggingface.co/pricing#pro) and [Enterprise Hub orgs](https://huggingface.co/enterprise). Would it fit your needs?" ]
https://api.github.com/repos/huggingface/datasets/issues/6138	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6138/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6138/comments	https://api.github.com/repos/huggingface/datasets/issues/6138/events	https://github.com/huggingface/datasets/pull/6138	1,844,952,496	PR_kwDODunzps5XoH2V	6,138	Ignore CI lint rule violation in Pickler.memoize	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "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}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova" }	[]	closed	false	null	[]	null	3	"2023-08-10T11:03:15Z"	"2023-08-10T11:31:45Z"	"2023-08-10T11:22:56Z"	MEMBER	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6138.diff", "html_url": "https://github.com/huggingface/datasets/pull/6138", "merged_at": "2023-08-10T11:22:56Z", "patch_url": "https://github.com/huggingface/datasets/pull/6138.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6138" }	This PR ignores the violation of the lint rule E721 in `Pickler.memoize`. The lint rule violation was introduced in this PR: - #3182 @lhoestq is there a reason you did not use `isinstance` instead? As a hotfix, we just ignore the violation of the lint rule. Fix #6136.	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6138/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6138/timeline	null	null	626	true	[ "<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) \| 0.006536 / 0.011353 (-0.004817) \| 0.003890 / 0.011008 (-0.007118) \| 0.084044 / 0.038508 (0.045536) \| 0.071893 / 0.023109 (0.048784) \| 0.346926 / 0.275898 (0.071028) \| 0.397487 / 0.323480 (0.074007) \| 0.004065 / 0.007986 (-0.003921) \| 0.003218 / 0.004328 (-0.001111) \| 0.064670 / 0.004250 (0.060420) \| 0.052414 / 0.037052 (0.015362) \| 0.355413 / 0.258489 (0.096924) \| 0.398894 / 0.293841 (0.105053) \| 0.030763 / 0.128546 (-0.097783) \| 0.008590 / 0.075646 (-0.067056) \| 0.286857 / 0.419271 (-0.132415) \| 0.051126 / 0.043533 (0.007593) \| 0.346125 / 0.255139 (0.090986) \| 0.395673 / 0.283200 (0.112474) \| 0.025766 / 0.141683 (-0.115917) \| 1.466238 / 1.452155 (0.014084) \| 1.543117 / 1.492716 (0.050400) \|\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) \| 0.213210 / 0.018006 (0.195204) \| 0.451981 / 0.000490 (0.451491) \| 0.003784 / 0.000200 (0.003585) \| 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) \| 0.027756 / 0.037411 (-0.009655) \| 0.082446 / 0.014526 (0.067920) \| 0.095414 / 0.176557 (-0.081142) \| 0.151812 / 0.737135 (-0.585323) \| 0.096296 / 0.296338 (-0.200042) \|\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) \| 0.383729 / 0.215209 (0.168520) \| 3.835126 / 2.077655 (1.757471) \| 1.891972 / 1.504120 (0.387852) \| 1.719934 / 1.541195 (0.178739) \| 1.899980 / 1.468490 (0.431490) \| 0.488741 / 4.584777 (-4.096036) \| 3.634120 / 3.745712 (-0.111592) \| 3.243314 / 5.269862 (-2.026547) \| 2.028382 / 4.565676 (-2.537294) \| 0.057355 / 0.424275 (-0.366920) \| 0.007717 / 0.007607 (0.000110) \| 0.459835 / 0.226044 (0.233790) \| 4.591793 / 2.268929 (2.322864) \| 2.346861 / 55.444624 (-53.097764) \| 2.067357 / 6.876477 (-4.809120) \| 2.254954 / 2.142072 (0.112882) \| 0.587016 / 4.805227 (-4.218211) \| 0.133918 / 6.500664 (-6.366746) \| 0.060311 / 0.075469 (-0.015158) \|\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) \| 1.250016 / 1.841788 (-0.591772) \| 19.674333 / 8.074308 (11.600025) \| 14.522764 / 10.191392 (4.331372) \| 0.145741 / 0.680424 (-0.534683) \| 0.018593 / 0.534201 (-0.515608) \| 0.392833 / 0.579283 (-0.186450) \| 0.408194 / 0.434364 (-0.026170) \| 0.455164 / 0.540337 (-0.085174) \| 0.622722 / 1.386936 (-0.764214) \|\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) \| 0.006583 / 0.011353 (-0.004770) \| 0.004008 / 0.011008 (-0.007000) \| 0.064688 / 0.038508 (0.026180) \| 0.074969 / 0.023109 (0.051860) \| 0.360504 / 0.275898 (0.084606) \| 0.396926 / 0.323480 (0.073446) \| 0.005190 / 0.007986 (-0.002796) \| 0.003363 / 0.004328 (-0.000966) \| 0.064372 / 0.004250 (0.060122) \| 0.054428 / 0.037052 (0.017376) \| 0.361204 / 0.258489 (0.102715) \| 0.400917 / 0.293841 (0.107077) \| 0.031117 / 0.128546 (-0.097429) \| 0.008406 / 0.075646 (-0.067241) \| 0.069655 / 0.419271 (-0.349617) \| 0.048582 / 0.043533 (0.005049) \| 0.365396 / 0.255139 (0.110257) \| 0.381344 / 0.283200 (0.098145) \| 0.023809 / 0.141683 (-0.117874) \| 1.472926 / 1.452155 (0.020772) \| 1.547298 / 1.492716 (0.054582) \|\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) \| 0.276912 / 0.018006 (0.258906) \| 0.449096 / 0.000490 (0.448607) \| 0.018921 / 0.000200 (0.018721) \| 0.000111 / 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) \| 0.030237 / 0.037411 (-0.007174) \| 0.088610 / 0.014526 (0.074084) \| 0.101529 / 0.176557 (-0.075027) \| 0.154070 / 0.737135 (-0.583065) \| 0.103471 / 0.296338 (-0.192867) \|\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) \| 0.416047 / 0.215209 (0.200838) \| 4.152374 / 2.077655 (2.074719) \| 2.111181 / 1.504120 (0.607061) \| 1.943582 / 1.541195 (0.402387) \| 2.031729 / 1.468490 (0.563239) \| 0.486740 / 4.584777 (-4.098037) \| 3.631547 / 3.745712 (-0.114165) \| 3.251202 / 5.269862 (-2.018660) \| 2.041272 / 4.565676 (-2.524405) \| 0.057287 / 0.424275 (-0.366988) \| 0.007303 / 0.007607 (-0.000304) \| 0.491027 / 0.226044 (0.264982) \| 4.906757 / 2.268929 (2.637829) \| 2.581694 / 55.444624 (-52.862931) \| 2.250996 / 6.876477 (-4.625481) \| 2.441771 / 2.142072 (0.299698) \| 0.600714 / 4.805227 (-4.204514) \| 0.133233 / 6.500664 (-6.367431) \| 0.060856 / 0.075469 (-0.014613) \|\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) \| 1.340062 / 1.841788 (-0.501725) \| 19.973899 / 8.074308 (11.899591) \| 14.347381 / 10.191392 (4.155989) \| 0.166651 / 0.680424 (-0.513773) \| 0.018691 / 0.534201 (-0.515510) \| 0.393580 / 0.579283 (-0.185703) \| 0.409425 / 0.434364 (-0.024939) \| 0.474409 / 0.540337 (-0.065929) \| 0.649423 / 1.386936 (-0.737514) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c5da68102297c3639207a7901952d2765a4cdb8b \"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) \| 0.006593 / 0.011353 (-0.004760) \| 0.004123 / 0.011008 (-0.006885) \| 0.084424 / 0.038508 (0.045916) \| 0.076867 / 0.023109 (0.053758) \| 0.309149 / 0.275898 (0.033251) \| 0.348572 / 0.323480 (0.025092) \| 0.005463 / 0.007986 (-0.002523) \| 0.003440 / 0.004328 (-0.000889) \| 0.064604 / 0.004250 (0.060353) \| 0.053920 / 0.037052 (0.016868) \| 0.345221 / 0.258489 (0.086732) \| 0.363209 / 0.293841 (0.069368) \| 0.031209 / 0.128546 (-0.097337) \| 0.008690 / 0.075646 (-0.066956) \| 0.288851 / 0.419271 (-0.130421) \| 0.052239 / 0.043533 (0.008707) \| 0.308643 / 0.255139 (0.053504) \| 0.346407 / 0.283200 (0.063207) \| 0.023935 / 0.141683 (-0.117748) \| 1.469207 / 1.452155 (0.017052) \| 1.532855 / 1.492716 (0.040138) \|\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) \| 0.290885 / 0.018006 (0.272879) \| 0.580561 / 0.000490 (0.580071) \| 0.004698 / 0.000200 (0.004498) \| 0.000286 / 0.000054 (0.000231) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028015 / 0.037411 (-0.009396) \| 0.081172 / 0.014526 (0.066646) \| 0.096822 / 0.176557 (-0.079735) \| 0.151355 / 0.737135 (-0.585781) \| 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) \| 0.384069 / 0.215209 (0.168859) \| 3.828635 / 2.077655 (1.750980) \| 1.829311 / 1.504120 (0.325192) \| 1.672520 / 1.541195 (0.131325) \| 1.743944 / 1.468490 (0.275453) \| 0.481594 / 4.584777 (-4.103183) \| 3.556204 / 3.745712 (-0.189509) \| 3.279499 / 5.269862 (-1.990363) \| 2.033243 / 4.565676 (-2.532434) \| 0.056525 / 0.424275 (-0.367750) \| 0.007717 / 0.007607 (0.000109) \| 0.466815 / 0.226044 (0.240771) \| 4.657022 / 2.268929 (2.388094) \| 2.438600 / 55.444624 (-53.006024) \| 2.097999 / 6.876477 (-4.778478) \| 2.263122 / 2.142072 (0.121049) \| 0.636001 / 4.805227 (-4.169226) \| 0.147727 / 6.500664 (-6.352937) \| 0.059293 / 0.075469 (-0.016176) \|\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) \| 1.243111 / 1.841788 (-0.598677) \| 19.558379 / 8.074308 (11.484071) \| 14.141017 / 10.191392 (3.949625) \| 0.169840 / 0.680424 (-0.510583) \| 0.017912 / 0.534201 (-0.516289) \| 0.391325 / 0.579283 (-0.187958) \| 0.417169 / 0.434364 (-0.017195) \| 0.457129 / 0.540337 (-0.083209) \| 0.629907 / 1.386936 (-0.757029) \|\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) \| 0.006687 / 0.011353 (-0.004666) \| 0.004165 / 0.011008 (-0.006844) \| 0.064738 / 0.038508 (0.026230) \| 0.077286 / 0.023109 (0.054177) \| 0.364236 / 0.275898 (0.088338) \| 0.393228 / 0.323480 (0.069748) \| 0.005451 / 0.007986 (-0.002535) \| 0.003547 / 0.004328 (-0.000781) \| 0.065761 / 0.004250 (0.061510) \| 0.056526 / 0.037052 (0.019474) \| 0.365523 / 0.258489 (0.107034) \| 0.403331 / 0.293841 (0.109490) \| 0.030900 / 0.128546 (-0.097646) \| 0.008757 / 0.075646 (-0.066889) \| 0.070961 / 0.419271 (-0.348311) \| 0.048394 / 0.043533 (0.004861) \| 0.365908 / 0.255139 (0.110769) \| 0.381197 / 0.283200 (0.097998) \| 0.022940 / 0.141683 (-0.118743) \| 1.487909 / 1.452155 (0.035754) \| 1.532931 / 1.492716 (0.040215) \|\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) \| 0.317506 / 0.018006 (0.299500) \| 0.513391 / 0.000490 (0.512902) \| 0.005464 / 0.000200 (0.005264) \| 0.000214 / 0.000054 (0.000159) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032289 / 0.037411 (-0.005122) \| 0.090157 / 0.014526 (0.075631) \| 0.103514 / 0.176557 (-0.073043) \| 0.158236 / 0.737135 (-0.578899) \| 0.106554 / 0.296338 (-0.189784) \|\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) \| 0.406455 / 0.215209 (0.191246) \| 4.061563 / 2.077655 (1.983908) \| 2.082201 / 1.504120 (0.578081) \| 1.914433 / 1.541195 (0.373238) \| 2.039342 / 1.468490 (0.570852) \| 0.478444 / 4.584777 (-4.106333) \| 3.599755 / 3.745712 (-0.145957) \| 3.294453 / 5.269862 (-1.975409) \| 2.028519 / 4.565676 (-2.537158) \| 0.056118 / 0.424275 (-0.368157) \| 0.007325 / 0.007607 (-0.000282) \| 0.493177 / 0.226044 (0.267132) \| 4.926218 / 2.268929 (2.657289) \| 2.605033 / 55.444624 (-52.839591) \| 2.239933 / 6.876477 (-4.636544) \| 2.454210 / 2.142072 (0.312137) \| 0.571905 / 4.805227 (-4.233322) \| 0.133251 / 6.500664 (-6.367413) \| 0.062422 / 0.075469 (-0.013047) \|\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) \| 1.352752 / 1.841788 (-0.489036) \| 20.265109 / 8.074308 (12.190801) \| 14.293064 / 10.191392 (4.101672) \| 0.169267 / 0.680424 (-0.511157) \| 0.018607 / 0.534201 (-0.515594) \| 0.393655 / 0.579283 (-0.185628) \| 0.402132 / 0.434364 (-0.032232) \| 0.477566 / 0.540337 (-0.062772) \| 0.651773 / 1.386936 (-0.735163) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#80023f36b2b6678347979421ef973d8969d31306 \"CML watermark\")\n" ]
https://api.github.com/repos/huggingface/datasets/issues/6137	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6137/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6137/comments	https://api.github.com/repos/huggingface/datasets/issues/6137/events	https://github.com/huggingface/datasets/issues/6137	1,844,952,312	I_kwDODunzps5t97z4	6,137	(`from_spark()`) Unable to connect HDFS in pyspark YARN setting	{ "avatar_url": "https://avatars.githubusercontent.com/u/1051900?v=4", "events_url": "https://api.github.com/users/kyoungrok0517/events{/privacy}", "followers_url": "https://api.github.com/users/kyoungrok0517/followers", "following_url": "https://api.github.com/users/kyoungrok0517/following{/other_user}", "gists_url": "https://api.github.com/users/kyoungrok0517/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/kyoungrok0517", "id": 1051900, "login": "kyoungrok0517", "node_id": "MDQ6VXNlcjEwNTE5MDA=", "organizations_url": "https://api.github.com/users/kyoungrok0517/orgs", "received_events_url": "https://api.github.com/users/kyoungrok0517/received_events", "repos_url": "https://api.github.com/users/kyoungrok0517/repos", "site_admin": false, "starred_url": "https://api.github.com/users/kyoungrok0517/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/kyoungrok0517/subscriptions", "type": "User", "url": "https://api.github.com/users/kyoungrok0517" }	[]	open	false	null	[]	null	0	"2023-08-10T11:03:08Z"	"2023-08-10T11:03:08Z"	null	NONE	null	null	null	### Describe the bug related issue: https://github.com/apache/arrow/issues/37057#issue-1841013613 --- Hello. I'm trying to interact with HDFS storage from a driver and workers of pyspark YARN cluster. Precisely I'm using huggingface's `datasets` ([link](https://github.com/huggingface/datasets)) library that relies on pyarrow to communicate with HDFS. The `from_spark()` ([link](https://huggingface.co/docs/datasets/use_with_spark#load-from-spark)) is what I'm invoking in my script. Below is the error I'm encountering. Note that I've masked sensitive paths. My code is sent to worker containers (docker) from driver container then executed. I confirmed that in both driver and worker images I can connect to HDFS using pyarrow since the envs and required jars are properly set, but strangely that becomes impossible when the same image runs as remote worker process. These are some peculiarities in my environment that might caused this issue. * Cluster requires kerberos authentication * But I think the error message implies that's not the problem in this case * The user that runs the worker process is different from that built the docker image * To avoid permission-related issues I made all directories that are accessed from the script accessible to everyone * Pyspark-part of my code has no problem interacting with HDFS. * Even pyarrow doesn't experience problem when I run the code in interactive session of the same docker images (driver, worker) * The problem occurs only when it runs as cluster's worker runtime Hope I could get some help. Thanks. ```bash 2023-08-08 18:51:19,638 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable 2023-08-08 18:51:20,280 WARN shortcircuit.DomainSocketFactory: The short-circuit local reads feature cannot be used because libhadoop cannot be loaded. 23/08/08 18:51:22 WARN TaskSetManager: Lost task 0.0 in stage 142.0 (TID 9732) (ac3bax2062.bdp.bdata.ai executor 1): org.apache.spark.api.python.PythonException: Traceback (most recent call last): File "<MASKED>/application_1682476586273_25865777/container_e143_1682476586273_25865777_01_000003/pyspark.zip/pyspark/worker.py", line 830, in main process() File "<MASKED>/application_1682476586273_25865777/container_e143_1682476586273_25865777_01_000003/pyspark.zip/pyspark/worker.py", line 820, in process out_iter = func(split_index, iterator) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/root/spark/python/pyspark/rdd.py", line 5405, in pipeline_func File "/root/spark/python/pyspark/rdd.py", line 828, in func File "/opt/conda/lib/python3.11/site-packages/datasets/packaged_modules/spark/spark.py", line 130, in create_cache_and_write_probe open(probe_file, "a") File "/opt/conda/lib/python3.11/site-packages/datasets/streaming.py", line 74, in wrapper return function(args, download_config=download_config, kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/site-packages/datasets/download/streaming_download_manager.py", line 496, in xopen file_obj = fsspec.open(file, mode=mode, args, kwargs).open() ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/site-packages/fsspec/core.py", line 439, in open out = open_files( ^^^^^^^^^^^ File "/opt/conda/lib/python3.11/site-packages/fsspec/core.py", line 282, in open_files fs, fs_token, paths = get_fs_token_paths( ^^^^^^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/site-packages/fsspec/core.py", line 609, in get_fs_token_paths fs = filesystem(protocol, inkwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/site-packages/fsspec/registry.py", line 267, in filesystem return cls(*storage_options) ^^^^^^^^^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/site-packages/fsspec/spec.py", line 79, in __call__ obj = super().__call__(args, *kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/site-packages/fsspec/implementations/arrow.py", line 278, in __init__ fs = HadoopFileSystem( ^^^^^^^^^^^^^^^^^ File "pyarrow/_hdfs.pyx", line 96, in pyarrow._hdfs.HadoopFileSystem.__init__ File "pyarrow/error.pxi", line 144, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 115, in pyarrow.lib.check_status OSError: HDFS connection failed at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:561) at org.apache.spark.api.python.PythonRunner$$anon$3.read(PythonRunner.scala:767) at org.apache.spark.api.python.PythonRunner$$anon$3.read(PythonRunner.scala:749) at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:514) at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37) at scala.collection.Iterator.foreach(Iterator.scala:943) at scala.collection.Iterator.foreach$(Iterator.scala:943) at org.apache.spark.InterruptibleIterator.foreach(InterruptibleIterator.scala:28) at scala.collection.generic.Growable.$plus$plus$eq(Growable.scala:62) at scala.collection.generic.Growable.$plus$plus$eq$(Growable.scala:53) at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:105) at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:49) at scala.collection.TraversableOnce.to(TraversableOnce.scala:366) at scala.collection.TraversableOnce.to$(TraversableOnce.scala:364) at org.apache.spark.InterruptibleIterator.to(InterruptibleIterator.scala:28) at scala.collection.TraversableOnce.toBuffer(TraversableOnce.scala:358) at scala.collection.TraversableOnce.toBuffer$(TraversableOnce.scala:358) at org.apache.spark.InterruptibleIterator.toBuffer(InterruptibleIterator.scala:28) at scala.collection.TraversableOnce.toArray(TraversableOnce.scala:345) at scala.collection.TraversableOnce.toArray$(TraversableOnce.scala:339) at org.apache.spark.InterruptibleIterator.toArray(InterruptibleIterator.scala:28) at org.apache.spark.rdd.RDD.$anonfun$collect$2(RDD.scala:1019) at org.apache.spark.SparkContext.$anonfun$runJob$5(SparkContext.scala:2303) at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:92) at org.apache.spark.TaskContext.runTaskWithListeners(TaskContext.scala:161) at org.apache.spark.scheduler.Task.run(Task.scala:139) at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:554) at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1529) at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:557) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617) at java.lang.Thread.run(Thread.java:745) 23/08/08 18:51:24 WARN TaskSetManager: Lost task 0.1 in stage 142.0 (TID 9733) (ac3iax2079.bdp.bdata.ai executor 2): org.apache.spark.api.python.PythonException: Traceback (most recent call last): File "<MASKED>/application_1682476586273_25865777/container_e143_1682476586273_25865777_01_000005/pyspark.zip/pyspark/worker.py", line 830, in main process() File "<MASKED>/application_1682476586273_25865777/container_e143_1682476586273_25865777_01_000005/pyspark.zip/pyspark/worker.py", line 820, in process out_iter = func(split_index, iterator) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/root/spark/python/pyspark/rdd.py", line 5405, in pipeline_func File "/root/spark/python/pyspark/rdd.py", line 828, in func File "/opt/conda/lib/python3.11/site-packages/datasets/packaged_modules/spark/spark.py", line 130, in create_cache_and_write_probe open(probe_file, "a") File "/opt/conda/lib/python3.11/site-packages/datasets/streaming.py", line 74, in wrapper return function(args, download_config=download_config, *kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/site-packages/datasets/download/streaming_download_manager.py", line 496, in xopen file_obj = fsspec.open(file, mode=mode, args, kwargs).open() ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/site-packages/fsspec/core.py", line 439, in open out = open_files( ^^^^^^^^^^^ File "/opt/conda/lib/python3.11/site-packages/fsspec/core.py", line 282, in open_files fs, fs_token, paths = get_fs_token_paths( ^^^^^^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/site-packages/fsspec/core.py", line 609, in get_fs_token_paths fs = filesystem(protocol, inkwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/site-packages/fsspec/registry.py", line 267, in filesystem return cls(*storage_options) ^^^^^^^^^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/site-packages/fsspec/spec.py", line 79, in __call__ obj = super().__call__(args, *kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/site-packages/fsspec/implementations/arrow.py", line 278, in __init__ fs = HadoopFileSystem( ^^^^^^^^^^^^^^^^^ File "pyarrow/_hdfs.pyx", line 96, in pyarrow._hdfs.HadoopFileSystem.__init__ File "pyarrow/error.pxi", line 144, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 115, in pyarrow.lib.check_status OSError: HDFS connection failed at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:561) at org.apache.spark.api.python.PythonRunner$$anon$3.read(PythonRunner.scala:767) at org.apache.spark.api.python.PythonRunner$$anon$3.read(PythonRunner.scala:749) at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:514) at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37) at scala.collection.Iterator.foreach(Iterator.scala:943) at scala.collection.Iterator.foreach$(Iterator.scala:943) at org.apache.spark.InterruptibleIterator.foreach(InterruptibleIterator.scala:28) at scala.collection.generic.Growable.$plus$plus$eq(Growable.scala:62) at scala.collection.generic.Growable.$plus$plus$eq$(Growable.scala:53) at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:105) at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:49) at scala.collection.TraversableOnce.to(TraversableOnce.scala:366) at scala.collection.TraversableOnce.to$(TraversableOnce.scala:364) at org.apache.spark.InterruptibleIterator.to(InterruptibleIterator.scala:28) at scala.collection.TraversableOnce.toBuffer(TraversableOnce.scala:358) at scala.collection.TraversableOnce.toBuffer$(TraversableOnce.scala:358) at org.apache.spark.InterruptibleIterator.toBuffer(InterruptibleIterator.scala:28) at scala.collection.TraversableOnce.toArray(TraversableOnce.scala:345) at scala.collection.TraversableOnce.toArray$(TraversableOnce.scala:339) at org.apache.spark.InterruptibleIterator.toArray(InterruptibleIterator.scala:28) at org.apache.spark.rdd.RDD.$anonfun$collect$2(RDD.scala:1019) at org.apache.spark.SparkContext.$anonfun$runJob$5(SparkContext.scala:2303) at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:92) at org.apache.spark.TaskContext.runTaskWithListeners(TaskContext.scala:161) at org.apache.spark.scheduler.Task.run(Task.scala:139) at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:554) at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1529) at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:557) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617) at java.lang.Thread.run(Thread.java:745) 23/08/08 18:51:38 WARN TaskSetManager: Lost task 0.2 in stage 142.0 (TID 9734) (<MASKED> executor 4): org.apache.spark.api.python.PythonException: Traceback (most recent call last): File "<MASKED>/application_1682476586273_25865777/container_e143_1682476586273_25865777_01_000008/pyspark.zip/pyspark/worker.py", line 830, in main process() File "<MASKED>/application_1682476586273_25865777/container_e143_1682476586273_25865777_01_000008/pyspark.zip/pyspark/worker.py", line 820, in process out_iter = func(split_index, iterator) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/root/spark/python/pyspark/rdd.py", line 5405, in pipeline_func File "/root/spark/python/pyspark/rdd.py", line 828, in func File "/opt/conda/lib/python3.11/site-packages/datasets/packaged_modules/spark/spark.py", line 130, in create_cache_and_write_probe open(probe_file, "a") File "/opt/conda/lib/python3.11/site-packages/datasets/streaming.py", line 74, in wrapper return function(args, download_config=download_config, *kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/site-packages/datasets/download/streaming_download_manager.py", line 496, in xopen file_obj = fsspec.open(file, mode=mode, args, kwargs).open() ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/site-packages/fsspec/core.py", line 439, in open out = open_files( ^^^^^^^^^^^ File "/opt/conda/lib/python3.11/site-packages/fsspec/core.py", line 282, in open_files fs, fs_token, paths = get_fs_token_paths( ^^^^^^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/site-packages/fsspec/core.py", line 609, in get_fs_token_paths fs = filesystem(protocol, inkwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/site-packages/fsspec/registry.py", line 267, in filesystem return cls(*storage_options) ^^^^^^^^^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/site-packages/fsspec/spec.py", line 79, in __call__ obj = super().__call__(args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/site-packages/fsspec/implementations/arrow.py", line 278, in __init__ fs = HadoopFileSystem( ^^^^^^^^^^^^^^^^^ File "pyarrow/_hdfs.pyx", line 96, in pyarrow._hdfs.HadoopFileSystem.__init__ File "pyarrow/error.pxi", line 144, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 115, in pyarrow.lib.check_status OSError: HDFS connection failed at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.handlePythonException(PythonRunner.scala:561) at org.apache.spark.api.python.PythonRunner$$anon$3.read(PythonRunner.scala:767) at org.apache.spark.api.python.PythonRunner$$anon$3.read(PythonRunner.scala:749) at org.apache.spark.api.python.BasePythonRunner$ReaderIterator.hasNext(PythonRunner.scala:514) at org.apache.spark.InterruptibleIterator.hasNext(InterruptibleIterator.scala:37) at scala.collection.Iterator.foreach(Iterator.scala:943) at scala.collection.Iterator.foreach$(Iterator.scala:943) at org.apache.spark.InterruptibleIterator.foreach(InterruptibleIterator.scala:28) at scala.collection.generic.Growable.$plus$plus$eq(Growable.scala:62) at scala.collection.generic.Growable.$plus$plus$eq$(Growable.scala:53) at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:105) at scala.collection.mutable.ArrayBuffer.$plus$plus$eq(ArrayBuffer.scala:49) at scala.collection.TraversableOnce.to(TraversableOnce.scala:366) at scala.collection.TraversableOnce.to$(TraversableOnce.scala:364) at org.apache.spark.InterruptibleIterator.to(InterruptibleIterator.scala:28) at scala.collection.TraversableOnce.toBuffer(TraversableOnce.scala:358) at scala.collection.TraversableOnce.toBuffer$(TraversableOnce.scala:358) at org.apache.spark.InterruptibleIterator.toBuffer(InterruptibleIterator.scala:28) at scala.collection.TraversableOnce.toArray(TraversableOnce.scala:345) at scala.collection.TraversableOnce.toArray$(TraversableOnce.scala:339) at org.apache.spark.InterruptibleIterator.toArray(InterruptibleIterator.scala:28) at org.apache.spark.rdd.RDD.$anonfun$collect$2(RDD.scala:1019) at org.apache.spark.SparkContext.$anonfun$runJob$5(SparkContext.scala:2303) at org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:92) at org.apache.spark.TaskContext.runTaskWithListeners(TaskContext.scala:161) at org.apache.spark.scheduler.Task.run(Task.scala:139) at org.apache.spark.executor.Executor$TaskRunner.$anonfun$run$3(Executor.scala:554) at org.apache.spark.util.Utils$.tryWithSafeFinally(Utils.scala:1529) at org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:557) at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142) at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617) at java.lang.Thread.run(Thread.java:745) ``` ### Steps to reproduce the bug Use `from_spark()` function in pyspark YARN setting. I set `cache_dir` to HDFS path. ### Expected behavior Work as described in document ### Environment info - `datasets` version: 2.14.4 - Platform: Linux-4.18.0-425.19.2.el8_7.x86_64-x86_64-with-glibc2.17 - Python version: 3.11.4 - Huggingface_hub version: 0.16.4 - PyArrow version: 10.0.1 - Pandas version: 1.5.3	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6137/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6137/timeline	null	null	627	false	[]
https://api.github.com/repos/huggingface/datasets/issues/6136	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6136/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6136/comments	https://api.github.com/repos/huggingface/datasets/issues/6136/events	https://github.com/huggingface/datasets/issues/6136	1,844,887,866	I_kwDODunzps5t9sE6	6,136	CI check_code_quality error: E721 Do not compare types, use `isinstance()`	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "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}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova" }	[ { "color": "d4c5f9", "default": false, "description": "Maintenance tasks", "id": 4296013012, "name": "maintenance", "node_id": "LA_kwDODunzps8AAAABAA_01A", "url": "https://api.github.com/repos/huggingface/datasets/labels/maintenance" } ]	closed	false	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "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}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova" }	[ { "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "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}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova" } ]	null	0	"2023-08-10T10:19:50Z"	"2023-08-10T11:22:58Z"	"2023-08-10T11:22:58Z"	MEMBER	null	null	null	After latest release of `ruff` (https://pypi.org/project/ruff/0.0.284/), we get the following CI error: ``` src/datasets/utils/py_utils.py:689:12: E721 Do not compare types, use `isinstance()` ```	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6136/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6136/timeline	null	completed	628	false	[]
https://api.github.com/repos/huggingface/datasets/issues/6135	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6135/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6135/comments	https://api.github.com/repos/huggingface/datasets/issues/6135/events	https://github.com/huggingface/datasets/pull/6135	1,844,870,943	PR_kwDODunzps5Xn2AT	6,135	Remove unused allowed_extensions param	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "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}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova" }	[]	closed	false	null	[]	null	4	"2023-08-10T10:09:54Z"	"2023-08-10T12:08:38Z"	"2023-08-10T12:00:02Z"	MEMBER	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6135.diff", "html_url": "https://github.com/huggingface/datasets/pull/6135", "merged_at": "2023-08-10T12:00:01Z", "patch_url": "https://github.com/huggingface/datasets/pull/6135.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6135" }	This PR removes unused `allowed_extensions` parameter from `create_builder_configs_from_metadata_configs`.	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6135/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6135/timeline	null	null	629	true	[ "_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) \| 0.009055 / 0.011353 (-0.002298) \| 0.008835 / 0.011008 (-0.002173) \| 0.117048 / 0.038508 (0.078540) \| 0.096268 / 0.023109 (0.073159) \| 0.474678 / 0.275898 (0.198780) \| 0.550509 / 0.323480 (0.227029) \| 0.005552 / 0.007986 (-0.002434) \| 0.004315 / 0.004328 (-0.000013) \| 0.094336 / 0.004250 (0.090086) \| 0.061945 / 0.037052 (0.024892) \| 0.461422 / 0.258489 (0.202933) \| 0.521271 / 0.293841 (0.227430) \| 0.049116 / 0.128546 (-0.079430) \| 0.015007 / 0.075646 (-0.060639) \| 0.414351 / 0.419271 (-0.004920) \| 0.137520 / 0.043533 (0.093987) \| 0.465627 / 0.255139 (0.210488) \| 0.537244 / 0.283200 (0.254044) \| 0.068577 / 0.141683 (-0.073106) \| 1.921373 / 1.452155 (0.469219) \| 2.506653 / 1.492716 (1.013937) \|\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) \| 0.273970 / 0.018006 (0.255963) \| 0.750295 / 0.000490 (0.749805) \| 0.004241 / 0.000200 (0.004041) \| 0.000128 / 0.000054 (0.000073) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033793 / 0.037411 (-0.003618) \| 0.105562 / 0.014526 (0.091037) \| 0.131771 / 0.176557 (-0.044786) \| 0.196890 / 0.737135 (-0.540245) \| 0.119842 / 0.296338 (-0.176496) \|\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) \| 0.634881 / 0.215209 (0.419672) \| 6.069221 / 2.077655 (3.991566) \| 2.678765 / 1.504120 (1.174646) \| 2.460309 / 1.541195 (0.919114) \| 2.517579 / 1.468490 (1.049089) \| 0.869558 / 4.584777 (-3.715219) \| 5.407686 / 3.745712 (1.661974) \| 4.920687 / 5.269862 (-0.349175) \| 3.130066 / 4.565676 (-1.435611) \| 0.100337 / 0.424275 (-0.323938) \| 0.009615 / 0.007607 (0.002008) \| 0.745275 / 0.226044 (0.519231) \| 7.577890 / 2.268929 (5.308962) \| 3.607887 / 55.444624 (-51.836738) \| 2.922211 / 6.876477 (-3.954266) \| 3.205592 / 2.142072 (1.063519) \| 1.052298 / 4.805227 (-3.752929) \| 0.218798 / 6.500664 (-6.281866) \| 0.082137 / 0.075469 (0.006667) \|\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) \| 1.696551 / 1.841788 (-0.145237) \| 24.946074 / 8.074308 (16.871766) \| 23.114202 / 10.191392 (12.922810) \| 0.220498 / 0.680424 (-0.459925) \| 0.029388 / 0.534201 (-0.504813) \| 0.494721 / 0.579283 (-0.084562) \| 0.603085 / 0.434364 (0.168722) \| 0.573093 / 0.540337 (0.032756) \| 0.784937 / 1.386936 (-0.601999) \|\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) \| 0.009642 / 0.011353 (-0.001711) \| 0.007551 / 0.011008 (-0.003457) \| 0.085224 / 0.038508 (0.046716) \| 0.099493 / 0.023109 (0.076384) \| 0.503824 / 0.275898 (0.227926) \| 0.546583 / 0.323480 (0.223103) \| 0.006385 / 0.007986 (-0.001601) \| 0.004751 / 0.004328 (0.000423) \| 0.084699 / 0.004250 (0.080449) \| 0.067875 / 0.037052 (0.030823) \| 0.485313 / 0.258489 (0.226824) \| 0.535808 / 0.293841 (0.241967) \| 0.049935 / 0.128546 (-0.078611) \| 0.014427 / 0.075646 (-0.061219) \| 0.095531 / 0.419271 (-0.323741) \| 0.068487 / 0.043533 (0.024954) \| 0.502204 / 0.255139 (0.247065) \| 0.514393 / 0.283200 (0.231193) \| 0.037350 / 0.141683 (-0.104333) \| 1.849380 / 1.452155 (0.397226) \| 1.920151 / 1.492716 (0.427434) \|\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) \| 0.298363 / 0.018006 (0.280357) \| 0.651555 / 0.000490 (0.651065) \| 0.005910 / 0.000200 (0.005710) \| 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) \| 0.039170 / 0.037411 (0.001758) \| 0.106436 / 0.014526 (0.091910) \| 0.129880 / 0.176557 (-0.046677) \| 0.185401 / 0.737135 (-0.551734) \| 0.125732 / 0.296338 (-0.170607) \|\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) \| 0.643248 / 0.215209 (0.428039) \| 6.374807 / 2.077655 (4.297152) \| 3.057296 / 1.504120 (1.553176) \| 2.779534 / 1.541195 (1.238340) \| 2.790165 / 1.468490 (1.321675) \| 0.841580 / 4.584777 (-3.743197) \| 5.371478 / 3.745712 (1.625766) \| 4.973251 / 5.269862 (-0.296610) \| 3.235817 / 4.565676 (-1.329860) \| 0.097276 / 0.424275 (-0.326999) \| 0.008840 / 0.007607 (0.001233) \| 0.728678 / 0.226044 (0.502634) \| 7.526382 / 2.268929 (5.257454) \| 3.792550 / 55.444624 (-51.652074) \| 3.439134 / 6.876477 (-3.437342) \| 3.466626 / 2.142072 (1.324553) \| 1.035894 / 4.805227 (-3.769333) \| 0.211670 / 6.500664 (-6.288994) \| 0.087596 / 0.075469 (0.012127) \|\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) \| 1.782755 / 1.841788 (-0.059033) \| 25.704407 / 8.074308 (17.630099) \| 23.799672 / 10.191392 (13.608280) \| 0.233952 / 0.680424 (-0.446472) \| 0.030810 / 0.534201 (-0.503391) \| 0.505857 / 0.579283 (-0.073426) \| 0.629331 / 0.434364 (0.194967) \| 0.608530 / 0.540337 (0.068192) \| 0.813688 / 1.386936 (-0.573248) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ed4d6bb5f1331576c41b04acd9872a5349a0915c \"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) \| 0.003916 / 0.011008 (-0.007092) \| 0.083976 / 0.038508 (0.045468) \| 0.072583 / 0.023109 (0.049474) \| 0.322747 / 0.275898 (0.046849) \| 0.345159 / 0.323480 (0.021679) \| 0.005366 / 0.007986 (-0.002620) \| 0.003399 / 0.004328 (-0.000930) \| 0.064232 / 0.004250 (0.059982) \| 0.053313 / 0.037052 (0.016261) \| 0.353127 / 0.258489 (0.094638) \| 0.361398 / 0.293841 (0.067557) \| 0.030604 / 0.128546 (-0.097942) \| 0.008615 / 0.075646 (-0.067031) \| 0.285806 / 0.419271 (-0.133466) \| 0.050887 / 0.043533 (0.007354) \| 0.312293 / 0.255139 (0.057154) \| 0.349716 / 0.283200 (0.066516) \| 0.024546 / 0.141683 (-0.117137) \| 1.472318 / 1.452155 (0.020163) \| 1.536063 / 1.492716 (0.043347) \|\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) \| 0.280012 / 0.018006 (0.262006) \| 0.593574 / 0.000490 (0.593085) \| 0.004083 / 0.000200 (0.003883) \| 0.000195 / 0.000054 (0.000141) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027715 / 0.037411 (-0.009696) \| 0.081392 / 0.014526 (0.066866) \| 0.096445 / 0.176557 (-0.080112) \| 0.152131 / 0.737135 (-0.585004) \| 0.094825 / 0.296338 (-0.201514) \|\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) \| 0.380749 / 0.215209 (0.165540) \| 3.806994 / 2.077655 (1.729339) \| 1.842544 / 1.504120 (0.338424) \| 1.682829 / 1.541195 (0.141635) \| 1.701679 / 1.468490 (0.233189) \| 0.484830 / 4.584777 (-4.099947) \| 3.517359 / 3.745712 (-0.228353) \| 3.231211 / 5.269862 (-2.038651) \| 2.029371 / 4.565676 (-2.536306) \| 0.057199 / 0.424275 (-0.367077) \| 0.007653 / 0.007607 (0.000046) \| 0.458572 / 0.226044 (0.232528) \| 4.579835 / 2.268929 (2.310907) \| 2.326467 / 55.444624 (-53.118157) \| 1.939646 / 6.876477 (-4.936831) \| 2.133150 / 2.142072 (-0.008922) \| 0.596251 / 4.805227 (-4.208976) \| 0.131979 / 6.500664 (-6.368686) \| 0.059226 / 0.075469 (-0.016243) \|\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) \| 1.234833 / 1.841788 (-0.606955) \| 19.475522 / 8.074308 (11.401214) \| 14.102760 / 10.191392 (3.911368) \| 0.159657 / 0.680424 (-0.520767) \| 0.018292 / 0.534201 (-0.515909) \| 0.391079 / 0.579283 (-0.188204) \| 0.406736 / 0.434364 (-0.027628) \| 0.459159 / 0.540337 (-0.081178) \| 0.618159 / 1.386936 (-0.768777) \|\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) \| 0.006592 / 0.011353 (-0.004761) \| 0.004052 / 0.011008 (-0.006957) \| 0.064536 / 0.038508 (0.026028) \| 0.075051 / 0.023109 (0.051942) \| 0.379596 / 0.275898 (0.103698) \| 0.412413 / 0.323480 (0.088933) \| 0.005377 / 0.007986 (-0.002608) \| 0.003466 / 0.004328 (-0.000863) \| 0.064958 / 0.004250 (0.060708) \| 0.055265 / 0.037052 (0.018213) \| 0.391505 / 0.258489 (0.133016) \| 0.425345 / 0.293841 (0.131504) \| 0.030750 / 0.128546 (-0.097796) \| 0.008652 / 0.075646 (-0.066994) \| 0.072107 / 0.419271 (-0.347165) \| 0.048340 / 0.043533 (0.004807) \| 0.387714 / 0.255139 (0.132575) \| 0.402602 / 0.283200 (0.119402) \| 0.023492 / 0.141683 (-0.118191) \| 1.528377 / 1.452155 (0.076222) \| 1.574827 / 1.492716 (0.082110) \|\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) \| 0.316999 / 0.018006 (0.298993) \| 0.528391 / 0.000490 (0.527901) \| 0.005183 / 0.000200 (0.004983) \| 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) \| 0.029670 / 0.037411 (-0.007741) \| 0.087130 / 0.014526 (0.072604) \| 0.099897 / 0.176557 (-0.076660) \| 0.154074 / 0.737135 (-0.583062) \| 0.104309 / 0.296338 (-0.192030) \|\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) \| 0.408804 / 0.215209 (0.193595) \| 4.072248 / 2.077655 (1.994593) \| 2.103333 / 1.504120 (0.599213) \| 1.931972 / 1.541195 (0.390777) \| 1.980132 / 1.468490 (0.511642) \| 0.482623 / 4.584777 (-4.102154) \| 3.532789 / 3.745712 (-0.212923) \| 3.304962 / 5.269862 (-1.964899) \| 2.036672 / 4.565676 (-2.529004) \| 0.056944 / 0.424275 (-0.367331) \| 0.007190 / 0.007607 (-0.000417) \| 0.490650 / 0.226044 (0.264606) \| 4.903604 / 2.268929 (2.634675) \| 2.586247 / 55.444624 (-52.858377) \| 2.227631 / 6.876477 (-4.648846) \| 2.397286 / 2.142072 (0.255214) \| 0.579167 / 4.805227 (-4.226060) \| 0.132037 / 6.500664 (-6.368627) \| 0.059971 / 0.075469 (-0.015498) \|\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) \| 1.336430 / 1.841788 (-0.505358) \| 19.915846 / 8.074308 (11.841538) \| 14.102781 / 10.191392 (3.911389) \| 0.147956 / 0.680424 (-0.532468) \| 0.018192 / 0.534201 (-0.516009) \| 0.397949 / 0.579283 (-0.181334) \| 0.408529 / 0.434364 (-0.025835) \| 0.479382 / 0.540337 (-0.060955) \| 0.659735 / 1.386936 (-0.727201) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#98074122449bc031f7269f298f1c55f20e39b975 \"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) \| 0.005880 / 0.011353 (-0.005473) \| 0.003677 / 0.011008 (-0.007332) \| 0.080022 / 0.038508 (0.041514) \| 0.055554 / 0.023109 (0.032445) \| 0.397449 / 0.275898 (0.121551) \| 0.428346 / 0.323480 (0.104867) \| 0.004613 / 0.007986 (-0.003373) \| 0.002873 / 0.004328 (-0.001455) \| 0.062226 / 0.004250 (0.057976) \| 0.044721 / 0.037052 (0.007669) \| 0.404792 / 0.258489 (0.146303) \| 0.437467 / 0.293841 (0.143626) \| 0.027166 / 0.128546 (-0.101381) \| 0.008077 / 0.075646 (-0.067569) \| 0.260469 / 0.419271 (-0.158803) \| 0.043551 / 0.043533 (0.000018) \| 0.401712 / 0.255139 (0.146573) \| 0.427294 / 0.283200 (0.144094) \| 0.021243 / 0.141683 (-0.120440) \| 1.464553 / 1.452155 (0.012398) \| 1.507112 / 1.492716 (0.014396) \|\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) \| 0.198415 / 0.018006 (0.180408) \| 0.427940 / 0.000490 (0.427450) \| 0.004236 / 0.000200 (0.004036) \| 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) \| 0.023759 / 0.037411 (-0.013652) \| 0.073262 / 0.014526 (0.058736) \| 0.677113 / 0.176557 (0.500557) \| 0.194964 / 0.737135 (-0.542172) \| 0.086121 / 0.296338 (-0.210217) \|\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) \| 0.401176 / 0.215209 (0.185967) \| 4.028688 / 2.077655 (1.951034) \| 2.026804 / 1.504120 (0.522685) \| 1.887964 / 1.541195 (0.346770) \| 2.008991 / 1.468490 (0.540501) \| 0.498847 / 4.584777 (-4.085930) \| 3.015920 / 3.745712 (-0.729792) \| 2.837019 / 5.269862 (-2.432843) \| 1.849976 / 4.565676 (-2.715701) \| 0.057545 / 0.424275 (-0.366730) \| 0.006645 / 0.007607 (-0.000962) \| 0.470225 / 0.226044 (0.244180) \| 4.720910 / 2.268929 (2.451982) \| 2.473693 / 55.444624 (-52.970931) \| 2.177525 / 6.876477 (-4.698952) \| 2.374702 / 2.142072 (0.232630) \| 0.588253 / 4.805227 (-4.216974) \| 0.125512 / 6.500664 (-6.375152) \| 0.061247 / 0.075469 (-0.014222) \|\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) \| 1.255829 / 1.841788 (-0.585959) \| 18.251689 / 8.074308 (10.177381) \| 13.690373 / 10.191392 (3.498981) \| 0.146928 / 0.680424 (-0.533496) \| 0.016534 / 0.534201 (-0.517667) \| 0.335249 / 0.579283 (-0.244034) \| 0.338940 / 0.434364 (-0.095424) \| 0.382170 / 0.540337 (-0.158168) \| 0.529570 / 1.386936 (-0.857366) \|\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) \| 0.005920 / 0.011353 (-0.005433) \| 0.003557 / 0.011008 (-0.007451) \| 0.062776 / 0.038508 (0.024267) \| 0.058473 / 0.023109 (0.035364) \| 0.358780 / 0.275898 (0.082882) \| 0.394161 / 0.323480 (0.070682) \| 0.004636 / 0.007986 (-0.003349) \| 0.002865 / 0.004328 (-0.001463) \| 0.062033 / 0.004250 (0.057782) \| 0.047154 / 0.037052 (0.010101) \| 0.367718 / 0.258489 (0.109229) \| 0.400814 / 0.293841 (0.106973) \| 0.026919 / 0.128546 (-0.101628) \| 0.008071 / 0.075646 (-0.067575) \| 0.067802 / 0.419271 (-0.351469) \| 0.040894 / 0.043533 (-0.002638) \| 0.358757 / 0.255139 (0.103618) \| 0.384971 / 0.283200 (0.101771) \| 0.020019 / 0.141683 (-0.121664) \| 1.458578 / 1.452155 (0.006423) \| 1.525059 / 1.492716 (0.032342) \|\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) \| 0.207795 / 0.018006 (0.189789) \| 0.413201 / 0.000490 (0.412712) \| 0.005199 / 0.000200 (0.004999) \| 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) \| 0.025716 / 0.037411 (-0.011696) \| 0.078434 / 0.014526 (0.063908) \| 0.086920 / 0.176557 (-0.089637) \| 0.138327 / 0.737135 (-0.598808) \| 0.088120 / 0.296338 (-0.208219) \|\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) \| 0.434344 / 0.215209 (0.219135) \| 4.343114 / 2.077655 (2.265459) \| 2.384439 / 1.504120 (0.880319) \| 2.253929 / 1.541195 (0.712735) \| 2.306811 / 1.468490 (0.838321) \| 0.497572 / 4.584777 (-4.087205) \| 3.028794 / 3.745712 (-0.716919) \| 2.833484 / 5.269862 (-2.436377) \| 1.878918 / 4.565676 (-2.686759) \| 0.057133 / 0.424275 (-0.367143) \| 0.006357 / 0.007607 (-0.001251) \| 0.508019 / 0.226044 (0.281975) \| 5.076935 / 2.268929 (2.808007) \| 2.745784 / 55.444624 (-52.698841) \| 2.476291 / 6.876477 (-4.400186) \| 2.677264 / 2.142072 (0.535191) \| 0.587173 / 4.805227 (-4.218054) \| 0.126373 / 6.500664 (-6.374291) \| 0.062815 / 0.075469 (-0.012654) \|\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) \| 1.355482 / 1.841788 (-0.486305) \| 18.818227 / 8.074308 (10.743919) \| 13.954289 / 10.191392 (3.762896) \| 0.143413 / 0.680424 (-0.537011) \| 0.016844 / 0.534201 (-0.517357) \| 0.338334 / 0.579283 (-0.240949) \| 0.344559 / 0.434364 (-0.089805) \| 0.400669 / 0.540337 (-0.139669) \| 0.563835 / 1.386936 (-0.823101) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c02a44715c036b5261686669727394b1308a3a4b \"CML watermark\")\n" ]
https://api.github.com/repos/huggingface/datasets/issues/6134	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6134/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6134/comments	https://api.github.com/repos/huggingface/datasets/issues/6134/events	https://github.com/huggingface/datasets/issues/6134	1,844,535,142	I_kwDODunzps5t8V9m	6,134	`datasets` cannot be installed alongside `apache-beam`	{ "avatar_url": "https://avatars.githubusercontent.com/u/6520892?v=4", "events_url": "https://api.github.com/users/boyleconnor/events{/privacy}", "followers_url": "https://api.github.com/users/boyleconnor/followers", "following_url": "https://api.github.com/users/boyleconnor/following{/other_user}", "gists_url": "https://api.github.com/users/boyleconnor/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/boyleconnor", "id": 6520892, "login": "boyleconnor", "node_id": "MDQ6VXNlcjY1MjA4OTI=", "organizations_url": "https://api.github.com/users/boyleconnor/orgs", "received_events_url": "https://api.github.com/users/boyleconnor/received_events", "repos_url": "https://api.github.com/users/boyleconnor/repos", "site_admin": false, "starred_url": "https://api.github.com/users/boyleconnor/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/boyleconnor/subscriptions", "type": "User", "url": "https://api.github.com/users/boyleconnor" }	[]	closed	false	null	[]	null	1	"2023-08-10T06:54:32Z"	"2023-09-01T03:19:49Z"	"2023-08-10T15:22:10Z"	NONE	null	null	null	### Describe the bug If one installs `apache-beam` alongside `datasets` (which is required for the [wikipedia](https://huggingface.co/datasets/wikipedia#dataset-summary) dataset) in certain environments (such as a Google Colab notebook), they appear to install successfully, however, actually trying to do something such as importing the `load_dataset` method from `datasets` results in a crashing error. I think the problem is that `apache-beam` version 2.49.0 requires `dill>=0.3.1.1,<0.3.2`, but the latest version of `multiprocess` (0.70.15) (on which `datasets` depends) requires `dill>=0.3.7,`, so this is causing the dependency resolver to use an older version of `multiprocess` which leads to the `datasets` crashing since it doesn't actually appear to be compatible with older versions. ### Steps to reproduce the bug See this [Google Colab notebook](https://colab.research.google.com/drive/1PTeGlshamFcJZix_GiS3vMXX_YzAhGv0?usp=sharing) to easily reproduce the bug. In some environments, I have been able to reproduce the bug by running the following in Bash: ```bash $ pip install datasets apache-beam ``` then the following in a Python shell: ```python from datasets import load_dataset ``` Here is my stacktrace from running on Google Colab: <details> <summary>stacktrace</summary> ``` [/usr/local/lib/python3.10/dist-packages/datasets/__init__.py](https://localhost:8080/#) in <module> 20 __version__ = "2.14.4" 21 ---> 22 from .arrow_dataset import Dataset 23 from .arrow_reader import ReadInstruction 24 from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder [/usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py](https://localhost:8080/#) in <module> 64 65 from . import config ---> 66 from .arrow_reader import ArrowReader 67 from .arrow_writer import ArrowWriter, OptimizedTypedSequence 68 from .data_files import sanitize_patterns [/usr/local/lib/python3.10/dist-packages/datasets/arrow_reader.py](https://localhost:8080/#) in <module> 28 import pyarrow.parquet as pq 29 ---> 30 from .download.download_config import DownloadConfig 31 from .naming import _split_re, filenames_for_dataset_split 32 from .table import InMemoryTable, MemoryMappedTable, Table, concat_tables [/usr/local/lib/python3.10/dist-packages/datasets/download/__init__.py](https://localhost:8080/#) in <module> 7 8 from .download_config import DownloadConfig ----> 9 from .download_manager import DownloadManager, DownloadMode 10 from .streaming_download_manager import StreamingDownloadManager [/usr/local/lib/python3.10/dist-packages/datasets/download/download_manager.py](https://localhost:8080/#) in <module> 33 from ..utils.info_utils import get_size_checksum_dict 34 from ..utils.logging import get_logger, is_progress_bar_enabled, tqdm ---> 35 from ..utils.py_utils import NestedDataStructure, map_nested, size_str 36 from .download_config import DownloadConfig 37 [/usr/local/lib/python3.10/dist-packages/datasets/utils/py_utils.py](https://localhost:8080/#) in <module> 38 import dill 39 import multiprocess ---> 40 import multiprocess.pool 41 import numpy as np 42 from packaging import version [/usr/local/lib/python3.10/dist-packages/multiprocess/pool.py](https://localhost:8080/#) in <module> 607 # 608 --> 609 class ThreadPool(Pool): 610 611 from .dummy import Process [/usr/local/lib/python3.10/dist-packages/multiprocess/pool.py](https://localhost:8080/#) in ThreadPool() 609 class ThreadPool(Pool): 610 --> 611 from .dummy import Process 612 613 def __init__(self, processes=None, initializer=None, initargs=()): [/usr/local/lib/python3.10/dist-packages/multiprocess/dummy/__init__.py](https://localhost:8080/#) in <module> 85 # 86 ---> 87 class Condition(threading._Condition): 88 # XXX 89 if sys.version_info < (3, 0): AttributeError: module 'threading' has no attribute '_Condition' ``` </details> I've also found that attempting to install these `datasets` and `apache-beam` in certain environments (e.g. via pip inside a conda env) simply causes pip to hang indefinitely. ### Expected behavior I would expect to be able to import methods from `datasets` without crashing. I have tested that this is possible as long as I do not attempt to install `apache-beam`. ### Environment info Google Colab	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6134/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6134/timeline	null	not_planned	630	false	[ "I noticed that this is actually covered by issue #5613, which for some reason I didn't see when I searched the issues in this repo the first time." ]
https://api.github.com/repos/huggingface/datasets/issues/6133	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6133/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6133/comments	https://api.github.com/repos/huggingface/datasets/issues/6133/events	https://github.com/huggingface/datasets/issues/6133	1,844,511,519	I_kwDODunzps5t8QMf	6,133	Dataset is slower after calling `to_iterable_dataset`	{ "avatar_url": "https://avatars.githubusercontent.com/u/11533479?v=4", "events_url": "https://api.github.com/users/npuichigo/events{/privacy}", "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}", "gravatar_id": "", "html_url": "https://github.com/npuichigo", "id": 11533479, "login": "npuichigo", "node_id": "MDQ6VXNlcjExNTMzNDc5", "organizations_url": "https://api.github.com/users/npuichigo/orgs", "received_events_url": "https://api.github.com/users/npuichigo/received_events", "repos_url": "https://api.github.com/users/npuichigo/repos", "site_admin": false, "starred_url": "https://api.github.com/users/npuichigo/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/npuichigo/subscriptions", "type": "User", "url": "https://api.github.com/users/npuichigo" }	[]	open	false	null	[]	null	2	"2023-08-10T06:36:23Z"	"2023-08-16T09:18:54Z"	null	CONTRIBUTOR	null	null	null	### Describe the bug Can anyone explain why looping over a dataset becomes slower after calling `to_iterable_dataset` to convert to `IterableDataset` ### Steps to reproduce the bug Any dataset after converting to `IterableDataset` ### Expected behavior Maybe it should be faster on big dataset? I only test on small dataset ### Environment info - `datasets` version: 2.14.4 - Platform: Linux-5.15.0-76-generic-x86_64-with-glibc2.17 - Python version: 3.8.15 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 1.5.3	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6133/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6133/timeline	null	null	631	false	[ "@lhoestq ", "It's roughly the same code between the two so we can expected roughly the same speed, could you share a benchmark ?" ]
https://api.github.com/repos/huggingface/datasets/issues/6132	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6132/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6132/comments	https://api.github.com/repos/huggingface/datasets/issues/6132/events	https://github.com/huggingface/datasets/issues/6132	1,843,491,020	I_kwDODunzps5t4XDM	6,132	to_iterable_dataset is missing in document	{ "avatar_url": "https://avatars.githubusercontent.com/u/11533479?v=4", "events_url": "https://api.github.com/users/npuichigo/events{/privacy}", "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}", "gravatar_id": "", "html_url": "https://github.com/npuichigo", "id": 11533479, "login": "npuichigo", "node_id": "MDQ6VXNlcjExNTMzNDc5", "organizations_url": "https://api.github.com/users/npuichigo/orgs", "received_events_url": "https://api.github.com/users/npuichigo/received_events", "repos_url": "https://api.github.com/users/npuichigo/repos", "site_admin": false, "starred_url": "https://api.github.com/users/npuichigo/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/npuichigo/subscriptions", "type": "User", "url": "https://api.github.com/users/npuichigo" }	[]	closed	false	null	[]	null	1	"2023-08-09T15:15:03Z"	"2023-08-16T04:43:36Z"	"2023-08-16T04:43:29Z"	CONTRIBUTOR	null	null	null	### Describe the bug to_iterable_dataset is missing in document ### Steps to reproduce the bug to_iterable_dataset is missing in document ### Expected behavior document enhancement ### Environment info unrelated	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6132/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6132/timeline	null	completed	632	false	[ "Fixed with PR" ]
https://api.github.com/repos/huggingface/datasets/issues/6130	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6130/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6130/comments	https://api.github.com/repos/huggingface/datasets/issues/6130/events	https://github.com/huggingface/datasets/issues/6130	1,843,158,846	I_kwDODunzps5t3F8-	6,130	default config name doesn't work when config kwargs are specified.	{ "avatar_url": "https://avatars.githubusercontent.com/u/11533479?v=4", "events_url": "https://api.github.com/users/npuichigo/events{/privacy}", "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}", "gravatar_id": "", "html_url": "https://github.com/npuichigo", "id": 11533479, "login": "npuichigo", "node_id": "MDQ6VXNlcjExNTMzNDc5", "organizations_url": "https://api.github.com/users/npuichigo/orgs", "received_events_url": "https://api.github.com/users/npuichigo/received_events", "repos_url": "https://api.github.com/users/npuichigo/repos", "site_admin": false, "starred_url": "https://api.github.com/users/npuichigo/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/npuichigo/subscriptions", "type": "User", "url": "https://api.github.com/users/npuichigo" }	[]	closed	false	null	[]	null	15	"2023-08-09T12:43:15Z"	"2023-11-22T11:50:49Z"	"2023-11-22T11:50:48Z"	CONTRIBUTOR	null	null	null	### Describe the bug https://github.com/huggingface/datasets/blob/12cfc1196e62847e2e8239fbd727a02cbc86ddec/src/datasets/builder.py#L518-L522 If `config_name` is `None`, `DEFAULT_CONFIG_NAME` should be select. But once users pass `config_kwargs` to their customized `BuilderConfig`, the logic is ignored, and dataset cannot select the default config from multiple configs. ### Steps to reproduce the bug ```python import datasets datasets.load_dataset('/dataset/with/multiple/config'') # Ok datasets.load_dataset('/dataset/with/multiple/config', some_field_in_config='some') # Err ``` ### Expected behavior Default config behavior should be consistent. ### Environment info - `datasets` version: 2.14.3 - Platform: Linux-5.15.0-76-generic-x86_64-with-glibc2.17 - Python version: 3.8.15 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 1.5.3	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6130/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6130/timeline	null	completed	633	false	[ "@lhoestq ", "What should be the behavior in this case ? Should it override the default config with the added parameter ?", "I know why it should be treated as a new config if overriding parameters are passed. But in some case, I just pass in some common fields like `data_dir`.\r\n\r\nFor example, I want to extend the FolderBasedBuilder as a multi-config version, the `data_dir` or `data_files` are always passed by user and should not be considered as overriding the default config. In current state, I cannot leverage the feature of default config since passing `data_dir` will disable the default config.", "Thinking more about it I think the current behavior is the right one.\r\n\r\nProvided parameters should be passed to instantiate a new BuilderConfig.\r\n\r\nWhat's the error you're getting ?", "For example, this works to use default config with name '_all_':\r\n```python\r\ndatasets.load_dataset(\"indonesian-nlp/librivox-indonesia\", split=\"train\")\r\n```\r\nwhile this failed to use default config\r\n```python\r\ndatasets.load_dataset(\"indonesian-nlp/librivox-indonesia\", split=\"train\", data_dir='.')\r\n```\r\nAfter manually specifying it, it works again.\r\n```python\r\ndatasets.load_dataset(\"indonesian-nlp/librivox-indonesia\", \"_all_\", split=\"train\", data_dir='.')\r\n```", "@lhoestq ", "It should work if you explicitly ask for the config you want to override\r\n\r\n```python\r\nload_dataset('/dataset/with/multiple/config', 'name_of_the_default_config', some_field_in_config='some')\r\n```\r\n\r\nAlternatively you can have a BuilderConfig class that when instantiated returns a config with the right default values. In this case this code would instantiate this config with the default values except for the parameter to override:\r\n\r\n```python\r\nload_dataset('/dataset/with/multiple/config', some_field_in_config='some')\r\n```", "@lhoestq Yes. But it doesn't work for me.\r\n\r\nHere's my dataset for example.\r\n```\r\nlass MyDatasetConfig(datasets.BuilderConfig):\r\n def __init__(self, name: str, version: str, kwargs):\r\n self.option1 = kwargs.pop(\"option1\", False)\r\n self.option2 = kwargs.pop(\"option2\", 5)\r\n\r\n super().__init__(\r\n name=name,\r\n version=datasets.Version(version),\r\n kwargs)\r\n\r\n\r\nclass MyDataset(datasets.GeneratorBasedBuilder):\r\n DEFAULT_CONFIG_NAME = \"v1\"\r\n\r\n BUILDER_CONFIGS = [\r\n UnifiedTtsDatasetConfig(\r\n name=\"v1\",\r\n version=\"1.0.0\",\r\n description=\"Initial version of the dataset\"\r\n ),\r\n ]\r\n\r\n def _info(self) -> DatasetInfo:\r\n _ = self.option1\r\n ....\r\n```\r\n\r\nHere it's okay to use `load_dataset('my_dataset.py')` for loading the default config `v1`.\r\n\r\nBut if I want to override the default values in config with `load_dataset('my_dataset.py', option2=3)`, it failed to find my default config `v1.\r\n\r\nUnless I use `load_dataset('my_dataset.py', 'v1', option2=3)`\r\n\r\nSo according to your advice, how can I modify my dataset to be able to override default config without manually specifying it.", "What's the error ? It should try to instantiate `MyDatasetConfig` with `option2=3`", "@lhoestq The error is\r\n```\r\ndef _info(self) -> DatasetInfo:\r\n _ = self.option1 <-\r\n ....\r\nAttributeError: 'BuilderConfig' object has no attribute 'option1'\r\n```\r\nwhich seems to find another unknown config.\r\n\r\nYou can try this line `datasets.load_dataset(\"indonesian-nlp/librivox-indonesia\", split=\"train\", data_dir='.')`, it's a multi-config dataset on HF hub and the error is the same.\r\n\r\nMy insights:\r\nhttps://github.com/huggingface/datasets/blob/12cfc1196e62847e2e8239fbd727a02cbc86ddec/src/datasets/builder.py#L518\r\nif `config_kwargs` is provided here, the if branch is skipped.", "I see, you just have to set this class attribute to your builder class :)\r\n\r\n```python\r\nBUILDER_CONFIG_CLASS = MyDatasetConfig\r\n```", "So what does this attribute do? In most cases it's not used and the [documents for multi-config dataset](https://huggingface.co/docs/datasets/main/en/image_dataset#multiple-configurations) never mentioned that.", "It tells which builder config class to instantiate if additional config parameters are passed to load_dataset", "@lhoestq maybe we can enhance the document to say something about the common attributes of `DatasetBuilder`", "Ah indeed it's missing in the docs, thanks for reporting. I'm opening a PR" ]
https://api.github.com/repos/huggingface/datasets/issues/6129	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6129/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6129/comments	https://api.github.com/repos/huggingface/datasets/issues/6129/events	https://github.com/huggingface/datasets/pull/6129	1,841,563,517	PR_kwDODunzps5Xcmuw	6,129	Release 2.14.4	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "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}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova" }	[]	closed	false	null	[]	null	5	"2023-08-08T15:43:56Z"	"2023-08-08T16:08:22Z"	"2023-08-08T15:49:06Z"	MEMBER	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6129.diff", "html_url": "https://github.com/huggingface/datasets/pull/6129", "merged_at": "2023-08-08T15:49:06Z", "patch_url": "https://github.com/huggingface/datasets/pull/6129.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6129" }	null	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6129/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6129/timeline	null	null	634	true	[ "_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) \| 0.006053 / 0.011353 (-0.005299) \| 0.003532 / 0.011008 (-0.007476) \| 0.081930 / 0.038508 (0.043422) \| 0.059043 / 0.023109 (0.035934) \| 0.322785 / 0.275898 (0.046887) \| 0.378158 / 0.323480 (0.054678) \| 0.004709 / 0.007986 (-0.003277) \| 0.002907 / 0.004328 (-0.001421) \| 0.061516 / 0.004250 (0.057266) \| 0.047209 / 0.037052 (0.010157) \| 0.346885 / 0.258489 (0.088396) \| 0.381011 / 0.293841 (0.087170) \| 0.027491 / 0.128546 (-0.101055) \| 0.008014 / 0.075646 (-0.067632) \| 0.260663 / 0.419271 (-0.158608) \| 0.045427 / 0.043533 (0.001894) \| 0.315277 / 0.255139 (0.060138) \| 0.377902 / 0.283200 (0.094703) \| 0.021371 / 0.141683 (-0.120311) \| 1.416350 / 1.452155 (-0.035804) \| 1.483345 / 1.492716 (-0.009372) \|\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) \| 0.203660 / 0.018006 (0.185654) \| 0.569081 / 0.000490 (0.568591) \| 0.002742 / 0.000200 (0.002542) \| 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) \| 0.023456 / 0.037411 (-0.013955) \| 0.073954 / 0.014526 (0.059428) \| 0.082991 / 0.176557 (-0.093566) \| 0.144781 / 0.737135 (-0.592354) \| 0.083346 / 0.296338 (-0.212992) \|\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) \| 0.391542 / 0.215209 (0.176333) \| 3.909505 / 2.077655 (1.831850) \| 1.862234 / 1.504120 (0.358114) \| 1.676076 / 1.541195 (0.134881) \| 1.727595 / 1.468490 (0.259105) \| 0.501769 / 4.584777 (-4.083008) \| 3.083697 / 3.745712 (-0.662016) \| 2.819751 / 5.269862 (-2.450111) \| 1.867265 / 4.565676 (-2.698411) \| 0.057575 / 0.424275 (-0.366700) \| 0.006478 / 0.007607 (-0.001129) \| 0.466684 / 0.226044 (0.240640) \| 4.657982 / 2.268929 (2.389054) \| 2.347052 / 55.444624 (-53.097573) \| 1.964688 / 6.876477 (-4.911789) \| 2.077821 / 2.142072 (-0.064252) \| 0.590591 / 4.805227 (-4.214636) \| 0.124585 / 6.500664 (-6.376079) \| 0.059468 / 0.075469 (-0.016001) \|\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) \| 1.223484 / 1.841788 (-0.618304) \| 18.104638 / 8.074308 (10.030330) \| 13.755126 / 10.191392 (3.563734) \| 0.143158 / 0.680424 (-0.537266) \| 0.017147 / 0.534201 (-0.517054) \| 0.337427 / 0.579283 (-0.241856) \| 0.352270 / 0.434364 (-0.082094) \| 0.383718 / 0.540337 (-0.156619) \| 0.534973 / 1.386936 (-0.851963) \|\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) \| 0.006039 / 0.011353 (-0.005314) \| 0.003735 / 0.011008 (-0.007274) \| 0.061954 / 0.038508 (0.023446) \| 0.061786 / 0.023109 (0.038677) \| 0.429420 / 0.275898 (0.153522) \| 0.457629 / 0.323480 (0.134149) \| 0.004748 / 0.007986 (-0.003237) \| 0.002843 / 0.004328 (-0.001485) \| 0.061811 / 0.004250 (0.057560) \| 0.048740 / 0.037052 (0.011687) \| 0.430066 / 0.258489 (0.171577) \| 0.465971 / 0.293841 (0.172130) \| 0.027577 / 0.128546 (-0.100969) \| 0.007981 / 0.075646 (-0.067665) \| 0.067580 / 0.419271 (-0.351692) \| 0.042058 / 0.043533 (-0.001475) \| 0.428412 / 0.255139 (0.173273) \| 0.451054 / 0.283200 (0.167855) \| 0.020850 / 0.141683 (-0.120833) \| 1.453907 / 1.452155 (0.001752) \| 1.509914 / 1.492716 (0.017197) \|\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) \| 0.237713 / 0.018006 (0.219707) \| 0.418064 / 0.000490 (0.417575) \| 0.006411 / 0.000200 (0.006211) \| 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) \| 0.024950 / 0.037411 (-0.012462) \| 0.076806 / 0.014526 (0.062281) \| 0.085237 / 0.176557 (-0.091320) \| 0.137940 / 0.737135 (-0.599196) \| 0.086266 / 0.296338 (-0.210072) \|\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) \| 0.418666 / 0.215209 (0.203457) \| 4.160547 / 2.077655 (2.082893) \| 2.135671 / 1.504120 (0.631551) \| 1.964985 / 1.541195 (0.423790) \| 2.009447 / 1.468490 (0.540957) \| 0.501377 / 4.584777 (-4.083400) \| 3.064293 / 3.745712 (-0.681419) \| 2.827153 / 5.269862 (-2.442709) \| 1.854698 / 4.565676 (-2.710978) \| 0.057662 / 0.424275 (-0.366613) \| 0.006829 / 0.007607 (-0.000778) \| 0.496730 / 0.226044 (0.270686) \| 4.964663 / 2.268929 (2.695735) \| 2.583133 / 55.444624 (-52.861491) \| 2.329700 / 6.876477 (-4.546776) \| 2.415521 / 2.142072 (0.273449) \| 0.591973 / 4.805227 (-4.213255) \| 0.126801 / 6.500664 (-6.373863) \| 0.062811 / 0.075469 (-0.012659) \|\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) \| 1.348575 / 1.841788 (-0.493212) \| 18.282861 / 8.074308 (10.208553) \| 13.734056 / 10.191392 (3.542664) \| 0.154987 / 0.680424 (-0.525437) \| 0.016996 / 0.534201 (-0.517205) \| 0.335264 / 0.579283 (-0.244019) \| 0.356907 / 0.434364 (-0.077456) \| 0.399185 / 0.540337 (-0.141152) \| 0.540209 / 1.386936 (-0.846727) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#887bef1217e0f4441d57bf0f4d1e806df12f2c50 \"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) \| 0.006768 / 0.011353 (-0.004585) \| 0.004250 / 0.011008 (-0.006758) \| 0.086780 / 0.038508 (0.048272) \| 0.080872 / 0.023109 (0.057762) \| 0.309281 / 0.275898 (0.033383) \| 0.352293 / 0.323480 (0.028814) \| 0.005604 / 0.007986 (-0.002382) \| 0.003544 / 0.004328 (-0.000784) \| 0.066910 / 0.004250 (0.062659) \| 0.055568 / 0.037052 (0.018516) \| 0.314931 / 0.258489 (0.056442) \| 0.366026 / 0.293841 (0.072185) \| 0.031247 / 0.128546 (-0.097300) \| 0.008860 / 0.075646 (-0.066786) \| 0.293210 / 0.419271 (-0.126061) \| 0.052868 / 0.043533 (0.009335) \| 0.316769 / 0.255139 (0.061630) \| 0.352128 / 0.283200 (0.068929) \| 0.025492 / 0.141683 (-0.116190) \| 1.478379 / 1.452155 (0.026224) \| 1.573652 / 1.492716 (0.080936) \|\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) \| 0.294975 / 0.018006 (0.276968) \| 0.615093 / 0.000490 (0.614603) \| 0.004279 / 0.000200 (0.004079) \| 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) \| 0.031557 / 0.037411 (-0.005855) \| 0.085026 / 0.014526 (0.070500) \| 0.101221 / 0.176557 (-0.075336) \| 0.157432 / 0.737135 (-0.579703) \| 0.102350 / 0.296338 (-0.193988) \|\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) \| 0.384158 / 0.215209 (0.168949) \| 3.826656 / 2.077655 (1.749001) \| 1.873510 / 1.504120 (0.369390) \| 1.721913 / 1.541195 (0.180718) \| 1.848779 / 1.468490 (0.380289) \| 0.485128 / 4.584777 (-4.099649) \| 3.656660 / 3.745712 (-0.089052) \| 3.441964 / 5.269862 (-1.827898) \| 2.150611 / 4.565676 (-2.415066) \| 0.056869 / 0.424275 (-0.367406) \| 0.007382 / 0.007607 (-0.000225) \| 0.458751 / 0.226044 (0.232707) \| 4.585028 / 2.268929 (2.316099) \| 2.439538 / 55.444624 (-53.005086) \| 2.116959 / 6.876477 (-4.759518) \| 2.459220 / 2.142072 (0.317147) \| 0.580907 / 4.805227 (-4.224321) \| 0.134502 / 6.500664 (-6.366162) \| 0.062528 / 0.075469 (-0.012941) \|\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) \| 1.251006 / 1.841788 (-0.590782) \| 20.755849 / 8.074308 (12.681541) \| 14.456950 / 10.191392 (4.265558) \| 0.167074 / 0.680424 (-0.513350) \| 0.018482 / 0.534201 (-0.515719) \| 0.395867 / 0.579283 (-0.183416) \| 0.415620 / 0.434364 (-0.018744) \| 0.462247 / 0.540337 (-0.078090) \| 0.645762 / 1.386936 (-0.741174) \|\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) \| 0.007050 / 0.011353 (-0.004303) \| 0.004421 / 0.011008 (-0.006587) \| 0.065312 / 0.038508 (0.026804) \| 0.089790 / 0.023109 (0.066681) \| 0.366318 / 0.275898 (0.090420) \| 0.403542 / 0.323480 (0.080062) \| 0.005695 / 0.007986 (-0.002290) \| 0.003642 / 0.004328 (-0.000687) \| 0.064540 / 0.004250 (0.060289) \| 0.060933 / 0.037052 (0.023881) \| 0.369004 / 0.258489 (0.110515) \| 0.408056 / 0.293841 (0.114215) \| 0.032124 / 0.128546 (-0.096422) \| 0.008960 / 0.075646 (-0.066686) \| 0.071267 / 0.419271 (-0.348005) \| 0.049745 / 0.043533 (0.006212) \| 0.367203 / 0.255139 (0.112064) \| 0.383009 / 0.283200 (0.099809) \| 0.025330 / 0.141683 (-0.116353) \| 1.518290 / 1.452155 (0.066135) \| 1.581738 / 1.492716 (0.089022) \|\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) \| 0.338281 / 0.018006 (0.320275) \| 0.538195 / 0.000490 (0.537706) \| 0.008498 / 0.000200 (0.008298) \| 0.000121 / 0.000054 (0.000067) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033279 / 0.037411 (-0.004133) \| 0.093233 / 0.014526 (0.078707) \| 0.106019 / 0.176557 (-0.070538) \| 0.161262 / 0.737135 (-0.575874) \| 0.109935 / 0.296338 (-0.186404) \|\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) \| 0.411563 / 0.215209 (0.196354) \| 4.102149 / 2.077655 (2.024495) \| 2.108513 / 1.504120 (0.604393) \| 1.945344 / 1.541195 (0.404150) \| 2.066964 / 1.468490 (0.598474) \| 0.482771 / 4.584777 (-4.102006) \| 3.659160 / 3.745712 (-0.086552) \| 3.420833 / 5.269862 (-1.849029) \| 2.147276 / 4.565676 (-2.418400) \| 0.056957 / 0.424275 (-0.367318) \| 0.007898 / 0.007607 (0.000290) \| 0.482401 / 0.226044 (0.256357) \| 4.821044 / 2.268929 (2.552115) \| 2.567993 / 55.444624 (-52.876631) \| 2.336165 / 6.876477 (-4.540312) \| 2.545066 / 2.142072 (0.402994) \| 0.580888 / 4.805227 (-4.224339) \| 0.134092 / 6.500664 (-6.366572) \| 0.062681 / 0.075469 (-0.012788) \|\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) \| 1.379124 / 1.841788 (-0.462664) \| 21.627949 / 8.074308 (13.553641) \| 15.064818 / 10.191392 (4.873426) \| 0.169707 / 0.680424 (-0.510716) \| 0.018671 / 0.534201 (-0.515530) \| 0.400496 / 0.579283 (-0.178787) \| 0.415542 / 0.434364 (-0.018822) \| 0.484351 / 0.540337 (-0.055986) \| 0.646046 / 1.386936 (-0.740890) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#53d55f33bfac9febb0c355e136f2847e5f3e3b53 \"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) \| 0.007113 / 0.011353 (-0.004240) \| 0.004436 / 0.011008 (-0.006572) \| 0.087422 / 0.038508 (0.048914) \| 0.085996 / 0.023109 (0.062887) \| 0.311772 / 0.275898 (0.035873) \| 0.353281 / 0.323480 (0.029801) \| 0.004562 / 0.007986 (-0.003423) \| 0.003840 / 0.004328 (-0.000488) \| 0.066500 / 0.004250 (0.062250) \| 0.061293 / 0.037052 (0.024241) \| 0.328840 / 0.258489 (0.070351) \| 0.365587 / 0.293841 (0.071746) \| 0.031802 / 0.128546 (-0.096744) \| 0.008881 / 0.075646 (-0.066765) \| 0.289671 / 0.419271 (-0.129601) \| 0.053348 / 0.043533 (0.009816) \| 0.307822 / 0.255139 (0.052683) \| 0.342559 / 0.283200 (0.059360) \| 0.025760 / 0.141683 (-0.115923) \| 1.509944 / 1.452155 (0.057789) \| 1.556634 / 1.492716 (0.063918) \|\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) \| 0.282036 / 0.018006 (0.264029) \| 0.608350 / 0.000490 (0.607860) \| 0.004843 / 0.000200 (0.004643) \| 0.000108 / 0.000054 (0.000054) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029810 / 0.037411 (-0.007601) \| 0.086215 / 0.014526 (0.071689) \| 0.102200 / 0.176557 (-0.074356) \| 0.158051 / 0.737135 (-0.579084) \| 0.103083 / 0.296338 (-0.193255) \|\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) \| 0.392119 / 0.215209 (0.176910) \| 3.895796 / 2.077655 (1.818141) \| 1.921118 / 1.504120 (0.416998) \| 1.754271 / 1.541195 (0.213076) \| 1.880991 / 1.468490 (0.412501) \| 0.481158 / 4.584777 (-4.103618) \| 3.609210 / 3.745712 (-0.136502) \| 3.412018 / 5.269862 (-1.857843) \| 2.131710 / 4.565676 (-2.433967) \| 0.057122 / 0.424275 (-0.367153) \| 0.007444 / 0.007607 (-0.000163) \| 0.468880 / 0.226044 (0.242835) \| 4.682441 / 2.268929 (2.413512) \| 2.505613 / 55.444624 (-52.939012) \| 2.149655 / 6.876477 (-4.726822) \| 2.465904 / 2.142072 (0.323832) \| 0.578877 / 4.805227 (-4.226350) \| 0.133504 / 6.500664 (-6.367160) \| 0.061422 / 0.075469 (-0.014047) \|\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) \| 1.269395 / 1.841788 (-0.572393) \| 21.107558 / 8.074308 (13.033250) \| 15.318502 / 10.191392 (5.127110) \| 0.165273 / 0.680424 (-0.515151) \| 0.018783 / 0.534201 (-0.515418) \| 0.396259 / 0.579283 (-0.183024) \| 0.412907 / 0.434364 (-0.021457) \| 0.465723 / 0.540337 (-0.074615) \| 0.638414 / 1.386936 (-0.748522) \|\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) \| 0.007083 / 0.011353 (-0.004270) \| 0.004216 / 0.011008 (-0.006793) \| 0.065362 / 0.038508 (0.026854) \| 0.095454 / 0.023109 (0.072345) \| 0.364220 / 0.275898 (0.088322) \| 0.417650 / 0.323480 (0.094170) \| 0.006114 / 0.007986 (-0.001872) \| 0.003577 / 0.004328 (-0.000751) \| 0.064830 / 0.004250 (0.060579) \| 0.062535 / 0.037052 (0.025483) \| 0.381844 / 0.258489 (0.123355) \| 0.418996 / 0.293841 (0.125155) \| 0.031386 / 0.128546 (-0.097160) \| 0.008913 / 0.075646 (-0.066733) \| 0.070860 / 0.419271 (-0.348411) \| 0.049132 / 0.043533 (0.005599) \| 0.360406 / 0.255139 (0.105267) \| 0.392407 / 0.283200 (0.109207) \| 0.024611 / 0.141683 (-0.117072) \| 1.509051 / 1.452155 (0.056896) \| 1.570288 / 1.492716 (0.077572) \|\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) \| 0.368611 / 0.018006 (0.350605) \| 0.537587 / 0.000490 (0.537098) \| 0.028056 / 0.000200 (0.027856) \| 0.000317 / 0.000054 (0.000262) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031570 / 0.037411 (-0.005841) \| 0.088985 / 0.014526 (0.074460) \| 0.105268 / 0.176557 (-0.071288) \| 0.156724 / 0.737135 (-0.580412) \| 0.105266 / 0.296338 (-0.191073) \|\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) \| 0.413861 / 0.215209 (0.198652) \| 4.127001 / 2.077655 (2.049347) \| 2.112114 / 1.504120 (0.607994) \| 1.945200 / 1.541195 (0.404005) \| 2.083031 / 1.468490 (0.614540) \| 0.488086 / 4.584777 (-4.096691) \| 3.565584 / 3.745712 (-0.180128) \| 3.380782 / 5.269862 (-1.889079) \| 2.103481 / 4.565676 (-2.462195) \| 0.058203 / 0.424275 (-0.366072) \| 0.007996 / 0.007607 (0.000389) \| 0.487986 / 0.226044 (0.261941) \| 4.871023 / 2.268929 (2.602095) \| 2.584632 / 55.444624 (-52.859992) \| 2.240103 / 6.876477 (-4.636374) \| 2.555165 / 2.142072 (0.413092) \| 0.591950 / 4.805227 (-4.213278) \| 0.134919 / 6.500664 (-6.365745) \| 0.062868 / 0.075469 (-0.012601) \|\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) \| 1.369731 / 1.841788 (-0.472057) \| 21.497888 / 8.074308 (13.423580) \| 14.555054 / 10.191392 (4.363662) \| 0.168768 / 0.680424 (-0.511656) \| 0.018837 / 0.534201 (-0.515364) \| 0.394512 / 0.579283 (-0.184771) \| 0.405459 / 0.434364 (-0.028905) \| 0.475479 / 0.540337 (-0.064858) \| 0.631994 / 1.386936 (-0.754942) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#53d55f33bfac9febb0c355e136f2847e5f3e3b53 \"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) \| 0.009072 / 0.011353 (-0.002280) \| 0.004894 / 0.011008 (-0.006114) \| 0.108790 / 0.038508 (0.070282) \| 0.081783 / 0.023109 (0.058674) \| 0.381963 / 0.275898 (0.106064) \| 0.450700 / 0.323480 (0.127220) \| 0.006961 / 0.007986 (-0.001025) \| 0.004035 / 0.004328 (-0.000293) \| 0.081420 / 0.004250 (0.077169) \| 0.058029 / 0.037052 (0.020976) \| 0.437453 / 0.258489 (0.178964) \| 0.472607 / 0.293841 (0.178766) \| 0.048663 / 0.128546 (-0.079884) \| 0.013512 / 0.075646 (-0.062134) \| 0.406009 / 0.419271 (-0.013262) \| 0.067616 / 0.043533 (0.024084) \| 0.383641 / 0.255139 (0.128502) \| 0.456734 / 0.283200 (0.173534) \| 0.033391 / 0.141683 (-0.108292) \| 1.753529 / 1.452155 (0.301375) \| 1.859831 / 1.492716 (0.367115) \|\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) \| 0.215128 / 0.018006 (0.197122) \| 0.538261 / 0.000490 (0.537771) \| 0.005430 / 0.000200 (0.005230) \| 0.000124 / 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) \| 0.032664 / 0.037411 (-0.004748) \| 0.093465 / 0.014526 (0.078939) \| 0.106637 / 0.176557 (-0.069919) \| 0.173642 / 0.737135 (-0.563494) \| 0.113944 / 0.296338 (-0.182394) \|\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) \| 0.629212 / 0.215209 (0.414003) \| 6.116729 / 2.077655 (4.039075) \| 2.818000 / 1.504120 (1.313880) \| 2.515317 / 1.541195 (0.974122) \| 2.466588 / 1.468490 (0.998098) \| 0.850815 / 4.584777 (-3.733962) \| 5.051292 / 3.745712 (1.305579) \| 4.472138 / 5.269862 (-0.797724) \| 2.968317 / 4.565676 (-1.597360) \| 0.100173 / 0.424275 (-0.324102) \| 0.008407 / 0.007607 (0.000800) \| 0.743972 / 0.226044 (0.517928) \| 7.397619 / 2.268929 (5.128690) \| 3.596681 / 55.444624 (-51.847943) \| 2.854674 / 6.876477 (-4.021803) \| 3.114274 / 2.142072 (0.972201) \| 1.064879 / 4.805227 (-3.740348) \| 0.215981 / 6.500664 (-6.284683) \| 0.078159 / 0.075469 (0.002690) \|\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) \| 1.543291 / 1.841788 (-0.298497) \| 23.244641 / 8.074308 (15.170333) \| 20.784610 / 10.191392 (10.593218) \| 0.222002 / 0.680424 (-0.458422) \| 0.028584 / 0.534201 (-0.505617) \| 0.478563 / 0.579283 (-0.100720) \| 0.556101 / 0.434364 (0.121737) \| 0.547446 / 0.540337 (0.007109) \| 0.764318 / 1.386936 (-0.622618) \|\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) \| 0.008651 / 0.011353 (-0.002702) \| 0.004925 / 0.011008 (-0.006083) \| 0.078995 / 0.038508 (0.040487) \| 0.092878 / 0.023109 (0.069769) \| 0.485615 / 0.275898 (0.209717) \| 0.532157 / 0.323480 (0.208677) \| 0.008228 / 0.007986 (0.000243) \| 0.004777 / 0.004328 (0.000449) \| 0.076892 / 0.004250 (0.072642) \| 0.066905 / 0.037052 (0.029853) \| 0.465497 / 0.258489 (0.207008) \| 0.520153 / 0.293841 (0.226312) \| 0.047357 / 0.128546 (-0.081189) \| 0.016870 / 0.075646 (-0.058776) \| 0.090481 / 0.419271 (-0.328791) \| 0.060774 / 0.043533 (0.017241) \| 0.474368 / 0.255139 (0.219229) \| 0.503981 / 0.283200 (0.220781) \| 0.036025 / 0.141683 (-0.105658) \| 1.769939 / 1.452155 (0.317784) \| 1.851518 / 1.492716 (0.358802) \|\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) \| 0.265947 / 0.018006 (0.247941) \| 0.532317 / 0.000490 (0.531828) \| 0.004997 / 0.000200 (0.004797) \| 0.000130 / 0.000054 (0.000076) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034112 / 0.037411 (-0.003299) \| 0.102290 / 0.014526 (0.087764) \| 0.109989 / 0.176557 (-0.066567) \| 0.182813 / 0.737135 (-0.554323) \| 0.111774 / 0.296338 (-0.184565) \|\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) \| 0.584893 / 0.215209 (0.369684) \| 6.138505 / 2.077655 (4.060850) \| 2.925761 / 1.504120 (1.421641) \| 2.607320 / 1.541195 (1.066125) \| 2.655827 / 1.468490 (1.187337) \| 0.871140 / 4.584777 (-3.713637) \| 5.051171 / 3.745712 (1.305459) \| 4.708008 / 5.269862 (-0.561854) \| 3.027485 / 4.565676 (-1.538191) \| 0.100970 / 0.424275 (-0.323305) \| 0.009640 / 0.007607 (0.002033) \| 0.747818 / 0.226044 (0.521774) \| 7.539930 / 2.268929 (5.271001) \| 3.611693 / 55.444624 (-51.832931) \| 2.924087 / 6.876477 (-3.952390) \| 3.141993 / 2.142072 (0.999920) \| 1.062921 / 4.805227 (-3.742306) \| 0.213185 / 6.500664 (-6.287479) \| 0.077146 / 0.075469 (0.001677) \|\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) \| 1.669182 / 1.841788 (-0.172606) \| 23.810242 / 8.074308 (15.735934) \| 21.220649 / 10.191392 (11.029257) \| 0.212639 / 0.680424 (-0.467785) \| 0.026705 / 0.534201 (-0.507496) \| 0.469231 / 0.579283 (-0.110053) \| 0.551672 / 0.434364 (0.117308) \| 0.575043 / 0.540337 (0.034706) \| 0.767511 / 1.386936 (-0.619425) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#53d55f33bfac9febb0c355e136f2847e5f3e3b53 \"CML watermark\")\n" ]
https://api.github.com/repos/huggingface/datasets/issues/6128	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6128/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6128/comments	https://api.github.com/repos/huggingface/datasets/issues/6128/events	https://github.com/huggingface/datasets/issues/6128	1,841,545,493	I_kwDODunzps5tw8EV	6,128	IndexError: Invalid key: 88 is out of bounds for size 0	{ "avatar_url": "https://avatars.githubusercontent.com/u/38727343?v=4", "events_url": "https://api.github.com/users/TomasAndersonFang/events{/privacy}", "followers_url": "https://api.github.com/users/TomasAndersonFang/followers", "following_url": "https://api.github.com/users/TomasAndersonFang/following{/other_user}", "gists_url": "https://api.github.com/users/TomasAndersonFang/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/TomasAndersonFang", "id": 38727343, "login": "TomasAndersonFang", "node_id": "MDQ6VXNlcjM4NzI3MzQz", "organizations_url": "https://api.github.com/users/TomasAndersonFang/orgs", "received_events_url": "https://api.github.com/users/TomasAndersonFang/received_events", "repos_url": "https://api.github.com/users/TomasAndersonFang/repos", "site_admin": false, "starred_url": "https://api.github.com/users/TomasAndersonFang/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/TomasAndersonFang/subscriptions", "type": "User", "url": "https://api.github.com/users/TomasAndersonFang" }	[]	closed	false	null	[]	null	5	"2023-08-08T15:32:08Z"	"2023-12-26T07:51:57Z"	"2023-08-11T13:35:09Z"	NONE	null	null	null	### Describe the bug This bug generates when I use torch.compile(model) in my code, which seems to raise an error in datasets lib. ### Steps to reproduce the bug I use the following code to fine-tune Falcon on my private dataset. ```python import transformers from transformers import ( AutoModelForCausalLM, AutoTokenizer, AutoConfig, DataCollatorForSeq2Seq, Trainer, Seq2SeqTrainer, HfArgumentParser, Seq2SeqTrainingArguments, BitsAndBytesConfig, ) from peft import ( LoraConfig, get_peft_model, get_peft_model_state_dict, prepare_model_for_int8_training, set_peft_model_state_dict, ) import torch import os import evaluate import functools from datasets import load_dataset import bitsandbytes as bnb import logging import json import copy from typing import Dict, Optional, Sequence from dataclasses import dataclass, field # Lora settings LORA_R = 8 LORA_ALPHA = 16 LORA_DROPOUT= 0.05 LORA_TARGET_MODULES = ["query_key_value"] @dataclass class ModelArguments: model_name_or_path: Optional[str] = field(default="Salesforce/codegen2-7B") @dataclass class DataArguments: data_path: str = field(default=None, metadata={"help": "Path to the training data."}) train_file: str = field(default=None, metadata={"help": "Path to the evaluation data."}) eval_file: str = field(default=None, metadata={"help": "Path to the evaluation data."}) cache_path: str = field(default=None, metadata={"help": "Path to the cache directory."}) num_proc: int = field(default=4, metadata={"help": "Number of processes to use for data preprocessing."}) @dataclass class TrainingArguments(transformers.TrainingArguments): # cache_dir: Optional[str] = field(default=None) optim: str = field(default="adamw_torch") model_max_length: int = field( default=512, metadata={"help": "Maximum sequence length. Sequences will be right padded (and possibly truncated)."}, ) is_lora: bool = field(default=True, metadata={"help": "Whether to use LORA."}) def tokenize(text, tokenizer, max_seq_len=512, add_eos_token=True): result = tokenizer( text, truncation=True, max_length=max_seq_len, padding=False, return_tensors=None, ) if ( result["input_ids"][-1] != tokenizer.eos_token_id and len(result["input_ids"]) < max_seq_len and add_eos_token ): result["input_ids"].append(tokenizer.eos_token_id) result["attention_mask"].append(1) if add_eos_token and len(result["input_ids"]) >= max_seq_len: result["input_ids"][max_seq_len - 1] = tokenizer.eos_token_id result["attention_mask"][max_seq_len - 1] = 1 result["labels"] = result["input_ids"].copy() return result def main(): parser = HfArgumentParser((ModelArguments, DataArguments, TrainingArguments)) model_args, data_args, training_args = parser.parse_args_into_dataclasses() config = AutoConfig.from_pretrained( model_args.model_name_or_path, cache_dir=data_args.cache_path, trust_remote_code=True, ) if training_args.is_lora: model = AutoModelForCausalLM.from_pretrained( model_args.model_name_or_path, cache_dir=data_args.cache_path, torch_dtype=torch.float16, trust_remote_code=True, load_in_8bit=True, quantization_config=BitsAndBytesConfig( load_in_8bit=True, llm_int8_threshold=6.0 ), ) model = prepare_model_for_int8_training(model) config = LoraConfig( r=LORA_R, lora_alpha=LORA_ALPHA, target_modules=LORA_TARGET_MODULES, lora_dropout=LORA_DROPOUT, bias="none", task_type="CAUSAL_LM", ) model = get_peft_model(model, config) else: model = AutoModelForCausalLM.from_pretrained( model_args.model_name_or_path, torch_dtype=torch.float16, cache_dir=data_args.cache_path, trust_remote_code=True, ) model.config.use_cache = False def print_trainable_parameters(model): """ Prints the number of trainable parameters in the model. """ trainable_params = 0 all_param = 0 for _, param in model.named_parameters(): all_param += param.numel() if param.requires_grad: trainable_params += param.numel() print( f"trainable params: {trainable_params} \|\| all params: {all_param} \|\| trainable%: {100 * trainable_params / all_param}" ) print_trainable_parameters(model) tokenizer = AutoTokenizer.from_pretrained( model_args.model_name_or_path, cache_dir=data_args.cache_path, model_max_length=training_args.model_max_length, padding_side="left", use_fast=True, trust_remote_code=True, ) tokenizer.pad_token = tokenizer.eos_token # Load dataset def generate_and_tokenize_prompt(sample): input_text = sample["input"] target_text = sample["output"] + tokenizer.eos_token full_text = input_text + target_text tokenized_full_text = tokenize(full_text, tokenizer, max_seq_len=512) tokenized_input_text = tokenize(input_text, tokenizer, max_seq_len=512) input_len = len(tokenized_input_text["input_ids"]) - 1 # -1 for eos token tokenized_full_text["labels"] = [-100] * input_len + tokenized_full_text["labels"][input_len:] return tokenized_full_text data_files = {} if data_args.train_file is not None: data_files["train"] = data_args.train_file if data_args.eval_file is not None: data_files["eval"] = data_args.eval_file dataset = load_dataset(data_args.data_path, data_files=data_files) train_dataset = dataset["train"] eval_dataset = dataset["eval"] train_dataset = train_dataset.map(generate_and_tokenize_prompt, num_proc=data_args.num_proc) eval_dataset = eval_dataset.map(generate_and_tokenize_prompt, num_proc=data_args.num_proc) data_collator = DataCollatorForSeq2Seq(tokenizer, pad_to_multiple_of=8, return_tensors="pt", padding=True) # Evaluation metrics def compute_metrics(eval_preds, tokenizer): metric = evaluate.load('exact_match') preds, labels = eval_preds # In case the model returns more than the prediction logits if isinstance(preds, tuple): preds = preds[0] decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True, clean_up_tokenization_spaces=False) # Replace -100s in the labels as we can't decode them labels[labels == -100] = tokenizer.pad_token_id decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True, clean_up_tokenization_spaces=False) # Some simple post-processing decoded_preds = [pred.strip() for pred in decoded_preds] decoded_labels = [label.strip() for label in decoded_labels] result = metric.compute(predictions=decoded_preds, references=decoded_labels) return {'exact_match': result['exact_match']} compute_metrics_fn = functools.partial(compute_metrics, tokenizer=tokenizer) model = torch.compile(model) # Training trainer = Trainer( model=model, train_dataset=train_dataset, eval_dataset=eval_dataset, args=training_args, data_collator=data_collator, compute_metrics=compute_metrics_fn, ) trainer.train() trainer.save_state() trainer.save_model(output_dir=training_args.output_dir) tokenizer.save_pretrained(save_directory=training_args.output_dir) if __name__ == "__main__": main() ``` When I didn't use `torch.cpmpile(model)`, my code worked well. But when I added this line to my code, It produced the following error: ``` Traceback (most recent call last): File "falcon_sft.py", line 230, in <module> main() File "falcon_sft.py", line 223, in main trainer.train() File "python3.10/site-packages/transformers/trainer.py", line 1539, in train return inner_training_loop( File "python3.10/site-packages/transformers/trainer.py", line 1787, in _inner_training_loop for step, inputs in enumerate(epoch_iterator): File "python3.10/site-packages/accelerate/data_loader.py", line 384, in __iter__ current_batch = next(dataloader_iter) File "python3.10/site-packages/torch/utils/data/dataloader.py", line 633, in __next__ data = self._next_data() File "python3.10/site-packages/torch/utils/data/dataloader.py", line 677, in _next_data data = self._dataset_fetcher.fetch(index) # may raise StopIteration File "python3.10/site-packages/torch/utils/data/_utils/fetch.py", line 49, in fetch data = self.dataset.__getitems__(possibly_batched_index) File "python3.10/site-packages/datasets/arrow_dataset.py", line 2807, in __getitems__ batch = self.__getitem__(keys) File "python3.10/site-packages/datasets/arrow_dataset.py", line 2803, in __getitem__ return self._getitem(key) File "python3.10/site-packages/datasets/arrow_dataset.py", line 2787, in _getitem pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None) File "python3.10/site-packages/datasets/formatting/formatting.py", line 583, in query_table _check_valid_index_key(key, size) File "python3.10/site-packages/datasets/formatting/formatting.py", line 536, in _check_valid_index_key _check_valid_index_key(int(max(key)), size=size) File "python3.10/site-packages/datasets/formatting/formatting.py", line 526, in _check_valid_index_key raise IndexError(f"Invalid key: {key} is out of bounds for size {size}") IndexError: Invalid key: 88 is out of bounds for size 0 ``` So I'm confused about why this error was generated, and how to fix it. Is this error produced by datasets or `torch.compile`? ### Expected behavior I want to use `torch.compile` in my code. ### Environment info - `datasets` version: 2.14.3 - Platform: Linux-4.18.0-425.19.2.el8_7.x86_64-x86_64-with-glibc2.28 - Python version: 3.10.8 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 2.0.3	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6128/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6128/timeline	null	completed	635	false	[ "Hi @TomasAndersonFang,\r\n\r\nHave you tried instead to use `torch_compile` in `transformers.TrainingArguments`? https://huggingface.co/docs/transformers/v4.31.0/en/main_classes/trainer#transformers.TrainingArguments.torch_compile", "> \r\n\r\nI tried this and got the following error:\r\n\r\n```\r\nTraceback (most recent call last):\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py\", line 324, in _compile\r\n out_code = transform_code_object(code, transform)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/bytecode_transformation.py\", line 445, in transform_code_object\r\n transformations(instructions, code_options)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py\", line 311, in transform\r\n tracer.run()\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py\", line 1726, in run\r\n super().run()\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py\", line 576, in run\r\n and self.step()\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py\", line 540, in step\r\n getattr(self, inst.opname)(inst)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/symbolic_convert.py\", line 1030, in LOAD_ATTR\r\n result = BuiltinVariable(getattr).call_function(\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/variables/builtin.py\", line 566, in call_function\r\n result = handler(tx, args, kwargs)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/variables/builtin.py\", line 931, in call_getattr\r\n return obj.var_getattr(tx, name).add_options(options)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/variables/nn_module.py\", line 124, in var_getattr\r\n subobj = inspect.getattr_static(base, name)\r\n File \"/apps/Arch/software/Python/3.10.8-GCCcore-12.2.0/lib/python3.10/inspect.py\", line 1777, in getattr_static\r\n raise AttributeError(attr)\r\nAttributeError: config\r\n\r\nfrom user code:\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/peft/peft_model.py\", line 909, in forward\r\n if self.base_model.config.model_type == \"mpt\":\r\n\r\nSet torch._dynamo.config.verbose=True for more information\r\n\r\n\r\nYou can suppress this exception and fall back to eager by setting:\r\n torch._dynamo.config.suppress_errors = True\r\n\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/llm-copt/fine-tune/falcon/falcon_sft.py\", line 228, in <module>\r\n main()\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/llm-copt/fine-tune/falcon/falcon_sft.py\", line 221, in main\r\n trainer.train()\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/transformers/trainer.py\", line 1539, in train\r\n return inner_training_loop(\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/transformers/trainer.py\", line 1809, in _inner_training_loop\r\n tr_loss_step = self.training_step(model, inputs)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/transformers/trainer.py\", line 2654, in training_step\r\n loss = self.compute_loss(model, inputs)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/transformers/trainer.py\", line 2679, in compute_loss\r\n outputs = model(inputs)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1501, in _call_impl\r\n return forward_call(args, *kwargs)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py\", line 82, in forward\r\n return self.dynamo_ctx(self._orig_mod.forward)(args, *kwargs)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py\", line 209, in _fn\r\n return fn(args, *kwargs)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/accelerate/utils/operations.py\", line 581, in forward\r\n return model_forward(args, *kwargs)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/accelerate/utils/operations.py\", line 569, in __call__\r\n return convert_to_fp32(self.model_forward(args, *kwargs))\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/amp/autocast_mode.py\", line 14, in decorate_autocast\r\n return func(args, *kwargs)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/eval_frame.py\", line 337, in catch_errors\r\n return callback(frame, cache_size, hooks)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py\", line 404, in _convert_frame\r\n result = inner_convert(frame, cache_size, hooks)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py\", line 104, in _fn\r\n return fn(args, *kwargs)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py\", line 262, in _convert_frame_assert\r\n return _compile(\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/utils.py\", line 163, in time_wrapper\r\n r = func(args, **kwargs)\r\n File \"/cephyr/NOBACKUP/groups/snic2021-23-24/LLM4-CodeOpt/env/lib/python3.10/site-packages/torch/_dynamo/convert_frame.py\", line 394, in _compile\r\n raise InternalTorchDynamoError() from e\r\ntorch._dynamo.exc.InternalTorchDynamoError\r\n```", "Hi @TomasAndersonFang,\r\n\r\nI guess in this case it may be an issue with `transformers` (or `PyTorch`). I would recommend you open an issue on their repo.", "@albertvillanova Thanks for your recommendation. I'll do it", "@TomasAndersonFang were you able to find a solution to this issue? I would highly appreciate any help. \r\n\r\nThanks!" ]
https://api.github.com/repos/huggingface/datasets/issues/6127	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6127/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6127/comments	https://api.github.com/repos/huggingface/datasets/issues/6127/events	https://github.com/huggingface/datasets/pull/6127	1,839,746,721	PR_kwDODunzps5XWdP5	6,127	Fix authentication issues	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "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}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova" }	[]	closed	false	null	[]	null	8	"2023-08-07T15:41:25Z"	"2023-08-08T15:24:59Z"	"2023-08-08T15:16:22Z"	MEMBER	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6127.diff", "html_url": "https://github.com/huggingface/datasets/pull/6127", "merged_at": "2023-08-08T15:16:22Z", "patch_url": "https://github.com/huggingface/datasets/pull/6127.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6127" }	This PR fixes 3 authentication issues: - Fix authentication when passing `token`. - Fix authentication in `Audio.decode_example` and `Image.decode_example`. - Fix authentication to resolve `data_files` in repositories without script. This PR also fixes our CI so that we properly test when passing `token` and we do not use the token stored in `HfFolder`. Fix #6126. ## Details ### Fix authentication when passing `token` See c0a77dc943de68a17f23f141517028c734c78623 The root issue was caused when the `token` was set in an already instantiated `DownloadConfig` and thus not propagated to `self._storage_options`: ```python download_config.token = token ``` As this usage pattern is very common, the fix consists in overriding `DownloadConfig.__setattr__`. This fixes authentication issues in the following functions: - `load_dataset` and `load_dataset_builder` - `Dataset.push_to_hub` and `Dataset.push_to_hub` - `inspect.get_dataset_config_info`, `inspect.get_dataset_infos` and `inspect.get_dataset_split_names` ### Fix authentication in `Audio.decode_example` and `Image.decode_example`. See: 58e62af004b6b8b84dcfd897a4bc71637cfa6c3f The `token` was not set because the `repo_id` was wrongly tried to be parsed from an HTTP URL (`"http://..."`), instead of an HFFileSystem URL (`"hf://"`) ### Fix authentication to resolve `data_files` in repositories without script See: e4684fc1032321abf0d494b0c130ea7c82ebda80 This is fixed by passing `download_config` to the function `create_builder_configs_from_metadata_configs`	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6127/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6127/timeline	null	null	636	true	[ "_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) \| 0.006103 / 0.011353 (-0.005250) \| 0.003588 / 0.011008 (-0.007420) \| 0.080335 / 0.038508 (0.041827) \| 0.059634 / 0.023109 (0.036525) \| 0.356093 / 0.275898 (0.080195) \| 0.407376 / 0.323480 (0.083896) \| 0.005343 / 0.007986 (-0.002643) \| 0.002928 / 0.004328 (-0.001400) \| 0.062580 / 0.004250 (0.058330) \| 0.047544 / 0.037052 (0.010491) \| 0.364305 / 0.258489 (0.105816) \| 0.421463 / 0.293841 (0.127623) \| 0.027249 / 0.128546 (-0.101298) \| 0.008010 / 0.075646 (-0.067636) \| 0.262543 / 0.419271 (-0.156728) \| 0.044978 / 0.043533 (0.001445) \| 0.339344 / 0.255139 (0.084205) \| 0.395288 / 0.283200 (0.112088) \| 0.021425 / 0.141683 (-0.120258) \| 1.439767 / 1.452155 (-0.012387) \| 1.498081 / 1.492716 (0.005365) \|\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) \| 0.196976 / 0.018006 (0.178970) \| 0.435383 / 0.000490 (0.434893) \| 0.004559 / 0.000200 (0.004359) \| 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) \| 0.023653 / 0.037411 (-0.013759) \| 0.072944 / 0.014526 (0.058418) \| 0.083651 / 0.176557 (-0.092906) \| 0.144590 / 0.737135 (-0.592545) \| 0.084844 / 0.296338 (-0.211494) \|\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) \| 0.398752 / 0.215209 (0.183543) \| 3.959539 / 2.077655 (1.881884) \| 1.935277 / 1.504120 (0.431157) \| 1.751994 / 1.541195 (0.210799) \| 1.828386 / 1.468490 (0.359896) \| 0.500492 / 4.584777 (-4.084284) \| 3.086630 / 3.745712 (-0.659082) \| 2.851664 / 5.269862 (-2.418198) \| 1.869792 / 4.565676 (-2.695885) \| 0.058509 / 0.424275 (-0.365766) \| 0.006500 / 0.007607 (-0.001107) \| 0.467468 / 0.226044 (0.241424) \| 4.686168 / 2.268929 (2.417240) \| 2.427632 / 55.444624 (-53.016993) \| 2.193194 / 6.876477 (-4.683283) \| 2.408574 / 2.142072 (0.266501) \| 0.592173 / 4.805227 (-4.213054) \| 0.125381 / 6.500664 (-6.375283) \| 0.060679 / 0.075469 (-0.014790) \|\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) \| 1.236066 / 1.841788 (-0.605722) \| 18.591689 / 8.074308 (10.517381) \| 14.138774 / 10.191392 (3.947382) \| 0.147455 / 0.680424 (-0.532968) \| 0.016921 / 0.534201 (-0.517280) \| 0.328129 / 0.579283 (-0.251154) \| 0.348872 / 0.434364 (-0.085491) \| 0.380311 / 0.540337 (-0.160026) \| 0.532901 / 1.386936 (-0.854035) \|\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 (-0.005438) \| 0.003614 / 0.011008 (-0.007394) \| 0.062857 / 0.038508 (0.024349) \| 0.060633 / 0.023109 (0.037524) \| 0.419684 / 0.275898 (0.143786) \| 0.449025 / 0.323480 (0.125546) \| 0.004595 / 0.007986 (-0.003391) \| 0.002861 / 0.004328 (-0.001467) \| 0.063253 / 0.004250 (0.059003) \| 0.048770 / 0.037052 (0.011718) \| 0.419838 / 0.258489 (0.161349) \| 0.465183 / 0.293841 (0.171342) \| 0.027350 / 0.128546 (-0.101196) \| 0.008065 / 0.075646 (-0.067582) \| 0.068321 / 0.419271 (-0.350950) \| 0.041083 / 0.043533 (-0.002449) \| 0.400831 / 0.255139 (0.145692) \| 0.449286 / 0.283200 (0.166086) \| 0.020472 / 0.141683 (-0.121210) \| 1.437215 / 1.452155 (-0.014940) \| 1.503679 / 1.492716 (0.010963) \|\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) \| 0.230764 / 0.018006 (0.212758) \| 0.420774 / 0.000490 (0.420285) \| 0.004012 / 0.000200 (0.003812) \| 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) \| 0.026009 / 0.037411 (-0.011402) \| 0.077943 / 0.014526 (0.063417) \| 0.087281 / 0.176557 (-0.089276) \| 0.139422 / 0.737135 (-0.597713) \| 0.089090 / 0.296338 (-0.207248) \|\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) \| 0.417298 / 0.215209 (0.202088) \| 4.152303 / 2.077655 (2.074648) \| 2.179996 / 1.504120 (0.675877) \| 2.020619 / 1.541195 (0.479424) \| 2.085241 / 1.468490 (0.616751) \| 0.501111 / 4.584777 (-4.083666) \| 3.079849 / 3.745712 (-0.665863) \| 2.820607 / 5.269862 (-2.449255) \| 1.863988 / 4.565676 (-2.701688) \| 0.057662 / 0.424275 (-0.366613) \| 0.006778 / 0.007607 (-0.000830) \| 0.498661 / 0.226044 (0.272616) \| 4.986503 / 2.268929 (2.717574) \| 2.620676 / 55.444624 (-52.823949) \| 2.297546 / 6.876477 (-4.578931) \| 2.458148 / 2.142072 (0.316075) \| 0.599490 / 4.805227 (-4.205738) \| 0.125102 / 6.500664 (-6.375562) \| 0.061411 / 0.075469 (-0.014059) \|\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) \| 1.323816 / 1.841788 (-0.517971) \| 18.462614 / 8.074308 (10.388306) \| 13.845826 / 10.191392 (3.654434) \| 0.146115 / 0.680424 (-0.534309) \| 0.016862 / 0.534201 (-0.517339) \| 0.335449 / 0.579283 (-0.243834) \| 0.343792 / 0.434364 (-0.090572) \| 0.394068 / 0.540337 (-0.146269) \| 0.536378 / 1.386936 (-0.850558) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#de3f00368c9236e9410821f5fddb95d6069883c1 \"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) \| 0.006825 / 0.011353 (-0.004527) \| 0.004005 / 0.011008 (-0.007003) \| 0.085504 / 0.038508 (0.046996) \| 0.077252 / 0.023109 (0.054143) \| 0.351891 / 0.275898 (0.075993) \| 0.383404 / 0.323480 (0.059924) \| 0.004153 / 0.007986 (-0.003833) \| 0.003344 / 0.004328 (-0.000985) \| 0.064936 / 0.004250 (0.060685) \| 0.057653 / 0.037052 (0.020601) \| 0.368155 / 0.258489 (0.109666) \| 0.406122 / 0.293841 (0.112282) \| 0.032049 / 0.128546 (-0.096497) \| 0.008698 / 0.075646 (-0.066949) \| 0.292394 / 0.419271 (-0.126878) \| 0.053634 / 0.043533 (0.010101) \| 0.358273 / 0.255139 (0.103134) \| 0.378441 / 0.283200 (0.095242) \| 0.026928 / 0.141683 (-0.114755) \| 1.458718 / 1.452155 (0.006563) \| 1.536231 / 1.492716 (0.043515) \|\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) \| 0.213956 / 0.018006 (0.195950) \| 0.458620 / 0.000490 (0.458130) \| 0.002718 / 0.000200 (0.002519) \| 0.000078 / 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) \| 0.027870 / 0.037411 (-0.009541) \| 0.083922 / 0.014526 (0.069396) \| 0.152056 / 0.176557 (-0.024501) \| 0.151584 / 0.737135 (-0.585552) \| 0.095698 / 0.296338 (-0.200641) \|\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) \| 0.407762 / 0.215209 (0.192553) \| 4.074324 / 2.077655 (1.996669) \| 2.089929 / 1.504120 (0.585809) \| 1.920024 / 1.541195 (0.378829) \| 2.013410 / 1.468490 (0.544920) \| 0.486056 / 4.584777 (-4.098721) \| 3.656869 / 3.745712 (-0.088843) \| 3.304008 / 5.269862 (-1.965854) \| 2.074363 / 4.565676 (-2.491313) \| 0.057293 / 0.424275 (-0.366982) \| 0.007240 / 0.007607 (-0.000367) \| 0.482696 / 0.226044 (0.256652) \| 4.833251 / 2.268929 (2.564322) \| 2.570391 / 55.444624 (-52.874233) \| 2.220619 / 6.876477 (-4.655857) \| 2.426316 / 2.142072 (0.284243) \| 0.584811 / 4.805227 (-4.220416) \| 0.134907 / 6.500664 (-6.365757) \| 0.061115 / 0.075469 (-0.014354) \|\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) \| 1.251969 / 1.841788 (-0.589818) \| 19.601611 / 8.074308 (11.527303) \| 14.190217 / 10.191392 (3.998825) \| 0.166296 / 0.680424 (-0.514128) \| 0.018334 / 0.534201 (-0.515867) \| 0.395172 / 0.579283 (-0.184111) \| 0.410440 / 0.434364 (-0.023924) \| 0.462263 / 0.540337 (-0.078074) \| 0.645504 / 1.386936 (-0.741432) \|\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) \| 0.006991 / 0.011353 (-0.004362) \| 0.004084 / 0.011008 (-0.006924) \| 0.065208 / 0.038508 (0.026700) \| 0.077809 / 0.023109 (0.054699) \| 0.386472 / 0.275898 (0.110574) \| 0.418686 / 0.323480 (0.095206) \| 0.005346 / 0.007986 (-0.002640) \| 0.003416 / 0.004328 (-0.000912) \| 0.066209 / 0.004250 (0.061958) \| 0.057517 / 0.037052 (0.020465) \| 0.407684 / 0.258489 (0.149195) \| 0.425438 / 0.293841 (0.131597) \| 0.032166 / 0.128546 (-0.096380) \| 0.008662 / 0.075646 (-0.066985) \| 0.071712 / 0.419271 (-0.347560) \| 0.049764 / 0.043533 (0.006231) \| 0.394882 / 0.255139 (0.139743) \| 0.403589 / 0.283200 (0.120389) \| 0.023688 / 0.141683 (-0.117995) \| 1.468488 / 1.452155 (0.016334) \| 1.533118 / 1.492716 (0.040401) \|\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) \| 0.252949 / 0.018006 (0.234943) \| 0.447355 / 0.000490 (0.446865) \| 0.011721 / 0.000200 (0.011521) \| 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) \| 0.031444 / 0.037411 (-0.005968) \| 0.089390 / 0.014526 (0.074864) \| 0.100103 / 0.176557 (-0.076454) \| 0.153301 / 0.737135 (-0.583835) \| 0.101336 / 0.296338 (-0.195003) \|\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) \| 0.408574 / 0.215209 (0.193365) \| 4.073135 / 2.077655 (1.995480) \| 2.086550 / 1.504120 (0.582430) \| 1.930651 / 1.541195 (0.389457) \| 2.013548 / 1.468490 (0.545058) \| 0.477235 / 4.584777 (-4.107542) \| 3.547545 / 3.745712 (-0.198167) \| 3.321957 / 5.269862 (-1.947905) \| 2.057705 / 4.565676 (-2.507971) \| 0.056730 / 0.424275 (-0.367545) \| 0.007882 / 0.007607 (0.000275) \| 0.487297 / 0.226044 (0.261253) \| 4.874184 / 2.268929 (2.605255) \| 2.631129 / 55.444624 (-52.813496) \| 2.235755 / 6.876477 (-4.640722) \| 2.463329 / 2.142072 (0.321257) \| 0.578308 / 4.805227 (-4.226919) \| 0.132726 / 6.500664 (-6.367938) \| 0.064883 / 0.075469 (-0.010586) \|\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) \| 1.347564 / 1.841788 (-0.494223) \| 20.192973 / 8.074308 (12.118665) \| 14.563553 / 10.191392 (4.372161) \| 0.168244 / 0.680424 (-0.512180) \| 0.018638 / 0.534201 (-0.515563) \| 0.394789 / 0.579283 (-0.184494) \| 0.419677 / 0.434364 (-0.014687) \| 0.480274 / 0.540337 (-0.060063) \| 0.641204 / 1.386936 (-0.745732) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9c7a0d56b60bf700d6a491fa30eaf66500969315 \"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) \| 0.005939 / 0.011353 (-0.005413) \| 0.003457 / 0.011008 (-0.007551) \| 0.079985 / 0.038508 (0.041477) \| 0.056492 / 0.023109 (0.033383) \| 0.312356 / 0.275898 (0.036458) \| 0.354038 / 0.323480 (0.030558) \| 0.004551 / 0.007986 (-0.003435) \| 0.002828 / 0.004328 (-0.001501) \| 0.062369 / 0.004250 (0.058119) \| 0.044712 / 0.037052 (0.007660) \| 0.318244 / 0.258489 (0.059755) \| 0.361977 / 0.293841 (0.068136) \| 0.026460 / 0.128546 (-0.102086) \| 0.007928 / 0.075646 (-0.067719) \| 0.261378 / 0.419271 (-0.157894) \| 0.044209 / 0.043533 (0.000676) \| 0.313931 / 0.255139 (0.058792) \| 0.339553 / 0.283200 (0.056354) \| 0.019776 / 0.141683 (-0.121907) \| 1.443126 / 1.452155 (-0.009029) \| 1.508149 / 1.492716 (0.015432) \|\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) \| 0.183801 / 0.018006 (0.165795) \| 0.427967 / 0.000490 (0.427477) \| 0.002028 / 0.000200 (0.001828) \| 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) \| 0.023697 / 0.037411 (-0.013715) \| 0.072128 / 0.014526 (0.057602) \| 0.083701 / 0.176557 (-0.092855) \| 0.142821 / 0.737135 (-0.594315) \| 0.082276 / 0.296338 (-0.214063) \|\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) \| 0.434427 / 0.215209 (0.219218) \| 4.325962 / 2.077655 (2.248308) \| 2.277115 / 1.504120 (0.772995) \| 2.093736 / 1.541195 (0.552541) \| 2.127984 / 1.468490 (0.659494) \| 0.502336 / 4.584777 (-4.082441) \| 3.023243 / 3.745712 (-0.722469) \| 2.805154 / 5.269862 (-2.464708) \| 1.821273 / 4.565676 (-2.744403) \| 0.057480 / 0.424275 (-0.366795) \| 0.006365 / 0.007607 (-0.001242) \| 0.508258 / 0.226044 (0.282213) \| 5.087950 / 2.268929 (2.819022) \| 2.705029 / 55.444624 (-52.739596) \| 2.378392 / 6.876477 (-4.498085) \| 2.515380 / 2.142072 (0.373307) \| 0.589283 / 4.805227 (-4.215944) \| 0.125719 / 6.500664 (-6.374945) \| 0.061074 / 0.075469 (-0.014395) \|\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) \| 1.221895 / 1.841788 (-0.619893) \| 18.025917 / 8.074308 (9.951609) \| 13.556901 / 10.191392 (3.365509) \| 0.142614 / 0.680424 (-0.537809) \| 0.016731 / 0.534201 (-0.517469) \| 0.328374 / 0.579283 (-0.250910) \| 0.342553 / 0.434364 (-0.091811) \| 0.374502 / 0.540337 (-0.165836) \| 0.534173 / 1.386936 (-0.852763) \|\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) \| 0.005817 / 0.011353 (-0.005536) \| 0.003500 / 0.011008 (-0.007509) \| 0.062240 / 0.038508 (0.023732) \| 0.058128 / 0.023109 (0.035019) \| 0.424014 / 0.275898 (0.148116) \| 0.468453 / 0.323480 (0.144973) \| 0.004641 / 0.007986 (-0.003345) \| 0.002821 / 0.004328 (-0.001508) \| 0.062180 / 0.004250 (0.057930) \| 0.047578 / 0.037052 (0.010526) \| 0.427367 / 0.258489 (0.168878) \| 0.467889 / 0.293841 (0.174048) \| 0.027144 / 0.128546 (-0.101403) \| 0.007969 / 0.075646 (-0.067678) \| 0.067764 / 0.419271 (-0.351508) \| 0.040719 / 0.043533 (-0.002814) \| 0.423663 / 0.255139 (0.168524) \| 0.458556 / 0.283200 (0.175356) \| 0.019196 / 0.141683 (-0.122487) \| 1.471546 / 1.452155 (0.019392) \| 1.547541 / 1.492716 (0.054825) \|\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) \| 0.228777 / 0.018006 (0.210770) \| 0.406663 / 0.000490 (0.406173) \| 0.003688 / 0.000200 (0.003488) \| 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) \| 0.025494 / 0.037411 (-0.011917) \| 0.076339 / 0.014526 (0.061814) \| 0.084233 / 0.176557 (-0.092324) \| 0.136995 / 0.737135 (-0.600140) \| 0.085443 / 0.296338 (-0.210895) \|\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) \| 0.420441 / 0.215209 (0.205232) \| 4.187018 / 2.077655 (2.109363) \| 2.142139 / 1.504120 (0.638019) \| 1.974530 / 1.541195 (0.433335) \| 2.027321 / 1.468490 (0.558831) \| 0.498116 / 4.584777 (-4.086661) \| 2.988514 / 3.745712 (-0.757198) \| 2.782046 / 5.269862 (-2.487816) \| 1.821725 / 4.565676 (-2.743951) \| 0.057711 / 0.424275 (-0.366564) \| 0.006664 / 0.007607 (-0.000944) \| 0.491015 / 0.226044 (0.264971) \| 4.921037 / 2.268929 (2.652108) \| 2.574964 / 55.444624 (-52.869661) \| 2.251703 / 6.876477 (-4.624774) \| 2.361154 / 2.142072 (0.219082) \| 0.593362 / 4.805227 (-4.211865) \| 0.126107 / 6.500664 (-6.374557) \| 0.061840 / 0.075469 (-0.013630) \|\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) \| 1.327459 / 1.841788 (-0.514328) \| 18.062960 / 8.074308 (9.988652) \| 13.669253 / 10.191392 (3.477861) \| 0.130719 / 0.680424 (-0.549705) \| 0.016564 / 0.534201 (-0.517637) \| 0.335821 / 0.579283 (-0.243462) \| 0.341691 / 0.434364 (-0.092673) \| 0.392651 / 0.540337 (-0.147686) \| 0.529650 / 1.386936 (-0.857286) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c65806b0542996e56825ab46a3ce8f9c07ab0df3 \"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) \| 0.009625 / 0.011353 (-0.001728) \| 0.005354 / 0.011008 (-0.005654) \| 0.114350 / 0.038508 (0.075842) \| 0.086637 / 0.023109 (0.063528) \| 0.465381 / 0.275898 (0.189483) \| 0.490411 / 0.323480 (0.166931) \| 0.006575 / 0.007986 (-0.001411) \| 0.004287 / 0.004328 (-0.000041) \| 0.093134 / 0.004250 (0.088884) \| 0.060209 / 0.037052 (0.023156) \| 0.459570 / 0.258489 (0.201080) \| 0.523320 / 0.293841 (0.229479) \| 0.047943 / 0.128546 (-0.080603) \| 0.014764 / 0.075646 (-0.060882) \| 0.383887 / 0.419271 (-0.035384) \| 0.069864 / 0.043533 (0.026331) \| 0.469122 / 0.255139 (0.213983) \| 0.509953 / 0.283200 (0.226753) \| 0.037800 / 0.141683 (-0.103883) \| 1.877589 / 1.452155 (0.425434) \| 2.014913 / 1.492716 (0.522197) \|\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) \| 0.309146 / 0.018006 (0.291140) \| 0.644390 / 0.000490 (0.643900) \| 0.005017 / 0.000200 (0.004817) \| 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) \| 0.032964 / 0.037411 (-0.004447) \| 0.103236 / 0.014526 (0.088711) \| 0.119950 / 0.176557 (-0.056607) \| 0.207674 / 0.737135 (-0.529461) \| 0.117278 / 0.296338 (-0.179060) \|\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) \| 0.605464 / 0.215209 (0.390255) \| 6.027805 / 2.077655 (3.950150) \| 2.719725 / 1.504120 (1.215605) \| 2.262752 / 1.541195 (0.721558) \| 2.330310 / 1.468490 (0.861820) \| 0.862537 / 4.584777 (-3.722240) \| 5.347080 / 3.745712 (1.601368) \| 4.792170 / 5.269862 (-0.477691) \| 3.103694 / 4.565676 (-1.461983) \| 0.103646 / 0.424275 (-0.320629) \| 0.009411 / 0.007607 (0.001804) \| 0.743052 / 0.226044 (0.517008) \| 7.289684 / 2.268929 (5.020755) \| 3.436530 / 55.444624 (-52.008094) \| 2.722440 / 6.876477 (-4.154036) \| 2.952380 / 2.142072 (0.810308) \| 1.047688 / 4.805227 (-3.757539) \| 0.212724 / 6.500664 (-6.287940) \| 0.081473 / 0.075469 (0.006004) \|\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) \| 1.714437 / 1.841788 (-0.127351) \| 24.384330 / 8.074308 (16.310022) \| 22.444162 / 10.191392 (12.252770) \| 0.226264 / 0.680424 (-0.454160) \| 0.030530 / 0.534201 (-0.503671) \| 0.473999 / 0.579283 (-0.105284) \| 0.575005 / 0.434364 (0.140641) \| 0.542789 / 0.540337 (0.002451) \| 0.776079 / 1.386936 (-0.610857) \|\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) \| 0.009366 / 0.011353 (-0.001987) \| 0.005239 / 0.011008 (-0.005769) \| 0.085116 / 0.038508 (0.046608) \| 0.089600 / 0.023109 (0.066491) \| 0.485778 / 0.275898 (0.209880) \| 0.540054 / 0.323480 (0.216574) \| 0.006290 / 0.007986 (-0.001695) \| 0.004054 / 0.004328 (-0.000274) \| 0.083535 / 0.004250 (0.079284) \| 0.067200 / 0.037052 (0.030148) \| 0.519520 / 0.258489 (0.261031) \| 0.544049 / 0.293841 (0.250208) \| 0.054300 / 0.128546 (-0.074246) \| 0.013650 / 0.075646 (-0.061996) \| 0.102515 / 0.419271 (-0.316757) \| 0.063054 / 0.043533 (0.019522) \| 0.491724 / 0.255139 (0.236585) \| 0.547498 / 0.283200 (0.264298) \| 0.039266 / 0.141683 (-0.102416) \| 1.801226 / 1.452155 (0.349071) \| 1.861778 / 1.492716 (0.369061) \|\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) \| 0.313009 / 0.018006 (0.295003) \| 0.587695 / 0.000490 (0.587205) \| 0.004972 / 0.000200 (0.004772) \| 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) \| 0.029230 / 0.037411 (-0.008181) \| 0.091154 / 0.014526 (0.076628) \| 0.110505 / 0.176557 (-0.066052) \| 0.164204 / 0.737135 (-0.572932) \| 0.107812 / 0.296338 (-0.188526) \|\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) \| 0.610535 / 0.215209 (0.395326) \| 6.162517 / 2.077655 (4.084862) \| 2.866718 / 1.504120 (1.362598) \| 2.542412 / 1.541195 (1.001218) \| 2.584136 / 1.468490 (1.115645) \| 0.874319 / 4.584777 (-3.710458) \| 5.257184 / 3.745712 (1.511472) \| 4.705840 / 5.269862 (-0.564022) \| 2.971708 / 4.565676 (-1.593969) \| 0.099026 / 0.424275 (-0.325249) \| 0.009142 / 0.007607 (0.001535) \| 0.728660 / 0.226044 (0.502615) \| 7.560922 / 2.268929 (5.291994) \| 3.439521 / 55.444624 (-52.005103) \| 2.854730 / 6.876477 (-4.021746) \| 3.088951 / 2.142072 (0.946879) \| 0.973621 / 4.805227 (-3.831606) \| 0.209792 / 6.500664 (-6.290872) \| 0.081107 / 0.075469 (0.005638) \|\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) \| 1.716809 / 1.841788 (-0.124978) \| 24.386927 / 8.074308 (16.312619) \| 20.715524 / 10.191392 (10.524131) \| 0.260831 / 0.680424 (-0.419592) \| 0.030701 / 0.534201 (-0.503500) \| 0.490018 / 0.579283 (-0.089265) \| 0.590424 / 0.434364 (0.156060) \| 0.589942 / 0.540337 (0.049604) \| 0.798094 / 1.386936 (-0.588842) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c0a77dc943de68a17f23f141517028c734c78623 \"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) \| 0.006592 / 0.011353 (-0.004761) \| 0.003880 / 0.011008 (-0.007128) \| 0.083761 / 0.038508 (0.045253) \| 0.075966 / 0.023109 (0.052857) \| 0.315291 / 0.275898 (0.039393) \| 0.355920 / 0.323480 (0.032440) \| 0.004972 / 0.007986 (-0.003014) \| 0.003053 / 0.004328 (-0.001275) \| 0.063553 / 0.004250 (0.059302) \| 0.050794 / 0.037052 (0.013742) \| 0.317681 / 0.258489 (0.059192) \| 0.361991 / 0.293841 (0.068150) \| 0.028119 / 0.128546 (-0.100427) \| 0.008203 / 0.075646 (-0.067443) \| 0.271756 / 0.419271 (-0.147516) \| 0.046701 / 0.043533 (0.003168) \| 0.316520 / 0.255139 (0.061381) \| 0.350499 / 0.283200 (0.067300) \| 0.022399 / 0.141683 (-0.119284) \| 1.416017 / 1.452155 (-0.036138) \| 1.503087 / 1.492716 (0.010371) \|\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) \| 0.208250 / 0.018006 (0.190244) \| 0.470345 / 0.000490 (0.469856) \| 0.003687 / 0.000200 (0.003487) \| 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) \| 0.026163 / 0.037411 (-0.011248) \| 0.083315 / 0.014526 (0.068789) \| 0.088541 / 0.176557 (-0.088015) \| 0.150078 / 0.737135 (-0.587057) \| 0.088862 / 0.296338 (-0.207476) \|\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) \| 0.404911 / 0.215209 (0.189702) \| 4.059257 / 2.077655 (1.981602) \| 1.890987 / 1.504120 (0.386867) \| 1.726608 / 1.541195 (0.185413) \| 1.767479 / 1.468490 (0.298989) \| 0.518826 / 4.584777 (-4.065951) \| 3.212145 / 3.745712 (-0.533567) \| 3.029933 / 5.269862 (-2.239929) \| 2.000203 / 4.565676 (-2.565474) \| 0.059631 / 0.424275 (-0.364644) \| 0.006707 / 0.007607 (-0.000900) \| 0.485741 / 0.226044 (0.259697) \| 4.871938 / 2.268929 (2.603010) \| 2.418856 / 55.444624 (-53.025769) \| 2.084847 / 6.876477 (-4.791630) \| 2.207992 / 2.142072 (0.065920) \| 0.614354 / 4.805227 (-4.190873) \| 0.128932 / 6.500664 (-6.371732) \| 0.062342 / 0.075469 (-0.013127) \|\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) \| 1.325792 / 1.841788 (-0.515995) \| 19.718995 / 8.074308 (11.644687) \| 15.278535 / 10.191392 (5.087143) \| 0.146719 / 0.680424 (-0.533705) \| 0.017718 / 0.534201 (-0.516483) \| 0.335709 / 0.579283 (-0.243574) \| 0.378060 / 0.434364 (-0.056304) \| 0.391135 / 0.540337 (-0.149202) \| 0.548045 / 1.386936 (-0.838891) \|\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) \| 0.003742 / 0.011008 (-0.007266) \| 0.064405 / 0.038508 (0.025897) \| 0.077618 / 0.023109 (0.054509) \| 0.365325 / 0.275898 (0.089427) \| 0.408109 / 0.323480 (0.084629) \| 0.004909 / 0.007986 (-0.003076) \| 0.002972 / 0.004328 (-0.001356) \| 0.063933 / 0.004250 (0.059682) \| 0.052916 / 0.037052 (0.015863) \| 0.370891 / 0.258489 (0.112402) \| 0.412134 / 0.293841 (0.118293) \| 0.028171 / 0.128546 (-0.100375) \| 0.008150 / 0.075646 (-0.067497) \| 0.069248 / 0.419271 (-0.350024) \| 0.042353 / 0.043533 (-0.001180) \| 0.368117 / 0.255139 (0.112978) \| 0.397548 / 0.283200 (0.114348) \| 0.022967 / 0.141683 (-0.118716) \| 1.472740 / 1.452155 (0.020586) \| 1.524028 / 1.492716 (0.031311) \|\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) \| 0.256854 / 0.018006 (0.238848) \| 0.471499 / 0.000490 (0.471009) \| 0.009609 / 0.000200 (0.009409) \| 0.000109 / 0.000054 (0.000054) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027978 / 0.037411 (-0.009433) \| 0.086741 / 0.014526 (0.072215) \| 0.091189 / 0.176557 (-0.085368) \| 0.146117 / 0.737135 (-0.591018) \| 0.092358 / 0.296338 (-0.203980) \|\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) \| 0.426356 / 0.215209 (0.211147) \| 4.263782 / 2.077655 (2.186127) \| 2.178198 / 1.504120 (0.674078) \| 2.015405 / 1.541195 (0.474211) \| 2.055966 / 1.468490 (0.587476) \| 0.507531 / 4.584777 (-4.077246) \| 3.175967 / 3.745712 (-0.569745) \| 3.055697 / 5.269862 (-2.214165) \| 1.987663 / 4.565676 (-2.578014) \| 0.058452 / 0.424275 (-0.365823) \| 0.006944 / 0.007607 (-0.000663) \| 0.502534 / 0.226044 (0.276489) \| 5.024693 / 2.268929 (2.755765) \| 2.754971 / 55.444624 (-52.689653) \| 2.470845 / 6.876477 (-4.405632) \| 2.698675 / 2.142072 (0.556602) \| 0.602357 / 4.805227 (-4.202871) \| 0.129490 / 6.500664 (-6.371174) \| 0.065127 / 0.075469 (-0.010342) \|\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) \| 1.398487 / 1.841788 (-0.443301) \| 19.692279 / 8.074308 (11.617971) \| 15.124064 / 10.191392 (4.932672) \| 0.148938 / 0.680424 (-0.531486) \| 0.017418 / 0.534201 (-0.516783) \| 0.340480 / 0.579283 (-0.238803) \| 0.377223 / 0.434364 (-0.057141) \| 0.405303 / 0.540337 (-0.135034) \| 0.548923 / 1.386936 (-0.838013) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#58e62af004b6b8b84dcfd897a4bc71637cfa6c3f \"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) \| 0.006433 / 0.011353 (-0.004920) \| 0.004002 / 0.011008 (-0.007006) \| 0.084130 / 0.038508 (0.045622) \| 0.070628 / 0.023109 (0.047519) \| 0.312372 / 0.275898 (0.036474) \| 0.343993 / 0.323480 (0.020513) \| 0.003936 / 0.007986 (-0.004050) \| 0.003336 / 0.004328 (-0.000993) \| 0.064715 / 0.004250 (0.060465) \| 0.052511 / 0.037052 (0.015458) \| 0.314092 / 0.258489 (0.055603) \| 0.363152 / 0.293841 (0.069311) \| 0.030898 / 0.128546 (-0.097648) \| 0.008396 / 0.075646 (-0.067250) \| 0.288083 / 0.419271 (-0.131188) \| 0.051654 / 0.043533 (0.008122) \| 0.315252 / 0.255139 (0.060113) \| 0.346756 / 0.283200 (0.063556) \| 0.025167 / 0.141683 (-0.116515) \| 1.487265 / 1.452155 (0.035110) \| 1.557528 / 1.492716 (0.064812) \|\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) \| 0.206517 / 0.018006 (0.188510) \| 0.458359 / 0.000490 (0.457869) \| 0.003719 / 0.000200 (0.003519) \| 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) \| 0.029631 / 0.037411 (-0.007780) \| 0.083856 / 0.014526 (0.069330) \| 0.340431 / 0.176557 (0.163875) \| 0.153864 / 0.737135 (-0.583271) \| 0.095951 / 0.296338 (-0.200388) \|\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) \| 0.379182 / 0.215209 (0.163973) \| 3.783396 / 2.077655 (1.705741) \| 1.835932 / 1.504120 (0.331813) \| 1.667563 / 1.541195 (0.126369) \| 1.739309 / 1.468490 (0.270818) \| 0.478957 / 4.584777 (-4.105820) \| 3.521974 / 3.745712 (-0.223738) \| 3.237635 / 5.269862 (-2.032227) \| 2.000300 / 4.565676 (-2.565377) \| 0.056389 / 0.424275 (-0.367887) \| 0.007242 / 0.007607 (-0.000365) \| 0.452642 / 0.226044 (0.226598) \| 4.524339 / 2.268929 (2.255411) \| 2.346210 / 55.444624 (-53.098414) \| 1.957196 / 6.876477 (-4.919281) \| 2.180051 / 2.142072 (0.037979) \| 0.570205 / 4.805227 (-4.235022) \| 0.131346 / 6.500664 (-6.369318) \| 0.059327 / 0.075469 (-0.016142) \|\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) \| 1.244709 / 1.841788 (-0.597079) \| 19.566277 / 8.074308 (11.491969) \| 14.172598 / 10.191392 (3.981206) \| 0.166493 / 0.680424 (-0.513931) \| 0.018281 / 0.534201 (-0.515920) \| 0.391608 / 0.579283 (-0.187675) \| 0.402642 / 0.434364 (-0.031722) \| 0.464974 / 0.540337 (-0.075364) \| 0.637565 / 1.386936 (-0.749371) \|\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) \| 0.006929 / 0.011353 (-0.004424) \| 0.004114 / 0.011008 (-0.006894) \| 0.064589 / 0.038508 (0.026081) \| 0.083334 / 0.023109 (0.060225) \| 0.391280 / 0.275898 (0.115382) \| 0.426157 / 0.323480 (0.102678) \| 0.005336 / 0.007986 (-0.002650) \| 0.003395 / 0.004328 (-0.000934) \| 0.064560 / 0.004250 (0.060310) \| 0.057094 / 0.037052 (0.020042) \| 0.398959 / 0.258489 (0.140470) \| 0.432470 / 0.293841 (0.138629) \| 0.031412 / 0.128546 (-0.097134) \| 0.008670 / 0.075646 (-0.066976) \| 0.071249 / 0.419271 (-0.348022) \| 0.048934 / 0.043533 (0.005401) \| 0.384207 / 0.255139 (0.129068) \| 0.407992 / 0.283200 (0.124792) \| 0.024492 / 0.141683 (-0.117191) \| 1.467788 / 1.452155 (0.015634) \| 1.541011 / 1.492716 (0.048295) \|\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) \| 0.279607 / 0.018006 (0.261600) \| 0.448899 / 0.000490 (0.448410) \| 0.020990 / 0.000200 (0.020790) \| 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) \| 0.030313 / 0.037411 (-0.007099) \| 0.089209 / 0.014526 (0.074684) \| 0.101024 / 0.176557 (-0.075532) \| 0.153468 / 0.737135 (-0.583667) \| 0.103219 / 0.296338 (-0.193120) \|\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) \| 0.429176 / 0.215209 (0.213967) \| 4.302234 / 2.077655 (2.224580) \| 2.291103 / 1.504120 (0.786983) \| 2.126257 / 1.541195 (0.585062) \| 2.207090 / 1.468490 (0.738600) \| 0.484643 / 4.584777 (-4.100134) \| 3.557429 / 3.745712 (-0.188283) \| 3.253804 / 5.269862 (-2.016058) \| 2.026087 / 4.565676 (-2.539589) \| 0.057793 / 0.424275 (-0.366482) \| 0.007761 / 0.007607 (0.000154) \| 0.504819 / 0.226044 (0.278775) \| 5.046868 / 2.268929 (2.777940) \| 2.773149 / 55.444624 (-52.671475) \| 2.398036 / 6.876477 (-4.478440) \| 2.608094 / 2.142072 (0.466021) \| 0.630499 / 4.805227 (-4.174729) \| 0.135496 / 6.500664 (-6.365168) \| 0.061329 / 0.075469 (-0.014140) \|\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) \| 1.327124 / 1.841788 (-0.514664) \| 19.889796 / 8.074308 (11.815488) \| 14.196100 / 10.191392 (4.004708) \| 0.161963 / 0.680424 (-0.518461) \| 0.018529 / 0.534201 (-0.515672) \| 0.392325 / 0.579283 (-0.186958) \| 0.404836 / 0.434364 (-0.029528) \| 0.475898 / 0.540337 (-0.064439) \| 0.633563 / 1.386936 (-0.753373) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e4684fc1032321abf0d494b0c130ea7c82ebda80 \"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) \| 0.006390 / 0.011353 (-0.004963) \| 0.003683 / 0.011008 (-0.007325) \| 0.081274 / 0.038508 (0.042766) \| 0.062193 / 0.023109 (0.039083) \| 0.355360 / 0.275898 (0.079462) \| 0.396471 / 0.323480 (0.072992) \| 0.003569 / 0.007986 (-0.004416) \| 0.003928 / 0.004328 (-0.000400) \| 0.062292 / 0.004250 (0.058041) \| 0.049700 / 0.037052 (0.012648) \| 0.354604 / 0.258489 (0.096115) \| 0.419436 / 0.293841 (0.125595) \| 0.027151 / 0.128546 (-0.101395) \| 0.007954 / 0.075646 (-0.067692) \| 0.262231 / 0.419271 (-0.157041) \| 0.045483 / 0.043533 (0.001950) \| 0.354285 / 0.255139 (0.099146) \| 0.385178 / 0.283200 (0.101978) \| 0.021183 / 0.141683 (-0.120500) \| 1.420785 / 1.452155 (-0.031370) \| 1.531545 / 1.492716 (0.038829) \|\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) \| 0.202298 / 0.018006 (0.184292) \| 0.442172 / 0.000490 (0.441683) \| 0.003565 / 0.000200 (0.003366) \| 0.000074 / 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) \| 0.024229 / 0.037411 (-0.013183) \| 0.074352 / 0.014526 (0.059826) \| 0.087530 / 0.176557 (-0.089026) \| 0.146478 / 0.737135 (-0.590658) \| 0.085145 / 0.296338 (-0.211194) \|\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) \| 0.388395 / 0.215209 (0.173186) \| 3.877623 / 2.077655 (1.799968) \| 1.882444 / 1.504120 (0.378324) \| 1.707871 / 1.541195 (0.166676) \| 1.772132 / 1.468490 (0.303642) \| 0.491937 / 4.584777 (-4.092840) \| 3.057947 / 3.745712 (-0.687765) \| 2.822390 / 5.269862 (-2.447471) \| 1.879719 / 4.565676 (-2.685957) \| 0.056830 / 0.424275 (-0.367445) \| 0.006415 / 0.007607 (-0.001192) \| 0.458945 / 0.226044 (0.232900) \| 4.594502 / 2.268929 (2.325574) \| 2.339677 / 55.444624 (-53.104948) \| 1.983750 / 6.876477 (-4.892727) \| 2.173792 / 2.142072 (0.031719) \| 0.580390 / 4.805227 (-4.224838) \| 0.124568 / 6.500664 (-6.376096) \| 0.061694 / 0.075469 (-0.013775) \|\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) \| 1.265108 / 1.841788 (-0.576680) \| 18.415254 / 8.074308 (10.340946) \| 13.963829 / 10.191392 (3.772437) \| 0.148926 / 0.680424 (-0.531498) \| 0.016919 / 0.534201 (-0.517282) \| 0.331082 / 0.579283 (-0.248201) \| 0.345777 / 0.434364 (-0.088587) \| 0.381123 / 0.540337 (-0.159214) \| 0.543297 / 1.386936 (-0.843639) \|\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) \| 0.003717 / 0.011008 (-0.007291) \| 0.063653 / 0.038508 (0.025144) \| 0.063723 / 0.023109 (0.040613) \| 0.360233 / 0.275898 (0.084335) \| 0.398353 / 0.323480 (0.074873) \| 0.004696 / 0.007986 (-0.003290) \| 0.002876 / 0.004328 (-0.001452) \| 0.063057 / 0.004250 (0.058806) \| 0.050258 / 0.037052 (0.013206) \| 0.362946 / 0.258489 (0.104457) \| 0.403260 / 0.293841 (0.109419) \| 0.027738 / 0.128546 (-0.100809) \| 0.008025 / 0.075646 (-0.067621) \| 0.068781 / 0.419271 (-0.350491) \| 0.042114 / 0.043533 (-0.001419) \| 0.363546 / 0.255139 (0.108407) \| 0.385640 / 0.283200 (0.102440) \| 0.021757 / 0.141683 (-0.119926) \| 1.482364 / 1.452155 (0.030209) \| 1.571859 / 1.492716 (0.079143) \|\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) \| 0.235628 / 0.018006 (0.217622) \| 0.439909 / 0.000490 (0.439419) \| 0.003070 / 0.000200 (0.002870) \| 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) \| 0.027045 / 0.037411 (-0.010366) \| 0.080413 / 0.014526 (0.065887) \| 0.088953 / 0.176557 (-0.087603) \| 0.141907 / 0.737135 (-0.595228) \| 0.090604 / 0.296338 (-0.205735) \|\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) \| 0.423250 / 0.215209 (0.208041) \| 4.216510 / 2.077655 (2.138855) \| 2.162946 / 1.504120 (0.658826) \| 2.014561 / 1.541195 (0.473366) \| 2.086347 / 1.468490 (0.617857) \| 0.496591 / 4.584777 (-4.088186) \| 3.089594 / 3.745712 (-0.656118) \| 2.853640 / 5.269862 (-2.416221) \| 1.878149 / 4.565676 (-2.687527) \| 0.056914 / 0.424275 (-0.367361) \| 0.006762 / 0.007607 (-0.000845) \| 0.493470 / 0.226044 (0.267426) \| 4.929966 / 2.268929 (2.661037) \| 2.640885 / 55.444624 (-52.803739) \| 2.335950 / 6.876477 (-4.540527) \| 2.565866 / 2.142072 (0.423793) \| 0.585433 / 4.805227 (-4.219794) \| 0.124969 / 6.500664 (-6.375695) \| 0.062361 / 0.075469 (-0.013108) \|\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) \| 1.369144 / 1.841788 (-0.472644) \| 19.037582 / 8.074308 (10.963274) \| 14.069141 / 10.191392 (3.877749) \| 0.146469 / 0.680424 (-0.533954) \| 0.016911 / 0.534201 (-0.517290) \| 0.336802 / 0.579283 (-0.242482) \| 0.336411 / 0.434364 (-0.097953) \| 0.392360 / 0.540337 (-0.147977) \| 0.536078 / 1.386936 (-0.850858) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#12cfc1196e62847e2e8239fbd727a02cbc86ddec \"CML watermark\")\n" ]
https://api.github.com/repos/huggingface/datasets/issues/6126	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6126/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6126/comments	https://api.github.com/repos/huggingface/datasets/issues/6126/events	https://github.com/huggingface/datasets/issues/6126	1,839,675,320	I_kwDODunzps5tpze4	6,126	Private datasets do not load when passing token	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "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}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova" }	[ { "color": "d73a4a", "default": true, "description": "Something isn't working", "id": 1935892857, "name": "bug", "node_id": "MDU6TGFiZWwxOTM1ODkyODU3", "url": "https://api.github.com/repos/huggingface/datasets/labels/bug" } ]	closed	false	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "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}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova" }	[ { "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "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}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova" } ]	null	4	"2023-08-07T15:06:47Z"	"2023-08-08T15:16:23Z"	"2023-08-08T15:16:23Z"	MEMBER	null	null	null	### Describe the bug Since the release of `datasets` 2.14, private/gated datasets do not load when passing `token`: they raise `EmptyDatasetError`. This is a non-planned backward incompatible breaking change. Note that private datasets do load if instead `download_config` is passed: ```python from datasets import DownloadConfig, load_dataset ds = load_dataset("albertvillanova/tmp-private", split="train", download_config=DownloadConfig(token="<MY-TOKEN>")) ds ``` gives ``` Dataset({ features: ['text'], num_rows: 4 }) ``` ### Steps to reproduce the bug ```python from datasets import load_dataset ds = load_dataset("albertvillanova/tmp-private", split="train", token="<MY-TOKEN>") ``` gives ``` --------------------------------------------------------------------------- EmptyDatasetError Traceback (most recent call last) [<ipython-input-2-25b48732107a>](https://localhost:8080/#) in <cell line: 3>() 1 from datasets import load_dataset 2 ----> 3 ds = load_dataset("albertvillanova/tmp-private", split="train", token="<MY-TOKEN>") 5 frames [/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://localhost:8080/#) 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, token, use_auth_token, task, streaming, num_proc, storage_options, config_kwargs) 2107 2108 # Create a dataset builder -> 2109 builder_instance = load_dataset_builder( 2110 path=path, 2111 name=name, [/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://localhost:8080/#) in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, token, use_auth_token, storage_options, config_kwargs) 1793 download_config = download_config.copy() if download_config else DownloadConfig() 1794 download_config.storage_options.update(storage_options) -> 1795 dataset_module = dataset_module_factory( 1796 path, 1797 revision=revision, [/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://localhost:8080/#) in dataset_module_factory(path, revision, download_config, download_mode, dynamic_modules_path, data_dir, data_files, download_kwargs) 1484 raise ConnectionError(f"Couldn't reach the Hugging Face Hub for dataset '{path}': {e1}") from None 1485 if isinstance(e1, EmptyDatasetError): -> 1486 raise e1 from None 1487 if isinstance(e1, FileNotFoundError): 1488 raise FileNotFoundError( [/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://localhost:8080/#) in dataset_module_factory(path, revision, download_config, download_mode, dynamic_modules_path, data_dir, data_files, download_kwargs) 1474 download_config=download_config, 1475 download_mode=download_mode, -> 1476 ).get_module() 1477 except ( 1478 Exception [/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://localhost:8080/#) in get_module(self) 1030 sanitize_patterns(self.data_files) 1031 if self.data_files is not None -> 1032 else get_data_patterns(base_path, download_config=self.download_config) 1033 ) 1034 data_files = DataFilesDict.from_patterns( [/usr/local/lib/python3.10/dist-packages/datasets/data_files.py](https://localhost:8080/#) in get_data_patterns(base_path, download_config) 457 return _get_data_files_patterns(resolver) 458 except FileNotFoundError: --> 459 raise EmptyDatasetError(f"The directory at {base_path} doesn't contain any data files") from None 460 461 EmptyDatasetError: The directory at hf://datasets/albertvillanova/tmp-private@79b9e4fe79670a9a050d6ebc385464891915a71d doesn't contain any data files ``` ### Expected behavior The dataset should load. ### Environment info - `datasets` version: 2.14.3 - Platform: Linux-5.15.109+-x86_64-with-glibc2.35 - Python version: 3.10.12 - Huggingface_hub version: 0.16.4 - PyArrow version: 9.0.0 - Pandas version: 1.5.3	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6126/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6126/timeline	null	completed	637	false	[ "Our CI did not catch this issue because with current implementation, stored token in `HfFolder` (which always exists) is used by default.", "I can confirm this and have the same problem (and just went almost crazy because I couldn't figure out the source of this problem because on another computer everything worked well even with `DownloadMode.FORCE_REDOWNLOAD`).", "We are planning to do a patch release today, after the merge of the fix:\r\n- #6127\r\n\r\nIn the meantime, the problem can be circumvented by passing `download_config` instead:\r\n```python\r\nfrom datasets import DownloadConfig, load_dataset\r\n\r\nload_dataset(\"<DATASET-NAME>\", split=\"train\", download_config=DownloadConfig(token=\"<TOKEN>\"))\r\n``` ", "> We are planning to do a patch release today, after the merge of the fix:\r\n> \r\n> * [Fix authentication issues #6127](https://github.com/huggingface/datasets/pull/6127)\r\n> \r\n> \r\n> In the meantime, the problem can be circumvented by passing `download_config` instead:\r\n> \r\n> ```python\r\n> from datasets import DownloadConfig, load_dataset\r\n> \r\n> load_dataset(\"<DATASET-NAME>\", split=\"train\", download_config=DownloadConfig(token=\"<TOKEN>\"))\r\n> ```\r\n\r\nThis did not work for me (there was some other error with the split being an unexpected size 0). Downgrading to 2.13 fixed it...." ]
https://api.github.com/repos/huggingface/datasets/issues/6125	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6125/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6125/comments	https://api.github.com/repos/huggingface/datasets/issues/6125/events	https://github.com/huggingface/datasets/issues/6125	1,837,980,986	I_kwDODunzps5tjV06	6,125	Reinforcement Learning and Robotics are not task categories in HF datasets metadata	{ "avatar_url": "https://avatars.githubusercontent.com/u/35373228?v=4", "events_url": "https://api.github.com/users/StoneT2000/events{/privacy}", "followers_url": "https://api.github.com/users/StoneT2000/followers", "following_url": "https://api.github.com/users/StoneT2000/following{/other_user}", "gists_url": "https://api.github.com/users/StoneT2000/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/StoneT2000", "id": 35373228, "login": "StoneT2000", "node_id": "MDQ6VXNlcjM1MzczMjI4", "organizations_url": "https://api.github.com/users/StoneT2000/orgs", "received_events_url": "https://api.github.com/users/StoneT2000/received_events", "repos_url": "https://api.github.com/users/StoneT2000/repos", "site_admin": false, "starred_url": "https://api.github.com/users/StoneT2000/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/StoneT2000/subscriptions", "type": "User", "url": "https://api.github.com/users/StoneT2000" }	[]	closed	false	null	[]	null	0	"2023-08-05T23:59:42Z"	"2023-08-18T12:28:42Z"	"2023-08-18T12:28:42Z"	NONE	null	null	null	### Describe the bug In https://huggingface.co/models there are task categories for RL and robotics but none in https://huggingface.co/datasets Our lab is currently moving our datasets over to hugging face and would like to be able to add those 2 tags Moreover we see some older datasets that do have that tag, but we can't seem to add it ourselves. ### Steps to reproduce the bug 1. Create a new dataset on Hugging face 2. Try to type reinforcemement-learning or robotics into the tasks categories, it does not allow you to commit ### Expected behavior Expected to be able to add RL and robotics as task categories as some previous datasets have these tags ### Environment info N/A	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6125/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6125/timeline	null	completed	638	false	[]
https://api.github.com/repos/huggingface/datasets/issues/6124	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6124/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6124/comments	https://api.github.com/repos/huggingface/datasets/issues/6124/events	https://github.com/huggingface/datasets/issues/6124	1,837,868,112	I_kwDODunzps5ti6RQ	6,124	Datasets crashing runs due to KeyError	{ "avatar_url": "https://avatars.githubusercontent.com/u/25208228?v=4", "events_url": "https://api.github.com/users/conceptofmind/events{/privacy}", "followers_url": "https://api.github.com/users/conceptofmind/followers", "following_url": "https://api.github.com/users/conceptofmind/following{/other_user}", "gists_url": "https://api.github.com/users/conceptofmind/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/conceptofmind", "id": 25208228, "login": "conceptofmind", "node_id": "MDQ6VXNlcjI1MjA4MjI4", "organizations_url": "https://api.github.com/users/conceptofmind/orgs", "received_events_url": "https://api.github.com/users/conceptofmind/received_events", "repos_url": "https://api.github.com/users/conceptofmind/repos", "site_admin": false, "starred_url": "https://api.github.com/users/conceptofmind/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/conceptofmind/subscriptions", "type": "User", "url": "https://api.github.com/users/conceptofmind" }	[]	closed	false	null	[]	null	7	"2023-08-05T17:48:56Z"	"2023-11-30T16:28:57Z"	"2023-11-30T16:28:57Z"	NONE	null	null	null	### Describe the bug Hi all, I have been running into a pretty persistent issue recently when trying to load datasets. ```python train_dataset = load_dataset( 'llama-2-7b-tokenized', split = 'train' ) ``` I receive a KeyError which crashes the runs. ``` Traceback (most recent call last): main() train_dataset = load_dataset( ^^^^^^^^^^^^^ builder_instance = load_dataset_builder( ^^^^^^^^^^^^^^^^^^^^^ dataset_module = dataset_module_factory( ^^^^^^^^^^^^^^^^^^^^^^^ raise e1 from None ).get_module() ^^^^^^^^^^^^ else get_data_patterns(base_path, download_config=self.download_config) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ return _get_data_files_patterns(resolver) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ data_files = pattern_resolver(pattern) ^^^^^^^^^^^^^^^^^^^^^^^^^ fs, _, _ = get_fs_token_paths(pattern, storage_options=storage_options) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ paths = [f for f in sorted(fs.glob(paths)) if not fs.isdir(f)] ^^^^^^^^^^^^^^ allpaths = self.find(root, maxdepth=depth, withdirs=True, detail=True, kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ for _, dirs, files in self.walk(path, maxdepth, detail=True, kwargs): listing = self.ls(path, detail=True, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ "last_modified": parse_datetime(tree_item["lastCommit"]["date"]), ~~~~~~~~~^^^^^^^^^^^^^^ KeyError: 'lastCommit' ``` Any help would be greatly appreciated. Thank you, Enrico ### Steps to reproduce the bug Load the dataset from the Huggingface hub. ```python train_dataset = load_dataset( 'llama-2-7b-tokenized', split = 'train' ) ``` ### Expected behavior Loads the dataset. ### Environment info datasets-2.14.3 CUDA 11.8 Python 3.11	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6124/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6124/timeline	null	completed	639	false	[ "i once had the same error and I could fix that by pushing a fake or a dummy commit on my hugging face dataset repo", "Hi! We need a reproducer to fix this. Can you provide a link to the dataset (if it's public)?", "> Hi! We need a reproducer to fix this. Can you provide a link to the dataset (if it's public)?\r\n\r\nHi Mario,\r\n\r\nUnfortunately, the dataset in question is currently private until the model is trained and released.\r\n\r\nThis is not happening with one dataset but numerous hosted private datasets.\r\n\r\nI am only loading the dataset and doing nothing else currently. It seems to happen completely sporadically.\r\n\r\nThank you,\r\n\r\nEnrico", "Hi,\r\n\r\nI have the same error in the dataset viewer with my dataset\r\nhttps://huggingface.co/datasets/elsaEU/ELSA10M_track1\r\n\r\nHas anyone solved this issue?\r\n\r\nEdit: After a dummy commit the error changed in ConfigNamesError", "@rs9000 The problem seems to be the (large) number of commits, as explained in https://huggingface.co/docs/hub/repositories-recommendations. This can be fixed by running:\r\n```python\r\nimport huggingface_hub\r\nhuggingface_hub.super_squash_history(repo_id=\"elsaEU/ELSA10M_track1\")\r\n``` \r\n\r\nThe issue stems from `push_to_hub` creating one commit per shard - https://github.com/huggingface/datasets/pull/6269 should fix this issue (will create one commit per 50 uploaded shards by default). The linked PR will be included in the next `datasets` release.\r\n\r\n\r\ncc @lhoestq @severo for visibility", "Thank you @mariosasko it works.", "#6269 has been merged, so I'm closing this issue" ]
https://api.github.com/repos/huggingface/datasets/issues/6123	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6123/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6123/comments	https://api.github.com/repos/huggingface/datasets/issues/6123/events	https://github.com/huggingface/datasets/issues/6123	1,837,789,294	I_kwDODunzps5tinBu	6,123	Inaccurate Bounding Boxes in "wildreceipt" Dataset	{ "avatar_url": "https://avatars.githubusercontent.com/u/50714796?v=4", "events_url": "https://api.github.com/users/HamzaGbada/events{/privacy}", "followers_url": "https://api.github.com/users/HamzaGbada/followers", "following_url": "https://api.github.com/users/HamzaGbada/following{/other_user}", "gists_url": "https://api.github.com/users/HamzaGbada/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/HamzaGbada", "id": 50714796, "login": "HamzaGbada", "node_id": "MDQ6VXNlcjUwNzE0Nzk2", "organizations_url": "https://api.github.com/users/HamzaGbada/orgs", "received_events_url": "https://api.github.com/users/HamzaGbada/received_events", "repos_url": "https://api.github.com/users/HamzaGbada/repos", "site_admin": false, "starred_url": "https://api.github.com/users/HamzaGbada/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/HamzaGbada/subscriptions", "type": "User", "url": "https://api.github.com/users/HamzaGbada" }	[]	closed	false	null	[]	null	1	"2023-08-05T14:34:13Z"	"2023-08-17T14:25:27Z"	"2023-08-17T14:25:26Z"	NONE	null	null	null	### Describe the bug I would like to bring to your attention an issue related to the accuracy of bounding boxes within the "wildreceipt" dataset, which is made available through the Hugging Face API. Specifically, I have identified a discrepancy between the bounding boxes generated by the dataset loading commands, namely `load_dataset("Theivaprakasham/wildreceipt")` and `load_dataset("jinhybr/WildReceipt")`, and the actual labels and corresponding bounding boxes present in the dataset. To illustrate this divergence, I've provided two examples in the form of screenshots. These screenshots highlight the contrasting outcomes between my personal implementation of the dataloader and the implementation offered by Hugging Face: Example 1: ![image](https://github.com/huggingface/datasets/assets/50714796/7a6604d2-899d-4102-a008-1a28c90698f1) ![image](https://github.com/huggingface/datasets/assets/50714796/eba458c7-d3af-4868-a520-8b683aa96f66) ![image](https://github.com/huggingface/datasets/assets/50714796/9f394891-5f5b-46f7-8e52-071b724aedab) Example 2: ![image](https://github.com/huggingface/datasets/assets/50714796/a2b2a8d3-124e-4990-b64a-5133cf4be2fe) ![image](https://github.com/huggingface/datasets/assets/50714796/6ee25642-35aa-40ad-ac1e-899d33be90df) ![image](https://github.com/huggingface/datasets/assets/50714796/5e42ff91-9fc4-4520-8803-0e225656f96c) It's important to note that my dataloader implementation is based on the same dataset files as utilized in the Hugging Face implementation. For your reference, you can access the dataset files through this link: [wildreceipt dataset files](https://download.openmmlab.com/mmocr/data/wildreceipt.tar). This inconsistency in bounding box accuracy warrants investigation and rectification for maintaining the integrity of the "wildreceipt" dataset. Your attention and assistance in addressing this matter would be greatly appreciated. ### Steps to reproduce the bug ```python import matplotlib.pyplot as plt from datasets import load_dataset # Define functions to convert bounding box formats def convert_format1(box): x, y, w, h = box x2, y2 = x + w, y + h return [x, y, x2, y2] def convert_format2(box): x1, y1, x2, y2 = box return [x1, y1, x2, y2] def plot_cropped_image(image, box, title): cropped_image = image.crop(box) plt.imshow(cropped_image) plt.title(title) plt.axis('off') plt.savefig(title+'.png') plt.show() doc_index = 1 word_index = 3 dataset = load_dataset("Theivaprakasham/wildreceipt")['train'] bbox_hugging_face = dataset[doc_index]['bboxes'][word_index] text_unit_face = dataset[doc_index]['words'][word_index] common_box_hugface_1 = convert_format1(bbox_hugging_face) common_box_hugface_2 = convert_format2(bbox_hugging_face) plot_cropped_image(image_hugging, common_box_hugface_1, f'Hugging Face Bouding boxes (x,y,w,h format) \n its associated text unit: {text_unit_face}') plot_cropped_image(image_hugging, common_box_hugface_2, f'Hugging Face Bouding boxes (x1,y1,x2, y2 format) \n its associated text unit: {text_unit_face}') ``` ### Expected behavior The bounding boxes generated by the "wildreceipt" dataset in HuggingFace implementation loading commands should accurately match the actual labels and bounding boxes of the dataset. ### Environment info - Python version: 3.8 - Hugging Face datasets version: 2.14.2 - Dataset file taken from this link: https://download.openmmlab.com/mmocr/data/wildreceipt.tar	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6123/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6123/timeline	null	completed	640	false	[ "Hi! Thanks for the investigation, but we are not the authors of these datasets, so please report this on the Hub instead so that the actual authors can fix it." ]
https://api.github.com/repos/huggingface/datasets/issues/6122	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6122/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6122/comments	https://api.github.com/repos/huggingface/datasets/issues/6122/events	https://github.com/huggingface/datasets/issues/6122	1,837,335,721	I_kwDODunzps5tg4Sp	6,122	Upload README via `push_to_hub`	{ "avatar_url": "https://avatars.githubusercontent.com/u/27999909?v=4", "events_url": "https://api.github.com/users/liyucheng09/events{/privacy}", "followers_url": "https://api.github.com/users/liyucheng09/followers", "following_url": "https://api.github.com/users/liyucheng09/following{/other_user}", "gists_url": "https://api.github.com/users/liyucheng09/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/liyucheng09", "id": 27999909, "login": "liyucheng09", "node_id": "MDQ6VXNlcjI3OTk5OTA5", "organizations_url": "https://api.github.com/users/liyucheng09/orgs", "received_events_url": "https://api.github.com/users/liyucheng09/received_events", "repos_url": "https://api.github.com/users/liyucheng09/repos", "site_admin": false, "starred_url": "https://api.github.com/users/liyucheng09/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/liyucheng09/subscriptions", "type": "User", "url": "https://api.github.com/users/liyucheng09" }	[ { "color": "a2eeef", "default": true, "description": "New feature or request", "id": 1935892871, "name": "enhancement", "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement" } ]	closed	false	null	[]	null	1	"2023-08-04T21:00:27Z"	"2023-08-21T18:18:54Z"	"2023-08-21T18:18:54Z"	NONE	null	null	null	### Feature request `push_to_hub` now allows users to upload datasets programmatically. However, based on the latest doc, we still need to open the dataset page to add readme file manually. However, I do discover snippets to intialize a README for every `push_to_hub`: ``` dataset_card = ( DatasetCard( "---\n" + str(dataset_card_data) + "\n---\n" + f'# Dataset Card for "{repo_id.split("/")[-1]}"\n\n[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)' ) if dataset_card is None else dataset_card ) HfApi(endpoint=config.HF_ENDPOINT).upload_file( path_or_fileobj=str(dataset_card).encode(), path_in_repo="README.md", repo_id=repo_id, token=token, repo_type="dataset", revision=branch, ) ``` So, if we can enable `push_to_hub` to upload a readme file by ourselves instead of using the auto generated ones, it can save ton of time, and will definitely alleviate the current "lack-of-dataset-card" situation. ### Motivation as elabrated above. ### Your contribution I might be able to make a pr.	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6122/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6122/timeline	null	not_planned	641	false	[ "You can use `huggingface_hub`'s [Card API](https://huggingface.co/docs/huggingface_hub/package_reference/cards) to programmatically push a dataset card to the Hub." ]
https://api.github.com/repos/huggingface/datasets/issues/6121	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6121/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6121/comments	https://api.github.com/repos/huggingface/datasets/issues/6121/events	https://github.com/huggingface/datasets/pull/6121	1,836,761,712	PR_kwDODunzps5XMsWd	6,121	Small typo in the code example of create imagefolder dataset	{ "avatar_url": "https://avatars.githubusercontent.com/u/19688994?v=4", "events_url": "https://api.github.com/users/WangXin93/events{/privacy}", "followers_url": "https://api.github.com/users/WangXin93/followers", "following_url": "https://api.github.com/users/WangXin93/following{/other_user}", "gists_url": "https://api.github.com/users/WangXin93/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/WangXin93", "id": 19688994, "login": "WangXin93", "node_id": "MDQ6VXNlcjE5Njg4OTk0", "organizations_url": "https://api.github.com/users/WangXin93/orgs", "received_events_url": "https://api.github.com/users/WangXin93/received_events", "repos_url": "https://api.github.com/users/WangXin93/repos", "site_admin": false, "starred_url": "https://api.github.com/users/WangXin93/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/WangXin93/subscriptions", "type": "User", "url": "https://api.github.com/users/WangXin93" }	[]	closed	false	null	[]	null	1	"2023-08-04T13:36:59Z"	"2023-08-04T13:45:32Z"	"2023-08-04T13:41:43Z"	NONE	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6121.diff", "html_url": "https://github.com/huggingface/datasets/pull/6121", "merged_at": null, "patch_url": "https://github.com/huggingface/datasets/pull/6121.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6121" }	Fix type of code example of load imagefolder dataset	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6121/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6121/timeline	null	null	642	true	[ "Hi,\r\n\r\nI found a small typo in the code example of create imagefolder dataset. It confused me a little when I first saw it.\r\n\r\nBest Regards.\r\n\r\nXin" ]
https://api.github.com/repos/huggingface/datasets/issues/6120	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6120/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6120/comments	https://api.github.com/repos/huggingface/datasets/issues/6120/events	https://github.com/huggingface/datasets/issues/6120	1,836,026,938	I_kwDODunzps5tb4w6	6,120	Lookahead streaming support?	{ "avatar_url": "https://avatars.githubusercontent.com/u/17175484?v=4", "events_url": "https://api.github.com/users/PicoCreator/events{/privacy}", "followers_url": "https://api.github.com/users/PicoCreator/followers", "following_url": "https://api.github.com/users/PicoCreator/following{/other_user}", "gists_url": "https://api.github.com/users/PicoCreator/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/PicoCreator", "id": 17175484, "login": "PicoCreator", "node_id": "MDQ6VXNlcjE3MTc1NDg0", "organizations_url": "https://api.github.com/users/PicoCreator/orgs", "received_events_url": "https://api.github.com/users/PicoCreator/received_events", "repos_url": "https://api.github.com/users/PicoCreator/repos", "site_admin": false, "starred_url": "https://api.github.com/users/PicoCreator/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/PicoCreator/subscriptions", "type": "User", "url": "https://api.github.com/users/PicoCreator" }	[ { "color": "a2eeef", "default": true, "description": "New feature or request", "id": 1935892871, "name": "enhancement", "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement" } ]	open	false	null	[]	null	1	"2023-08-04T04:01:52Z"	"2023-08-17T17:48:42Z"	null	NONE	null	null	null	### Feature request From what I understand, streaming dataset currently pulls the data, and process the data as it is requested. This can introduce significant latency delays when data is loaded into the training process, needing to wait for each segment. While the delays might be dataset specific (or even mapping instruction/tokenizer specific) Is it possible to introduce a `streaming_lookahead` parameter, which is used for predictable workloads (even shuffled dataset with fixed seed). As we can predict in advance what the next few datasamples will be. And fetch them while the current set is being trained. With enough CPU & bandwidth to keep up with the training process, and a sufficiently large lookahead, this will reduce the various latency involved while waiting for the dataset to be ready between batches. ### Motivation Faster streaming performance, while training over extra large TB sized datasets ### Your contribution I currently use HF dataset, with pytorch lightning trainer for RWKV project, and would be able to help test this feature if supported.	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6120/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6120/timeline	null	null	643	false	[ "In which format is your dataset? We could expose the `pre_buffer` flag for Parquet to use PyArrow's background thread pool to speed up loading. " ]
https://api.github.com/repos/huggingface/datasets/issues/6119	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6119/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6119/comments	https://api.github.com/repos/huggingface/datasets/issues/6119/events	https://github.com/huggingface/datasets/pull/6119	1,835,996,350	PR_kwDODunzps5XKI19	6,119	[Docs] Add description of `select_columns` to guide	{ "avatar_url": "https://avatars.githubusercontent.com/u/18213435?v=4", "events_url": "https://api.github.com/users/unifyh/events{/privacy}", "followers_url": "https://api.github.com/users/unifyh/followers", "following_url": "https://api.github.com/users/unifyh/following{/other_user}", "gists_url": "https://api.github.com/users/unifyh/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/unifyh", "id": 18213435, "login": "unifyh", "node_id": "MDQ6VXNlcjE4MjEzNDM1", "organizations_url": "https://api.github.com/users/unifyh/orgs", "received_events_url": "https://api.github.com/users/unifyh/received_events", "repos_url": "https://api.github.com/users/unifyh/repos", "site_admin": false, "starred_url": "https://api.github.com/users/unifyh/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/unifyh/subscriptions", "type": "User", "url": "https://api.github.com/users/unifyh" }	[]	closed	false	null	[]	null	2	"2023-08-04T03:13:30Z"	"2023-08-16T10:13:02Z"	"2023-08-16T10:02:52Z"	CONTRIBUTOR	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6119.diff", "html_url": "https://github.com/huggingface/datasets/pull/6119", "merged_at": "2023-08-16T10:02:52Z", "patch_url": "https://github.com/huggingface/datasets/pull/6119.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6119" }	Closes #6116	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6119/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6119/timeline	null	null	644	true	[ "_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) \| 0.007755 / 0.011353 (-0.003598) \| 0.004618 / 0.011008 (-0.006391) \| 0.098132 / 0.038508 (0.059624) \| 0.086759 / 0.023109 (0.063650) \| 0.374668 / 0.275898 (0.098770) \| 0.417131 / 0.323480 (0.093651) \| 0.004604 / 0.007986 (-0.003382) \| 0.005461 / 0.004328 (0.001132) \| 0.077249 / 0.004250 (0.072999) \| 0.063247 / 0.037052 (0.026195) \| 0.391801 / 0.258489 (0.133312) \| 0.432139 / 0.293841 (0.138298) \| 0.036755 / 0.128546 (-0.091791) \| 0.010011 / 0.075646 (-0.065636) \| 0.346175 / 0.419271 (-0.073097) \| 0.061503 / 0.043533 (0.017971) \| 0.374063 / 0.255139 (0.118924) \| 0.435873 / 0.283200 (0.152673) \| 0.029476 / 0.141683 (-0.112207) \| 1.786945 / 1.452155 (0.334790) \| 1.857190 / 1.492716 (0.364474) \|\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) \| 0.253939 / 0.018006 (0.235933) \| 0.506847 / 0.000490 (0.506358) \| 0.007278 / 0.000200 (0.007079) \| 0.000451 / 0.000054 (0.000397) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032938 / 0.037411 (-0.004474) \| 0.097493 / 0.014526 (0.082967) \| 0.112090 / 0.176557 (-0.064467) \| 0.177986 / 0.737135 (-0.559149) \| 0.112060 / 0.296338 (-0.184278) \|\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) \| 0.481858 / 0.215209 (0.266649) \| 4.814894 / 2.077655 (2.737239) \| 2.496428 / 1.504120 (0.992308) \| 2.309965 / 1.541195 (0.768770) \| 2.393819 / 1.468490 (0.925329) \| 0.564670 / 4.584777 (-4.020107) \| 4.151222 / 3.745712 (0.405510) \| 3.676115 / 5.269862 (-1.593747) \| 2.346165 / 4.565676 (-2.219512) \| 0.066344 / 0.424275 (-0.357931) \| 0.009006 / 0.007607 (0.001399) \| 0.567699 / 0.226044 (0.341654) \| 5.686799 / 2.268929 (3.417871) \| 3.031044 / 55.444624 (-52.413580) \| 2.606259 / 6.876477 (-4.270217) \| 2.864876 / 2.142072 (0.722804) \| 0.681730 / 4.805227 (-4.123498) \| 0.155405 / 6.500664 (-6.345259) \| 0.071492 / 0.075469 (-0.003977) \|\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) \| 1.514446 / 1.841788 (-0.327341) \| 22.624912 / 8.074308 (14.550604) \| 16.754145 / 10.191392 (6.562753) \| 0.193113 / 0.680424 (-0.487311) \| 0.021808 / 0.534201 (-0.512393) \| 0.468241 / 0.579283 (-0.111042) \| 0.499647 / 0.434364 (0.065283) \| 0.539571 / 0.540337 (-0.000766) \| 0.771268 / 1.386936 (-0.615668) \|\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) \| 0.007562 / 0.011353 (-0.003791) \| 0.004548 / 0.011008 (-0.006460) \| 0.075998 / 0.038508 (0.037490) \| 0.081648 / 0.023109 (0.058539) \| 0.462876 / 0.275898 (0.186978) \| 0.499366 / 0.323480 (0.175886) \| 0.005839 / 0.007986 (-0.002147) \| 0.003753 / 0.004328 (-0.000576) \| 0.075918 / 0.004250 (0.071668) \| 0.063233 / 0.037052 (0.026181) \| 0.459024 / 0.258489 (0.200535) \| 0.506388 / 0.293841 (0.212547) \| 0.036179 / 0.128546 (-0.092367) \| 0.009961 / 0.075646 (-0.065685) \| 0.082061 / 0.419271 (-0.337211) \| 0.056469 / 0.043533 (0.012936) \| 0.459567 / 0.255139 (0.204428) \| 0.482578 / 0.283200 (0.199378) \| 0.026363 / 0.141683 (-0.115320) \| 1.742247 / 1.452155 (0.290092) \| 1.807166 / 1.492716 (0.314450) \|\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) \| 0.330526 / 0.018006 (0.312520) \| 0.511674 / 0.000490 (0.511184) \| 0.040969 / 0.000200 (0.040769) \| 0.000176 / 0.000054 (0.000121) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.035492 / 0.037411 (-0.001920) \| 0.104338 / 0.014526 (0.089813) \| 0.116973 / 0.176557 (-0.059583) \| 0.180218 / 0.737135 (-0.556917) \| 0.118801 / 0.296338 (-0.177538) \|\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) \| 0.492196 / 0.215209 (0.276987) \| 4.910271 / 2.077655 (2.832616) \| 2.542562 / 1.504120 (1.038442) \| 2.333516 / 1.541195 (0.792321) \| 2.439682 / 1.468490 (0.971192) \| 0.571966 / 4.584777 (-4.012811) \| 4.089801 / 3.745712 (0.344089) \| 3.732129 / 5.269862 (-1.537733) \| 2.375887 / 4.565676 (-2.189789) \| 0.067376 / 0.424275 (-0.356900) \| 0.008350 / 0.007607 (0.000743) \| 0.583942 / 0.226044 (0.357897) \| 5.840002 / 2.268929 (3.571074) \| 3.062520 / 55.444624 (-52.382104) \| 2.722512 / 6.876477 (-4.153965) \| 2.938307 / 2.142072 (0.796234) \| 0.689459 / 4.805227 (-4.115769) \| 0.155632 / 6.500664 (-6.345032) \| 0.072387 / 0.075469 (-0.003082) \|\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) \| 1.595587 / 1.841788 (-0.246201) \| 23.035478 / 8.074308 (14.961170) \| 16.457675 / 10.191392 (6.266283) \| 0.170819 / 0.680424 (-0.509605) \| 0.022042 / 0.534201 (-0.512159) \| 0.466824 / 0.579283 (-0.112459) \| 0.486350 / 0.434364 (0.051986) \| 0.574330 / 0.540337 (0.033993) \| 0.764913 / 1.386936 (-0.622023) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#664a1cb72ea1e6ef7c47e671e2686ca4a35e8d63 \"CML watermark\")\n" ]
https://api.github.com/repos/huggingface/datasets/issues/6118	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6118/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6118/comments	https://api.github.com/repos/huggingface/datasets/issues/6118/events	https://github.com/huggingface/datasets/issues/6118	1,835,940,417	I_kwDODunzps5tbjpB	6,118	IterableDataset.from_generator() fails with pickle error when provided a generator or iterator	{ "avatar_url": "https://avatars.githubusercontent.com/u/1281051?v=4", "events_url": "https://api.github.com/users/finkga/events{/privacy}", "followers_url": "https://api.github.com/users/finkga/followers", "following_url": "https://api.github.com/users/finkga/following{/other_user}", "gists_url": "https://api.github.com/users/finkga/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/finkga", "id": 1281051, "login": "finkga", "node_id": "MDQ6VXNlcjEyODEwNTE=", "organizations_url": "https://api.github.com/users/finkga/orgs", "received_events_url": "https://api.github.com/users/finkga/received_events", "repos_url": "https://api.github.com/users/finkga/repos", "site_admin": false, "starred_url": "https://api.github.com/users/finkga/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/finkga/subscriptions", "type": "User", "url": "https://api.github.com/users/finkga" }	[]	open	false	null	[]	null	2	"2023-08-04T01:45:04Z"	"2023-12-04T09:28:50Z"	null	NONE	null	null	null	### Describe the bug Description Providing a generator in an instantiation of IterableDataset.from_generator() fails with `TypeError: cannot pickle 'generator' object` when the generator argument is supplied with a generator. Code example ``` def line_generator(files: List[Path]): if isinstance(files, str): files = [Path(files)] for file in files: if isinstance(file, str): file = Path(file) yield from open(file,'r').readlines() ... model_training_files = ['file1.txt', 'file2.txt', 'file3.txt'] train_dataset = IterableDataset.from_generator(generator=line_generator(model_training_files)) ``` Traceback Traceback (most recent call last): File "/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.9/lib/python3.9/contextlib.py", line 135, in __exit__ self.gen.throw(type, value, traceback) File "/Users/d3p692/code/clem_bert/venv/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 691, in _no_cache_fields yield File "/Users/d3p692/code/clem_bert/venv/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 701, in dumps dump(obj, file) File "/Users/d3p692/code/clem_bert/venv/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 676, in dump Pickler(file, recurse=True).dump(obj) File "/Users/d3p692/code/clem_bert/venv/lib/python3.9/site-packages/dill/_dill.py", line 394, in dump StockPickler.dump(self, obj) File "/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.9/lib/python3.9/pickle.py", line 487, in dump self.save(obj) File "/Users/d3p692/code/clem_bert/venv/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 666, in save dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id) File "/Users/d3p692/code/clem_bert/venv/lib/python3.9/site-packages/dill/_dill.py", line 388, in save StockPickler.save(self, obj, save_persistent_id) File "/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.9/lib/python3.9/pickle.py", line 560, in save f(self, obj) # Call unbound method with explicit self File "/Users/d3p692/code/clem_bert/venv/lib/python3.9/site-packages/dill/_dill.py", line 1186, in save_module_dict StockPickler.save_dict(pickler, obj) File "/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.9/lib/python3.9/pickle.py", line 971, in save_dict self._batch_setitems(obj.items()) File "/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.9/lib/python3.9/pickle.py", line 997, in _batch_setitems save(v) File "/Users/d3p692/code/clem_bert/venv/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 666, in save dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id) File "/Users/d3p692/code/clem_bert/venv/lib/python3.9/site-packages/dill/_dill.py", line 388, in save StockPickler.save(self, obj, save_persistent_id) File "/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.9/lib/python3.9/pickle.py", line 578, in save rv = reduce(self.proto) TypeError: cannot pickle 'generator' object ### Steps to reproduce the bug 1. Create a set of text files to iterate over. 2. Create a generator that returns the lines in each file until all files are exhausted. 3. Instantiate the dataset over the generator by instantiating an IterableDataset.from_generator(). 4. Wait for the explosion. ### Expected behavior I would expect that since the function claims to accept a generator that there would be no crash. Instead, I would expect the dataset to return all the lines in the files as queued up in the `line_generator()` function. ### Environment info datasets.__version__ == '2.13.1' Python 3.9.6 Platform: Darwin WE35261 22.5.0 Darwin Kernel Version 22.5.0: Thu Jun 8 22:22:22 PDT 2023; root:xnu-8796.121.3~7/RELEASE_X86_64 x86_64	{ "+1": 1, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 1, "url": "https://api.github.com/repos/huggingface/datasets/issues/6118/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6118/timeline	null	null	645	false	[ "Hi! `IterableDataset.from_generator` expects a generator function, not the object (to be consistent with `Dataset.from_generator`).\r\n\r\nYou can fix the above snippet as follows:\r\n```python\r\ntrain_dataset = IterableDataset.from_generator(line_generator, fn_kwargs={\"files\": model_training_files})\r\n```", "to anyone reaching this issue, the argument is `gen_kwargs`:\r\n```py\r\ntrain_dataset = IterableDataset.from_generator(line_generator, gen_kwargs={\"files\": model_training_files})\r\n```" ]
https://api.github.com/repos/huggingface/datasets/issues/6117	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6117/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6117/comments	https://api.github.com/repos/huggingface/datasets/issues/6117/events	https://github.com/huggingface/datasets/pull/6117	1,835,213,848	PR_kwDODunzps5XHktw	6,117	Set dev version	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "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}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova" }	[]	closed	false	null	[]	null	3	"2023-08-03T14:46:04Z"	"2023-08-03T14:56:59Z"	"2023-08-03T14:46:18Z"	MEMBER	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6117.diff", "html_url": "https://github.com/huggingface/datasets/pull/6117", "merged_at": "2023-08-03T14:46:18Z", "patch_url": "https://github.com/huggingface/datasets/pull/6117.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6117" }	null	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6117/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6117/timeline	null	null	646	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6117). 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) \| 0.012516 / 0.011353 (0.001163) \| 0.004725 / 0.011008 (-0.006283) \| 0.112245 / 0.038508 (0.073736) \| 0.079146 / 0.023109 (0.056037) \| 0.386415 / 0.275898 (0.110517) \| 0.420441 / 0.323480 (0.096961) \| 0.005682 / 0.007986 (-0.002304) \| 0.004169 / 0.004328 (-0.000160) \| 0.077847 / 0.004250 (0.073597) \| 0.055763 / 0.037052 (0.018711) \| 0.385529 / 0.258489 (0.127040) \| 0.422711 / 0.293841 (0.128870) \| 0.047212 / 0.128546 (-0.081334) \| 0.013711 / 0.075646 (-0.061935) \| 0.342856 / 0.419271 (-0.076416) \| 0.066788 / 0.043533 (0.023255) \| 0.380728 / 0.255139 (0.125589) \| 0.416241 / 0.283200 (0.133041) \| 0.034676 / 0.141683 (-0.107007) \| 1.679661 / 1.452155 (0.227506) \| 1.838014 / 1.492716 (0.345297) \|\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) \| 0.219556 / 0.018006 (0.201550) \| 0.524728 / 0.000490 (0.524238) \| 0.005045 / 0.000200 (0.004845) \| 0.000124 / 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) \| 0.025475 / 0.037411 (-0.011936) \| 0.085937 / 0.014526 (0.071412) \| 0.099245 / 0.176557 (-0.077311) \| 0.158995 / 0.737135 (-0.578141) \| 0.101504 / 0.296338 (-0.194835) \|\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) \| 0.582200 / 0.215209 (0.366991) \| 5.794340 / 2.077655 (3.716685) \| 2.473635 / 1.504120 (0.969515) \| 2.168135 / 1.541195 (0.626941) \| 2.215886 / 1.468490 (0.747396) \| 0.855599 / 4.584777 (-3.729178) \| 5.003067 / 3.745712 (1.257354) \| 4.503566 / 5.269862 (-0.766295) \| 2.912248 / 4.565676 (-1.653428) \| 0.103267 / 0.424275 (-0.321008) \| 0.012114 / 0.007607 (0.004507) \| 0.712240 / 0.226044 (0.486196) \| 7.131946 / 2.268929 (4.863017) \| 3.280052 / 55.444624 (-52.164573) \| 2.583472 / 6.876477 (-4.293004) \| 2.820758 / 2.142072 (0.678686) \| 1.132097 / 4.805227 (-3.673131) \| 0.232191 / 6.500664 (-6.268473) \| 0.082966 / 0.075469 (0.007497) \|\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) \| 1.581125 / 1.841788 (-0.260662) \| 22.723878 / 8.074308 (14.649570) \| 19.969347 / 10.191392 (9.777955) \| 0.234365 / 0.680424 (-0.446059) \| 0.030245 / 0.534201 (-0.503956) \| 0.470843 / 0.579283 (-0.108440) \| 0.558069 / 0.434364 (0.123705) \| 0.534878 / 0.540337 (-0.005460) \| 0.801025 / 1.386936 (-0.585911) \|\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) \| 0.008524 / 0.011353 (-0.002829) \| 0.005083 / 0.011008 (-0.005925) \| 0.078054 / 0.038508 (0.039546) \| 0.082025 / 0.023109 (0.058915) \| 0.458027 / 0.275898 (0.182129) \| 0.498232 / 0.323480 (0.174752) \| 0.005938 / 0.007986 (-0.002048) \| 0.003776 / 0.004328 (-0.000553) \| 0.080413 / 0.004250 (0.076163) \| 0.060485 / 0.037052 (0.023433) \| 0.462816 / 0.258489 (0.204327) \| 0.513970 / 0.293841 (0.220129) \| 0.047574 / 0.128546 (-0.080973) \| 0.013424 / 0.075646 (-0.062222) \| 0.087707 / 0.419271 (-0.331565) \| 0.065007 / 0.043533 (0.021474) \| 0.465844 / 0.255139 (0.210705) \| 0.498474 / 0.283200 (0.215274) \| 0.033518 / 0.141683 (-0.108164) \| 1.737507 / 1.452155 (0.285352) \| 1.848291 / 1.492716 (0.355574) \|\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) \| 0.316710 / 0.018006 (0.298703) \| 0.504415 / 0.000490 (0.503925) \| 0.042128 / 0.000200 (0.041928) \| 0.000171 / 0.000054 (0.000117) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032097 / 0.037411 (-0.005314) \| 0.099371 / 0.014526 (0.084845) \| 0.109311 / 0.176557 (-0.067246) \| 0.177373 / 0.737135 (-0.559762) \| 0.110753 / 0.296338 (-0.185585) \|\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) \| 0.688060 / 0.215209 (0.472851) \| 6.255219 / 2.077655 (4.177564) \| 2.696845 / 1.504120 (1.192725) \| 2.395424 / 1.541195 (0.854230) \| 2.414870 / 1.468490 (0.946380) \| 0.865704 / 4.584777 (-3.719073) \| 5.086828 / 3.745712 (1.341116) \| 4.648107 / 5.269862 (-0.621754) \| 3.091119 / 4.565676 (-1.474558) \| 0.101787 / 0.424275 (-0.322489) \| 0.008829 / 0.007607 (0.001222) \| 0.772398 / 0.226044 (0.546354) \| 7.700366 / 2.268929 (5.431438) \| 3.608632 / 55.444624 (-51.835992) \| 2.923309 / 6.876477 (-3.953168) \| 2.952141 / 2.142072 (0.810069) \| 1.093006 / 4.805227 (-3.712221) \| 0.224363 / 6.500664 (-6.276301) \| 0.074927 / 0.075469 (-0.000542) \|\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) \| 1.638414 / 1.841788 (-0.203374) \| 23.486781 / 8.074308 (15.412473) \| 21.129104 / 10.191392 (10.937712) \| 0.259955 / 0.680424 (-0.420469) \| 0.027305 / 0.534201 (-0.506895) \| 0.464448 / 0.579283 (-0.114835) \| 0.553737 / 0.434364 (0.119373) \| 0.571318 / 0.540337 (0.030981) \| 0.772917 / 1.386936 (-0.614019) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3ec5ee9e78b464364796651d995823c7ecb0f951 \"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) \| 0.009093 / 0.011353 (-0.002260) \| 0.005283 / 0.011008 (-0.005725) \| 0.112299 / 0.038508 (0.073791) \| 0.081341 / 0.023109 (0.058232) \| 0.363799 / 0.275898 (0.087901) \| 0.409261 / 0.323480 (0.085781) \| 0.006400 / 0.007986 (-0.001586) \| 0.003965 / 0.004328 (-0.000363) \| 0.074389 / 0.004250 (0.070139) \| 0.060654 / 0.037052 (0.023602) \| 0.391046 / 0.258489 (0.132557) \| 0.430514 / 0.293841 (0.136673) \| 0.054900 / 0.128546 (-0.073646) \| 0.017972 / 0.075646 (-0.057675) \| 0.410875 / 0.419271 (-0.008396) \| 0.067405 / 0.043533 (0.023873) \| 0.371468 / 0.255139 (0.116329) \| 0.435061 / 0.283200 (0.151861) \| 0.038063 / 0.141683 (-0.103620) \| 1.733509 / 1.452155 (0.281354) \| 1.833899 / 1.492716 (0.341182) \|\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) \| 0.243230 / 0.018006 (0.225224) \| 0.605636 / 0.000490 (0.605146) \| 0.004890 / 0.000200 (0.004690) \| 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) \| 0.027624 / 0.037411 (-0.009787) \| 0.084799 / 0.014526 (0.070273) \| 0.104405 / 0.176557 (-0.072152) \| 0.165383 / 0.737135 (-0.571752) \| 0.102083 / 0.296338 (-0.194255) \|\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) \| 0.578334 / 0.215209 (0.363125) \| 5.369520 / 2.077655 (3.291866) \| 2.294174 / 1.504120 (0.790055) \| 2.054195 / 1.541195 (0.513000) \| 2.007304 / 1.468490 (0.538814) \| 0.839283 / 4.584777 (-3.745494) \| 5.262288 / 3.745712 (1.516576) \| 4.363346 / 5.269862 (-0.906516) \| 2.854903 / 4.565676 (-1.710773) \| 0.096975 / 0.424275 (-0.327300) \| 0.008237 / 0.007607 (0.000630) \| 0.646746 / 0.226044 (0.420702) \| 6.250621 / 2.268929 (3.981693) \| 2.900377 / 55.444624 (-52.544247) \| 2.283238 / 6.876477 (-4.593239) \| 2.443785 / 2.142072 (0.301713) \| 0.991719 / 4.805227 (-3.813508) \| 0.189755 / 6.500664 (-6.310909) \| 0.067906 / 0.075469 (-0.007563) \|\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) \| 1.515563 / 1.841788 (-0.326225) \| 21.956499 / 8.074308 (13.882191) \| 19.161750 / 10.191392 (8.970358) \| 0.238199 / 0.680424 (-0.442225) \| 0.026771 / 0.534201 (-0.507430) \| 0.450195 / 0.579283 (-0.129088) \| 0.585168 / 0.434364 (0.150804) \| 0.522945 / 0.540337 (-0.017393) \| 0.776244 / 1.386936 (-0.610693) \|\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) \| 0.005021 / 0.011008 (-0.005988) \| 0.087308 / 0.038508 (0.048800) \| 0.077760 / 0.023109 (0.054650) \| 0.425313 / 0.275898 (0.149415) \| 0.451470 / 0.323480 (0.127990) \| 0.006848 / 0.007986 (-0.001137) \| 0.004812 / 0.004328 (0.000484) \| 0.071198 / 0.004250 (0.066947) \| 0.058325 / 0.037052 (0.021273) \| 0.427411 / 0.258489 (0.168922) \| 0.466069 / 0.293841 (0.172228) \| 0.048686 / 0.128546 (-0.079861) \| 0.011841 / 0.075646 (-0.063806) \| 0.086225 / 0.419271 (-0.333047) \| 0.060500 / 0.043533 (0.016967) \| 0.435580 / 0.255139 (0.180441) \| 0.456919 / 0.283200 (0.173719) \| 0.035094 / 0.141683 (-0.106588) \| 1.582805 / 1.452155 (0.130650) \| 1.717838 / 1.492716 (0.225122) \|\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) \| 0.283967 / 0.018006 (0.265960) \| 0.517496 / 0.000490 (0.517006) \| 0.014747 / 0.000200 (0.014547) \| 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) \| 0.027870 / 0.037411 (-0.009541) \| 0.083835 / 0.014526 (0.069309) \| 0.099157 / 0.176557 (-0.077400) \| 0.173210 / 0.737135 (-0.563925) \| 0.094212 / 0.296338 (-0.202127) \|\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) \| 0.535720 / 0.215209 (0.320511) \| 5.273730 / 2.077655 (3.196075) \| 2.422560 / 1.504120 (0.918440) \| 2.131416 / 1.541195 (0.590222) \| 2.192000 / 1.468490 (0.723510) \| 0.708469 / 4.584777 (-3.876308) \| 4.758092 / 3.745712 (1.012380) \| 3.940729 / 5.269862 (-1.329133) \| 2.553093 / 4.565676 (-2.012583) \| 0.084895 / 0.424275 (-0.339380) \| 0.008730 / 0.007607 (0.001123) \| 0.646975 / 0.226044 (0.420930) \| 6.294811 / 2.268929 (4.025883) \| 3.293964 / 55.444624 (-52.150660) \| 2.568985 / 6.876477 (-4.307492) \| 2.743786 / 2.142072 (0.601713) \| 0.899733 / 4.805227 (-3.905494) \| 0.193484 / 6.500664 (-6.307181) \| 0.070012 / 0.075469 (-0.005457) \|\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) \| 1.502255 / 1.841788 (-0.339532) \| 20.690234 / 8.074308 (12.615926) \| 18.375791 / 10.191392 (8.184399) \| 0.200135 / 0.680424 (-0.480289) \| 0.029434 / 0.534201 (-0.504767) \| 0.477267 / 0.579283 (-0.102016) \| 0.566869 / 0.434364 (0.132505) \| 0.543756 / 0.540337 (0.003418) \| 0.700476 / 1.386936 (-0.686460) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ef17d9fd6c648bb41d43ba301c3de4d7b6f833d8 \"CML watermark\")\n" ]
https://api.github.com/repos/huggingface/datasets/issues/6116	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6116/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6116/comments	https://api.github.com/repos/huggingface/datasets/issues/6116/events	https://github.com/huggingface/datasets/issues/6116	1,835,098,484	I_kwDODunzps5tYWF0	6,116	[Docs] The "Process" how-to guide lacks description of `select_columns` function	{ "avatar_url": "https://avatars.githubusercontent.com/u/18213435?v=4", "events_url": "https://api.github.com/users/unifyh/events{/privacy}", "followers_url": "https://api.github.com/users/unifyh/followers", "following_url": "https://api.github.com/users/unifyh/following{/other_user}", "gists_url": "https://api.github.com/users/unifyh/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/unifyh", "id": 18213435, "login": "unifyh", "node_id": "MDQ6VXNlcjE4MjEzNDM1", "organizations_url": "https://api.github.com/users/unifyh/orgs", "received_events_url": "https://api.github.com/users/unifyh/received_events", "repos_url": "https://api.github.com/users/unifyh/repos", "site_admin": false, "starred_url": "https://api.github.com/users/unifyh/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/unifyh/subscriptions", "type": "User", "url": "https://api.github.com/users/unifyh" }	[ { "color": "a2eeef", "default": true, "description": "New feature or request", "id": 1935892871, "name": "enhancement", "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement" } ]	closed	false	null	[]	null	1	"2023-08-03T13:45:10Z"	"2023-08-16T10:02:53Z"	"2023-08-16T10:02:53Z"	CONTRIBUTOR	null	null	null	### Feature request The [how to process dataset guide](https://huggingface.co/docs/datasets/main/en/process) currently does not mention the [`select_columns`](https://huggingface.co/docs/datasets/main/en/package_reference/main_classes#datasets.Dataset.select_columns) function. It would be nice to include it in the guide. ### Motivation This function is a commonly requested feature (see this [forum thread](https://discuss.huggingface.co/t/how-to-create-a-new-dataset-from-another-dataset-and-select-specific-columns-and-the-data-along-with-the-column/15120) and #5468 #5474). However, it has not been included in the guide since its implementation by PR #5480. Mentioning it in the guide would help future users discover this added feature. ### Your contribution I could submit a PR to add a brief description of the function to said guide.	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6116/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6116/timeline	null	completed	647	false	[ "Great idea, feel free to open a PR! :)" ]
https://api.github.com/repos/huggingface/datasets/issues/6115	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6115/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6115/comments	https://api.github.com/repos/huggingface/datasets/issues/6115/events	https://github.com/huggingface/datasets/pull/6115	1,834,765,485	PR_kwDODunzps5XGChP	6,115	Release: 2.14.3	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "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}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova" }	[]	closed	false	null	[]	null	6	"2023-08-03T10:18:32Z"	"2023-08-03T15:08:02Z"	"2023-08-03T10:24:57Z"	MEMBER	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6115.diff", "html_url": "https://github.com/huggingface/datasets/pull/6115", "merged_at": "2023-08-03T10:24:57Z", "patch_url": "https://github.com/huggingface/datasets/pull/6115.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6115" }	null	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6115/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6115/timeline	null	null	648	true	[ "<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) \| 0.007578 / 0.011353 (-0.003775) \| 0.004271 / 0.011008 (-0.006738) \| 0.086607 / 0.038508 (0.048098) \| 0.063209 / 0.023109 (0.040099) \| 0.351724 / 0.275898 (0.075826) \| 0.399261 / 0.323480 (0.075781) \| 0.004767 / 0.007986 (-0.003219) \| 0.003487 / 0.004328 (-0.000842) \| 0.071483 / 0.004250 (0.067233) \| 0.051281 / 0.037052 (0.014229) \| 0.387726 / 0.258489 (0.129237) \| 0.408446 / 0.293841 (0.114605) \| 0.041189 / 0.128546 (-0.087357) \| 0.012446 / 0.075646 (-0.063200) \| 0.331147 / 0.419271 (-0.088124) \| 0.056721 / 0.043533 (0.013188) \| 0.361306 / 0.255139 (0.106167) \| 0.409651 / 0.283200 (0.126451) \| 0.035485 / 0.141683 (-0.106198) \| 1.461391 / 1.452155 (0.009236) \| 1.554820 / 1.492716 (0.062104) \|\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) \| 0.237119 / 0.018006 (0.219113) \| 0.518731 / 0.000490 (0.518241) \| 0.004192 / 0.000200 (0.003992) \| 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) \| 0.024912 / 0.037411 (-0.012499) \| 0.089420 / 0.014526 (0.074894) \| 0.091209 / 0.176557 (-0.085347) \| 0.152580 / 0.737135 (-0.584555) \| 0.089660 / 0.296338 (-0.206678) \|\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) \| 0.515223 / 0.215209 (0.300014) \| 5.328359 / 2.077655 (3.250705) \| 1.974326 / 1.504120 (0.470206) \| 1.665216 / 1.541195 (0.124021) \| 1.736040 / 1.468490 (0.267550) \| 0.734746 / 4.584777 (-3.850031) \| 4.186613 / 3.745712 (0.440901) \| 3.535760 / 5.269862 (-1.734102) \| 2.333247 / 4.565676 (-2.232429) \| 0.071845 / 0.424275 (-0.352430) \| 0.006147 / 0.007607 (-0.001460) \| 0.546649 / 0.226044 (0.320605) \| 5.452281 / 2.268929 (3.183353) \| 2.512984 / 55.444624 (-52.931640) \| 2.104210 / 6.876477 (-4.772267) \| 2.409251 / 2.142072 (0.267178) \| 0.822797 / 4.805227 (-3.982430) \| 0.166648 / 6.500664 (-6.334016) \| 0.056350 / 0.075469 (-0.019119) \|\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) \| 1.397798 / 1.841788 (-0.443989) \| 20.549399 / 8.074308 (12.475091) \| 19.118168 / 10.191392 (8.926776) \| 0.216361 / 0.680424 (-0.464063) \| 0.027064 / 0.534201 (-0.507136) \| 0.410762 / 0.579283 (-0.168521) \| 0.559225 / 0.434364 (0.124861) \| 0.468028 / 0.540337 (-0.072309) \| 0.691520 / 1.386936 (-0.695416) \|\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) \| 0.003879 / 0.011008 (-0.007130) \| 0.058723 / 0.038508 (0.020215) \| 0.057202 / 0.023109 (0.034092) \| 0.344397 / 0.275898 (0.068499) \| 0.360388 / 0.323480 (0.036908) \| 0.005502 / 0.007986 (-0.002483) \| 0.004101 / 0.004328 (-0.000227) \| 0.058168 / 0.004250 (0.053917) \| 0.059112 / 0.037052 (0.022060) \| 0.362206 / 0.258489 (0.103717) \| 0.386444 / 0.293841 (0.092603) \| 0.036613 / 0.128546 (-0.091934) \| 0.010482 / 0.075646 (-0.065165) \| 0.065850 / 0.419271 (-0.353421) \| 0.046528 / 0.043533 (0.002995) \| 0.349568 / 0.255139 (0.094429) \| 0.360181 / 0.283200 (0.076981) \| 0.029030 / 0.141683 (-0.112653) \| 1.314569 / 1.452155 (-0.137586) \| 1.422393 / 1.492716 (-0.070324) \|\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) \| 0.281554 / 0.018006 (0.263548) \| 0.608018 / 0.000490 (0.607528) \| 0.004568 / 0.000200 (0.004368) \| 0.000182 / 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) \| 0.023515 / 0.037411 (-0.013896) \| 0.072994 / 0.014526 (0.058468) \| 0.080688 / 0.176557 (-0.095868) \| 0.125904 / 0.737135 (-0.611232) \| 0.085457 / 0.296338 (-0.210882) \|\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) \| 0.471530 / 0.215209 (0.256321) \| 4.796197 / 2.077655 (2.718542) \| 2.189181 / 1.504120 (0.685061) \| 1.886649 / 1.541195 (0.345454) \| 1.871067 / 1.468490 (0.402577) \| 0.661043 / 4.584777 (-3.923734) \| 4.344027 / 3.745712 (0.598315) \| 3.656967 / 5.269862 (-1.612895) \| 2.286033 / 4.565676 (-2.279644) \| 0.079146 / 0.424275 (-0.345129) \| 0.006840 / 0.007607 (-0.000767) \| 0.588750 / 0.226044 (0.362706) \| 6.301286 / 2.268929 (4.032357) \| 3.074702 / 55.444624 (-52.369923) \| 2.398739 / 6.876477 (-4.477738) \| 2.555057 / 2.142072 (0.412985) \| 0.874189 / 4.805227 (-3.931038) \| 0.191423 / 6.500664 (-6.309241) \| 0.061227 / 0.075469 (-0.014242) \|\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) \| 1.472763 / 1.841788 (-0.369024) \| 19.441304 / 8.074308 (11.366996) \| 15.974276 / 10.191392 (5.782884) \| 0.172503 / 0.680424 (-0.507921) \| 0.027016 / 0.534201 (-0.507185) \| 0.356085 / 0.579283 (-0.223198) \| 0.473251 / 0.434364 (0.038887) \| 0.427949 / 0.540337 (-0.112388) \| 0.588924 / 1.386936 (-0.798013) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0973da6e60ac7c1d24229ba6aa6881747b21858a \"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) \| 0.006166 / 0.011353 (-0.005187) \| 0.003558 / 0.011008 (-0.007450) \| 0.080576 / 0.038508 (0.042068) \| 0.066542 / 0.023109 (0.043432) \| 0.323997 / 0.275898 (0.048099) \| 0.369828 / 0.323480 (0.046348) \| 0.004896 / 0.007986 (-0.003090) \| 0.002909 / 0.004328 (-0.001419) \| 0.062553 / 0.004250 (0.058302) \| 0.049795 / 0.037052 (0.012742) \| 0.321369 / 0.258489 (0.062880) \| 0.422860 / 0.293841 (0.129019) \| 0.027394 / 0.128546 (-0.101152) \| 0.007954 / 0.075646 (-0.067693) \| 0.264122 / 0.419271 (-0.155149) \| 0.044881 / 0.043533 (0.001349) \| 0.316702 / 0.255139 (0.061563) \| 0.374718 / 0.283200 (0.091518) \| 0.021728 / 0.141683 (-0.119955) \| 1.394456 / 1.452155 (-0.057699) \| 1.474936 / 1.492716 (-0.017780) \|\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) \| 0.191902 / 0.018006 (0.173896) \| 0.430468 / 0.000490 (0.429979) \| 0.003790 / 0.000200 (0.003590) \| 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) \| 0.024974 / 0.037411 (-0.012438) \| 0.073053 / 0.014526 (0.058527) \| 0.083801 / 0.176557 (-0.092756) \| 0.143457 / 0.737135 (-0.593678) \| 0.085099 / 0.296338 (-0.211240) \|\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) \| 0.428411 / 0.215209 (0.213202) \| 4.278077 / 2.077655 (2.200422) \| 2.230039 / 1.504120 (0.725919) \| 2.057191 / 1.541195 (0.515996) \| 2.120109 / 1.468490 (0.651619) \| 0.495242 / 4.584777 (-4.089535) \| 3.031299 / 3.745712 (-0.714413) \| 2.802685 / 5.269862 (-2.467176) \| 1.839828 / 4.565676 (-2.725849) \| 0.056875 / 0.424275 (-0.367401) \| 0.006446 / 0.007607 (-0.001161) \| 0.498958 / 0.226044 (0.272913) \| 4.980440 / 2.268929 (2.711511) \| 2.659659 / 55.444624 (-52.784965) \| 2.315174 / 6.876477 (-4.561303) \| 2.475920 / 2.142072 (0.333848) \| 0.586946 / 4.805227 (-4.218282) \| 0.124291 / 6.500664 (-6.376373) \| 0.060701 / 0.075469 (-0.014768) \|\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) \| 1.245062 / 1.841788 (-0.596725) \| 18.201444 / 8.074308 (10.127136) \| 13.723271 / 10.191392 (3.531879) \| 0.130203 / 0.680424 (-0.550221) \| 0.016773 / 0.534201 (-0.517428) \| 0.332909 / 0.579283 (-0.246374) \| 0.347469 / 0.434364 (-0.086895) \| 0.381364 / 0.540337 (-0.158973) \| 0.541723 / 1.386936 (-0.845213) \|\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) \| 0.005934 / 0.011353 (-0.005419) \| 0.003573 / 0.011008 (-0.007435) \| 0.062195 / 0.038508 (0.023687) \| 0.059026 / 0.023109 (0.035917) \| 0.413993 / 0.275898 (0.138095) \| 0.459552 / 0.323480 (0.136072) \| 0.004610 / 0.007986 (-0.003376) \| 0.002907 / 0.004328 (-0.001421) \| 0.062983 / 0.004250 (0.058733) \| 0.047797 / 0.037052 (0.010745) \| 0.415461 / 0.258489 (0.156972) \| 0.417424 / 0.293841 (0.123583) \| 0.027098 / 0.128546 (-0.101449) \| 0.008106 / 0.075646 (-0.067540) \| 0.067600 / 0.419271 (-0.351672) \| 0.041432 / 0.043533 (-0.002101) \| 0.407861 / 0.255139 (0.152722) \| 0.430774 / 0.283200 (0.147575) \| 0.020738 / 0.141683 (-0.120945) \| 1.435127 / 1.452155 (-0.017028) \| 1.486961 / 1.492716 (-0.005755) \|\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) \| 0.231174 / 0.018006 (0.213168) \| 0.421208 / 0.000490 (0.420718) \| 0.005411 / 0.000200 (0.005211) \| 0.000078 / 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) \| 0.025362 / 0.037411 (-0.012049) \| 0.078534 / 0.014526 (0.064008) \| 0.085304 / 0.176557 (-0.091252) \| 0.139048 / 0.737135 (-0.598087) \| 0.087015 / 0.296338 (-0.209323) \|\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) \| 0.448506 / 0.215209 (0.233297) \| 4.486694 / 2.077655 (2.409039) \| 2.488022 / 1.504120 (0.983902) \| 2.325321 / 1.541195 (0.784126) \| 2.381311 / 1.468490 (0.912821) \| 0.502102 / 4.584777 (-4.082675) \| 3.018326 / 3.745712 (-0.727386) \| 2.824922 / 5.269862 (-2.444940) \| 1.857414 / 4.565676 (-2.708263) \| 0.057514 / 0.424275 (-0.366761) \| 0.006829 / 0.007607 (-0.000779) \| 0.521939 / 0.226044 (0.295895) \| 5.224393 / 2.268929 (2.955465) \| 2.933132 / 55.444624 (-52.511492) \| 2.661187 / 6.876477 (-4.215290) \| 2.781950 / 2.142072 (0.639878) \| 0.592927 / 4.805227 (-4.212300) \| 0.126685 / 6.500664 (-6.373979) \| 0.064188 / 0.075469 (-0.011281) \|\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) \| 1.351107 / 1.841788 (-0.490681) \| 18.344453 / 8.074308 (10.270145) \| 13.838788 / 10.191392 (3.647396) \| 0.157881 / 0.680424 (-0.522543) \| 0.016636 / 0.534201 (-0.517565) \| 0.331597 / 0.579283 (-0.247686) \| 0.345573 / 0.434364 (-0.088791) \| 0.397361 / 0.540337 (-0.142976) \| 0.534289 / 1.386936 (-0.852647) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#582e722a76534904c0f3038d32ebb8db88ce9128 \"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) \| 0.006399 / 0.011353 (-0.004954) \| 0.003872 / 0.011008 (-0.007136) \| 0.083722 / 0.038508 (0.045214) \| 0.068845 / 0.023109 (0.045736) \| 0.329112 / 0.275898 (0.053214) \| 0.343295 / 0.323480 (0.019815) \| 0.005137 / 0.007986 (-0.002849) \| 0.003303 / 0.004328 (-0.001026) \| 0.064495 / 0.004250 (0.060245) \| 0.051448 / 0.037052 (0.014395) \| 0.322554 / 0.258489 (0.064065) \| 0.361934 / 0.293841 (0.068093) \| 0.030821 / 0.128546 (-0.097726) \| 0.008482 / 0.075646 (-0.067164) \| 0.288136 / 0.419271 (-0.131135) \| 0.051935 / 0.043533 (0.008402) \| 0.308283 / 0.255139 (0.053144) \| 0.343421 / 0.283200 (0.060221) \| 0.023639 / 0.141683 (-0.118044) \| 1.485442 / 1.452155 (0.033288) \| 1.533282 / 1.492716 (0.040565) \|\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) \| 0.218163 / 0.018006 (0.200157) \| 0.464473 / 0.000490 (0.463983) \| 0.003097 / 0.000200 (0.002897) \| 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) \| 0.028650 / 0.037411 (-0.008761) \| 0.083295 / 0.014526 (0.068769) \| 0.096468 / 0.176557 (-0.080088) \| 0.152086 / 0.737135 (-0.585050) \| 0.102586 / 0.296338 (-0.193752) \|\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) \| 0.393038 / 0.215209 (0.177829) \| 3.925514 / 2.077655 (1.847859) \| 1.938419 / 1.504120 (0.434300) \| 1.760265 / 1.541195 (0.219071) \| 1.810024 / 1.468490 (0.341534) \| 0.486232 / 4.584777 (-4.098545) \| 3.618747 / 3.745712 (-0.126965) \| 3.206950 / 5.269862 (-2.062912) \| 1.999240 / 4.565676 (-2.566436) \| 0.056986 / 0.424275 (-0.367289) \| 0.007193 / 0.007607 (-0.000415) \| 0.469313 / 0.226044 (0.243269) \| 4.688670 / 2.268929 (2.419741) \| 2.400332 / 55.444624 (-53.044292) \| 2.074197 / 6.876477 (-4.802279) \| 2.290823 / 2.142072 (0.148751) \| 0.582339 / 4.805227 (-4.222888) \| 0.134127 / 6.500664 (-6.366537) \| 0.061061 / 0.075469 (-0.014408) \|\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) \| 1.272782 / 1.841788 (-0.569006) \| 19.463375 / 8.074308 (11.389067) \| 14.306819 / 10.191392 (4.115427) \| 0.164608 / 0.680424 (-0.515816) \| 0.018626 / 0.534201 (-0.515575) \| 0.395225 / 0.579283 (-0.184058) \| 0.408984 / 0.434364 (-0.025380) \| 0.463364 / 0.540337 (-0.076974) \| 0.630425 / 1.386936 (-0.756511) \|\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) \| 0.006465 / 0.011353 (-0.004888) \| 0.003975 / 0.011008 (-0.007033) \| 0.063643 / 0.038508 (0.025134) \| 0.075214 / 0.023109 (0.052105) \| 0.361734 / 0.275898 (0.085836) \| 0.396664 / 0.323480 (0.073184) \| 0.005251 / 0.007986 (-0.002735) \| 0.003249 / 0.004328 (-0.001080) \| 0.063841 / 0.004250 (0.059591) \| 0.054504 / 0.037052 (0.017451) \| 0.374791 / 0.258489 (0.116302) \| 0.399205 / 0.293841 (0.105364) \| 0.031355 / 0.128546 (-0.097192) \| 0.008483 / 0.075646 (-0.067163) \| 0.070234 / 0.419271 (-0.349037) \| 0.048336 / 0.043533 (0.004803) \| 0.373484 / 0.255139 (0.118345) \| 0.382174 / 0.283200 (0.098974) \| 0.022560 / 0.141683 (-0.119123) \| 1.449799 / 1.452155 (-0.002355) \| 1.525255 / 1.492716 (0.032539) \|\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) \| 0.228350 / 0.018006 (0.210343) \| 0.444344 / 0.000490 (0.443855) \| 0.003699 / 0.000200 (0.003499) \| 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) \| 0.030681 / 0.037411 (-0.006731) \| 0.087340 / 0.014526 (0.072814) \| 0.098636 / 0.176557 (-0.077920) \| 0.151665 / 0.737135 (-0.585471) \| 0.100840 / 0.296338 (-0.195498) \|\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) \| 0.417857 / 0.215209 (0.202648) \| 4.168407 / 2.077655 (2.090752) \| 2.201758 / 1.504120 (0.697638) \| 1.997834 / 1.541195 (0.456639) \| 2.127693 / 1.468490 (0.659202) \| 0.486429 / 4.584777 (-4.098348) \| 3.676335 / 3.745712 (-0.069378) \| 3.226268 / 5.269862 (-2.043594) \| 2.027255 / 4.565676 (-2.538422) \| 0.056759 / 0.424275 (-0.367516) \| 0.007628 / 0.007607 (0.000021) \| 0.500482 / 0.226044 (0.274438) \| 4.996236 / 2.268929 (2.727307) \| 2.628884 / 55.444624 (-52.815740) \| 2.347611 / 6.876477 (-4.528866) \| 2.551328 / 2.142072 (0.409255) \| 0.582449 / 4.805227 (-4.222778) \| 0.132844 / 6.500664 (-6.367821) \| 0.061791 / 0.075469 (-0.013678) \|\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) \| 1.373718 / 1.841788 (-0.468070) \| 19.921217 / 8.074308 (11.846909) \| 14.209642 / 10.191392 (4.018250) \| 0.185334 / 0.680424 (-0.495090) \| 0.018228 / 0.534201 (-0.515973) \| 0.395549 / 0.579283 (-0.183734) \| 0.404446 / 0.434364 (-0.029918) \| 0.472456 / 0.540337 (-0.067882) \| 0.622739 / 1.386936 (-0.764197) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#33f736eafa0f77de03aa6894ea4a6c923702e5d1 \"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) \| 0.006007 / 0.011353 (-0.005346) \| 0.003588 / 0.011008 (-0.007420) \| 0.080334 / 0.038508 (0.041826) \| 0.058932 / 0.023109 (0.035823) \| 0.404613 / 0.275898 (0.128715) \| 0.438377 / 0.323480 (0.114897) \| 0.003468 / 0.007986 (-0.004518) \| 0.003702 / 0.004328 (-0.000627) \| 0.062936 / 0.004250 (0.058686) \| 0.047987 / 0.037052 (0.010934) \| 0.411409 / 0.258489 (0.152920) \| 0.450244 / 0.293841 (0.156403) \| 0.027007 / 0.128546 (-0.101539) \| 0.007932 / 0.075646 (-0.067714) \| 0.261390 / 0.419271 (-0.157882) \| 0.044992 / 0.043533 (0.001459) \| 0.409730 / 0.255139 (0.154591) \| 0.433331 / 0.283200 (0.150131) \| 0.020446 / 0.141683 (-0.121237) \| 1.425418 / 1.452155 (-0.026736) \| 1.479242 / 1.492716 (-0.013475) \|\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) \| 0.187375 / 0.018006 (0.169368) \| 0.428532 / 0.000490 (0.428043) \| 0.003406 / 0.000200 (0.003206) \| 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) \| 0.024390 / 0.037411 (-0.013022) \| 0.072571 / 0.014526 (0.058045) \| 0.083513 / 0.176557 (-0.093044) \| 0.144395 / 0.737135 (-0.592741) \| 0.084813 / 0.296338 (-0.211526) \|\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) \| 0.409176 / 0.215209 (0.193967) \| 4.078082 / 2.077655 (2.000428) \| 1.913596 / 1.504120 (0.409476) \| 1.718470 / 1.541195 (0.177275) \| 1.753106 / 1.468490 (0.284616) \| 0.494167 / 4.584777 (-4.090610) \| 3.029531 / 3.745712 (-0.716181) \| 2.807331 / 5.269862 (-2.462531) \| 1.839471 / 4.565676 (-2.726206) \| 0.057169 / 0.424275 (-0.367106) \| 0.006433 / 0.007607 (-0.001175) \| 0.482666 / 0.226044 (0.256621) \| 4.817601 / 2.268929 (2.548673) \| 2.449967 / 55.444624 (-52.994658) \| 2.113891 / 6.876477 (-4.762586) \| 2.399293 / 2.142072 (0.257221) \| 0.578903 / 4.805227 (-4.226324) \| 0.124306 / 6.500664 (-6.376358) \| 0.061572 / 0.075469 (-0.013897) \|\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) \| 1.254692 / 1.841788 (-0.587096) \| 18.414049 / 8.074308 (10.339741) \| 13.992059 / 10.191392 (3.800667) \| 0.146671 / 0.680424 (-0.533753) \| 0.016925 / 0.534201 (-0.517275) \| 0.333124 / 0.579283 (-0.246159) \| 0.348007 / 0.434364 (-0.086357) \| 0.378519 / 0.540337 (-0.161819) \| 0.532540 / 1.386936 (-0.854396) \|\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) \| 0.006050 / 0.011353 (-0.005303) \| 0.003614 / 0.011008 (-0.007394) \| 0.061707 / 0.038508 (0.023199) \| 0.062874 / 0.023109 (0.039765) \| 0.364760 / 0.275898 (0.088862) \| 0.398136 / 0.323480 (0.074656) \| 0.005598 / 0.007986 (-0.002388) \| 0.002836 / 0.004328 (-0.001493) \| 0.061880 / 0.004250 (0.057630) \| 0.048165 / 0.037052 (0.011113) \| 0.372656 / 0.258489 (0.114167) \| 0.403967 / 0.293841 (0.110126) \| 0.027046 / 0.128546 (-0.101501) \| 0.008091 / 0.075646 (-0.067555) \| 0.066783 / 0.419271 (-0.352489) \| 0.041186 / 0.043533 (-0.002347) \| 0.376009 / 0.255139 (0.120870) \| 0.391769 / 0.283200 (0.108569) \| 0.021020 / 0.141683 (-0.120663) \| 1.514593 / 1.452155 (0.062438) \| 1.548506 / 1.492716 (0.055790) \|\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) \| 0.237610 / 0.018006 (0.219604) \| 0.434274 / 0.000490 (0.433784) \| 0.009720 / 0.000200 (0.009520) \| 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) \| 0.025605 / 0.037411 (-0.011807) \| 0.078971 / 0.014526 (0.064445) \| 0.088154 / 0.176557 (-0.088403) \| 0.139112 / 0.737135 (-0.598023) \| 0.088890 / 0.296338 (-0.207449) \|\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) \| 0.420027 / 0.215209 (0.204818) \| 4.189493 / 2.077655 (2.111838) \| 2.143907 / 1.504120 (0.639787) \| 1.967032 / 1.541195 (0.425837) \| 2.011845 / 1.468490 (0.543355) \| 0.496692 / 4.584777 (-4.088085) \| 3.025456 / 3.745712 (-0.720256) \| 2.828436 / 5.269862 (-2.441426) \| 1.860673 / 4.565676 (-2.705003) \| 0.057199 / 0.424275 (-0.367076) \| 0.006770 / 0.007607 (-0.000838) \| 0.491281 / 0.226044 (0.265236) \| 4.918065 / 2.268929 (2.649136) \| 2.593172 / 55.444624 (-52.851452) \| 2.250750 / 6.876477 (-4.625727) \| 2.406235 / 2.142072 (0.264162) \| 0.588648 / 4.805227 (-4.216579) \| 0.125635 / 6.500664 (-6.375029) \| 0.061697 / 0.075469 (-0.013773) \|\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) \| 1.374065 / 1.841788 (-0.467722) \| 18.439315 / 8.074308 (10.365007) \| 14.031660 / 10.191392 (3.840268) \| 0.153665 / 0.680424 (-0.526759) \| 0.016980 / 0.534201 (-0.517221) \| 0.331799 / 0.579283 (-0.247484) \| 0.343201 / 0.434364 (-0.091163) \| 0.392445 / 0.540337 (-0.147892) \| 0.530387 / 1.386936 (-0.856549) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#33f736eafa0f77de03aa6894ea4a6c923702e5d1 \"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) \| 0.008189 / 0.011353 (-0.003164) \| 0.004598 / 0.011008 (-0.006410) \| 0.102199 / 0.038508 (0.063691) \| 0.077961 / 0.023109 (0.054852) \| 0.364936 / 0.275898 (0.089038) \| 0.402606 / 0.323480 (0.079126) \| 0.005522 / 0.007986 (-0.002464) \| 0.004007 / 0.004328 (-0.000322) \| 0.071560 / 0.004250 (0.067310) \| 0.055818 / 0.037052 (0.018765) \| 0.378394 / 0.258489 (0.119905) \| 0.428990 / 0.293841 (0.135149) \| 0.043142 / 0.128546 (-0.085404) \| 0.013254 / 0.075646 (-0.062392) \| 0.331102 / 0.419271 (-0.088170) \| 0.061407 / 0.043533 (0.017875) \| 0.387397 / 0.255139 (0.132258) \| 0.416062 / 0.283200 (0.132862) \| 0.036330 / 0.141683 (-0.105353) \| 1.735352 / 1.452155 (0.283198) \| 1.773329 / 1.492716 (0.280613) \|\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) \| 0.188587 / 0.018006 (0.170581) \| 0.519506 / 0.000490 (0.519016) \| 0.004702 / 0.000200 (0.004502) \| 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) \| 0.027152 / 0.037411 (-0.010260) \| 0.094296 / 0.014526 (0.079770) \| 0.098155 / 0.176557 (-0.078402) \| 0.162541 / 0.737135 (-0.574595) \| 0.112092 / 0.296338 (-0.184246) \|\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) \| 0.537555 / 0.215209 (0.322346) \| 5.486821 / 2.077655 (3.409166) \| 2.377127 / 1.504120 (0.873008) \| 2.073205 / 1.541195 (0.532011) \| 2.075130 / 1.468490 (0.606640) \| 0.783779 / 4.584777 (-3.800998) \| 5.029524 / 3.745712 (1.283812) \| 4.382724 / 5.269862 (-0.887138) \| 2.836180 / 4.565676 (-1.729496) \| 0.108840 / 0.424275 (-0.315435) \| 0.008123 / 0.007607 (0.000516) \| 0.673460 / 0.226044 (0.447416) \| 6.674030 / 2.268929 (4.405102) \| 3.208922 / 55.444624 (-52.235702) \| 2.464908 / 6.876477 (-4.411568) \| 2.661929 / 2.142072 (0.519856) \| 0.962529 / 4.805227 (-3.842698) \| 0.197974 / 6.500664 (-6.302690) \| 0.066656 / 0.075469 (-0.008813) \|\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) \| 1.430373 / 1.841788 (-0.411415) \| 21.180540 / 8.074308 (13.106232) \| 19.027491 / 10.191392 (8.836099) \| 0.217520 / 0.680424 (-0.462904) \| 0.028038 / 0.534201 (-0.506163) \| 0.435266 / 0.579283 (-0.144017) \| 0.529510 / 0.434364 (0.095147) \| 0.511011 / 0.540337 (-0.029327) \| 0.728940 / 1.386936 (-0.657996) \|\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) \| 0.007883 / 0.011353 (-0.003470) \| 0.004448 / 0.011008 (-0.006560) \| 0.071350 / 0.038508 (0.032842) \| 0.075269 / 0.023109 (0.052160) \| 0.396705 / 0.275898 (0.120807) \| 0.457809 / 0.323480 (0.134329) \| 0.005193 / 0.007986 (-0.002792) \| 0.003695 / 0.004328 (-0.000633) \| 0.078087 / 0.004250 (0.073836) \| 0.054276 / 0.037052 (0.017224) \| 0.412184 / 0.258489 (0.153695) \| 0.452400 / 0.293841 (0.158559) \| 0.049762 / 0.128546 (-0.078784) \| 0.013206 / 0.075646 (-0.062440) \| 0.085985 / 0.419271 (-0.333287) \| 0.058837 / 0.043533 (0.015304) \| 0.432481 / 0.255139 (0.177342) \| 0.433260 / 0.283200 (0.150060) \| 0.031190 / 0.141683 (-0.110493) \| 1.582707 / 1.452155 (0.130552) \| 1.664457 / 1.492716 (0.171741) \|\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) \| 0.223639 / 0.018006 (0.205633) \| 0.524388 / 0.000490 (0.523899) \| 0.005489 / 0.000200 (0.005289) \| 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) \| 0.030182 / 0.037411 (-0.007230) \| 0.089309 / 0.014526 (0.074783) \| 0.103306 / 0.176557 (-0.073250) \| 0.162624 / 0.737135 (-0.574511) \| 0.108957 / 0.296338 (-0.187381) \|\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) \| 0.577423 / 0.215209 (0.362214) \| 5.900154 / 2.077655 (3.822500) \| 2.687369 / 1.504120 (1.183249) \| 2.513061 / 1.541195 (0.971866) \| 2.506453 / 1.468490 (1.037963) \| 0.830838 / 4.584777 (-3.753939) \| 5.032195 / 3.745712 (1.286483) \| 4.396827 / 5.269862 (-0.873035) \| 2.884230 / 4.565676 (-1.681447) \| 0.102239 / 0.424275 (-0.322036) \| 0.008178 / 0.007607 (0.000571) \| 0.710027 / 0.226044 (0.483983) \| 7.149626 / 2.268929 (4.880698) \| 3.403605 / 55.444624 (-52.041019) \| 2.661970 / 6.876477 (-4.214506) \| 2.760227 / 2.142072 (0.618154) \| 1.043981 / 4.805227 (-3.761246) \| 0.195028 / 6.500664 (-6.305636) \| 0.065211 / 0.075469 (-0.010258) \|\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) \| 1.581265 / 1.841788 (-0.260522) \| 21.640230 / 8.074308 (13.565922) \| 19.031860 / 10.191392 (8.840468) \| 0.196903 / 0.680424 (-0.483520) \| 0.027061 / 0.534201 (-0.507140) \| 0.444995 / 0.579283 (-0.134288) \| 0.528195 / 0.434364 (0.093831) \| 0.521540 / 0.540337 (-0.018797) \| 0.730204 / 1.386936 (-0.656732) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#33f736eafa0f77de03aa6894ea4a6c923702e5d1 \"CML watermark\")\n" ]
https://api.github.com/repos/huggingface/datasets/issues/6114	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6114/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6114/comments	https://api.github.com/repos/huggingface/datasets/issues/6114/events	https://github.com/huggingface/datasets/issues/6114	1,834,015,584	I_kwDODunzps5tUNtg	6,114	Cache not being used when loading commonvoice 8.0.0	{ "avatar_url": "https://avatars.githubusercontent.com/u/31082141?v=4", "events_url": "https://api.github.com/users/clabornd/events{/privacy}", "followers_url": "https://api.github.com/users/clabornd/followers", "following_url": "https://api.github.com/users/clabornd/following{/other_user}", "gists_url": "https://api.github.com/users/clabornd/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/clabornd", "id": 31082141, "login": "clabornd", "node_id": "MDQ6VXNlcjMxMDgyMTQx", "organizations_url": "https://api.github.com/users/clabornd/orgs", "received_events_url": "https://api.github.com/users/clabornd/received_events", "repos_url": "https://api.github.com/users/clabornd/repos", "site_admin": false, "starred_url": "https://api.github.com/users/clabornd/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/clabornd/subscriptions", "type": "User", "url": "https://api.github.com/users/clabornd" }	[]	closed	false	null	[]	null	2	"2023-08-02T23:18:11Z"	"2023-08-18T23:59:00Z"	"2023-08-18T23:59:00Z"	NONE	null	null	null	### Describe the bug I have commonvoice 8.0.0 downloaded in `~/.cache/huggingface/datasets/mozilla-foundation___common_voice_8_0/en/8.0.0/b2f8b72f8f30b2e98c41ccf855954d9e35a5fa498c43332df198534ff9797a4a`. The folder contains all the arrow files etc, and was used as the cached version last time I touched the ec2 instance I'm working on. Now, with the same command that downloaded it initially: ``` dataset = load_dataset("mozilla-foundation/common_voice_8_0", "en", use_auth_token="<mytoken>") ``` it tries to redownload the dataset to `~/.cache/huggingface/datasets/mozilla-foundation___common_voice_8_0/en/8.0.0/05bdc7940b0a336ceeaeef13470c89522c29a8e4494cbeece64fb472a87acb32` ### Steps to reproduce the bug Steps to reproduce the behavior: 1. ```dataset = load_dataset("mozilla-foundation/common_voice_8_0", "en", use_auth_token="<mytoken>")``` 2. dataset is updated by maintainers 3. ```dataset = load_dataset("mozilla-foundation/common_voice_8_0", "en", use_auth_token="<mytoken>")``` ### Expected behavior I expect that it uses the already downloaded data in `~/.cache/huggingface/datasets/mozilla-foundation___common_voice_8_0/en/8.0.0/b2f8b72f8f30b2e98c41ccf855954d9e35a5fa498c43332df198534ff9797a4a`. Not sure what's happening in 2. but if, say it's an issue with the dataset referenced by "mozilla-foundation/common_voice_8_0" being modified by the maintainers, how would I force datasets to point to the original version I downloaded? EDIT: It was indeed that the maintainers had updated the dataset (v 8.0.0). However I still cant load the dataset from disk instead of redownloading, with for example: ``` load_dataset(".cache/huggingface/datasets/downloads/extracted/<hash>/cv-corpus-8.0-2022-01-19/en/", "en") > ... > File [~/miniconda3/envs/aa_torch2/lib/python3.10/site-packages/datasets/table.py:1938](.../ python3.10/site-packages/datasets/table.py:1938), in cast_array_to_feature(array, feature, allow_number_to_str) 1937 elif not isinstance(feature, (Sequence, dict, list, tuple)): -> 1938 return array_cast(array, feature(), allow_number_to_str=allow_number_to_str) ... 1794 e = e.__context__ -> 1795 raise DatasetGenerationError("An error occurred while generating the dataset") from e 1797 yield job_id, True, (total_num_examples, total_num_bytes, writer._features, num_shards, shard_lengths) DatasetGenerationError: An error occurred while generating the dataset ``` ### Environment info datasets==2.7.0 python==3.10.8 OS: AWS Linux	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6114/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6114/timeline	null	completed	649	false	[ "You can avoid this by using the `revision` parameter in `load_dataset` to always force downloading a specific commit (if not specified it defaults to HEAD, hence the redownload).", "Thanks @mariosasko this works well, looks like I should have read the documentation a bit more carefully. \r\n\r\nIt is still a bit confusing which hash I should provide: passing `revision = c8fd66e85f086e3abb11eeee55b1737a3d1e8487` from https://huggingface.co/datasets/mozilla-foundation/common_voice_8_0/commits/main caused the cached version at `~/.cache/huggingface/datasets/mozilla-foundation___common_voice_8_0/en/8.0.0/b2f8b72f8f30b2e98c41ccf855954d9e35a5fa498c43332df198534ff9797a4a` to be loaded, so I had to know that it was the previous commit unless I've missed something else." ]
https://api.github.com/repos/huggingface/datasets/issues/6113	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6113/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6113/comments	https://api.github.com/repos/huggingface/datasets/issues/6113/events	https://github.com/huggingface/datasets/issues/6113	1,833,854,030	I_kwDODunzps5tTmRO	6,113	load_dataset() fails with streamlit caching inside docker	{ "avatar_url": "https://avatars.githubusercontent.com/u/987574?v=4", "events_url": "https://api.github.com/users/fierval/events{/privacy}", "followers_url": "https://api.github.com/users/fierval/followers", "following_url": "https://api.github.com/users/fierval/following{/other_user}", "gists_url": "https://api.github.com/users/fierval/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/fierval", "id": 987574, "login": "fierval", "node_id": "MDQ6VXNlcjk4NzU3NA==", "organizations_url": "https://api.github.com/users/fierval/orgs", "received_events_url": "https://api.github.com/users/fierval/received_events", "repos_url": "https://api.github.com/users/fierval/repos", "site_admin": false, "starred_url": "https://api.github.com/users/fierval/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/fierval/subscriptions", "type": "User", "url": "https://api.github.com/users/fierval" }	[]	closed	false	null	[]	null	1	"2023-08-02T20:20:26Z"	"2023-08-21T18:18:27Z"	"2023-08-21T18:18:27Z"	NONE	null	null	null	### Describe the bug When calling `load_dataset` in a streamlit application running within a docker container, get a failure with the error message: EmptyDatasetError: The directory at hf://datasets/fetch-rewards/inc-rings-2000@bea27cf60842b3641eae418f38864a2ec4cde684 doesn't contain any data files Traceback: File "/opt/conda/lib/python3.10/site-packages/streamlit/runtime/scriptrunner/script_runner.py", line 552, in _run_script exec(code, module.__dict__) File "/home/user/app/app.py", line 62, in <module> dashboard() File "/home/user/app/app.py", line 47, in dashboard feat_dict, path_gml = load_data(hf_repo, model_gml_dict[selected_model], hf_token) File "/opt/conda/lib/python3.10/site-packages/streamlit/runtime/caching/cache_utils.py", line 211, in wrapper return cached_func(args, kwargs) File "/opt/conda/lib/python3.10/site-packages/streamlit/runtime/caching/cache_utils.py", line 240, in __call__ return self._get_or_create_cached_value(args, kwargs) File "/opt/conda/lib/python3.10/site-packages/streamlit/runtime/caching/cache_utils.py", line 266, in _get_or_create_cached_value return self._handle_cache_miss(cache, value_key, func_args, func_kwargs) File "/opt/conda/lib/python3.10/site-packages/streamlit/runtime/caching/cache_utils.py", line 320, in _handle_cache_miss computed_value = self._info.func(func_args, **func_kwargs) File "/home/user/app/hf_interface.py", line 16, in load_data hf_dataset = load_dataset(repo_id, use_auth_token=hf_token) File "/opt/conda/lib/python3.10/site-packages/datasets/load.py", line 2109, in load_dataset builder_instance = load_dataset_builder( File "/opt/conda/lib/python3.10/site-packages/datasets/load.py", line 1795, in load_dataset_builder dataset_module = dataset_module_factory( File "/opt/conda/lib/python3.10/site-packages/datasets/load.py", line 1486, in dataset_module_factory raise e1 from None File "/opt/conda/lib/python3.10/site-packages/datasets/load.py", line 1476, in dataset_module_factory ).get_module() File "/opt/conda/lib/python3.10/site-packages/datasets/load.py", line 1032, in get_module else get_data_patterns(base_path, download_config=self.download_config) File "/opt/conda/lib/python3.10/site-packages/datasets/data_files.py", line 458, in get_data_patterns raise EmptyDatasetError(f"The directory at {base_path} doesn't contain any data files") from None ### Steps to reproduce the bug ```python @st.cache_resource def load_data(repo_id: str, hf_token=None): """Load data from HuggingFace Hub """ hf_dataset = load_dataset(repo_id, use_auth_token=hf_token) hf_dataset = hf_dataset.map(lambda x: json.loads(x["ground_truth"]), remove_columns=["ground_truth"]) return hf_dataset ``` ### Expected behavior Expect to load. Note: works fine with datasets==2.13.1 ### Environment info datasets==2.14.2, Ubuntu bionic-based Docker container.	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6113/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6113/timeline	null	completed	650	false	[ "Hi! This should be fixed in the latest (patch) release (run `pip install -U datasets` to install it). This behavior was due to a bug in our authentication logic." ]
https://api.github.com/repos/huggingface/datasets/issues/6112	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6112/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6112/comments	https://api.github.com/repos/huggingface/datasets/issues/6112/events	https://github.com/huggingface/datasets/issues/6112	1,833,693,299	I_kwDODunzps5tS_Bz	6,112	yaml error using push_to_hub with generated README.md	{ "avatar_url": "https://avatars.githubusercontent.com/u/1643887?v=4", "events_url": "https://api.github.com/users/kevintee/events{/privacy}", "followers_url": "https://api.github.com/users/kevintee/followers", "following_url": "https://api.github.com/users/kevintee/following{/other_user}", "gists_url": "https://api.github.com/users/kevintee/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/kevintee", "id": 1643887, "login": "kevintee", "node_id": "MDQ6VXNlcjE2NDM4ODc=", "organizations_url": "https://api.github.com/users/kevintee/orgs", "received_events_url": "https://api.github.com/users/kevintee/received_events", "repos_url": "https://api.github.com/users/kevintee/repos", "site_admin": false, "starred_url": "https://api.github.com/users/kevintee/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/kevintee/subscriptions", "type": "User", "url": "https://api.github.com/users/kevintee" }	[]	closed	false	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "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}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko" }	[ { "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "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}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko" } ]	null	1	"2023-08-02T18:21:21Z"	"2023-12-12T15:00:44Z"	"2023-12-12T15:00:44Z"	NONE	null	null	null	### Describe the bug When I construct a dataset with the following features: ``` features = Features( { "pixel_values": Array3D(dtype="float64", shape=(3, 224, 224)), "input_ids": Sequence(feature=Value(dtype="int64")), "attention_mask": Sequence(Value(dtype="int64")), "tokens": Sequence(Value(dtype="string")), "bbox": Array2D(dtype="int64", shape=(512, 4)), } ) ``` and run `push_to_hub`, the individual `.parquet` files are pushed, but when trying to upload the auto-generated README, I run into the following error: ``` Traceback (most recent call last): File "/Users/kevintee/.pyenv/versions/dev2/lib/python3.10/site-packages/huggingface_hub/utils/_errors.py", line 261, in hf_raise_for_status response.raise_for_status() File "/Users/kevintee/.pyenv/versions/dev2/lib/python3.10/site-packages/requests/models.py", line 1021, in raise_for_status raise HTTPError(http_error_msg, response=self) requests.exceptions.HTTPError: 400 Client Error: Bad Request for url: https://huggingface.co/api/datasets/looppayments/multitask_document_classification_dataset/commit/main The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/Users/kevintee/loop-payments/ml/src/ml/data_scripts/build_document_classification_training_data.py", line 297, in <module> build_dataset() File "/Users/kevintee/loop-payments/ml/src/ml/data_scripts/build_document_classification_training_data.py", line 290, in build_dataset push_to_hub(dataset, "multitask_document_classification_dataset") File "/Users/kevintee/loop-payments/ml/src/ml/data_scripts/build_document_classification_training_data.py", line 135, in push_to_hub dataset.push_to_hub(f"looppayments/{dataset_name}", private=True) File "/Users/kevintee/.pyenv/versions/dev2/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 5577, in push_to_hub HfApi(endpoint=config.HF_ENDPOINT).upload_file( File "/Users/kevintee/.pyenv/versions/dev2/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py", line 118, in _inner_fn return fn(args, *kwargs) File "/Users/kevintee/.pyenv/versions/dev2/lib/python3.10/site-packages/huggingface_hub/hf_api.py", line 828, in _inner return fn(self, args, *kwargs) File "/Users/kevintee/.pyenv/versions/dev2/lib/python3.10/site-packages/huggingface_hub/hf_api.py", line 3221, in upload_file commit_info = self.create_commit( File "/Users/kevintee/.pyenv/versions/dev2/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py", line 118, in _inner_fn return fn(args, *kwargs) File "/Users/kevintee/.pyenv/versions/dev2/lib/python3.10/site-packages/huggingface_hub/hf_api.py", line 828, in _inner return fn(self, args, **kwargs) File "/Users/kevintee/.pyenv/versions/dev2/lib/python3.10/site-packages/huggingface_hub/hf_api.py", line 2728, in create_commit hf_raise_for_status(commit_resp, endpoint_name="commit") File "/Users/kevintee/.pyenv/versions/dev2/lib/python3.10/site-packages/huggingface_hub/utils/_errors.py", line 299, in hf_raise_for_status raise BadRequestError(message, response=response) from e huggingface_hub.utils._errors.BadRequestError: (Request ID: Root=1-64ca9c3d-2d2bbef354e102482a9a168e;bc00371c-8549-4859-9f41-43ff140ad36e) Bad request for commit endpoint: Invalid YAML in README.md: unknown tag !<tag:yaml.org,2002:python/tuple> (10:9) 7 \| - 3 8 \| - 224 9 \| - 224 10 \| dtype: float64 --------------^ 11 \| - name: input_ids 12 \| sequence: int64 ``` My guess is that the auto-generated yaml is unable to be parsed for some reason. ### Steps to reproduce the bug The description contains most of what's needed to reproduce the issue, but I've added a shortened code snippet: ``` from datasets import Array2D, Array3D, ClassLabel, Dataset, Features, Sequence, Value from PIL import Image from transformers import AutoProcessor features = Features( { "pixel_values": Array3D(dtype="float64", shape=(3, 224, 224)), "input_ids": Sequence(feature=Value(dtype="int64")), "attention_mask": Sequence(Value(dtype="int64")), "tokens": Sequence(Value(dtype="string")), "bbox": Array2D(dtype="int64", shape=(512, 4)), } ) processor = AutoProcessor.from_pretrained("microsoft/layoutlmv3-base", apply_ocr=False) def preprocess_dataset(rows): # Get images images = [ Image.open(png_filename).convert("RGB") for png_filename in rows["png_filename"] ] encoding = processor( images, rows["tokens"], boxes=rows["bbox"], truncation=True, padding="max_length", ) encoding["tokens"] = rows["tokens"] return encoding dataset = dataset.map( preprocess_dataset, batched=True, batch_size=5, features=features, ) ``` ### Expected behavior Using datasets==2.11.0, I'm able to succesfully push_to_hub, no issues, but with datasets==2.14.2, I run into the above error. ### Environment info - `datasets` version: 2.14.2 - Platform: macOS-12.5-arm64-arm-64bit - Python version: 3.10.12 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 1.5.3	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6112/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6112/timeline	null	completed	651	false	[ "Thanks for reporting! This is a bug in converting the `ArrayXD` types to YAML. It will be fixed soon." ]
https://api.github.com/repos/huggingface/datasets/issues/6111	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6111/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6111/comments	https://api.github.com/repos/huggingface/datasets/issues/6111/events	https://github.com/huggingface/datasets/issues/6111	1,832,781,654	I_kwDODunzps5tPgdW	6,111	raise FileNotFoundError("Directory {dataset_path} is neither a `Dataset` directory nor a `DatasetDict` directory." )	{ "avatar_url": "https://avatars.githubusercontent.com/u/41530341?v=4", "events_url": "https://api.github.com/users/2catycm/events{/privacy}", "followers_url": "https://api.github.com/users/2catycm/followers", "following_url": "https://api.github.com/users/2catycm/following{/other_user}", "gists_url": "https://api.github.com/users/2catycm/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/2catycm", "id": 41530341, "login": "2catycm", "node_id": "MDQ6VXNlcjQxNTMwMzQx", "organizations_url": "https://api.github.com/users/2catycm/orgs", "received_events_url": "https://api.github.com/users/2catycm/received_events", "repos_url": "https://api.github.com/users/2catycm/repos", "site_admin": false, "starred_url": "https://api.github.com/users/2catycm/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/2catycm/subscriptions", "type": "User", "url": "https://api.github.com/users/2catycm" }	[]	closed	false	null	[]	null	3	"2023-08-02T09:17:29Z"	"2023-08-29T02:00:28Z"	"2023-08-29T02:00:28Z"	NONE	null	null	null	### Describe the bug For researchers in some countries or regions, it is usually the case that the download ability of `load_dataset` is disabled due to the complex network environment. People in these regions often prefer to use git clone or other programming tricks to manually download the files to the disk (for example, [How to elegantly download hf models, zhihu zhuanlan](https://zhuanlan.zhihu.com/p/475260268) proposed a crawlder based solution, and [Is there any mirror for hf_hub, zhihu answer](https://www.zhihu.com/question/371644077) provided some cloud based solutions, and [How to avoid pitfalls on Hugging face downloading, zhihu zhuanlan] gave some useful suggestions), and then use `load_from_disk` to get the dataset object. However, when one finally has the local files on the disk, it is still buggy when trying to load the files into objects. ### Steps to reproduce the bug Steps to reproduce the bug: 1. Found CIFAR dataset in hugging face: https://huggingface.co/datasets/cifar100/tree/main 2. Click ":" button to show "Clone repository" option, and then follow the prompts on the box: ```bash cd my_directory_absolute git lfs install git clone https://huggingface.co/datasets/cifar100 ls my_directory_absolute/cifar100 # confirm that the directory exists and it is OK. ``` 3. Write A python file to try to load the dataset ```python from datasets import load_dataset, load_from_disk dataset = load_from_disk("my_directory_absolute/cifar100") ``` Notice that according to issue #3700 , it is wrong to use load_dataset("my_directory_absolute/cifar100"), so we must use load_from_disk instead. 4. Then you will see the error reported: ```log --------------------------------------------------------------------------- FileNotFoundError Traceback (most recent call last) Cell In[5], line 9 1 from datasets import load_dataset, load_from_disk ----> 9 dataset = load_from_disk("my_directory_absolute/cifar100") File [~/miniconda3/envs/ai/lib/python3.10/site-packages/datasets/load.py:2232), in load_from_disk(dataset_path, fs, keep_in_memory, storage_options) 2230 return DatasetDict.load_from_disk(dataset_path, keep_in_memory=keep_in_memory, storage_options=storage_options) 2231 else: -> 2232 raise FileNotFoundError( 2233 f"Directory {dataset_path} is neither a `Dataset` directory nor a `DatasetDict` directory." 2234 ) FileNotFoundError: Directory my_directory_absolute/cifar100 is neither a `Dataset` directory nor a `DatasetDict` directory. ``` ### Expected behavior The dataset should be load successfully. ### Environment info ```bash datasets-cli env ``` -> results: ```txt Copy-and-paste the text below in your GitHub issue. - `datasets` version: 2.14.2 - Platform: Linux-4.18.0-372.32.1.el8_6.x86_64-x86_64-with-glibc2.28 - Python version: 3.10.12 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 2.0.3 ```	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6111/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6111/timeline	null	completed	652	false	[ "any idea?", "This should work: `load_dataset(\"path/to/downloaded_repo\")`\r\n\r\n`load_from_disk` is intended to be used on directories created with `Dataset.save_to_disk` or `DatasetDict.save_to_disk`", "> This should work: `load_dataset(\"path/to/downloaded_repo\")`\r\n> \r\n> `load_from_disk` is intended to be used on directories created with `Dataset.save_to_disk` or `DatasetDict.save_to_disk`\r\n\r\nThanks for your help. This works." ]
https://api.github.com/repos/huggingface/datasets/issues/6110	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6110/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6110/comments	https://api.github.com/repos/huggingface/datasets/issues/6110/events	https://github.com/huggingface/datasets/issues/6110	1,831,110,633	I_kwDODunzps5tJIfp	6,110	[BUG] Dataset initialized from in-memory data does not create cache.	{ "avatar_url": "https://avatars.githubusercontent.com/u/57797966?v=4", "events_url": "https://api.github.com/users/MattYoon/events{/privacy}", "followers_url": "https://api.github.com/users/MattYoon/followers", "following_url": "https://api.github.com/users/MattYoon/following{/other_user}", "gists_url": "https://api.github.com/users/MattYoon/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/MattYoon", "id": 57797966, "login": "MattYoon", "node_id": "MDQ6VXNlcjU3Nzk3OTY2", "organizations_url": "https://api.github.com/users/MattYoon/orgs", "received_events_url": "https://api.github.com/users/MattYoon/received_events", "repos_url": "https://api.github.com/users/MattYoon/repos", "site_admin": false, "starred_url": "https://api.github.com/users/MattYoon/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/MattYoon/subscriptions", "type": "User", "url": "https://api.github.com/users/MattYoon" }	[]	closed	false	null	[]	null	1	"2023-08-01T11:58:58Z"	"2023-08-17T14:03:01Z"	"2023-08-17T14:03:00Z"	NONE	null	null	null	### Describe the bug `Dataset` initialized from in-memory data (dictionary in my case, haven't tested with other types) does not create cache when processed with the `map` method, unlike `Dataset` initialized by other methods such as `load_dataset`. ### Steps to reproduce the bug ```python # below code was run the second time so the map function can be loaded from cache if exists from datasets import load_dataset, Dataset dataset = load_dataset("tatsu-lab/alpaca")['train'] dataset = dataset.map(lambda x: {'input': x['input'] + 'hi'}) # some random map print(len(dataset.cache_files)) # 1 # copy the exact same data but initialize from a dictionary memory_dataset = Dataset.from_dict({ 'instruction': dataset['instruction'], 'input': dataset['input'], 'output': dataset['output'], 'text': dataset['text']}) memory_dataset = memory_dataset.map(lambda x: {'input': x['input'] + 'hi'}) # exact same map print(len(memory_dataset.cache_files)) # Map: 100%\|██████████\| 52002[/52002] # 0 ``` ### Expected behavior The `map` function should create cache regardless of the method the `Dataset` was created. ### Environment info - `datasets` version: 2.14.2 - Platform: Linux-5.15.0-41-generic-x86_64-with-glibc2.31 - Python version: 3.9.16 - Huggingface_hub version: 0.14.1 - PyArrow version: 11.0.0 - Pandas version: 1.5.3	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6110/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6110/timeline	null	completed	653	false	[ "This is expected behavior. You must provide `cache_file_name` when performing `.map` on an in-memory dataset for the result to be cached." ]
https://api.github.com/repos/huggingface/datasets/issues/6109	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6109/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6109/comments	https://api.github.com/repos/huggingface/datasets/issues/6109/events	https://github.com/huggingface/datasets/issues/6109	1,830,753,793	I_kwDODunzps5tHxYB	6,109	Problems in downloading Amazon reviews from HF	{ "avatar_url": "https://avatars.githubusercontent.com/u/52964960?v=4", "events_url": "https://api.github.com/users/610v4nn1/events{/privacy}", "followers_url": "https://api.github.com/users/610v4nn1/followers", "following_url": "https://api.github.com/users/610v4nn1/following{/other_user}", "gists_url": "https://api.github.com/users/610v4nn1/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/610v4nn1", "id": 52964960, "login": "610v4nn1", "node_id": "MDQ6VXNlcjUyOTY0OTYw", "organizations_url": "https://api.github.com/users/610v4nn1/orgs", "received_events_url": "https://api.github.com/users/610v4nn1/received_events", "repos_url": "https://api.github.com/users/610v4nn1/repos", "site_admin": false, "starred_url": "https://api.github.com/users/610v4nn1/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/610v4nn1/subscriptions", "type": "User", "url": "https://api.github.com/users/610v4nn1" }	[]	closed	false	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "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}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova" }	[ { "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "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}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova" } ]	null	1	"2023-08-01T08:38:29Z"	"2023-08-02T07:12:07Z"	"2023-08-02T07:12:07Z"	NONE	null	null	null	### Describe the bug I have a script downloading `amazon_reviews_multi`. When the download starts, I get ``` Downloading data files: 0%\| \| 0/1 [00:00<?, ?it/s] Downloading data: 243B [00:00, 1.43MB/s] Downloading data files: 100%\|██████████\| 1/1 [00:01<00:00, 1.54s/it] Extracting data files: 100%\|██████████\| 1/1 [00:00<00:00, 842.40it/s] Downloading data files: 0%\| \| 0/1 [00:00<?, ?it/s] Downloading data: 243B [00:00, 928kB/s] Downloading data files: 100%\|██████████\| 1/1 [00:01<00:00, 1.42s/it] Extracting data files: 100%\|██████████\| 1/1 [00:00<00:00, 832.70it/s] Downloading data files: 0%\| \| 0/1 [00:00<?, ?it/s] Downloading data: 243B [00:00, 1.81MB/s] Downloading data files: 100%\|██████████\| 1/1 [00:01<00:00, 1.40s/it] Extracting data files: 100%\|██████████\| 1/1 [00:00<00:00, 1294.14it/s] Generating train split: 0%\| \| 0/200000 [00:00<?, ? examples/s] ``` the file is clearly too small to contain the requested dataset, in fact it contains en error message: ``` <?xml version="1.0" encoding="UTF-8"?> <Error><Code>AccessDenied</Code><Message>Access Denied</Message><RequestId>AGJWSY3ZADT2QVWE</RequestId><HostId>Gx1O2KXnxtQFqvzDLxyVSTq3+TTJuTnuVFnJL3SP89Yp8UzvYLPTVwd1PpniE4EvQzT3tCaqEJw=</HostId></Error> ``` obviously the script fails: ``` > raise DatasetGenerationError("An error occurred while generating the dataset") from e E datasets.builder.DatasetGenerationError: An error occurred while generating the dataset ``` ### Steps to reproduce the bug 1. load_dataset("amazon_reviews_multi", name="en", split="train", cache_dir="ADDYOURPATHHERE") ### Expected behavior I would expect the dataset to be downloaded and processed ### Environment info * The problem is present with both datasets 2.12.0 and 2.14.2 * python version 3.10.12	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6109/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6109/timeline	null	not_planned	654	false	[ "Thanks for reporting, @610v4nn1.\r\n\r\nIndeed, the source data files are no longer available. We have contacted the authors of the dataset and they report that Amazon has decided to stop distributing the multilingual reviews dataset.\r\n\r\nWe are adding a notification about this issue to the dataset card.\r\n\r\nSee: https://huggingface.co/datasets/amazon_reviews_multi/discussions/4#64c3898db63057f1fd3ce1a0 " ]
https://api.github.com/repos/huggingface/datasets/issues/6108	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6108/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6108/comments	https://api.github.com/repos/huggingface/datasets/issues/6108/events	https://github.com/huggingface/datasets/issues/6108	1,830,347,187	I_kwDODunzps5tGOGz	6,108	Loading local datasets got strangely stuck	{ "avatar_url": "https://avatars.githubusercontent.com/u/48412571?v=4", "events_url": "https://api.github.com/users/LoveCatc/events{/privacy}", "followers_url": "https://api.github.com/users/LoveCatc/followers", "following_url": "https://api.github.com/users/LoveCatc/following{/other_user}", "gists_url": "https://api.github.com/users/LoveCatc/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/LoveCatc", "id": 48412571, "login": "LoveCatc", "node_id": "MDQ6VXNlcjQ4NDEyNTcx", "organizations_url": "https://api.github.com/users/LoveCatc/orgs", "received_events_url": "https://api.github.com/users/LoveCatc/received_events", "repos_url": "https://api.github.com/users/LoveCatc/repos", "site_admin": false, "starred_url": "https://api.github.com/users/LoveCatc/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/LoveCatc/subscriptions", "type": "User", "url": "https://api.github.com/users/LoveCatc" }	[]	open	false	null	[]	null	6	"2023-08-01T02:28:06Z"	"2024-02-05T08:55:16Z"	null	NONE	null	null	null	### Describe the bug I try to use `load_dataset()` to load several local `.jsonl` files as a dataset. Every line of these files is a json structure only containing one key `text` (yeah it is a dataset for NLP model). The code snippet is as: ```python ds = load_dataset("json", data_files=LIST_OF_FILE_PATHS, num_proc=16)['train'] ``` However, I found that the loading process can get stuck -- the progress bar `Generating train split` no more proceed. When I was trying to find the cause and solution, I found a really strange behavior. If I load the dataset in this way: ```python dlist = list() for _ in LIST_OF_FILE_PATHS: dlist.append(load_dataset("json", data_files=_)['train']) ds = concatenate_datasets(dlist) ``` I can actually successfully load all the files despite its slow speed. But if I load them in batch like above, things go wrong. I did try to use Control-C to trace the stuck point but the program cannot be terminated in this way when `num_proc` is set to `None`. The only thing I can do is use Control-Z to hang it up then kill it. If I use more than 2 cpus, a Control-C would simply cause the following error: ```bash ^C Process ForkPoolWorker-1: Traceback (most recent call last): File "/usr/local/lib/python3.10/dist-packages/multiprocess/process.py", line 314, in _bootstrap self.run() File "/usr/local/lib/python3.10/dist-packages/multiprocess/process.py", line 108, in run self._target(self._args, self._kwargs) File "/usr/local/lib/python3.10/dist-packages/multiprocess/pool.py", line 114, in worker task = get() File "/usr/local/lib/python3.10/dist-packages/multiprocess/queues.py", line 368, in get res = self._reader.recv_bytes() File "/usr/local/lib/python3.10/dist-packages/multiprocess/connection.py", line 224, in recv_bytes buf = self._recv_bytes(maxlength) File "/usr/local/lib/python3.10/dist-packages/multiprocess/connection.py", line 422, in _recv_bytes buf = self._recv(4) File "/usr/local/lib/python3.10/dist-packages/multiprocess/connection.py", line 387, in _recv chunk = read(handle, remaining) KeyboardInterrupt Generating train split: 92431 examples [01:23, 1104.25 examples/s] Traceback (most recent call last): File "/usr/local/lib/python3.10/dist-packages/datasets/utils/py_utils.py", line 1373, in iflatmap_unordered yield queue.get(timeout=0.05) File "<string>", line 2, in get File "/usr/local/lib/python3.10/dist-packages/multiprocess/managers.py", line 818, in _callmethod kind, result = conn.recv() File "/usr/local/lib/python3.10/dist-packages/multiprocess/connection.py", line 258, in recv buf = self._recv_bytes() File "/usr/local/lib/python3.10/dist-packages/multiprocess/connection.py", line 422, in _recv_bytes buf = self._recv(4) File "/usr/local/lib/python3.10/dist-packages/multiprocess/connection.py", line 387, in _recv chunk = read(handle, remaining) KeyboardInterrupt During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/mnt/data/liyongyuan/source/batch_load.py", line 11, in <module> a = load_dataset( File "/usr/local/lib/python3.10/dist-packages/datasets/load.py", line 2133, in load_dataset builder_instance.download_and_prepare( File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 954, in download_and_prepare self._download_and_prepare( File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 1049, in _download_and_prepare self._prepare_split(split_generator, *prepare_split_kwargs) File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 1842, in _prepare_split for job_id, done, content in iflatmap_unordered( File "/usr/local/lib/python3.10/dist-packages/datasets/utils/py_utils.py", line 1387, in iflatmap_unordered [async_result.get(timeout=0.05) for async_result in async_results] File "/usr/local/lib/python3.10/dist-packages/datasets/utils/py_utils.py", line 1387, in <listcomp> [async_result.get(timeout=0.05) for async_result in async_results] File "/usr/local/lib/python3.10/dist-packages/multiprocess/pool.py", line 770, in get raise TimeoutError multiprocess.context.TimeoutError ``` I have validated the basic correctness of these `.jsonl` files. They are correctly formatted (or they cannot be loaded singly by `load_dataset`) though some of the json may contain too long text (more than 1e7 characters). I do not know if this could be the problem. And there should not be any bottleneck in system's resource. The whole dataset is ~300GB, and I am using a cloud server with plenty of storage and 1TB ram. Thanks for your efforts and patience! Any suggestion or help would be appreciated. ### Steps to reproduce the bug 1. use load_dataset() with `data_files = LIST_OF_FILES` ### Expected behavior All the files should be smoothly loaded. ### Environment info - Datasets: A private dataset. ~2500 `.jsonl` files. ~300GB in total. Each json structure only contains one key: `text`. Format checked. - `datasets` version: 2.14.2 - Platform: Linux-4.19.91-014.kangaroo.alios7.x86_64-x86_64-with-glibc2.35 - Python version: 3.10.6 - Huggingface_hub version: 0.15.1 - PyArrow version: 10.0.1.dev0+ga6eabc2b.d20230609 - Pandas version: 1.5.2	{ "+1": 1, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 1, "url": "https://api.github.com/repos/huggingface/datasets/issues/6108/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6108/timeline	null	null	655	false	[ "Yesterday I waited for more than 12 hours to make sure it was really stuck instead of proceeding too slow.", "I've had similar weird issues with `load_dataset` as well. Not multiple files, but dataset is quite big, about 50G.", "We use a generic multiprocessing code, so there is little we can do about this - unfortunately, turning off multiprocessing seems to be the only solution. Multithreading would make our code easier to maintain and (most likely) avoid issues such as this one, but we cannot use it until the GIL is dropped (no-GIL Python should be released in 2024, so we can start exploring this then)", "The problem seems to be the `Generating train split`. Is it possible to avoid that? I have a dataset saved, just want to load it but somehow running into issues with that again.", "Hey guys, recently I ran into this problem again and I spent one whole day trying to locate the problem. Finally I found the problem seems to be with `pyarrow`'s json parser, and it seems a long-existing problem. Similar issue can be found in #2181. Anyway, my solution is to adjust the `load_dataset`'s parameter `chunksize`. You can inspect the parameter set in `datasets/packaged_modules/json/json.py`, now the actual chunksize should be very small, and you can increase the value. For me, `chunksize=10<<23` could solve the stuck problem. But I also find that too big `chunksize`, like `10 << 30`, would also cause a stuck, which is rather weird. I think I may explore this when I am free. And hope this can help those who also encounter the same problem. ", "Experiencing the same issue with the `kaist-ai/Feedback-Collection` dataset, which is comparatively small i.e. 100k rows.\r\nCode to reproduce\r\n\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset(\"kaist-ai/Feedback-Collection\")\r\n```\r\n\r\nI have tried setting `num_proc=1` as well as `chunksize=1024, 64` but problem persists. Any pointers?" ]
https://api.github.com/repos/huggingface/datasets/issues/6107	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6107/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6107/comments	https://api.github.com/repos/huggingface/datasets/issues/6107/events	https://github.com/huggingface/datasets/pull/6107	1,829,625,320	PR_kwDODunzps5W0rLR	6,107	Fix deprecation of use_auth_token in file_utils	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "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}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova" }	[]	closed	false	null	[]	null	3	"2023-07-31T16:32:01Z"	"2023-08-03T10:13:32Z"	"2023-08-03T10:04:18Z"	MEMBER	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6107.diff", "html_url": "https://github.com/huggingface/datasets/pull/6107", "merged_at": "2023-08-03T10:04:18Z", "patch_url": "https://github.com/huggingface/datasets/pull/6107.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6107" }	Fix issues with the deprecation of `use_auth_token` introduced by: - #5996 in functions: - `get_authentication_headers_for_url` - `request_etag` - `get_from_cache` Currently, `TypeError` is raised: https://github.com/huggingface/datasets-server/actions/runs/5711650666/job/15484685570?pr=1588 ``` FAILED tests/job_runners/config/test_parquet_and_info.py::test__is_too_big_external_files[None-None-False] - TypeError: get_authentication_headers_for_url() got an unexpected keyword argument 'use_auth_token' FAILED tests/job_runners/config/test_parquet_and_info.py::test_fill_builder_info[None-False] - libcommon.exceptions.FileSystemError: Could not read the parquet files: get_authentication_headers_for_url() got an unexpected keyword argument 'use_auth_token' ``` Related to: - #6094	{ "+1": 1, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 1, "url": "https://api.github.com/repos/huggingface/datasets/issues/6107/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6107/timeline	null	null	656	true	[ "<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) \| 0.007678 / 0.011353 (-0.003675) \| 0.004233 / 0.011008 (-0.006776) \| 0.095934 / 0.038508 (0.057426) \| 0.064201 / 0.023109 (0.041092) \| 0.345765 / 0.275898 (0.069867) \| 0.383089 / 0.323480 (0.059609) \| 0.004084 / 0.007986 (-0.003902) \| 0.003311 / 0.004328 (-0.001017) \| 0.072367 / 0.004250 (0.068117) \| 0.048252 / 0.037052 (0.011200) \| 0.338340 / 0.258489 (0.079851) \| 0.391627 / 0.293841 (0.097786) \| 0.045203 / 0.128546 (-0.083343) \| 0.013494 / 0.075646 (-0.062153) \| 0.314097 / 0.419271 (-0.105174) \| 0.058183 / 0.043533 (0.014650) \| 0.353946 / 0.255139 (0.098807) \| 0.385181 / 0.283200 (0.101981) \| 0.033111 / 0.141683 (-0.108572) \| 1.578489 / 1.452155 (0.126335) \| 1.631660 / 1.492716 (0.138944) \|\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) \| 0.202592 / 0.018006 (0.184586) \| 0.506450 / 0.000490 (0.505961) \| 0.004630 / 0.000200 (0.004430) \| 0.000105 / 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) \| 0.024761 / 0.037411 (-0.012651) \| 0.086295 / 0.014526 (0.071769) \| 0.094063 / 0.176557 (-0.082494) \| 0.154189 / 0.737135 (-0.582947) \| 0.096273 / 0.296338 (-0.200065) \|\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) \| 0.581731 / 0.215209 (0.366522) \| 5.552020 / 2.077655 (3.474365) \| 2.430800 / 1.504120 (0.926680) \| 2.130864 / 1.541195 (0.589669) \| 2.092802 / 1.468490 (0.624312) \| 0.833956 / 4.584777 (-3.750821) \| 4.840859 / 3.745712 (1.095147) \| 4.267812 / 5.269862 (-1.002050) \| 2.663245 / 4.565676 (-1.902432) \| 0.093195 / 0.424275 (-0.331080) \| 0.007942 / 0.007607 (0.000335) \| 0.651457 / 0.226044 (0.425413) \| 6.782986 / 2.268929 (4.514058) \| 3.103307 / 55.444624 (-52.341318) \| 2.373933 / 6.876477 (-4.502544) \| 2.571613 / 2.142072 (0.429540) \| 0.981389 / 4.805227 (-3.823839) \| 0.199019 / 6.500664 (-6.301645) \| 0.065828 / 0.075469 (-0.009641) \|\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) \| 1.429778 / 1.841788 (-0.412009) \| 20.967563 / 8.074308 (12.893255) \| 19.329723 / 10.191392 (9.138331) \| 0.222048 / 0.680424 (-0.458376) \| 0.033507 / 0.534201 (-0.500694) \| 0.436801 / 0.579283 (-0.142482) \| 0.530197 / 0.434364 (0.095833) \| 0.491532 / 0.540337 (-0.048805) \| 0.718216 / 1.386936 (-0.668720) \|\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) \| 0.007798 / 0.011353 (-0.003555) \| 0.004748 / 0.011008 (-0.006260) \| 0.070847 / 0.038508 (0.032339) \| 0.069338 / 0.023109 (0.046229) \| 0.400890 / 0.275898 (0.124992) \| 0.429482 / 0.323480 (0.106002) \| 0.006469 / 0.007986 (-0.001517) \| 0.003514 / 0.004328 (-0.000814) \| 0.069049 / 0.004250 (0.064798) \| 0.059800 / 0.037052 (0.022748) \| 0.415644 / 0.258489 (0.157155) \| 0.432562 / 0.293841 (0.138721) \| 0.043778 / 0.128546 (-0.084768) \| 0.015141 / 0.075646 (-0.060506) \| 0.081521 / 0.419271 (-0.337750) \| 0.054692 / 0.043533 (0.011160) \| 0.404497 / 0.255139 (0.149358) \| 0.419783 / 0.283200 (0.136583) \| 0.029588 / 0.141683 (-0.112094) \| 1.593506 / 1.452155 (0.141351) \| 1.615977 / 1.492716 (0.123261) \|\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) \| 0.270981 / 0.018006 (0.252975) \| 0.522074 / 0.000490 (0.521584) \| 0.026568 / 0.000200 (0.026368) \| 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) \| 0.031551 / 0.037411 (-0.005861) \| 0.086723 / 0.014526 (0.072197) \| 0.103315 / 0.176557 (-0.073242) \| 0.154692 / 0.737135 (-0.582443) \| 0.099472 / 0.296338 (-0.196866) \|\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) \| 0.570238 / 0.215209 (0.355029) \| 5.655963 / 2.077655 (3.578308) \| 2.662670 / 1.504120 (1.158550) \| 2.380903 / 1.541195 (0.839709) \| 2.409467 / 1.468490 (0.940977) \| 0.828055 / 4.584777 (-3.756722) \| 4.964698 / 3.745712 (1.218986) \| 4.299995 / 5.269862 (-0.969867) \| 2.824162 / 4.565676 (-1.741514) \| 0.095872 / 0.424275 (-0.328403) \| 0.007907 / 0.007607 (0.000300) \| 0.701595 / 0.226044 (0.475551) \| 7.131965 / 2.268929 (4.863036) \| 3.250554 / 55.444624 (-52.194070) \| 2.531916 / 6.876477 (-4.344561) \| 2.717908 / 2.142072 (0.575835) \| 1.014479 / 4.805227 (-3.790748) \| 0.223804 / 6.500664 (-6.276861) \| 0.071893 / 0.075469 (-0.003576) \|\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) \| 1.541702 / 1.841788 (-0.300086) \| 21.668219 / 8.074308 (13.593911) \| 18.916032 / 10.191392 (8.724640) \| 0.205915 / 0.680424 (-0.474508) \| 0.026356 / 0.534201 (-0.507845) \| 0.429122 / 0.579283 (-0.150161) \| 0.506110 / 0.434364 (0.071746) \| 0.510148 / 0.540337 (-0.030190) \| 0.724699 / 1.386936 (-0.662237) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c4ca93ff86551b398c979862e7be7305725a240b \"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) \| 0.006884 / 0.011353 (-0.004469) \| 0.004492 / 0.011008 (-0.006516) \| 0.085439 / 0.038508 (0.046931) \| 0.083905 / 0.023109 (0.060796) \| 0.313604 / 0.275898 (0.037706) \| 0.354683 / 0.323480 (0.031203) \| 0.006535 / 0.007986 (-0.001451) \| 0.004318 / 0.004328 (-0.000011) \| 0.066129 / 0.004250 (0.061879) \| 0.057568 / 0.037052 (0.020516) \| 0.317162 / 0.258489 (0.058672) \| 0.372501 / 0.293841 (0.078660) \| 0.031059 / 0.128546 (-0.097488) \| 0.009013 / 0.075646 (-0.066634) \| 0.288794 / 0.419271 (-0.130478) \| 0.053326 / 0.043533 (0.009793) \| 0.314318 / 0.255139 (0.059179) \| 0.357505 / 0.283200 (0.074305) \| 0.027020 / 0.141683 (-0.114663) \| 1.530653 / 1.452155 (0.078498) \| 1.599782 / 1.492716 (0.107066) \|\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) \| 0.278788 / 0.018006 (0.260782) \| 0.626822 / 0.000490 (0.626333) \| 0.003780 / 0.000200 (0.003580) \| 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) \| 0.031703 / 0.037411 (-0.005708) \| 0.085654 / 0.014526 (0.071128) \| 0.754858 / 0.176557 (0.578301) \| 0.212251 / 0.737135 (-0.524885) \| 0.171344 / 0.296338 (-0.124994) \|\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) \| 0.382291 / 0.215209 (0.167082) \| 3.825612 / 2.077655 (1.747958) \| 1.874553 / 1.504120 (0.370433) \| 1.712574 / 1.541195 (0.171379) \| 1.791479 / 1.468490 (0.322989) \| 0.481005 / 4.584777 (-4.103772) \| 3.530559 / 3.745712 (-0.215153) \| 3.395305 / 5.269862 (-1.874557) \| 2.133747 / 4.565676 (-2.431930) \| 0.056139 / 0.424275 (-0.368136) \| 0.007424 / 0.007607 (-0.000183) \| 0.458321 / 0.226044 (0.232277) \| 4.577665 / 2.268929 (2.308736) \| 2.380233 / 55.444624 (-53.064392) \| 2.004060 / 6.876477 (-4.872417) \| 2.290712 / 2.142072 (0.148639) \| 0.570157 / 4.805227 (-4.235070) \| 0.131670 / 6.500664 (-6.368994) \| 0.060684 / 0.075469 (-0.014785) \|\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) \| 21.386663 / 8.074308 (13.312355) \| 14.389440 / 10.191392 (4.198048) \| 0.171177 / 0.680424 (-0.509247) \| 0.018660 / 0.534201 (-0.515541) \| 0.394385 / 0.579283 (-0.184898) \| 0.424942 / 0.434364 (-0.009422) \| 0.463618 / 0.540337 (-0.076719) \| 0.651499 / 1.386936 (-0.735437) \|\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) \| 0.007079 / 0.011353 (-0.004274) \| 0.004615 / 0.011008 (-0.006393) \| 0.066300 / 0.038508 (0.027792) \| 0.092636 / 0.023109 (0.069527) \| 0.399080 / 0.275898 (0.123182) \| 0.429873 / 0.323480 (0.106393) \| 0.006689 / 0.007986 (-0.001297) \| 0.004358 / 0.004328 (0.000029) \| 0.067155 / 0.004250 (0.062905) \| 0.064040 / 0.037052 (0.026988) \| 0.399905 / 0.258489 (0.141416) \| 0.448237 / 0.293841 (0.154397) \| 0.031985 / 0.128546 (-0.096561) \| 0.009053 / 0.075646 (-0.066593) \| 0.071904 / 0.419271 (-0.347368) \| 0.048759 / 0.043533 (0.005227) \| 0.386797 / 0.255139 (0.131658) \| 0.411240 / 0.283200 (0.128040) \| 0.028568 / 0.141683 (-0.113115) \| 1.501037 / 1.452155 (0.048882) \| 1.594560 / 1.492716 (0.101844) \|\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) \| 0.300756 / 0.018006 (0.282750) \| 0.631220 / 0.000490 (0.630730) \| 0.010163 / 0.000200 (0.009963) \| 0.000144 / 0.000054 (0.000089) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033716 / 0.037411 (-0.003695) \| 0.093562 / 0.014526 (0.079037) \| 0.106975 / 0.176557 (-0.069582) \| 0.161919 / 0.737135 (-0.575216) \| 0.113397 / 0.296338 (-0.182942) \|\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) \| 0.410392 / 0.215209 (0.195183) \| 4.094411 / 2.077655 (2.016756) \| 2.085868 / 1.504120 (0.581748) \| 1.959589 / 1.541195 (0.418394) \| 2.096683 / 1.468490 (0.628193) \| 0.494593 / 4.584777 (-4.090184) \| 3.854302 / 3.745712 (0.108590) \| 3.742303 / 5.269862 (-1.527558) \| 2.379983 / 4.565676 (-2.185693) \| 0.058640 / 0.424275 (-0.365635) \| 0.008092 / 0.007607 (0.000484) \| 0.486957 / 0.226044 (0.260912) \| 4.855784 / 2.268929 (2.586855) \| 2.654029 / 55.444624 (-52.790595) \| 2.237627 / 6.876477 (-4.638850) \| 2.536955 / 2.142072 (0.394882) \| 0.622398 / 4.805227 (-4.182829) \| 0.139212 / 6.500664 (-6.361452) \| 0.062805 / 0.075469 (-0.012664) \|\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) \| 1.374862 / 1.841788 (-0.466926) \| 22.797015 / 8.074308 (14.722707) \| 14.393995 / 10.191392 (4.202603) \| 0.196603 / 0.680424 (-0.483821) \| 0.018602 / 0.534201 (-0.515599) \| 0.394568 / 0.579283 (-0.184715) \| 0.408792 / 0.434364 (-0.025572) \| 0.486706 / 0.540337 (-0.053631) \| 0.652365 / 1.386936 (-0.734571) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5713299a88f527ea162a099c2bf2cbceada8fb86 \"CML watermark\")\n" ]
https://api.github.com/repos/huggingface/datasets/issues/6106	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6106/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6106/comments	https://api.github.com/repos/huggingface/datasets/issues/6106/events	https://github.com/huggingface/datasets/issues/6106	1,829,131,223	I_kwDODunzps5tBlPX	6,106	load local json_file as dataset	{ "avatar_url": "https://avatars.githubusercontent.com/u/39040787?v=4", "events_url": "https://api.github.com/users/CiaoHe/events{/privacy}", "followers_url": "https://api.github.com/users/CiaoHe/followers", "following_url": "https://api.github.com/users/CiaoHe/following{/other_user}", "gists_url": "https://api.github.com/users/CiaoHe/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/CiaoHe", "id": 39040787, "login": "CiaoHe", "node_id": "MDQ6VXNlcjM5MDQwNzg3", "organizations_url": "https://api.github.com/users/CiaoHe/orgs", "received_events_url": "https://api.github.com/users/CiaoHe/received_events", "repos_url": "https://api.github.com/users/CiaoHe/repos", "site_admin": false, "starred_url": "https://api.github.com/users/CiaoHe/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/CiaoHe/subscriptions", "type": "User", "url": "https://api.github.com/users/CiaoHe" }	[]	closed	false	null	[]	null	2	"2023-07-31T12:53:49Z"	"2023-08-18T01:46:35Z"	"2023-08-18T01:46:35Z"	NONE	null	null	null	### Describe the bug I tried to load local json file as dataset but failed to parsing json file because some columns are 'float' type. ### Steps to reproduce the bug 1. load json file with certain columns are 'float' type. For example `data = load_data("json", data_files=JSON_PATH)` 2. Then, the error will be triggered like `ArrowInvalid: Could not convert '-0.2253' with type str: tried to convert to double ### Expected behavior Should allow some columns are 'float' type, at least it should convert those columns to str type. I tried to avoid the error by naively convert the float item to str: ```python # if col type is not str, we need to convert it to str mapping = {} for col in keys: if isinstance(dataset[0][col], str): mapping[col] = [row.get(col) for row in dataset] else: mapping[col] = [str(row.get(col)) for row in dataset] ``` ### Environment info - `datasets` version: 2.14.2 - Platform: Linux-5.4.0-52-generic-x86_64-with-glibc2.31 - Python version: 3.9.16 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.0 - Pandas version: 2.0.1	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6106/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6106/timeline	null	completed	657	false	[ "Hi! We use PyArrow to read JSON files, and PyArrow doesn't allow different value types in the same column. #5776 should address this.\r\n\r\nIn the meantime, you can combine `Dataset.from_generator` with the above code to cast the values to the same type. ", "Thanks for your help!" ]
https://api.github.com/repos/huggingface/datasets/issues/6105	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6105/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6105/comments	https://api.github.com/repos/huggingface/datasets/issues/6105/events	https://github.com/huggingface/datasets/pull/6105	1,829,008,430	PR_kwDODunzps5WyiJD	6,105	Fix error when loading from GCP bucket	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "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}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova" }	[]	closed	false	null	[]	null	5	"2023-07-31T11:44:46Z"	"2023-08-01T10:48:52Z"	"2023-08-01T10:38:54Z"	MEMBER	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6105.diff", "html_url": "https://github.com/huggingface/datasets/pull/6105", "merged_at": "2023-08-01T10:38:54Z", "patch_url": "https://github.com/huggingface/datasets/pull/6105.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6105" }	Fix `resolve_pattern` for filesystems with tuple protocol. Fix #6100. The bug code lines were introduced by: - #6028	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6105/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6105/timeline	null	null	658	true	[ "_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) \| 0.006706 / 0.011353 (-0.004647) \| 0.004016 / 0.011008 (-0.006992) \| 0.083696 / 0.038508 (0.045188) \| 0.074340 / 0.023109 (0.051230) \| 0.327338 / 0.275898 (0.051440) \| 0.366663 / 0.323480 (0.043183) \| 0.004052 / 0.007986 (-0.003934) \| 0.003423 / 0.004328 (-0.000906) \| 0.064576 / 0.004250 (0.060326) \| 0.055037 / 0.037052 (0.017985) \| 0.325089 / 0.258489 (0.066600) \| 0.379986 / 0.293841 (0.086145) \| 0.031614 / 0.128546 (-0.096932) \| 0.008553 / 0.075646 (-0.067094) \| 0.287430 / 0.419271 (-0.131841) \| 0.053032 / 0.043533 (0.009499) \| 0.318990 / 0.255139 (0.063851) \| 0.364426 / 0.283200 (0.081226) \| 0.024926 / 0.141683 (-0.116757) \| 1.461835 / 1.452155 (0.009680) \| 1.557172 / 1.492716 (0.064456) \|\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) \| 0.212430 / 0.018006 (0.194424) \| 0.512891 / 0.000490 (0.512402) \| 0.004772 / 0.000200 (0.004572) \| 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) \| 0.027873 / 0.037411 (-0.009538) \| 0.085598 / 0.014526 (0.071072) \| 0.097330 / 0.176557 (-0.079226) \| 0.152235 / 0.737135 (-0.584900) \| 0.097787 / 0.296338 (-0.198552) \|\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) \| 0.384645 / 0.215209 (0.169436) \| 3.841161 / 2.077655 (1.763506) \| 1.863696 / 1.504120 (0.359577) \| 1.685082 / 1.541195 (0.143887) \| 1.772904 / 1.468490 (0.304414) \| 0.480177 / 4.584777 (-4.104599) \| 3.601537 / 3.745712 (-0.144175) \| 3.273647 / 5.269862 (-1.996214) \| 2.014415 / 4.565676 (-2.551261) \| 0.056668 / 0.424275 (-0.367607) \| 0.007257 / 0.007607 (-0.000350) \| 0.458194 / 0.226044 (0.232150) \| 4.577311 / 2.268929 (2.308382) \| 2.333983 / 55.444624 (-53.110641) \| 1.964508 / 6.876477 (-4.911969) \| 2.193379 / 2.142072 (0.051307) \| 0.577557 / 4.805227 (-4.227670) \| 0.133899 / 6.500664 (-6.366765) \| 0.060804 / 0.075469 (-0.014665) \|\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) \| 1.249490 / 1.841788 (-0.592298) \| 19.791875 / 8.074308 (11.717567) \| 14.418728 / 10.191392 (4.227336) \| 0.167788 / 0.680424 (-0.512636) \| 0.018993 / 0.534201 (-0.515208) \| 0.396141 / 0.579283 (-0.183142) \| 0.412427 / 0.434364 (-0.021937) \| 0.456718 / 0.540337 (-0.083619) \| 0.641383 / 1.386936 (-0.745553) \|\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) \| 0.006546 / 0.011353 (-0.004807) \| 0.004059 / 0.011008 (-0.006949) \| 0.064523 / 0.038508 (0.026015) \| 0.074988 / 0.023109 (0.051878) \| 0.388932 / 0.275898 (0.113034) \| 0.424496 / 0.323480 (0.101016) \| 0.005226 / 0.007986 (-0.002760) \| 0.003409 / 0.004328 (-0.000920) \| 0.064284 / 0.004250 (0.060034) \| 0.056829 / 0.037052 (0.019777) \| 0.386457 / 0.258489 (0.127968) \| 0.428063 / 0.293841 (0.134222) \| 0.031411 / 0.128546 (-0.097136) \| 0.008577 / 0.075646 (-0.067070) \| 0.070357 / 0.419271 (-0.348915) \| 0.048920 / 0.043533 (0.005388) \| 0.385197 / 0.255139 (0.130058) \| 0.407167 / 0.283200 (0.123967) \| 0.024469 / 0.141683 (-0.117214) \| 1.482733 / 1.452155 (0.030578) \| 1.539027 / 1.492716 (0.046311) \|\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) \| 0.227532 / 0.018006 (0.209526) \| 0.448792 / 0.000490 (0.448302) \| 0.004139 / 0.000200 (0.003939) \| 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) \| 0.031004 / 0.037411 (-0.006408) \| 0.088163 / 0.014526 (0.073637) \| 0.101452 / 0.176557 (-0.075105) \| 0.152907 / 0.737135 (-0.584229) \| 0.102325 / 0.296338 (-0.194014) \|\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) \| 0.418092 / 0.215209 (0.202883) \| 4.162277 / 2.077655 (2.084623) \| 2.232987 / 1.504120 (0.728867) \| 2.143583 / 1.541195 (0.602388) \| 2.246142 / 1.468490 (0.777652) \| 0.490181 / 4.584777 (-4.094596) \| 3.631514 / 3.745712 (-0.114198) \| 3.315025 / 5.269862 (-1.954837) \| 2.101853 / 4.565676 (-2.463823) \| 0.057905 / 0.424275 (-0.366370) \| 0.007686 / 0.007607 (0.000079) \| 0.489965 / 0.226044 (0.263921) \| 4.894375 / 2.268929 (2.625447) \| 2.655459 / 55.444624 (-52.789165) \| 2.262211 / 6.876477 (-4.614266) \| 2.505335 / 2.142072 (0.363263) \| 0.591329 / 4.805227 (-4.213898) \| 0.133554 / 6.500664 (-6.367110) \| 0.061922 / 0.075469 (-0.013547) \|\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) \| 1.347483 / 1.841788 (-0.494304) \| 20.027011 / 8.074308 (11.952703) \| 14.430737 / 10.191392 (4.239345) \| 0.165767 / 0.680424 (-0.514657) \| 0.018460 / 0.534201 (-0.515741) \| 0.393790 / 0.579283 (-0.185494) \| 0.407213 / 0.434364 (-0.027151) \| 0.474459 / 0.540337 (-0.065879) \| 0.635054 / 1.386936 (-0.751882) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7f575111481e2e2f4d4fc9180771797f69ebcc44 \"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) \| 0.007652 / 0.011353 (-0.003701) \| 0.004581 / 0.011008 (-0.006427) \| 0.101629 / 0.038508 (0.063121) \| 0.090233 / 0.023109 (0.067124) \| 0.392789 / 0.275898 (0.116891) \| 0.432163 / 0.323480 (0.108683) \| 0.004694 / 0.007986 (-0.003292) \| 0.003927 / 0.004328 (-0.000401) \| 0.076533 / 0.004250 (0.072282) \| 0.064442 / 0.037052 (0.027390) \| 0.397539 / 0.258489 (0.139050) \| 0.441323 / 0.293841 (0.147482) \| 0.036278 / 0.128546 (-0.092268) \| 0.009810 / 0.075646 (-0.065836) \| 0.343537 / 0.419271 (-0.075734) \| 0.060273 / 0.043533 (0.016740) \| 0.395023 / 0.255139 (0.139884) \| 0.427210 / 0.283200 (0.144011) \| 0.031717 / 0.141683 (-0.109966) \| 1.771221 / 1.452155 (0.319066) \| 1.896336 / 1.492716 (0.403620) \|\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) \| 0.235081 / 0.018006 (0.217075) \| 0.512781 / 0.000490 (0.512292) \| 0.004920 / 0.000200 (0.004721) \| 0.000097 / 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) \| 0.033525 / 0.037411 (-0.003887) \| 0.104416 / 0.014526 (0.089890) \| 0.115695 / 0.176557 (-0.060861) \| 0.182216 / 0.737135 (-0.554919) \| 0.116259 / 0.296338 (-0.180079) \|\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) \| 0.454817 / 0.215209 (0.239608) \| 4.527753 / 2.077655 (2.450098) \| 2.222273 / 1.504120 (0.718153) \| 2.038448 / 1.541195 (0.497253) \| 2.179444 / 1.468490 (0.710953) \| 0.573665 / 4.584777 (-4.011112) \| 4.504943 / 3.745712 (0.759231) \| 3.848435 / 5.269862 (-1.421427) \| 2.455185 / 4.565676 (-2.110491) \| 0.067985 / 0.424275 (-0.356290) \| 0.008719 / 0.007607 (0.001112) \| 0.552405 / 0.226044 (0.326360) \| 5.515251 / 2.268929 (3.246322) \| 2.851557 / 55.444624 (-52.593067) \| 2.463070 / 6.876477 (-4.413407) \| 2.761596 / 2.142072 (0.619524) \| 0.688561 / 4.805227 (-4.116667) \| 0.159946 / 6.500664 (-6.340718) \| 0.075435 / 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) \| 1.505178 / 1.841788 (-0.336610) \| 23.555236 / 8.074308 (15.480928) \| 17.272759 / 10.191392 (7.081367) \| 0.206495 / 0.680424 (-0.473928) \| 0.021869 / 0.534201 (-0.512332) \| 0.469271 / 0.579283 (-0.110012) \| 0.469200 / 0.434364 (0.034837) \| 0.542437 / 0.540337 (0.002100) \| 0.792864 / 1.386936 (-0.594072) \|\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) \| 0.008151 / 0.011353 (-0.003202) \| 0.004992 / 0.011008 (-0.006016) \| 0.079545 / 0.038508 (0.041037) \| 0.100234 / 0.023109 (0.077125) \| 0.492791 / 0.275898 (0.216893) \| 0.511315 / 0.323480 (0.187835) \| 0.006878 / 0.007986 (-0.001108) \| 0.003807 / 0.004328 (-0.000522) \| 0.080876 / 0.004250 (0.076625) \| 0.076734 / 0.037052 (0.039681) \| 0.518247 / 0.258489 (0.259758) \| 0.524202 / 0.293841 (0.230361) \| 0.039896 / 0.128546 (-0.088650) \| 0.016581 / 0.075646 (-0.059065) \| 0.101228 / 0.419271 (-0.318043) \| 0.061990 / 0.043533 (0.018457) \| 0.490611 / 0.255139 (0.235472) \| 0.514930 / 0.283200 (0.231730) \| 0.028680 / 0.141683 (-0.113002) \| 1.966215 / 1.452155 (0.514061) \| 2.047757 / 1.492716 (0.555040) \|\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) \| 0.286807 / 0.018006 (0.268801) \| 0.506448 / 0.000490 (0.505959) \| 0.005867 / 0.000200 (0.005667) \| 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) \| 0.037141 / 0.037411 (-0.000270) \| 0.113232 / 0.014526 (0.098706) \| 0.121201 / 0.176557 (-0.055356) \| 0.185472 / 0.737135 (-0.551663) \| 0.122896 / 0.296338 (-0.173442) \|\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) \| 0.514491 / 0.215209 (0.299282) \| 4.942457 / 2.077655 (2.864802) \| 2.533519 / 1.504120 (1.029399) \| 2.371011 / 1.541195 (0.829817) \| 2.495604 / 1.468490 (1.027114) \| 0.576224 / 4.584777 (-4.008553) \| 4.368584 / 3.745712 (0.622872) \| 3.885598 / 5.269862 (-1.384263) \| 2.443596 / 4.565676 (-2.122080) \| 0.068905 / 0.424275 (-0.355371) \| 0.009171 / 0.007607 (0.001564) \| 0.584977 / 0.226044 (0.358932) \| 5.835220 / 2.268929 (3.566291) \| 3.189037 / 55.444624 (-52.255588) \| 2.753228 / 6.876477 (-4.123249) \| 3.009062 / 2.142072 (0.866990) \| 0.690179 / 4.805227 (-4.115048) \| 0.157981 / 6.500664 (-6.342683) \| 0.074518 / 0.075469 (-0.000951) \|\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) \| 1.599907 / 1.841788 (-0.241880) \| 23.853903 / 8.074308 (15.779595) \| 17.419796 / 10.191392 (7.228404) \| 0.204974 / 0.680424 (-0.475450) \| 0.022014 / 0.534201 (-0.512187) \| 0.473379 / 0.579283 (-0.105905) \| 0.461346 / 0.434364 (0.026982) \| 0.564881 / 0.540337 (0.024543) \| 0.752933 / 1.386936 (-0.634003) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f49c9ca993fa600fae0e327636d52657328e7ffb \"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) \| 0.006547 / 0.011353 (-0.004805) \| 0.004020 / 0.011008 (-0.006988) \| 0.086828 / 0.038508 (0.048320) \| 0.072924 / 0.023109 (0.049815) \| 0.312847 / 0.275898 (0.036949) \| 0.344605 / 0.323480 (0.021125) \| 0.004117 / 0.007986 (-0.003868) \| 0.004365 / 0.004328 (0.000037) \| 0.066755 / 0.004250 (0.062505) \| 0.053248 / 0.037052 (0.016195) \| 0.315744 / 0.258489 (0.057255) \| 0.362426 / 0.293841 (0.068585) \| 0.030732 / 0.128546 (-0.097814) \| 0.008516 / 0.075646 (-0.067130) \| 0.289927 / 0.419271 (-0.129345) \| 0.052115 / 0.043533 (0.008582) \| 0.308026 / 0.255139 (0.052887) \| 0.343115 / 0.283200 (0.059915) \| 0.024131 / 0.141683 (-0.117551) \| 1.464290 / 1.452155 (0.012135) \| 1.559359 / 1.492716 (0.066642) \|\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) \| 0.216744 / 0.018006 (0.198738) \| 0.473156 / 0.000490 (0.472666) \| 0.004176 / 0.000200 (0.003977) \| 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) \| 0.028500 / 0.037411 (-0.008911) \| 0.083892 / 0.014526 (0.069366) \| 0.131851 / 0.176557 (-0.044705) \| 0.162202 / 0.737135 (-0.574933) \| 0.127989 / 0.296338 (-0.168349) \|\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) \| 0.404555 / 0.215209 (0.189346) \| 4.035989 / 2.077655 (1.958334) \| 2.025174 / 1.504120 (0.521054) \| 1.835785 / 1.541195 (0.294590) \| 1.909819 / 1.468490 (0.441329) \| 0.475352 / 4.584777 (-4.109425) \| 3.548055 / 3.745712 (-0.197657) \| 3.234782 / 5.269862 (-2.035080) \| 2.010305 / 4.565676 (-2.555371) \| 0.056507 / 0.424275 (-0.367768) \| 0.007259 / 0.007607 (-0.000348) \| 0.482021 / 0.226044 (0.255977) \| 4.818559 / 2.268929 (2.549631) \| 2.528765 / 55.444624 (-52.915860) \| 2.159804 / 6.876477 (-4.716673) \| 2.380640 / 2.142072 (0.238567) \| 0.585005 / 4.805227 (-4.220222) \| 0.133811 / 6.500664 (-6.366853) \| 0.060686 / 0.075469 (-0.014783) \|\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) \| 1.260902 / 1.841788 (-0.580886) \| 19.500215 / 8.074308 (11.425907) \| 14.164698 / 10.191392 (3.973306) \| 0.172492 / 0.680424 (-0.507932) \| 0.018221 / 0.534201 (-0.515980) \| 0.392609 / 0.579283 (-0.186674) \| 0.423265 / 0.434364 (-0.011099) \| 0.454705 / 0.540337 (-0.085633) \| 0.639856 / 1.386936 (-0.747080) \|\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) \| 0.006656 / 0.011353 (-0.004697) \| 0.003903 / 0.011008 (-0.007106) \| 0.063780 / 0.038508 (0.025272) \| 0.076848 / 0.023109 (0.053739) \| 0.379429 / 0.275898 (0.103531) \| 0.442554 / 0.323480 (0.119074) \| 0.005327 / 0.007986 (-0.002658) \| 0.003318 / 0.004328 (-0.001010) \| 0.064307 / 0.004250 (0.060056) \| 0.057183 / 0.037052 (0.020131) \| 0.398163 / 0.258489 (0.139674) \| 0.448532 / 0.293841 (0.154691) \| 0.031322 / 0.128546 (-0.097224) \| 0.008462 / 0.075646 (-0.067184) \| 0.070354 / 0.419271 (-0.348917) \| 0.048420 / 0.043533 (0.004887) \| 0.368304 / 0.255139 (0.113165) \| 0.428786 / 0.283200 (0.145587) \| 0.023921 / 0.141683 (-0.117762) \| 1.499281 / 1.452155 (0.047126) \| 1.554448 / 1.492716 (0.061731) \|\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) \| 0.238830 / 0.018006 (0.220824) \| 0.464196 / 0.000490 (0.463706) \| 0.004812 / 0.000200 (0.004613) \| 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) \| 0.031642 / 0.037411 (-0.005770) \| 0.089205 / 0.014526 (0.074679) \| 0.101577 / 0.176557 (-0.074980) \| 0.154993 / 0.737135 (-0.582142) \| 0.102935 / 0.296338 (-0.193403) \|\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) \| 0.415218 / 0.215209 (0.200009) \| 4.137711 / 2.077655 (2.060056) \| 2.128757 / 1.504120 (0.624637) \| 1.961086 / 1.541195 (0.419891) \| 2.047552 / 1.468490 (0.579061) \| 0.486953 / 4.584777 (-4.097824) \| 3.587851 / 3.745712 (-0.157861) \| 3.280771 / 5.269862 (-1.989090) \| 2.016980 / 4.565676 (-2.548697) \| 0.057284 / 0.424275 (-0.366991) \| 0.007705 / 0.007607 (0.000097) \| 0.492242 / 0.226044 (0.266197) \| 4.923213 / 2.268929 (2.654285) \| 2.672528 / 55.444624 (-52.772097) \| 2.292862 / 6.876477 (-4.583614) \| 2.517410 / 2.142072 (0.375337) \| 0.614798 / 4.805227 (-4.190429) \| 0.149642 / 6.500664 (-6.351023) \| 0.062898 / 0.075469 (-0.012571) \|\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) \| 1.323266 / 1.841788 (-0.518522) \| 19.891504 / 8.074308 (11.817196) \| 14.115069 / 10.191392 (3.923677) \| 0.169859 / 0.680424 (-0.510564) \| 0.018538 / 0.534201 (-0.515663) \| 0.398456 / 0.579283 (-0.180827) \| 0.410111 / 0.434364 (-0.024253) \| 0.483198 / 0.540337 (-0.057139) \| 0.639283 / 1.386936 (-0.747653) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#01e2194f2aab6aa98686a2069ee5201b69a53c14 \"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) \| 0.007731 / 0.011353 (-0.003622) \| 0.004064 / 0.011008 (-0.006944) \| 0.095261 / 0.038508 (0.056753) \| 0.081594 / 0.023109 (0.058485) \| 0.390413 / 0.275898 (0.114515) \| 0.415542 / 0.323480 (0.092063) \| 0.006031 / 0.007986 (-0.001954) \| 0.003817 / 0.004328 (-0.000512) \| 0.066381 / 0.004250 (0.062131) \| 0.058262 / 0.037052 (0.021210) \| 0.383626 / 0.258489 (0.125137) \| 0.443237 / 0.293841 (0.149396) \| 0.034358 / 0.128546 (-0.094188) \| 0.010002 / 0.075646 (-0.065644) \| 0.317472 / 0.419271 (-0.101800) \| 0.057428 / 0.043533 (0.013895) \| 0.393929 / 0.255139 (0.138790) \| 0.444572 / 0.283200 (0.161373) \| 0.026295 / 0.141683 (-0.115388) \| 1.603639 / 1.452155 (0.151484) \| 1.707750 / 1.492716 (0.215034) \|\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) \| 0.222171 / 0.018006 (0.204165) \| 0.491762 / 0.000490 (0.491272) \| 0.003389 / 0.000200 (0.003189) \| 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) \| 0.029420 / 0.037411 (-0.007991) \| 0.086201 / 0.014526 (0.071676) \| 0.100150 / 0.176557 (-0.076406) \| 0.162338 / 0.737135 (-0.574797) \| 0.099349 / 0.296338 (-0.196989) \|\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) \| 0.445976 / 0.215209 (0.230767) \| 4.460197 / 2.077655 (2.382542) \| 2.211767 / 1.504120 (0.707647) \| 1.988740 / 1.541195 (0.447545) \| 2.052289 / 1.468490 (0.583799) \| 0.570321 / 4.584777 (-4.014456) \| 4.148777 / 3.745712 (0.403065) \| 3.750977 / 5.269862 (-1.518885) \| 2.309443 / 4.565676 (-2.256234) \| 0.064552 / 0.424275 (-0.359724) \| 0.008167 / 0.007607 (0.000560) \| 0.523283 / 0.226044 (0.297238) \| 5.349347 / 2.268929 (3.080419) \| 2.710292 / 55.444624 (-52.734332) \| 2.344252 / 6.876477 (-4.532225) \| 2.549903 / 2.142072 (0.407831) \| 0.665942 / 4.805227 (-4.139285) \| 0.154108 / 6.500664 (-6.346556) \| 0.070181 / 0.075469 (-0.005289) \|\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) \| 1.455733 / 1.841788 (-0.386054) \| 21.846958 / 8.074308 (13.772650) \| 15.133865 / 10.191392 (4.942473) \| 0.199009 / 0.680424 (-0.481415) \| 0.021299 / 0.534201 (-0.512902) \| 0.421555 / 0.579283 (-0.157729) \| 0.437639 / 0.434364 (0.003275) \| 0.498568 / 0.540337 (-0.041769) \| 0.719649 / 1.386936 (-0.667287) \|\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) \| 0.007858 / 0.011353 (-0.003495) \| 0.004629 / 0.011008 (-0.006380) \| 0.075701 / 0.038508 (0.037193) \| 0.084425 / 0.023109 (0.061316) \| 0.436650 / 0.275898 (0.160752) \| 0.466046 / 0.323480 (0.142566) \| 0.006042 / 0.007986 (-0.001944) \| 0.003834 / 0.004328 (-0.000495) \| 0.074729 / 0.004250 (0.070478) \| 0.065983 / 0.037052 (0.028931) \| 0.447239 / 0.258489 (0.188750) \| 0.466728 / 0.293841 (0.172887) \| 0.035814 / 0.128546 (-0.092733) \| 0.009919 / 0.075646 (-0.065727) \| 0.081151 / 0.419271 (-0.338120) \| 0.057256 / 0.043533 (0.013723) \| 0.435609 / 0.255139 (0.180470) \| 0.448901 / 0.283200 (0.165701) \| 0.026325 / 0.141683 (-0.115357) \| 1.745658 / 1.452155 (0.293503) \| 1.804137 / 1.492716 (0.311421) \|\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) \| 0.302551 / 0.018006 (0.284544) \| 0.498438 / 0.000490 (0.497948) \| 0.038562 / 0.000200 (0.038362) \| 0.000411 / 0.000054 (0.000356) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.035573 / 0.037411 (-0.001839) \| 0.104957 / 0.014526 (0.090431) \| 0.117208 / 0.176557 (-0.059349) \| 0.178935 / 0.737135 (-0.558200) \| 0.124577 / 0.296338 (-0.171761) \|\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) \| 0.467076 / 0.215209 (0.251867) \| 4.698852 / 2.077655 (2.621197) \| 2.453389 / 1.504120 (0.949269) \| 2.257378 / 1.541195 (0.716183) \| 2.338615 / 1.468490 (0.870125) \| 0.542379 / 4.584777 (-4.042398) \| 4.066895 / 3.745712 (0.321183) \| 3.689540 / 5.269862 (-1.580321) \| 2.268997 / 4.565676 (-2.296679) \| 0.064754 / 0.424275 (-0.359521) \| 0.008866 / 0.007607 (0.001259) \| 0.546732 / 0.226044 (0.320687) \| 5.487765 / 2.268929 (3.218836) \| 2.974126 / 55.444624 (-52.470498) \| 2.585492 / 6.876477 (-4.290985) \| 2.754417 / 2.142072 (0.612345) \| 0.652045 / 4.805227 (-4.153183) \| 0.145597 / 6.500664 (-6.355067) \| 0.065415 / 0.075469 (-0.010054) \|\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) \| 1.553970 / 1.841788 (-0.287818) \| 22.300954 / 8.074308 (14.226646) \| 15.640990 / 10.191392 (5.449598) \| 0.170903 / 0.680424 (-0.509521) \| 0.021750 / 0.534201 (-0.512451) \| 0.455316 / 0.579283 (-0.123967) \| 0.455051 / 0.434364 (0.020687) \| 0.536174 / 0.540337 (-0.004164) \| 0.735930 / 1.386936 (-0.651006) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f68139846c26b43631bd235114854f4bf6cb9954 \"CML watermark\")\n" ]
https://api.github.com/repos/huggingface/datasets/issues/6104	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6104/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6104/comments	https://api.github.com/repos/huggingface/datasets/issues/6104/events	https://github.com/huggingface/datasets/issues/6104	1,828,959,107	I_kwDODunzps5tA7OD	6,104	HF Datasets data access is extremely slow even when in memory	{ "avatar_url": "https://avatars.githubusercontent.com/u/36224762?v=4", "events_url": "https://api.github.com/users/NightMachinery/events{/privacy}", "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}", "gravatar_id": "", "html_url": "https://github.com/NightMachinery", "id": 36224762, "login": "NightMachinery", "node_id": "MDQ6VXNlcjM2MjI0NzYy", "organizations_url": "https://api.github.com/users/NightMachinery/orgs", "received_events_url": "https://api.github.com/users/NightMachinery/received_events", "repos_url": "https://api.github.com/users/NightMachinery/repos", "site_admin": false, "starred_url": "https://api.github.com/users/NightMachinery/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/NightMachinery/subscriptions", "type": "User", "url": "https://api.github.com/users/NightMachinery" }	[]	open	false	null	[]	null	1	"2023-07-31T11:12:19Z"	"2023-08-01T11:22:43Z"	null	CONTRIBUTOR	null	null	null	### Describe the bug Doing a simple `some_dataset[:10]` can take more than a minute. Profiling it: <img width="1280" alt="image" src="https://github.com/huggingface/datasets/assets/36224762/e641fb95-ff02-4072-9016-5416a65f75ab"> `some_dataset` is completely in memory with no disk cache. This is proving fatal to my usage of HF Datasets. Is there a way I can forgo the arrow format and store the dataset as PyTorch tensors so that `_tensorize` is not needed? And is `_consolidate` supposed to take this long? It's faster to produce the dataset from scratch than to access it from HF Datasets! ### Steps to reproduce the bug I have uploaded the dataset that causes this problem [here](https://huggingface.co/datasets/NightMachinery/hf_datasets_bug1). ```python #!/usr/bin/env python3 import sys import time import torch from datasets import load_dataset def main(dataset_name): # Start the timer start_time = time.time() # Load the dataset from Hugging Face Hub dataset = load_dataset(dataset_name) # Set the dataset format as torch dataset.set_format(type="torch") # Perform an identity map dataset = dataset.map(lambda example: example, batched=True, batch_size=20) # End the timer end_time = time.time() # Print the time taken print(f"Time taken: {end_time - start_time:.2f} seconds") if __name__ == "__main__": dataset_name = "NightMachinery/hf_datasets_bug1" print(f"dataset_name: {dataset_name}") main(dataset_name) ``` ### Expected behavior _ ### Environment info - `datasets` version: 2.13.1 - Platform: Linux-5.15.0-76-generic-x86_64-with-glibc2.35 - Python version: 3.10.12 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 2.0.3	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6104/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6104/timeline	null	null	659	false	[ "Possibly related:\r\n- https://github.com/pytorch/pytorch/issues/22462" ]
https://api.github.com/repos/huggingface/datasets/issues/6103	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6103/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6103/comments	https://api.github.com/repos/huggingface/datasets/issues/6103/events	https://github.com/huggingface/datasets/pull/6103	1,828,515,165	PR_kwDODunzps5Ww2gV	6,103	Set dev version	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "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}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova" }	[]	closed	false	null	[]	null	3	"2023-07-31T06:44:05Z"	"2023-07-31T06:55:58Z"	"2023-07-31T06:45:41Z"	MEMBER	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6103.diff", "html_url": "https://github.com/huggingface/datasets/pull/6103", "merged_at": "2023-07-31T06:45:41Z", "patch_url": "https://github.com/huggingface/datasets/pull/6103.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6103" }	null	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6103/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6103/timeline	null	null	660	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6103). 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) \| 0.006528 / 0.011353 (-0.004825) \| 0.003909 / 0.011008 (-0.007099) \| 0.083954 / 0.038508 (0.045446) \| 0.070513 / 0.023109 (0.047404) \| 0.344362 / 0.275898 (0.068464) \| 0.370278 / 0.323480 (0.046798) \| 0.005395 / 0.007986 (-0.002591) \| 0.003323 / 0.004328 (-0.001005) \| 0.064538 / 0.004250 (0.060288) \| 0.055616 / 0.037052 (0.018564) \| 0.353590 / 0.258489 (0.095101) \| 0.382159 / 0.293841 (0.088318) \| 0.031133 / 0.128546 (-0.097414) \| 0.008429 / 0.075646 (-0.067217) \| 0.288665 / 0.419271 (-0.130606) \| 0.052626 / 0.043533 (0.009093) \| 0.347676 / 0.255139 (0.092537) \| 0.363726 / 0.283200 (0.080526) \| 0.021956 / 0.141683 (-0.119727) \| 1.506091 / 1.452155 (0.053936) \| 1.563940 / 1.492716 (0.071223) \|\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) \| 0.207658 / 0.018006 (0.189652) \| 0.473411 / 0.000490 (0.472922) \| 0.005437 / 0.000200 (0.005237) \| 0.000087 / 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) \| 0.027769 / 0.037411 (-0.009643) \| 0.082566 / 0.014526 (0.068040) \| 0.092700 / 0.176557 (-0.083857) \| 0.152589 / 0.737135 (-0.584546) \| 0.093772 / 0.296338 (-0.202566) \|\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) \| 0.401072 / 0.215209 (0.185863) \| 3.997922 / 2.077655 (1.920267) \| 2.028223 / 1.504120 (0.524103) \| 1.845229 / 1.541195 (0.304035) \| 1.883980 / 1.468490 (0.415489) \| 0.485112 / 4.584777 (-4.099665) \| 3.657048 / 3.745712 (-0.088664) \| 4.998475 / 5.269862 (-0.271386) \| 3.007417 / 4.565676 (-1.558259) \| 0.057003 / 0.424275 (-0.367272) \| 0.007270 / 0.007607 (-0.000338) \| 0.482220 / 0.226044 (0.256176) \| 4.817560 / 2.268929 (2.548631) \| 2.484285 / 55.444624 (-52.960340) \| 2.163327 / 6.876477 (-4.713149) \| 2.326412 / 2.142072 (0.184339) \| 0.600349 / 4.805227 (-4.204878) \| 0.134245 / 6.500664 (-6.366419) \| 0.060705 / 0.075469 (-0.014764) \|\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) \| 1.281440 / 1.841788 (-0.560347) \| 19.165591 / 8.074308 (11.091283) \| 14.007728 / 10.191392 (3.816336) \| 0.168367 / 0.680424 (-0.512057) \| 0.018149 / 0.534201 (-0.516052) \| 0.391688 / 0.579283 (-0.187595) \| 0.414528 / 0.434364 (-0.019836) \| 0.456964 / 0.540337 (-0.083373) \| 0.613807 / 1.386936 (-0.773129) \|\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) \| 0.006502 / 0.011353 (-0.004851) \| 0.003956 / 0.011008 (-0.007052) \| 0.064297 / 0.038508 (0.025789) \| 0.073430 / 0.023109 (0.050321) \| 0.364113 / 0.275898 (0.088215) \| 0.389021 / 0.323480 (0.065541) \| 0.005375 / 0.007986 (-0.002611) \| 0.003363 / 0.004328 (-0.000966) \| 0.064404 / 0.004250 (0.060153) \| 0.056664 / 0.037052 (0.019612) \| 0.365504 / 0.258489 (0.107015) \| 0.398477 / 0.293841 (0.104636) \| 0.031739 / 0.128546 (-0.096807) \| 0.008663 / 0.075646 (-0.066984) \| 0.070757 / 0.419271 (-0.348515) \| 0.051014 / 0.043533 (0.007481) \| 0.368287 / 0.255139 (0.113148) \| 0.382941 / 0.283200 (0.099742) \| 0.024642 / 0.141683 (-0.117041) \| 1.516721 / 1.452155 (0.064567) \| 1.557625 / 1.492716 (0.064908) \|\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) \| 0.208248 / 0.018006 (0.190242) \| 0.443560 / 0.000490 (0.443070) \| 0.004004 / 0.000200 (0.003805) \| 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) \| 0.031116 / 0.037411 (-0.006295) \| 0.086814 / 0.014526 (0.072288) \| 0.099111 / 0.176557 (-0.077445) \| 0.155032 / 0.737135 (-0.582104) \| 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) \| 0.413080 / 0.215209 (0.197871) \| 4.115546 / 2.077655 (2.037891) \| 2.162073 / 1.504120 (0.657953) \| 2.008107 / 1.541195 (0.466912) \| 2.052317 / 1.468490 (0.583827) \| 0.485158 / 4.584777 (-4.099619) \| 3.617478 / 3.745712 (-0.128234) \| 5.030564 / 5.269862 (-0.239298) \| 2.787812 / 4.565676 (-1.777865) \| 0.057466 / 0.424275 (-0.366809) \| 0.007656 / 0.007607 (0.000049) \| 0.490037 / 0.226044 (0.263993) \| 4.887896 / 2.268929 (2.618968) \| 2.639644 / 55.444624 (-52.804981) \| 2.258051 / 6.876477 (-4.618426) \| 2.417573 / 2.142072 (0.275500) \| 0.604473 / 4.805227 (-4.200754) \| 0.134770 / 6.500664 (-6.365894) \| 0.061709 / 0.075469 (-0.013760) \|\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) \| 1.342500 / 1.841788 (-0.499288) \| 19.354990 / 8.074308 (11.280682) \| 14.161975 / 10.191392 (3.970583) \| 0.157084 / 0.680424 (-0.523339) \| 0.018227 / 0.534201 (-0.515974) \| 0.391819 / 0.579283 (-0.187464) \| 0.399157 / 0.434364 (-0.035207) \| 0.460582 / 0.540337 (-0.079756) \| 0.612183 / 1.386936 (-0.774753) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b20f6a82410dd47e89585bb932616a22e0eaf2e6 \"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) \| 0.009318 / 0.011353 (-0.002035) \| 0.005515 / 0.011008 (-0.005493) \| 0.108532 / 0.038508 (0.070024) \| 0.103583 / 0.023109 (0.080473) \| 0.419249 / 0.275898 (0.143351) \| 0.453573 / 0.323480 (0.130093) \| 0.006601 / 0.007986 (-0.001384) \| 0.005297 / 0.004328 (0.000968) \| 0.082737 / 0.004250 (0.078487) \| 0.064708 / 0.037052 (0.027656) \| 0.425679 / 0.258489 (0.167190) \| 0.462028 / 0.293841 (0.168187) \| 0.048104 / 0.128546 (-0.080442) \| 0.014069 / 0.075646 (-0.061577) \| 0.377780 / 0.419271 (-0.041491) \| 0.067510 / 0.043533 (0.023977) \| 0.422421 / 0.255139 (0.167282) \| 0.447127 / 0.283200 (0.163927) \| 0.037745 / 0.141683 (-0.103938) \| 1.855306 / 1.452155 (0.403152) \| 1.943876 / 1.492716 (0.451160) \|\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) \| 0.280161 / 0.018006 (0.262155) \| 0.598001 / 0.000490 (0.597512) \| 0.001130 / 0.000200 (0.000930) \| 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) \| 0.036064 / 0.037411 (-0.001347) \| 0.113256 / 0.014526 (0.098730) \| 0.120598 / 0.176557 (-0.055959) \| 0.191386 / 0.737135 (-0.545750) \| 0.118125 / 0.296338 (-0.178214) \|\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) \| 0.616887 / 0.215209 (0.401678) \| 6.085498 / 2.077655 (4.007844) \| 2.639428 / 1.504120 (1.135308) \| 2.215444 / 1.541195 (0.674249) \| 2.311990 / 1.468490 (0.843500) \| 0.820539 / 4.584777 (-3.764238) \| 5.306010 / 3.745712 (1.560298) \| 4.731726 / 5.269862 (-0.538136) \| 3.053933 / 4.565676 (-1.511744) \| 0.098862 / 0.424275 (-0.325413) \| 0.009456 / 0.007607 (0.001849) \| 0.725455 / 0.226044 (0.499411) \| 7.367385 / 2.268929 (5.098457) \| 3.464921 / 55.444624 (-51.979703) \| 2.833868 / 6.876477 (-4.042608) \| 3.033008 / 2.142072 (0.890935) \| 1.036751 / 4.805227 (-3.768476) \| 0.243646 / 6.500664 (-6.257018) \| 0.081079 / 0.075469 (0.005610) \|\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) \| 1.584695 / 1.841788 (-0.257093) \| 25.150355 / 8.074308 (17.076047) \| 21.826622 / 10.191392 (11.635230) \| 0.212502 / 0.680424 (-0.467921) \| 0.029865 / 0.534201 (-0.504335) \| 0.496814 / 0.579283 (-0.082470) \| 0.611959 / 0.434364 (0.177595) \| 0.550434 / 0.540337 (0.010097) \| 0.800897 / 1.386936 (-0.586039) \|\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) \| 0.009117 / 0.011353 (-0.002236) \| 0.005236 / 0.011008 (-0.005772) \| 0.082402 / 0.038508 (0.043894) \| 0.090578 / 0.023109 (0.067468) \| 0.487302 / 0.275898 (0.211404) \| 0.523639 / 0.323480 (0.200159) \| 0.006684 / 0.007986 (-0.001302) \| 0.004306 / 0.004328 (-0.000023) \| 0.083273 / 0.004250 (0.079023) \| 0.068585 / 0.037052 (0.031532) \| 0.487751 / 0.258489 (0.229262) \| 0.538972 / 0.293841 (0.245131) \| 0.048915 / 0.128546 (-0.079632) \| 0.014312 / 0.075646 (-0.061335) \| 0.091863 / 0.419271 (-0.327409) \| 0.066114 / 0.043533 (0.022581) \| 0.483552 / 0.255139 (0.228413) \| 0.522250 / 0.283200 (0.239050) \| 0.038533 / 0.141683 (-0.103150) \| 1.803834 / 1.452155 (0.351680) \| 1.891927 / 1.492716 (0.399211) \|\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) \| 0.336662 / 0.018006 (0.318656) \| 0.611408 / 0.000490 (0.610918) \| 0.014310 / 0.000200 (0.014110) \| 0.000152 / 0.000054 (0.000097) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034755 / 0.037411 (-0.002656) \| 0.101008 / 0.014526 (0.086483) \| 0.124530 / 0.176557 (-0.052026) \| 0.179844 / 0.737135 (-0.557292) \| 0.125027 / 0.296338 (-0.171312) \|\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) \| 0.618341 / 0.215209 (0.403132) \| 6.146848 / 2.077655 (4.069193) \| 2.893305 / 1.504120 (1.389185) \| 2.608722 / 1.541195 (1.067528) \| 2.671276 / 1.468490 (1.202786) \| 0.860096 / 4.584777 (-3.724681) \| 5.440671 / 3.745712 (1.694959) \| 4.776958 / 5.269862 (-0.492903) \| 3.098300 / 4.565676 (-1.467376) \| 0.098664 / 0.424275 (-0.325611) \| 0.009270 / 0.007607 (0.001663) \| 0.712780 / 0.226044 (0.486735) \| 7.199721 / 2.268929 (4.930793) \| 3.620723 / 55.444624 (-51.823902) \| 3.052218 / 6.876477 (-3.824259) \| 3.321093 / 2.142072 (1.179021) \| 1.070992 / 4.805227 (-3.734235) \| 0.224091 / 6.500664 (-6.276573) \| 0.083395 / 0.075469 (0.007926) \|\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) \| 1.716867 / 1.841788 (-0.124921) \| 25.534617 / 8.074308 (17.460309) \| 25.221014 / 10.191392 (15.029621) \| 0.248098 / 0.680424 (-0.432326) \| 0.029659 / 0.534201 (-0.504542) \| 0.492929 / 0.579283 (-0.086355) \| 0.618253 / 0.434364 (0.183889) \| 0.577108 / 0.540337 (0.036771) \| 0.803188 / 1.386936 (-0.583748) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#584db360eed9155e173b199ba5fc037562b7b862 \"CML watermark\")\n" ]
https://api.github.com/repos/huggingface/datasets/issues/6102	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6102/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6102/comments	https://api.github.com/repos/huggingface/datasets/issues/6102/events	https://github.com/huggingface/datasets/pull/6102	1,828,494,896	PR_kwDODunzps5WwyGy	6,102	Release 2.14.2	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "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}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova" }	[]	closed	false	null	[]	null	4	"2023-07-31T06:27:47Z"	"2023-07-31T06:48:09Z"	"2023-07-31T06:32:58Z"	MEMBER	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6102.diff", "html_url": "https://github.com/huggingface/datasets/pull/6102", "merged_at": "2023-07-31T06:32:58Z", "patch_url": "https://github.com/huggingface/datasets/pull/6102.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6102" }	null	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6102/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6102/timeline	null	null	661	true	[ "<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) \| 0.006517 / 0.011353 (-0.004836) \| 0.004217 / 0.011008 (-0.006792) \| 0.083162 / 0.038508 (0.044654) \| 0.074476 / 0.023109 (0.051367) \| 0.321193 / 0.275898 (0.045295) \| 0.358348 / 0.323480 (0.034868) \| 0.005531 / 0.007986 (-0.002455) \| 0.003621 / 0.004328 (-0.000707) \| 0.063819 / 0.004250 (0.059568) \| 0.056524 / 0.037052 (0.019471) \| 0.322145 / 0.258489 (0.063656) \| 0.371415 / 0.293841 (0.077574) \| 0.030612 / 0.128546 (-0.097934) \| 0.008907 / 0.075646 (-0.066739) \| 0.289451 / 0.419271 (-0.129821) \| 0.051959 / 0.043533 (0.008426) \| 0.317729 / 0.255139 (0.062590) \| 0.339750 / 0.283200 (0.056550) \| 0.022430 / 0.141683 (-0.119253) \| 1.487661 / 1.452155 (0.035506) \| 1.554916 / 1.492716 (0.062199) \|\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) \| 0.296673 / 0.018006 (0.278667) \| 0.599183 / 0.000490 (0.598694) \| 0.002524 / 0.000200 (0.002324) \| 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) \| 0.027898 / 0.037411 (-0.009514) \| 0.080870 / 0.014526 (0.066344) \| 0.094894 / 0.176557 (-0.081662) \| 0.152350 / 0.737135 (-0.584785) \| 0.095765 / 0.296338 (-0.200573) \|\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) \| 0.415442 / 0.215209 (0.200233) \| 4.161155 / 2.077655 (2.083500) \| 2.117061 / 1.504120 (0.612941) \| 1.937846 / 1.541195 (0.396651) \| 1.979635 / 1.468490 (0.511145) \| 0.488381 / 4.584777 (-4.096396) \| 3.509836 / 3.745712 (-0.235876) \| 3.833074 / 5.269862 (-1.436788) \| 2.307536 / 4.565676 (-2.258141) \| 0.057059 / 0.424275 (-0.367216) \| 0.007366 / 0.007607 (-0.000241) \| 0.487752 / 0.226044 (0.261708) \| 4.869406 / 2.268929 (2.600478) \| 2.594775 / 55.444624 (-52.849849) \| 2.191712 / 6.876477 (-4.684765) \| 2.413220 / 2.142072 (0.271147) \| 0.584513 / 4.805227 (-4.220714) \| 0.132162 / 6.500664 (-6.368502) \| 0.061059 / 0.075469 (-0.014410) \|\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) \| 1.245178 / 1.841788 (-0.596610) \| 20.624563 / 8.074308 (12.550255) \| 14.675545 / 10.191392 (4.484153) \| 0.165838 / 0.680424 (-0.514586) \| 0.018700 / 0.534201 (-0.515501) \| 0.392475 / 0.579283 (-0.186808) \| 0.399884 / 0.434364 (-0.034480) \| 0.457478 / 0.540337 (-0.082859) \| 0.624553 / 1.386936 (-0.762383) \|\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) \| 0.006716 / 0.011353 (-0.004637) \| 0.004308 / 0.011008 (-0.006700) \| 0.064495 / 0.038508 (0.025987) \| 0.083194 / 0.023109 (0.060085) \| 0.371994 / 0.275898 (0.096096) \| 0.433045 / 0.323480 (0.109566) \| 0.005535 / 0.007986 (-0.002450) \| 0.003469 / 0.004328 (-0.000859) \| 0.064342 / 0.004250 (0.060092) \| 0.059362 / 0.037052 (0.022309) \| 0.393819 / 0.258489 (0.135330) \| 0.442591 / 0.293841 (0.148750) \| 0.031594 / 0.128546 (-0.096952) \| 0.008943 / 0.075646 (-0.066703) \| 0.070689 / 0.419271 (-0.348582) \| 0.049219 / 0.043533 (0.005686) \| 0.361568 / 0.255139 (0.106429) \| 0.417085 / 0.283200 (0.133886) \| 0.025112 / 0.141683 (-0.116571) \| 1.497204 / 1.452155 (0.045049) \| 1.552781 / 1.492716 (0.060064) \|\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) \| 0.325254 / 0.018006 (0.307248) \| 0.528399 / 0.000490 (0.527909) \| 0.007429 / 0.000200 (0.007229) \| 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) \| 0.029908 / 0.037411 (-0.007504) \| 0.087114 / 0.014526 (0.072588) \| 0.103366 / 0.176557 (-0.073191) \| 0.155145 / 0.737135 (-0.581990) \| 0.103458 / 0.296338 (-0.192880) \|\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) \| 0.409432 / 0.215209 (0.194223) \| 4.093327 / 2.077655 (2.015673) \| 2.154115 / 1.504120 (0.649995) \| 1.953492 / 1.541195 (0.412297) \| 2.021532 / 1.468490 (0.553042) \| 0.478928 / 4.584777 (-4.105849) \| 3.515287 / 3.745712 (-0.230426) \| 4.976239 / 5.269862 (-0.293623) \| 2.832803 / 4.565676 (-1.732873) \| 0.057239 / 0.424275 (-0.367036) \| 0.007718 / 0.007607 (0.000111) \| 0.484102 / 0.226044 (0.258057) \| 4.833020 / 2.268929 (2.564092) \| 2.564550 / 55.444624 (-52.880074) \| 2.268969 / 6.876477 (-4.607508) \| 2.513308 / 2.142072 (0.371235) \| 0.582822 / 4.805227 (-4.222406) \| 0.133989 / 6.500664 (-6.366675) \| 0.062078 / 0.075469 (-0.013391) \|\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) \| 1.393766 / 1.841788 (-0.448021) \| 20.224546 / 8.074308 (12.150238) \| 14.359438 / 10.191392 (4.168046) \| 0.166358 / 0.680424 (-0.514066) \| 0.018840 / 0.534201 (-0.515361) \| 0.393206 / 0.579283 (-0.186077) \| 0.404220 / 0.434364 (-0.030144) \| 0.462346 / 0.540337 (-0.077992) \| 0.603078 / 1.386936 (-0.783858) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#53e8007baeff133aaad8cbb366196be18a5e57fd \"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) \| 0.006835 / 0.011353 (-0.004518) \| 0.004530 / 0.011008 (-0.006478) \| 0.087506 / 0.038508 (0.048997) \| 0.088289 / 0.023109 (0.065180) \| 0.351575 / 0.275898 (0.075677) \| 0.391873 / 0.323480 (0.068393) \| 0.005627 / 0.007986 (-0.002359) \| 0.003735 / 0.004328 (-0.000594) \| 0.065747 / 0.004250 (0.061497) \| 0.058779 / 0.037052 (0.021726) \| 0.358076 / 0.258489 (0.099587) \| 0.408466 / 0.293841 (0.114626) \| 0.031369 / 0.128546 (-0.097178) \| 0.008807 / 0.075646 (-0.066839) \| 0.293253 / 0.419271 (-0.126019) \| 0.052950 / 0.043533 (0.009417) \| 0.350411 / 0.255139 (0.095272) \| 0.384827 / 0.283200 (0.101627) \| 0.026219 / 0.141683 (-0.115464) \| 1.464290 / 1.452155 (0.012136) \| 1.549688 / 1.492716 (0.056972) \|\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) \| 0.270354 / 0.018006 (0.252348) \| 0.593436 / 0.000490 (0.592946) \| 0.003872 / 0.000200 (0.003673) \| 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) \| 0.031625 / 0.037411 (-0.005787) \| 0.092599 / 0.014526 (0.078073) \| 0.104619 / 0.176557 (-0.071938) \| 0.163183 / 0.737135 (-0.573952) \| 0.103245 / 0.296338 (-0.193094) \|\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) \| 0.390213 / 0.215209 (0.175004) \| 3.894519 / 2.077655 (1.816864) \| 1.905739 / 1.504120 (0.401619) \| 1.728873 / 1.541195 (0.187678) \| 1.838692 / 1.468490 (0.370202) \| 0.484730 / 4.584777 (-4.100047) \| 3.706749 / 3.745712 (-0.038963) \| 5.572311 / 5.269862 (0.302449) \| 3.389949 / 4.565676 (-1.175727) \| 0.057315 / 0.424275 (-0.366960) \| 0.007475 / 0.007607 (-0.000132) \| 0.464690 / 0.226044 (0.238645) \| 4.622242 / 2.268929 (2.353314) \| 2.380957 / 55.444624 (-53.063667) \| 2.038225 / 6.876477 (-4.838251) \| 2.358881 / 2.142072 (0.216809) \| 0.606358 / 4.805227 (-4.198869) \| 0.133584 / 6.500664 (-6.367080) \| 0.061894 / 0.075469 (-0.013575) \|\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) \| 1.259575 / 1.841788 (-0.582213) \| 20.915216 / 8.074308 (12.840908) \| 14.971952 / 10.191392 (4.780560) \| 0.160206 / 0.680424 (-0.520218) \| 0.018675 / 0.534201 (-0.515526) \| 0.396821 / 0.579283 (-0.182462) \| 0.430982 / 0.434364 (-0.003382) \| 0.452895 / 0.540337 (-0.087443) \| 0.647869 / 1.386936 (-0.739067) \|\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) \| 0.007194 / 0.011353 (-0.004158) \| 0.004340 / 0.011008 (-0.006669) \| 0.065125 / 0.038508 (0.026617) \| 0.096243 / 0.023109 (0.073134) \| 0.374361 / 0.275898 (0.098463) \| 0.411863 / 0.323480 (0.088383) \| 0.005813 / 0.007986 (-0.002172) \| 0.003615 / 0.004328 (-0.000713) \| 0.064953 / 0.004250 (0.060703) \| 0.063171 / 0.037052 (0.026119) \| 0.376238 / 0.258489 (0.117749) \| 0.415826 / 0.293841 (0.121985) \| 0.031926 / 0.128546 (-0.096620) \| 0.008821 / 0.075646 (-0.066825) \| 0.072150 / 0.419271 (-0.347122) \| 0.049484 / 0.043533 (0.005951) \| 0.369691 / 0.255139 (0.114552) \| 0.390669 / 0.283200 (0.107470) \| 0.025732 / 0.141683 (-0.115950) \| 1.493833 / 1.452155 (0.041679) \| 1.601786 / 1.492716 (0.109070) \|\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) \| 0.284279 / 0.018006 (0.266272) \| 0.585909 / 0.000490 (0.585419) \| 0.000411 / 0.000200 (0.000211) \| 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) \| 0.033642 / 0.037411 (-0.003769) \| 0.095328 / 0.014526 (0.080802) \| 0.105810 / 0.176557 (-0.070746) \| 0.159779 / 0.737135 (-0.577357) \| 0.108938 / 0.296338 (-0.187400) \|\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) \| 0.408112 / 0.215209 (0.192902) \| 4.067035 / 2.077655 (1.989380) \| 2.114504 / 1.504120 (0.610384) \| 1.944027 / 1.541195 (0.402832) \| 2.066117 / 1.468490 (0.597627) \| 0.486441 / 4.584777 (-4.098336) \| 3.622659 / 3.745712 (-0.123053) \| 3.399310 / 5.269862 (-1.870552) \| 2.183151 / 4.565676 (-2.382525) \| 0.057490 / 0.424275 (-0.366785) \| 0.007955 / 0.007607 (0.000347) \| 0.490221 / 0.226044 (0.264177) \| 4.887301 / 2.268929 (2.618373) \| 2.679806 / 55.444624 (-52.764819) \| 2.258992 / 6.876477 (-4.617484) \| 2.592493 / 2.142072 (0.450420) \| 0.606515 / 4.805227 (-4.198712) \| 0.135645 / 6.500664 (-6.365019) \| 0.063956 / 0.075469 (-0.011513) \|\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) \| 1.331304 / 1.841788 (-0.510483) \| 21.458611 / 8.074308 (13.384303) \| 14.898964 / 10.191392 (4.707572) \| 0.172110 / 0.680424 (-0.508314) \| 0.018791 / 0.534201 (-0.515409) \| 0.395944 / 0.579283 (-0.183339) \| 0.424526 / 0.434364 (-0.009838) \| 0.462517 / 0.540337 (-0.077821) \| 0.610139 / 1.386936 (-0.776797) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#09492ba523518289a84175ddb7ab3bc555e742ee \"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) \| 0.005957 / 0.011353 (-0.005396) \| 0.003581 / 0.011008 (-0.007427) \| 0.079624 / 0.038508 (0.041116) \| 0.058004 / 0.023109 (0.034895) \| 0.309345 / 0.275898 (0.033447) \| 0.346653 / 0.323480 (0.023173) \| 0.005420 / 0.007986 (-0.002566) \| 0.002906 / 0.004328 (-0.001423) \| 0.061970 / 0.004250 (0.057720) \| 0.047627 / 0.037052 (0.010575) \| 0.314096 / 0.258489 (0.055607) \| 0.361368 / 0.293841 (0.067527) \| 0.027211 / 0.128546 (-0.101335) \| 0.007853 / 0.075646 (-0.067793) \| 0.260202 / 0.419271 (-0.159070) \| 0.045308 / 0.043533 (0.001775) \| 0.312150 / 0.255139 (0.057011) \| 0.341085 / 0.283200 (0.057886) \| 0.021302 / 0.141683 (-0.120381) \| 1.430315 / 1.452155 (-0.021840) \| 1.608989 / 1.492716 (0.116273) \|\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) \| 0.185289 / 0.018006 (0.167283) \| 0.423318 / 0.000490 (0.422828) \| 0.005741 / 0.000200 (0.005541) \| 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) \| 0.023777 / 0.037411 (-0.013634) \| 0.071937 / 0.014526 (0.057412) \| 0.079406 / 0.176557 (-0.097151) \| 0.143815 / 0.737135 (-0.593320) \| 0.081648 / 0.296338 (-0.214690) \|\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) \| 0.431514 / 0.215209 (0.216305) \| 4.314471 / 2.077655 (2.236817) \| 2.305167 / 1.504120 (0.801047) \| 2.137894 / 1.541195 (0.596699) \| 2.161034 / 1.468490 (0.692544) \| 0.511701 / 4.584777 (-4.073076) \| 3.098213 / 3.745712 (-0.647499) \| 4.086837 / 5.269862 (-1.183024) \| 2.517184 / 4.565676 (-2.048492) \| 0.058272 / 0.424275 (-0.366003) \| 0.006415 / 0.007607 (-0.001192) \| 0.504792 / 0.226044 (0.278747) \| 5.046758 / 2.268929 (2.777829) \| 2.752049 / 55.444624 (-52.692576) \| 2.407707 / 6.876477 (-4.468770) \| 2.532162 / 2.142072 (0.390090) \| 0.597562 / 4.805227 (-4.207666) \| 0.125935 / 6.500664 (-6.374729) \| 0.060837 / 0.075469 (-0.014632) \|\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) \| 1.257048 / 1.841788 (-0.584740) \| 17.877849 / 8.074308 (9.803541) \| 13.904805 / 10.191392 (3.713413) \| 0.131647 / 0.680424 (-0.548776) \| 0.016975 / 0.534201 (-0.517226) \| 0.329651 / 0.579283 (-0.249633) \| 0.354358 / 0.434364 (-0.080006) \| 0.377545 / 0.540337 (-0.162792) \| 0.545593 / 1.386936 (-0.841343) \|\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) \| 0.005839 / 0.011353 (-0.005514) \| 0.003580 / 0.011008 (-0.007428) \| 0.062204 / 0.038508 (0.023696) \| 0.057943 / 0.023109 (0.034834) \| 0.400165 / 0.275898 (0.124267) \| 0.427911 / 0.323480 (0.104431) \| 0.004412 / 0.007986 (-0.003574) \| 0.002794 / 0.004328 (-0.001534) \| 0.062933 / 0.004250 (0.058683) \| 0.046243 / 0.037052 (0.009191) \| 0.413640 / 0.258489 (0.155151) \| 0.418592 / 0.293841 (0.124751) \| 0.027020 / 0.128546 (-0.101526) \| 0.007927 / 0.075646 (-0.067720) \| 0.067581 / 0.419271 (-0.351691) \| 0.041927 / 0.043533 (-0.001606) \| 0.381863 / 0.255139 (0.126724) \| 0.415711 / 0.283200 (0.132511) \| 0.019827 / 0.141683 (-0.121856) \| 1.464049 / 1.452155 (0.011894) \| 1.528387 / 1.492716 (0.035671) \|\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) \| 0.224999 / 0.018006 (0.206993) \| 0.419167 / 0.000490 (0.418678) \| 0.000363 / 0.000200 (0.000163) \| 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) \| 0.024827 / 0.037411 (-0.012585) \| 0.077134 / 0.014526 (0.062608) \| 0.085142 / 0.176557 (-0.091414) \| 0.137400 / 0.737135 (-0.599735) \| 0.086434 / 0.296338 (-0.209905) \|\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) \| 0.452716 / 0.215209 (0.237507) \| 4.530610 / 2.077655 (2.452955) \| 2.467309 / 1.504120 (0.963189) \| 2.300441 / 1.541195 (0.759246) \| 2.323475 / 1.468490 (0.854985) \| 0.501847 / 4.584777 (-4.082930) \| 3.079432 / 3.745712 (-0.666280) \| 2.793107 / 5.269862 (-2.476755) \| 1.835010 / 4.565676 (-2.730666) \| 0.057698 / 0.424275 (-0.366577) \| 0.006756 / 0.007607 (-0.000851) \| 0.529062 / 0.226044 (0.303017) \| 5.287822 / 2.268929 (3.018894) \| 2.908411 / 55.444624 (-52.536214) \| 2.571627 / 6.876477 (-4.304850) \| 2.691188 / 2.142072 (0.549116) \| 0.592289 / 4.805227 (-4.212938) \| 0.126091 / 6.500664 (-6.374573) \| 0.062312 / 0.075469 (-0.013157) \|\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) \| 1.328854 / 1.841788 (-0.512933) \| 18.185628 / 8.074308 (10.111320) \| 13.858781 / 10.191392 (3.667389) \| 0.142421 / 0.680424 (-0.538003) \| 0.016535 / 0.534201 (-0.517666) \| 0.330839 / 0.579283 (-0.248444) \| 0.346559 / 0.434364 (-0.087805) \| 0.389153 / 0.540337 (-0.151185) \| 0.516897 / 1.386936 (-0.870039) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#09492ba523518289a84175ddb7ab3bc555e742ee \"CML watermark\")\n" ]
https://api.github.com/repos/huggingface/datasets/issues/6101	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6101/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6101/comments	https://api.github.com/repos/huggingface/datasets/issues/6101/events	https://github.com/huggingface/datasets/pull/6101	1,828,469,648	PR_kwDODunzps5WwspW	6,101	Release 2.14.2	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "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}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova" }	[]	closed	false	null	[]	null	3	"2023-07-31T06:05:36Z"	"2023-07-31T06:33:00Z"	"2023-07-31T06:18:17Z"	MEMBER	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6101.diff", "html_url": "https://github.com/huggingface/datasets/pull/6101", "merged_at": "2023-07-31T06:18:17Z", "patch_url": "https://github.com/huggingface/datasets/pull/6101.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6101" }	null	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6101/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6101/timeline	null	null	662	true	[ "<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 (-0.004810) \| 0.003894 / 0.011008 (-0.007115) \| 0.084742 / 0.038508 (0.046234) \| 0.072942 / 0.023109 (0.049833) \| 0.310722 / 0.275898 (0.034824) \| 0.346806 / 0.323480 (0.023326) \| 0.005373 / 0.007986 (-0.002613) \| 0.003270 / 0.004328 (-0.001059) \| 0.064379 / 0.004250 (0.060128) \| 0.054876 / 0.037052 (0.017824) \| 0.316794 / 0.258489 (0.058305) \| 0.350353 / 0.293841 (0.056512) \| 0.030683 / 0.128546 (-0.097863) \| 0.008275 / 0.075646 (-0.067371) \| 0.288747 / 0.419271 (-0.130525) \| 0.051892 / 0.043533 (0.008359) \| 0.315060 / 0.255139 (0.059921) \| 0.331664 / 0.283200 (0.048464) \| 0.023334 / 0.141683 (-0.118349) \| 1.499734 / 1.452155 (0.047579) \| 1.542006 / 1.492716 (0.049290) \|\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) \| 0.210488 / 0.018006 (0.192482) \| 0.462187 / 0.000490 (0.461697) \| 0.001280 / 0.000200 (0.001080) \| 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) \| 0.027812 / 0.037411 (-0.009599) \| 0.082492 / 0.014526 (0.067966) \| 0.096504 / 0.176557 (-0.080053) \| 0.158164 / 0.737135 (-0.578972) \| 0.096678 / 0.296338 (-0.199661) \|\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) \| 0.403317 / 0.215209 (0.188108) \| 4.008367 / 2.077655 (1.930713) \| 2.033067 / 1.504120 (0.528947) \| 1.869484 / 1.541195 (0.328290) \| 1.947450 / 1.468490 (0.478960) \| 0.494048 / 4.584777 (-4.090729) \| 3.631673 / 3.745712 (-0.114039) \| 5.322167 / 5.269862 (0.052306) \| 3.125570 / 4.565676 (-1.440107) \| 0.057341 / 0.424275 (-0.366934) \| 0.007318 / 0.007607 (-0.000289) \| 0.483990 / 0.226044 (0.257945) \| 4.830573 / 2.268929 (2.561645) \| 2.543267 / 55.444624 (-52.901358) \| 2.217890 / 6.876477 (-4.658587) \| 2.435111 / 2.142072 (0.293038) \| 0.597920 / 4.805227 (-4.207307) \| 0.132690 / 6.500664 (-6.367974) \| 0.060160 / 0.075469 (-0.015309) \|\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) \| 1.247656 / 1.841788 (-0.594131) \| 19.436984 / 8.074308 (11.362675) \| 14.504249 / 10.191392 (4.312857) \| 0.167444 / 0.680424 (-0.512980) \| 0.018214 / 0.534201 (-0.515987) \| 0.394790 / 0.579283 (-0.184493) \| 0.413770 / 0.434364 (-0.020594) \| 0.474290 / 0.540337 (-0.066048) \| 0.646782 / 1.386936 (-0.740154) \|\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) \| 0.006575 / 0.011353 (-0.004778) \| 0.003924 / 0.011008 (-0.007084) \| 0.064402 / 0.038508 (0.025893) \| 0.072569 / 0.023109 (0.049460) \| 0.361981 / 0.275898 (0.086083) \| 0.398660 / 0.323480 (0.075180) \| 0.005380 / 0.007986 (-0.002605) \| 0.003355 / 0.004328 (-0.000974) \| 0.065173 / 0.004250 (0.060923) \| 0.057120 / 0.037052 (0.020067) \| 0.366347 / 0.258489 (0.107858) \| 0.402723 / 0.293841 (0.108882) \| 0.031258 / 0.128546 (-0.097288) \| 0.008499 / 0.075646 (-0.067147) \| 0.070558 / 0.419271 (-0.348714) \| 0.050089 / 0.043533 (0.006556) \| 0.361280 / 0.255139 (0.106141) \| 0.384497 / 0.283200 (0.101297) \| 0.024789 / 0.141683 (-0.116893) \| 1.492577 / 1.452155 (0.040422) \| 1.572242 / 1.492716 (0.079525) \|\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) \| 0.228054 / 0.018006 (0.210048) \| 0.448317 / 0.000490 (0.447828) \| 0.000368 / 0.000200 (0.000168) \| 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) \| 0.030575 / 0.037411 (-0.006836) \| 0.088604 / 0.014526 (0.074078) \| 0.099317 / 0.176557 (-0.077239) \| 0.152455 / 0.737135 (-0.584680) \| 0.100444 / 0.296338 (-0.195894) \|\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) \| 0.411876 / 0.215209 (0.196667) \| 4.108187 / 2.077655 (2.030532) \| 2.096371 / 1.504120 (0.592251) \| 1.923532 / 1.541195 (0.382337) \| 1.998345 / 1.468490 (0.529855) \| 0.483853 / 4.584777 (-4.100924) \| 3.622433 / 3.745712 (-0.123279) \| 3.254430 / 5.269862 (-2.015431) \| 2.044342 / 4.565676 (-2.521334) \| 0.056756 / 0.424275 (-0.367519) \| 0.007720 / 0.007607 (0.000113) \| 0.487656 / 0.226044 (0.261612) \| 4.882024 / 2.268929 (2.613096) \| 2.585008 / 55.444624 (-52.859616) \| 2.229251 / 6.876477 (-4.647225) \| 2.408318 / 2.142072 (0.266246) \| 0.617537 / 4.805227 (-4.187691) \| 0.132102 / 6.500664 (-6.368562) \| 0.061694 / 0.075469 (-0.013775) \|\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) \| 1.362077 / 1.841788 (-0.479711) \| 19.750714 / 8.074308 (11.676406) \| 14.545299 / 10.191392 (4.353907) \| 0.168666 / 0.680424 (-0.511758) \| 0.018606 / 0.534201 (-0.515595) \| 0.394760 / 0.579283 (-0.184523) \| 0.410030 / 0.434364 (-0.024334) \| 0.464742 / 0.540337 (-0.075596) \| 0.610881 / 1.386936 (-0.776055) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#53e8007baeff133aaad8cbb366196be18a5e57fd \"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) \| 0.005836 / 0.011353 (-0.005517) \| 0.003493 / 0.011008 (-0.007515) \| 0.079877 / 0.038508 (0.041369) \| 0.057299 / 0.023109 (0.034190) \| 0.332945 / 0.275898 (0.057047) \| 0.386615 / 0.323480 (0.063135) \| 0.004437 / 0.007986 (-0.003548) \| 0.002758 / 0.004328 (-0.001571) \| 0.062668 / 0.004250 (0.058418) \| 0.046135 / 0.037052 (0.009083) \| 0.346160 / 0.258489 (0.087671) \| 0.416720 / 0.293841 (0.122879) \| 0.026678 / 0.128546 (-0.101868) \| 0.007893 / 0.075646 (-0.067753) \| 0.260427 / 0.419271 (-0.158845) \| 0.044240 / 0.043533 (0.000707) \| 0.328101 / 0.255139 (0.072963) \| 0.380072 / 0.283200 (0.096872) \| 0.020813 / 0.141683 (-0.120870) \| 1.400202 / 1.452155 (-0.051952) \| 1.475627 / 1.492716 (-0.017089) \|\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) \| 0.174479 / 0.018006 (0.156473) \| 0.413810 / 0.000490 (0.413320) \| 0.003059 / 0.000200 (0.002860) \| 0.000212 / 0.000054 (0.000157) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023422 / 0.037411 (-0.013990) \| 0.071519 / 0.014526 (0.056993) \| 0.080555 / 0.176557 (-0.096001) \| 0.143825 / 0.737135 (-0.593311) \| 0.081182 / 0.296338 (-0.215157) \|\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) \| 0.406858 / 0.215209 (0.191648) \| 4.161475 / 2.077655 (2.083820) \| 1.991800 / 1.504120 (0.487680) \| 1.811224 / 1.541195 (0.270030) \| 1.828809 / 1.468490 (0.360318) \| 0.504882 / 4.584777 (-4.079895) \| 2.985010 / 3.745712 (-0.760703) \| 3.984856 / 5.269862 (-1.285006) \| 2.477936 / 4.565676 (-2.087740) \| 0.057553 / 0.424275 (-0.366722) \| 0.006436 / 0.007607 (-0.001172) \| 0.488061 / 0.226044 (0.262016) \| 4.805501 / 2.268929 (2.536573) \| 2.446508 / 55.444624 (-52.998116) \| 2.051406 / 6.876477 (-4.825071) \| 2.177696 / 2.142072 (0.035623) \| 0.588021 / 4.805227 (-4.217207) \| 0.125118 / 6.500664 (-6.375546) \| 0.060885 / 0.075469 (-0.014584) \|\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) \| 1.197130 / 1.841788 (-0.644658) \| 17.867450 / 8.074308 (9.793142) \| 13.536895 / 10.191392 (3.345503) \| 0.137603 / 0.680424 (-0.542821) \| 0.016706 / 0.534201 (-0.517495) \| 0.327642 / 0.579283 (-0.251641) \| 0.347201 / 0.434364 (-0.087163) \| 0.379570 / 0.540337 (-0.160768) \| 0.517825 / 1.386936 (-0.869111) \|\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) \| 0.005769 / 0.011353 (-0.005584) \| 0.003414 / 0.011008 (-0.007594) \| 0.063198 / 0.038508 (0.024690) \| 0.056020 / 0.023109 (0.032911) \| 0.393333 / 0.275898 (0.117435) \| 0.421166 / 0.323480 (0.097686) \| 0.004360 / 0.007986 (-0.003626) \| 0.002860 / 0.004328 (-0.001469) \| 0.062712 / 0.004250 (0.058461) \| 0.045363 / 0.037052 (0.008311) \| 0.413156 / 0.258489 (0.154667) \| 0.422897 / 0.293841 (0.129056) \| 0.027092 / 0.128546 (-0.101455) \| 0.007960 / 0.075646 (-0.067687) \| 0.068531 / 0.419271 (-0.350740) \| 0.041402 / 0.043533 (-0.002131) \| 0.377008 / 0.255139 (0.121869) \| 0.409142 / 0.283200 (0.125942) \| 0.019707 / 0.141683 (-0.121976) \| 1.440556 / 1.452155 (-0.011599) \| 1.487403 / 1.492716 (-0.005314) \|\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) \| 0.224355 / 0.018006 (0.206349) \| 0.397855 / 0.000490 (0.397365) \| 0.000363 / 0.000200 (0.000163) \| 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) \| 0.025107 / 0.037411 (-0.012305) \| 0.076404 / 0.014526 (0.061878) \| 0.083194 / 0.176557 (-0.093362) \| 0.135347 / 0.737135 (-0.601789) \| 0.084786 / 0.296338 (-0.211553) \|\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) \| 0.433024 / 0.215209 (0.217815) \| 4.323879 / 2.077655 (2.246224) \| 2.263004 / 1.504120 (0.758884) \| 2.072053 / 1.541195 (0.530858) \| 2.113916 / 1.468490 (0.645426) \| 0.502742 / 4.584777 (-4.082035) \| 3.001716 / 3.745712 (-0.743996) \| 2.777960 / 5.269862 (-2.491901) \| 1.826514 / 4.565676 (-2.739162) \| 0.057735 / 0.424275 (-0.366540) \| 0.006671 / 0.007607 (-0.000937) \| 0.503347 / 0.226044 (0.277303) \| 5.037308 / 2.268929 (2.768380) \| 2.679146 / 55.444624 (-52.765478) \| 2.410899 / 6.876477 (-4.465577) \| 2.467341 / 2.142072 (0.325268) \| 0.589824 / 4.805227 (-4.215403) \| 0.125529 / 6.500664 (-6.375135) \| 0.061950 / 0.075469 (-0.013520) \|\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) \| 1.304128 / 1.841788 (-0.537659) \| 17.950215 / 8.074308 (9.875907) \| 13.673768 / 10.191392 (3.482376) \| 0.129863 / 0.680424 (-0.550561) \| 0.016720 / 0.534201 (-0.517481) \| 0.329795 / 0.579283 (-0.249488) \| 0.339057 / 0.434364 (-0.095307) \| 0.382279 / 0.540337 (-0.158059) \| 0.507337 / 1.386936 (-0.879599) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ef05b6f99a2b19990c6f5e4e28d95d28781570db \"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) \| 0.006199 / 0.011353 (-0.005154) \| 0.003749 / 0.011008 (-0.007259) \| 0.080600 / 0.038508 (0.042092) \| 0.061017 / 0.023109 (0.037908) \| 0.319966 / 0.275898 (0.044067) \| 0.354937 / 0.323480 (0.031457) \| 0.004854 / 0.007986 (-0.003131) \| 0.002996 / 0.004328 (-0.001333) \| 0.063100 / 0.004250 (0.058849) \| 0.050063 / 0.037052 (0.013011) \| 0.316744 / 0.258489 (0.058255) \| 0.358001 / 0.293841 (0.064160) \| 0.027503 / 0.128546 (-0.101043) \| 0.007876 / 0.075646 (-0.067771) \| 0.262211 / 0.419271 (-0.157060) \| 0.045717 / 0.043533 (0.002184) \| 0.317188 / 0.255139 (0.062049) \| 0.342404 / 0.283200 (0.059205) \| 0.020194 / 0.141683 (-0.121489) \| 1.498672 / 1.452155 (0.046517) \| 1.545479 / 1.492716 (0.052762) \|\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) \| 0.210985 / 0.018006 (0.192979) \| 0.433592 / 0.000490 (0.433102) \| 0.002864 / 0.000200 (0.002664) \| 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) \| 0.023463 / 0.037411 (-0.013948) \| 0.073375 / 0.014526 (0.058850) \| 0.083082 / 0.176557 (-0.093475) \| 0.142583 / 0.737135 (-0.594552) \| 0.084267 / 0.296338 (-0.212071) \|\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) \| 0.412890 / 0.215209 (0.197681) \| 4.131421 / 2.077655 (2.053766) \| 1.969164 / 1.504120 (0.465044) \| 1.772379 / 1.541195 (0.231185) \| 1.834154 / 1.468490 (0.365664) \| 0.496290 / 4.584777 (-4.088487) \| 3.056504 / 3.745712 (-0.689208) \| 3.400962 / 5.269862 (-1.868900) \| 2.120575 / 4.565676 (-2.445101) \| 0.056932 / 0.424275 (-0.367343) \| 0.006412 / 0.007607 (-0.001195) \| 0.484521 / 0.226044 (0.258477) \| 4.817474 / 2.268929 (2.548545) \| 2.464075 / 55.444624 (-52.980549) \| 2.085056 / 6.876477 (-4.791421) \| 2.324516 / 2.142072 (0.182444) \| 0.592013 / 4.805227 (-4.213214) \| 0.132232 / 6.500664 (-6.368432) \| 0.062825 / 0.075469 (-0.012645) \|\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) \| 1.228080 / 1.841788 (-0.613708) \| 18.555385 / 8.074308 (10.481077) \| 13.939565 / 10.191392 (3.748173) \| 0.145979 / 0.680424 (-0.534445) \| 0.016823 / 0.534201 (-0.517377) \| 0.330569 / 0.579283 (-0.248714) \| 0.358094 / 0.434364 (-0.076270) \| 0.384642 / 0.540337 (-0.155696) \| 0.518347 / 1.386936 (-0.868589) \|\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) \| 0.006198 / 0.011353 (-0.005155) \| 0.003670 / 0.011008 (-0.007338) \| 0.062502 / 0.038508 (0.023994) \| 0.064339 / 0.023109 (0.041229) \| 0.428414 / 0.275898 (0.152516) \| 0.463899 / 0.323480 (0.140420) \| 0.005524 / 0.007986 (-0.002462) \| 0.002915 / 0.004328 (-0.001413) \| 0.062521 / 0.004250 (0.058270) \| 0.051182 / 0.037052 (0.014130) \| 0.431144 / 0.258489 (0.172655) \| 0.469465 / 0.293841 (0.175624) \| 0.027463 / 0.128546 (-0.101083) \| 0.007974 / 0.075646 (-0.067673) \| 0.068029 / 0.419271 (-0.351242) \| 0.042123 / 0.043533 (-0.001409) \| 0.428667 / 0.255139 (0.173528) \| 0.455917 / 0.283200 (0.172717) \| 0.023264 / 0.141683 (-0.118419) \| 1.426986 / 1.452155 (-0.025168) \| 1.500049 / 1.492716 (0.007332) \|\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) \| 0.207264 / 0.018006 (0.189258) \| 0.440738 / 0.000490 (0.440248) \| 0.000802 / 0.000200 (0.000602) \| 0.000062 / 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) \| 0.026245 / 0.037411 (-0.011166) \| 0.078749 / 0.014526 (0.064223) \| 0.087873 / 0.176557 (-0.088684) \| 0.141518 / 0.737135 (-0.595617) \| 0.089811 / 0.296338 (-0.206527) \|\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) \| 0.418955 / 0.215209 (0.203746) \| 4.177881 / 2.077655 (2.100226) \| 2.162678 / 1.504120 (0.658558) \| 1.998969 / 1.541195 (0.457775) \| 2.066720 / 1.468490 (0.598230) \| 0.496850 / 4.584777 (-4.087927) \| 3.041179 / 3.745712 (-0.704534) \| 4.126039 / 5.269862 (-1.143823) \| 2.740507 / 4.565676 (-1.825169) \| 0.058025 / 0.424275 (-0.366250) \| 0.006846 / 0.007607 (-0.000761) \| 0.493281 / 0.226044 (0.267237) \| 4.930196 / 2.268929 (2.661268) \| 2.685152 / 55.444624 (-52.759472) \| 2.378247 / 6.876477 (-4.498230) \| 2.469103 / 2.142072 (0.327031) \| 0.585346 / 4.805227 (-4.219882) \| 0.126099 / 6.500664 (-6.374565) \| 0.062946 / 0.075469 (-0.012523) \|\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) \| 1.313892 / 1.841788 (-0.527896) \| 19.177117 / 8.074308 (11.102809) \| 14.081321 / 10.191392 (3.889929) \| 0.133948 / 0.680424 (-0.546476) \| 0.017128 / 0.534201 (-0.517073) \| 0.332241 / 0.579283 (-0.247042) \| 0.373218 / 0.434364 (-0.061145) \| 0.395308 / 0.540337 (-0.145030) \| 0.529883 / 1.386936 (-0.857053) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#16f7c7677942083436062b904b74643accb9bcac \"CML watermark\")\n" ]
https://api.github.com/repos/huggingface/datasets/issues/6100	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6100/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6100/comments	https://api.github.com/repos/huggingface/datasets/issues/6100/events	https://github.com/huggingface/datasets/issues/6100	1,828,118,930	I_kwDODunzps5s9uGS	6,100	TypeError when loading from GCP bucket	{ "avatar_url": "https://avatars.githubusercontent.com/u/16692099?v=4", "events_url": "https://api.github.com/users/bilelomrani1/events{/privacy}", "followers_url": "https://api.github.com/users/bilelomrani1/followers", "following_url": "https://api.github.com/users/bilelomrani1/following{/other_user}", "gists_url": "https://api.github.com/users/bilelomrani1/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/bilelomrani1", "id": 16692099, "login": "bilelomrani1", "node_id": "MDQ6VXNlcjE2NjkyMDk5", "organizations_url": "https://api.github.com/users/bilelomrani1/orgs", "received_events_url": "https://api.github.com/users/bilelomrani1/received_events", "repos_url": "https://api.github.com/users/bilelomrani1/repos", "site_admin": false, "starred_url": "https://api.github.com/users/bilelomrani1/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/bilelomrani1/subscriptions", "type": "User", "url": "https://api.github.com/users/bilelomrani1" }	[]	closed	false	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "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}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova" }	[ { "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "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}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova" } ]	null	2	"2023-07-30T23:03:00Z"	"2023-08-03T10:00:48Z"	"2023-08-01T10:38:55Z"	NONE	null	null	null	### Describe the bug Loading a dataset from a GCP bucket raises a type error. This bug was introduced recently (either in 2.14 or 2.14.1), and appeared during a migration from 2.13.1. ### Steps to reproduce the bug Load any file from a GCP bucket: ```python import datasets datasets.load_dataset("json", data_files=["gs://..."]) ``` The following exception is raised: ```python Traceback (most recent call last): ... packages/datasets/data_files.py", line 335, in resolve_pattern protocol_prefix = fs.protocol + "://" if fs.protocol != "file" else "" TypeError: can only concatenate tuple (not "str") to tuple ``` With a `GoogleFileSystem`, the attribute `fs.protocol` is a tuple `('gs', 'gcs')` and hence cannot be concatenated with a string. ### Expected behavior The file should be loaded without exception. ### Environment info - `datasets` version: 2.14.1 - Platform: macOS-13.2.1-x86_64-i386-64bit - Python version: 3.10.12 - Huggingface_hub version: 0.16.4 - PyArrow version: 12.0.1 - Pandas version: 2.0.3	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6100/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6100/timeline	null	completed	663	false	[ "Thanks for reporting, @bilelomrani1.\r\n\r\nWe are fixing it. ", "We have fixed it. We are planning to do a patch release today." ]
https://api.github.com/repos/huggingface/datasets/issues/6099	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6099/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6099/comments	https://api.github.com/repos/huggingface/datasets/issues/6099/events	https://github.com/huggingface/datasets/issues/6099	1,827,893,576	I_kwDODunzps5s83FI	6,099	How do i get "amazon_us_reviews	{ "avatar_url": "https://avatars.githubusercontent.com/u/57810189?v=4", "events_url": "https://api.github.com/users/IqraBaluch/events{/privacy}", "followers_url": "https://api.github.com/users/IqraBaluch/followers", "following_url": "https://api.github.com/users/IqraBaluch/following{/other_user}", "gists_url": "https://api.github.com/users/IqraBaluch/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/IqraBaluch", "id": 57810189, "login": "IqraBaluch", "node_id": "MDQ6VXNlcjU3ODEwMTg5", "organizations_url": "https://api.github.com/users/IqraBaluch/orgs", "received_events_url": "https://api.github.com/users/IqraBaluch/received_events", "repos_url": "https://api.github.com/users/IqraBaluch/repos", "site_admin": false, "starred_url": "https://api.github.com/users/IqraBaluch/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/IqraBaluch/subscriptions", "type": "User", "url": "https://api.github.com/users/IqraBaluch" }	[ { "color": "a2eeef", "default": true, "description": "New feature or request", "id": 1935892871, "name": "enhancement", "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement" } ]	closed	false	null	[]	null	10	"2023-07-30T11:02:17Z"	"2023-08-21T05:08:08Z"	"2023-08-10T05:02:35Z"	NONE	null	null	null	### Feature request I have been trying to load 'amazon_us_dataset" but unable to do so. `amazon_us_reviews = load_dataset('amazon_us_reviews')` `print(amazon_us_reviews)` > [ValueError: Config name is missing. Please pick one among the available configs: ['Wireless_v1_00', 'Watches_v1_00', 'Video_Games_v1_00', 'Video_DVD_v1_00', 'Video_v1_00', 'Toys_v1_00', 'Tools_v1_00', 'Sports_v1_00', 'Software_v1_00', 'Shoes_v1_00', 'Pet_Products_v1_00', 'Personal_Care_Appliances_v1_00', 'PC_v1_00', 'Outdoors_v1_00', 'Office_Products_v1_00', 'Musical_Instruments_v1_00', 'Music_v1_00', 'Mobile_Electronics_v1_00', 'Mobile_Apps_v1_00', 'Major_Appliances_v1_00', 'Luggage_v1_00', 'Lawn_and_Garden_v1_00', 'Kitchen_v1_00', 'Jewelry_v1_00', 'Home_Improvement_v1_00', 'Home_Entertainment_v1_00', 'Home_v1_00', 'Health_Personal_Care_v1_00', 'Grocery_v1_00', 'Gift_Card_v1_00', 'Furniture_v1_00', 'Electronics_v1_00', 'Digital_Video_Games_v1_00', 'Digital_Video_Download_v1_00', 'Digital_Software_v1_00', 'Digital_Music_Purchase_v1_00', 'Digital_Ebook_Purchase_v1_00', 'Camera_v1_00', 'Books_v1_00', 'Beauty_v1_00', 'Baby_v1_00', 'Automotive_v1_00', 'Apparel_v1_00', 'Digital_Ebook_Purchase_v1_01', 'Books_v1_01', 'Books_v1_02'] Example of usage: `load_dataset('amazon_us_reviews', 'Wireless_v1_00')`] __________________________________________________________________________ `amazon_us_reviews = load_dataset('amazon_us_reviews', 'Watches_v1_00') print(amazon_us_reviews)` ERROR `Generating` train split: 0% 0/960872 [00:00<?, ? examples/s] --------------------------------------------------------------------------- KeyError Traceback (most recent call last) /usr/local/lib/python3.10/dist-packages/datasets/builder.py in _prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, split_info, check_duplicate_keys, job_id) 1692 ) -> 1693 example = self.info.features.encode_example(record) if self.info.features is not None else record 1694 writer.write(example, key) 11 frames KeyError: 'marketplace' The above exception was the direct cause of the following exception: DatasetGenerationError Traceback (most recent call last) /usr/local/lib/python3.10/dist-packages/datasets/builder.py in _prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, split_info, check_duplicate_keys, job_id) 1710 if isinstance(e, SchemaInferenceError) and e.__context__ is not None: 1711 e = e.__context__ -> 1712 raise DatasetGenerationError("An error occurred while generating the dataset") from e 1713 1714 yield job_id, True, (total_num_examples, total_num_bytes, writer._features, num_shards, shard_lengths) DatasetGenerationError: An error occurred while generating the dataset ### Motivation The dataset I'm using https://huggingface.co/datasets/amazon_us_reviews ### Your contribution What is the best way to load this data	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6099/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6099/timeline	null	completed	664	false	[ "Seems like the problem isn't with the library, but the dataset itself hosted on AWS S3.\r\n\r\nIts [homepage](https://s3.amazonaws.com/amazon-reviews-pds/readme.html) returns an `AccessDenied` XML response, which is the same thing you get if you try to log the `record` that triggers the exception\r\n\r\n```python\r\ntry:\r\n example = self.info.features.encode_example(record) if self.info.features is not None else record\r\nexcept Exception as e:\r\n print(record)\r\n```\r\n\r\n⬇️\r\n\r\n```\r\n{'<?xml version=\"1.0\" encoding=\"UTF-8\"?>': '<Error><Code>AccessDenied</Code><Message>Access Denied</Message><RequestId>N2HFJ82ZV8SZW9BV</RequestId><HostId>Zw2DQ0V2GdRmvH5qWEpumK4uj5+W8YPcilQbN9fLBr3VqQOcKPHOhUZLG3LcM9X5fkOetxp48Os=</HostId></Error>'}\r\n```", "I'm getting same errors when loading this dataset", "I have figured it out. there was an option of parquet formated files i downloaded some from there. ", "this dataset is unfortunately no longer public", "Thanks for reporting, @IqraBaluch.\r\n\r\nWe contacted the authors and unfortunately they reported that Amazon has decided to stop distributing this dataset.", "If anyone still needs this dataset, you could find it on kaggle here : https://www.kaggle.com/datasets/cynthiarempel/amazon-us-customer-reviews-dataset", "Thanks @Maryam-Mostafa ", "@albertvillanova don't tell 'em, we have figured it out. XD", "I noticed that some book data is missing, we can only get Books_v1_02 data. \r\nIs there any way we can get the Books_v1_00 and Books_v1_01? \r\nReally appreciate !!!", "@albertvillanova will this dataset be retired given the data are no longer hosted on S3? What is done in cases such as these?" ]
https://api.github.com/repos/huggingface/datasets/issues/6098	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6098/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6098/comments	https://api.github.com/repos/huggingface/datasets/issues/6098/events	https://github.com/huggingface/datasets/pull/6098	1,827,655,071	PR_kwDODunzps5WuCn1	6,098	Expanduser in save_to_disk()	{ "avatar_url": "https://avatars.githubusercontent.com/u/51715864?v=4", "events_url": "https://api.github.com/users/Unknown3141592/events{/privacy}", "followers_url": "https://api.github.com/users/Unknown3141592/followers", "following_url": "https://api.github.com/users/Unknown3141592/following{/other_user}", "gists_url": "https://api.github.com/users/Unknown3141592/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/Unknown3141592", "id": 51715864, "login": "Unknown3141592", "node_id": "MDQ6VXNlcjUxNzE1ODY0", "organizations_url": "https://api.github.com/users/Unknown3141592/orgs", "received_events_url": "https://api.github.com/users/Unknown3141592/received_events", "repos_url": "https://api.github.com/users/Unknown3141592/repos", "site_admin": false, "starred_url": "https://api.github.com/users/Unknown3141592/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/Unknown3141592/subscriptions", "type": "User", "url": "https://api.github.com/users/Unknown3141592" }	[]	closed	false	null	[]	null	3	"2023-07-29T20:50:45Z"	"2023-10-27T14:14:11Z"	"2023-10-27T14:04:36Z"	CONTRIBUTOR	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6098.diff", "html_url": "https://github.com/huggingface/datasets/pull/6098", "merged_at": "2023-10-27T14:04:36Z", "patch_url": "https://github.com/huggingface/datasets/pull/6098.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6098" }	Fixes #5651. The same problem occurs when loading from disk so I fixed it there too. I am not sure why the case distinction between local and remote filesystems is even necessary for `DatasetDict` when saving to disk. Imo this could be removed (leaving only `fs.makedirs(dataset_dict_path, exist_ok=True)`).	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6098/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6098/timeline	null	null	665	true	[ "_The documentation is not available anymore as the PR was closed or merged._", "> I am not sure why the case distinction between local and remote filesystems is even necessary for DatasetDict when saving to disk. Imo this could be removed (leaving only fs.makedirs(dataset_dict_path, exist_ok=True)).\r\n\r\nIndeed. But it's better to address this in a separate PR.", "<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) \| 0.007696 / 0.011353 (-0.003656) \| 0.004497 / 0.011008 (-0.006511) \| 0.099302 / 0.038508 (0.060794) \| 0.083360 / 0.023109 (0.060251) \| 0.393483 / 0.275898 (0.117585) \| 0.450505 / 0.323480 (0.127025) \| 0.004610 / 0.007986 (-0.003376) \| 0.003637 / 0.004328 (-0.000692) \| 0.075752 / 0.004250 (0.071501) \| 0.064034 / 0.037052 (0.026982) \| 0.397785 / 0.258489 (0.139296) \| 0.462948 / 0.293841 (0.169107) \| 0.035902 / 0.128546 (-0.092644) \| 0.009640 / 0.075646 (-0.066007) \| 0.342299 / 0.419271 (-0.076973) \| 0.059586 / 0.043533 (0.016053) \| 0.404918 / 0.255139 (0.149779) \| 0.440889 / 0.283200 (0.157690) \| 0.028981 / 0.141683 (-0.112702) \| 1.775380 / 1.452155 (0.323226) \| 1.866663 / 1.492716 (0.373946) \|\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) \| 0.249080 / 0.018006 (0.231074) \| 0.456460 / 0.000490 (0.455970) \| 0.028145 / 0.000200 (0.027945) \| 0.000402 / 0.000054 (0.000347) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030373 / 0.037411 (-0.007038) \| 0.088562 / 0.014526 (0.074036) \| 0.122837 / 0.176557 (-0.053720) \| 0.167122 / 0.737135 (-0.570014) \| 0.103953 / 0.296338 (-0.192385) \|\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) \| 0.431714 / 0.215209 (0.216505) \| 4.182224 / 2.077655 (2.104570) \| 2.025650 / 1.504120 (0.521530) \| 1.838905 / 1.541195 (0.297710) \| 1.868710 / 1.468490 (0.400219) \| 0.538422 / 4.584777 (-4.046355) \| 4.038941 / 3.745712 (0.293228) \| 3.717695 / 5.269862 (-1.552166) \| 2.313197 / 4.565676 (-2.252479) \| 0.061060 / 0.424275 (-0.363215) \| 0.008248 / 0.007607 (0.000641) \| 0.497438 / 0.226044 (0.271394) \| 4.946663 / 2.268929 (2.677734) \| 2.571841 / 55.444624 (-52.872784) \| 2.155894 / 6.876477 (-4.720583) \| 2.183180 / 2.142072 (0.041107) \| 0.639810 / 4.805227 (-4.165417) \| 0.153273 / 6.500664 (-6.347391) \| 0.068606 / 0.075469 (-0.006863) \|\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) \| 1.376152 / 1.841788 (-0.465635) \| 20.747088 / 8.074308 (12.672780) \| 15.200311 / 10.191392 (5.008919) \| 0.166380 / 0.680424 (-0.514043) \| 0.021417 / 0.534201 (-0.512784) \| 0.435677 / 0.579283 (-0.143606) \| 0.460412 / 0.434364 (0.026048) \| 0.509978 / 0.540337 (-0.030359) \| 0.702506 / 1.386936 (-0.684430) \|\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) \| 0.007378 / 0.011353 (-0.003975) \| 0.003938 / 0.011008 (-0.007070) \| 0.067095 / 0.038508 (0.028587) \| 0.082252 / 0.023109 (0.059143) \| 0.420317 / 0.275898 (0.144419) \| 0.477496 / 0.323480 (0.154017) \| 0.006259 / 0.007986 (-0.001727) \| 0.003513 / 0.004328 (-0.000816) \| 0.072107 / 0.004250 (0.067856) \| 0.061737 / 0.037052 (0.024684) \| 0.444142 / 0.258489 (0.185653) \| 0.488926 / 0.293841 (0.195085) \| 0.033623 / 0.128546 (-0.094923) \| 0.008091 / 0.075646 (-0.067555) \| 0.073997 / 0.419271 (-0.345274) \| 0.051295 / 0.043533 (0.007762) \| 0.442551 / 0.255139 (0.187412) \| 0.462713 / 0.283200 (0.179513) \| 0.023115 / 0.141683 (-0.118568) \| 1.645759 / 1.452155 (0.193604) \| 1.758121 / 1.492716 (0.265405) \|\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) \| 0.233450 / 0.018006 (0.215444) \| 0.445384 / 0.000490 (0.444894) \| 0.006412 / 0.000200 (0.006212) \| 0.000111 / 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) \| 0.032446 / 0.037411 (-0.004965) \| 0.098515 / 0.014526 (0.083989) \| 0.109095 / 0.176557 (-0.067462) \| 0.167645 / 0.737135 (-0.569490) \| 0.110403 / 0.296338 (-0.185936) \|\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) \| 0.470189 / 0.215209 (0.254980) \| 4.663224 / 2.077655 (2.585569) \| 2.504474 / 1.504120 (1.000354) \| 2.282867 / 1.541195 (0.741673) \| 2.331598 / 1.468490 (0.863108) \| 0.554421 / 4.584777 (-4.030356) \| 4.078657 / 3.745712 (0.332945) \| 3.516339 / 5.269862 (-1.753523) \| 2.239134 / 4.565676 (-2.326542) \| 0.062690 / 0.424275 (-0.361585) \| 0.008406 / 0.007607 (0.000799) \| 0.533827 / 0.226044 (0.307782) \| 5.423984 / 2.268929 (3.155055) \| 2.972784 / 55.444624 (-52.471840) \| 2.699056 / 6.876477 (-4.177421) \| 2.844403 / 2.142072 (0.702331) \| 0.639194 / 4.805227 (-4.166033) \| 0.142097 / 6.500664 (-6.358567) \| 0.064646 / 0.075469 (-0.010823) \|\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) \| 1.544640 / 1.841788 (-0.297148) \| 21.453429 / 8.074308 (13.379121) \| 15.610723 / 10.191392 (5.419331) \| 0.207796 / 0.680424 (-0.472628) \| 0.021912 / 0.534201 (-0.512289) \| 0.430472 / 0.579283 (-0.148811) \| 0.467530 / 0.434364 (0.033166) \| 0.541339 / 0.540337 (0.001002) \| 0.721976 / 1.386936 (-0.664960) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#691c4bc65888005f3aadea5c104fdbc87694882d \"CML watermark\")\n" ]
https://api.github.com/repos/huggingface/datasets/issues/6097	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6097/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6097/comments	https://api.github.com/repos/huggingface/datasets/issues/6097/events	https://github.com/huggingface/datasets/issues/6097	1,827,054,143	I_kwDODunzps5s5qI_	6,097	Dataset.get_nearest_examples does not return all feature values for the k most similar datapoints - side effect of Dataset.set_format	{ "avatar_url": "https://avatars.githubusercontent.com/u/2538048?v=4", "events_url": "https://api.github.com/users/aschoenauer-sebag/events{/privacy}", "followers_url": "https://api.github.com/users/aschoenauer-sebag/followers", "following_url": "https://api.github.com/users/aschoenauer-sebag/following{/other_user}", "gists_url": "https://api.github.com/users/aschoenauer-sebag/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/aschoenauer-sebag", "id": 2538048, "login": "aschoenauer-sebag", "node_id": "MDQ6VXNlcjI1MzgwNDg=", "organizations_url": "https://api.github.com/users/aschoenauer-sebag/orgs", "received_events_url": "https://api.github.com/users/aschoenauer-sebag/received_events", "repos_url": "https://api.github.com/users/aschoenauer-sebag/repos", "site_admin": false, "starred_url": "https://api.github.com/users/aschoenauer-sebag/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/aschoenauer-sebag/subscriptions", "type": "User", "url": "https://api.github.com/users/aschoenauer-sebag" }	[]	closed	false	null	[]	null	1	"2023-07-28T20:31:59Z"	"2023-07-28T20:49:58Z"	"2023-07-28T20:49:58Z"	NONE	null	null	null	### Describe the bug Hi team! I observe that there seems to be a side effect of `Dataset.set_format`: after setting a format and creating a FAISS index, the method `get_nearest_examples` from the `Dataset` class, fails to retrieve anything else but the embeddings themselves - not super useful. This is not the case if not using the `set_format` method: you can also retrieve any other feature value, such as an index/id/etc. Are you able to reproduce what I observe? ### Steps to reproduce the bug ```python from datasets import Dataset import numpy as np foo = {'vectors': np.random.random((100,1024)), 'ids': [str(u) for u in range(100)]} foo = Dataset.from_dict(foo) foo.set_format('numpy', ['vectors']) foo.add_faiss_index('vectors') new_vector = np.random.random(1024) scores, res = foo.get_nearest_examples('vectors', new_vector, k=3) ``` This will return, for the resulting most similar vectors to `new_vector` - in particular it will not return the `ids` feature: ``` {'vectors': array([[random values ...]])} ``` ### Expected behavior The expected behavior happens when the `set_format` method is not called: ```python from datasets import Dataset import numpy as np foo = {'vectors': np.random.random((100,1024)), 'ids': [str(u) for u in range(100)]} foo = Dataset.from_dict(foo) # foo.set_format('numpy', ['vectors']) foo.add_faiss_index('vectors') new_vector = np.random.random(1024) scores, res = foo.get_nearest_examples('vectors', new_vector, k=3) ``` This will return the `ids` of the similar vectors - with unfortunately a list of lists in lieu of the array I think for caching reasons - read it elsewhere ``` {'vectors': [[random values on multiple lines...]], 'ids': ['x', 'y', 'z']} ``` ### Environment info - `datasets` version: 2.12.0 - Platform: Linux-5.4.0-155-generic-x86_64-with-glibc2.31 - Python version: 3.10.6 - Huggingface_hub version: 0.15.1 - PyArrow version: 11.0.0 - Pandas version: 1.5.3	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6097/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6097/timeline	null	completed	666	false	[ "Actually, my bad -- specifying\r\n```python\r\nfoo.set_format('numpy', ['vectors'], output_all_columns=True)\r\n```\r\nfixes it." ]
https://api.github.com/repos/huggingface/datasets/issues/6096	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6096/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6096/comments	https://api.github.com/repos/huggingface/datasets/issues/6096/events	https://github.com/huggingface/datasets/pull/6096	1,826,731,091	PR_kwDODunzps5Wq9Hb	6,096	Add `fsspec` support for `to_json`, `to_csv`, and `to_parquet`	{ "avatar_url": "https://avatars.githubusercontent.com/u/36760800?v=4", "events_url": "https://api.github.com/users/alvarobartt/events{/privacy}", "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}", "gravatar_id": "", "html_url": "https://github.com/alvarobartt", "id": 36760800, "login": "alvarobartt", "node_id": "MDQ6VXNlcjM2NzYwODAw", "organizations_url": "https://api.github.com/users/alvarobartt/orgs", "received_events_url": "https://api.github.com/users/alvarobartt/received_events", "repos_url": "https://api.github.com/users/alvarobartt/repos", "site_admin": false, "starred_url": "https://api.github.com/users/alvarobartt/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/alvarobartt/subscriptions", "type": "User", "url": "https://api.github.com/users/alvarobartt" }	[]	open	false	null	[]	null	2	"2023-07-28T16:36:59Z"	"2023-09-06T13:58:09Z"	null	CONTRIBUTOR	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6096.diff", "html_url": "https://github.com/huggingface/datasets/pull/6096", "merged_at": null, "patch_url": "https://github.com/huggingface/datasets/pull/6096.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6096" }	Hi to whoever is reading this! 🤗 (Most likely @mariosasko) ## What's in this PR? This PR replaces the `open` from Python with `fsspec.open` and adds the argument `storage_options` for the methods `to_json`, `to_csv`, and `to_parquet`, to allow users to export any 🤗`Dataset` into a file in a file-system as requested at #6086. ## What's missing in this PR? As per `to_json`, `to_csv`, and `to_parquet` docstrings for the recently included `storage_options` arg, I've scoped it to 2.15.0, so we should check that before merging in case we want to scope that for 2.14.2 instead. Additionally, should we also add `fsspec` support for the `from_csv`, `from_json`, and `from_parquet` methods? If you want me to do so @mariosasko just let me know and I'll create another PR to support that too!	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6096/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6096/timeline	null	null	667	true	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6096). All of your documentation changes will be reflected on that endpoint.", "Hi @mariosasko and/or @lhoestq, friendly pining you guys here! Let me know if there's anything else to be included within this PR, thanks! 🤗 " ]
https://api.github.com/repos/huggingface/datasets/issues/6095	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6095/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6095/comments	https://api.github.com/repos/huggingface/datasets/issues/6095/events	https://github.com/huggingface/datasets/pull/6095	1,826,496,967	PR_kwDODunzps5WqJtr	6,095	Fix deprecation of errors in TextConfig	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "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}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova" }	[]	closed	false	null	[]	null	3	"2023-07-28T14:08:37Z"	"2023-07-31T05:26:32Z"	"2023-07-31T05:17:38Z"	MEMBER	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6095.diff", "html_url": "https://github.com/huggingface/datasets/pull/6095", "merged_at": "2023-07-31T05:17:38Z", "patch_url": "https://github.com/huggingface/datasets/pull/6095.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6095" }	This PR fixes an issue with the deprecation of `errors` in `TextConfig` introduced by: - #5974 ```python In [1]: ds = load_dataset("text", data_files="test.txt", errors="strict") --------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-13-701c27131a5d> in <module> ----> 1 ds = load_dataset("text", data_files="test.txt", errors="strict") ~/huggingface/datasets/src/datasets/load.py 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, token, use_auth_token, task, streaming, num_proc, storage_options, config_kwargs) 2107 2108 # Create a dataset builder -> 2109 builder_instance = load_dataset_builder( 2110 path=path, 2111 name=name, ~/huggingface/datasets/src/datasets/load.py in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, token, use_auth_token, storage_options, config_kwargs) 1830 builder_cls = get_dataset_builder_class(dataset_module, dataset_name=dataset_name) 1831 # Instantiate the dataset builder -> 1832 builder_instance: DatasetBuilder = builder_cls( 1833 cache_dir=cache_dir, 1834 dataset_name=dataset_name, ~/huggingface/datasets/src/datasets/builder.py in __init__(self, cache_dir, dataset_name, config_name, hash, base_path, info, features, token, use_auth_token, repo_id, data_files, data_dir, storage_options, writer_batch_size, name, config_kwargs) 371 if data_dir is not None: 372 config_kwargs["data_dir"] = data_dir --> 373 self.config, self.config_id = self._create_builder_config( 374 config_name=config_name, 375 custom_features=features, ~/huggingface/datasets/src/datasets/builder.py in _create_builder_config(self, config_name, custom_features, config_kwargs) 550 if "version" not in config_kwargs and hasattr(self, "VERSION") and self.VERSION: 551 config_kwargs["version"] = self.VERSION --> 552 builder_config = self.BUILDER_CONFIG_CLASS(**config_kwargs) 553 554 # otherwise use the config_kwargs to overwrite the attributes TypeError: __init__() got an unexpected keyword argument 'errors' ``` Similar to: - #6094	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6095/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6095/timeline	null	null	668	true	[ "_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) \| 0.012497 / 0.011353 (0.001144) \| 0.005355 / 0.011008 (-0.005654) \| 0.106018 / 0.038508 (0.067510) \| 0.093069 / 0.023109 (0.069960) \| 0.394699 / 0.275898 (0.118801) \| 0.449723 / 0.323480 (0.126243) \| 0.006434 / 0.007986 (-0.001552) \| 0.004187 / 0.004328 (-0.000141) \| 0.079620 / 0.004250 (0.075370) \| 0.062513 / 0.037052 (0.025460) \| 0.410305 / 0.258489 (0.151816) \| 0.467231 / 0.293841 (0.173390) \| 0.048130 / 0.128546 (-0.080416) \| 0.013747 / 0.075646 (-0.061899) \| 0.357979 / 0.419271 (-0.061293) \| 0.064764 / 0.043533 (0.021231) \| 0.411029 / 0.255139 (0.155890) \| 0.454734 / 0.283200 (0.171534) \| 0.037215 / 0.141683 (-0.104468) \| 1.801331 / 1.452155 (0.349176) \| 1.951628 / 1.492716 (0.458912) \|\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) \| 0.231073 / 0.018006 (0.213067) \| 0.564179 / 0.000490 (0.563689) \| 0.000947 / 0.000200 (0.000747) \| 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) \| 0.030629 / 0.037411 (-0.006783) \| 0.092522 / 0.014526 (0.077996) \| 0.109781 / 0.176557 (-0.066775) \| 0.183185 / 0.737135 (-0.553950) \| 0.109679 / 0.296338 (-0.186660) \|\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) \| 0.600095 / 0.215209 (0.384886) \| 6.072868 / 2.077655 (3.995213) \| 2.684109 / 1.504120 (1.179989) \| 2.436204 / 1.541195 (0.895010) \| 2.514667 / 1.468490 (1.046177) \| 0.865455 / 4.584777 (-3.719322) \| 5.245561 / 3.745712 (1.499849) \| 5.628688 / 5.269862 (0.358826) \| 3.457343 / 4.565676 (-1.108333) \| 0.107563 / 0.424275 (-0.316712) \| 0.008803 / 0.007607 (0.001196) \| 0.754014 / 0.226044 (0.527970) \| 7.341226 / 2.268929 (5.072297) \| 3.482090 / 55.444624 (-51.962534) \| 2.726071 / 6.876477 (-4.150406) \| 3.168494 / 2.142072 (1.026422) \| 1.023517 / 4.805227 (-3.781710) \| 0.207440 / 6.500664 (-6.293224) \| 0.073642 / 0.075469 (-0.001827) \|\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) \| 1.588636 / 1.841788 (-0.253152) \| 23.305257 / 8.074308 (15.230949) \| 22.071476 / 10.191392 (11.880084) \| 0.242044 / 0.680424 (-0.438379) \| 0.028830 / 0.534201 (-0.505371) \| 0.461414 / 0.579283 (-0.117869) \| 0.591024 / 0.434364 (0.156660) \| 0.548984 / 0.540337 (0.008646) \| 0.783318 / 1.386936 (-0.603618) \|\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) \| 0.008724 / 0.011353 (-0.002629) \| 0.004638 / 0.011008 (-0.006371) \| 0.081024 / 0.038508 (0.042516) \| 0.077533 / 0.023109 (0.054423) \| 0.444827 / 0.275898 (0.168929) \| 0.507812 / 0.323480 (0.184332) \| 0.006017 / 0.007986 (-0.001968) \| 0.004204 / 0.004328 (-0.000124) \| 0.082154 / 0.004250 (0.077904) \| 0.063818 / 0.037052 (0.026765) \| 0.463468 / 0.258489 (0.204979) \| 0.536784 / 0.293841 (0.242943) \| 0.046393 / 0.128546 (-0.082153) \| 0.014349 / 0.075646 (-0.061298) \| 0.089213 / 0.419271 (-0.330059) \| 0.058313 / 0.043533 (0.014780) \| 0.463674 / 0.255139 (0.208535) \| 0.495865 / 0.283200 (0.212665) \| 0.036586 / 0.141683 (-0.105096) \| 1.801601 / 1.452155 (0.349447) \| 1.871219 / 1.492716 (0.378502) \|\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) \| 0.273411 / 0.018006 (0.255405) \| 0.531745 / 0.000490 (0.531255) \| 0.000424 / 0.000200 (0.000224) \| 0.000130 / 0.000054 (0.000076) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.037689 / 0.037411 (0.000278) \| 0.109544 / 0.014526 (0.095019) \| 0.124053 / 0.176557 (-0.052504) \| 0.179960 / 0.737135 (-0.557175) \| 0.118218 / 0.296338 (-0.178120) \|\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) \| 0.639859 / 0.215209 (0.424650) \| 6.347385 / 2.077655 (4.269730) \| 2.910188 / 1.504120 (1.406068) \| 2.698821 / 1.541195 (1.157626) \| 2.802652 / 1.468490 (1.334161) \| 0.816109 / 4.584777 (-3.768668) \| 5.190313 / 3.745712 (1.444601) \| 4.642684 / 5.269862 (-0.627178) \| 2.948092 / 4.565676 (-1.617584) \| 0.095877 / 0.424275 (-0.328398) \| 0.009631 / 0.007607 (0.002024) \| 0.779136 / 0.226044 (0.553091) \| 7.611586 / 2.268929 (5.342658) \| 3.760804 / 55.444624 (-51.683820) \| 3.139355 / 6.876477 (-3.737122) \| 3.419660 / 2.142072 (1.277587) \| 1.036397 / 4.805227 (-3.768831) \| 0.224015 / 6.500664 (-6.276649) \| 0.084037 / 0.075469 (0.008568) \|\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) \| 1.710608 / 1.841788 (-0.131179) \| 24.447646 / 8.074308 (16.373338) \| 21.345322 / 10.191392 (11.153930) \| 0.232383 / 0.680424 (-0.448040) \| 0.026381 / 0.534201 (-0.507820) \| 0.475995 / 0.579283 (-0.103289) \| 0.611939 / 0.434364 (0.177575) \| 0.541441 / 0.540337 (0.001104) \| 0.742796 / 1.386936 (-0.644140) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7929929525e734f7232cfc68d1d22fb8d53c54a3 \"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) \| 0.006140 / 0.011353 (-0.005213) \| 0.003664 / 0.011008 (-0.007344) \| 0.080765 / 0.038508 (0.042257) \| 0.065009 / 0.023109 (0.041900) \| 0.312787 / 0.275898 (0.036889) \| 0.354637 / 0.323480 (0.031157) \| 0.004846 / 0.007986 (-0.003140) \| 0.003019 / 0.004328 (-0.001310) \| 0.062823 / 0.004250 (0.058573) \| 0.050446 / 0.037052 (0.013394) \| 0.314478 / 0.258489 (0.055989) \| 0.360206 / 0.293841 (0.066365) \| 0.027282 / 0.128546 (-0.101265) \| 0.008024 / 0.075646 (-0.067622) \| 0.262125 / 0.419271 (-0.157146) \| 0.045793 / 0.043533 (0.002260) \| 0.310508 / 0.255139 (0.055369) \| 0.340899 / 0.283200 (0.057699) \| 0.021850 / 0.141683 (-0.119833) \| 1.510791 / 1.452155 (0.058636) \| 1.570661 / 1.492716 (0.077944) \|\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) \| 0.449310 / 0.000490 (0.448820) \| 0.004556 / 0.000200 (0.004356) \| 0.000078 / 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) \| 0.023689 / 0.037411 (-0.013722) \| 0.076316 / 0.014526 (0.061791) \| 0.084800 / 0.176557 (-0.091757) \| 0.153154 / 0.737135 (-0.583981) \| 0.086467 / 0.296338 (-0.209871) \|\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) \| 0.432254 / 0.215209 (0.217045) \| 4.305098 / 2.077655 (2.227443) \| 2.304267 / 1.504120 (0.800147) \| 2.139503 / 1.541195 (0.598309) \| 2.220414 / 1.468490 (0.751924) \| 0.498595 / 4.584777 (-4.086182) \| 3.058593 / 3.745712 (-0.687119) \| 4.324501 / 5.269862 (-0.945361) \| 2.667731 / 4.565676 (-1.897946) \| 0.059917 / 0.424275 (-0.364358) \| 0.006829 / 0.007607 (-0.000778) \| 0.504608 / 0.226044 (0.278564) \| 5.044480 / 2.268929 (2.775552) \| 2.753080 / 55.444624 (-52.691545) \| 2.449265 / 6.876477 (-4.427212) \| 2.635113 / 2.142072 (0.493040) \| 0.590760 / 4.805227 (-4.214467) \| 0.130133 / 6.500664 (-6.370532) \| 0.062759 / 0.075469 (-0.012710) \|\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) \| 1.267014 / 1.841788 (-0.574773) \| 18.562890 / 8.074308 (10.488581) \| 13.991257 / 10.191392 (3.799865) \| 0.147108 / 0.680424 (-0.533315) \| 0.017216 / 0.534201 (-0.516985) \| 0.330317 / 0.579283 (-0.248966) \| 0.351328 / 0.434364 (-0.083036) \| 0.381097 / 0.540337 (-0.159241) \| 0.558718 / 1.386936 (-0.828218) \|\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) \| 0.006385 / 0.011353 (-0.004967) \| 0.003668 / 0.011008 (-0.007340) \| 0.062581 / 0.038508 (0.024073) \| 0.067006 / 0.023109 (0.043896) \| 0.428465 / 0.275898 (0.152567) \| 0.466106 / 0.323480 (0.142626) \| 0.005806 / 0.007986 (-0.002180) \| 0.003117 / 0.004328 (-0.001212) \| 0.063554 / 0.004250 (0.059303) \| 0.054404 / 0.037052 (0.017352) \| 0.431168 / 0.258489 (0.172679) \| 0.467578 / 0.293841 (0.173737) \| 0.027779 / 0.128546 (-0.100767) \| 0.008055 / 0.075646 (-0.067592) \| 0.067718 / 0.419271 (-0.351554) \| 0.043042 / 0.043533 (-0.000491) \| 0.425926 / 0.255139 (0.170787) \| 0.453699 / 0.283200 (0.170500) \| 0.023495 / 0.141683 (-0.118187) \| 1.435356 / 1.452155 (-0.016799) \| 1.509340 / 1.492716 (0.016624) \|\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) \| 0.242322 / 0.018006 (0.224316) \| 0.446865 / 0.000490 (0.446376) \| 0.001079 / 0.000200 (0.000879) \| 0.000065 / 0.000054 (0.000010) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025376 / 0.037411 (-0.012035) \| 0.079373 / 0.014526 (0.064847) \| 0.088554 / 0.176557 (-0.088002) \| 0.141026 / 0.737135 (-0.596109) \| 0.090666 / 0.296338 (-0.205672) \|\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) \| 0.434800 / 0.215209 (0.219590) \| 4.314491 / 2.077655 (2.236836) \| 2.320688 / 1.504120 (0.816568) \| 2.163941 / 1.541195 (0.622747) \| 2.292576 / 1.468490 (0.824086) \| 0.500226 / 4.584777 (-4.084551) \| 3.114604 / 3.745712 (-0.631108) \| 4.206997 / 5.269862 (-1.062864) \| 2.461126 / 4.565676 (-2.104551) \| 0.057717 / 0.424275 (-0.366558) \| 0.006989 / 0.007607 (-0.000618) \| 0.515623 / 0.226044 (0.289579) \| 5.155301 / 2.268929 (2.886372) \| 2.733589 / 55.444624 (-52.711035) \| 2.542111 / 6.876477 (-4.334366) \| 2.697035 / 2.142072 (0.554963) \| 0.594213 / 4.805227 (-4.211014) \| 0.128537 / 6.500664 (-6.372127) \| 0.065223 / 0.075469 (-0.010246) \|\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) \| 1.306738 / 1.841788 (-0.535050) \| 19.065370 / 8.074308 (10.991062) \| 14.242096 / 10.191392 (4.050704) \| 0.146177 / 0.680424 (-0.534246) \| 0.017186 / 0.534201 (-0.517015) \| 0.337224 / 0.579283 (-0.242059) \| 0.349997 / 0.434364 (-0.084367) \| 0.390408 / 0.540337 (-0.149930) \| 0.524597 / 1.386936 (-0.862339) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#69ec36948b0ef1f194e9dcd43ec53a50b7708962 \"CML watermark\")\n" ]
https://api.github.com/repos/huggingface/datasets/issues/6094	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6094/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6094/comments	https://api.github.com/repos/huggingface/datasets/issues/6094/events	https://github.com/huggingface/datasets/pull/6094	1,826,293,414	PR_kwDODunzps5WpdpA	6,094	Fix deprecation of use_auth_token in DownloadConfig	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "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}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova" }	[]	closed	false	null	[]	null	3	"2023-07-28T11:52:21Z"	"2023-07-31T05:08:41Z"	"2023-07-31T04:59:50Z"	MEMBER	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6094.diff", "html_url": "https://github.com/huggingface/datasets/pull/6094", "merged_at": "2023-07-31T04:59:50Z", "patch_url": "https://github.com/huggingface/datasets/pull/6094.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6094" }	This PR fixes an issue with the deprecation of `use_auth_token` in `DownloadConfig` introduced by: - #5996 ```python In [1]: from datasets import DownloadConfig In [2]: DownloadConfig(use_auth_token=False) --------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-3-41927b449e72> in <module> ----> 1 DownloadConfig(use_auth_token=False) TypeError: __init__() got an unexpected keyword argument 'use_auth_token' ``` ```python In [1]: from datasets import get_dataset_config_names In [2]: get_dataset_config_names("squad", use_auth_token=False) --------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-22-4671992ead50> in <module> ----> 1 get_dataset_config_names("squad", use_auth_token=False) ~/huggingface/datasets/src/datasets/inspect.py in get_dataset_config_names(path, revision, download_config, download_mode, dynamic_modules_path, data_files, download_kwargs) 349 ``` 350 """ --> 351 dataset_module = dataset_module_factory( 352 path, 353 revision=revision, ~/huggingface/datasets/src/datasets/load.py in dataset_module_factory(path, revision, download_config, download_mode, dynamic_modules_path, data_dir, data_files, download_kwargs) 1374 """ 1375 if download_config is None: -> 1376 download_config = DownloadConfig(**download_kwargs) 1377 download_mode = DownloadMode(download_mode or DownloadMode.REUSE_DATASET_IF_EXISTS) 1378 download_config.extract_compressed_file = True TypeError: __init__() got an unexpected keyword argument 'use_auth_token' ```	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6094/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6094/timeline	null	null	669	true	[ "_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) \| 0.008996 / 0.011353 (-0.002357) \| 0.004976 / 0.011008 (-0.006033) \| 0.114495 / 0.038508 (0.075987) \| 0.083958 / 0.023109 (0.060849) \| 0.408395 / 0.275898 (0.132497) \| 0.456757 / 0.323480 (0.133278) \| 0.006396 / 0.007986 (-0.001589) \| 0.004315 / 0.004328 (-0.000014) \| 0.093558 / 0.004250 (0.089307) \| 0.062067 / 0.037052 (0.025014) \| 0.423452 / 0.258489 (0.164963) \| 0.463947 / 0.293841 (0.170106) \| 0.049934 / 0.128546 (-0.078613) \| 0.013937 / 0.075646 (-0.061709) \| 0.365809 / 0.419271 (-0.053463) \| 0.067382 / 0.043533 (0.023849) \| 0.418860 / 0.255139 (0.163721) \| 0.463264 / 0.283200 (0.180065) \| 0.034392 / 0.141683 (-0.107291) \| 1.870685 / 1.452155 (0.418530) \| 1.975313 / 1.492716 (0.482597) \|\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) \| 0.261748 / 0.018006 (0.243742) \| 0.645510 / 0.000490 (0.645020) \| 0.000376 / 0.000200 (0.000176) \| 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) \| 0.032129 / 0.037411 (-0.005282) \| 0.104309 / 0.014526 (0.089783) \| 0.113154 / 0.176557 (-0.063403) \| 0.186795 / 0.737135 (-0.550341) \| 0.115584 / 0.296338 (-0.180755) \|\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) \| 0.577755 / 0.215209 (0.362546) \| 5.984988 / 2.077655 (3.907333) \| 2.581967 / 1.504120 (1.077848) \| 2.305744 / 1.541195 (0.764549) \| 2.359618 / 1.468490 (0.891128) \| 0.882892 / 4.584777 (-3.701885) \| 5.755578 / 3.745712 (2.009866) \| 8.718373 / 5.269862 (3.448511) \| 5.217586 / 4.565676 (0.651909) \| 0.099785 / 0.424275 (-0.324490) \| 0.009008 / 0.007607 (0.001401) \| 0.730937 / 0.226044 (0.504892) \| 7.265309 / 2.268929 (4.996381) \| 3.487167 / 55.444624 (-51.957457) \| 2.750090 / 6.876477 (-4.126386) \| 3.060198 / 2.142072 (0.918125) \| 1.069945 / 4.805227 (-3.735282) \| 0.227143 / 6.500664 (-6.273521) \| 0.083601 / 0.075469 (0.008132) \|\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) \| 1.754375 / 1.841788 (-0.087412) \| 25.448731 / 8.074308 (17.374423) \| 22.385943 / 10.191392 (12.194551) \| 0.249921 / 0.680424 (-0.430503) \| 0.034138 / 0.534201 (-0.500063) \| 0.535170 / 0.579283 (-0.044113) \| 0.605474 / 0.434364 (0.171110) \| 0.580025 / 0.540337 (0.039688) \| 0.810537 / 1.386936 (-0.576399) \|\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) \| 0.009117 / 0.011353 (-0.002236) \| 0.005029 / 0.011008 (-0.005979) \| 0.082200 / 0.038508 (0.043691) \| 0.082386 / 0.023109 (0.059277) \| 0.491869 / 0.275898 (0.215971) \| 0.546735 / 0.323480 (0.223255) \| 0.006893 / 0.007986 (-0.001093) \| 0.004571 / 0.004328 (0.000243) \| 0.085361 / 0.004250 (0.081111) \| 0.063342 / 0.037052 (0.026290) \| 0.522522 / 0.258489 (0.264033) \| 0.560784 / 0.293841 (0.266943) \| 0.047685 / 0.128546 (-0.080861) \| 0.017741 / 0.075646 (-0.057905) \| 0.098204 / 0.419271 (-0.321067) \| 0.062919 / 0.043533 (0.019386) \| 0.504005 / 0.255139 (0.248866) \| 0.547022 / 0.283200 (0.263823) \| 0.033731 / 0.141683 (-0.107952) \| 1.869765 / 1.452155 (0.417610) \| 1.935867 / 1.492716 (0.443151) \|\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) \| 0.304756 / 0.018006 (0.286750) \| 0.623647 / 0.000490 (0.623157) \| 0.000508 / 0.000200 (0.000308) \| 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) \| 0.043627 / 0.037411 (0.006216) \| 0.107183 / 0.014526 (0.092657) \| 0.119304 / 0.176557 (-0.057253) \| 0.192651 / 0.737135 (-0.544485) \| 0.125118 / 0.296338 (-0.171221) \|\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) \| 0.669980 / 0.215209 (0.454771) \| 6.566068 / 2.077655 (4.488413) \| 3.136271 / 1.504120 (1.632152) \| 2.964643 / 1.541195 (1.423448) \| 2.936772 / 1.468490 (1.468282) \| 0.885205 / 4.584777 (-3.699572) \| 5.539062 / 3.745712 (1.793350) \| 5.006133 / 5.269862 (-0.263729) \| 3.313697 / 4.565676 (-1.251979) \| 0.102975 / 0.424275 (-0.321301) \| 0.010759 / 0.007607 (0.003152) \| 0.791176 / 0.226044 (0.565132) \| 7.822195 / 2.268929 (5.553266) \| 3.982315 / 55.444624 (-51.462309) \| 3.357026 / 6.876477 (-3.519451) \| 3.561307 / 2.142072 (1.419234) \| 1.056966 / 4.805227 (-3.748261) \| 0.220476 / 6.500664 (-6.280188) \| 0.090535 / 0.075469 (0.015066) \|\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) \| 1.897984 / 1.841788 (0.056196) \| 26.411411 / 8.074308 (18.337103) \| 22.951939 / 10.191392 (12.760547) \| 0.216091 / 0.680424 (-0.464333) \| 0.037005 / 0.534201 (-0.497196) \| 0.505585 / 0.579283 (-0.073698) \| 0.617794 / 0.434364 (0.183430) \| 0.604631 / 0.540337 (0.064293) \| 0.826356 / 1.386936 (-0.560580) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ca6342c0177adc3a1d114740444e207b8525ed6e \"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) \| 0.006850 / 0.011353 (-0.004503) \| 0.004062 / 0.011008 (-0.006947) \| 0.086587 / 0.038508 (0.048079) \| 0.079587 / 0.023109 (0.056478) \| 0.353601 / 0.275898 (0.077702) \| 0.396399 / 0.323480 (0.072919) \| 0.004182 / 0.007986 (-0.003804) \| 0.004445 / 0.004328 (0.000117) \| 0.065100 / 0.004250 (0.060849) \| 0.057386 / 0.037052 (0.020334) \| 0.356945 / 0.258489 (0.098456) \| 0.407093 / 0.293841 (0.113252) \| 0.031949 / 0.128546 (-0.096597) \| 0.008525 / 0.075646 (-0.067121) \| 0.291310 / 0.419271 (-0.127961) \| 0.053638 / 0.043533 (0.010105) \| 0.359381 / 0.255139 (0.104242) \| 0.399473 / 0.283200 (0.116273) \| 0.025880 / 0.141683 (-0.115803) \| 1.487604 / 1.452155 (0.035449) \| 1.550528 / 1.492716 (0.057812) \|\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) \| 0.201106 / 0.018006 (0.183099) \| 0.457538 / 0.000490 (0.457048) \| 0.003995 / 0.000200 (0.003795) \| 0.000087 / 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) \| 0.030365 / 0.037411 (-0.007046) \| 0.088064 / 0.014526 (0.073538) \| 0.096432 / 0.176557 (-0.080124) \| 0.158063 / 0.737135 (-0.579072) \| 0.098258 / 0.296338 (-0.198080) \|\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) \| 0.405351 / 0.215209 (0.190142) \| 4.032639 / 2.077655 (1.954984) \| 2.018357 / 1.504120 (0.514237) \| 1.848493 / 1.541195 (0.307298) \| 1.929401 / 1.468490 (0.460910) \| 0.488729 / 4.584777 (-4.096048) \| 3.586114 / 3.745712 (-0.159598) \| 5.279054 / 5.269862 (0.009193) \| 3.113275 / 4.565676 (-1.452402) \| 0.057373 / 0.424275 (-0.366902) \| 0.007416 / 0.007607 (-0.000191) \| 0.485514 / 0.226044 (0.259470) \| 4.854389 / 2.268929 (2.585461) \| 2.493113 / 55.444624 (-52.951512) \| 2.128836 / 6.876477 (-4.747641) \| 2.383669 / 2.142072 (0.241596) \| 0.588266 / 4.805227 (-4.216962) \| 0.133603 / 6.500664 (-6.367061) \| 0.061812 / 0.075469 (-0.013657) \|\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) \| 1.260841 / 1.841788 (-0.580947) \| 20.086954 / 8.074308 (12.012646) \| 14.620932 / 10.191392 (4.429540) \| 0.161525 / 0.680424 (-0.518899) \| 0.018102 / 0.534201 (-0.516099) \| 0.393810 / 0.579283 (-0.185473) \| 0.406974 / 0.434364 (-0.027390) \| 0.462732 / 0.540337 (-0.077606) \| 0.634221 / 1.386936 (-0.752715) \|\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) \| 0.006692 / 0.011353 (-0.004661) \| 0.004068 / 0.011008 (-0.006940) \| 0.068009 / 0.038508 (0.029501) \| 0.081298 / 0.023109 (0.058189) \| 0.363531 / 0.275898 (0.087633) \| 0.408482 / 0.323480 (0.085002) \| 0.005601 / 0.007986 (-0.002384) \| 0.003385 / 0.004328 (-0.000943) \| 0.068043 / 0.004250 (0.063792) \| 0.059739 / 0.037052 (0.022687) \| 0.374043 / 0.258489 (0.115553) \| 0.407219 / 0.293841 (0.113378) \| 0.031194 / 0.128546 (-0.097352) \| 0.008630 / 0.075646 (-0.067017) \| 0.073755 / 0.419271 (-0.345517) \| 0.049831 / 0.043533 (0.006298) \| 0.363664 / 0.255139 (0.108525) \| 0.381515 / 0.283200 (0.098315) \| 0.026331 / 0.141683 (-0.115352) \| 1.507771 / 1.452155 (0.055617) \| 1.554403 / 1.492716 (0.061686) \|\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) \| 0.226309 / 0.018006 (0.208302) \| 0.452428 / 0.000490 (0.451938) \| 0.000937 / 0.000200 (0.000737) \| 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) \| 0.031899 / 0.037411 (-0.005513) \| 0.092090 / 0.014526 (0.077564) \| 0.100838 / 0.176557 (-0.075718) \| 0.153722 / 0.737135 (-0.583413) \| 0.101950 / 0.296338 (-0.194389) \|\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) \| 0.417879 / 0.215209 (0.202669) \| 4.171939 / 2.077655 (2.094284) \| 2.312937 / 1.504120 (0.808817) \| 2.209991 / 1.541195 (0.668796) \| 2.329469 / 1.468490 (0.860979) \| 0.484576 / 4.584777 (-4.100201) \| 3.659198 / 3.745712 (-0.086514) \| 5.255227 / 5.269862 (-0.014634) \| 3.047430 / 4.565676 (-1.518247) \| 0.057029 / 0.424275 (-0.367246) \| 0.007735 / 0.007607 (0.000127) \| 0.499962 / 0.226044 (0.273918) \| 4.991655 / 2.268929 (2.722727) \| 2.755999 / 55.444624 (-52.688625) \| 2.374034 / 6.876477 (-4.502443) \| 2.599759 / 2.142072 (0.457687) \| 0.600319 / 4.805227 (-4.204908) \| 0.146176 / 6.500664 (-6.354488) \| 0.062328 / 0.075469 (-0.013142) \|\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) \| 1.346065 / 1.841788 (-0.495722) \| 20.430343 / 8.074308 (12.356035) \| 14.632959 / 10.191392 (4.441567) \| 0.167007 / 0.680424 (-0.513417) \| 0.018588 / 0.534201 (-0.515613) \| 0.396015 / 0.579283 (-0.183268) \| 0.429384 / 0.434364 (-0.004980) \| 0.467746 / 0.540337 (-0.072591) \| 0.615166 / 1.386936 (-0.771770) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#289bcc2ae9bf98c9414b6846ae603178a1816d3f \"CML watermark\")\n" ]
https://api.github.com/repos/huggingface/datasets/issues/6093	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6093/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6093/comments	https://api.github.com/repos/huggingface/datasets/issues/6093/events	https://github.com/huggingface/datasets/pull/6093	1,826,210,490	PR_kwDODunzps5WpLfh	6,093	Deprecate `download_custom`	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "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}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko" }	[]	closed	false	null	[]	null	6	"2023-07-28T10:49:06Z"	"2023-08-21T17:51:34Z"	"2023-07-28T11:30:02Z"	CONTRIBUTOR	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6093.diff", "html_url": "https://github.com/huggingface/datasets/pull/6093", "merged_at": "2023-07-28T11:30:02Z", "patch_url": "https://github.com/huggingface/datasets/pull/6093.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6093" }	Deprecate `DownloadManager.download_custom`. Users should use `fsspec` URLs (cacheable) or make direct requests with `fsspec`/`requests` (not cacheable) instead. We should deprecate this method as it's not compatible with streaming, and implementing the streaming version of it is hard/impossible. There have been requests to implement the streaming version of this method on the forum, but the reason for this seems to be a tip in the docs that "promotes" this method (this PR removes it).	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6093/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6093/timeline	null	null	670	true	[ "_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) \| 0.007498 / 0.011353 (-0.003855) \| 0.004158 / 0.011008 (-0.006850) \| 0.087568 / 0.038508 (0.049060) \| 0.083265 / 0.023109 (0.060156) \| 0.378505 / 0.275898 (0.102607) \| 0.399025 / 0.323480 (0.075545) \| 0.006173 / 0.007986 (-0.001813) \| 0.003743 / 0.004328 (-0.000586) \| 0.071958 / 0.004250 (0.067707) \| 0.059323 / 0.037052 (0.022271) \| 0.377084 / 0.258489 (0.118595) \| 0.408358 / 0.293841 (0.114517) \| 0.035191 / 0.128546 (-0.093356) \| 0.009408 / 0.075646 (-0.066238) \| 0.312587 / 0.419271 (-0.106685) \| 0.058073 / 0.043533 (0.014540) \| 0.381977 / 0.255139 (0.126838) \| 0.395611 / 0.283200 (0.112411) \| 0.024191 / 0.141683 (-0.117491) \| 1.572735 / 1.452155 (0.120581) \| 1.687186 / 1.492716 (0.194470) \|\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) \| 0.208886 / 0.018006 (0.190879) \| 0.474625 / 0.000490 (0.474135) \| 0.006261 / 0.000200 (0.006061) \| 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) \| 0.031401 / 0.037411 (-0.006011) \| 0.086433 / 0.014526 (0.071907) \| 0.108405 / 0.176557 (-0.068152) \| 0.174564 / 0.737135 (-0.562571) \| 0.099932 / 0.296338 (-0.196407) \|\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) \| 0.407059 / 0.215209 (0.191850) \| 4.102056 / 2.077655 (2.024401) \| 1.975397 / 1.504120 (0.471277) \| 1.807117 / 1.541195 (0.265922) \| 1.908667 / 1.468490 (0.440177) \| 0.525880 / 4.584777 (-4.058897) \| 3.899639 / 3.745712 (0.153927) \| 4.358664 / 5.269862 (-0.911198) \| 2.586185 / 4.565676 (-1.979492) \| 0.061967 / 0.424275 (-0.362308) \| 0.007656 / 0.007607 (0.000049) \| 0.504851 / 0.226044 (0.278807) \| 5.004429 / 2.268929 (2.735500) \| 2.515540 / 55.444624 (-52.929084) \| 2.183142 / 6.876477 (-4.693334) \| 2.369835 / 2.142072 (0.227763) \| 0.623527 / 4.805227 (-4.181700) \| 0.145105 / 6.500664 (-6.355559) \| 0.063924 / 0.075469 (-0.011546) \|\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) \| 1.472661 / 1.841788 (-0.369126) \| 21.781655 / 8.074308 (13.707347) \| 15.628820 / 10.191392 (5.437428) \| 0.182342 / 0.680424 (-0.498082) \| 0.021139 / 0.534201 (-0.513062) \| 0.438610 / 0.579283 (-0.140673) \| 0.451343 / 0.434364 (0.016979) \| 0.563320 / 0.540337 (0.022983) \| 0.740976 / 1.386936 (-0.645960) \|\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) \| 0.007492 / 0.011353 (-0.003861) \| 0.004429 / 0.011008 (-0.006579) \| 0.068517 / 0.038508 (0.030008) \| 0.078533 / 0.023109 (0.055424) \| 0.383530 / 0.275898 (0.107632) \| 0.435061 / 0.323480 (0.111581) \| 0.005955 / 0.007986 (-0.002030) \| 0.003645 / 0.004328 (-0.000683) \| 0.068792 / 0.004250 (0.064541) \| 0.062452 / 0.037052 (0.025399) \| 0.408768 / 0.258489 (0.150279) \| 0.438538 / 0.293841 (0.144697) \| 0.032038 / 0.128546 (-0.096508) \| 0.009196 / 0.075646 (-0.066450) \| 0.074495 / 0.419271 (-0.344776) \| 0.051322 / 0.043533 (0.007789) \| 0.394458 / 0.255139 (0.139319) \| 0.424763 / 0.283200 (0.141564) \| 0.024890 / 0.141683 (-0.116793) \| 1.568322 / 1.452155 (0.116167) \| 1.703903 / 1.492716 (0.211187) \|\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) \| 0.249630 / 0.018006 (0.231624) \| 0.471412 / 0.000490 (0.470923) \| 0.000435 / 0.000200 (0.000235) \| 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) \| 0.033054 / 0.037411 (-0.004358) \| 0.100150 / 0.014526 (0.085624) \| 0.101704 / 0.176557 (-0.074853) \| 0.164031 / 0.737135 (-0.573104) \| 0.112497 / 0.296338 (-0.183841) \|\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) \| 0.487150 / 0.215209 (0.271941) \| 4.662335 / 2.077655 (2.584681) \| 2.477285 / 1.504120 (0.973165) \| 2.294033 / 1.541195 (0.752838) \| 2.380143 / 1.468490 (0.911653) \| 0.519182 / 4.584777 (-4.065595) \| 3.983589 / 3.745712 (0.237877) \| 3.669895 / 5.269862 (-1.599967) \| 2.267147 / 4.565676 (-2.298529) \| 0.063300 / 0.424275 (-0.360975) \| 0.008839 / 0.007607 (0.001232) \| 0.566766 / 0.226044 (0.340721) \| 5.533475 / 2.268929 (3.264546) \| 3.033412 / 55.444624 (-52.411212) \| 2.701793 / 6.876477 (-4.174684) \| 2.899444 / 2.142072 (0.757372) \| 0.614236 / 4.805227 (-4.190991) \| 0.139533 / 6.500664 (-6.361131) \| 0.067537 / 0.075469 (-0.007932) \|\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) \| 1.505572 / 1.841788 (-0.336216) \| 22.859062 / 8.074308 (14.784754) \| 15.044777 / 10.191392 (4.853385) \| 0.169153 / 0.680424 (-0.511271) \| 0.021027 / 0.534201 (-0.513174) \| 0.447979 / 0.579283 (-0.131304) \| 0.460676 / 0.434364 (0.026312) \| 0.506327 / 0.540337 (-0.034010) \| 0.737880 / 1.386936 (-0.649057) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#db7180eb7e3ebf52b9d1f2c6629db6d92d8a29ba \"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) \| 0.006118 / 0.011353 (-0.005235) \| 0.003692 / 0.011008 (-0.007316) \| 0.080606 / 0.038508 (0.042098) \| 0.062014 / 0.023109 (0.038905) \| 0.391886 / 0.275898 (0.115988) \| 0.423978 / 0.323480 (0.100498) \| 0.004968 / 0.007986 (-0.003017) \| 0.002911 / 0.004328 (-0.001417) \| 0.062867 / 0.004250 (0.058617) \| 0.049493 / 0.037052 (0.012441) \| 0.395656 / 0.258489 (0.137167) \| 0.432406 / 0.293841 (0.138565) \| 0.027242 / 0.128546 (-0.101304) \| 0.007938 / 0.075646 (-0.067709) \| 0.261703 / 0.419271 (-0.157569) \| 0.045922 / 0.043533 (0.002389) \| 0.391544 / 0.255139 (0.136405) \| 0.417902 / 0.283200 (0.134703) \| 0.021339 / 0.141683 (-0.120344) \| 1.508391 / 1.452155 (0.056236) \| 1.518970 / 1.492716 (0.026254) \|\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) \| 0.181159 / 0.018006 (0.163153) \| 0.431402 / 0.000490 (0.430912) \| 0.003849 / 0.000200 (0.003649) \| 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) \| 0.024498 / 0.037411 (-0.012914) \| 0.072758 / 0.014526 (0.058233) \| 0.084910 / 0.176557 (-0.091646) \| 0.148314 / 0.737135 (-0.588821) \| 0.085212 / 0.296338 (-0.211126) \|\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) \| 0.386693 / 0.215209 (0.171484) \| 3.852652 / 2.077655 (1.774997) \| 1.891758 / 1.504120 (0.387638) \| 1.718793 / 1.541195 (0.177598) \| 1.747595 / 1.468490 (0.279104) \| 0.498593 / 4.584777 (-4.086184) \| 3.057907 / 3.745712 (-0.687805) \| 4.728449 / 5.269862 (-0.541413) \| 2.966368 / 4.565676 (-1.599308) \| 0.057538 / 0.424275 (-0.366737) \| 0.006415 / 0.007607 (-0.001192) \| 0.461652 / 0.226044 (0.235608) \| 4.625944 / 2.268929 (2.357015) \| 2.306938 / 55.444624 (-53.137686) \| 1.974670 / 6.876477 (-4.901806) \| 2.146327 / 2.142072 (0.004254) \| 0.585033 / 4.805227 (-4.220195) \| 0.125936 / 6.500664 (-6.374728) \| 0.062365 / 0.075469 (-0.013104) \|\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) \| 1.263415 / 1.841788 (-0.578373) \| 18.380651 / 8.074308 (10.306343) \| 13.853410 / 10.191392 (3.662018) \| 0.144674 / 0.680424 (-0.535749) \| 0.016833 / 0.534201 (-0.517368) \| 0.330812 / 0.579283 (-0.248471) \| 0.357553 / 0.434364 (-0.076810) \| 0.383529 / 0.540337 (-0.156809) \| 0.558923 / 1.386936 (-0.828013) \|\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) \| 0.006074 / 0.011353 (-0.005278) \| 0.003655 / 0.011008 (-0.007353) \| 0.062981 / 0.038508 (0.024473) \| 0.061457 / 0.023109 (0.038348) \| 0.366471 / 0.275898 (0.090573) \| 0.408463 / 0.323480 (0.084983) \| 0.004854 / 0.007986 (-0.003132) \| 0.002916 / 0.004328 (-0.001412) \| 0.062745 / 0.004250 (0.058494) \| 0.051136 / 0.037052 (0.014084) \| 0.380313 / 0.258489 (0.121824) \| 0.416945 / 0.293841 (0.123104) \| 0.027228 / 0.128546 (-0.101318) \| 0.008031 / 0.075646 (-0.067615) \| 0.067941 / 0.419271 (-0.351331) \| 0.042886 / 0.043533 (-0.000647) \| 0.370112 / 0.255139 (0.114973) \| 0.397111 / 0.283200 (0.113911) \| 0.023063 / 0.141683 (-0.118620) \| 1.476955 / 1.452155 (0.024800) \| 1.534783 / 1.492716 (0.042066) \|\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) \| 0.231462 / 0.018006 (0.213456) \| 0.439559 / 0.000490 (0.439069) \| 0.000364 / 0.000200 (0.000164) \| 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) \| 0.026925 / 0.037411 (-0.010486) \| 0.079623 / 0.014526 (0.065097) \| 0.088694 / 0.176557 (-0.087862) \| 0.143163 / 0.737135 (-0.593972) \| 0.089900 / 0.296338 (-0.206438) \|\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) \| 0.451429 / 0.215209 (0.236220) \| 4.510723 / 2.077655 (2.433069) \| 2.491853 / 1.504120 (0.987733) \| 2.334670 / 1.541195 (0.793475) \| 2.395519 / 1.468490 (0.927029) \| 0.501369 / 4.584777 (-4.083408) \| 3.014019 / 3.745712 (-0.731693) \| 2.809199 / 5.269862 (-2.460662) \| 1.842195 / 4.565676 (-2.723481) \| 0.057675 / 0.424275 (-0.366600) \| 0.006742 / 0.007607 (-0.000865) \| 0.524402 / 0.226044 (0.298358) \| 5.245296 / 2.268929 (2.976367) \| 2.957990 / 55.444624 (-52.486634) \| 2.649807 / 6.876477 (-4.226670) \| 2.755909 / 2.142072 (0.613836) \| 0.589610 / 4.805227 (-4.215617) \| 0.125708 / 6.500664 (-6.374956) \| 0.062237 / 0.075469 (-0.013232) \|\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) \| 1.362758 / 1.841788 (-0.479030) \| 18.343694 / 8.074308 (10.269386) \| 13.621521 / 10.191392 (3.430129) \| 0.128866 / 0.680424 (-0.551558) \| 0.016608 / 0.534201 (-0.517593) \| 0.333071 / 0.579283 (-0.246212) \| 0.341917 / 0.434364 (-0.092447) \| 0.381075 / 0.540337 (-0.159263) \| 0.512485 / 1.386936 (-0.874451) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ab3f0165d4a2a8ab1aee1ebc4628893e17e27387 \"CML watermark\")\n", "I forgot to mention this in the initial comment, but only one public dataset (excluding gated) uses this method - `pg19`, which I just fixed.\r\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) \| 0.007838 / 0.011353 (-0.003515) \| 0.004791 / 0.011008 (-0.006217) \| 0.102596 / 0.038508 (0.064088) \| 0.087678 / 0.023109 (0.064569) \| 0.373858 / 0.275898 (0.097960) \| 0.416643 / 0.323480 (0.093163) \| 0.006147 / 0.007986 (-0.001839) \| 0.003837 / 0.004328 (-0.000491) \| 0.076706 / 0.004250 (0.072456) \| 0.063449 / 0.037052 (0.026396) \| 0.378392 / 0.258489 (0.119903) \| 0.431768 / 0.293841 (0.137927) \| 0.036648 / 0.128546 (-0.091898) \| 0.010042 / 0.075646 (-0.065604) \| 0.350277 / 0.419271 (-0.068995) \| 0.062892 / 0.043533 (0.019359) \| 0.376151 / 0.255139 (0.121012) \| 0.420929 / 0.283200 (0.137729) \| 0.027816 / 0.141683 (-0.113867) \| 1.791607 / 1.452155 (0.339452) \| 1.903045 / 1.492716 (0.410328) \|\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) \| 0.224688 / 0.018006 (0.206682) \| 0.491941 / 0.000490 (0.491451) \| 0.004482 / 0.000200 (0.004282) \| 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) \| 0.033495 / 0.037411 (-0.003917) \| 0.099855 / 0.014526 (0.085329) \| 0.114593 / 0.176557 (-0.061964) \| 0.190947 / 0.737135 (-0.546189) \| 0.116202 / 0.296338 (-0.180136) \|\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) \| 0.488581 / 0.215209 (0.273372) \| 4.869531 / 2.077655 (2.791876) \| 2.527920 / 1.504120 (1.023800) \| 2.340021 / 1.541195 (0.798826) \| 2.432661 / 1.468490 (0.964171) \| 0.569646 / 4.584777 (-4.015131) \| 4.392036 / 3.745712 (0.646324) \| 4.987253 / 5.269862 (-0.282608) \| 2.866604 / 4.565676 (-1.699073) \| 0.067393 / 0.424275 (-0.356882) \| 0.008759 / 0.007607 (0.001152) \| 0.584327 / 0.226044 (0.358283) \| 5.853000 / 2.268929 (3.584072) \| 3.206721 / 55.444624 (-52.237904) \| 2.730867 / 6.876477 (-4.145610) \| 2.944814 / 2.142072 (0.802742) \| 0.703336 / 4.805227 (-4.101891) \| 0.173985 / 6.500664 (-6.326679) \| 0.075333 / 0.075469 (-0.000137) \|\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) \| 1.519755 / 1.841788 (-0.322033) \| 22.918038 / 8.074308 (14.843730) \| 17.211160 / 10.191392 (7.019768) \| 0.196941 / 0.680424 (-0.483483) \| 0.021833 / 0.534201 (-0.512368) \| 0.476835 / 0.579283 (-0.102448) \| 0.464513 / 0.434364 (0.030149) \| 0.559180 / 0.540337 (0.018843) \| 0.748232 / 1.386936 (-0.638704) \|\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) \| 0.008461 / 0.011353 (-0.002892) \| 0.004799 / 0.011008 (-0.006209) \| 0.077466 / 0.038508 (0.038958) \| 0.103562 / 0.023109 (0.080453) \| 0.453661 / 0.275898 (0.177763) \| 0.531126 / 0.323480 (0.207647) \| 0.006618 / 0.007986 (-0.001367) \| 0.004048 / 0.004328 (-0.000280) \| 0.075446 / 0.004250 (0.071196) \| 0.072815 / 0.037052 (0.035762) \| 0.497145 / 0.258489 (0.238656) \| 0.533828 / 0.293841 (0.239987) \| 0.037657 / 0.128546 (-0.090890) \| 0.010139 / 0.075646 (-0.065507) \| 0.083759 / 0.419271 (-0.335512) \| 0.061401 / 0.043533 (0.017868) \| 0.441785 / 0.255139 (0.186646) \| 0.491678 / 0.283200 (0.208479) \| 0.033100 / 0.141683 (-0.108583) \| 1.753612 / 1.452155 (0.301458) \| 1.838956 / 1.492716 (0.346240) \|\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) \| 0.395023 / 0.018006 (0.377017) \| 0.509362 / 0.000490 (0.508872) \| 0.060742 / 0.000200 (0.060542) \| 0.000545 / 0.000054 (0.000491) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.039327 / 0.037411 (0.001916) \| 0.117345 / 0.014526 (0.102819) \| 0.124540 / 0.176557 (-0.052017) \| 0.200743 / 0.737135 (-0.536392) \| 0.126750 / 0.296338 (-0.169589) \|\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) \| 0.488597 / 0.215209 (0.273388) \| 4.875534 / 2.077655 (2.797880) \| 2.714364 / 1.504120 (1.210244) \| 2.603707 / 1.541195 (1.062513) \| 2.733547 / 1.468490 (1.265057) \| 0.575183 / 4.584777 (-4.009594) \| 4.126096 / 3.745712 (0.380384) \| 3.853803 / 5.269862 (-1.416058) \| 2.395160 / 4.565676 (-2.170516) \| 0.067391 / 0.424275 (-0.356884) \| 0.009108 / 0.007607 (0.001501) \| 0.585865 / 0.226044 (0.359820) \| 5.864878 / 2.268929 (3.595949) \| 3.153369 / 55.444624 (-52.291256) \| 2.759064 / 6.876477 (-4.117413) \| 3.032489 / 2.142072 (0.890416) \| 0.702615 / 4.805227 (-4.102613) \| 0.160034 / 6.500664 (-6.340630) \| 0.077294 / 0.075469 (0.001825) \|\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) \| 1.595069 / 1.841788 (-0.246719) \| 23.231191 / 8.074308 (15.156883) \| 16.365137 / 10.191392 (6.173745) \| 0.188360 / 0.680424 (-0.492063) \| 0.021704 / 0.534201 (-0.512497) \| 0.469996 / 0.579283 (-0.109287) \| 0.463255 / 0.434364 (0.028891) \| 0.560506 / 0.540337 (0.020169) \| 0.751006 / 1.386936 (-0.635930) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#50d9a70c666ff46ff9974c47cedc77d9f88d6471 \"CML watermark\")\n", "@mariosasko How would you stream a split zip file with just [download_and_extract or download](https://github.com/huggingface/datasets/blob/main/src/datasets/download/download_manager.py#L353)? With download_custom, it is possible to combine a split zip file. Perhaps add an option in [download](https://huggingface.co/docs/datasets/v2.2.1/en/package_reference/builder_classes#datasets.DownloadManager.download) to combine split zips. This issue may apply to other multipart file-types.\r\n\r\nEdit - \r\nIn case asked why I use split zips, I haven't been able to upload zips larger than 50 GB to HuggingFace.\r\n\r\nEdit2 -\r\nIssue is [tackled](https://discuss.huggingface.co/t/download-custom-method-of-streamingdownloadmanager-not-implemented/28298/8) for split zips. " ]
https://api.github.com/repos/huggingface/datasets/issues/6092	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6092/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6092/comments	https://api.github.com/repos/huggingface/datasets/issues/6092/events	https://github.com/huggingface/datasets/pull/6092	1,826,111,806	PR_kwDODunzps5Wo1mh	6,092	Minor fix in `iter_files` for hidden files	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "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}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko" }	[]	closed	false	null	[]	null	3	"2023-07-28T09:50:12Z"	"2023-07-28T10:59:28Z"	"2023-07-28T10:50:10Z"	CONTRIBUTOR	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6092.diff", "html_url": "https://github.com/huggingface/datasets/pull/6092", "merged_at": "2023-07-28T10:50:09Z", "patch_url": "https://github.com/huggingface/datasets/pull/6092.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6092" }	Fix #6090	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6092/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6092/timeline	null	null	671	true	[ "_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) \| 0.007873 / 0.011353 (-0.003480) \| 0.004585 / 0.011008 (-0.006423) \| 0.101622 / 0.038508 (0.063114) \| 0.092459 / 0.023109 (0.069350) \| 0.365157 / 0.275898 (0.089259) \| 0.405943 / 0.323480 (0.082463) \| 0.006229 / 0.007986 (-0.001756) \| 0.003811 / 0.004328 (-0.000518) \| 0.073831 / 0.004250 (0.069580) \| 0.065097 / 0.037052 (0.028045) \| 0.378912 / 0.258489 (0.120423) \| 0.422174 / 0.293841 (0.128333) \| 0.036244 / 0.128546 (-0.092302) \| 0.009677 / 0.075646 (-0.065970) \| 0.345164 / 0.419271 (-0.074107) \| 0.061632 / 0.043533 (0.018099) \| 0.370350 / 0.255139 (0.115211) \| 0.418245 / 0.283200 (0.135046) \| 0.027272 / 0.141683 (-0.114411) \| 1.774047 / 1.452155 (0.321892) \| 1.880278 / 1.492716 (0.387562) \|\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) \| 0.217238 / 0.018006 (0.199231) \| 0.489560 / 0.000490 (0.489071) \| 0.004013 / 0.000200 (0.003813) \| 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) \| 0.034139 / 0.037411 (-0.003272) \| 0.103831 / 0.014526 (0.089305) \| 0.114353 / 0.176557 (-0.062204) \| 0.182034 / 0.737135 (-0.555102) \| 0.116171 / 0.296338 (-0.180168) \|\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) \| 0.448658 / 0.215209 (0.233449) \| 4.520849 / 2.077655 (2.443195) \| 2.216121 / 1.504120 (0.712001) \| 2.034596 / 1.541195 (0.493402) \| 2.193216 / 1.468490 (0.724725) \| 0.568166 / 4.584777 (-4.016611) \| 4.133587 / 3.745712 (0.387875) \| 4.641117 / 5.269862 (-0.628744) \| 2.772913 / 4.565676 (-1.792764) \| 0.067664 / 0.424275 (-0.356611) \| 0.008719 / 0.007607 (0.001112) \| 0.547723 / 0.226044 (0.321678) \| 5.438325 / 2.268929 (3.169397) \| 2.877667 / 55.444624 (-52.566958) \| 2.477503 / 6.876477 (-4.398974) \| 2.688209 / 2.142072 (0.546136) \| 0.692593 / 4.805227 (-4.112634) \| 0.154549 / 6.500664 (-6.346115) \| 0.073286 / 0.075469 (-0.002183) \|\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) \| 1.610927 / 1.841788 (-0.230861) \| 23.413345 / 8.074308 (15.339037) \| 16.851819 / 10.191392 (6.660427) \| 0.170076 / 0.680424 (-0.510348) \| 0.021428 / 0.534201 (-0.512773) \| 0.468184 / 0.579283 (-0.111099) \| 0.491820 / 0.434364 (0.057456) \| 0.553453 / 0.540337 (0.013115) \| 0.762303 / 1.386936 (-0.624633) \|\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) \| 0.008033 / 0.011353 (-0.003320) \| 0.004638 / 0.011008 (-0.006370) \| 0.077044 / 0.038508 (0.038536) \| 0.096529 / 0.023109 (0.073420) \| 0.428735 / 0.275898 (0.152837) \| 0.477303 / 0.323480 (0.153823) \| 0.006040 / 0.007986 (-0.001946) \| 0.003808 / 0.004328 (-0.000521) \| 0.076042 / 0.004250 (0.071791) \| 0.066123 / 0.037052 (0.029071) \| 0.445482 / 0.258489 (0.186993) \| 0.481350 / 0.293841 (0.187509) \| 0.036951 / 0.128546 (-0.091595) \| 0.009944 / 0.075646 (-0.065703) \| 0.082731 / 0.419271 (-0.336541) \| 0.057490 / 0.043533 (0.013958) \| 0.432668 / 0.255139 (0.177529) \| 0.461146 / 0.283200 (0.177947) \| 0.027330 / 0.141683 (-0.114353) \| 1.784195 / 1.452155 (0.332040) \| 1.834776 / 1.492716 (0.342059) \|\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) \| 0.254104 / 0.018006 (0.236097) \| 0.475810 / 0.000490 (0.475321) \| 0.000459 / 0.000200 (0.000259) \| 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) \| 0.037058 / 0.037411 (-0.000353) \| 0.114962 / 0.014526 (0.100436) \| 0.123725 / 0.176557 (-0.052832) \| 0.188885 / 0.737135 (-0.548251) \| 0.125668 / 0.296338 (-0.170670) \|\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) \| 0.492627 / 0.215209 (0.277418) \| 4.900625 / 2.077655 (2.822970) \| 2.546349 / 1.504120 (1.042229) \| 2.360350 / 1.541195 (0.819155) \| 2.477975 / 1.468490 (1.009485) \| 0.574042 / 4.584777 (-4.010735) \| 4.408414 / 3.745712 (0.662702) \| 3.836640 / 5.269862 (-1.433222) \| 2.438450 / 4.565676 (-2.127227) \| 0.067706 / 0.424275 (-0.356569) \| 0.009165 / 0.007607 (0.001558) \| 0.580313 / 0.226044 (0.354269) \| 5.798211 / 2.268929 (3.529283) \| 3.098480 / 55.444624 (-52.346145) \| 2.740180 / 6.876477 (-4.136296) \| 2.984548 / 2.142072 (0.842476) \| 0.702550 / 4.805227 (-4.102677) \| 0.158248 / 6.500664 (-6.342416) \| 0.073999 / 0.075469 (-0.001470) \|\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) \| 1.636034 / 1.841788 (-0.205754) \| 24.068000 / 8.074308 (15.993692) \| 17.123987 / 10.191392 (6.932595) \| 0.210101 / 0.680424 (-0.470323) \| 0.022555 / 0.534201 (-0.511646) \| 0.509354 / 0.579283 (-0.069929) \| 0.540739 / 0.434364 (0.106375) \| 0.546048 / 0.540337 (0.005711) \| 0.719155 / 1.386936 (-0.667781) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#40530382ba98f54445de8820943b1236d4a4704f \"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) \| 0.007342 / 0.011353 (-0.004010) \| 0.004579 / 0.011008 (-0.006429) \| 0.087050 / 0.038508 (0.048542) \| 0.089001 / 0.023109 (0.065892) \| 0.307319 / 0.275898 (0.031421) \| 0.377573 / 0.323480 (0.054093) \| 0.006472 / 0.007986 (-0.001514) \| 0.004287 / 0.004328 (-0.000041) \| 0.067226 / 0.004250 (0.062976) \| 0.063147 / 0.037052 (0.026094) \| 0.314541 / 0.258489 (0.056052) \| 0.369919 / 0.293841 (0.076078) \| 0.031283 / 0.128546 (-0.097263) \| 0.009175 / 0.075646 (-0.066471) \| 0.289211 / 0.419271 (-0.130061) \| 0.053444 / 0.043533 (0.009911) \| 0.307308 / 0.255139 (0.052169) \| 0.346221 / 0.283200 (0.063021) \| 0.027948 / 0.141683 (-0.113735) \| 1.475177 / 1.452155 (0.023022) \| 1.575971 / 1.492716 (0.083255) \|\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) \| 0.291092 / 0.018006 (0.273086) \| 0.696951 / 0.000490 (0.696461) \| 0.005211 / 0.000200 (0.005011) \| 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) \| 0.031787 / 0.037411 (-0.005625) \| 0.084382 / 0.014526 (0.069857) \| 0.106474 / 0.176557 (-0.070083) \| 0.161472 / 0.737135 (-0.575663) \| 0.108650 / 0.296338 (-0.187688) \|\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) \| 0.379656 / 0.215209 (0.164447) \| 3.784072 / 2.077655 (1.706417) \| 1.826580 / 1.504120 (0.322460) \| 1.654916 / 1.541195 (0.113721) \| 1.730698 / 1.468490 (0.262208) \| 0.478003 / 4.584777 (-4.106774) \| 3.564920 / 3.745712 (-0.180792) \| 5.824873 / 5.269862 (0.555012) \| 3.454563 / 4.565676 (-1.111113) \| 0.056646 / 0.424275 (-0.367629) \| 0.007410 / 0.007607 (-0.000197) \| 0.461781 / 0.226044 (0.235737) \| 4.600928 / 2.268929 (2.331999) \| 2.351887 / 55.444624 (-53.092738) \| 1.986470 / 6.876477 (-4.890007) \| 2.311623 / 2.142072 (0.169551) \| 0.571247 / 4.805227 (-4.233980) \| 0.132191 / 6.500664 (-6.368473) \| 0.059943 / 0.075469 (-0.015526) \|\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) \| 1.253142 / 1.841788 (-0.588646) \| 21.294983 / 8.074308 (13.220675) \| 14.522429 / 10.191392 (4.331037) \| 0.166663 / 0.680424 (-0.513761) \| 0.019694 / 0.534201 (-0.514507) \| 0.395908 / 0.579283 (-0.183375) \| 0.413283 / 0.434364 (-0.021081) \| 0.457739 / 0.540337 (-0.082599) \| 0.664361 / 1.386936 (-0.722575) \|\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) \| 0.007228 / 0.011353 (-0.004124) \| 0.004941 / 0.011008 (-0.006067) \| 0.065381 / 0.038508 (0.026873) \| 0.090790 / 0.023109 (0.067681) \| 0.391315 / 0.275898 (0.115417) \| 0.416518 / 0.323480 (0.093038) \| 0.007015 / 0.007986 (-0.000970) \| 0.004417 / 0.004328 (0.000089) \| 0.067235 / 0.004250 (0.062985) \| 0.068092 / 0.037052 (0.031039) \| 0.403031 / 0.258489 (0.144542) \| 0.434013 / 0.293841 (0.140172) \| 0.032004 / 0.128546 (-0.096542) \| 0.009242 / 0.075646 (-0.066404) \| 0.071222 / 0.419271 (-0.348050) \| 0.054207 / 0.043533 (0.010674) \| 0.386198 / 0.255139 (0.131059) \| 0.404350 / 0.283200 (0.121150) \| 0.036284 / 0.141683 (-0.105399) \| 1.488814 / 1.452155 (0.036660) \| 1.587785 / 1.492716 (0.095069) \|\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) \| 0.313760 / 0.018006 (0.295754) \| 0.747778 / 0.000490 (0.747289) \| 0.003307 / 0.000200 (0.003107) \| 0.000113 / 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) \| 0.034321 / 0.037411 (-0.003090) \| 0.088266 / 0.014526 (0.073740) \| 0.112874 / 0.176557 (-0.063682) \| 0.171554 / 0.737135 (-0.565581) \| 0.111356 / 0.296338 (-0.184982) \|\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) \| 0.422624 / 0.215209 (0.207415) \| 4.212079 / 2.077655 (2.134425) \| 2.242742 / 1.504120 (0.738622) \| 2.072555 / 1.541195 (0.531360) \| 2.192648 / 1.468490 (0.724158) \| 0.488214 / 4.584777 (-4.096563) \| 3.597013 / 3.745712 (-0.148699) \| 3.477556 / 5.269862 (-1.792305) \| 2.184340 / 4.565676 (-2.381337) \| 0.057170 / 0.424275 (-0.367105) \| 0.007772 / 0.007607 (0.000165) \| 0.499455 / 0.226044 (0.273411) \| 4.988953 / 2.268929 (2.720024) \| 2.797894 / 55.444624 (-52.646731) \| 2.402215 / 6.876477 (-4.474262) \| 2.725069 / 2.142072 (0.582997) \| 0.596213 / 4.805227 (-4.209014) \| 0.136564 / 6.500664 (-6.364100) \| 0.061799 / 0.075469 (-0.013670) \|\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) \| 1.360739 / 1.841788 (-0.481049) \| 21.846457 / 8.074308 (13.772149) \| 14.568842 / 10.191392 (4.377450) \| 0.168980 / 0.680424 (-0.511444) \| 0.018795 / 0.534201 (-0.515406) \| 0.396173 / 0.579283 (-0.183110) \| 0.418651 / 0.434364 (-0.015713) \| 0.480042 / 0.540337 (-0.060295) \| 0.650803 / 1.386936 (-0.736133) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b7d460304487d4daab0a64ca0ca707e896367ca1 \"CML watermark\")\n" ]
https://api.github.com/repos/huggingface/datasets/issues/6091	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6091/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6091/comments	https://api.github.com/repos/huggingface/datasets/issues/6091/events	https://github.com/huggingface/datasets/pull/6091	1,826,086,487	PR_kwDODunzps5Wov9Q	6,091	Bump fsspec from 2021.11.1 to 2022.3.0	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "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}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko" }	[]	closed	false	null	[]	null	3	"2023-07-28T09:37:15Z"	"2023-07-28T10:16:11Z"	"2023-07-28T10:07:02Z"	CONTRIBUTOR	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6091.diff", "html_url": "https://github.com/huggingface/datasets/pull/6091", "merged_at": "2023-07-28T10:07:02Z", "patch_url": "https://github.com/huggingface/datasets/pull/6091.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6091" }	Fix https://github.com/huggingface/datasets/issues/6087 (Colab installs 2023.6.0, so we should be good)	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6091/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6091/timeline	null	null	672	true	[ "_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) \| 0.006640 / 0.011353 (-0.004713) \| 0.004077 / 0.011008 (-0.006931) \| 0.084905 / 0.038508 (0.046397) \| 0.074004 / 0.023109 (0.050895) \| 0.315968 / 0.275898 (0.040070) \| 0.351594 / 0.323480 (0.028114) \| 0.005623 / 0.007986 (-0.002362) \| 0.003476 / 0.004328 (-0.000852) \| 0.065089 / 0.004250 (0.060839) \| 0.054683 / 0.037052 (0.017631) \| 0.314983 / 0.258489 (0.056494) \| 0.371776 / 0.293841 (0.077935) \| 0.031727 / 0.128546 (-0.096819) \| 0.008786 / 0.075646 (-0.066860) \| 0.289905 / 0.419271 (-0.129367) \| 0.053340 / 0.043533 (0.009807) \| 0.311802 / 0.255139 (0.056663) \| 0.351927 / 0.283200 (0.068727) \| 0.024453 / 0.141683 (-0.117229) \| 1.491727 / 1.452155 (0.039572) \| 1.585027 / 1.492716 (0.092310) \|\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) \| 0.238757 / 0.018006 (0.220750) \| 0.557691 / 0.000490 (0.557202) \| 0.005158 / 0.000200 (0.004958) \| 0.000204 / 0.000054 (0.000149) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028435 / 0.037411 (-0.008977) \| 0.082219 / 0.014526 (0.067693) \| 0.096932 / 0.176557 (-0.079625) \| 0.153802 / 0.737135 (-0.583333) \| 0.098338 / 0.296338 (-0.198001) \|\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) \| 0.383448 / 0.215209 (0.168238) \| 3.816074 / 2.077655 (1.738420) \| 1.835111 / 1.504120 (0.330991) \| 1.662326 / 1.541195 (0.121131) \| 1.720202 / 1.468490 (0.251712) \| 0.483107 / 4.584777 (-4.101669) \| 3.648528 / 3.745712 (-0.097184) \| 4.020929 / 5.269862 (-1.248932) \| 2.433141 / 4.565676 (-2.132536) \| 0.057081 / 0.424275 (-0.367194) \| 0.007303 / 0.007607 (-0.000304) \| 0.461366 / 0.226044 (0.235322) \| 4.609090 / 2.268929 (2.340162) \| 2.355940 / 55.444624 (-53.088684) \| 1.989833 / 6.876477 (-4.886644) \| 2.201451 / 2.142072 (0.059378) \| 0.586156 / 4.805227 (-4.219071) \| 0.133486 / 6.500664 (-6.367178) \| 0.060062 / 0.075469 (-0.015407) \|\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) \| 1.247845 / 1.841788 (-0.593942) \| 19.624252 / 8.074308 (11.549944) \| 14.305975 / 10.191392 (4.114583) \| 0.168687 / 0.680424 (-0.511737) \| 0.018075 / 0.534201 (-0.516126) \| 0.393859 / 0.579283 (-0.185424) \| 0.407272 / 0.434364 (-0.027092) \| 0.463760 / 0.540337 (-0.076578) \| 0.629930 / 1.386936 (-0.757006) \|\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) \| 0.006760 / 0.011353 (-0.004593) \| 0.004345 / 0.011008 (-0.006663) \| 0.064379 / 0.038508 (0.025871) \| 0.078295 / 0.023109 (0.055186) \| 0.364532 / 0.275898 (0.088633) \| 0.395852 / 0.323480 (0.072372) \| 0.005659 / 0.007986 (-0.002327) \| 0.003515 / 0.004328 (-0.000813) \| 0.065030 / 0.004250 (0.060780) \| 0.059950 / 0.037052 (0.022898) \| 0.375420 / 0.258489 (0.116931) \| 0.411579 / 0.293841 (0.117738) \| 0.031575 / 0.128546 (-0.096972) \| 0.008737 / 0.075646 (-0.066910) \| 0.070350 / 0.419271 (-0.348922) \| 0.050607 / 0.043533 (0.007075) \| 0.359785 / 0.255139 (0.104646) \| 0.382638 / 0.283200 (0.099438) \| 0.025533 / 0.141683 (-0.116150) \| 1.564379 / 1.452155 (0.112225) \| 1.620642 / 1.492716 (0.127925) \|\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) \| 0.212779 / 0.018006 (0.194773) \| 0.563827 / 0.000490 (0.563337) \| 0.003767 / 0.000200 (0.003567) \| 0.000103 / 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) \| 0.030275 / 0.037411 (-0.007136) \| 0.088108 / 0.014526 (0.073582) \| 0.102454 / 0.176557 (-0.074103) \| 0.156107 / 0.737135 (-0.581028) \| 0.103961 / 0.296338 (-0.192378) \|\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) \| 0.421395 / 0.215209 (0.206186) \| 4.204935 / 2.077655 (2.127280) \| 2.144929 / 1.504120 (0.640809) \| 1.999341 / 1.541195 (0.458147) \| 2.066966 / 1.468490 (0.598476) \| 0.486135 / 4.584777 (-4.098642) \| 3.628139 / 3.745712 (-0.117573) \| 5.652683 / 5.269862 (0.382821) \| 3.216721 / 4.565676 (-1.348956) \| 0.057513 / 0.424275 (-0.366762) \| 0.007553 / 0.007607 (-0.000055) \| 0.494470 / 0.226044 (0.268426) \| 4.949343 / 2.268929 (2.680414) \| 2.654222 / 55.444624 (-52.790402) \| 2.322257 / 6.876477 (-4.554220) \| 2.555633 / 2.142072 (0.413561) \| 0.588355 / 4.805227 (-4.216872) \| 0.134481 / 6.500664 (-6.366183) \| 0.062415 / 0.075469 (-0.013054) \|\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) \| 1.377578 / 1.841788 (-0.464209) \| 19.805201 / 8.074308 (11.730893) \| 14.128536 / 10.191392 (3.937144) \| 0.164343 / 0.680424 (-0.516081) \| 0.018553 / 0.534201 (-0.515648) \| 0.398191 / 0.579283 (-0.181093) \| 0.414268 / 0.434364 (-0.020096) \| 0.462270 / 0.540337 (-0.078068) \| 0.608497 / 1.386936 (-0.778439) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3af05ba487f361fae90a4c80af72de5c4ed70162 \"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) \| 0.006966 / 0.011353 (-0.004387) \| 0.004339 / 0.011008 (-0.006669) \| 0.086682 / 0.038508 (0.048174) \| 0.086143 / 0.023109 (0.063034) \| 0.316106 / 0.275898 (0.040208) \| 0.351422 / 0.323480 (0.027942) \| 0.005916 / 0.007986 (-0.002069) \| 0.003630 / 0.004328 (-0.000698) \| 0.066980 / 0.004250 (0.062730) \| 0.060031 / 0.037052 (0.022979) \| 0.317487 / 0.258489 (0.058998) \| 0.356280 / 0.293841 (0.062439) \| 0.031816 / 0.128546 (-0.096730) \| 0.008797 / 0.075646 (-0.066849) \| 0.289848 / 0.419271 (-0.129424) \| 0.055431 / 0.043533 (0.011898) \| 0.318881 / 0.255139 (0.063742) \| 0.332315 / 0.283200 (0.049116) \| 0.025946 / 0.141683 (-0.115737) \| 1.472904 / 1.452155 (0.020749) \| 1.577973 / 1.492716 (0.085257) \|\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) \| 0.239056 / 0.018006 (0.221050) \| 0.565406 / 0.000490 (0.564917) \| 0.003606 / 0.000200 (0.003406) \| 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) \| 0.029771 / 0.037411 (-0.007640) \| 0.085534 / 0.014526 (0.071008) \| 0.107008 / 0.176557 (-0.069548) \| 0.631583 / 0.737135 (-0.105552) \| 0.104210 / 0.296338 (-0.192128) \|\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) \| 0.390675 / 0.215209 (0.175466) \| 3.898746 / 2.077655 (1.821091) \| 1.933048 / 1.504120 (0.428928) \| 1.792162 / 1.541195 (0.250967) \| 1.958045 / 1.468490 (0.489555) \| 0.488632 / 4.584777 (-4.096144) \| 3.696306 / 3.745712 (-0.049406) \| 3.454600 / 5.269862 (-1.815262) \| 2.176292 / 4.565676 (-2.389385) \| 0.057617 / 0.424275 (-0.366658) \| 0.007603 / 0.007607 (-0.000004) \| 0.467843 / 0.226044 (0.241798) \| 4.672928 / 2.268929 (2.404000) \| 2.441096 / 55.444624 (-53.003529) \| 2.133506 / 6.876477 (-4.742970) \| 2.431167 / 2.142072 (0.289095) \| 0.588567 / 4.805227 (-4.216661) \| 0.136070 / 6.500664 (-6.364594) \| 0.063395 / 0.075469 (-0.012074) \|\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) \| 1.255003 / 1.841788 (-0.586784) \| 20.587656 / 8.074308 (12.513348) \| 15.147817 / 10.191392 (4.956425) \| 0.152039 / 0.680424 (-0.528384) \| 0.018815 / 0.534201 (-0.515386) \| 0.397458 / 0.579283 (-0.181825) \| 0.431433 / 0.434364 (-0.002931) \| 0.487890 / 0.540337 (-0.052448) \| 0.675367 / 1.386936 (-0.711569) \|\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) \| 0.007209 / 0.011353 (-0.004144) \| 0.004372 / 0.011008 (-0.006636) \| 0.066288 / 0.038508 (0.027780) \| 0.091776 / 0.023109 (0.068667) \| 0.390724 / 0.275898 (0.114826) \| 0.434711 / 0.323480 (0.111231) \| 0.005790 / 0.007986 (-0.002196) \| 0.003562 / 0.004328 (-0.000767) \| 0.066155 / 0.004250 (0.061904) \| 0.062459 / 0.037052 (0.025406) \| 0.406622 / 0.258489 (0.148133) \| 0.433976 / 0.293841 (0.140135) \| 0.032590 / 0.128546 (-0.095957) \| 0.008856 / 0.075646 (-0.066790) \| 0.072327 / 0.419271 (-0.346945) \| 0.049958 / 0.043533 (0.006426) \| 0.400164 / 0.255139 (0.145025) \| 0.413339 / 0.283200 (0.130139) \| 0.025283 / 0.141683 (-0.116399) \| 1.487668 / 1.452155 (0.035514) \| 1.537679 / 1.492716 (0.044962) \|\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) \| 0.257814 / 0.018006 (0.239808) \| 0.571741 / 0.000490 (0.571251) \| 0.000412 / 0.000200 (0.000212) \| 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) \| 0.033893 / 0.037411 (-0.003518) \| 0.094533 / 0.014526 (0.080008) \| 0.105876 / 0.176557 (-0.070680) \| 0.158675 / 0.737135 (-0.578460) \| 0.107790 / 0.296338 (-0.188548) \|\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) \| 0.425796 / 0.215209 (0.210587) \| 4.229159 / 2.077655 (2.151505) \| 2.239613 / 1.504120 (0.735493) \| 2.073830 / 1.541195 (0.532635) \| 2.185508 / 1.468490 (0.717018) \| 0.483984 / 4.584777 (-4.100793) \| 3.645575 / 3.745712 (-0.100137) \| 3.454767 / 5.269862 (-1.815095) \| 2.141387 / 4.565676 (-2.424290) \| 0.057570 / 0.424275 (-0.366705) \| 0.007901 / 0.007607 (0.000294) \| 0.501160 / 0.226044 (0.275116) \| 5.012283 / 2.268929 (2.743355) \| 2.701267 / 55.444624 (-52.743357) \| 2.465409 / 6.876477 (-4.411068) \| 2.696812 / 2.142072 (0.554739) \| 0.587160 / 4.805227 (-4.218067) \| 0.134175 / 6.500664 (-6.366489) \| 0.062028 / 0.075469 (-0.013441) \|\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) \| 1.345632 / 1.841788 (-0.496155) \| 21.077279 / 8.074308 (13.002971) \| 14.700826 / 10.191392 (4.509434) \| 0.156191 / 0.680424 (-0.524233) \| 0.018991 / 0.534201 (-0.515210) \| 0.400413 / 0.579283 (-0.178870) \| 0.420597 / 0.434364 (-0.013767) \| 0.486534 / 0.540337 (-0.053804) \| 0.646606 / 1.386936 (-0.740330) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5bb8fabb135ca8adf47151ad3de050e3a258ccab \"CML watermark\")\n" ]
https://api.github.com/repos/huggingface/datasets/issues/6090	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6090/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6090/comments	https://api.github.com/repos/huggingface/datasets/issues/6090/events	https://github.com/huggingface/datasets/issues/6090	1,825,865,043	I_kwDODunzps5s1H1T	6,090	FilesIterable skips all the files after a hidden file	{ "avatar_url": "https://avatars.githubusercontent.com/u/10785413?v=4", "events_url": "https://api.github.com/users/dkrivosic/events{/privacy}", "followers_url": "https://api.github.com/users/dkrivosic/followers", "following_url": "https://api.github.com/users/dkrivosic/following{/other_user}", "gists_url": "https://api.github.com/users/dkrivosic/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/dkrivosic", "id": 10785413, "login": "dkrivosic", "node_id": "MDQ6VXNlcjEwNzg1NDEz", "organizations_url": "https://api.github.com/users/dkrivosic/orgs", "received_events_url": "https://api.github.com/users/dkrivosic/received_events", "repos_url": "https://api.github.com/users/dkrivosic/repos", "site_admin": false, "starred_url": "https://api.github.com/users/dkrivosic/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/dkrivosic/subscriptions", "type": "User", "url": "https://api.github.com/users/dkrivosic" }	[]	closed	false	null	[]	null	1	"2023-07-28T07:25:57Z"	"2023-07-28T10:51:14Z"	"2023-07-28T10:50:11Z"	NONE	null	null	null	### Describe the bug When initializing `FilesIterable` with a list of file paths using `FilesIterable.from_paths`, it will discard all the files after a hidden file. The problem is in [this line](https://github.com/huggingface/datasets/blob/88896a7b28610ace95e444b94f9a4bc332cc1ee3/src/datasets/download/download_manager.py#L233C26-L233C26) where `return` should be replaced by `continue`. ### Steps to reproduce the bug https://colab.research.google.com/drive/1SQlxs4y_LSo1Q89KnFoYDSyyKEISun_J#scrollTo=93K4_blkW-8- ### Expected behavior The script should print all the files except the hidden one. ### Environment info - `datasets` version: 2.14.1 - Platform: Linux-5.15.109+-x86_64-with-glibc2.35 - Python version: 3.10.6 - Huggingface_hub version: 0.16.4 - PyArrow version: 9.0.0 - Pandas version: 1.5.3	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6090/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6090/timeline	null	completed	673	false	[ "Thanks for reporting. We've merged a PR with a fix." ]
https://api.github.com/repos/huggingface/datasets/issues/6089	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6089/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6089/comments	https://api.github.com/repos/huggingface/datasets/issues/6089/events	https://github.com/huggingface/datasets/issues/6089	1,825,761,476	I_kwDODunzps5s0ujE	6,089	AssertionError: daemonic processes are not allowed to have children	{ "avatar_url": "https://avatars.githubusercontent.com/u/138426806?v=4", "events_url": "https://api.github.com/users/codingl2k1/events{/privacy}", "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}", "gravatar_id": "", "html_url": "https://github.com/codingl2k1", "id": 138426806, "login": "codingl2k1", "node_id": "U_kgDOCEA5tg", "organizations_url": "https://api.github.com/users/codingl2k1/orgs", "received_events_url": "https://api.github.com/users/codingl2k1/received_events", "repos_url": "https://api.github.com/users/codingl2k1/repos", "site_admin": false, "starred_url": "https://api.github.com/users/codingl2k1/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/codingl2k1/subscriptions", "type": "User", "url": "https://api.github.com/users/codingl2k1" }	[]	open	false	null	[]	null	2	"2023-07-28T06:04:00Z"	"2023-07-31T02:34:02Z"	null	NONE	null	null	null	### Describe the bug When I load_dataset with num_proc > 0 in a deamon process, I got an error: ```python File "/Users/codingl2k1/Work/datasets/src/datasets/download/download_manager.py", line 564, in download_and_extract return self.extract(self.download(url_or_urls)) ^^^^^^^^^^^^^^^^^ File "/Users/codingl2k1/Work/datasets/src/datasets/download/download_manager.py", line 427, in download downloaded_path_or_paths = map_nested( ^^^^^^^^^^^^^^^^^ File "/Users/codingl2k1/Work/datasets/src/datasets/utils/py_utils.py", line 468, in map_nested mapped = parallel_map(function, iterable, num_proc, types, disable_tqdm, desc, _single_map_nested) ^^^^^^^^^^^^^^^^^ File "/Users/codingl2k1/Work/datasets/src/datasets/utils/experimental.py", line 40, in _inner_fn return fn(args, *kwargs) ^^^^^^^^^^^^^^^^^ File "/Users/codingl2k1/Work/datasets/src/datasets/parallel/parallel.py", line 34, in parallel_map return _map_with_multiprocessing_pool( ^^^^^^^^^^^^^^^^^ File "/Users/codingl2k1/Work/datasets/src/datasets/parallel/parallel.py", line 64, in _map_with_multiprocessing_pool with Pool(num_proc, initargs=initargs, initializer=initializer) as pool: ^^^^^^^^^^^^^^^^^ File "/Users/codingl2k1/.pyenv/versions/3.11.4/lib/python3.11/multiprocessing/context.py", line 119, in Pool return Pool(processes, initializer, initargs, maxtasksperchild, ^^^^^^^^^^^^^^^^^ File "/Users/codingl2k1/.pyenv/versions/3.11.4/lib/python3.11/multiprocessing/pool.py", line 215, in __init__ self._repopulate_pool() ^^^^^^^^^^^^^^^^^ File "/Users/codingl2k1/.pyenv/versions/3.11.4/lib/python3.11/multiprocessing/pool.py", line 306, in _repopulate_pool return self._repopulate_pool_static(self._ctx, self.Process, ^^^^^^^^^^^^^^^^^ File "/Users/codingl2k1/.pyenv/versions/3.11.4/lib/python3.11/multiprocessing/pool.py", line 329, in _repopulate_pool_static w.start() File "/Users/codingl2k1/.pyenv/versions/3.11.4/lib/python3.11/multiprocessing/process.py", line 118, in start assert not _current_process._config.get('daemon'), ^^^^^^^^^^^^^^^^^ AssertionError: daemonic processes are not allowed to have children ``` The download is io-intensive computing, may be datasets can replece the multi processing pool by a multi threading pool if in a deamon process. ### Steps to reproduce the bug 1. start a deamon process 2. run load_dataset with num_proc > 0 ### Expected behavior No error. ### Environment info Python 3.11.4 datasets latest master	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6089/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6089/timeline	null	null	674	false	[ "We could add a \"threads\" parallel backend to `datasets.parallel.parallel_backend` to support downloading with threads but note that `download_and_extract` also decompresses archives, and this is a CPU-intensive task, which is not ideal for (Python) threads (good for IO-intensive tasks).", "> We could add a \"threads\" parallel backend to `datasets.parallel.parallel_backend` to support downloading with threads but note that `download_and_extract` also decompresses archives, and this is a CPU-intensive task, which is not ideal for (Python) threads (good for IO-intensive tasks).\r\n\r\nGreat! Download takes more time than extract, multiple threads can download in parallel, which can speed up a lot." ]
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	{ "avatar_url": "https://avatars.githubusercontent.com/u/23375707?v=4", "events_url": "https://api.github.com/users/lytning98/events{/privacy}", "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}", "gravatar_id": "", "html_url": "https://github.com/lytning98", "id": 23375707, "login": "lytning98", "node_id": "MDQ6VXNlcjIzMzc1NzA3", "organizations_url": "https://api.github.com/users/lytning98/orgs", "received_events_url": "https://api.github.com/users/lytning98/received_events", "repos_url": "https://api.github.com/users/lytning98/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lytning98/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lytning98/subscriptions", "type": "User", "url": "https://api.github.com/users/lytning98" }	[]	closed	false	null	[]	null	0	"2023-07-28T04:06:26Z"	"2023-07-28T05:02:22Z"	"2023-07-28T05:02:22Z"	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?	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6088/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6088/timeline	null	completed	675	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	{ "avatar_url": "https://avatars.githubusercontent.com/u/1085885?v=4", "events_url": "https://api.github.com/users/iXce/events{/privacy}", "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}", "gravatar_id": "", "html_url": "https://github.com/iXce", "id": 1085885, "login": "iXce", "node_id": "MDQ6VXNlcjEwODU4ODU=", "organizations_url": "https://api.github.com/users/iXce/orgs", "received_events_url": "https://api.github.com/users/iXce/received_events", "repos_url": "https://api.github.com/users/iXce/repos", "site_admin": false, "starred_url": "https://api.github.com/users/iXce/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/iXce/subscriptions", "type": "User", "url": "https://api.github.com/users/iXce" }	[]	closed	false	null	[]	null	1	"2023-07-27T20:08:22Z"	"2023-07-28T10:07:56Z"	"2023-07-28T10:07:03Z"	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	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6087/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6087/timeline	null	completed	676	false	[ "Thanks for reporting! A PR with a fix has just been merged." ]
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	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "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}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko" }	[ { "color": "a2eeef", "default": true, "description": "New feature or request", "id": 1935892871, "name": "enhancement", "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement" } ]	open	false	{ "avatar_url": "https://avatars.githubusercontent.com/u/36760800?v=4", "events_url": "https://api.github.com/users/alvarobartt/events{/privacy}", "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}", "gravatar_id": "", "html_url": "https://github.com/alvarobartt", "id": 36760800, "login": "alvarobartt", "node_id": "MDQ6VXNlcjM2NzYwODAw", "organizations_url": "https://api.github.com/users/alvarobartt/orgs", "received_events_url": "https://api.github.com/users/alvarobartt/received_events", "repos_url": "https://api.github.com/users/alvarobartt/repos", "site_admin": false, "starred_url": "https://api.github.com/users/alvarobartt/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/alvarobartt/subscriptions", "type": "User", "url": "https://api.github.com/users/alvarobartt" }	[ { "avatar_url": "https://avatars.githubusercontent.com/u/36760800?v=4", "events_url": "https://api.github.com/users/alvarobartt/events{/privacy}", "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}", "gravatar_id": "", "html_url": "https://github.com/alvarobartt", "id": 36760800, "login": "alvarobartt", "node_id": "MDQ6VXNlcjM2NzYwODAw", "organizations_url": "https://api.github.com/users/alvarobartt/orgs", "received_events_url": "https://api.github.com/users/alvarobartt/received_events", "repos_url": "https://api.github.com/users/alvarobartt/repos", "site_admin": false, "starred_url": "https://api.github.com/users/alvarobartt/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/alvarobartt/subscriptions", "type": "User", "url": "https://api.github.com/users/alvarobartt" } ]	null	4	"2023-07-27T19:08:37Z"	"2023-07-28T15:28:26Z"	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).	{ "+1": 1, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 1, "url": "https://api.github.com/repos/huggingface/datasets/issues/6086/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6086/timeline	null	null	677	false	[ "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?" ]
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	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "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}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko" }	[]	open	false	null	[]	null	3	"2023-07-27T18:54:47Z"	"2023-07-27T19:06:13Z"	null	CONTRIBUTOR	null	1	{ "diff_url": "https://github.com/huggingface/datasets/pull/6085.diff", "html_url": "https://github.com/huggingface/datasets/pull/6085", "merged_at": null, "patch_url": "https://github.com/huggingface/datasets/pull/6085.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6085" }	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	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6085/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6085/timeline	null	null	678	true	[ "<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" ]
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	{ "avatar_url": "https://avatars.githubusercontent.com/u/90359895?v=4", "events_url": "https://api.github.com/users/ZitengWangNYU/events{/privacy}", "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}", "gravatar_id": "", "html_url": "https://github.com/ZitengWangNYU", "id": 90359895, "login": "ZitengWangNYU", "node_id": "MDQ6VXNlcjkwMzU5ODk1", "organizations_url": "https://api.github.com/users/ZitengWangNYU/orgs", "received_events_url": "https://api.github.com/users/ZitengWangNYU/received_events", "repos_url": "https://api.github.com/users/ZitengWangNYU/repos", "site_admin": false, "starred_url": "https://api.github.com/users/ZitengWangNYU/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/ZitengWangNYU/subscriptions", "type": "User", "url": "https://api.github.com/users/ZitengWangNYU" }	[]	open	false	null	[]	null	0	"2023-07-27T17:55:35Z"	"2023-07-27T17:55:35Z"	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?	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6084/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6084/timeline	null	null	679	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	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "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}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq" }	[]	closed	false	null	[]	null	3	"2023-07-27T17:10:41Z"	"2023-07-27T17:22:05Z"	"2023-07-27T17:11:01Z"	MEMBER	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6083.diff", "html_url": "https://github.com/huggingface/datasets/pull/6083", "merged_at": "2023-07-27T17:11:01Z", "patch_url": "https://github.com/huggingface/datasets/pull/6083.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6083" }	null	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6083/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6083/timeline	null	null	680	true	[ "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" ]
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	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "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}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq" }	[]	closed	false	null	[]	null	6	"2023-07-27T17:05:54Z"	"2023-07-31T06:32:16Z"	"2023-07-27T17:08:38Z"	MEMBER	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6082.diff", "html_url": "https://github.com/huggingface/datasets/pull/6082", "merged_at": "2023-07-27T17:08:38Z", "patch_url": "https://github.com/huggingface/datasets/pull/6082.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6082" }	null	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6082/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6082/timeline	null	null	681	true	[ "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" ]
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`	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "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}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko" }	[]	closed	false	null	[]	null	2	"2023-07-27T14:22:18Z"	"2023-07-28T11:09:54Z"	"2023-07-28T11:01:04Z"	CONTRIBUTOR	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6081.diff", "html_url": "https://github.com/huggingface/datasets/pull/6081", "merged_at": "2023-07-28T11:01:04Z", "patch_url": "https://github.com/huggingface/datasets/pull/6081.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6081" }	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.	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6081/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6081/timeline	null	null	682	true	[ "_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" ]
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	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "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}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko" }	[]	closed	false	null	[]	null	3	"2023-07-26T15:27:49Z"	"2023-07-26T16:24:43Z"	"2023-07-26T16:14:34Z"	CONTRIBUTOR	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6080.diff", "html_url": "https://github.com/huggingface/datasets/pull/6080", "merged_at": "2023-07-26T16:14:34Z", "patch_url": "https://github.com/huggingface/datasets/pull/6080.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6080" }	null	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6080/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6080/timeline	null	null	683	true	[ "_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" ]
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	{ "avatar_url": "https://avatars.githubusercontent.com/u/5454868?v=4", "events_url": "https://api.github.com/users/arindamsarkar93/events{/privacy}", "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}", "gravatar_id": "", "html_url": "https://github.com/arindamsarkar93", "id": 5454868, "login": "arindamsarkar93", "node_id": "MDQ6VXNlcjU0NTQ4Njg=", "organizations_url": "https://api.github.com/users/arindamsarkar93/orgs", "received_events_url": "https://api.github.com/users/arindamsarkar93/received_events", "repos_url": "https://api.github.com/users/arindamsarkar93/repos", "site_admin": false, "starred_url": "https://api.github.com/users/arindamsarkar93/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/arindamsarkar93/subscriptions", "type": "User", "url": "https://api.github.com/users/arindamsarkar93" }	[]	closed	false	null	[]	null	15	"2023-07-26T14:52:37Z"	"2024-02-07T17:46:52Z"	"2023-07-30T14:09:06Z"	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	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6079/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6079/timeline	null	completed	684	false	[ "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", "I'm facing a similar situation in the local VS Code. \r\n\r\nDatasets version 2.14.4\r\nTorch 2.0.1+cu118\r\n\r\nSame code runs without issues in Colab\r\n\r\n```\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"Supermaxman/esa-hubble\", streaming=True)\r\nsample = next(iter(dataset[\"train\"]))\r\n```\r\n\r\nis stuck for minutes. If I interrupt, I get\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nKeyboardInterrupt Traceback (most recent call last)\r\nCell In[5], line 5\r\n 1 from datasets import load_dataset\r\n 3 dataset = load_dataset(\"Supermaxman/esa-hubble\", streaming=True)\r\n----> 5 sample = next(iter(dataset[\"train\"]))\r\n 6 print(sample[\"text\"])\r\n 7 sample[\"image\"]\r\n\r\nFile [~/miniconda3/envs/book/lib/python3.10/site-packages/datasets/iterable_dataset.py:1353](https://file+.vscode-resource.vscode-cdn.net/home/osanseviero/Desktop/workspace/genai/nbs/~/miniconda3/envs/book/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 [~/miniconda3/envs/book/lib/python3.10/site-packages/datasets/iterable_dataset.py:255](https://file+.vscode-resource.vscode-cdn.net/home/osanseviero/Desktop/workspace/genai/nbs/~/miniconda3/envs/book/lib/python3.10/site-packages/datasets/iterable_dataset.py:255), in ArrowExamplesIterable.__iter__(self)\r\n 253 def __iter__(self):\r\n 254 formatter = PythonFormatter()\r\n--> 255 for key, pa_table in self.generate_tables_fn(**self.kwargs):\r\n 256 for pa_subtable in pa_table.to_reader(max_chunksize=config.ARROW_READER_BATCH_SIZE_IN_DATASET_ITER):\r\n...\r\n-> 1130 return self._sslobj.read(len, buffer)\r\n 1131 else:\r\n 1132 return self._sslobj.read(len)\r\n```", "@osanseviero I assume the `self._sslobj.read(len, buffer)` line comes from the built-in `ssl` module, so this probably has something to do with your network. Please open a new issue with the full stack trace in case you haven't resolved this yet.", "Thank you reporting this and sharing the solution, I ran into this as well!", "Ran into same issue after upgrading to pytorch-2.0. Disabling KMP_AFFINITY as mentioned above worked for me. Thanks!\r\n" ]
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	{ "avatar_url": "https://avatars.githubusercontent.com/u/72763959?v=4", "events_url": "https://api.github.com/users/NicolasMICAUX/events{/privacy}", "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}", "gravatar_id": "", "html_url": "https://github.com/NicolasMICAUX", "id": 72763959, "login": "NicolasMICAUX", "node_id": "MDQ6VXNlcjcyNzYzOTU5", "organizations_url": "https://api.github.com/users/NicolasMICAUX/orgs", "received_events_url": "https://api.github.com/users/NicolasMICAUX/received_events", "repos_url": "https://api.github.com/users/NicolasMICAUX/repos", "site_admin": false, "starred_url": "https://api.github.com/users/NicolasMICAUX/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/NicolasMICAUX/subscriptions", "type": "User", "url": "https://api.github.com/users/NicolasMICAUX" }	[]	closed	false	null	[]	null	3	"2023-07-26T14:08:22Z"	"2023-07-28T11:05:03Z"	"2023-07-28T11:05:03Z"	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	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6078/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6078/timeline	null	completed	685	false	[ "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" ]
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%	{ "avatar_url": "https://avatars.githubusercontent.com/u/21087104?v=4", "events_url": "https://api.github.com/users/Laurent2916/events{/privacy}", "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}", "gravatar_id": "", "html_url": "https://github.com/Laurent2916", "id": 21087104, "login": "Laurent2916", "node_id": "MDQ6VXNlcjIxMDg3MTA0", "organizations_url": "https://api.github.com/users/Laurent2916/orgs", "received_events_url": "https://api.github.com/users/Laurent2916/received_events", "repos_url": "https://api.github.com/users/Laurent2916/repos", "site_admin": false, "starred_url": "https://api.github.com/users/Laurent2916/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/Laurent2916/subscriptions", "type": "User", "url": "https://api.github.com/users/Laurent2916" }	[]	open	false	null	[]	null	4	"2023-07-26T14:00:40Z"	"2023-07-28T09:21:07Z"	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	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6077/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6077/timeline	null	null	686	false	[ "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" ]
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	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "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}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq" }	[]	closed	false	null	[]	null	3	"2023-07-26T12:46:07Z"	"2023-07-27T16:15:11Z"	"2023-07-27T16:14:40Z"	MEMBER	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6076.diff", "html_url": "https://github.com/huggingface/datasets/pull/6076", "merged_at": "2023-07-27T16:14:40Z", "patch_url": "https://github.com/huggingface/datasets/pull/6076.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6076" }	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	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6076/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6076/timeline	null	null	687	true	[ "_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" ]
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`	{ "avatar_url": "https://avatars.githubusercontent.com/u/56069179?v=4", "events_url": "https://api.github.com/users/susnato/events{/privacy}", "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}", "gravatar_id": "", "html_url": "https://github.com/susnato", "id": 56069179, "login": "susnato", "node_id": "MDQ6VXNlcjU2MDY5MTc5", "organizations_url": "https://api.github.com/users/susnato/orgs", "received_events_url": "https://api.github.com/users/susnato/received_events", "repos_url": "https://api.github.com/users/susnato/repos", "site_admin": false, "starred_url": "https://api.github.com/users/susnato/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/susnato/subscriptions", "type": "User", "url": "https://api.github.com/users/susnato" }	[]	closed	false	null	[]	null	2	"2023-07-26T12:44:05Z"	"2023-07-26T13:08:08Z"	"2023-07-26T13:08:08Z"	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	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6075/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6075/timeline	null	completed	688	false	[ "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!" ]
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	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "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}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko" }	[]	closed	false	null	[]	null	3	"2023-07-26T12:20:54Z"	"2023-07-27T16:16:28Z"	"2023-07-27T16:16:02Z"	CONTRIBUTOR	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6074.diff", "html_url": "https://github.com/huggingface/datasets/pull/6074", "merged_at": "2023-07-27T16:16:02Z", "patch_url": "https://github.com/huggingface/datasets/pull/6074.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6074" }	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).	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6074/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6074/timeline	null	null	689	true	[ "<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" ]
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	{ "avatar_url": "https://avatars.githubusercontent.com/u/45893496?v=4", "events_url": "https://api.github.com/users/BUAAChuanWang/events{/privacy}", "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}", "gravatar_id": "", "html_url": "https://github.com/BUAAChuanWang", "id": 45893496, "login": "BUAAChuanWang", "node_id": "MDQ6VXNlcjQ1ODkzNDk2", "organizations_url": "https://api.github.com/users/BUAAChuanWang/orgs", "received_events_url": "https://api.github.com/users/BUAAChuanWang/received_events", "repos_url": "https://api.github.com/users/BUAAChuanWang/repos", "site_admin": false, "starred_url": "https://api.github.com/users/BUAAChuanWang/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/BUAAChuanWang/subscriptions", "type": "User", "url": "https://api.github.com/users/BUAAChuanWang" }	[]	closed	false	null	[]	null	1	"2023-07-26T11:09:31Z"	"2023-08-29T15:53:59Z"	"2023-08-29T15:53:59Z"	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	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6073/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6073/timeline	null	completed	690	false	[ "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)." ]
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	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "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}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq" }	[]	closed	false	null	[]	null	6	"2023-07-26T10:44:23Z"	"2023-07-27T12:51:51Z"	"2023-07-27T12:42:57Z"	MEMBER	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6072.diff", "html_url": "https://github.com/huggingface/datasets/pull/6072", "merged_at": "2023-07-27T12:42:57Z", "patch_url": "https://github.com/huggingface/datasets/pull/6072.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6072" }	close https://github.com/huggingface/datasets/issues/6071	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6072/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6072/timeline	null	null	691	true	[ "_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" ]
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	{ "avatar_url": "https://avatars.githubusercontent.com/u/128361578?v=4", "events_url": "https://api.github.com/users/exs-avianello/events{/privacy}", "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}", "gravatar_id": "", "html_url": "https://github.com/exs-avianello", "id": 128361578, "login": "exs-avianello", "node_id": "U_kgDOB6akag", "organizations_url": "https://api.github.com/users/exs-avianello/orgs", "received_events_url": "https://api.github.com/users/exs-avianello/received_events", "repos_url": "https://api.github.com/users/exs-avianello/repos", "site_admin": false, "starred_url": "https://api.github.com/users/exs-avianello/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/exs-avianello/subscriptions", "type": "User", "url": "https://api.github.com/users/exs-avianello" }	[]	closed	false	null	[]	null	2	"2023-07-26T09:37:20Z"	"2023-07-27T12:42:58Z"	"2023-07-27T12:42:58Z"	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	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6071/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6071/timeline	null	completed	692	false	[ "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" ]
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	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "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}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko" }	[]	closed	false	null	[]	null	3	"2023-07-25T17:48:37Z"	"2023-07-25T18:19:01Z"	"2023-07-25T18:10:16Z"	CONTRIBUTOR	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6070.diff", "html_url": "https://github.com/huggingface/datasets/pull/6070", "merged_at": "2023-07-25T18:10:16Z", "patch_url": "https://github.com/huggingface/datasets/pull/6070.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6070" }	Reported in https://github.com/huggingface/datasets/pull/5902#issuecomment-1649885621 (cc @alvarobartt)	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6070/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6070/timeline	null	null	693	true	[ "_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" ]
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"	{ "avatar_url": "https://avatars.githubusercontent.com/u/28512232?v=4", "events_url": "https://api.github.com/users/etetteh/events{/privacy}", "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}", "gravatar_id": "", "html_url": "https://github.com/etetteh", "id": 28512232, "login": "etetteh", "node_id": "MDQ6VXNlcjI4NTEyMjMy", "organizations_url": "https://api.github.com/users/etetteh/orgs", "received_events_url": "https://api.github.com/users/etetteh/received_events", "repos_url": "https://api.github.com/users/etetteh/repos", "site_admin": false, "starred_url": "https://api.github.com/users/etetteh/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/etetteh/subscriptions", "type": "User", "url": "https://api.github.com/users/etetteh" }	[]	closed	false	null	[]	null	6	"2023-07-25T17:45:50Z"	"2023-07-27T12:42:17Z"	"2023-07-27T12:42:17Z"	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	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6069/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6069/timeline	null	completed	694	false	[ "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. " ]
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	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "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}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq" }	[]	closed	false	null	[]	null	5	"2023-07-25T11:17:25Z"	"2023-07-25T15:29:39Z"	"2023-07-25T15:17:50Z"	MEMBER	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6068.diff", "html_url": "https://github.com/huggingface/datasets/pull/6068", "merged_at": "2023-07-25T15:17:50Z", "patch_url": "https://github.com/huggingface/datasets/pull/6068.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6068" }	related to https://github.com/huggingface/datasets/issues/6066	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6068/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6068/timeline	null	null	695	true	[ "_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" ]
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	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "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}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq" }	[]	closed	false	null	[]	null	3	"2023-07-25T09:32:16Z"	"2023-07-25T10:02:43Z"	"2023-07-25T09:54:12Z"	MEMBER	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6067.diff", "html_url": "https://github.com/huggingface/datasets/pull/6067", "merged_at": "2023-07-25T09:54:12Z", "patch_url": "https://github.com/huggingface/datasets/pull/6067.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6067" }	close https://github.com/huggingface/datasets/issues/6066	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6067/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6067/timeline	null	null	696	true	[ "_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" ]
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'	{ "avatar_url": "https://avatars.githubusercontent.com/u/138426806?v=4", "events_url": "https://api.github.com/users/codingl2k1/events{/privacy}", "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}", "gravatar_id": "", "html_url": "https://github.com/codingl2k1", "id": 138426806, "login": "codingl2k1", "node_id": "U_kgDOCEA5tg", "organizations_url": "https://api.github.com/users/codingl2k1/orgs", "received_events_url": "https://api.github.com/users/codingl2k1/received_events", "repos_url": "https://api.github.com/users/codingl2k1/repos", "site_admin": false, "starred_url": "https://api.github.com/users/codingl2k1/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/codingl2k1/subscriptions", "type": "User", "url": "https://api.github.com/users/codingl2k1" }	[]	closed	false	null	[]	null	7	"2023-07-25T07:24:36Z"	"2023-07-26T10:56:25Z"	"2023-07-26T10:56:24Z"	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	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6066/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6066/timeline	null	completed	697	false	[ "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" ]
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	{ "avatar_url": "https://avatars.githubusercontent.com/u/1712066?v=4", "events_url": "https://api.github.com/users/piercefreeman/events{/privacy}", "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}", "gravatar_id": "", "html_url": "https://github.com/piercefreeman", "id": 1712066, "login": "piercefreeman", "node_id": "MDQ6VXNlcjE3MTIwNjY=", "organizations_url": "https://api.github.com/users/piercefreeman/orgs", "received_events_url": "https://api.github.com/users/piercefreeman/received_events", "repos_url": "https://api.github.com/users/piercefreeman/repos", "site_admin": false, "starred_url": "https://api.github.com/users/piercefreeman/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/piercefreeman/subscriptions", "type": "User", "url": "https://api.github.com/users/piercefreeman" }	[]	closed	false	null	[]	null	5	"2023-07-25T00:34:17Z"	"2023-07-26T15:13:45Z"	"2023-07-26T15:13:44Z"	NONE	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6065.diff", "html_url": "https://github.com/huggingface/datasets/pull/6065", "merged_at": null, "patch_url": "https://github.com/huggingface/datasets/pull/6065.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6065" }	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).	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6065/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6065/timeline	null	null	698	true	[ "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." ]
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	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "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}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq" }	[]	closed	false	null	[]	null	3	"2023-07-24T15:56:00Z"	"2023-07-24T16:05:19Z"	"2023-07-24T15:56:10Z"	MEMBER	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6064.diff", "html_url": "https://github.com/huggingface/datasets/pull/6064", "merged_at": "2023-07-24T15:56:10Z", "patch_url": "https://github.com/huggingface/datasets/pull/6064.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6064" }	null	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6064/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6064/timeline	null	null	699	true	[ "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" ]

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