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https://api.github.com/repos/huggingface/datasets/issues/7074 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7074/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7074/comments | https://api.github.com/repos/huggingface/datasets/issues/7074/events | https://github.com/huggingface/datasets/pull/7074 | 2,431,772,703 | PR_kwDODunzps52jkw4 | 7,074 | Fix CI by temporarily marking test_convert_to_parquet as expected to fail | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7074). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<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) | 0.005168 / 0.011353 (-0.006185) | 0.003572 / 0.011008 (-0.007436) | 0.062755 / 0.038508 (0.024247) | 0.030371 / 0.023109 (0.007262) | 0.250240 / 0.275898 (-0.025658) | 0.268091 / 0.323480 (-0.055389) | 0.003260 / 0.007986 (-0.004726) | 0.002706 / 0.004328 (-0.001622) | 0.048957 / 0.004250 (0.044706) | 0.044441 / 0.037052 (0.007389) | 0.251801 / 0.258489 (-0.006688) | 0.289401 / 0.293841 (-0.004440) | 0.028991 / 0.128546 (-0.099555) | 0.011871 / 0.075646 (-0.063775) | 0.203722 / 0.419271 (-0.215549) | 0.035911 / 0.043533 (-0.007622) | 0.248070 / 0.255139 (-0.007069) | 0.266480 / 0.283200 (-0.016720) | 0.019831 / 0.141683 (-0.121852) | 1.143429 / 1.452155 (-0.308726) | 1.160102 / 1.492716 (-0.332614) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.096740 / 0.018006 (0.078734) | 0.302473 / 0.000490 (0.301983) | 0.000219 / 0.000200 (0.000019) | 0.000043 / 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) | 0.018367 / 0.037411 (-0.019045) | 0.062346 / 0.014526 (0.047820) | 0.074416 / 0.176557 (-0.102140) | 0.120507 / 0.737135 (-0.616628) | 0.076536 / 0.296338 (-0.219802) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.284093 / 0.215209 (0.068884) | 2.738805 / 2.077655 (0.661150) | 1.469263 / 1.504120 (-0.034856) | 1.349122 / 1.541195 (-0.192073) | 1.355578 / 1.468490 (-0.112912) | 0.720364 / 4.584777 (-3.864413) | 2.360339 / 3.745712 (-1.385373) | 2.941134 / 5.269862 (-2.328728) | 1.888692 / 4.565676 (-2.676984) | 0.077111 / 0.424275 (-0.347164) | 0.005070 / 0.007607 (-0.002537) | 0.334122 / 0.226044 (0.108078) | 3.298378 / 2.268929 (1.029450) | 1.868514 / 55.444624 (-53.576111) | 1.528561 / 6.876477 (-5.347916) | 1.535319 / 2.142072 (-0.606754) | 0.778591 / 4.805227 (-4.026636) | 0.131364 / 6.500664 (-6.369300) | 0.041697 / 0.075469 (-0.033773) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.970243 / 1.841788 (-0.871544) | 11.324752 / 8.074308 (3.250443) | 9.612381 / 10.191392 (-0.579011) | 0.138842 / 0.680424 (-0.541582) | 0.014479 / 0.534201 (-0.519722) | 0.309415 / 0.579283 (-0.269868) | 0.264654 / 0.434364 (-0.169710) | 0.343695 / 0.540337 (-0.196642) | 0.435323 / 1.386936 (-0.951613) |\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) | 0.005680 / 0.011353 (-0.005673) | 0.003614 / 0.011008 (-0.007394) | 0.060575 / 0.038508 (0.022067) | 0.031103 / 0.023109 (0.007994) | 0.269083 / 0.275898 (-0.006815) | 0.291556 / 0.323480 (-0.031923) | 0.004354 / 0.007986 (-0.003632) | 0.002739 / 0.004328 (-0.001589) | 0.049056 / 0.004250 (0.044806) | 0.039759 / 0.037052 (0.002707) | 0.280608 / 0.258489 (0.022119) | 0.324798 / 0.293841 (0.030957) | 0.032030 / 0.128546 (-0.096516) | 0.011862 / 0.075646 (-0.063784) | 0.060011 / 0.419271 (-0.359261) | 0.033960 / 0.043533 (-0.009573) | 0.271114 / 0.255139 (0.015975) | 0.293922 / 0.283200 (0.010722) | 0.019497 / 0.141683 (-0.122185) | 1.137871 / 1.452155 (-0.314284) | 1.180656 / 1.492716 (-0.312061) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094201 / 0.018006 (0.076194) | 0.306657 / 0.000490 (0.306167) | 0.000215 / 0.000200 (0.000015) | 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) | 0.022562 / 0.037411 (-0.014850) | 0.077170 / 0.014526 (0.062644) | 0.088915 / 0.176557 (-0.087642) | 0.129455 / 0.737135 (-0.607680) | 0.091571 / 0.296338 (-0.204767) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.300753 / 0.215209 (0.085544) | 2.941929 / 2.077655 (0.864274) | 1.613451 / 1.504120 (0.109331) | 1.498365 / 1.541195 (-0.042830) | 1.517124 / 1.468490 (0.048634) | 0.709209 / 4.584777 (-3.875568) | 0.950478 / 3.745712 (-2.795235) | 2.799328 / 5.269862 (-2.470533) | 1.872895 / 4.565676 (-2.692782) | 0.078233 / 0.424275 (-0.346042) | 0.005613 / 0.007607 (-0.001994) | 0.349590 / 0.226044 (0.123545) | 3.500213 / 2.268929 (1.231284) | 2.001155 / 55.444624 (-53.443469) | 1.704845 / 6.876477 (-5.171632) | 1.810722 / 2.142072 (-0.331350) | 0.795326 / 4.805227 (-4.009901) | 0.132913 / 6.500664 (-6.367751) | 0.041209 / 0.075469 (-0.034260) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.029513 / 1.841788 (-0.812274) | 12.005617 / 8.074308 (3.931309) | 10.119379 / 10.191392 (-0.072013) | 0.139767 / 0.680424 (-0.540657) | 0.015241 / 0.534201 (-0.518960) | 0.301164 / 0.579283 (-0.278119) | 0.121563 / 0.434364 (-0.312801) | 0.336672 / 0.540337 (-0.203666) | 0.431526 / 1.386936 (-0.955410) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#92bdab56a3c7d5ded10e8ae4134c943e32d3bc86 \"CML watermark\")\n"
] | "2024-07-26T09:03:33Z" | "2024-07-26T09:23:33Z" | "2024-07-26T09:16:12Z" | MEMBER | null | As a hotfix for CI, temporarily mark test_convert_to_parquet as expected to fail.
Fix #7073.
Revert once root cause is fixed. | {
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https://api.github.com/repos/huggingface/datasets/issues/7073 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7073/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7073/comments | https://api.github.com/repos/huggingface/datasets/issues/7073/events | https://github.com/huggingface/datasets/issues/7073 | 2,431,706,568 | I_kwDODunzps6Q8OXI | 7,073 | CI is broken for convert_to_parquet: Invalid rev id: refs/pr/1 404 error causes RevisionNotFoundError | {
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"Any recent change in the API backend rejecting parameter `revision=\"refs/pr/1\"` to `HfApi.preupload_lfs_files`?\r\n```\r\nf\"{endpoint}/api/{repo_type}s/{repo_id}/preupload/{revision}\"\r\n\r\nhttps://hub-ci.huggingface.co/api/datasets/__DUMMY_TRANSFORMERS_USER__/test-dataset-5188a8-17219154347516/preupload/refs%2Fpr%2F1.\r\nInvalid rev id: refs/pr/1\r\n```\r\n@Wauplin @huggingface/datasets @huggingface/moon-landing @huggingface/moon-landing-back ",
"I have temporarily fixed the CI with:\r\n- #7074\r\n\r\nHowever, the underlying issue must be fixed and #7074 must be reverted.",
"Hmm does it do the preupload call before creating the ref cc @Wauplin ?\r\n\r\n(in that case it should do a preupload call on the base branch with `?create_pr=1`)",
"@coyotte508, the CI test was implemented 2 months ago and it was working OK until yesterday. See the CI status of the commits in the main branch of `datasets`: https://github.com/huggingface/datasets/commits/main/",
"Yes i get that\r\n\r\nWe changed the preupload response to return the commit id in https://github.com/huggingface-internal/moon-landing/pull/10756\r\n\r\nThis line is probably causing the error: https://github.com/huggingface-internal/moon-landing/pull/10756/files#diff-558f6f9865e30bfa091b94d6a4a900138103ddb4eb0bec96b6deec5bf5626fa0R2322\r\n\r\nIt's weird the error is returned, it means that maybe a ref with 0 history (not even the first commit) was created\r\n\r\nDoes this change have any impact in production, or just the CI test? If it's just the CI test it should be fixed on your side, if it impacts production we can look at a solution",
"@coyotte508 it impacts production: `convert_to_parquet` raises the above error when the dataset has more that one configs/subsets:\r\n- First subset calls `push_to_hub` with `create_pr=True`\r\n- Second subset uses the `refs/pr/#` returned by the call above, and calls `push_to_hub` with `revision=\"refs/pr/#\"`",
"I tried removing the `mock_commit` call: https://github.com/huggingface/datasets/pull/7076\r\n\r\nAnd the tests seem to work.\r\n\r\nSo it's probably because the commit is not actually called, it doesn't actually create the pull request on the remote (and the associated `refs/pr/1`). But the `preupload` call is not mocked.\r\n\r\nAnyway it shouldn't impact production, since production isn't mocked",
"@coyotte508 thanks a lot for the investigation and sorry for the noise. \r\nI promise not trying to fix things when I have a slight fever: my head does not work well.\r\n\r\nWe need indeed to mock `preupload_lfs_files`: before it was not necessary, but now it is.",
"I fixed the test in:\r\n- #7078\r\n\r\nThanks again, @coyotte508."
] | "2024-07-26T08:27:41Z" | "2024-07-27T05:48:02Z" | "2024-07-26T09:16:13Z" | MEMBER | null | See: https://github.com/huggingface/datasets/actions/runs/10095313567/job/27915185756
```
FAILED tests/test_hub.py::test_convert_to_parquet - huggingface_hub.utils._errors.RevisionNotFoundError: 404 Client Error. (Request ID: Root=1-66a25839-31ce7b475e70e7db1e4d44c2;b0c8870f-d5ef-4bf2-a6ff-0191f3df0f64)
Revision Not Found for url: https://hub-ci.huggingface.co/api/datasets/__DUMMY_TRANSFORMERS_USER__/test-dataset-5188a8-17219154347516/preupload/refs%2Fpr%2F1.
Invalid rev id: refs/pr/1
```
```
/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/datasets/hub.py:86: in convert_to_parquet
dataset.push_to_hub(
/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/datasets/dataset_dict.py:1722: in push_to_hub
split_additions, uploaded_size, dataset_nbytes = self[split]._push_parquet_shards_to_hub(
/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/datasets/arrow_dataset.py:5511: in _push_parquet_shards_to_hub
api.preupload_lfs_files(
/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/huggingface_hub/hf_api.py:4231: in preupload_lfs_files
_fetch_upload_modes(
/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/huggingface_hub/utils/_validators.py:118: in _inner_fn
return fn(*args, **kwargs)
/opt/hostedtoolcache/Python/3.8.18/x64/lib/python3.8/site-packages/huggingface_hub/_commit_api.py:507: in _fetch_upload_modes
hf_raise_for_status(resp)
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https://api.github.com/repos/huggingface/datasets/issues/7071 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7071/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7071/comments | https://api.github.com/repos/huggingface/datasets/issues/7071/events | https://github.com/huggingface/datasets/issues/7071 | 2,430,313,011 | I_kwDODunzps6Q26Iz | 7,071 | Filter hangs | {
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} | [] | open | false | null | [] | null | [] | "2024-07-25T15:29:05Z" | "2024-07-25T15:36:59Z" | null | NONE | null | ### Describe the bug
When trying to filter my custom dataset, the process hangs, regardless of the lambda function used. It appears to be an issue with the way the Images are being handled. The dataset in question is a preprocessed version of https://huggingface.co/datasets/danaaubakirova/patfig where notably, I have converted the data to the Parquet format.
### Steps to reproduce the bug
```python
from datasets import load_dataset
ds = load_dataset('lcolonn/patfig', split='test')
ds_filtered = ds.filter(lambda row: row['cpc_class'] != 'Y')
```
Eventually I ctrl+C and I obtain this stack trace:
```
>>> ds_filtered = ds.filter(lambda row: row['cpc_class'] != 'Y')
Filter: 0%| | 0/998 [00:00<?, ? examples/s]Filter: 0%| | 0/998 [00:35<?, ? examples/s]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/arrow_dataset.py", line 567, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/fingerprint.py", line 482, in wrapper
out = func(dataset, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/arrow_dataset.py", line 3714, in filter
indices = self.map(
^^^^^^^^^
File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/arrow_dataset.py", line 602, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/arrow_dataset.py", line 567, in wrapper
out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/arrow_dataset.py", line 3161, in map
for rank, done, content in Dataset._map_single(**dataset_kwargs):
File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/arrow_dataset.py", line 3552, in _map_single
batch = apply_function_on_filtered_inputs(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/arrow_dataset.py", line 3421, in apply_function_on_filtered_inputs
processed_inputs = function(*fn_args, *additional_args, **fn_kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/arrow_dataset.py", line 6478, in get_indices_from_mask_function
num_examples = len(batch[next(iter(batch.keys()))])
~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/formatting/formatting.py", line 273, in __getitem__
value = self.format(key)
^^^^^^^^^^^^^^^^
File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/formatting/formatting.py", line 376, in format
return self.formatter.format_column(self.pa_table.select([key]))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/formatting/formatting.py", line 443, in format_column
column = self.python_features_decoder.decode_column(column, pa_table.column_names[0])
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/formatting/formatting.py", line 219, in decode_column
return self.features.decode_column(column, column_name) if self.features else column
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/features/features.py", line 2008, in decode_column
[decode_nested_example(self[column_name], value) if value is not None else None for value in column]
File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/features/features.py", line 2008, in <listcomp>
[decode_nested_example(self[column_name], value) if value is not None else None for value in column]
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/features/features.py", line 1351, in decode_nested_example
return schema.decode_example(obj, token_per_repo_id=token_per_repo_id)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/datasets/features/image.py", line 188, in decode_example
image.load() # to avoid "Too many open files" errors
^^^^^^^^^^^^
File "/home/l-walewski/miniconda3/envs/patentqa/lib/python3.11/site-packages/PIL/ImageFile.py", line 293, in load
n, err_code = decoder.decode(b)
^^^^^^^^^^^^^^^^^
KeyboardInterrupt
```
Warning! This can even seem to cause some computers to crash.
### Expected behavior
Should return the filtered dataset
### Environment info
- `datasets` version: 2.20.0
- Platform: Linux-6.5.0-41-generic-x86_64-with-glibc2.35
- Python version: 3.11.9
- `huggingface_hub` version: 0.24.0
- PyArrow version: 17.0.0
- Pandas version: 2.2.2
- `fsspec` version: 2024.5.0 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7070 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7070/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7070/comments | https://api.github.com/repos/huggingface/datasets/issues/7070/events | https://github.com/huggingface/datasets/issues/7070 | 2,430,285,235 | I_kwDODunzps6Q2zWz | 7,070 | how set_transform affects batch size? | {
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} | [] | open | false | null | [] | null | [] | "2024-07-25T15:19:34Z" | "2024-07-25T15:19:34Z" | null | NONE | null | ### Describe the bug
I am trying to fine-tune w2v-bert for ASR task. Since my dataset is so big, I preferred to use the on-the-fly method with set_transform. So i change the preprocessing function to this:
```
def prepare_dataset(batch):
input_features = processor(batch["audio"], sampling_rate=16000).input_features[0]
input_length = len(input_features)
labels = processor.tokenizer(batch["text"], padding=False).input_ids
batch = {
"input_features": [input_features],
"input_length": [input_length],
"labels": [labels]
}
return batch
train_ds.set_transform(prepare_dataset)
val_ds.set_transform(prepare_dataset)
```
After this, I also had to change the DataCollatorCTCWithPadding class like this:
```
@dataclass
class DataCollatorCTCWithPadding:
processor: Wav2Vec2BertProcessor
padding: Union[bool, str] = True
def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:
# Separate input_features and labels
input_features = [{"input_features": feature["input_features"][0]} for feature in features]
labels = [feature["labels"][0] for feature in features]
# Pad input features
batch = self.processor.pad(
input_features,
padding=self.padding,
return_tensors="pt",
)
# Pad and process labels
label_features = self.processor.tokenizer.pad(
{"input_ids": labels},
padding=self.padding,
return_tensors="pt",
)
labels = label_features["input_ids"]
attention_mask = label_features["attention_mask"]
# Replace padding with -100 to ignore these tokens during loss calculation
labels = labels.masked_fill(attention_mask.ne(1), -100)
batch["labels"] = labels
return batch
```
But now a strange thing is happening, no matter how much I increase the batch size, the amount of V-RAM GPU usage does not change, while the number of total steps in the progress-bar (logging) changes. Is this normal or have I made a mistake?
### Steps to reproduce the bug
i can share my code if needed
### Expected behavior
Equal to the batch size value, the set_transform function is applied to the dataset and given to the model as a batch.
### Environment info
all updated versions | null | {
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} | [] | closed | false | null | [] | null | [
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7069). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"cc @Wauplin maybe it's a `huggingface_hub` bug ?\r\n\r\nEDIT: ah actually the issue is opened at https://github.com/huggingface/huggingface_hub/issues/2419",
"I think we need to make this fix anyway, ~~unless we pin the lower version of huggingface-hub (once they release the patch)~~.\r\n- Calling create_branch with a PR ref raises an error",
"Comment by @Wauplin: https://github.com/huggingface/huggingface_hub/pull/2426#issuecomment-2257657543\r\n> I think this should be something to fix in datasets directly. Having a 400 Bad request when trying to create the branch refs/pr/1 seems normal to me since it's not a branch.",
"does this mean we should use `create_pull_request()` in that case ?",
"> does this mean we should use create_pull_request() in that case ?\r\n\r\nIf user wants to push some data to a new PR, they can already pass `create_pr=True` which will automatically do the job for you (without using `revision`). If user is passing `revision=\"refs/pr/1\"` explicitly, you should assume the PR already exists.",
"ah yes we do pass create_pr in `preupload_lfs_files()` ! sounds good then",
"<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) | 0.005806 / 0.011353 (-0.005547) | 0.004082 / 0.011008 (-0.006927) | 0.064277 / 0.038508 (0.025769) | 0.032289 / 0.023109 (0.009180) | 0.242066 / 0.275898 (-0.033832) | 0.272574 / 0.323480 (-0.050906) | 0.003281 / 0.007986 (-0.004705) | 0.002957 / 0.004328 (-0.001371) | 0.049930 / 0.004250 (0.045679) | 0.047306 / 0.037052 (0.010253) | 0.252216 / 0.258489 (-0.006273) | 0.286678 / 0.293841 (-0.007163) | 0.030182 / 0.128546 (-0.098364) | 0.012967 / 0.075646 (-0.062680) | 0.204949 / 0.419271 (-0.214323) | 0.036999 / 0.043533 (-0.006534) | 0.243577 / 0.255139 (-0.011562) | 0.265044 / 0.283200 (-0.018156) | 0.021149 / 0.141683 (-0.120534) | 1.112293 / 1.452155 (-0.339862) | 1.186483 / 1.492716 (-0.306233) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093239 / 0.018006 (0.075233) | 0.286372 / 0.000490 (0.285883) | 0.000224 / 0.000200 (0.000024) | 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) | 0.019042 / 0.037411 (-0.018369) | 0.063690 / 0.014526 (0.049164) | 0.075034 / 0.176557 (-0.101523) | 0.123053 / 0.737135 (-0.614083) | 0.076843 / 0.296338 (-0.219495) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.276554 / 0.215209 (0.061345) | 2.749338 / 2.077655 (0.671683) | 1.442764 / 1.504120 (-0.061356) | 1.327860 / 1.541195 (-0.213335) | 1.369885 / 1.468490 (-0.098606) | 0.722645 / 4.584777 (-3.862132) | 2.430707 / 3.745712 (-1.315005) | 3.105293 / 5.269862 (-2.164568) | 1.961617 / 4.565676 (-2.604060) | 0.077728 / 0.424275 (-0.346547) | 0.005189 / 0.007607 (-0.002418) | 0.335511 / 0.226044 (0.109467) | 3.315618 / 2.268929 (1.046690) | 1.858254 / 55.444624 (-53.586371) | 1.552173 / 6.876477 (-5.324304) | 1.627086 / 2.142072 (-0.514987) | 0.790871 / 4.805227 (-4.014356) | 0.136958 / 6.500664 (-6.363706) | 0.043207 / 0.075469 (-0.032262) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.969314 / 1.841788 (-0.872473) | 12.145318 / 8.074308 (4.071010) | 9.834839 / 10.191392 (-0.356553) | 0.141896 / 0.680424 (-0.538528) | 0.014304 / 0.534201 (-0.519897) | 0.306091 / 0.579283 (-0.273192) | 0.260994 / 0.434364 (-0.173369) | 0.348096 / 0.540337 (-0.192242) | 0.441458 / 1.386936 (-0.945478) |\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) | 0.005989 / 0.011353 (-0.005363) | 0.003907 / 0.011008 (-0.007102) | 0.050819 / 0.038508 (0.012310) | 0.033178 / 0.023109 (0.010069) | 0.279059 / 0.275898 (0.003161) | 0.300161 / 0.323480 (-0.023319) | 0.004383 / 0.007986 (-0.003603) | 0.002834 / 0.004328 (-0.001495) | 0.048779 / 0.004250 (0.044528) | 0.040502 / 0.037052 (0.003450) | 0.291786 / 0.258489 (0.033297) | 0.323827 / 0.293841 (0.029986) | 0.032175 / 0.128546 (-0.096371) | 0.012157 / 0.075646 (-0.063489) | 0.060796 / 0.419271 (-0.358476) | 0.033924 / 0.043533 (-0.009609) | 0.278047 / 0.255139 (0.022908) | 0.297878 / 0.283200 (0.014678) | 0.019137 / 0.141683 (-0.122546) | 1.138996 / 1.452155 (-0.313158) | 1.172731 / 1.492716 (-0.319985) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.110148 / 0.018006 (0.092142) | 0.307232 / 0.000490 (0.306742) | 0.000209 / 0.000200 (0.000009) | 0.000044 / 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) | 0.023082 / 0.037411 (-0.014330) | 0.076590 / 0.014526 (0.062065) | 0.088444 / 0.176557 (-0.088113) | 0.129293 / 0.737135 (-0.607842) | 0.090470 / 0.296338 (-0.205868) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.305016 / 0.215209 (0.089807) | 2.931671 / 2.077655 (0.854016) | 1.586055 / 1.504120 (0.081935) | 1.463517 / 1.541195 (-0.077678) | 1.479654 / 1.468490 (0.011164) | 0.726194 / 4.584777 (-3.858583) | 0.970512 / 3.745712 (-2.775200) | 2.850496 / 5.269862 (-2.419365) | 1.920112 / 4.565676 (-2.645564) | 0.079921 / 0.424275 (-0.344354) | 0.005367 / 0.007607 (-0.002240) | 0.347022 / 0.226044 (0.120978) | 3.472425 / 2.268929 (1.203497) | 1.965400 / 55.444624 (-53.479225) | 1.669116 / 6.876477 (-5.207361) | 1.859504 / 2.142072 (-0.282568) | 0.802703 / 4.805227 (-4.002525) | 0.134776 / 6.500664 (-6.365888) | 0.041800 / 0.075469 (-0.033669) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.039665 / 1.841788 (-0.802122) | 12.024071 / 8.074308 (3.949763) | 10.338743 / 10.191392 (0.147351) | 0.139495 / 0.680424 (-0.540929) | 0.015249 / 0.534201 (-0.518952) | 0.298580 / 0.579283 (-0.280703) | 0.124625 / 0.434364 (-0.309739) | 0.341868 / 0.540337 (-0.198470) | 0.431396 / 1.386936 (-0.955540) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#65b9499348fa4c6e5bfa977ee9b5e8574bf64eea \"CML watermark\")\n"
] | "2024-07-25T07:50:04Z" | "2024-07-31T07:10:07Z" | "2024-07-30T10:51:01Z" | MEMBER | null | Fix push_to_hub by not calling create_branch if PR branch (e.g. `refs/pr/1`).
Note that currently create_branch raises a 400 Bad Request error if the user passes a PR branch (e.g. `refs/pr/1`).
EDIT:
~~Fix push_to_hub by not calling create_branch if branch exists.~~
Note that currently create_branch raises a 403 Forbidden error even if all these conditions are met:
- exist_ok is passed
- the branch already exists
- the user does not have WRITE permission
Fix #7067.
Related issue:
- https://github.com/huggingface/huggingface_hub/issues/2419 | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7068). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<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) | 0.005725 / 0.011353 (-0.005628) | 0.004149 / 0.011008 (-0.006859) | 0.065051 / 0.038508 (0.026543) | 0.030220 / 0.023109 (0.007111) | 0.256768 / 0.275898 (-0.019130) | 0.269767 / 0.323480 (-0.053713) | 0.003256 / 0.007986 (-0.004730) | 0.003378 / 0.004328 (-0.000951) | 0.049407 / 0.004250 (0.045156) | 0.046041 / 0.037052 (0.008988) | 0.270977 / 0.258489 (0.012488) | 0.288771 / 0.293841 (-0.005070) | 0.030401 / 0.128546 (-0.098145) | 0.012203 / 0.075646 (-0.063443) | 0.227365 / 0.419271 (-0.191906) | 0.036356 / 0.043533 (-0.007176) | 0.262763 / 0.255139 (0.007624) | 0.268172 / 0.283200 (-0.015028) | 0.020698 / 0.141683 (-0.120984) | 1.171679 / 1.452155 (-0.280476) | 1.155353 / 1.492716 (-0.337363) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.138740 / 0.018006 (0.120733) | 0.300962 / 0.000490 (0.300473) | 0.000240 / 0.000200 (0.000040) | 0.000050 / 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) | 0.019056 / 0.037411 (-0.018355) | 0.062922 / 0.014526 (0.048396) | 0.075339 / 0.176557 (-0.101218) | 0.122587 / 0.737135 (-0.614548) | 0.078622 / 0.296338 (-0.217716) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.273878 / 0.215209 (0.058669) | 2.753188 / 2.077655 (0.675533) | 1.446877 / 1.504120 (-0.057243) | 1.325034 / 1.541195 (-0.216160) | 1.332849 / 1.468490 (-0.135641) | 0.721042 / 4.584777 (-3.863735) | 2.457241 / 3.745712 (-1.288471) | 3.008013 / 5.269862 (-2.261848) | 1.925773 / 4.565676 (-2.639903) | 0.077725 / 0.424275 (-0.346550) | 0.005232 / 0.007607 (-0.002375) | 0.331398 / 0.226044 (0.105354) | 3.273689 / 2.268929 (1.004761) | 1.818291 / 55.444624 (-53.626334) | 1.532233 / 6.876477 (-5.344244) | 1.545236 / 2.142072 (-0.596837) | 0.809853 / 4.805227 (-3.995374) | 0.137571 / 6.500664 (-6.363093) | 0.042829 / 0.075469 (-0.032640) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.962599 / 1.841788 (-0.879189) | 11.593394 / 8.074308 (3.519086) | 9.564848 / 10.191392 (-0.626544) | 0.131547 / 0.680424 (-0.548876) | 0.014724 / 0.534201 (-0.519477) | 0.309343 / 0.579283 (-0.269940) | 0.263476 / 0.434364 (-0.170888) | 0.350755 / 0.540337 (-0.189582) | 0.445279 / 1.386936 (-0.941657) |\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) | 0.005818 / 0.011353 (-0.005534) | 0.004028 / 0.011008 (-0.006980) | 0.050337 / 0.038508 (0.011829) | 0.033234 / 0.023109 (0.010125) | 0.273498 / 0.275898 (-0.002400) | 0.299130 / 0.323480 (-0.024350) | 0.004391 / 0.007986 (-0.003595) | 0.002854 / 0.004328 (-0.001474) | 0.048616 / 0.004250 (0.044365) | 0.040354 / 0.037052 (0.003302) | 0.287980 / 0.258489 (0.029491) | 0.323940 / 0.293841 (0.030099) | 0.033031 / 0.128546 (-0.095515) | 0.012539 / 0.075646 (-0.063108) | 0.061129 / 0.419271 (-0.358143) | 0.034410 / 0.043533 (-0.009123) | 0.276367 / 0.255139 (0.021228) | 0.295266 / 0.283200 (0.012066) | 0.018558 / 0.141683 (-0.123125) | 1.149051 / 1.452155 (-0.303104) | 1.207995 / 1.492716 (-0.284721) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.095732 / 0.018006 (0.077726) | 0.305774 / 0.000490 (0.305284) | 0.000222 / 0.000200 (0.000022) | 0.000044 / 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) | 0.023680 / 0.037411 (-0.013731) | 0.077147 / 0.014526 (0.062621) | 0.088850 / 0.176557 (-0.087706) | 0.130219 / 0.737135 (-0.606917) | 0.090582 / 0.296338 (-0.205756) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.306099 / 0.215209 (0.090890) | 2.952515 / 2.077655 (0.874861) | 1.593090 / 1.504120 (0.088970) | 1.471887 / 1.541195 (-0.069308) | 1.484277 / 1.468490 (0.015787) | 0.741158 / 4.584777 (-3.843619) | 0.976520 / 3.745712 (-2.769192) | 2.904631 / 5.269862 (-2.365231) | 1.940287 / 4.565676 (-2.625389) | 0.079828 / 0.424275 (-0.344447) | 0.005482 / 0.007607 (-0.002125) | 0.353376 / 0.226044 (0.127332) | 3.502412 / 2.268929 (1.233483) | 1.976571 / 55.444624 (-53.468053) | 1.675141 / 6.876477 (-5.201336) | 1.821075 / 2.142072 (-0.320998) | 0.814290 / 4.805227 (-3.990937) | 0.135227 / 6.500664 (-6.365437) | 0.041631 / 0.075469 (-0.033838) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.041495 / 1.841788 (-0.800293) | 12.275647 / 8.074308 (4.201339) | 10.569540 / 10.191392 (0.378148) | 0.143136 / 0.680424 (-0.537288) | 0.015010 / 0.534201 (-0.519191) | 0.302177 / 0.579283 (-0.277106) | 0.125924 / 0.434364 (-0.308440) | 0.340977 / 0.540337 (-0.199360) | 0.438467 / 1.386936 (-0.948469) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#baea190799dfa22493621fe06584b006b57f16ce \"CML watermark\")\n"
] | "2024-07-24T05:52:34Z" | "2024-07-29T07:02:07Z" | "2024-07-29T06:56:15Z" | MEMBER | null | Fix `_prepare_single_hop_path_and_storage_options`:
- Do not pass HF authentication headers and HF user-agent to non-HF HTTP URLs
- Do not overwrite passed `storage_options` nested values:
- Before, when passed
```DownloadConfig(storage_options={"https": {"client_kwargs": {"raise_for_status": True}}})```,
it was overwritten to
```{"https": {"client_kwargs": {"trust_env": True}}}```
- Now, the result combines both:
```{"https": {"client_kwargs": {"trust_env": True, "raise_for_status": True}}}``` | {
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https://api.github.com/repos/huggingface/datasets/issues/7067 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7067/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7067/comments | https://api.github.com/repos/huggingface/datasets/issues/7067/events | https://github.com/huggingface/datasets/issues/7067 | 2,425,460,168 | I_kwDODunzps6QkZXI | 7,067 | Convert_to_parquet fails for datasets with multiple configs | {
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"Many users have encountered the same issue, which has caused inconvenience.\r\n\r\nhttps://discuss.huggingface.co/t/convert-to-parquet-fails-for-datasets-with-multiple-configs/86733\r\n",
"Thanks for reporting.\r\n\r\nI will make the code more robust.",
"I have opened an issue in the huggingface-hub repo:\r\n- https://github.com/huggingface/huggingface_hub/issues/2419\r\n\r\nI am opening a PR to avoid calling `create_branch` if the branch already exists."
] | "2024-07-23T15:09:33Z" | "2024-07-30T10:51:02Z" | "2024-07-30T10:51:02Z" | NONE | null | If the dataset has multiple configs, when using the `datasets-cli convert_to_parquet` command to avoid issues with the data viewer caused by loading scripts, the conversion process only successfully converts the data corresponding to the first config. When it starts converting the second config, it throws an error:
```
Traceback (most recent call last):
File "/opt/anaconda3/envs/dl/bin/datasets-cli", line 8, in <module>
sys.exit(main())
File "/opt/anaconda3/envs/dl/lib/python3.10/site-packages/datasets/commands/datasets_cli.py", line 41, in main
service.run()
File "/opt/anaconda3/envs/dl/lib/python3.10/site-packages/datasets/commands/convert_to_parquet.py", line 83, in run
dataset.push_to_hub(
File "/opt/anaconda3/envs/dl/lib/python3.10/site-packages/datasets/dataset_dict.py", line 1713, in push_to_hub
api.create_branch(repo_id, branch=revision, token=token, repo_type="dataset", exist_ok=True)
File "/opt/anaconda3/envs/dl/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py", line 114, in _inner_fn
return fn(*args, **kwargs)
File "/opt/anaconda3/envs/dl/lib/python3.10/site-packages/huggingface_hub/hf_api.py", line 5503, in create_branch
hf_raise_for_status(response)
File "/opt/anaconda3/envs/dl/lib/python3.10/site-packages/huggingface_hub/utils/_errors.py", line 358, in hf_raise_for_status
raise BadRequestError(message, response=response) from e
huggingface_hub.utils._errors.BadRequestError: (Request ID: Root=1-669fc665-7c2e80d75f4337496ee95402;731fcdc7-0950-4eec-99cf-ce047b8d003f)
Bad request:
Invalid reference for a branch: refs/pr/1
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/7066 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7066/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7066/comments | https://api.github.com/repos/huggingface/datasets/issues/7066/events | https://github.com/huggingface/datasets/issues/7066 | 2,425,125,160 | I_kwDODunzps6QjHko | 7,066 | One subset per file in repo ? | {
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} | [] | open | false | null | [] | null | [] | "2024-07-23T12:43:59Z" | "2024-07-23T12:43:59Z" | null | MEMBER | null | Right now we consider all the files of a dataset to be the same data, e.g.
```
single_subset_dataset/
├── train0.jsonl
├── train1.jsonl
└── train2.jsonl
```
but in cases like this, each file is actually a different subset of the dataset and should be loaded separately
```
many_subsets_dataset/
├── animals.jsonl
├── trees.jsonl
└── metadata.jsonl
```
It would be nice to detect those subsets automatically using a simple heuristic. For example we can group files together if their paths names are the same except some digits ? | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7065 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7065/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7065/comments | https://api.github.com/repos/huggingface/datasets/issues/7065/events | https://github.com/huggingface/datasets/issues/7065 | 2,424,734,953 | I_kwDODunzps6QhoTp | 7,065 | Cannot get item after loading from disk and then converting to iterable. | {
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} | [] | open | false | null | [] | null | [] | "2024-07-23T09:37:56Z" | "2024-07-23T09:37:56Z" | null | NONE | null | ### Describe the bug
The dataset generated from local file works fine.
```py
root = "/home/data/train"
file_list1 = glob(os.path.join(root, "*part1.flac"))
file_list2 = glob(os.path.join(root, "*part2.flac"))
ds = (
Dataset.from_dict({"part1": file_list1, "part2": file_list2})
.cast_column("part1", Audio(sampling_rate=None, mono=False))
.cast_column("part2", Audio(sampling_rate=None, mono=False))
)
ids = ds.to_iterable_dataset(128)
ids = ids.shuffle(buffer_size=10000, seed=42)
dataloader = DataLoader(ids, num_workers=4, batch_size=8, persistent_workers=True)
for batch in dataloader:
break
```
But after saving it to disk and then loading it from disk, I cannot get data as expected.
```py
root = "/home/data/train"
file_list1 = glob(os.path.join(root, "*part1.flac"))
file_list2 = glob(os.path.join(root, "*part2.flac"))
ds = (
Dataset.from_dict({"part1": file_list1, "part2": file_list2})
.cast_column("part1", Audio(sampling_rate=None, mono=False))
.cast_column("part2", Audio(sampling_rate=None, mono=False))
)
ds.save_to_disk("./train")
ds = datasets.load_from_disk("./train")
ids = ds.to_iterable_dataset(128)
ids = ids.shuffle(buffer_size=10000, seed=42)
dataloader = DataLoader(ids, num_workers=4, batch_size=8, persistent_workers=True)
for batch in dataloader:
break
```
After a long time waiting, an error occurs:
```
Loading dataset from disk: 100%|█████████████████████████████████████████████████████████████████████████| 165/165 [00:00<00:00, 6422.18it/s]
Traceback (most recent call last):
File "/home/hanzerui/.conda/envs/mss/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 1133, in _try_get_data
data = self._data_queue.get(timeout=timeout)
File "/home/hanzerui/.conda/envs/mss/lib/python3.10/multiprocessing/queues.py", line 113, in get
if not self._poll(timeout):
File "/home/hanzerui/.conda/envs/mss/lib/python3.10/multiprocessing/connection.py", line 257, in poll
return self._poll(timeout)
File "/home/hanzerui/.conda/envs/mss/lib/python3.10/multiprocessing/connection.py", line 424, in _poll
r = wait([self], timeout)
File "/home/hanzerui/.conda/envs/mss/lib/python3.10/multiprocessing/connection.py", line 931, in wait
ready = selector.select(timeout)
File "/home/hanzerui/.conda/envs/mss/lib/python3.10/selectors.py", line 416, in select
fd_event_list = self._selector.poll(timeout)
File "/home/hanzerui/.conda/envs/mss/lib/python3.10/site-packages/torch/utils/data/_utils/signal_handling.py", line 66, in handler
_error_if_any_worker_fails()
RuntimeError: DataLoader worker (pid 3490529) is killed by signal: Killed.
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/home/hanzerui/.conda/envs/mss/lib/python3.10/runpy.py", line 196, in _run_module_as_main
return _run_code(code, main_globals, None,
File "/home/hanzerui/.conda/envs/mss/lib/python3.10/runpy.py", line 86, in _run_code
exec(code, run_globals)
File "/home/hanzerui/.vscode-server/extensions/ms-python.debugpy-2024.9.12011011/bundled/libs/debugpy/adapter/../../debugpy/launcher/../../debugpy/__main__.py", line 39, in <module>
cli.main()
File "/home/hanzerui/.vscode-server/extensions/ms-python.debugpy-2024.9.12011011/bundled/libs/debugpy/adapter/../../debugpy/launcher/../../debugpy/../debugpy/server/cli.py", line 430, in main
run()
File "/home/hanzerui/.vscode-server/extensions/ms-python.debugpy-2024.9.12011011/bundled/libs/debugpy/adapter/../../debugpy/launcher/../../debugpy/../debugpy/server/cli.py", line 284, in run_file
runpy.run_path(target, run_name="__main__")
File "/home/hanzerui/.vscode-server/extensions/ms-python.debugpy-2024.9.12011011/bundled/libs/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_runpy.py", line 321, in run_path
return _run_module_code(code, init_globals, run_name,
File "/home/hanzerui/.vscode-server/extensions/ms-python.debugpy-2024.9.12011011/bundled/libs/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_runpy.py", line 135, in _run_module_code
_run_code(code, mod_globals, init_globals,
File "/home/hanzerui/.vscode-server/extensions/ms-python.debugpy-2024.9.12011011/bundled/libs/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_runpy.py", line 124, in _run_code
exec(code, run_globals)
File "/home/hanzerui/workspace/NetEase/test/test_datasets.py", line 60, in <module>
for batch in dataloader:
File "/home/hanzerui/.conda/envs/mss/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 631, in __next__
data = self._next_data()
File "/home/hanzerui/.conda/envs/mss/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 1329, in _next_data
idx, data = self._get_data()
File "/home/hanzerui/.conda/envs/mss/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 1295, in _get_data
success, data = self._try_get_data()
File "/home/hanzerui/.conda/envs/mss/lib/python3.10/site-packages/torch/utils/data/dataloader.py", line 1146, in _try_get_data
raise RuntimeError(f'DataLoader worker (pid(s) {pids_str}) exited unexpectedly') from e
RuntimeError: DataLoader worker (pid(s) 3490529) exited unexpectedly
```
It seems that streaming is not supported by `laod_from_disk`, so does that mean I cannot convert it to iterable?
### Steps to reproduce the bug
1. Create a `Dataset` from local files with `from_dict`
2. Save it to disk with `save_to_disk`
3. Load it from disk with `load_from_disk`
4. Convert to iterable with `to_iterable_dataset`
5. Loop the dataset
### Expected behavior
Get items faster than the original dataset generated from dict.
### Environment info
- `datasets` version: 2.20.0
- Platform: Linux-6.5.0-41-generic-x86_64-with-glibc2.35
- Python version: 3.10.14
- `huggingface_hub` version: 0.23.2
- PyArrow version: 17.0.0
- Pandas version: 2.2.2
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"Looks good to me ! :)\r\n\r\nyou might want to add the `map` num_proc argument as well, for people who want to make it run faster",
"Thanks for the feedback @lhoestq! The last commits include:\r\n- Adding the `num_proc` parameter to `batch`\r\n- Adding tests similar to the one done for `IterableDataset.batch()`\r\n- Updated the documentation -> I think they are actually misplaced in the `Stream` page. But could not find a better place atm. Where would you put this documentation?\r\n\r\nWDYT?",
"You can put the documentation in process.mdx :)",
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7064). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"I reset the head to the commit before I added the `Dataset.batch()` documentation to `stream.mdx` and instead added the documentation to `process.mdx`. ",
"<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) | 0.005736 / 0.011353 (-0.005617) | 0.003959 / 0.011008 (-0.007049) | 0.063259 / 0.038508 (0.024751) | 0.030705 / 0.023109 (0.007596) | 0.245706 / 0.275898 (-0.030192) | 0.278766 / 0.323480 (-0.044714) | 0.003354 / 0.007986 (-0.004632) | 0.004246 / 0.004328 (-0.000082) | 0.049346 / 0.004250 (0.045095) | 0.046439 / 0.037052 (0.009386) | 0.257930 / 0.258489 (-0.000559) | 0.295562 / 0.293841 (0.001722) | 0.030529 / 0.128546 (-0.098017) | 0.012465 / 0.075646 (-0.063182) | 0.205595 / 0.419271 (-0.213677) | 0.036319 / 0.043533 (-0.007214) | 0.243872 / 0.255139 (-0.011267) | 0.275834 / 0.283200 (-0.007366) | 0.020330 / 0.141683 (-0.121353) | 1.108337 / 1.452155 (-0.343817) | 1.150406 / 1.492716 (-0.342310) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.113498 / 0.018006 (0.095491) | 0.306654 / 0.000490 (0.306164) | 0.000238 / 0.000200 (0.000038) | 0.000043 / 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) | 0.019092 / 0.037411 (-0.018319) | 0.063180 / 0.014526 (0.048654) | 0.078244 / 0.176557 (-0.098313) | 0.126106 / 0.737135 (-0.611030) | 0.078651 / 0.296338 (-0.217687) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.284132 / 0.215209 (0.068923) | 2.781250 / 2.077655 (0.703595) | 1.471864 / 1.504120 (-0.032256) | 1.354661 / 1.541195 (-0.186534) | 1.362839 / 1.468490 (-0.105651) | 0.719126 / 4.584777 (-3.865651) | 2.396969 / 3.745712 (-1.348743) | 2.987924 / 5.269862 (-2.281938) | 1.910555 / 4.565676 (-2.655121) | 0.078612 / 0.424275 (-0.345663) | 0.005170 / 0.007607 (-0.002437) | 0.333876 / 0.226044 (0.107832) | 3.298340 / 2.268929 (1.029412) | 1.853332 / 55.444624 (-53.591292) | 1.551919 / 6.876477 (-5.324557) | 1.585677 / 2.142072 (-0.556395) | 0.802487 / 4.805227 (-4.002741) | 0.134828 / 6.500664 (-6.365837) | 0.041966 / 0.075469 (-0.033503) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.992277 / 1.841788 (-0.849511) | 11.626887 / 8.074308 (3.552578) | 9.715623 / 10.191392 (-0.475769) | 0.140306 / 0.680424 (-0.540117) | 0.014528 / 0.534201 (-0.519673) | 0.306247 / 0.579283 (-0.273036) | 0.263067 / 0.434364 (-0.171297) | 0.342325 / 0.540337 (-0.198013) | 0.432299 / 1.386936 (-0.954637) |\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) | 0.006004 / 0.011353 (-0.005349) | 0.003890 / 0.011008 (-0.007118) | 0.050408 / 0.038508 (0.011900) | 0.031880 / 0.023109 (0.008771) | 0.273114 / 0.275898 (-0.002784) | 0.296653 / 0.323480 (-0.026826) | 0.004569 / 0.007986 (-0.003416) | 0.002831 / 0.004328 (-0.001497) | 0.050032 / 0.004250 (0.045782) | 0.040468 / 0.037052 (0.003415) | 0.284718 / 0.258489 (0.026229) | 0.321754 / 0.293841 (0.027913) | 0.033863 / 0.128546 (-0.094684) | 0.012183 / 0.075646 (-0.063463) | 0.060805 / 0.419271 (-0.358466) | 0.034919 / 0.043533 (-0.008614) | 0.274354 / 0.255139 (0.019215) | 0.293477 / 0.283200 (0.010277) | 0.019418 / 0.141683 (-0.122265) | 1.151571 / 1.452155 (-0.300584) | 1.217174 / 1.492716 (-0.275542) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.097326 / 0.018006 (0.079320) | 0.316277 / 0.000490 (0.315787) | 0.000225 / 0.000200 (0.000025) | 0.000045 / 0.000054 (-0.000009) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022932 / 0.037411 (-0.014479) | 0.077455 / 0.014526 (0.062929) | 0.088949 / 0.176557 (-0.087608) | 0.129447 / 0.737135 (-0.607688) | 0.093705 / 0.296338 (-0.202634) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.303918 / 0.215209 (0.088709) | 2.973866 / 2.077655 (0.896211) | 1.593165 / 1.504120 (0.089045) | 1.465312 / 1.541195 (-0.075883) | 1.484503 / 1.468490 (0.016013) | 0.731849 / 4.584777 (-3.852928) | 0.953337 / 3.745712 (-2.792375) | 2.887815 / 5.269862 (-2.382047) | 1.923618 / 4.565676 (-2.642058) | 0.080073 / 0.424275 (-0.344202) | 0.005460 / 0.007607 (-0.002148) | 0.359876 / 0.226044 (0.133832) | 3.532251 / 2.268929 (1.263323) | 1.987778 / 55.444624 (-53.456846) | 1.685572 / 6.876477 (-5.190905) | 1.827141 / 2.142072 (-0.314932) | 0.815953 / 4.805227 (-3.989274) | 0.136698 / 6.500664 (-6.363967) | 0.042185 / 0.075469 (-0.033285) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.032508 / 1.841788 (-0.809280) | 12.526918 / 8.074308 (4.452610) | 10.202942 / 10.191392 (0.011550) | 0.145920 / 0.680424 (-0.534504) | 0.015643 / 0.534201 (-0.518558) | 0.300465 / 0.579283 (-0.278818) | 0.126786 / 0.434364 (-0.307578) | 0.342885 / 0.540337 (-0.197453) | 0.438139 / 1.386936 (-0.948797) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9c98e069b47d40a219b6f27e62ed85a5bb17449e \"CML watermark\")\n"
] | "2024-07-23T08:40:43Z" | "2024-07-25T13:51:25Z" | "2024-07-25T13:45:20Z" | CONTRIBUTOR | null | This PR introduces a new `batch` method to the `Dataset` class, aligning its functionality with the `IterableDataset.batch()` method (implemented in #7054). The implementation uses as well the existing `map` method for efficient batching of examples.
Key changes:
- Add `batch` method to `Dataset` class in `arrow_dataset.py`
- Utilize `map` method for batching
Closes #7063
Once the approach is approved, i will create the tests and update the documentation. | {
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] | closed | false | null | [] | null | [] | "2024-07-23T07:36:59Z" | "2024-07-25T13:45:21Z" | "2024-07-25T13:45:21Z" | CONTRIBUTOR | null | ### Feature request
Add a `batch` method to the Dataset class, similar to the one recently implemented for `IterableDataset` in PR #7054.
### Motivation
A batched iteration speeds up data loading significantly (see e.g. #6279)
### Your contribution
I plan to open a PR to implement this. | {
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https://api.github.com/repos/huggingface/datasets/issues/7062 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7062/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7062/comments | https://api.github.com/repos/huggingface/datasets/issues/7062/events | https://github.com/huggingface/datasets/pull/7062 | 2,424,467,484 | PR_kwDODunzps52LUPR | 7,062 | Avoid calling http_head for non-HTTP URLs | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7062). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<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) | 0.005591 / 0.011353 (-0.005761) | 0.003992 / 0.011008 (-0.007016) | 0.063932 / 0.038508 (0.025424) | 0.034572 / 0.023109 (0.011463) | 0.252532 / 0.275898 (-0.023366) | 0.271233 / 0.323480 (-0.052247) | 0.005146 / 0.007986 (-0.002840) | 0.002844 / 0.004328 (-0.001484) | 0.049555 / 0.004250 (0.045305) | 0.044111 / 0.037052 (0.007059) | 0.270131 / 0.258489 (0.011642) | 0.318109 / 0.293841 (0.024269) | 0.030247 / 0.128546 (-0.098300) | 0.012438 / 0.075646 (-0.063209) | 0.205160 / 0.419271 (-0.214112) | 0.036228 / 0.043533 (-0.007305) | 0.250664 / 0.255139 (-0.004475) | 0.263884 / 0.283200 (-0.019315) | 0.018141 / 0.141683 (-0.123541) | 1.128504 / 1.452155 (-0.323650) | 1.182543 / 1.492716 (-0.310173) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094576 / 0.018006 (0.076570) | 0.301153 / 0.000490 (0.300664) | 0.000246 / 0.000200 (0.000046) | 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) | 0.019143 / 0.037411 (-0.018268) | 0.062788 / 0.014526 (0.048262) | 0.074688 / 0.176557 (-0.101869) | 0.121799 / 0.737135 (-0.615336) | 0.076200 / 0.296338 (-0.220138) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.277002 / 0.215209 (0.061793) | 2.735738 / 2.077655 (0.658083) | 1.430408 / 1.504120 (-0.073712) | 1.309795 / 1.541195 (-0.231400) | 1.339083 / 1.468490 (-0.129407) | 0.702540 / 4.584777 (-3.882237) | 2.352468 / 3.745712 (-1.393244) | 2.913698 / 5.269862 (-2.356164) | 1.871739 / 4.565676 (-2.693938) | 0.077054 / 0.424275 (-0.347221) | 0.005055 / 0.007607 (-0.002552) | 0.330550 / 0.226044 (0.104505) | 3.272556 / 2.268929 (1.003627) | 1.805268 / 55.444624 (-53.639356) | 1.504791 / 6.876477 (-5.371686) | 1.511361 / 2.142072 (-0.630712) | 0.784451 / 4.805227 (-4.020776) | 0.132182 / 6.500664 (-6.368482) | 0.042516 / 0.075469 (-0.032954) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.946939 / 1.841788 (-0.894849) | 11.369607 / 8.074308 (3.295299) | 9.667350 / 10.191392 (-0.524042) | 0.138689 / 0.680424 (-0.541735) | 0.014416 / 0.534201 (-0.519785) | 0.300685 / 0.579283 (-0.278598) | 0.259709 / 0.434364 (-0.174655) | 0.341271 / 0.540337 (-0.199066) | 0.435609 / 1.386936 (-0.951327) |\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) | 0.005726 / 0.011353 (-0.005627) | 0.004071 / 0.011008 (-0.006937) | 0.050837 / 0.038508 (0.012329) | 0.047000 / 0.023109 (0.023890) | 0.278543 / 0.275898 (0.002645) | 0.300526 / 0.323480 (-0.022954) | 0.004483 / 0.007986 (-0.003503) | 0.002835 / 0.004328 (-0.001494) | 0.050925 / 0.004250 (0.046675) | 0.041834 / 0.037052 (0.004782) | 0.285059 / 0.258489 (0.026570) | 0.324557 / 0.293841 (0.030716) | 0.038949 / 0.128546 (-0.089597) | 0.012145 / 0.075646 (-0.063501) | 0.061791 / 0.419271 (-0.357481) | 0.034493 / 0.043533 (-0.009040) | 0.274034 / 0.255139 (0.018895) | 0.295886 / 0.283200 (0.012686) | 0.018524 / 0.141683 (-0.123159) | 1.148766 / 1.452155 (-0.303388) | 1.207966 / 1.492716 (-0.284750) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094078 / 0.018006 (0.076071) | 0.307850 / 0.000490 (0.307361) | 0.000224 / 0.000200 (0.000024) | 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) | 0.023502 / 0.037411 (-0.013910) | 0.077321 / 0.014526 (0.062795) | 0.091147 / 0.176557 (-0.085410) | 0.131111 / 0.737135 (-0.606025) | 0.090906 / 0.296338 (-0.205432) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.290700 / 0.215209 (0.075491) | 2.833655 / 2.077655 (0.756001) | 1.546371 / 1.504120 (0.042251) | 1.415337 / 1.541195 (-0.125858) | 1.445752 / 1.468490 (-0.022738) | 0.737880 / 4.584777 (-3.846897) | 0.961549 / 3.745712 (-2.784164) | 2.844021 / 5.269862 (-2.425841) | 2.023547 / 4.565676 (-2.542130) | 0.079791 / 0.424275 (-0.344484) | 0.005449 / 0.007607 (-0.002158) | 0.356381 / 0.226044 (0.130337) | 3.515555 / 2.268929 (1.246627) | 1.920407 / 55.444624 (-53.524217) | 1.628637 / 6.876477 (-5.247839) | 1.752995 / 2.142072 (-0.389077) | 0.807264 / 4.805227 (-3.997963) | 0.133627 / 6.500664 (-6.367037) | 0.041861 / 0.075469 (-0.033609) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.035643 / 1.841788 (-0.806144) | 12.114792 / 8.074308 (4.040484) | 10.185844 / 10.191392 (-0.005548) | 0.142354 / 0.680424 (-0.538070) | 0.015466 / 0.534201 (-0.518734) | 0.304681 / 0.579283 (-0.274603) | 0.124297 / 0.434364 (-0.310067) | 0.339907 / 0.540337 (-0.200430) | 0.436266 / 1.386936 (-0.950670) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#856eb84569006ab9389ddbcce8b7141befeab9cc \"CML watermark\")\n"
] | "2024-07-23T07:25:09Z" | "2024-07-23T14:28:27Z" | "2024-07-23T14:21:08Z" | MEMBER | null | Avoid calling `http_head` for non-HTTP URLs, by adding and `else` statement.
Currently, it makes an unnecessary HTTP call (which adds latency) for non-HTTP protocols, like FTP, S3,...
I discovered this while working in an unrelated issue. | {
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https://api.github.com/repos/huggingface/datasets/issues/7061 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7061/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7061/comments | https://api.github.com/repos/huggingface/datasets/issues/7061/events | https://github.com/huggingface/datasets/issues/7061 | 2,423,786,881 | I_kwDODunzps6QeA2B | 7,061 | Custom Dataset | Still Raise Error while handling errors in _generate_examples | {
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} | [] | open | false | null | [] | null | [] | "2024-07-22T21:18:12Z" | "2024-09-09T14:48:07Z" | null | NONE | null | ### Describe the bug
I follow this [example](https://discuss.huggingface.co/t/error-handling-in-iterabledataset/72827/3) to handle errors in custom dataset. I am writing a dataset script which read jsonl files and i need to handle errors and continue reading files without raising exception and exit the execution.
```
def _generate_examples(self, filepaths):
errors=[]
id_ = 0
for filepath in filepaths:
try:
with open(filepath, 'r') as f:
for line in f:
json_obj = json.loads(line)
yield id_, json_obj
id_ += 1
except Exception as exc:
logger.error(f"error occur at filepath: {filepath}")
errors.append(error)
```
seems the logger.error is printed but still exception is raised the the run is exit.
```
Downloading and preparing dataset custom_dataset/default to /home/myuser/.cache/huggingface/datasets/custom_dataset/default-a14cdd566afee0a6/1.0.0/acfcc9fb9c57034b580c4252841
ERROR: datasets_modules.datasets.custom_dataset.acfcc9fb9c57034b580c4252841bb890a5617cbd28678dd4be5e52b81188ad02.custom_dataset: 2024-07-22 10:47:42,167: error occur at filepath: '/home/myuser/ds/corrupted-file.jsonl
Traceback (most recent call last):
File "/home/myuser/.cache/huggingface/modules/datasets_modules/datasets/custom_dataset/ac..2/custom_dataset.py", line 48, in _generate_examples
json_obj = json.loads(line)
File "myenv/lib/python3.8/json/__init__.py", line 357, in loads
return _default_decoder.decode(s)
File "myenv/lib/python3.8/json/decoder.py", line 337, in decode
obj, end = self.raw_decode(s, idx=_w(s, 0).end())
File "myenv/lib/python3.8/json/decoder.py", line 353, in raw_decode
obj, end = self.scan_once(s, idx)
json.decoder.JSONDecodeError: Invalid control character at: line 1 column 4 (char 3)
Generating train split: 0 examples [00:06, ? examples/s]>
RemoteTraceback:
"""
Traceback (most recent call last):
File "myenv/lib/python3.8/site-packages/datasets/builder.py", line 1637, in _prepare_split_single
num_examples, num_bytes = writer.finalize()
File "myenv/lib/python3.8/site-packages/datasets/arrow_writer.py", line 594, in finalize
raise SchemaInferenceError("Please pass `features` or at least one example when writing data")
datasets.arrow_writer.SchemaInferenceError: Please pass `features` or at least one example when writing data
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "myenv/lib/python3.8/site-packages/multiprocess/pool.py", line 125, in worker
result = (True, func(*args, **kwds))
File "myenv/lib/python3.8/site-packages/datasets/utils/py_utils.py", line 1353, in
_write_generator_to_queue
for i, result in enumerate(func(**kwargs)):
File "myenv/lib/python3.8/site-packages/datasets/builder.py", line 1646, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.builder.DatasetGenerationError: An error occurred while generating the dataset
"""
The above exception was the direct cause of the following exception:
│ │
│ myenv/lib/python3.8/site-packages/datasets/utils/py_utils. │
│ py:1377 in <listcomp> │
│ │
│ 1374 │ │ │ │ if all(async_result.ready() for async_result in async_results) and queue │
│ 1375 │ │ │ │ │ break │
│ 1376 │ │ # we get the result in case there's an error to raise │
│ ❱ 1377 │ │ [async_result.get() for async_result in async_results] │
│ 1378 │
│ │
│ ╭──────────────────────────────── locals ─────────────────────────────────╮ │
│ │ .0 = <list_iterator object at 0x7f2cc1f0ce20> │ │
│ │ async_result = <multiprocess.pool.ApplyResult object at 0x7f2cc1f79c10> │ │
│ ╰─────────────────────────────────────────────────────────────────────────╯ │
│ │
│ myenv/lib/python3.8/site-packages/multiprocess/pool.py:771 │
│ in get │
│ │
│ 768 │ │ if self._success: │
│ 769 │ │ │ return self._value │
│ 770 │ │ else: │
│ ❱ 771 │ │ │ raise self._value │
│ 772 │ │
│ 773 │ def _set(self, i, obj): │
│ 774 │ │ self._success, self._value = obj │
│ │
│ ╭────────────────────────────── locals ──────────────────────────────╮ │
│ │ self = <multiprocess.pool.ApplyResult object at 0x7f2cc1f79c10> │ │
│ │ timeout = None │ │
│ ╰────────────────────────────────────────────────────────────────────╯ │
DatasetGenerationError: An error occurred while generating the dataset
```
### Steps to reproduce the bug
same as above
### Expected behavior
should handle error and continue reading remaining files
### Environment info
python 3.9 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7060 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7060/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7060/comments | https://api.github.com/repos/huggingface/datasets/issues/7060/events | https://github.com/huggingface/datasets/pull/7060 | 2,423,188,419 | PR_kwDODunzps52G71g | 7,060 | WebDataset BuilderConfig | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7060). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update."
] | "2024-07-22T15:41:07Z" | "2024-07-23T13:28:44Z" | "2024-07-23T13:28:44Z" | NONE | null | This PR adds `WebDatasetConfig`.
Closes #7055 | {
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https://api.github.com/repos/huggingface/datasets/issues/7059 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7059/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7059/comments | https://api.github.com/repos/huggingface/datasets/issues/7059/events | https://github.com/huggingface/datasets/issues/7059 | 2,422,827,892 | I_kwDODunzps6QaWt0 | 7,059 | None values are skipped when reading jsonl in subobjects | {
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} | [] | open | false | null | [] | null | [] | "2024-07-22T13:02:42Z" | "2024-07-22T13:02:53Z" | null | NONE | null | ### Describe the bug
I have been fighting against my machine since this morning only to find out this is some kind of a bug.
When loading a dataset composed of `metadata.jsonl`, if you have nullable values (Optional[str]), they can be ignored by the parser, shifting things around.
E.g., let's take this example
Here are two version of a same dataset:
[not-buggy.tar.gz](https://github.com/user-attachments/files/16333532/not-buggy.tar.gz)
[buggy.tar.gz](https://github.com/user-attachments/files/16333553/buggy.tar.gz)
### Steps to reproduce the bug
1. Load the `buggy.tar.gz` dataset
2. Print baseline of `dts = load_dataset("./data")["train"][0]["baselines]`
3. Load the `not-buggy.tar.gz` dataset
4. Print baseline of `dts = load_dataset("./data")["train"][0]["baselines]`
### Expected behavior
Both should have 4 baseline entries:
1. Buggy should have None followed by three lists
2. Non-Buggy should have four lists, and the first one should be an empty list.
One does not work, 2 works. Despite accepting None in another position than the first one.
### Environment info
- `datasets` version: 2.19.1
- Platform: Linux-6.5.0-44-generic-x86_64-with-glibc2.35
- Python version: 3.10.12
- `huggingface_hub` version: 0.23.0
- PyArrow version: 16.1.0
- Pandas version: 2.2.2
- `fsspec` version: 2024.3.1
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} | [] | open | false | null | [] | null | [] | "2024-07-22T10:49:20Z" | "2024-07-22T10:49:20Z" | null | COLLABORATOR | null | It would be useful for PDF.
https://github.com/huggingface/dataset-viewer/issues/2991#issuecomment-2242656069 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7057 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7057/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7057/comments | https://api.github.com/repos/huggingface/datasets/issues/7057/events | https://github.com/huggingface/datasets/pull/7057 | 2,422,498,520 | PR_kwDODunzps52EjGC | 7,057 | Update load_hub.mdx | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7057). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<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) | 0.005617 / 0.011353 (-0.005736) | 0.003994 / 0.011008 (-0.007014) | 0.064188 / 0.038508 (0.025680) | 0.030939 / 0.023109 (0.007829) | 0.248712 / 0.275898 (-0.027186) | 0.273417 / 0.323480 (-0.050063) | 0.003340 / 0.007986 (-0.004646) | 0.002823 / 0.004328 (-0.001506) | 0.049985 / 0.004250 (0.045734) | 0.046872 / 0.037052 (0.009820) | 0.254554 / 0.258489 (-0.003935) | 0.288142 / 0.293841 (-0.005699) | 0.030540 / 0.128546 (-0.098006) | 0.012295 / 0.075646 (-0.063352) | 0.204589 / 0.419271 (-0.214683) | 0.036383 / 0.043533 (-0.007150) | 0.254277 / 0.255139 (-0.000862) | 0.267962 / 0.283200 (-0.015237) | 0.021173 / 0.141683 (-0.120510) | 1.126933 / 1.452155 (-0.325221) | 1.190841 / 1.492716 (-0.301875) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093622 / 0.018006 (0.075616) | 0.297967 / 0.000490 (0.297477) | 0.000241 / 0.000200 (0.000041) | 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) | 0.018623 / 0.037411 (-0.018789) | 0.062210 / 0.014526 (0.047684) | 0.074369 / 0.176557 (-0.102187) | 0.120585 / 0.737135 (-0.616550) | 0.075966 / 0.296338 (-0.220372) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.285440 / 0.215209 (0.070231) | 2.804275 / 2.077655 (0.726620) | 1.484539 / 1.504120 (-0.019580) | 1.366587 / 1.541195 (-0.174607) | 1.355269 / 1.468490 (-0.113221) | 0.722289 / 4.584777 (-3.862488) | 2.344567 / 3.745712 (-1.401145) | 2.831779 / 5.269862 (-2.438083) | 1.899800 / 4.565676 (-2.665876) | 0.078657 / 0.424275 (-0.345619) | 0.005188 / 0.007607 (-0.002420) | 0.340150 / 0.226044 (0.114106) | 3.390915 / 2.268929 (1.121986) | 1.836473 / 55.444624 (-53.608152) | 1.520718 / 6.876477 (-5.355759) | 1.723448 / 2.142072 (-0.418624) | 0.810281 / 4.805227 (-3.994946) | 0.136008 / 6.500664 (-6.364657) | 0.044005 / 0.075469 (-0.031465) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.989982 / 1.841788 (-0.851806) | 11.671075 / 8.074308 (3.596767) | 9.805471 / 10.191392 (-0.385921) | 0.141637 / 0.680424 (-0.538787) | 0.014551 / 0.534201 (-0.519650) | 0.310077 / 0.579283 (-0.269206) | 0.266838 / 0.434364 (-0.167526) | 0.348894 / 0.540337 (-0.191444) | 0.451530 / 1.386936 (-0.935406) |\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) | 0.005639 / 0.011353 (-0.005713) | 0.003935 / 0.011008 (-0.007074) | 0.050147 / 0.038508 (0.011639) | 0.031023 / 0.023109 (0.007914) | 0.268361 / 0.275898 (-0.007537) | 0.295774 / 0.323480 (-0.027706) | 0.005029 / 0.007986 (-0.002956) | 0.002832 / 0.004328 (-0.001496) | 0.049806 / 0.004250 (0.045556) | 0.040515 / 0.037052 (0.003463) | 0.283298 / 0.258489 (0.024809) | 0.321946 / 0.293841 (0.028105) | 0.031833 / 0.128546 (-0.096714) | 0.012137 / 0.075646 (-0.063510) | 0.060510 / 0.419271 (-0.358761) | 0.033754 / 0.043533 (-0.009779) | 0.268079 / 0.255139 (0.012940) | 0.292468 / 0.283200 (0.009268) | 0.017268 / 0.141683 (-0.124414) | 1.159922 / 1.452155 (-0.292233) | 1.188961 / 1.492716 (-0.303755) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.096930 / 0.018006 (0.078923) | 0.306921 / 0.000490 (0.306431) | 0.000226 / 0.000200 (0.000026) | 0.000050 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022811 / 0.037411 (-0.014600) | 0.077298 / 0.014526 (0.062772) | 0.088949 / 0.176557 (-0.087608) | 0.130763 / 0.737135 (-0.606372) | 0.090429 / 0.296338 (-0.205909) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.300866 / 0.215209 (0.085657) | 2.963375 / 2.077655 (0.885720) | 1.595753 / 1.504120 (0.091633) | 1.463091 / 1.541195 (-0.078104) | 1.481182 / 1.468490 (0.012692) | 0.712939 / 4.584777 (-3.871838) | 0.956694 / 3.745712 (-2.789018) | 2.802890 / 5.269862 (-2.466971) | 1.891092 / 4.565676 (-2.674585) | 0.077570 / 0.424275 (-0.346706) | 0.005536 / 0.007607 (-0.002072) | 0.351958 / 0.226044 (0.125914) | 3.459114 / 2.268929 (1.190185) | 1.989488 / 55.444624 (-53.455137) | 1.676271 / 6.876477 (-5.200205) | 1.808073 / 2.142072 (-0.334000) | 0.786920 / 4.805227 (-4.018307) | 0.132220 / 6.500664 (-6.368444) | 0.041602 / 0.075469 (-0.033867) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.031759 / 1.841788 (-0.810029) | 12.007776 / 8.074308 (3.933467) | 10.568254 / 10.191392 (0.376862) | 0.143176 / 0.680424 (-0.537248) | 0.015556 / 0.534201 (-0.518645) | 0.304484 / 0.579283 (-0.274799) | 0.125508 / 0.434364 (-0.308855) | 0.340017 / 0.540337 (-0.200320) | 0.434285 / 1.386936 (-0.952651) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#16fa4421f44b22bbbc607f379a93f45af468d1fc \"CML watermark\")\n"
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"Oh cool !\r\n\r\nThe time it takes to resume depends on the expected maximum distance in this case right ? Do you know its relationship with $B$ ?\r\n\r\nIn your test it already as high as 15k for $B=1024$, which is ok for text datasets but is maybe not ideal for datasets with heavy samples like audio/image/video ? Though for heavy samples datasets the buffer size is generally much smaller to avoid memory issues.\r\n\r\nMaybe we could just add a warning message on resuming to tell the user that it might take some time to recover the shuffle buffer (with a progress bar maybe ?), and have the option to stop + re-run with an env variable to disable shuffle buffer recovering ? WDYT ?",
"> The time it takes to resume depends on the expected maximum distance in this case right ? Do you know its relationship with $B$\r\n\r\nHi, I created a histogram to visualize the distances in the simulation exp.\r\n\r\nI think there is no guarantee as to when the oldest example will be yielded. It could stay in the buffer until the entire shard is consumed. However, this can be rare, and in most cases, the pushed examples will be yielded very quickly. In the figure above, most examples are yielded within $2B$ steps. Things will improve if the dataset is split into enough shards and each shard is not too large.\r\n\r\nI agree that we may need to add some warnings or provide some options to allow users to make their own choices.",
"Maybe there's a middle ground between rebuilding the buffer from scratch and storing the entire buffer, but the logic is a bit complicated and takes time to implement. At least for now, we have a way to make shuffled `IterableDataset` resumable :)",
"@lhoestq I'm not sure if it's ok to use progress bar when having multiple workers. \r\nHow about passing an arg `resumable=True` to `IterableDataset.shuffle` to allow for controling of the behaviors?",
"I feel like the default behavior should ideally be fast and perfect resuming.\r\n\r\nLoading from disk is a good option for this (although it's not always possible to serialize the content of the buffer, in that case the buffer would restart empty and we can show a warning). \r\n\r\nThe state_dict() would be part of the training state_dict that is saved to disk along with the model and optimizer anyway. Cc @muellerzr from that worked on storing training state_dicts for the `accelerate` lib, in case you have an opinion.\r\n\r\nI also feel like it is simpler and more intuitive to users. It doesn't require to explain why we need to stream a lot of data just to recover a buffer.\r\n\r\n> Maybe there's a middle ground between rebuilding the buffer from scratch and storing the entire buffer, but the logic is a bit complicated and takes time to implement.\r\n\r\ndefinitely, and it would also make things even harder to understand to users",
"@lhoestq \r\n> Loading from disk is a good option for this (although it's not always possible to serialize the content of the buffer, in that case the buffer would restart empty and we can show a warning).\r\nThe state_dict() would be part of the training state_dict that is saved to disk along with the model and optimizer anyway. Cc @muellerzr from that worked on storing training state_dicts for the accelerate lib, in case you have an opinion.\r\nI also feel like it is simpler and more intuitive to users. It doesn't require to explain why we need to stream a lot of data just to recover a buffer.\r\n\r\nYea, agree with you. But here's the thing: saving buffers as state dict can get pretty tricky. When it comes to tokenized text data, working with multi-worker shuffle can take around x hundreds GB of memories in my case. That's just not feasible for most machine envs out there, and can be more severe for audio/video data.\r\n\r\nAlso, serializing the buffer does take a major toll on performance, and in my experience, I've had to lean heavily on numpy/torch tensor operations to manage those tokenized text data efficiently, which isn't easily transferable to other scenarios—it's kind of a custom fix that works for now, but it's not a one-size-fits-all solution. So, for me it's not that ideal to directly serialize the buffer content with those limitations.\r\n\r\n",
"> When it comes to tokenized text data, working with multi-worker shuffle can taken around x hundreds GB memories in my case.\r\n\r\nit's kinda close to the size of a model + optimizer no ?\r\n\r\nAnyway that makes sense and adding the feature to recover a buffer shuffle (at least as an opt-in for now, we can decide on the default later based on users feedback and experience).\r\n\r\nAre you ok with adding `buffer_resuming_mode=` to `.shuffle()` to enable buffer recovering using your method with `buffer_resuming_mode=\"recover_from_source\"` ? (feel free to suggest other names for the parameter and value)",
"@lhoestq \r\n> Are you ok with adding buffer_resuming_mode= to .shuffle() to enable buffer recovering using your method with buffer_resuming_mode=\"recover_from_source\" ? (feel free to suggest other names for the parameter and value)\r\n\r\nOf course, appreciate your feedbacks."
] | "2024-07-22T07:50:02Z" | "2024-07-22T15:37:01Z" | null | CONTRIBUTOR | null | This PR aims to implement a resumable `BufferShuffledExamplesIterable`.
Instead of saving the entire buffer content, which is very memory-intensive, the newly implemented `BufferShuffledExamplesIterable` saves only the minimal state necessary for recovery, e.g., the random generator states and the state of the first example in the buffer dict.
The idea is that since the buffer size is limited, even if the entire buffer is discarded, we can rebuild it as long as the state of the oldest example is recorded. For buffer size $B$, the expected distance between when an example is pushed and when it is yielded is
$d = \sum_{k=1}^{\infty} k\frac{1}{B} (1 - \frac{1}{B} )^{k-1} =B$.
Simulation experiments support these claims:
```py
from random import randint
BUFFER_SIZE = 1024
dists = []
buffer = []
for i in range(10000000):
if i < BUFFER_SIZE:
buffer.append(i)
else:
index = randint(0, BUFFER_SIZE - 1)
dists.append(i - buffer[index])
buffer[index] = i
print(f"MIN DIST: {min(dists)}\nMAX DIST: {max(dists)}\nAVG DIST: {sum(dists) / len(dists):.2f}\n")
```
which produces the following output:
```py
MIN DIST: 1
MAX DIST: 15136
AVG DIST: 1023.95
```
The overall time for reconstructing the buffer and recovery should not be too long.
The following code mimics the cases of resuming online tokenization by `datasets` and `StatefulDataLoader` under distributed scenarios,
```py
import pickle
import time
from itertools import chain
from typing import Any, Dict, List
import torch
from datasets import load_dataset
from torchdata.stateful_dataloader import StatefulDataLoader
from tqdm import tqdm
from transformers import AutoTokenizer, DataCollatorForLanguageModeling
tokenizer = AutoTokenizer.from_pretrained('fla-hub/gla-1.3B-100B')
tokenizer.pad_token = tokenizer.eos_token
data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)
torch.manual_seed(42)
def tokenize(examples: Dict[str, List[Any]]) -> Dict[str, List[List[int]]]:
input_ids = tokenizer(examples['text'])['input_ids']
input_ids = list(chain(*input_ids))
total_length = len(input_ids)
chunk_size = 2048
total_length = (total_length // chunk_size) * chunk_size
# the last chunk smaller than chunk_size will be discarded
return {'input_ids': [input_ids[i: i+chunk_size] for i in range(0, total_length, chunk_size)]}
batch_size = 16
num_workers = 5
context_length = 2048
rank = 1
world_size = 32
prefetch_factor = 2
steps = 2048
path = 'fla-hub/slimpajama-test'
dataset = load_dataset(
path=path,
split='train',
streaming=True,
trust_remote_code=True
)
dataset = dataset.map(tokenize, batched=True, remove_columns=next(iter(dataset)).keys())
dataset = dataset.shuffle(seed=42)
loader = StatefulDataLoader(dataset=dataset,
batch_size=batch_size,
collate_fn=data_collator,
num_workers=num_workers,
persistent_workers=False,
prefetch_factor=prefetch_factor)
start = time.time()
for i, batch in tqdm(enumerate(loader)):
if i == 0:
print(f'{i}\n{batch["input_ids"]}')
if i == steps - 1:
print(f'{i}\n{batch["input_ids"]}')
state_dict = loader.state_dict()
if i == steps:
print(f'{i}\n{batch["input_ids"]}')
break
print(f"{time.time() - start:.2f}s elapsed")
print(f"{len(pickle.dumps(state_dict)) / 1024**2:.2f}MB states in total")
for worker in state_dict['_snapshot']['_worker_snapshots'].keys():
print(f"{worker} {len(pickle.dumps(state_dict['_snapshot']['_worker_snapshots'][worker])) / 1024**2:.2f}MB")
print(state_dict['_snapshot']['_worker_snapshots']['worker_0']['dataset_state'])
loader = StatefulDataLoader(dataset=dataset,
batch_size=batch_size,
collate_fn=data_collator,
num_workers=num_workers,
persistent_workers=False,
prefetch_factor=prefetch_factor)
print("Loading state dict")
loader.load_state_dict(state_dict)
start = time.time()
for batch in loader:
print(batch['input_ids'])
break
print(f"{time.time() - start:.2f}s elapsed")
```
and the outputs are
```py
0
tensor([[ 909, 395, 19082, ..., 13088, 16232, 395],
[ 601, 28705, 28770, ..., 28733, 923, 288],
[21753, 15071, 13977, ..., 9369, 28723, 415],
...,
[21763, 28751, 20300, ..., 28781, 28734, 4775],
[ 354, 396, 10214, ..., 298, 429, 28770],
[ 333, 6149, 28768, ..., 2773, 340, 351]])
2047
tensor([[28723, 415, 3889, ..., 272, 3065, 2609],
[ 403, 3214, 3629, ..., 403, 21163, 16434],
[28723, 13, 28749, ..., 28705, 28750, 28734],
...,
[ 2778, 2251, 28723, ..., 354, 684, 429],
[ 5659, 298, 1038, ..., 5290, 297, 22153],
[ 938, 28723, 1537, ..., 9123, 28733, 12154]])
2048
tensor([[ 769, 278, 12531, ..., 28721, 19309, 28739],
[ 415, 23347, 622, ..., 3937, 2426, 28725],
[28745, 4345, 28723, ..., 338, 28725, 583],
...,
[ 1670, 28709, 5809, ..., 28734, 28760, 393],
[ 340, 1277, 624, ..., 325, 28790, 1329],
[ 523, 1144, 3409, ..., 359, 359, 17422]])
65.97s elapsed
0.00MB states in total
worker_0 0.00MB
worker_1 0.00MB
worker_2 0.00MB
worker_3 0.00MB
worker_4 0.00MB
{'ex_iterable': {'ex_iterable': {'shard_idx': 0, 'shard_example_idx': 14000}, 'num_examples_since_previous_state': 166, 'previous_state_example_idx': 7394, 'previous_state': {'shard_idx': 0, 'shard_example_idx': 13000}}, 'num_taken': 6560, 'global_example_idx': 7560, 'buffer_state_dict': {'num_taken': 6560, 'global_example_idx': 356, 'index_offset': 0, 'first_state': {'ex_iterable': {'shard_idx': 0, 'shard_example_idx': 1000}, 'num_examples_since_previous_state': 356, 'previous_state_example_idx': 0, 'previous_state': {'shard_idx': 0, 'shard_example_idx': 0}}, 'bit_generator_state': {'state': {'state': 274674114334540486603088602300644985544, 'inc': 332724090758049132448979897138935081983}, 'bit_generator': 'PCG64', 'has_uint32': 0, 'uinteger': 0}}}
Loading state dict
tensor([[ 769, 278, 12531, ..., 28721, 19309, 28739],
[ 415, 23347, 622, ..., 3937, 2426, 28725],
[28745, 4345, 28723, ..., 338, 28725, 583],
...,
[ 1670, 28709, 5809, ..., 28734, 28760, 393],
[ 340, 1277, 624, ..., 325, 28790, 1329],
[ 523, 1144, 3409, ..., 359, 359, 17422]])
24.60s elapsed
```
Not sure if this PR complies with the `datasets` code style. Looking for your help @lhoestq, also very willing to further improve the code if any suggestions are given.
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https://api.github.com/repos/huggingface/datasets/issues/7055 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7055/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7055/comments | https://api.github.com/repos/huggingface/datasets/issues/7055/events | https://github.com/huggingface/datasets/issues/7055 | 2,421,708,891 | I_kwDODunzps6QWFhb | 7,055 | WebDataset with different prefixes are unsupported | {
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} | [] | closed | false | null | [] | null | [
"Since `datasets` uses is built on Arrow to store the data, it requires each sample to have the same columns.\r\n\r\nThis can be fixed by specifyign in advance the name of all the possible columns in the `dataset_info` in YAML, and missing values will be `None`",
"Thanks. This currently doesn't work for WebDataset because there's no `BuilderConfig` with `features` and in turn `_info` is missing `features=self.config.features`. I'll prepare a PR to fix this.\r\n\r\nNote it may be useful to add the [expected format of `features`](https://github.com/huggingface/datasets/blob/16fa4421f44b22bbbc607f379a93f45af468d1fc/src/datasets/features/features.py#L1757) to the documentation for [`Builder Parameters`](https://huggingface.co/docs/datasets/repository_structure#builder-parameters).\r\n",
"Oh good catch ! thanks\r\n\r\n> Note it may be useful to add the [expected format of features](https://github.com/huggingface/datasets/blob/16fa4421f44b22bbbc607f379a93f45af468d1fc/src/datasets/features/features.py#L1757) to the documentation for [Buil](https://huggingface.co/docs/datasets/repository_structure#builder-parameters)\r\n\r\nGood idea, let me open a PR",
"#7060 ",
"Actually I just tried with `datasets` on the `main` branch and having `features` defined in `dataset_info` worked for me\r\n\r\n```python\r\n>>> list(load_dataset(\"/Users/quentinlhoest/tmp\", streaming=True, split=\"train\"))\r\n[{'txt': 'hello there\\n', 'other': None}]\r\n```\r\nwhere `tmp` contains data.tar with \"hello there\\n\" in a text file and the README.md:\r\n```\r\n---\r\ndataset_info:\r\n features:\r\n - name: txt\r\n dtype: string\r\n - name: other\r\n dtype: string\r\n---\r\n\r\nThis is a dataset card\r\n```\r\n\r\nWhat error did you get when you tried to specify the columns in `dataset_info` ?",
"If you review the changes in #7060 you'll note that `features` are not passed to `DatasetInfo`.\r\n\r\nIn your case the features are being extracted by [this code](https://github.com/huggingface/datasets/blob/e83d6fa574710fcb44e341087239d2687183f62b/src/datasets/packaged_modules/webdataset/webdataset.py#L72-L98).\r\n\r\nTry with the `Steps to reproduce the bug`. It's the same error mentioned in `Describe the bug` because `features` are not passed to `DatasetInfo`.\r\n\r\n`features` are [not used](https://github.com/huggingface/datasets/blob/e83d6fa574710fcb44e341087239d2687183f62b/src/datasets/builder.py#L365-L366) when the `BuilderConfig` has no `features` attribute. `WebDataset` uses the default [`BuilderConfig`](https://github.com/huggingface/datasets/blob/e83d6fa574710fcb44e341087239d2687183f62b/src/datasets/builder.py#L101-L124).\r\n\r\nThere is a [warning](https://github.com/huggingface/datasets/blob/e83d6fa574710fcb44e341087239d2687183f62b/src/datasets/load.py#L640-L648) that `features` are ignored.\r\n\r\nNote that as mentioned in `Describe the bug` this could also be resolved by removing the check [here](https://github.com/huggingface/datasets/blob/e83d6fa574710fcb44e341087239d2687183f62b/src/datasets/packaged_modules/webdataset/webdataset.py#L76-L80) because Arrow actually handles this itself, Arrow sets any missing fields to `None`, at least in my case.",
"Note for anyone else who encounters this issue, every dataset type except folder-based types supported features in the [documented](https://huggingface.co/docs/datasets/repository_structure#builder-parameters) manner; [Arrow](https://github.com/huggingface/datasets/blob/e83d6fa574710fcb44e341087239d2687183f62b/src/datasets/packaged_modules/arrow/arrow.py#L15-L21), [csv](https://github.com/huggingface/datasets/blob/e83d6fa574710fcb44e341087239d2687183f62b/src/datasets/packaged_modules/csv/csv.py#L25-L68), [generator](https://github.com/huggingface/datasets/blob/e83d6fa574710fcb44e341087239d2687183f62b/src/datasets/packaged_modules/generator/generator.py#L8-L19), [json](https://github.com/huggingface/datasets/blob/e83d6fa574710fcb44e341087239d2687183f62b/src/datasets/packaged_modules/json/json.py#L42-L52), [pandas](https://github.com/huggingface/datasets/blob/e83d6fa574710fcb44e341087239d2687183f62b/src/datasets/packaged_modules/pandas/pandas.py#L14-L20), [parquet](https://github.com/huggingface/datasets/blob/e83d6fa574710fcb44e341087239d2687183f62b/src/datasets/packaged_modules/parquet/parquet.py#L16-L24), [spark](https://github.com/huggingface/datasets/blob/e83d6fa574710fcb44e341087239d2687183f62b/src/datasets/packaged_modules/spark/spark.py#L31-L37), [sql](https://github.com/huggingface/datasets/blob/e83d6fa574710fcb44e341087239d2687183f62b/src/datasets/packaged_modules/sql/sql.py#L24-L35) and [text](https://github.com/huggingface/datasets/blob/e83d6fa574710fcb44e341087239d2687183f62b/src/datasets/packaged_modules/text/text.py#L18-L27). `WebDataset` is different and requires [`dataset_info` which is vaguely documented](https://huggingface.co/docs/datasets/dataset_script#optional-generate-dataset-metadata) under dataset loading scripts.",
"Thanks for explaining. I see the Dataset Viewer is still failing - I'll update `datasets` in the Viewer to fix this"
] | "2024-07-22T01:14:19Z" | "2024-07-24T13:26:30Z" | "2024-07-23T13:28:46Z" | NONE | null | ### Describe the bug
Consider a WebDataset with multiple images for each item where the number of images may vary: [example](https://huggingface.co/datasets/bigdata-pw/fashion-150k)
Due to this [code](https://github.com/huggingface/datasets/blob/87f4c2088854ff33e817e724e75179e9975c1b02/src/datasets/packaged_modules/webdataset/webdataset.py#L76-L80) an error is given.
```
The TAR archives of the dataset should be in WebDataset format, but the files in the archive don't share the same prefix or the same types.
```
The purpose of this check is unclear because PyArrow supports different keys.
Removing the check allows the dataset to be loaded and there's no issue when iterating through the dataset.
```
>>> from datasets import load_dataset
>>> path = "shards/*.tar"
>>> dataset = load_dataset("webdataset", data_files={"train": path}, split="train", streaming=True)
Resolving data files: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 152/152 [00:00<00:00, 56458.93it/s]
>>> dataset
IterableDataset({
features: ['__key__', '__url__', '1.jpg', '2.jpg', '3.jpg', '4.jpg', 'json'],
n_shards: 152
})
```
### Steps to reproduce the bug
```python
from datasets import load_dataset
load_dataset("bigdata-pw/fashion-150k")
```
### Expected behavior
Dataset loads without error
### Environment info
- `datasets` version: 2.20.0
- Platform: Linux-5.14.0-467.el9.x86_64-x86_64-with-glibc2.34
- Python version: 3.9.19
- `huggingface_hub` version: 0.23.4
- PyArrow version: 17.0.0
- Pandas version: 2.2.2
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"Cool ! Thanks for diving into it :)\r\n\r\nYour implementation is great and indeed supports shuffling and batching, you just need to additionally account for state_dict (for dataset [checkpointing+resuming](https://huggingface.co/docs/datasets/main/en/use_with_pytorch#checkpoint-and-resume))\r\n\r\nThat being said, I believe the implementation can be made simpler by relying on `IterableDataset.map()` which already implements all this. Maybe something like\r\n\r\n```python\r\n\r\ndef batch(self, batch_size: int, drop_last_batch: bool = False) -> \"IterableDataset\":\r\n def batch(unbatched: dict[str, list]) -> dict[str, list]:\r\n return {k: [v] for k, v in unbatched}\r\n\r\n return self.map(batch, batched=True, batch_size=batch_size, drop_last_batch=drop_last_batch)\r\n```\r\n\r\nAnd this way no need to reimplement everything !\r\n\r\n(my only small concern is that it's not an Arrow-optimized function so it requires the examples to be manipulated as python objects even if the original data is in Arrow format (e.g. when streaming Parquet files) but it's not a big deal and we can see later if we need to optimize this)",
"Thanks a lot for the feedback @lhoestq! I definitely could have saved some time looking into it properly first. 😅 \r\n\r\nImplemented the `.batch()` method, added a proper docsrtring for documentation, and added tests.\r\n\r\nLet me know what you think and if this needs some update.",
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7054). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"Thanks for the feedbak @lhoestq!\r\n\r\nApplied it and referenced the `batched=True` option in the `map` function and highlighted the difference. Hope i got this right.",
"<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) | 0.005181 / 0.011353 (-0.006172) | 0.003714 / 0.011008 (-0.007294) | 0.063060 / 0.038508 (0.024552) | 0.030885 / 0.023109 (0.007776) | 0.239060 / 0.275898 (-0.036838) | 0.262480 / 0.323480 (-0.061000) | 0.004103 / 0.007986 (-0.003883) | 0.002696 / 0.004328 (-0.001632) | 0.048706 / 0.004250 (0.044456) | 0.042577 / 0.037052 (0.005525) | 0.249928 / 0.258489 (-0.008561) | 0.283252 / 0.293841 (-0.010589) | 0.029304 / 0.128546 (-0.099242) | 0.012001 / 0.075646 (-0.063646) | 0.204467 / 0.419271 (-0.214804) | 0.035639 / 0.043533 (-0.007894) | 0.243850 / 0.255139 (-0.011289) | 0.261609 / 0.283200 (-0.021590) | 0.018302 / 0.141683 (-0.123381) | 1.096040 / 1.452155 (-0.356115) | 1.135917 / 1.492716 (-0.356800) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091976 / 0.018006 (0.073970) | 0.296396 / 0.000490 (0.295906) | 0.000203 / 0.000200 (0.000003) | 0.000043 / 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) | 0.018405 / 0.037411 (-0.019007) | 0.062470 / 0.014526 (0.047944) | 0.073340 / 0.176557 (-0.103216) | 0.119474 / 0.737135 (-0.617661) | 0.075750 / 0.296338 (-0.220588) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.279586 / 0.215209 (0.064377) | 2.768542 / 2.077655 (0.690887) | 1.449158 / 1.504120 (-0.054962) | 1.328760 / 1.541195 (-0.212435) | 1.336338 / 1.468490 (-0.132152) | 0.732582 / 4.584777 (-3.852195) | 2.325558 / 3.745712 (-1.420154) | 2.898077 / 5.269862 (-2.371784) | 1.893107 / 4.565676 (-2.672569) | 0.078788 / 0.424275 (-0.345487) | 0.005273 / 0.007607 (-0.002335) | 0.334887 / 0.226044 (0.108842) | 3.304173 / 2.268929 (1.035244) | 1.834743 / 55.444624 (-53.609882) | 1.527463 / 6.876477 (-5.349014) | 1.538824 / 2.142072 (-0.603249) | 0.785646 / 4.805227 (-4.019581) | 0.134876 / 6.500664 (-6.365788) | 0.042894 / 0.075469 (-0.032575) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.976635 / 1.841788 (-0.865152) | 11.217156 / 8.074308 (3.142848) | 9.616971 / 10.191392 (-0.574421) | 0.127276 / 0.680424 (-0.553148) | 0.014344 / 0.534201 (-0.519857) | 0.301896 / 0.579283 (-0.277387) | 0.259615 / 0.434364 (-0.174749) | 0.340693 / 0.540337 (-0.199645) | 0.429145 / 1.386936 (-0.957791) |\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) | 0.005534 / 0.011353 (-0.005819) | 0.003795 / 0.011008 (-0.007213) | 0.049761 / 0.038508 (0.011253) | 0.031311 / 0.023109 (0.008202) | 0.276032 / 0.275898 (0.000134) | 0.297316 / 0.323480 (-0.026164) | 0.004396 / 0.007986 (-0.003590) | 0.002693 / 0.004328 (-0.001635) | 0.049025 / 0.004250 (0.044775) | 0.039707 / 0.037052 (0.002654) | 0.284264 / 0.258489 (0.025775) | 0.319962 / 0.293841 (0.026121) | 0.031842 / 0.128546 (-0.096705) | 0.012192 / 0.075646 (-0.063454) | 0.059895 / 0.419271 (-0.359376) | 0.033676 / 0.043533 (-0.009856) | 0.275917 / 0.255139 (0.020778) | 0.292637 / 0.283200 (0.009437) | 0.017992 / 0.141683 (-0.123691) | 1.199329 / 1.452155 (-0.252826) | 1.259083 / 1.492716 (-0.233633) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092770 / 0.018006 (0.074764) | 0.313363 / 0.000490 (0.312873) | 0.000212 / 0.000200 (0.000013) | 0.000052 / 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) | 0.022977 / 0.037411 (-0.014434) | 0.076839 / 0.014526 (0.062314) | 0.088289 / 0.176557 (-0.088267) | 0.128625 / 0.737135 (-0.608510) | 0.089348 / 0.296338 (-0.206990) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.300881 / 0.215209 (0.085672) | 2.946499 / 2.077655 (0.868845) | 1.599686 / 1.504120 (0.095566) | 1.479332 / 1.541195 (-0.061862) | 1.476910 / 1.468490 (0.008420) | 0.720536 / 4.584777 (-3.864241) | 0.944822 / 3.745712 (-2.800890) | 2.771864 / 5.269862 (-2.497998) | 1.886573 / 4.565676 (-2.679103) | 0.078462 / 0.424275 (-0.345813) | 0.005392 / 0.007607 (-0.002215) | 0.354984 / 0.226044 (0.128939) | 3.516449 / 2.268929 (1.247520) | 1.977033 / 55.444624 (-53.467592) | 1.671922 / 6.876477 (-5.204555) | 1.785755 / 2.142072 (-0.356318) | 0.795330 / 4.805227 (-4.009897) | 0.132895 / 6.500664 (-6.367769) | 0.041178 / 0.075469 (-0.034291) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.031780 / 1.841788 (-0.810008) | 11.855600 / 8.074308 (3.781292) | 10.245599 / 10.191392 (0.054207) | 0.140649 / 0.680424 (-0.539775) | 0.015332 / 0.534201 (-0.518869) | 0.299402 / 0.579283 (-0.279881) | 0.120007 / 0.434364 (-0.314357) | 0.337770 / 0.540337 (-0.202568) | 0.433679 / 1.386936 (-0.953257) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e83d6fa574710fcb44e341087239d2687183f62b \"CML watermark\")\n"
] | "2024-07-19T10:11:47Z" | "2024-07-23T13:25:13Z" | "2024-07-23T10:34:28Z" | CONTRIBUTOR | null | I've taken a try at implementing a batched `IterableDataset` as requested in issue #6279. This PR adds a new `BatchedExamplesIterable` class and a `.batch()` method to the `IterableDataset` class.
The main changes are:
1. A new `BatchedExamplesIterable` that groups examples into batches.
2. A `.batch()` method for `IterableDataset` to easily create batched versions.
3. Support for shuffling and sharding to work with PyTorch DataLoader and multiple workers.
I'm not sure if this is exactly what you had in mind and also have not fully tested it atm, so I'd really appreciate your feedback. Does this seem like it's heading in the right direction? I'm happy to make any changes or explore different approaches if needed.
Pinging @lhoestq | {
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"Hi,\r\n\r\nThis issue was fixed in `datasets` 2.15.0:\r\n- #6105\r\n\r\nYou will need to update your `datasets`:\r\n```\r\npip install -U datasets\r\n```",
"Duplicate of:\r\n- #6100"
] | "2024-07-18T13:42:35Z" | "2024-07-18T15:17:42Z" | "2024-07-18T15:16:18Z" | NONE | null | ### Describe the bug
in data_files.py, line 332,
`fs, _, _ = get_fs_token_paths(pattern, storage_options=storage_options)`
If we run the code on AWS, as fs.protocol will be a tuple like: `('file', 'local')`
So, `isinstance(fs.protocol, str) == False` and
`protocol_prefix = fs.protocol + "://" if fs.protocol != "file" else ""` will raise
`TypeError: can only concatenate tuple (not "str") to tuple`.
### Steps to reproduce the bug
Steps to reproduce:
1. Run on a cloud server like AWS,
2. `import datasets.data_files as datafile`
3. datafile.resolve_pattern('path/to/dataset', '.')
4. `TypeError: can only concatenate tuple (not "str") to tuple`
### Expected behavior
Should return path of the dataset, with fs.protocol at the beginning
### Environment info
- `datasets` version: 2.14.0
- Platform: Linux-3.10.0-1160.119.1.el7.x86_64-x86_64-with-glibc2.17
- Python version: 3.8.19
- Huggingface_hub version: 0.23.5
- PyArrow version: 16.1.0
- Pandas version: 1.1.5 | {
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https://api.github.com/repos/huggingface/datasets/issues/7052 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7052/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7052/comments | https://api.github.com/repos/huggingface/datasets/issues/7052/events | https://github.com/huggingface/datasets/pull/7052 | 2,411,682,730 | PR_kwDODunzps51iuop | 7,052 | Adding `Music` feature for symbolic music modality (MIDI, abc) | {
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} | [] | closed | false | null | [] | null | [] | "2024-07-16T17:26:04Z" | "2024-07-29T06:47:55Z" | "2024-07-29T06:47:55Z" | NONE | null | ⚠️ (WIP) ⚠️
### What this PR does
This PR adds a `Music` feature for the symbolic music modality, in particular [MIDI](https://en.wikipedia.org/wiki/Musical_Instrument_Digital_Interface) and [abc](https://en.wikipedia.org/wiki/ABC_notation) files.
### Motivations
These two file formats are widely used in the [Music Information Retrieval (MIR)](https://en.wikipedia.org/wiki/Music_information_retrieval) for tasks such as music generation, music transcription, music synthesis or music transcription. Having a dedicated feature in the datasets library would allow to both encourage researchers to share datasets of this modality as well as making them more easily usable for end users, benefitting from the perks of the library.
These file formats are supported by [symusic](https://github.com/Yikai-Liao/symusic), a lightweight Python library with C bindings (using nanobind) allowing to efficiently read, write and manipulate them. The library is actively developed, and can in the future also implement other file formats such as [musicXML](https://en.wikipedia.org/wiki/MusicXML). As such, this PR relies on it.
The music data can then easily be tokenized with appropriate tokenizers such as [MidiTok](https://github.com/Natooz/MidiTok) or converted to pianorolls matrices by symusic.
**Jul 16th 2024:**
* the tests for the `Music` feature are currently failing due to non-supported access to the LazyBatch in `test_dataset_with_music_feature_map` and `test_dataset_with_music_feature_map_resample_music` (see TODOs). I am a beginner with pyArrow, I'll take any advice to make this work;
* additional tests including the `Music` feature with parquet and WebDataset should be implemented. As of right now, I am waiting for your feedback before taking further steps;
* a `MusicFolder` should also be implemented to comply with the usages of the `Image` and `Audio` features, waiting for your feedback too.
CCing @lhoestq and @albertvillanova | {
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https://api.github.com/repos/huggingface/datasets/issues/7051 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7051/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7051/comments | https://api.github.com/repos/huggingface/datasets/issues/7051/events | https://github.com/huggingface/datasets/issues/7051 | 2,409,353,929 | I_kwDODunzps6Pm9LJ | 7,051 | How to set_epoch with interleave_datasets? | {
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"This is not possible right now afaik :/\r\n\r\nMaybe we could have something like this ? wdyt ?\r\n\r\n```python\r\nds = interleave_datasets(\r\n [shuffled_dataset_a, dataset_b],\r\n probabilities=probabilities,\r\n stopping_strategy='all_exhausted',\r\n reshuffle_each_iteration=True,\r\n)",
"That would be helpful for this case! \r\n\r\nIf there was some way for from_generator to iterate over just a single shard of some dataset that would probably be more ideal. Maybe something like\r\n\r\n```\r\ndef from_dataset_generator(dataset, generator_fn, gen_kwargs):\r\n # calls generator_fn(dataset=dataset_shard, **gen_kwargs)\r\n```\r\n\r\nAnother transform I was trying to implement is an input bucketing transform. Essentially you need to iterate through a dataset and reorder the examples in them, which is not really possible with a `map()` call. But using `from_generator()` causes the final dataset to be a single shard and loses speed gains from multiple dataloader workers",
"I see, there are some internal functions to get a single shard already but the public `.shard()` method hasn't been implemented yet for `IterableDataset` :/\r\n\r\n(see the use of `ex_iterable.shard_data_sources` in `IterableDataset._prepare_ex_iterable_for_iteration` for example)",
"Would that be something planned on the roadmap for the near future, or do you suggest hacking through with internal APIs for now?",
"Ok this turned out to be not too difficult. Are there any obvious issues with my implementation?\r\n\r\n```\r\nclass ShuffleEveryEpochIterable(iterable_dataset._BaseExamplesIterable):\r\n \"\"\"ExamplesIterable that reshuffles the dataset every epoch.\"\"\"\r\n\r\n def __init__(\r\n self,\r\n ex_iterable: iterable_dataset._BaseExamplesIterable,\r\n generator: np.random.Generator,\r\n ):\r\n \"\"\"Constructor.\"\"\"\r\n super().__init__()\r\n self.ex_iterable = ex_iterable\r\n self.generator = generator\r\n\r\n def _init_state_dict(self) -> dict:\r\n self._state_dict = {\r\n 'ex_iterable': self.ex_iterable._init_state_dict(),\r\n 'epoch': 0,\r\n }\r\n return self._state_dict\r\n\r\n @typing.override\r\n def __iter__(self):\r\n epoch = self._state_dict['epoch'] if self._state_dict else 0\r\n for i in itertools.count(epoch):\r\n # Create effective seed using i (subtract in order to avoir overflow in long_scalars)\r\n effective_seed = copy.deepcopy(self.generator).integers(0, 1 << 63) - i\r\n effective_seed = (1 << 63) + effective_seed if effective_seed < 0 else effective_seed\r\n generator = np.random.default_rng(effective_seed)\r\n self.ex_iterable = self.ex_iterable.shuffle_data_sources(generator)\r\n if self._state_dict:\r\n self._state_dict['epoch'] = i\r\n self._state_dict['ex_iterable'] = self.ex_iterable._init_state_dict()\r\n it = iter(self.ex_iterable)\r\n yield from it\r\n\r\n @typing.override\r\n def shuffle_data_sources(self, generator):\r\n ex_iterable = self.ex_iterable.shuffle_data_sources(generator)\r\n return ShuffleEveryEpochIterable(ex_iterable, generator=generator)\r\n\r\n @typing.override\r\n def shard_data_sources(self, worker_id: int, num_workers: int):\r\n ex_iterable = self.ex_iterable.shard_data_sources(worker_id, num_workers)\r\n return ShuffleEveryEpochIterable(ex_iterable, generator=self.generator)\r\n\r\n @typing.override\r\n @property\r\n def n_shards(self) -> int:\r\n return self.ex_iterable.n_shards\r\n \r\ngenerator = np.random.default_rng(seed)\r\nshuffling = iterable_dataset.ShufflingConfig(generator=generator, _original_seed=seed)\r\nex_iterable = iterable_dataset.BufferShuffledExamplesIterable(\r\n dataset._ex_iterable, buffer_size=buffer_size, generator=generator\r\n)\r\nex_iterable = ShuffleEveryEpochIterable(ex_iterable, generator=generator)\r\ndataset = datasets.IterableDataset(\r\n ex_iterable=ex_iterable,\r\n info=dataset._info.copy(),\r\n split=dataset._split,\r\n formatting=dataset._formatting,\r\n shuffling=shuffling,\r\n distributed=copy.deepcopy(dataset._distributed),\r\n token_per_repo_id=dataset._token_per_repo_id,\r\n)\r\n```\r\n",
"Nice ! This iterable is infinite though no ? How would `interleave_dataset` know when to stop ?\r\n\r\nMaybe the re-shuffling can be implemented directly in `RandomlyCyclingMultiSourcesExamplesIterable` (which is the iterable used by `interleave_dataset`) ?",
"Infinite is fine for my usecases fortunately."
] | "2024-07-15T18:24:52Z" | "2024-08-05T20:58:04Z" | "2024-08-05T20:58:04Z" | NONE | null | Let's say I have dataset A which has 100k examples, and dataset B which has 100m examples.
I want to train on an interleaved dataset of A+B, with stopping_strategy='all_exhausted' so dataset B doesn't repeat any examples. But every time A is exhausted I want it to be reshuffled (eg. calling set_epoch)
Of course I want to interleave as IterableDatasets / streaming mode so B doesn't have to get tokenized completely at the start.
How could I achieve this? I was thinking something like, if I wrap dataset A in some new IterableDataset with from_generator() and manually call set_epoch before interleaving it? But I'm not sure how to keep the number of shards in that dataset...
Something like
```
dataset_a = load_dataset(...)
dataset_b = load_dataset(...)
def epoch_shuffled_dataset(ds):
# How to make this maintain the number of shards in ds??
for epoch in itertools.count():
ds.set_epoch(epoch)
yield from iter(ds)
shuffled_dataset_a = IterableDataset.from_generator(epoch_shuffled_dataset, gen_kwargs={'ds': dataset_a})
interleaved = interleave_datasets([shuffled_dataset_a, dataset_b], probs, stopping_strategy='all_exhausted')
``` | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7050). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<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) | 0.005707 / 0.011353 (-0.005646) | 0.004381 / 0.011008 (-0.006627) | 0.063711 / 0.038508 (0.025202) | 0.031882 / 0.023109 (0.008772) | 0.250056 / 0.275898 (-0.025842) | 0.287616 / 0.323480 (-0.035863) | 0.003327 / 0.007986 (-0.004658) | 0.003717 / 0.004328 (-0.000611) | 0.049103 / 0.004250 (0.044853) | 0.048821 / 0.037052 (0.011769) | 0.259688 / 0.258489 (0.001199) | 0.311469 / 0.293841 (0.017628) | 0.030667 / 0.128546 (-0.097879) | 0.013091 / 0.075646 (-0.062555) | 0.204737 / 0.419271 (-0.214534) | 0.038312 / 0.043533 (-0.005221) | 0.250055 / 0.255139 (-0.005084) | 0.272199 / 0.283200 (-0.011001) | 0.021161 / 0.141683 (-0.120522) | 1.116095 / 1.452155 (-0.336060) | 1.153588 / 1.492716 (-0.339129) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.107828 / 0.018006 (0.089822) | 0.315898 / 0.000490 (0.315408) | 0.000228 / 0.000200 (0.000028) | 0.000048 / 0.000054 (-0.000006) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018873 / 0.037411 (-0.018539) | 0.063374 / 0.014526 (0.048848) | 0.076424 / 0.176557 (-0.100133) | 0.123468 / 0.737135 (-0.613667) | 0.077432 / 0.296338 (-0.218906) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.288931 / 0.215209 (0.073722) | 2.828745 / 2.077655 (0.751091) | 1.471061 / 1.504120 (-0.033059) | 1.332289 / 1.541195 (-0.208906) | 1.379797 / 1.468490 (-0.088693) | 0.708053 / 4.584777 (-3.876724) | 2.382431 / 3.745712 (-1.363281) | 2.952672 / 5.269862 (-2.317190) | 1.957517 / 4.565676 (-2.608160) | 0.078730 / 0.424275 (-0.345546) | 0.005093 / 0.007607 (-0.002514) | 0.338147 / 0.226044 (0.112102) | 3.340841 / 2.268929 (1.071912) | 1.857083 / 55.444624 (-53.587541) | 1.533659 / 6.876477 (-5.342818) | 1.750549 / 2.142072 (-0.391523) | 0.804125 / 4.805227 (-4.001103) | 0.134618 / 6.500664 (-6.366046) | 0.042517 / 0.075469 (-0.032952) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.968608 / 1.841788 (-0.873180) | 12.326994 / 8.074308 (4.252686) | 9.464889 / 10.191392 (-0.726503) | 0.143979 / 0.680424 (-0.536445) | 0.014577 / 0.534201 (-0.519624) | 0.303205 / 0.579283 (-0.276078) | 0.269866 / 0.434364 (-0.164498) | 0.344846 / 0.540337 (-0.195491) | 0.443794 / 1.386936 (-0.943142) |\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) | 0.006452 / 0.011353 (-0.004900) | 0.004264 / 0.011008 (-0.006745) | 0.051355 / 0.038508 (0.012847) | 0.035188 / 0.023109 (0.012079) | 0.267697 / 0.275898 (-0.008201) | 0.295853 / 0.323480 (-0.027627) | 0.004611 / 0.007986 (-0.003374) | 0.005395 / 0.004328 (0.001066) | 0.049903 / 0.004250 (0.045652) | 0.044582 / 0.037052 (0.007530) | 0.284706 / 0.258489 (0.026217) | 0.321623 / 0.293841 (0.027782) | 0.033228 / 0.128546 (-0.095318) | 0.013077 / 0.075646 (-0.062569) | 0.061867 / 0.419271 (-0.357405) | 0.034625 / 0.043533 (-0.008908) | 0.269088 / 0.255139 (0.013949) | 0.284899 / 0.283200 (0.001699) | 0.019972 / 0.141683 (-0.121710) | 1.157976 / 1.452155 (-0.294178) | 1.181658 / 1.492716 (-0.311058) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.111072 / 0.018006 (0.093066) | 0.333310 / 0.000490 (0.332820) | 0.000251 / 0.000200 (0.000051) | 0.000059 / 0.000054 (0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023760 / 0.037411 (-0.013652) | 0.080746 / 0.014526 (0.066221) | 0.090231 / 0.176557 (-0.086326) | 0.132200 / 0.737135 (-0.604936) | 0.095679 / 0.296338 (-0.200660) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.297404 / 0.215209 (0.082195) | 2.919779 / 2.077655 (0.842124) | 1.577470 / 1.504120 (0.073350) | 1.452924 / 1.541195 (-0.088271) | 1.523683 / 1.468490 (0.055193) | 0.743801 / 4.584777 (-3.840976) | 1.006944 / 3.745712 (-2.738768) | 3.218161 / 5.269862 (-2.051701) | 2.069762 / 4.565676 (-2.495914) | 0.082900 / 0.424275 (-0.341375) | 0.005239 / 0.007607 (-0.002368) | 0.360124 / 0.226044 (0.134080) | 3.505349 / 2.268929 (1.236420) | 1.959324 / 55.444624 (-53.485300) | 1.663782 / 6.876477 (-5.212694) | 1.725745 / 2.142072 (-0.416327) | 0.825268 / 4.805227 (-3.979959) | 0.138577 / 6.500664 (-6.362087) | 0.042716 / 0.075469 (-0.032753) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.021138 / 1.841788 (-0.820650) | 13.907954 / 8.074308 (5.833646) | 11.023796 / 10.191392 (0.832404) | 0.135224 / 0.680424 (-0.545200) | 0.016232 / 0.534201 (-0.517969) | 0.330389 / 0.579283 (-0.248894) | 0.131702 / 0.434364 (-0.302662) | 0.372499 / 0.540337 (-0.167838) | 0.472702 / 1.386936 (-0.914234) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#87f4c2088854ff33e817e724e75179e9975c1b02 \"CML watermark\")\n"
] | "2024-07-15T15:38:04Z" | "2024-07-15T16:06:15Z" | "2024-07-15T15:59:56Z" | MEMBER | null | (minor) just to make it more prominent in the docs page for the soon-to-be-released new torchdata | {
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https://api.github.com/repos/huggingface/datasets/issues/7049 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7049/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7049/comments | https://api.github.com/repos/huggingface/datasets/issues/7049/events | https://github.com/huggingface/datasets/issues/7049 | 2,408,514,366 | I_kwDODunzps6PjwM- | 7,049 | Save nparray as list | {
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"In addition, when I use `set_format ` and index the ds, the following error occurs:\r\nthe code\r\n```python\r\nds.set_format(type=\"np\", colums=\"pixel_values\")\r\n```\r\nerror\r\n<img width=\"918\" alt=\"image\" src=\"https://github.com/user-attachments/assets/b28bbff2-20ea-4d28-ab62-b4ed2d944996\">\r\n",
"> Some people use the set_format function to convert the column back, but doesn't this lose precision?\r\n\r\nUnder the hood the data is saved in Arrow format using the same precision as your numpy arrays?\r\nBy default the Arrow data is read as python lists, but you can indeed read them back as numpy arrays with the same precision",
"(you can fix your second issue by fixing the typo `colums` -> `columns`)",
"> (you can fix your second issue by fixing the typo `colums` -> `columns`)\r\n\r\nYou are right, I was careless. Thank you.",
"> > Some people use the set_format function to convert the column back, but doesn't this lose precision?\r\n> \r\n> Under the hood the data is saved in Arrow format using the same precision as your numpy arrays? By default the Arrow data is read as python lists, but you can indeed read them back as numpy arrays with the same precision\r\n\r\nYes, after testing I found that there was no loss of precision. Thanks again for your answer."
] | "2024-07-15T11:36:11Z" | "2024-07-18T11:33:34Z" | "2024-07-18T11:33:34Z" | NONE | null | ### Describe the bug
When I use the `map` function to convert images into features, datasets saves nparray as a list. Some people use the `set_format` function to convert the column back, but doesn't this lose precision?
### Steps to reproduce the bug
the map function
```python
def convert_image_to_features(inst, processor, image_dir):
image_file = inst["image_url"]
file = image_file.split("/")[-1]
image_path = os.path.join(image_dir, file)
image = Image.open(image_path)
image = image.convert("RGBA")
inst["pixel_values"] = processor(images=image, return_tensors="np")["pixel_values"]
return inst
```
main function
```python
map_fun = partial(
convert_image_to_features, processor=processor, image_dir=image_dir
)
ds = ds.map(map_fun, batched=False, num_proc=20)
print(type(ds[0]["pixel_values"])
```
### Expected behavior
(type < list>)
### Environment info
- `datasets` version: 2.16.1
- Platform: Linux-4.19.91-009.ali4000.alios7.x86_64-x86_64-with-glibc2.35
- Python version: 3.11.5
- `huggingface_hub` version: 0.23.4
- PyArrow version: 14.0.2
- Pandas version: 2.1.4
- `fsspec` version: 2023.10.0 | {
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https://api.github.com/repos/huggingface/datasets/issues/7048 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7048/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7048/comments | https://api.github.com/repos/huggingface/datasets/issues/7048/events | https://github.com/huggingface/datasets/issues/7048 | 2,408,487,547 | I_kwDODunzps6Pjpp7 | 7,048 | ImportError: numpy.core.multiarray when using `filter` | {
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"Could you please check your `numpy` version?",
"I got this issue while using numpy version 2.0. \r\n\r\nI solved it by switching back to numpy 1.26.0 :) ",
"We recently added support for numpy 2.0, but it is not released yet.",
"Ok I see, thanks! I think we can close this issue for now as switching back to version 1.26.0 solves the problem :) "
] | "2024-07-15T11:21:04Z" | "2024-07-16T10:11:25Z" | "2024-07-16T10:11:25Z" | NONE | null | ### Describe the bug
I can't apply the filter method on my dataset.
### Steps to reproduce the bug
The following snippet generates a bug:
```python
from datasets import load_dataset
ami = load_dataset('kamilakesbi/ami', 'ihm')
ami['train'].filter(
lambda example: example["file_name"] == 'EN2001a'
)
```
I get the following error:
`ImportError: numpy.core.multiarray failed to import (auto-generated because you didn't call 'numpy.import_array()' after cimporting numpy; use '<void>numpy._import_array' to disable if you are certain you don't need it).`
### Expected behavior
It should work properly!
### Environment info
- `datasets` version: 2.20.0
- Platform: Linux-5.15.0-67-generic-x86_64-with-glibc2.35
- Python version: 3.10.6
- `huggingface_hub` version: 0.23.4
- PyArrow version: 16.1.0
- Pandas version: 2.2.2
- `fsspec` version: 2024.5.0 | {
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https://api.github.com/repos/huggingface/datasets/issues/7047 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7047/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7047/comments | https://api.github.com/repos/huggingface/datasets/issues/7047/events | https://github.com/huggingface/datasets/issues/7047 | 2,406,495,084 | I_kwDODunzps6PcDNs | 7,047 | Save Dataset as Sharded Parquet | {
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"To anyone else who finds themselves in this predicament, it's possible to read the parquet file in the same way that datasets writes it, and then manually break it into pieces. Although, you need a couple of magic options (`thrift_*`) to deal with the huge metadata, otherwise pyarrow immediately crashes.\r\n```python\r\nimport pyarrow.parquet as pq\r\nimport pyarrow as pa\r\n\r\nr = pq.ParquetReader()\r\n\r\nr.open(\"./outrageous-file.parquet\",thrift_string_size_limit=2**31-1, thrift_container_size_limit=2**31-1)\r\n\r\nfrom more_itertools import chunked\r\nimport tqdm\r\n\r\nfor i,chunk in tqdm.tqdm(enumerate(chunked(range(r.num_row_groups),10000))):\r\n w = pq.ParquetWriter(f\"./chunks.parquet/chunk{i}.parquet\",schema=r.schema_arrow)\r\n for idx in chunk:\r\n w.write_table(r.read_row_group(idx))\r\n w.close()\r\n```",
"You can also use `.shard()` and call `to_parquet()` on each shard in the meantime:\r\n\r\n```python\r\nnum_shards = 128\r\noutput_path_template = \"output_dir/{index:05d}.parquet\"\r\nfor index in range(num_shards):\r\n shard = ds.shard(index=index, num_shards=num_shards, contiguous=True)\r\n shard.to_parquet(output_path_template.format(index=index))\r\n```"
] | "2024-07-12T23:47:51Z" | "2024-07-17T12:07:08Z" | null | NONE | null | ### Feature request
`to_parquet` currently saves the dataset as one massive, monolithic parquet file, rather than as several small parquet files. It should shard large datasets automatically.
### Motivation
This default behavior makes me very sad because a program I ran for 6 hours saved its results using `to_parquet`, putting the entire billion+ row dataset into a 171 GB *single shard parquet file* which pyarrow, apache spark, etc. all cannot work with without completely exhausting the memory of my system. I was previously able to work with larger-than-memory parquet files, but not this one. I *assume* the reason why this is happening is because it is a single shard. Making sharding the default behavior puts datasets in parity with other frameworks, such as spark, which automatically shard when a large dataset is saved as parquet.
### Your contribution
I could change the logic here https://github.com/huggingface/datasets/blob/bf6f41e94d9b2f1c620cf937a2e85e5754a8b960/src/datasets/io/parquet.py#L109-L158
to use `pyarrow.dataset.write_dataset`, which seems to support sharding, or periodically open new files. We would only shard if the user passed in a path rather than file handle. | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7046 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7046/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7046/comments | https://api.github.com/repos/huggingface/datasets/issues/7046/events | https://github.com/huggingface/datasets/pull/7046 | 2,405,485,582 | PR_kwDODunzps51N05n | 7,046 | Support librosa and numpy 2.0 for Python 3.10 | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7046). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<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) | 0.005897 / 0.011353 (-0.005456) | 0.003958 / 0.011008 (-0.007050) | 0.063684 / 0.038508 (0.025176) | 0.031743 / 0.023109 (0.008634) | 0.246725 / 0.275898 (-0.029173) | 0.275519 / 0.323480 (-0.047961) | 0.003347 / 0.007986 (-0.004639) | 0.004089 / 0.004328 (-0.000240) | 0.049591 / 0.004250 (0.045341) | 0.049386 / 0.037052 (0.012333) | 0.264929 / 0.258489 (0.006440) | 0.317157 / 0.293841 (0.023316) | 0.029929 / 0.128546 (-0.098617) | 0.012264 / 0.075646 (-0.063382) | 0.209208 / 0.419271 (-0.210064) | 0.037073 / 0.043533 (-0.006460) | 0.247999 / 0.255139 (-0.007140) | 0.273457 / 0.283200 (-0.009742) | 0.020354 / 0.141683 (-0.121328) | 1.109874 / 1.452155 (-0.342281) | 1.180085 / 1.492716 (-0.312631) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.099935 / 0.018006 (0.081929) | 0.305607 / 0.000490 (0.305118) | 0.000214 / 0.000200 (0.000014) | 0.000044 / 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) | 0.020019 / 0.037411 (-0.017392) | 0.066608 / 0.014526 (0.052083) | 0.079354 / 0.176557 (-0.097202) | 0.123416 / 0.737135 (-0.613719) | 0.078171 / 0.296338 (-0.218167) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.281627 / 0.215209 (0.066418) | 2.809807 / 2.077655 (0.732152) | 1.467007 / 1.504120 (-0.037112) | 1.351367 / 1.541195 (-0.189828) | 1.396782 / 1.468490 (-0.071708) | 0.735605 / 4.584777 (-3.849172) | 2.378455 / 3.745712 (-1.367257) | 2.971739 / 5.269862 (-2.298122) | 2.004970 / 4.565676 (-2.560707) | 0.078156 / 0.424275 (-0.346119) | 0.005276 / 0.007607 (-0.002331) | 0.340370 / 0.226044 (0.114325) | 3.347552 / 2.268929 (1.078624) | 1.851098 / 55.444624 (-53.593527) | 1.518079 / 6.876477 (-5.358398) | 1.703145 / 2.142072 (-0.438927) | 0.799574 / 4.805227 (-4.005654) | 0.133591 / 6.500664 (-6.367074) | 0.043329 / 0.075469 (-0.032141) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.977268 / 1.841788 (-0.864520) | 12.720209 / 8.074308 (4.645901) | 9.798126 / 10.191392 (-0.393266) | 0.132106 / 0.680424 (-0.548318) | 0.014456 / 0.534201 (-0.519745) | 0.312965 / 0.579283 (-0.266318) | 0.271348 / 0.434364 (-0.163016) | 0.343951 / 0.540337 (-0.196386) | 0.449814 / 1.386936 (-0.937122) |\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) | 0.005944 / 0.011353 (-0.005409) | 0.004054 / 0.011008 (-0.006954) | 0.050573 / 0.038508 (0.012065) | 0.034580 / 0.023109 (0.011470) | 0.261439 / 0.275898 (-0.014459) | 0.286057 / 0.323480 (-0.037423) | 0.004463 / 0.007986 (-0.003523) | 0.002891 / 0.004328 (-0.001437) | 0.049169 / 0.004250 (0.044919) | 0.041622 / 0.037052 (0.004570) | 0.275216 / 0.258489 (0.016727) | 0.305847 / 0.293841 (0.012006) | 0.032615 / 0.128546 (-0.095932) | 0.012304 / 0.075646 (-0.063343) | 0.062890 / 0.419271 (-0.356382) | 0.033846 / 0.043533 (-0.009687) | 0.262758 / 0.255139 (0.007619) | 0.279451 / 0.283200 (-0.003748) | 0.018953 / 0.141683 (-0.122730) | 1.149158 / 1.452155 (-0.302997) | 1.173981 / 1.492716 (-0.318735) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.100462 / 0.018006 (0.082456) | 0.308390 / 0.000490 (0.307900) | 0.000207 / 0.000200 (0.000007) | 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) | 0.023089 / 0.037411 (-0.014322) | 0.078610 / 0.014526 (0.064084) | 0.090348 / 0.176557 (-0.086208) | 0.130784 / 0.737135 (-0.606351) | 0.092538 / 0.296338 (-0.203801) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.296255 / 0.215209 (0.081046) | 2.899159 / 2.077655 (0.821504) | 1.603524 / 1.504120 (0.099404) | 1.418002 / 1.541195 (-0.123192) | 1.470221 / 1.468490 (0.001731) | 0.722129 / 4.584777 (-3.862648) | 0.956146 / 3.745712 (-2.789566) | 3.011640 / 5.269862 (-2.258222) | 1.910966 / 4.565676 (-2.654711) | 0.078771 / 0.424275 (-0.345504) | 0.005154 / 0.007607 (-0.002453) | 0.354001 / 0.226044 (0.127956) | 3.484224 / 2.268929 (1.215296) | 1.913612 / 55.444624 (-53.531012) | 1.634492 / 6.876477 (-5.241985) | 1.693292 / 2.142072 (-0.448780) | 0.816837 / 4.805227 (-3.988390) | 0.136631 / 6.500664 (-6.364033) | 0.042291 / 0.075469 (-0.033178) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.994887 / 1.841788 (-0.846901) | 13.144865 / 8.074308 (5.070557) | 10.820098 / 10.191392 (0.628706) | 0.132557 / 0.680424 (-0.547867) | 0.015467 / 0.534201 (-0.518734) | 0.302026 / 0.579283 (-0.277257) | 0.128763 / 0.434364 (-0.305601) | 0.347908 / 0.540337 (-0.192430) | 0.444829 / 1.386936 (-0.942107) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#bf6f41e94d9b2f1c620cf937a2e85e5754a8b960 \"CML watermark\")\n"
] | "2024-07-12T12:42:47Z" | "2024-07-12T13:04:40Z" | "2024-07-12T12:58:17Z" | MEMBER | null | Support librosa and numpy 2.0 for Python 3.10 by installing soxr 0.4.0b1 pre-release:
- https://github.com/dofuuz/python-soxr/releases/tag/v0.4.0b1
- https://github.com/dofuuz/python-soxr/issues/28 | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7045). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<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) | 0.005426 / 0.011353 (-0.005927) | 0.003896 / 0.011008 (-0.007112) | 0.063492 / 0.038508 (0.024984) | 0.030199 / 0.023109 (0.007090) | 0.249892 / 0.275898 (-0.026006) | 0.291311 / 0.323480 (-0.032168) | 0.004389 / 0.007986 (-0.003597) | 0.002829 / 0.004328 (-0.001500) | 0.049685 / 0.004250 (0.045435) | 0.043351 / 0.037052 (0.006299) | 0.264265 / 0.258489 (0.005776) | 0.290463 / 0.293841 (-0.003378) | 0.030007 / 0.128546 (-0.098539) | 0.012146 / 0.075646 (-0.063500) | 0.203841 / 0.419271 (-0.215430) | 0.037159 / 0.043533 (-0.006373) | 0.253377 / 0.255139 (-0.001762) | 0.275990 / 0.283200 (-0.007209) | 0.018334 / 0.141683 (-0.123349) | 1.112616 / 1.452155 (-0.339539) | 1.157507 / 1.492716 (-0.335209) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.097781 / 0.018006 (0.079775) | 0.314381 / 0.000490 (0.313891) | 0.000217 / 0.000200 (0.000017) | 0.000043 / 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) | 0.018704 / 0.037411 (-0.018708) | 0.062293 / 0.014526 (0.047767) | 0.073997 / 0.176557 (-0.102559) | 0.120309 / 0.737135 (-0.616826) | 0.075592 / 0.296338 (-0.220747) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.283178 / 0.215209 (0.067969) | 2.798027 / 2.077655 (0.720372) | 1.431320 / 1.504120 (-0.072800) | 1.316135 / 1.541195 (-0.225060) | 1.345528 / 1.468490 (-0.122962) | 0.717300 / 4.584777 (-3.867477) | 2.401019 / 3.745712 (-1.344693) | 2.866411 / 5.269862 (-2.403451) | 1.933198 / 4.565676 (-2.632479) | 0.079505 / 0.424275 (-0.344771) | 0.005089 / 0.007607 (-0.002519) | 0.333614 / 0.226044 (0.107569) | 3.315449 / 2.268929 (1.046520) | 1.807667 / 55.444624 (-53.636957) | 1.490537 / 6.876477 (-5.385939) | 1.633305 / 2.142072 (-0.508767) | 0.807732 / 4.805227 (-3.997495) | 0.133825 / 6.500664 (-6.366839) | 0.041696 / 0.075469 (-0.033774) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.969063 / 1.841788 (-0.872724) | 11.825985 / 8.074308 (3.751677) | 9.808041 / 10.191392 (-0.383351) | 0.143338 / 0.680424 (-0.537085) | 0.014714 / 0.534201 (-0.519487) | 0.304360 / 0.579283 (-0.274923) | 0.266863 / 0.434364 (-0.167501) | 0.342374 / 0.540337 (-0.197963) | 0.442120 / 1.386936 (-0.944816) |\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) | 0.005574 / 0.011353 (-0.005778) | 0.003735 / 0.011008 (-0.007273) | 0.051021 / 0.038508 (0.012513) | 0.032825 / 0.023109 (0.009716) | 0.267775 / 0.275898 (-0.008123) | 0.286015 / 0.323480 (-0.037464) | 0.004332 / 0.007986 (-0.003653) | 0.002796 / 0.004328 (-0.001532) | 0.050183 / 0.004250 (0.045933) | 0.040191 / 0.037052 (0.003138) | 0.279777 / 0.258489 (0.021288) | 0.312161 / 0.293841 (0.018320) | 0.031993 / 0.128546 (-0.096553) | 0.012168 / 0.075646 (-0.063478) | 0.061622 / 0.419271 (-0.357650) | 0.033577 / 0.043533 (-0.009956) | 0.267300 / 0.255139 (0.012161) | 0.284595 / 0.283200 (0.001396) | 0.018476 / 0.141683 (-0.123207) | 1.135917 / 1.452155 (-0.316237) | 1.164516 / 1.492716 (-0.328200) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.108194 / 0.018006 (0.090188) | 0.309514 / 0.000490 (0.309025) | 0.000211 / 0.000200 (0.000011) | 0.000053 / 0.000054 (-0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022998 / 0.037411 (-0.014413) | 0.077126 / 0.014526 (0.062600) | 0.088779 / 0.176557 (-0.087778) | 0.128646 / 0.737135 (-0.608489) | 0.089895 / 0.296338 (-0.206443) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.295131 / 0.215209 (0.079922) | 2.887380 / 2.077655 (0.809726) | 1.586450 / 1.504120 (0.082330) | 1.449831 / 1.541195 (-0.091363) | 1.468805 / 1.468490 (0.000315) | 0.721578 / 4.584777 (-3.863199) | 0.970499 / 3.745712 (-2.775214) | 2.975604 / 5.269862 (-2.294258) | 1.935809 / 4.565676 (-2.629867) | 0.078504 / 0.424275 (-0.345771) | 0.005219 / 0.007607 (-0.002388) | 0.347168 / 0.226044 (0.121124) | 3.417040 / 2.268929 (1.148111) | 1.928707 / 55.444624 (-53.515917) | 1.629398 / 6.876477 (-5.247078) | 1.653014 / 2.142072 (-0.489058) | 0.796097 / 4.805227 (-4.009130) | 0.133956 / 6.500664 (-6.366708) | 0.041567 / 0.075469 (-0.033902) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.995511 / 1.841788 (-0.846277) | 12.577211 / 8.074308 (4.502903) | 10.562561 / 10.191392 (0.371169) | 0.144288 / 0.680424 (-0.536136) | 0.016345 / 0.534201 (-0.517856) | 0.304364 / 0.579283 (-0.274920) | 0.134630 / 0.434364 (-0.299734) | 0.341494 / 0.540337 (-0.198843) | 0.436238 / 1.386936 (-0.950698) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3b708bb6611a88c3f00f58ec3c63fe0da2c2b1e1 \"CML watermark\")\n"
] | "2024-07-12T12:20:23Z" | "2024-07-12T12:38:53Z" | "2024-07-12T12:33:00Z" | MEMBER | null | Fix tensorflow min version depending on Python version.
Related to:
- #6991 | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7044). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<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) | 0.005797 / 0.011353 (-0.005556) | 0.004017 / 0.011008 (-0.006991) | 0.063829 / 0.038508 (0.025321) | 0.031329 / 0.023109 (0.008220) | 0.249388 / 0.275898 (-0.026510) | 0.273129 / 0.323480 (-0.050351) | 0.004250 / 0.007986 (-0.003736) | 0.002821 / 0.004328 (-0.001507) | 0.049250 / 0.004250 (0.044999) | 0.046175 / 0.037052 (0.009123) | 0.252040 / 0.258489 (-0.006449) | 0.296537 / 0.293841 (0.002696) | 0.030579 / 0.128546 (-0.097967) | 0.012436 / 0.075646 (-0.063210) | 0.205829 / 0.419271 (-0.213443) | 0.036979 / 0.043533 (-0.006554) | 0.251354 / 0.255139 (-0.003785) | 0.272262 / 0.283200 (-0.010938) | 0.019047 / 0.141683 (-0.122636) | 1.112410 / 1.452155 (-0.339745) | 1.137445 / 1.492716 (-0.355271) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.097270 / 0.018006 (0.079264) | 0.309329 / 0.000490 (0.308839) | 0.000221 / 0.000200 (0.000021) | 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) | 0.019021 / 0.037411 (-0.018390) | 0.066801 / 0.014526 (0.052276) | 0.075280 / 0.176557 (-0.101276) | 0.122499 / 0.737135 (-0.614637) | 0.077424 / 0.296338 (-0.218914) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.279469 / 0.215209 (0.064259) | 2.787511 / 2.077655 (0.709856) | 1.411389 / 1.504120 (-0.092731) | 1.285796 / 1.541195 (-0.255399) | 1.354252 / 1.468490 (-0.114238) | 0.735341 / 4.584777 (-3.849436) | 2.418557 / 3.745712 (-1.327155) | 2.983406 / 5.269862 (-2.286455) | 2.005853 / 4.565676 (-2.559823) | 0.080440 / 0.424275 (-0.343835) | 0.005242 / 0.007607 (-0.002365) | 0.343557 / 0.226044 (0.117513) | 3.358984 / 2.268929 (1.090055) | 1.816709 / 55.444624 (-53.627915) | 1.500225 / 6.876477 (-5.376252) | 1.715405 / 2.142072 (-0.426667) | 0.829054 / 4.805227 (-3.976174) | 0.138352 / 6.500664 (-6.362312) | 0.043709 / 0.075469 (-0.031760) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.969135 / 1.841788 (-0.872652) | 12.510750 / 8.074308 (4.436442) | 10.140368 / 10.191392 (-0.051024) | 0.133117 / 0.680424 (-0.547307) | 0.015775 / 0.534201 (-0.518426) | 0.302203 / 0.579283 (-0.277080) | 0.268214 / 0.434364 (-0.166150) | 0.347041 / 0.540337 (-0.193296) | 0.456095 / 1.386936 (-0.930841) |\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) | 0.006255 / 0.011353 (-0.005098) | 0.004453 / 0.011008 (-0.006555) | 0.052298 / 0.038508 (0.013790) | 0.034808 / 0.023109 (0.011699) | 0.274723 / 0.275898 (-0.001175) | 0.297199 / 0.323480 (-0.026281) | 0.004499 / 0.007986 (-0.003486) | 0.003086 / 0.004328 (-0.001242) | 0.051315 / 0.004250 (0.047065) | 0.042764 / 0.037052 (0.005712) | 0.285636 / 0.258489 (0.027147) | 0.321819 / 0.293841 (0.027978) | 0.033350 / 0.128546 (-0.095196) | 0.013457 / 0.075646 (-0.062189) | 0.063930 / 0.419271 (-0.355342) | 0.034537 / 0.043533 (-0.008996) | 0.272630 / 0.255139 (0.017491) | 0.289245 / 0.283200 (0.006045) | 0.018910 / 0.141683 (-0.122773) | 1.153064 / 1.452155 (-0.299091) | 1.207065 / 1.492716 (-0.285651) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093008 / 0.018006 (0.075002) | 0.301313 / 0.000490 (0.300823) | 0.000214 / 0.000200 (0.000014) | 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) | 0.023168 / 0.037411 (-0.014244) | 0.080837 / 0.014526 (0.066312) | 0.089667 / 0.176557 (-0.086889) | 0.135849 / 0.737135 (-0.601286) | 0.092082 / 0.296338 (-0.204257) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.298933 / 0.215209 (0.083723) | 2.847736 / 2.077655 (0.770082) | 1.550268 / 1.504120 (0.046148) | 1.425675 / 1.541195 (-0.115520) | 1.469251 / 1.468490 (0.000761) | 0.720446 / 4.584777 (-3.864331) | 0.976149 / 3.745712 (-2.769563) | 3.081804 / 5.269862 (-2.188057) | 1.982797 / 4.565676 (-2.582880) | 0.078598 / 0.424275 (-0.345677) | 0.005229 / 0.007607 (-0.002379) | 0.345475 / 0.226044 (0.119430) | 3.421312 / 2.268929 (1.152384) | 1.929034 / 55.444624 (-53.515590) | 1.631523 / 6.876477 (-5.244953) | 1.671996 / 2.142072 (-0.470077) | 0.776916 / 4.805227 (-4.028311) | 0.133966 / 6.500664 (-6.366699) | 0.042183 / 0.075469 (-0.033286) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.993023 / 1.841788 (-0.848764) | 12.981642 / 8.074308 (4.907334) | 10.610457 / 10.191392 (0.419065) | 0.146748 / 0.680424 (-0.533676) | 0.016556 / 0.534201 (-0.517645) | 0.303613 / 0.579283 (-0.275670) | 0.132671 / 0.434364 (-0.301693) | 0.344786 / 0.540337 (-0.195552) | 0.443049 / 1.386936 (-0.943887) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8419c40a085d67eb5832cecebf3ef8213112857d \"CML watermark\")\n"
] | "2024-07-12T08:06:59Z" | "2024-07-12T09:06:32Z" | "2024-07-12T09:00:09Z" | MEMBER | null | Mark tests that require `librosa`.
Note that `librosa` is an optional dependency (installed with `audio` option) and we should be able to test environments without that library installed. This is the case if we want to test Numpy 2.0, which is currently incompatible with `librosa` due to its dependency on `soxr`:
- https://github.com/dofuuz/python-soxr/issues/28 | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7043). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<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) | 0.005147 / 0.011353 (-0.006205) | 0.003403 / 0.011008 (-0.007605) | 0.061367 / 0.038508 (0.022859) | 0.030295 / 0.023109 (0.007186) | 0.233503 / 0.275898 (-0.042395) | 0.252644 / 0.323480 (-0.070836) | 0.004072 / 0.007986 (-0.003913) | 0.002678 / 0.004328 (-0.001650) | 0.049099 / 0.004250 (0.044848) | 0.043032 / 0.037052 (0.005979) | 0.248823 / 0.258489 (-0.009666) | 0.274895 / 0.293841 (-0.018946) | 0.029307 / 0.128546 (-0.099239) | 0.011186 / 0.075646 (-0.064460) | 0.197142 / 0.419271 (-0.222129) | 0.035924 / 0.043533 (-0.007609) | 0.234728 / 0.255139 (-0.020411) | 0.252990 / 0.283200 (-0.030209) | 0.017589 / 0.141683 (-0.124094) | 1.108252 / 1.452155 (-0.343903) | 1.135949 / 1.492716 (-0.356767) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093096 / 0.018006 (0.075090) | 0.289284 / 0.000490 (0.288794) | 0.000208 / 0.000200 (0.000008) | 0.000038 / 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) | 0.017633 / 0.037411 (-0.019778) | 0.060621 / 0.014526 (0.046095) | 0.073194 / 0.176557 (-0.103363) | 0.120176 / 0.737135 (-0.616959) | 0.073575 / 0.296338 (-0.222764) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.277168 / 0.215209 (0.061959) | 2.689714 / 2.077655 (0.612060) | 1.427558 / 1.504120 (-0.076562) | 1.331350 / 1.541195 (-0.209844) | 1.353069 / 1.468490 (-0.115421) | 0.716657 / 4.584777 (-3.868120) | 2.321145 / 3.745712 (-1.424567) | 2.757986 / 5.269862 (-2.511876) | 1.851604 / 4.565676 (-2.714072) | 0.089530 / 0.424275 (-0.334745) | 0.004884 / 0.007607 (-0.002723) | 0.327859 / 0.226044 (0.101814) | 3.290749 / 2.268929 (1.021821) | 1.831090 / 55.444624 (-53.613535) | 1.509247 / 6.876477 (-5.367229) | 1.616545 / 2.142072 (-0.525527) | 0.775228 / 4.805227 (-4.029999) | 0.133794 / 6.500664 (-6.366870) | 0.040644 / 0.075469 (-0.034825) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.950816 / 1.841788 (-0.890972) | 11.109938 / 8.074308 (3.035630) | 9.560673 / 10.191392 (-0.630719) | 0.130685 / 0.680424 (-0.549738) | 0.014096 / 0.534201 (-0.520105) | 0.297222 / 0.579283 (-0.282061) | 0.262777 / 0.434364 (-0.171587) | 0.340983 / 0.540337 (-0.199355) | 0.426107 / 1.386936 (-0.960829) |\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) | 0.005547 / 0.011353 (-0.005806) | 0.003425 / 0.011008 (-0.007584) | 0.049791 / 0.038508 (0.011283) | 0.032660 / 0.023109 (0.009550) | 0.257640 / 0.275898 (-0.018258) | 0.283483 / 0.323480 (-0.039997) | 0.004330 / 0.007986 (-0.003655) | 0.002297 / 0.004328 (-0.002032) | 0.047999 / 0.004250 (0.043748) | 0.039875 / 0.037052 (0.002822) | 0.273300 / 0.258489 (0.014811) | 0.303384 / 0.293841 (0.009543) | 0.031696 / 0.128546 (-0.096851) | 0.011913 / 0.075646 (-0.063733) | 0.060330 / 0.419271 (-0.358942) | 0.033253 / 0.043533 (-0.010280) | 0.255378 / 0.255139 (0.000240) | 0.271647 / 0.283200 (-0.011553) | 0.018772 / 0.141683 (-0.122910) | 1.116079 / 1.452155 (-0.336075) | 1.165133 / 1.492716 (-0.327583) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094325 / 0.018006 (0.076319) | 0.297523 / 0.000490 (0.297034) | 0.000210 / 0.000200 (0.000011) | 0.000047 / 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) | 0.022485 / 0.037411 (-0.014926) | 0.073731 / 0.014526 (0.059205) | 0.089039 / 0.176557 (-0.087518) | 0.124035 / 0.737135 (-0.613101) | 0.088053 / 0.296338 (-0.208286) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.286676 / 0.215209 (0.071467) | 2.794678 / 2.077655 (0.717024) | 1.541401 / 1.504120 (0.037281) | 1.432928 / 1.541195 (-0.108267) | 1.454940 / 1.468490 (-0.013550) | 0.721779 / 4.584777 (-3.862998) | 0.956514 / 3.745712 (-2.789198) | 2.889533 / 5.269862 (-2.380329) | 1.863980 / 4.565676 (-2.701696) | 0.078366 / 0.424275 (-0.345909) | 0.005137 / 0.007607 (-0.002470) | 0.338835 / 0.226044 (0.112791) | 3.320921 / 2.268929 (1.051993) | 1.903654 / 55.444624 (-53.540970) | 1.615294 / 6.876477 (-5.261182) | 1.624777 / 2.142072 (-0.517295) | 0.792417 / 4.805227 (-4.012810) | 0.133321 / 6.500664 (-6.367343) | 0.040127 / 0.075469 (-0.035342) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.982357 / 1.841788 (-0.859430) | 11.585106 / 8.074308 (3.510798) | 9.991577 / 10.191392 (-0.199815) | 0.149292 / 0.680424 (-0.531131) | 0.015693 / 0.534201 (-0.518508) | 0.297416 / 0.579283 (-0.281867) | 0.118565 / 0.434364 (-0.315799) | 0.335640 / 0.540337 (-0.204697) | 0.429484 / 1.386936 (-0.957452) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3091d7608f20e182f21bb7d0b68be66c0798509a \"CML watermark\")\n"
] | "2024-07-12T07:35:23Z" | "2024-07-12T08:12:55Z" | "2024-07-12T08:07:10Z" | MEMBER | null | Add decorator as explicit test dependency.
We use `decorator` library in our CI test since PR:
- #4845
However we did not add it as an explicit test requirement, and we depended on it indirectly through other libraries' dependencies.
I discovered this while testing Numpy 2.0 and removing incompatible libraries. | {
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"<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) | 0.005135 / 0.011353 (-0.006218) | 0.003389 / 0.011008 (-0.007619) | 0.063053 / 0.038508 (0.024545) | 0.031597 / 0.023109 (0.008487) | 0.237519 / 0.275898 (-0.038379) | 0.263101 / 0.323480 (-0.060379) | 0.003109 / 0.007986 (-0.004877) | 0.002699 / 0.004328 (-0.001630) | 0.048611 / 0.004250 (0.044361) | 0.042937 / 0.037052 (0.005884) | 0.253760 / 0.258489 (-0.004729) | 0.275444 / 0.293841 (-0.018397) | 0.028952 / 0.128546 (-0.099594) | 0.011837 / 0.075646 (-0.063809) | 0.207620 / 0.419271 (-0.211651) | 0.035727 / 0.043533 (-0.007806) | 0.241770 / 0.255139 (-0.013369) | 0.270509 / 0.283200 (-0.012691) | 0.020709 / 0.141683 (-0.120974) | 1.135722 / 1.452155 (-0.316432) | 1.200355 / 1.492716 (-0.292361) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092555 / 0.018006 (0.074549) | 0.284719 / 0.000490 (0.284229) | 0.000210 / 0.000200 (0.000010) | 0.000049 / 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) | 0.018431 / 0.037411 (-0.018980) | 0.063618 / 0.014526 (0.049092) | 0.075371 / 0.176557 (-0.101185) | 0.120982 / 0.737135 (-0.616153) | 0.075718 / 0.296338 (-0.220620) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.279439 / 0.215209 (0.064230) | 2.722274 / 2.077655 (0.644619) | 1.442314 / 1.504120 (-0.061806) | 1.323166 / 1.541195 (-0.218029) | 1.339642 / 1.468490 (-0.128848) | 0.723451 / 4.584777 (-3.861326) | 2.334879 / 3.745712 (-1.410833) | 2.938745 / 5.269862 (-2.331116) | 1.867278 / 4.565676 (-2.698398) | 0.078704 / 0.424275 (-0.345571) | 0.005128 / 0.007607 (-0.002479) | 0.338634 / 0.226044 (0.112589) | 3.266239 / 2.268929 (0.997311) | 1.815276 / 55.444624 (-53.629349) | 1.487158 / 6.876477 (-5.389319) | 1.547550 / 2.142072 (-0.594522) | 0.804458 / 4.805227 (-4.000769) | 0.139186 / 6.500664 (-6.361479) | 0.042935 / 0.075469 (-0.032534) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.978223 / 1.841788 (-0.863564) | 11.350997 / 8.074308 (3.276689) | 10.082980 / 10.191392 (-0.108412) | 0.145067 / 0.680424 (-0.535357) | 0.014132 / 0.534201 (-0.520069) | 0.302162 / 0.579283 (-0.277121) | 0.264603 / 0.434364 (-0.169761) | 0.338466 / 0.540337 (-0.201871) | 0.427891 / 1.386936 (-0.959045) |\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) | 0.006078 / 0.011353 (-0.005275) | 0.004030 / 0.011008 (-0.006978) | 0.051646 / 0.038508 (0.013138) | 0.031263 / 0.023109 (0.008154) | 0.279437 / 0.275898 (0.003539) | 0.304489 / 0.323480 (-0.018991) | 0.004553 / 0.007986 (-0.003433) | 0.002869 / 0.004328 (-0.001459) | 0.050638 / 0.004250 (0.046387) | 0.041091 / 0.037052 (0.004038) | 0.290681 / 0.258489 (0.032192) | 0.332059 / 0.293841 (0.038218) | 0.033353 / 0.128546 (-0.095193) | 0.012506 / 0.075646 (-0.063141) | 0.061788 / 0.419271 (-0.357484) | 0.034150 / 0.043533 (-0.009382) | 0.278258 / 0.255139 (0.023119) | 0.298084 / 0.283200 (0.014885) | 0.019106 / 0.141683 (-0.122577) | 1.164475 / 1.452155 (-0.287679) | 1.204804 / 1.492716 (-0.287912) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.100053 / 0.018006 (0.082047) | 0.301255 / 0.000490 (0.300765) | 0.000220 / 0.000200 (0.000020) | 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) | 0.023536 / 0.037411 (-0.013876) | 0.078513 / 0.014526 (0.063987) | 0.090281 / 0.176557 (-0.086276) | 0.129607 / 0.737135 (-0.607528) | 0.090742 / 0.296338 (-0.205596) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.304082 / 0.215209 (0.088873) | 2.909401 / 2.077655 (0.831747) | 1.587210 / 1.504120 (0.083090) | 1.458713 / 1.541195 (-0.082482) | 1.472579 / 1.468490 (0.004089) | 0.716542 / 4.584777 (-3.868235) | 0.947557 / 3.745712 (-2.798155) | 2.908044 / 5.269862 (-2.361817) | 1.886382 / 4.565676 (-2.679294) | 0.078105 / 0.424275 (-0.346170) | 0.005802 / 0.007607 (-0.001805) | 0.357883 / 0.226044 (0.131839) | 3.490958 / 2.268929 (1.222029) | 1.946574 / 55.444624 (-53.498050) | 1.645167 / 6.876477 (-5.231310) | 1.649242 / 2.142072 (-0.492830) | 0.796864 / 4.805227 (-4.008363) | 0.134206 / 6.500664 (-6.366458) | 0.041439 / 0.075469 (-0.034030) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.012311 / 1.841788 (-0.829477) | 12.396967 / 8.074308 (4.322659) | 10.382494 / 10.191392 (0.191102) | 0.157395 / 0.680424 (-0.523029) | 0.015154 / 0.534201 (-0.519047) | 0.302209 / 0.579283 (-0.277074) | 0.127430 / 0.434364 (-0.306934) | 0.348933 / 0.540337 (-0.191404) | 0.442930 / 1.386936 (-0.944006) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#69d9f455c3c51625e6c9ffcade122313e9098f3c \"CML watermark\")\n"
] | "2024-07-12T03:49:54Z" | "2024-08-15T10:07:44Z" | "2024-08-15T10:01:59Z" | CONTRIBUTOR | null | Improved the tutorial by letting readers know about loading datasets with common files and including a link. I left the local files section alone because the methods were already listed with code snippets. | {
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https://api.github.com/repos/huggingface/datasets/issues/7041 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7041/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7041/comments | https://api.github.com/repos/huggingface/datasets/issues/7041/events | https://github.com/huggingface/datasets/issues/7041 | 2,404,576,038 | I_kwDODunzps6PUusm | 7,041 | `sort` after `filter` unreasonably slow | {
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"`filter` add an indices mapping on top of the dataset, so `sort` has to gather all the rows that are kept to form a new Arrow table and sort the table. Gathering all the rows can take some time, but is a necessary step. You can try calling `ds = ds.flatten_indices()` before sorting to remove the indices mapping."
] | "2024-07-12T03:29:27Z" | "2024-07-22T13:55:17Z" | null | NONE | null | ### Describe the bug
as the tittle says ...
### Steps to reproduce the bug
`sort` seems to be normal.
```python
from datasets import Dataset
import random
nums = [{"k":random.choice(range(0,1000))} for _ in range(100000)]
ds = Dataset.from_list(nums)
print("start sort")
ds = ds.sort("k")
print("finish sort")
```
but `sort` after `filter` is extremely slow.
```python
from datasets import Dataset
import random
nums = [{"k":random.choice(range(0,1000))} for _ in range(100000)]
ds = Dataset.from_list(nums)
ds = ds.filter(lambda x:x > 100, input_columns="k")
print("start sort")
ds = ds.sort("k")
print("finish sort")
```
### Expected behavior
Is this a bug, or is it a misuse of the `sort` function?
### Environment info
- `datasets` version: 2.20.0
- Platform: Linux-3.10.0-1127.19.1.el7.x86_64-x86_64-with-glibc2.17
- Python version: 3.10.13
- `huggingface_hub` version: 0.23.4
- PyArrow version: 16.1.0
- Pandas version: 2.2.2
- `fsspec` version: 2023.10.0 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7040 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7040/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7040/comments | https://api.github.com/repos/huggingface/datasets/issues/7040/events | https://github.com/huggingface/datasets/issues/7040 | 2,402,918,335 | I_kwDODunzps6POZ-_ | 7,040 | load `streaming=True` dataset with downloaded cache | {
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"When you pass `streaming=True`, the cache is ignored. The remote data URL is used instead and the data is streamed from the remote server.",
"Thanks for your reply! So is there any solution to get my expected behavior besides clone the whole repo ? Or could I adjust my script to load the downloaded arrow files and generate the dataset streamingly?"
] | "2024-07-11T11:14:13Z" | "2024-07-11T14:11:56Z" | null | NONE | null | ### Describe the bug
We build a dataset which contains several hdf5 files and write a script using `h5py` to generate the dataset. The hdf5 files are large and the processed dataset cache takes more disk space. So we hope to try streaming iterable dataset. Unfortunately, `h5py` can't convert a remote URL into a hdf5 file descriptor. So we use `fsspec` as an interface like below:
```python
def _generate_examples(self, filepath, split):
for file in filepath:
with fsspec.open(file, "rb") as fs:
with h5py.File(fs, "r") as fp:
# for event_id in sorted(list(fp.keys())):
event_ids = list(fp.keys())
......
```
### Steps to reproduce the bug
The `fsspec` works, but it takes 10+ min to print the first 10 examples, which is even longer than the downloading time. I'm not sure if it just caches the whole hdf5 file and generates the examples.
### Expected behavior
So does the following make sense so far?
1. download the files
```python
dataset = datasets.load('path/to/myscripts', split="train", name="event", trust_remote_code=True)
```
2. load the iterable dataset faster (using the raw file cache at path `.cache/huggingface/datasets/downloads`)
```python
dataset = datasets.load('path/to/myscripts', split="train", name="event", trust_remote_code=True, streaming=true)
```
I made some tests, but the code above can't get the expected result. I'm not sure if this is supported. I also find the issue #6327 . It seemed similar to mine, but I couldn't find a solution.
### Environment info
- `datasets` = 2.18.0
- `h5py` = 3.10.0
- `fsspec` = 2023.10.0 | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7039 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7039/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7039/comments | https://api.github.com/repos/huggingface/datasets/issues/7039/events | https://github.com/huggingface/datasets/pull/7039 | 2,402,403,390 | PR_kwDODunzps51DgCY | 7,039 | Fix export to JSON when dataset larger than batch size | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7039). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"The test before confirms the bug.\r\n\r\nThere are different possible solutions to this issue:\r\n- the easiest would be to write multiple JSON files, one for each batch; this solution can be done in parallel if `num_proc` is passed\r\n- alternatively, we could tweak the writing and remove the extra `[` and `]` characters; this solution will only be valid if `orient=\"records\"`\r\n- others?",
"@albertvillanova I was planning to approach it in the second way for `orient=\"records\"` , `orient=\"values\"` and `orient=\"index\"`. For `orient=\"split\"`, the columns and index can be written in one go and the data can be written in streaming manner. For `orient=\"columns\"`, each column can be written in a streaming way. LMK if I should go ahead with this.\r\n\r\n> The test before confirms the bug.\r\n> \r\n> There are different possible solutions to this issue:\r\n> \r\n> * the easiest would be to write multiple JSON files, one for each batch; this solution can be done in parallel if `num_proc` is passed\r\n> \r\n> * alternatively, we could tweak the writing and remove the extra `[` and `]` characters; this solution will only be valid if `orient=\"records\"`\r\n> \r\n> * others?\r\n\r\n"
] | "2024-07-11T06:52:22Z" | "2024-09-28T06:10:00Z" | null | MEMBER | null | Fix export to JSON (`lines=False`) when dataset larger than batch size.
Fix #7037. | null | {
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"Thanks for reporting, @LinglingGreat.\r\n\r\nI confirm this is a bug.",
"@albertvillanova I would like to take a shot at this if you aren't working on it currently. Let me know!"
] | "2024-07-10T09:11:22Z" | "2024-09-22T13:16:07Z" | null | NONE | null | ### Describe the bug
When using the Dataset.to_json() function, an unexpected error occurs if the parameter is set to lines=False. The stored data should be in the form of a list, but it actually turns into multiple lists, which causes an error when reading the data again.
The reason is that to_json() writes to the file in several segments based on the batch size. This is not a problem when lines=True, but it is incorrect when lines=False, because writing in several times will produce multiple lists(when len(dataset) > batch_size).
### Steps to reproduce the bug
try this code:
```python
from datasets import load_dataset
import json
train_dataset = load_dataset("Anthropic/hh-rlhf", data_dir="harmless-base")["train"]
output_path = "./harmless-base_hftojs.json"
print(len(train_dataset))
train_dataset.to_json(output_path, lines=False, force_ascii=False, indent=2)
with open(output_path, encoding="utf-8") as f:
data = json.loads(f.read())
```
it raise error: json.decoder.JSONDecodeError: Extra data: line 4003 column 1 (char 1373709)
Extra square brackets have appeared here:
<img width="265" alt="image" src="https://github.com/huggingface/datasets/assets/26499566/81492332-386d-42e8-88d1-b6d4ae3682cc">
### Expected behavior
The code runs normally.
### Environment info
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7036). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<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) | 0.005582 / 0.011353 (-0.005771) | 0.003968 / 0.011008 (-0.007041) | 0.063672 / 0.038508 (0.025164) | 0.032360 / 0.023109 (0.009251) | 0.241351 / 0.275898 (-0.034547) | 0.264926 / 0.323480 (-0.058554) | 0.003186 / 0.007986 (-0.004800) | 0.003423 / 0.004328 (-0.000906) | 0.049600 / 0.004250 (0.045350) | 0.045558 / 0.037052 (0.008506) | 0.253326 / 0.258489 (-0.005163) | 0.289474 / 0.293841 (-0.004367) | 0.030285 / 0.128546 (-0.098261) | 0.012424 / 0.075646 (-0.063222) | 0.203914 / 0.419271 (-0.215358) | 0.036569 / 0.043533 (-0.006964) | 0.245252 / 0.255139 (-0.009887) | 0.261971 / 0.283200 (-0.021228) | 0.018276 / 0.141683 (-0.123406) | 1.120386 / 1.452155 (-0.331769) | 1.181736 / 1.492716 (-0.310980) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.095427 / 0.018006 (0.077421) | 0.300666 / 0.000490 (0.300176) | 0.000205 / 0.000200 (0.000005) | 0.000045 / 0.000054 (-0.000009) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019255 / 0.037411 (-0.018156) | 0.062645 / 0.014526 (0.048119) | 0.074822 / 0.176557 (-0.101734) | 0.121222 / 0.737135 (-0.615913) | 0.076136 / 0.296338 (-0.220202) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.279756 / 0.215209 (0.064547) | 2.769680 / 2.077655 (0.692025) | 1.466156 / 1.504120 (-0.037964) | 1.348337 / 1.541195 (-0.192857) | 1.348311 / 1.468490 (-0.120179) | 0.710414 / 4.584777 (-3.874363) | 2.379192 / 3.745712 (-1.366520) | 2.990227 / 5.269862 (-2.279635) | 1.909749 / 4.565676 (-2.655928) | 0.079677 / 0.424275 (-0.344598) | 0.005116 / 0.007607 (-0.002491) | 0.335442 / 0.226044 (0.109398) | 3.308757 / 2.268929 (1.039828) | 1.831681 / 55.444624 (-53.612944) | 1.528642 / 6.876477 (-5.347835) | 1.554577 / 2.142072 (-0.587496) | 0.777722 / 4.805227 (-4.027505) | 0.132164 / 6.500664 (-6.368501) | 0.042277 / 0.075469 (-0.033193) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.964461 / 1.841788 (-0.877327) | 11.436569 / 8.074308 (3.362261) | 9.801367 / 10.191392 (-0.390025) | 0.130214 / 0.680424 (-0.550210) | 0.015288 / 0.534201 (-0.518913) | 0.303992 / 0.579283 (-0.275292) | 0.258128 / 0.434364 (-0.176236) | 0.347259 / 0.540337 (-0.193078) | 0.438156 / 1.386936 (-0.948780) |\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) | 0.006019 / 0.011353 (-0.005334) | 0.003872 / 0.011008 (-0.007136) | 0.050763 / 0.038508 (0.012255) | 0.033993 / 0.023109 (0.010884) | 0.271789 / 0.275898 (-0.004109) | 0.298849 / 0.323480 (-0.024631) | 0.004486 / 0.007986 (-0.003500) | 0.002789 / 0.004328 (-0.001540) | 0.049926 / 0.004250 (0.045676) | 0.040470 / 0.037052 (0.003418) | 0.287533 / 0.258489 (0.029044) | 0.320066 / 0.293841 (0.026225) | 0.033039 / 0.128546 (-0.095508) | 0.011842 / 0.075646 (-0.063804) | 0.061016 / 0.419271 (-0.358256) | 0.034807 / 0.043533 (-0.008726) | 0.272079 / 0.255139 (0.016940) | 0.291603 / 0.283200 (0.008403) | 0.018676 / 0.141683 (-0.123007) | 1.171214 / 1.452155 (-0.280940) | 1.210691 / 1.492716 (-0.282025) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093045 / 0.018006 (0.075038) | 0.301045 / 0.000490 (0.300556) | 0.000213 / 0.000200 (0.000013) | 0.000052 / 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) | 0.022616 / 0.037411 (-0.014795) | 0.077271 / 0.014526 (0.062746) | 0.088959 / 0.176557 (-0.087598) | 0.129961 / 0.737135 (-0.607174) | 0.090495 / 0.296338 (-0.205843) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.301864 / 0.215209 (0.086655) | 2.947486 / 2.077655 (0.869831) | 1.587123 / 1.504120 (0.083003) | 1.453799 / 1.541195 (-0.087396) | 1.474296 / 1.468490 (0.005806) | 0.718609 / 4.584777 (-3.866168) | 0.948426 / 3.745712 (-2.797286) | 2.877275 / 5.269862 (-2.392586) | 1.930940 / 4.565676 (-2.634736) | 0.079207 / 0.424275 (-0.345068) | 0.005379 / 0.007607 (-0.002228) | 0.357969 / 0.226044 (0.131925) | 3.576455 / 2.268929 (1.307527) | 1.985058 / 55.444624 (-53.459566) | 1.663730 / 6.876477 (-5.212747) | 1.812752 / 2.142072 (-0.329320) | 0.800200 / 4.805227 (-4.005027) | 0.135124 / 6.500664 (-6.365540) | 0.041211 / 0.075469 (-0.034258) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.032394 / 1.841788 (-0.809394) | 12.082436 / 8.074308 (4.008128) | 10.198703 / 10.191392 (0.007311) | 0.143578 / 0.680424 (-0.536846) | 0.015576 / 0.534201 (-0.518625) | 0.301450 / 0.579283 (-0.277833) | 0.126596 / 0.434364 (-0.307768) | 0.339437 / 0.540337 (-0.200900) | 0.445454 / 1.386936 (-0.941482) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#347f1664a31c1c0fcb6a1a0914ebfb99c134e116 \"CML watermark\")\n"
] | "2024-07-10T07:58:46Z" | "2024-07-26T07:58:00Z" | "2024-07-26T07:51:52Z" | MEMBER | null | Fix doc generation when `NamedSplit` is used as parameter default value.
Fix #7035. | {
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https://api.github.com/repos/huggingface/datasets/issues/7035 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7035/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7035/comments | https://api.github.com/repos/huggingface/datasets/issues/7035/events | https://github.com/huggingface/datasets/issues/7035 | 2,400,021,225 | I_kwDODunzps6PDWrp | 7,035 | Docs are not generated when a parameter defaults to a NamedSplit value | {
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] | null | [] | "2024-07-10T07:51:24Z" | "2024-07-26T07:51:53Z" | "2024-07-26T07:51:53Z" | MEMBER | null | While generating the docs, we get an error when some parameter defaults to a `NamedSplit` value, like:
```python
def call_function(split=Split.TRAIN):
...
```
The error is: ValueError: Equality not supported between split train and <class 'inspect._empty'>
See: https://github.com/huggingface/datasets/actions/runs/9869660902/job/27254359863?pr=7015
```
Building the MDX files: 97%|█████████▋| 58/60 [00:00<00:00, 91.94it/s]
Traceback (most recent call last):
File "/home/runner/work/datasets/datasets/.venv/lib/python3.10/site-packages/doc_builder/build_doc.py", line 197, in build_mdx_files
content, new_anchors, source_files, errors = resolve_autodoc(
File "/home/runner/work/datasets/datasets/.venv/lib/python3.10/site-packages/doc_builder/build_doc.py", line 123, in resolve_autodoc
doc = autodoc(
File "/home/runner/work/datasets/datasets/.venv/lib/python3.10/site-packages/doc_builder/autodoc.py", line 499, in autodoc
method_doc, check = document_object(
File "/home/runner/work/datasets/datasets/.venv/lib/python3.10/site-packages/doc_builder/autodoc.py", line 395, in document_object
signature = format_signature(obj)
File "/home/runner/work/datasets/datasets/.venv/lib/python3.10/site-packages/doc_builder/autodoc.py", line 126, in format_signature
if param.default != inspect._empty:
File "/home/runner/work/datasets/datasets/.venv/lib/python3.10/site-packages/datasets/splits.py", line 136, in __ne__
return not self.__eq__(other)
File "/home/runner/work/datasets/datasets/.venv/lib/python3.10/site-packages/datasets/splits.py", line 379, in __eq__
raise ValueError(f"Equality not supported between split {self} and {other}")
ValueError: Equality not supported between split train and <class 'inspect._empty'>
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/home/runner/work/datasets/datasets/.venv/bin/doc-builder", line 8, in <module>
sys.exit(main())
File "/home/runner/work/datasets/datasets/.venv/lib/python3.10/site-packages/doc_builder/commands/doc_builder_cli.py", line 47, in main
args.func(args)
File "/home/runner/work/datasets/datasets/.venv/lib/python3.10/site-packages/doc_builder/commands/build.py", line 102, in build_command
build_doc(
File "/home/runner/work/datasets/datasets/.venv/lib/python3.10/site-packages/doc_builder/build_doc.py", line 367, in build_doc
anchors_mapping, source_files_mapping = build_mdx_files(
File "/home/runner/work/datasets/datasets/.venv/lib/python3.10/site-packages/doc_builder/build_doc.py", line 230, in build_mdx_files
raise type(e)(f"There was an error when converting {file} to the MDX format.\n" + e.args[0]) from e
ValueError: There was an error when converting ../datasets/docs/source/package_reference/main_classes.mdx to the MDX format.
Equality not supported between split train and <class 'inspect._empty'>
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/7034 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7034/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7034/comments | https://api.github.com/repos/huggingface/datasets/issues/7034/events | https://github.com/huggingface/datasets/pull/7034 | 2,397,525,974 | PR_kwDODunzps50y-ya | 7,034 | chore: fix typos in docs | {
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"<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) | 0.005319 / 0.011353 (-0.006034) | 0.003979 / 0.011008 (-0.007030) | 0.063858 / 0.038508 (0.025350) | 0.031064 / 0.023109 (0.007955) | 0.232761 / 0.275898 (-0.043137) | 0.260362 / 0.323480 (-0.063118) | 0.004271 / 0.007986 (-0.003715) | 0.002801 / 0.004328 (-0.001527) | 0.049471 / 0.004250 (0.045220) | 0.043432 / 0.037052 (0.006379) | 0.247467 / 0.258489 (-0.011022) | 0.271926 / 0.293841 (-0.021915) | 0.030063 / 0.128546 (-0.098483) | 0.012659 / 0.075646 (-0.062988) | 0.204650 / 0.419271 (-0.214622) | 0.036340 / 0.043533 (-0.007192) | 0.237480 / 0.255139 (-0.017659) | 0.255955 / 0.283200 (-0.027244) | 0.017922 / 0.141683 (-0.123761) | 1.152251 / 1.452155 (-0.299904) | 1.195610 / 1.492716 (-0.297106) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.095411 / 0.018006 (0.077405) | 0.296836 / 0.000490 (0.296346) | 0.000226 / 0.000200 (0.000026) | 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) | 0.018547 / 0.037411 (-0.018865) | 0.063423 / 0.014526 (0.048897) | 0.073587 / 0.176557 (-0.102970) | 0.120327 / 0.737135 (-0.616808) | 0.076185 / 0.296338 (-0.220154) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.282815 / 0.215209 (0.067606) | 2.781204 / 2.077655 (0.703549) | 1.432489 / 1.504120 (-0.071631) | 1.312018 / 1.541195 (-0.229177) | 1.328290 / 1.468490 (-0.140200) | 0.734169 / 4.584777 (-3.850608) | 2.380654 / 3.745712 (-1.365058) | 2.904945 / 5.269862 (-2.364916) | 1.872079 / 4.565676 (-2.693598) | 0.078329 / 0.424275 (-0.345946) | 0.005151 / 0.007607 (-0.002457) | 0.338957 / 0.226044 (0.112912) | 3.353638 / 2.268929 (1.084709) | 1.812223 / 55.444624 (-53.632401) | 1.514860 / 6.876477 (-5.361617) | 1.528539 / 2.142072 (-0.613533) | 0.798711 / 4.805227 (-4.006516) | 0.135129 / 6.500664 (-6.365535) | 0.042355 / 0.075469 (-0.033114) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.954665 / 1.841788 (-0.887122) | 11.431925 / 8.074308 (3.357617) | 9.652583 / 10.191392 (-0.538809) | 0.132538 / 0.680424 (-0.547886) | 0.015517 / 0.534201 (-0.518683) | 0.303826 / 0.579283 (-0.275457) | 0.267530 / 0.434364 (-0.166834) | 0.340775 / 0.540337 (-0.199562) | 0.429909 / 1.386936 (-0.957027) |\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) | 0.005819 / 0.011353 (-0.005533) | 0.003829 / 0.011008 (-0.007179) | 0.049707 / 0.038508 (0.011199) | 0.030810 / 0.023109 (0.007701) | 0.269637 / 0.275898 (-0.006261) | 0.295857 / 0.323480 (-0.027623) | 0.004462 / 0.007986 (-0.003523) | 0.002823 / 0.004328 (-0.001505) | 0.048544 / 0.004250 (0.044294) | 0.039692 / 0.037052 (0.002639) | 0.286837 / 0.258489 (0.028348) | 0.319874 / 0.293841 (0.026034) | 0.033319 / 0.128546 (-0.095227) | 0.012318 / 0.075646 (-0.063329) | 0.060319 / 0.419271 (-0.358953) | 0.034341 / 0.043533 (-0.009192) | 0.271132 / 0.255139 (0.015993) | 0.292577 / 0.283200 (0.009377) | 0.018298 / 0.141683 (-0.123384) | 1.136871 / 1.452155 (-0.315284) | 1.192894 / 1.492716 (-0.299822) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.098890 / 0.018006 (0.080884) | 0.307830 / 0.000490 (0.307341) | 0.000214 / 0.000200 (0.000014) | 0.000044 / 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) | 0.023066 / 0.037411 (-0.014346) | 0.076732 / 0.014526 (0.062206) | 0.088154 / 0.176557 (-0.088403) | 0.129849 / 0.737135 (-0.607286) | 0.089368 / 0.296338 (-0.206970) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.298298 / 0.215209 (0.083089) | 2.914801 / 2.077655 (0.837147) | 1.609280 / 1.504120 (0.105160) | 1.486971 / 1.541195 (-0.054223) | 1.496254 / 1.468490 (0.027764) | 0.723780 / 4.584777 (-3.860997) | 0.972436 / 3.745712 (-2.773276) | 2.993773 / 5.269862 (-2.276089) | 1.911170 / 4.565676 (-2.654506) | 0.080599 / 0.424275 (-0.343677) | 0.005713 / 0.007607 (-0.001894) | 0.350510 / 0.226044 (0.124465) | 3.464035 / 2.268929 (1.195107) | 2.001558 / 55.444624 (-53.443066) | 1.691888 / 6.876477 (-5.184589) | 1.732348 / 2.142072 (-0.409724) | 0.818572 / 4.805227 (-3.986655) | 0.136770 / 6.500664 (-6.363894) | 0.041722 / 0.075469 (-0.033748) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.021225 / 1.841788 (-0.820563) | 11.941224 / 8.074308 (3.866915) | 10.118500 / 10.191392 (-0.072892) | 0.146167 / 0.680424 (-0.534257) | 0.015700 / 0.534201 (-0.518501) | 0.301511 / 0.579283 (-0.277772) | 0.122716 / 0.434364 (-0.311648) | 0.349048 / 0.540337 (-0.191290) | 0.444940 / 1.386936 (-0.941996) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a5c7fe5484e3b487eed4750fc6cc27c04bf90bd8 \"CML watermark\")\n"
] | "2024-07-09T08:35:05Z" | "2024-08-13T08:22:25Z" | "2024-08-13T08:16:22Z" | CONTRIBUTOR | null | null | {
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"Thanks for reporting, @pminervini.\r\n\r\nI agree we should give the option to define the split name.\r\n\r\nIndeed, there is a PR that addresses precisely this issue:\r\n- #7015\r\n\r\nI am reviewing it.",
"Booom! thank you guys :)"
] | "2024-07-09T07:47:58Z" | "2024-07-26T12:56:16Z" | "2024-07-26T09:31:56Z" | CONTRIBUTOR | null | ### Describe the bug
I'm building train, dev, and test using `from_generator`; however, in all three cases, the logger prints `Generating train split:`
It's not possible to change the split name since it seems to be hardcoded: https://github.com/huggingface/datasets/blob/main/src/datasets/packaged_modules/generator/generator.py
### Steps to reproduce the bug
```
In [1]: from datasets import Dataset
In [2]: def gen():
...: yield {"pokemon": "bulbasaur", "type": "grass"}
...:
In [3]: ds = Dataset.from_generator(gen)
Generating train split: 1 examples [00:00, 133.89 examples/s]
```
### Expected behavior
It should be possible to specify any split name
### Environment info
- `datasets` version: 2.19.2
- Platform: macOS-10.16-x86_64-i386-64bit
- Python version: 3.8.5
- `huggingface_hub` version: 0.23.3
- PyArrow version: 15.0.0
- Pandas version: 2.0.3
- `fsspec` version: 2023.10.0 | {
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https://api.github.com/repos/huggingface/datasets/issues/7032 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7032/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7032/comments | https://api.github.com/repos/huggingface/datasets/issues/7032/events | https://github.com/huggingface/datasets/pull/7032 | 2,395,531,699 | PR_kwDODunzps50sJTq | 7,032 | Register `.zstd` extension for zstd-compressed files | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7032). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"@albertvillanova hm I don't know tbh, it's just that \"mlfoundations/dclm-baseline-1.0\" dataset contains [files](https://huggingface.co/datasets/mlfoundations/dclm-baseline-1.0/tree/main/global-shard_01_of_10/local-shard_0_of_10) with this extension and these files seem to be valid ",
"not sure why CI is failing but seems to be unrelated to this pr? can I merge @lhoestq @albertvillanova ?",
"yes you can merge, the CI failure is unrelated (surely an issue with hub-ci)",
"ah why not, you could try opening a PR\r\n\r\nbtw there is a channel with them at (internal) https://app.slack.com/client/T1RCG4490/C079AKTV11P if you want to let them know",
"@lhoestq, your previous comment was addressed to me or Polina?\r\n\r\n@polinaeterna let me know if it is OK for you.",
"I opened https://huggingface.co/datasets/mlfoundations/dclm-baseline-1.0/discussions/7",
"Should we close this PR then?"
] | "2024-07-08T12:39:50Z" | "2024-07-12T15:07:03Z" | "2024-07-12T15:07:03Z" | CONTRIBUTOR | null | For example, https://huggingface.co/datasets/mlfoundations/dclm-baseline-1.0 dataset files have `.zstd` extension which is currently ignored (only `.zst` is registered). | {
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```
error: `ruff <path>` has been removed. Use `ruff check <path>` instead.
``` | {
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https://api.github.com/repos/huggingface/datasets/issues/7030 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7030/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7030/comments | https://api.github.com/repos/huggingface/datasets/issues/7030/events | https://github.com/huggingface/datasets/issues/7030 | 2,393,411,631 | I_kwDODunzps6OqJAv | 7,030 | Add option to disable progress bar when reading a dataset ("Loading dataset from disk") | {
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"You can disable progress bars for all of `datasets` with `disable_progress_bars`. [Link](https://huggingface.co/docs/datasets/en/package_reference/utilities#datasets.enable_progress_bars)\r\n\r\nSo you could do something like:\r\n\r\n```python\r\nfrom datasets import load_from_disk, enable_progress_bars, disable_progress_bars\r\n\r\ndisable_progress_bars()\r\n# Your code\r\nload_from_disk(....)\r\n\r\nenable_progress_bars()\r\n```\r\n",
"Thank you! Closing the issue."
] | "2024-07-06T05:43:37Z" | "2024-07-13T14:35:59Z" | "2024-07-13T14:35:59Z" | NONE | null | ### Feature request
Add an option in load_from_disk to disable the progress bar even if the number of files is larger than 16.
### Motivation
I am reading a lot of datasets that it creates lots of logs.
<img width="1432" alt="image" src="https://github.com/huggingface/datasets/assets/57996478/8d4bbf03-6b89-44b6-937c-932f01b4eb2a">
### Your contribution
Seems like an easy fix to make. I can create a PR if necessary. | {
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"hi ! can you share the full stack trace ? this should help locate what files is not written in the cache_dir"
] | "2024-07-04T19:15:16Z" | "2024-07-17T12:44:03Z" | null | NONE | null | ### Describe the bug
I'm using AWS lambda to run a python application. I run the `load_dataset` function with cache_dir="/tmp" and is still throws the OSError(30, 'Read-only file system') error. Is even updated all the HF envs to point to /tmp dir but the issue still persists. I can confirm that the I can write to /tmp directory.
### Steps to reproduce the bug
```python
d = load_dataset(
path=hugging_face_link,
split=split,
token=token,
cache_dir="/tmp/hugging_face_cache",
)
```
### Expected behavior
Everything written to the file system as part of the load_datasets function should be in the /tmp directory.
### Environment info
datasets version: 2.16.1
Platform: Linux-5.10.216-225.855.amzn2.x86_64-x86_64-with-glibc2.26
Python version: 3.11.9
huggingface_hub version: 0.19.4
PyArrow version: 16.1.0
Pandas version: 2.2.2
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7028). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<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) | 0.005748 / 0.011353 (-0.005605) | 0.004109 / 0.011008 (-0.006899) | 0.067017 / 0.038508 (0.028509) | 0.031950 / 0.023109 (0.008841) | 0.239939 / 0.275898 (-0.035959) | 0.266339 / 0.323480 (-0.057141) | 0.003176 / 0.007986 (-0.004809) | 0.003556 / 0.004328 (-0.000773) | 0.050725 / 0.004250 (0.046475) | 0.047711 / 0.037052 (0.010658) | 0.251048 / 0.258489 (-0.007441) | 0.287049 / 0.293841 (-0.006792) | 0.029919 / 0.128546 (-0.098627) | 0.012562 / 0.075646 (-0.063085) | 0.212903 / 0.419271 (-0.206369) | 0.036570 / 0.043533 (-0.006963) | 0.240975 / 0.255139 (-0.014164) | 0.266473 / 0.283200 (-0.016726) | 0.019959 / 0.141683 (-0.121724) | 1.152224 / 1.452155 (-0.299931) | 1.186046 / 1.492716 (-0.306671) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.095836 / 0.018006 (0.077829) | 0.303402 / 0.000490 (0.302913) | 0.000210 / 0.000200 (0.000010) | 0.000042 / 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) | 0.020552 / 0.037411 (-0.016859) | 0.063619 / 0.014526 (0.049093) | 0.076969 / 0.176557 (-0.099588) | 0.123368 / 0.737135 (-0.613767) | 0.077005 / 0.296338 (-0.219334) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.282005 / 0.215209 (0.066796) | 2.794144 / 2.077655 (0.716489) | 1.463569 / 1.504120 (-0.040551) | 1.334295 / 1.541195 (-0.206899) | 1.387198 / 1.468490 (-0.081292) | 0.707654 / 4.584777 (-3.877123) | 2.341698 / 3.745712 (-1.404014) | 2.865131 / 5.269862 (-2.404731) | 1.945168 / 4.565676 (-2.620509) | 0.077926 / 0.424275 (-0.346349) | 0.005470 / 0.007607 (-0.002137) | 0.336498 / 0.226044 (0.110454) | 3.330262 / 2.268929 (1.061334) | 1.865574 / 55.444624 (-53.579050) | 1.536932 / 6.876477 (-5.339545) | 1.720960 / 2.142072 (-0.421113) | 0.794753 / 4.805227 (-4.010475) | 0.133491 / 6.500664 (-6.367173) | 0.042437 / 0.075469 (-0.033032) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.976788 / 1.841788 (-0.865000) | 11.895137 / 8.074308 (3.820829) | 9.211969 / 10.191392 (-0.979423) | 0.141798 / 0.680424 (-0.538626) | 0.014354 / 0.534201 (-0.519847) | 0.306044 / 0.579283 (-0.273239) | 0.265016 / 0.434364 (-0.169348) | 0.340877 / 0.540337 (-0.199460) | 0.470449 / 1.386936 (-0.916487) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006134 / 0.011353 (-0.005219) | 0.004023 / 0.011008 (-0.006985) | 0.050419 / 0.038508 (0.011911) | 0.033853 / 0.023109 (0.010744) | 0.266799 / 0.275898 (-0.009099) | 0.291248 / 0.323480 (-0.032232) | 0.004474 / 0.007986 (-0.003511) | 0.002847 / 0.004328 (-0.001481) | 0.049895 / 0.004250 (0.045645) | 0.041160 / 0.037052 (0.004108) | 0.278818 / 0.258489 (0.020329) | 0.314027 / 0.293841 (0.020186) | 0.032303 / 0.128546 (-0.096243) | 0.012367 / 0.075646 (-0.063279) | 0.061495 / 0.419271 (-0.357776) | 0.033512 / 0.043533 (-0.010021) | 0.266168 / 0.255139 (0.011029) | 0.283129 / 0.283200 (-0.000071) | 0.018674 / 0.141683 (-0.123009) | 1.124453 / 1.452155 (-0.327701) | 1.164527 / 1.492716 (-0.328189) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.098522 / 0.018006 (0.080516) | 0.315069 / 0.000490 (0.314579) | 0.000202 / 0.000200 (0.000002) | 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) | 0.022809 / 0.037411 (-0.014602) | 0.078409 / 0.014526 (0.063883) | 0.088558 / 0.176557 (-0.087998) | 0.130004 / 0.737135 (-0.607131) | 0.090507 / 0.296338 (-0.205832) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.291323 / 0.215209 (0.076114) | 2.836363 / 2.077655 (0.758708) | 1.548889 / 1.504120 (0.044769) | 1.423857 / 1.541195 (-0.117337) | 1.461667 / 1.468490 (-0.006823) | 0.714956 / 4.584777 (-3.869821) | 0.948170 / 3.745712 (-2.797542) | 3.036151 / 5.269862 (-2.233711) | 1.923824 / 4.565676 (-2.641853) | 0.078002 / 0.424275 (-0.346273) | 0.005198 / 0.007607 (-0.002409) | 0.337007 / 0.226044 (0.110963) | 3.310255 / 2.268929 (1.041327) | 1.910371 / 55.444624 (-53.534253) | 1.619855 / 6.876477 (-5.256622) | 1.682093 / 2.142072 (-0.459979) | 0.789903 / 4.805227 (-4.015324) | 0.132117 / 6.500664 (-6.368547) | 0.041312 / 0.075469 (-0.034157) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.997658 / 1.841788 (-0.844130) | 12.447878 / 8.074308 (4.373570) | 10.277662 / 10.191392 (0.086270) | 0.143580 / 0.680424 (-0.536844) | 0.016472 / 0.534201 (-0.517729) | 0.307235 / 0.579283 (-0.272048) | 0.125469 / 0.434364 (-0.308895) | 0.339525 / 0.540337 (-0.200813) | 0.427371 / 1.386936 (-0.959566) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#689447f8c86f777829a4db9ccc5d8133c12ec84c \"CML watermark\")\n"
] | "2024-07-04T15:11:08Z" | "2024-07-04T15:26:35Z" | "2024-07-04T15:19:16Z" | MEMBER | null | ...after last pr errors | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7027). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<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) | 0.005612 / 0.011353 (-0.005741) | 0.004023 / 0.011008 (-0.006985) | 0.065578 / 0.038508 (0.027070) | 0.030476 / 0.023109 (0.007367) | 0.237131 / 0.275898 (-0.038767) | 0.269388 / 0.323480 (-0.054092) | 0.003364 / 0.007986 (-0.004622) | 0.002938 / 0.004328 (-0.001390) | 0.050867 / 0.004250 (0.046617) | 0.049456 / 0.037052 (0.012403) | 0.249587 / 0.258489 (-0.008902) | 0.291132 / 0.293841 (-0.002709) | 0.029373 / 0.128546 (-0.099174) | 0.012266 / 0.075646 (-0.063380) | 0.206239 / 0.419271 (-0.213033) | 0.037192 / 0.043533 (-0.006340) | 0.244902 / 0.255139 (-0.010237) | 0.269779 / 0.283200 (-0.013421) | 0.019870 / 0.141683 (-0.121813) | 1.123697 / 1.452155 (-0.328458) | 1.181256 / 1.492716 (-0.311460) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.108535 / 0.018006 (0.090529) | 0.317838 / 0.000490 (0.317348) | 0.000216 / 0.000200 (0.000016) | 0.000043 / 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) | 0.019097 / 0.037411 (-0.018315) | 0.063836 / 0.014526 (0.049310) | 0.075446 / 0.176557 (-0.101111) | 0.124503 / 0.737135 (-0.612632) | 0.077730 / 0.296338 (-0.218608) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.284688 / 0.215209 (0.069479) | 2.817832 / 2.077655 (0.740178) | 1.487342 / 1.504120 (-0.016778) | 1.354037 / 1.541195 (-0.187158) | 1.426904 / 1.468490 (-0.041586) | 0.728754 / 4.584777 (-3.856022) | 2.361140 / 3.745712 (-1.384573) | 2.926215 / 5.269862 (-2.343647) | 1.981767 / 4.565676 (-2.583909) | 0.079278 / 0.424275 (-0.344997) | 0.005567 / 0.007607 (-0.002040) | 0.336590 / 0.226044 (0.110546) | 3.371062 / 2.268929 (1.102134) | 1.845343 / 55.444624 (-53.599282) | 1.537699 / 6.876477 (-5.338777) | 1.731407 / 2.142072 (-0.410665) | 0.796148 / 4.805227 (-4.009079) | 0.133830 / 6.500664 (-6.366835) | 0.043117 / 0.075469 (-0.032352) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.980786 / 1.841788 (-0.861001) | 12.653553 / 8.074308 (4.579245) | 9.402636 / 10.191392 (-0.788756) | 0.143756 / 0.680424 (-0.536667) | 0.014896 / 0.534201 (-0.519304) | 0.328796 / 0.579283 (-0.250487) | 0.275108 / 0.434364 (-0.159255) | 0.343397 / 0.540337 (-0.196940) | 0.472301 / 1.386936 (-0.914635) |\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) | 0.005882 / 0.011353 (-0.005471) | 0.003982 / 0.011008 (-0.007026) | 0.050484 / 0.038508 (0.011976) | 0.035217 / 0.023109 (0.012108) | 0.271683 / 0.275898 (-0.004215) | 0.291498 / 0.323480 (-0.031982) | 0.004429 / 0.007986 (-0.003557) | 0.002928 / 0.004328 (-0.001401) | 0.049386 / 0.004250 (0.045136) | 0.040868 / 0.037052 (0.003815) | 0.280968 / 0.258489 (0.022479) | 0.314880 / 0.293841 (0.021039) | 0.032590 / 0.128546 (-0.095956) | 0.012319 / 0.075646 (-0.063327) | 0.060354 / 0.419271 (-0.358917) | 0.034138 / 0.043533 (-0.009394) | 0.267491 / 0.255139 (0.012352) | 0.283077 / 0.283200 (-0.000123) | 0.017784 / 0.141683 (-0.123899) | 1.154835 / 1.452155 (-0.297320) | 1.179271 / 1.492716 (-0.313446) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.100519 / 0.018006 (0.082513) | 0.309043 / 0.000490 (0.308553) | 0.000222 / 0.000200 (0.000022) | 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) | 0.024056 / 0.037411 (-0.013356) | 0.077810 / 0.014526 (0.063284) | 0.092682 / 0.176557 (-0.083875) | 0.132101 / 0.737135 (-0.605034) | 0.091986 / 0.296338 (-0.204352) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.298186 / 0.215209 (0.082977) | 2.905134 / 2.077655 (0.827479) | 1.552364 / 1.504120 (0.048245) | 1.424644 / 1.541195 (-0.116551) | 1.457667 / 1.468490 (-0.010823) | 0.717606 / 4.584777 (-3.867171) | 0.944470 / 3.745712 (-2.801242) | 3.056236 / 5.269862 (-2.213626) | 1.946453 / 4.565676 (-2.619223) | 0.080525 / 0.424275 (-0.343750) | 0.005235 / 0.007607 (-0.002372) | 0.348561 / 0.226044 (0.122516) | 3.449350 / 2.268929 (1.180421) | 1.930165 / 55.444624 (-53.514459) | 1.620883 / 6.876477 (-5.255593) | 1.671963 / 2.142072 (-0.470109) | 0.801978 / 4.805227 (-4.003249) | 0.134494 / 6.500664 (-6.366170) | 0.041888 / 0.075469 (-0.033581) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.005961 / 1.841788 (-0.835826) | 12.687638 / 8.074308 (4.613330) | 10.398730 / 10.191392 (0.207338) | 0.134503 / 0.680424 (-0.545920) | 0.015839 / 0.534201 (-0.518362) | 0.307465 / 0.579283 (-0.271819) | 0.130805 / 0.434364 (-0.303559) | 0.349079 / 0.540337 (-0.191259) | 0.437609 / 1.386936 (-0.949327) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#cc6ac9e5f70811a450198203ddc077c0c7bff206 \"CML watermark\")\n"
] | "2024-07-04T14:34:29Z" | "2024-07-04T14:40:46Z" | "2024-07-04T14:34:36Z" | MEMBER | null | null | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7026). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<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) | 0.005637 / 0.011353 (-0.005716) | 0.003967 / 0.011008 (-0.007041) | 0.064187 / 0.038508 (0.025679) | 0.031356 / 0.023109 (0.008246) | 0.239203 / 0.275898 (-0.036695) | 0.261033 / 0.323480 (-0.062447) | 0.003256 / 0.007986 (-0.004730) | 0.003416 / 0.004328 (-0.000913) | 0.049673 / 0.004250 (0.045423) | 0.047021 / 0.037052 (0.009969) | 0.252146 / 0.258489 (-0.006343) | 0.283663 / 0.293841 (-0.010178) | 0.030223 / 0.128546 (-0.098324) | 0.012342 / 0.075646 (-0.063304) | 0.213061 / 0.419271 (-0.206211) | 0.036867 / 0.043533 (-0.006665) | 0.242589 / 0.255139 (-0.012550) | 0.265584 / 0.283200 (-0.017616) | 0.019149 / 0.141683 (-0.122533) | 1.108909 / 1.452155 (-0.343246) | 1.148484 / 1.492716 (-0.344232) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.096815 / 0.018006 (0.078809) | 0.299633 / 0.000490 (0.299143) | 0.000212 / 0.000200 (0.000013) | 0.000043 / 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) | 0.018947 / 0.037411 (-0.018464) | 0.061640 / 0.014526 (0.047114) | 0.074621 / 0.176557 (-0.101935) | 0.120830 / 0.737135 (-0.616305) | 0.075472 / 0.296338 (-0.220866) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.284626 / 0.215209 (0.069417) | 2.805299 / 2.077655 (0.727644) | 1.469879 / 1.504120 (-0.034241) | 1.355524 / 1.541195 (-0.185671) | 1.388246 / 1.468490 (-0.080244) | 0.726740 / 4.584777 (-3.858037) | 2.387461 / 3.745712 (-1.358251) | 2.834137 / 5.269862 (-2.435724) | 1.915750 / 4.565676 (-2.649927) | 0.079223 / 0.424275 (-0.345052) | 0.005489 / 0.007607 (-0.002118) | 0.335517 / 0.226044 (0.109473) | 3.299332 / 2.268929 (1.030403) | 1.817726 / 55.444624 (-53.626898) | 1.520834 / 6.876477 (-5.355642) | 1.696285 / 2.142072 (-0.445788) | 0.815147 / 4.805227 (-3.990080) | 0.136566 / 6.500664 (-6.364098) | 0.043482 / 0.075469 (-0.031987) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.981382 / 1.841788 (-0.860406) | 11.472890 / 8.074308 (3.398582) | 9.274181 / 10.191392 (-0.917211) | 0.133051 / 0.680424 (-0.547373) | 0.015417 / 0.534201 (-0.518784) | 0.306098 / 0.579283 (-0.273185) | 0.261424 / 0.434364 (-0.172940) | 0.338946 / 0.540337 (-0.201391) | 0.460776 / 1.386936 (-0.926160) |\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) | 0.005806 / 0.011353 (-0.005547) | 0.004274 / 0.011008 (-0.006734) | 0.050831 / 0.038508 (0.012323) | 0.033717 / 0.023109 (0.010607) | 0.280561 / 0.275898 (0.004663) | 0.302437 / 0.323480 (-0.021043) | 0.004543 / 0.007986 (-0.003442) | 0.002905 / 0.004328 (-0.001424) | 0.048897 / 0.004250 (0.044646) | 0.041089 / 0.037052 (0.004037) | 0.291439 / 0.258489 (0.032950) | 0.319762 / 0.293841 (0.025921) | 0.033178 / 0.128546 (-0.095368) | 0.012336 / 0.075646 (-0.063311) | 0.061033 / 0.419271 (-0.358238) | 0.034018 / 0.043533 (-0.009515) | 0.278514 / 0.255139 (0.023375) | 0.295648 / 0.283200 (0.012448) | 0.018621 / 0.141683 (-0.123062) | 1.160250 / 1.452155 (-0.291905) | 1.183867 / 1.492716 (-0.308850) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.096354 / 0.018006 (0.078348) | 0.301907 / 0.000490 (0.301417) | 0.000205 / 0.000200 (0.000006) | 0.000044 / 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) | 0.022357 / 0.037411 (-0.015054) | 0.076218 / 0.014526 (0.061692) | 0.088172 / 0.176557 (-0.088385) | 0.128621 / 0.737135 (-0.608515) | 0.089250 / 0.296338 (-0.207089) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.292633 / 0.215209 (0.077424) | 2.862456 / 2.077655 (0.784801) | 1.581967 / 1.504120 (0.077847) | 1.459822 / 1.541195 (-0.081373) | 1.475896 / 1.468490 (0.007406) | 0.728550 / 4.584777 (-3.856226) | 0.958819 / 3.745712 (-2.786893) | 3.011074 / 5.269862 (-2.258788) | 1.934393 / 4.565676 (-2.631283) | 0.079831 / 0.424275 (-0.344444) | 0.005249 / 0.007607 (-0.002358) | 0.346334 / 0.226044 (0.120290) | 3.438979 / 2.268929 (1.170051) | 1.935567 / 55.444624 (-53.509057) | 1.648723 / 6.876477 (-5.227754) | 1.685489 / 2.142072 (-0.456583) | 0.800992 / 4.805227 (-4.004236) | 0.139388 / 6.500664 (-6.361276) | 0.042518 / 0.075469 (-0.032951) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.031715 / 1.841788 (-0.810072) | 12.486711 / 8.074308 (4.412403) | 10.430191 / 10.191392 (0.238799) | 0.146884 / 0.680424 (-0.533540) | 0.015735 / 0.534201 (-0.518466) | 0.303938 / 0.579283 (-0.275346) | 0.140374 / 0.434364 (-0.293989) | 0.338508 / 0.540337 (-0.201830) | 0.429551 / 1.386936 (-0.957385) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e32336195f3ea69988148df5f129f9f59d3ab595 \"CML watermark\")\n"
] | "2024-07-04T14:18:38Z" | "2024-07-04T14:28:36Z" | "2024-07-04T14:20:02Z" | MEMBER | null | move it to after the `trust_remote_code` check
Note that it only affects local datasets that already exist on disk, not datasets loaded from HF directly | {
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https://api.github.com/repos/huggingface/datasets/issues/7025 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7025/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7025/comments | https://api.github.com/repos/huggingface/datasets/issues/7025/events | https://github.com/huggingface/datasets/pull/7025 | 2,390,488,546 | PR_kwDODunzps50bSyD | 7,025 | feat: support non streamable arrow file binary format | {
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"requesting review - @albertvillanova @lhoestq ",
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7025). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"@lhoestq rebased the PR, It would be really helpful to have this feature into datasets, please let me know if there is anything pending on this PR, thanks. ",
"@lhoestq \r\n\r\nHave added the unit test to generate tables for both the arrow formats - file and streaming.\r\n\r\nLet me know if we have any docs changes as well. Thanks\r\n\r\n<img width=\"568\" alt=\"Screenshot 2024-07-25 at 7 04 26 PM\" src=\"https://github.com/user-attachments/assets/69fd0906-bda9-45fa-8f7e-8092e351ac29\">\r\n",
"@lhoestq any update on this thread? Thanks",
"Timely PR!\r\nCan we please look into this?",
"<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) | 0.005737 / 0.011353 (-0.005615) | 0.003894 / 0.011008 (-0.007114) | 0.067510 / 0.038508 (0.029002) | 0.033431 / 0.023109 (0.010321) | 0.262766 / 0.275898 (-0.013132) | 0.283776 / 0.323480 (-0.039704) | 0.003296 / 0.007986 (-0.004689) | 0.003577 / 0.004328 (-0.000752) | 0.052165 / 0.004250 (0.047915) | 0.047815 / 0.037052 (0.010763) | 0.263528 / 0.258489 (0.005039) | 0.292980 / 0.293841 (-0.000861) | 0.031535 / 0.128546 (-0.097011) | 0.012966 / 0.075646 (-0.062680) | 0.218827 / 0.419271 (-0.200444) | 0.039181 / 0.043533 (-0.004352) | 0.263768 / 0.255139 (0.008629) | 0.288012 / 0.283200 (0.004813) | 0.020562 / 0.141683 (-0.121121) | 1.180547 / 1.452155 (-0.271608) | 1.269283 / 1.492716 (-0.223433) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.098951 / 0.018006 (0.080944) | 0.318922 / 0.000490 (0.318433) | 0.000214 / 0.000200 (0.000014) | 0.000044 / 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) | 0.021315 / 0.037411 (-0.016097) | 0.067728 / 0.014526 (0.053202) | 0.079428 / 0.176557 (-0.097129) | 0.127472 / 0.737135 (-0.609663) | 0.080455 / 0.296338 (-0.215883) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.308725 / 0.215209 (0.093516) | 3.043555 / 2.077655 (0.965900) | 1.587419 / 1.504120 (0.083299) | 1.444421 / 1.541195 (-0.096774) | 1.470703 / 1.468490 (0.002213) | 0.784005 / 4.584777 (-3.800772) | 2.582064 / 3.745712 (-1.163648) | 3.140269 / 5.269862 (-2.129592) | 2.031099 / 4.565676 (-2.534577) | 0.086999 / 0.424275 (-0.337277) | 0.005923 / 0.007607 (-0.001684) | 0.361333 / 0.226044 (0.135289) | 3.587173 / 2.268929 (1.318244) | 1.961448 / 55.444624 (-53.483177) | 1.649868 / 6.876477 (-5.226609) | 1.698595 / 2.142072 (-0.443478) | 0.858552 / 4.805227 (-3.946676) | 0.146001 / 6.500664 (-6.354663) | 0.046049 / 0.075469 (-0.029421) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.022644 / 1.841788 (-0.819144) | 12.655994 / 8.074308 (4.581686) | 10.205832 / 10.191392 (0.014440) | 0.156073 / 0.680424 (-0.524351) | 0.015550 / 0.534201 (-0.518651) | 0.327762 / 0.579283 (-0.251521) | 0.299212 / 0.434364 (-0.135152) | 0.367549 / 0.540337 (-0.172788) | 0.474499 / 1.386936 (-0.912437) |\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) | 0.005904 / 0.011353 (-0.005448) | 0.004245 / 0.011008 (-0.006763) | 0.054309 / 0.038508 (0.015801) | 0.037490 / 0.023109 (0.014381) | 0.293540 / 0.275898 (0.017642) | 0.324068 / 0.323480 (0.000588) | 0.004675 / 0.007986 (-0.003311) | 0.003091 / 0.004328 (-0.001238) | 0.052972 / 0.004250 (0.048721) | 0.045545 / 0.037052 (0.008493) | 0.301465 / 0.258489 (0.042976) | 0.342822 / 0.293841 (0.048981) | 0.033958 / 0.128546 (-0.094588) | 0.013311 / 0.075646 (-0.062336) | 0.064050 / 0.419271 (-0.355222) | 0.038127 / 0.043533 (-0.005406) | 0.297383 / 0.255139 (0.042244) | 0.312244 / 0.283200 (0.029044) | 0.019395 / 0.141683 (-0.122288) | 1.244335 / 1.452155 (-0.207820) | 1.305547 / 1.492716 (-0.187169) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.101847 / 0.018006 (0.083840) | 0.330827 / 0.000490 (0.330337) | 0.000211 / 0.000200 (0.000011) | 0.000047 / 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) | 0.025734 / 0.037411 (-0.011677) | 0.085020 / 0.014526 (0.070494) | 0.096724 / 0.176557 (-0.079833) | 0.141276 / 0.737135 (-0.595859) | 0.099150 / 0.296338 (-0.197189) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.316058 / 0.215209 (0.100849) | 3.059459 / 2.077655 (0.981804) | 1.638394 / 1.504120 (0.134274) | 1.505313 / 1.541195 (-0.035881) | 1.526635 / 1.468490 (0.058145) | 0.777259 / 4.584777 (-3.807518) | 1.059575 / 3.745712 (-2.686137) | 2.952334 / 5.269862 (-2.317528) | 2.003894 / 4.565676 (-2.561782) | 0.084464 / 0.424275 (-0.339811) | 0.007343 / 0.007607 (-0.000265) | 0.366218 / 0.226044 (0.140174) | 3.705588 / 2.268929 (1.436660) | 2.047029 / 55.444624 (-53.397595) | 1.766970 / 6.876477 (-5.109507) | 1.883804 / 2.142072 (-0.258268) | 0.865780 / 4.805227 (-3.939447) | 0.143180 / 6.500664 (-6.357485) | 0.044943 / 0.075469 (-0.030527) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.141391 / 1.841788 (-0.700397) | 13.244917 / 8.074308 (5.170609) | 10.907863 / 10.191392 (0.716471) | 0.156087 / 0.680424 (-0.524337) | 0.016487 / 0.534201 (-0.517714) | 0.331377 / 0.579283 (-0.247906) | 0.148863 / 0.434364 (-0.285501) | 0.370443 / 0.540337 (-0.169895) | 0.499647 / 1.386936 (-0.887289) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ce4a0c573920607bc6c814605734091b06b860e7 \"CML watermark\")\n"
] | "2024-07-04T10:11:12Z" | "2024-07-31T06:15:50Z" | "2024-07-31T06:09:31Z" | CONTRIBUTOR | null | Support Arrow files (`.arrow`) that are in non streamable binary file formats. | {
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https://api.github.com/repos/huggingface/datasets/issues/7024 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7024/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7024/comments | https://api.github.com/repos/huggingface/datasets/issues/7024/events | https://github.com/huggingface/datasets/issues/7024 | 2,390,141,626 | I_kwDODunzps6Odqq6 | 7,024 | Streaming dataset not returning data | {
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} | [] | open | false | null | [] | null | [] | "2024-07-04T07:21:47Z" | "2024-07-04T07:21:47Z" | null | NONE | null | ### Describe the bug
I'm deciding to post here because I'm still not sure what the issue is, or if I am using IterableDatasets wrongly.
I'm following the guide on here https://huggingface.co/learn/cookbook/en/fine_tuning_code_llm_on_single_gpu pretty much to a tee and have verified that it works when I'm fine-tuning on the provided dataset.
However, I'm doing some data preprocessing steps (filtering out entries), when I try to swap out the dataset for mine, it fails to train. However, I eventually fixed this by simply setting `stream=False` in `load_dataset`.
Coud this be some sort of network / firewall issue I'm facing?
### Steps to reproduce the bug
I made a post with greater description about how I reproduced this problem before I found my workaround: https://discuss.huggingface.co/t/problem-with-custom-iterator-of-streaming-dataset-not-returning-anything/94551
Here is the problematic dataset snippet, which works when streaming=False (and with buffer keyword removed from shuffle)
```
commitpackft = load_dataset(
"chargoddard/commitpack-ft-instruct", split="train", streaming=True
).filter(lambda example: example["language"] == "Python")
def form_template(example):
"""Forms a template for each example following the alpaca format for CommitPack"""
example["content"] = (
"### Human: " + example["instruction"] + " " + example["input"] + " ### Assistant: " + example["output"]
)
return example
dataset = commitpackft.map(
form_template,
remove_columns=["id", "language", "license", "instruction", "input", "output"],
).shuffle(
seed=42, buffer_size=10000
) # remove everything since its all inside "content" now
validation_data = dataset.take(4000)
train_data = dataset.skip(4000)
```
The annoying part about this is that it only fails during training and I don't know when it will fail, except that it always fails during evaluation.
### Expected behavior
The expected behavior is that I should be able to get something from the iterator when called instead of getting nothing / stuck in a loop somewhere.
### Environment info
- `datasets` version: 2.20.0
- Platform: Linux-5.4.0-121-generic-x86_64-with-glibc2.31
- Python version: 3.11.7
- `huggingface_hub` version: 0.23.4
- PyArrow version: 16.1.0
- Pandas version: 2.2.2
- `fsspec` version: 2024.5.0
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https://api.github.com/repos/huggingface/datasets/issues/7023 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7023/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7023/comments | https://api.github.com/repos/huggingface/datasets/issues/7023/events | https://github.com/huggingface/datasets/pull/7023 | 2,388,090,424 | PR_kwDODunzps50TDot | 7,023 | Remove dead code for pyarrow < 15.0.0 | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7023). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<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) | 0.005669 / 0.011353 (-0.005684) | 0.004233 / 0.011008 (-0.006775) | 0.063550 / 0.038508 (0.025041) | 0.031269 / 0.023109 (0.008160) | 0.234280 / 0.275898 (-0.041618) | 0.264517 / 0.323480 (-0.058963) | 0.003310 / 0.007986 (-0.004676) | 0.003640 / 0.004328 (-0.000688) | 0.050139 / 0.004250 (0.045889) | 0.046909 / 0.037052 (0.009856) | 0.253101 / 0.258489 (-0.005388) | 0.280281 / 0.293841 (-0.013560) | 0.029558 / 0.128546 (-0.098989) | 0.012537 / 0.075646 (-0.063110) | 0.209624 / 0.419271 (-0.209648) | 0.036857 / 0.043533 (-0.006676) | 0.236957 / 0.255139 (-0.018182) | 0.260510 / 0.283200 (-0.022689) | 0.019802 / 0.141683 (-0.121881) | 1.141747 / 1.452155 (-0.310407) | 1.172617 / 1.492716 (-0.320099) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.107381 / 0.018006 (0.089375) | 0.308401 / 0.000490 (0.307911) | 0.000227 / 0.000200 (0.000027) | 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) | 0.019504 / 0.037411 (-0.017907) | 0.063920 / 0.014526 (0.049394) | 0.075375 / 0.176557 (-0.101181) | 0.122707 / 0.737135 (-0.614428) | 0.080015 / 0.296338 (-0.216324) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.288716 / 0.215209 (0.073507) | 2.862022 / 2.077655 (0.784368) | 1.472510 / 1.504120 (-0.031610) | 1.332989 / 1.541195 (-0.208206) | 1.395140 / 1.468490 (-0.073350) | 0.728042 / 4.584777 (-3.856735) | 2.409914 / 3.745712 (-1.335799) | 2.912514 / 5.269862 (-2.357347) | 1.986980 / 4.565676 (-2.578697) | 0.078587 / 0.424275 (-0.345688) | 0.005601 / 0.007607 (-0.002006) | 0.342510 / 0.226044 (0.116466) | 3.354621 / 2.268929 (1.085692) | 1.852472 / 55.444624 (-53.592153) | 1.542567 / 6.876477 (-5.333910) | 1.726756 / 2.142072 (-0.415317) | 0.794567 / 4.805227 (-4.010660) | 0.135279 / 6.500664 (-6.365386) | 0.042591 / 0.075469 (-0.032878) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.968336 / 1.841788 (-0.873452) | 12.334614 / 8.074308 (4.260305) | 9.638775 / 10.191392 (-0.552617) | 0.143625 / 0.680424 (-0.536799) | 0.015475 / 0.534201 (-0.518726) | 0.313357 / 0.579283 (-0.265926) | 0.271257 / 0.434364 (-0.163107) | 0.362074 / 0.540337 (-0.178263) | 0.468595 / 1.386936 (-0.918341) |\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) | 0.006243 / 0.011353 (-0.005110) | 0.004496 / 0.011008 (-0.006512) | 0.051271 / 0.038508 (0.012763) | 0.035718 / 0.023109 (0.012609) | 0.272623 / 0.275898 (-0.003275) | 0.297060 / 0.323480 (-0.026420) | 0.004801 / 0.007986 (-0.003185) | 0.003060 / 0.004328 (-0.001269) | 0.049990 / 0.004250 (0.045740) | 0.042413 / 0.037052 (0.005360) | 0.281268 / 0.258489 (0.022779) | 0.327224 / 0.293841 (0.033383) | 0.033745 / 0.128546 (-0.094801) | 0.012777 / 0.075646 (-0.062869) | 0.061808 / 0.419271 (-0.357464) | 0.034428 / 0.043533 (-0.009105) | 0.272211 / 0.255139 (0.017072) | 0.327260 / 0.283200 (0.044061) | 0.019756 / 0.141683 (-0.121927) | 1.137768 / 1.452155 (-0.314387) | 1.220347 / 1.492716 (-0.272369) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.099737 / 0.018006 (0.081731) | 0.304627 / 0.000490 (0.304137) | 0.000210 / 0.000200 (0.000011) | 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) | 0.023177 / 0.037411 (-0.014234) | 0.077505 / 0.014526 (0.062979) | 0.088957 / 0.176557 (-0.087599) | 0.129187 / 0.737135 (-0.607948) | 0.090386 / 0.296338 (-0.205953) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.291558 / 0.215209 (0.076349) | 2.874297 / 2.077655 (0.796642) | 1.562316 / 1.504120 (0.058196) | 1.439950 / 1.541195 (-0.101244) | 1.492316 / 1.468490 (0.023826) | 0.729885 / 4.584777 (-3.854892) | 0.985075 / 3.745712 (-2.760637) | 3.108313 / 5.269862 (-2.161549) | 1.998072 / 4.565676 (-2.567604) | 0.079367 / 0.424275 (-0.344908) | 0.005210 / 0.007607 (-0.002398) | 0.347335 / 0.226044 (0.121290) | 3.519375 / 2.268929 (1.250446) | 1.949395 / 55.444624 (-53.495229) | 1.650379 / 6.876477 (-5.226097) | 1.691606 / 2.142072 (-0.450466) | 0.816023 / 4.805227 (-3.989204) | 0.135318 / 6.500664 (-6.365346) | 0.041390 / 0.075469 (-0.034079) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.018964 / 1.841788 (-0.822823) | 13.120135 / 8.074308 (5.045827) | 10.618095 / 10.191392 (0.426703) | 0.134507 / 0.680424 (-0.545917) | 0.015895 / 0.534201 (-0.518306) | 0.302864 / 0.579283 (-0.276420) | 0.131117 / 0.434364 (-0.303247) | 0.342374 / 0.540337 (-0.197964) | 0.441640 / 1.386936 (-0.945296) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c5fdb68cd12d069f05a3db8add8e6feab3c06930 \"CML watermark\")\n"
] | "2024-07-03T09:05:03Z" | "2024-07-03T09:24:46Z" | "2024-07-03T09:17:35Z" | MEMBER | null | Remove dead code for pyarrow < 15.0.0.
Code is dead since the merge of:
- #6892
Fix #7022. | {
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] | null | [] | "2024-07-03T08:52:57Z" | "2024-07-03T09:17:36Z" | "2024-07-03T09:17:36Z" | MEMBER | null | There are code lines specific for pyarrow versions < 15.0.0.
However, we require pyarrow >= 15.0.0 since the merge of PR:
- #6892
Those code lines are now dead code and should be removed. | {
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https://api.github.com/repos/huggingface/datasets/issues/7021 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7021/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7021/comments | https://api.github.com/repos/huggingface/datasets/issues/7021/events | https://github.com/huggingface/datasets/pull/7021 | 2,387,948,935 | PR_kwDODunzps50SlKR | 7,021 | Fix casting list array to fixed size list | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7021). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<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) | 0.005126 / 0.011353 (-0.006227) | 0.003417 / 0.011008 (-0.007591) | 0.063274 / 0.038508 (0.024766) | 0.030896 / 0.023109 (0.007787) | 0.246661 / 0.275898 (-0.029237) | 0.275037 / 0.323480 (-0.048443) | 0.003243 / 0.007986 (-0.004742) | 0.003460 / 0.004328 (-0.000868) | 0.049665 / 0.004250 (0.045414) | 0.045826 / 0.037052 (0.008773) | 0.254360 / 0.258489 (-0.004129) | 0.294934 / 0.293841 (0.001094) | 0.029115 / 0.128546 (-0.099431) | 0.011908 / 0.075646 (-0.063738) | 0.207429 / 0.419271 (-0.211842) | 0.036371 / 0.043533 (-0.007162) | 0.249127 / 0.255139 (-0.006012) | 0.273982 / 0.283200 (-0.009218) | 0.019318 / 0.141683 (-0.122365) | 1.108985 / 1.452155 (-0.343169) | 1.147234 / 1.492716 (-0.345482) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.104830 / 0.018006 (0.086824) | 0.313453 / 0.000490 (0.312964) | 0.000213 / 0.000200 (0.000013) | 0.000043 / 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) | 0.019140 / 0.037411 (-0.018271) | 0.062160 / 0.014526 (0.047634) | 0.073537 / 0.176557 (-0.103020) | 0.119605 / 0.737135 (-0.617530) | 0.074707 / 0.296338 (-0.221632) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.282600 / 0.215209 (0.067391) | 2.805560 / 2.077655 (0.727906) | 1.471312 / 1.504120 (-0.032808) | 1.360920 / 1.541195 (-0.180275) | 1.361132 / 1.468490 (-0.107358) | 0.714791 / 4.584777 (-3.869986) | 2.405224 / 3.745712 (-1.340488) | 2.814498 / 5.269862 (-2.455363) | 1.896792 / 4.565676 (-2.668884) | 0.078138 / 0.424275 (-0.346137) | 0.005430 / 0.007607 (-0.002177) | 0.345529 / 0.226044 (0.119485) | 3.366205 / 2.268929 (1.097277) | 1.862820 / 55.444624 (-53.581805) | 1.555970 / 6.876477 (-5.320507) | 1.665102 / 2.142072 (-0.476970) | 0.798679 / 4.805227 (-4.006548) | 0.132601 / 6.500664 (-6.368064) | 0.041819 / 0.075469 (-0.033650) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.972545 / 1.841788 (-0.869242) | 11.250626 / 8.074308 (3.176318) | 9.211127 / 10.191392 (-0.980265) | 0.130818 / 0.680424 (-0.549605) | 0.014123 / 0.534201 (-0.520078) | 0.298384 / 0.579283 (-0.280899) | 0.269736 / 0.434364 (-0.164628) | 0.341322 / 0.540337 (-0.199015) | 0.466915 / 1.386936 (-0.920021) |\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) | 0.005884 / 0.011353 (-0.005469) | 0.003983 / 0.011008 (-0.007025) | 0.050295 / 0.038508 (0.011787) | 0.033906 / 0.023109 (0.010797) | 0.271364 / 0.275898 (-0.004534) | 0.290652 / 0.323480 (-0.032828) | 0.004503 / 0.007986 (-0.003483) | 0.002946 / 0.004328 (-0.001382) | 0.049336 / 0.004250 (0.045086) | 0.040987 / 0.037052 (0.003935) | 0.283088 / 0.258489 (0.024599) | 0.313132 / 0.293841 (0.019291) | 0.032545 / 0.128546 (-0.096001) | 0.012622 / 0.075646 (-0.063024) | 0.060574 / 0.419271 (-0.358698) | 0.033625 / 0.043533 (-0.009908) | 0.266765 / 0.255139 (0.011626) | 0.286164 / 0.283200 (0.002964) | 0.018840 / 0.141683 (-0.122843) | 1.167874 / 1.452155 (-0.284281) | 1.170767 / 1.492716 (-0.321950) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.102266 / 0.018006 (0.084260) | 0.309530 / 0.000490 (0.309040) | 0.000210 / 0.000200 (0.000010) | 0.000043 / 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) | 0.023879 / 0.037411 (-0.013533) | 0.076837 / 0.014526 (0.062311) | 0.088718 / 0.176557 (-0.087839) | 0.129422 / 0.737135 (-0.607714) | 0.090051 / 0.296338 (-0.206287) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.287325 / 0.215209 (0.072116) | 2.844051 / 2.077655 (0.766397) | 1.552338 / 1.504120 (0.048218) | 1.422390 / 1.541195 (-0.118804) | 1.458580 / 1.468490 (-0.009910) | 0.712103 / 4.584777 (-3.872674) | 0.935116 / 3.745712 (-2.810596) | 2.891878 / 5.269862 (-2.377984) | 1.884683 / 4.565676 (-2.680994) | 0.077810 / 0.424275 (-0.346465) | 0.005087 / 0.007607 (-0.002520) | 0.337981 / 0.226044 (0.111937) | 3.346176 / 2.268929 (1.077248) | 1.892525 / 55.444624 (-53.552100) | 1.595472 / 6.876477 (-5.281004) | 1.595617 / 2.142072 (-0.546455) | 0.779581 / 4.805227 (-4.025647) | 0.131042 / 6.500664 (-6.369623) | 0.040665 / 0.075469 (-0.034804) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.063560 / 1.841788 (-0.778227) | 12.030321 / 8.074308 (3.956013) | 10.213963 / 10.191392 (0.022571) | 0.142954 / 0.680424 (-0.537470) | 0.015700 / 0.534201 (-0.518501) | 0.311536 / 0.579283 (-0.267747) | 0.127064 / 0.434364 (-0.307300) | 0.351636 / 0.540337 (-0.188702) | 0.442281 / 1.386936 (-0.944655) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9ccc1f3d533712baf15cb7a93182add3e5446165 \"CML watermark\")\n"
] | "2024-07-03T07:58:57Z" | "2024-07-03T08:47:49Z" | "2024-07-03T08:41:55Z" | MEMBER | null | Fix casting list array to fixed size list.
This bug was introduced in [datasets-2.17.0](https://github.com/huggingface/datasets/releases/tag/2.17.0) by PR: https://github.com/huggingface/datasets/pull/6283/files#diff-1cb2b66aa9311d729cfd83013dad56cf5afcda35b39dfd0bfe9c3813a049eab0R1899
- #6283
Fix #7020. | {
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] | null | [] | "2024-07-03T07:54:49Z" | "2024-07-03T08:41:56Z" | "2024-07-03T08:41:56Z" | MEMBER | null | When trying to cast a list array to fixed size list, an AttributeError is raised:
> AttributeError: 'pyarrow.lib.FixedSizeListType' object has no attribute 'length'
Steps to reproduce the bug:
```python
import pyarrow as pa
from datasets.table import array_cast
arr = pa.array([[0, 1]])
array_cast(arr, pa.list_(pa.int64(), 2))
```
Stack trace:
```
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-12-6cb90a1d8216> in <module>
3
4 arr = pa.array([[0, 1]])
----> 5 array_cast(arr, pa.list_(pa.int64(), 2))
~/huggingface/datasets/src/datasets/table.py in wrapper(array, *args, **kwargs)
1802 return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
1803 else:
-> 1804 return func(array, *args, **kwargs)
1805
1806 return wrapper
~/huggingface/datasets/src/datasets/table.py in array_cast(array, pa_type, allow_primitive_to_str, allow_decimal_to_str)
1920 else:
1921 array_values = array.values[
-> 1922 array.offset * pa_type.length : (array.offset + len(array)) * pa_type.length
1923 ]
1924 return pa.FixedSizeListArray.from_arrays(_c(array_values, pa_type.value_type), pa_type.list_size)
AttributeError: 'pyarrow.lib.FixedSizeListType' object has no attribute 'length'
``` | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7019). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"@albertvillanova really happy to see this fix.\r\n\r\nHave you attempted to save a dataset to disk after this? I attempted to utilize your fix in a build from source, and while I can now successfully get a dataset object from a polars df containing a large list, I am getting the following error when attempting to save the resulting dataset to disk:\r\n```\r\nFile \"/Users/x/VSCodeProjects/HuggingFace/hf.py\", line 9, in <module>\r\n dataset.save_to_disk(\"data/test.hf\")\r\n File \"/Users/x/VSCodeProjects/HuggingFace/datasets/src/datasets/arrow_dataset.py\", line 1591, in save_to_disk\r\n for kwargs in kwargs_per_job:\r\n File \"/Users/x/VSCodeProjects/HuggingFace/datasets/src/datasets/arrow_dataset.py\", line 1568, in <genexpr>\r\n \"shard\": self.shard(num_shards=num_shards, index=shard_idx, contiguous=True),\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/Users/x/VSCodeProjects/HuggingFace/datasets/src/datasets/arrow_dataset.py\", line 4757, in shard\r\n return self.select(\r\n ^^^^^^^^^^^^\r\n File \"/Users/x/VSCodeProjects/HuggingFace/datasets/src/datasets/arrow_dataset.py\", line 567, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/Users/x/VSCodeProjects/HuggingFace/datasets/src/datasets/fingerprint.py\", line 482, in wrapper\r\n out = func(dataset, *args, **kwargs)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/Users/x/VSCodeProjects/HuggingFace/datasets/src/datasets/arrow_dataset.py\", line 3892, in select\r\n return self._select_contiguous(start, length, new_fingerprint=new_fingerprint)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/Users/x/VSCodeProjects/HuggingFace/datasets/src/datasets/arrow_dataset.py\", line 567, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/Users/x/VSCodeProjects/HuggingFace/datasets/src/datasets/fingerprint.py\", line 482, in wrapper\r\n out = func(dataset, *args, **kwargs)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/Users/x/VSCodeProjects/HuggingFace/datasets/src/datasets/arrow_dataset.py\", line 3955, in _select_contiguous\r\n return Dataset(\r\n ^^^^^^^^\r\n File \"/Users/x/VSCodeProjects/HuggingFace/datasets/src/datasets/arrow_dataset.py\", line 731, in __init__\r\n raise ValueError(\r\nValueError: External features info don't match the dataset:\r\nGot\r\n{'0': Value(dtype='int64', id=None), '1': Value(dtype='int64', id=None), '2': Value(dtype='int64', id=None), '3': Value(dtype='int64', id=None), '4': Value(dtype='int64', id=None), '5': Value(dtype='int64', id=None), '6': Value(dtype='int64', id=None), '7': Value(dtype='int64', id=None), '8': Value(dtype='int64', id=None), '9': Value(dtype='int64', id=None), '10': Value(dtype='int64', id=None), '11': Value(dtype='int64', id=None), '12': Value(dtype='int64', id=None), '13': Value(dtype='int64', id=None), '14': Value(dtype='int64', id=None), '15': Value(dtype='int64', id=None), '16': Value(dtype='int64', id=None), '17': Value(dtype='int64', id=None), '18': Value(dtype='int64', id=None), '19': Value(dtype='int64', id=None), 'A': Sequence(feature=Value(dtype='int64', id=None), length=-1, large=False, id=None), 'B': Sequence(feature=Value(dtype='int64', id=None), length=-1, large=False, id=None), 'C': Sequence(feature=Value(dtype='int64', id=None), length=-1, large=False, id=None), 'D': Sequence(feature=Value(dtype='int64', id=None), length=-1, large=False, id=None), '__index_level_0__': Value(dtype='int64', id=None)}\r\nwith type\r\nstruct<0: int64, 1: int64, 2: int64, 3: int64, 4: int64, 5: int64, 6: int64, 7: int64, 8: int64, 9: int64, 10: int64, 11: int64, 12: int64, 13: int64, 14: int64, 15: int64, 16: int64, 17: int64, 18: int64, 19: int64, A: list<item: int64>, B: list<item: int64>, C: list<item: int64>, D: list<item: int64>, __index_level_0__: int64>\r\n\r\nbut expected something like\r\n{'0': Value(dtype='int64', id=None), '1': Value(dtype='int64', id=None), '2': Value(dtype='int64', id=None), '3': Value(dtype='int64', id=None), '4': Value(dtype='int64', id=None), '5': Value(dtype='int64', id=None), '6': Value(dtype='int64', id=None), '7': Value(dtype='int64', id=None), '8': Value(dtype='int64', id=None), '9': Value(dtype='int64', id=None), '10': Value(dtype='int64', id=None), '11': Value(dtype='int64', id=None), '12': Value(dtype='int64', id=None), '13': Value(dtype='int64', id=None), '14': Value(dtype='int64', id=None), '15': Value(dtype='int64', id=None), '16': Value(dtype='int64', id=None), '17': Value(dtype='int64', id=None), '18': Value(dtype='int64', id=None), '19': Value(dtype='int64', id=None), 'A': Sequence(feature=Value(dtype='int64', id=None), length=-1, large=True, id=None), 'B': Sequence(feature=Value(dtype='int64', id=None), length=-1, large=True, id=None), 'C': Sequence(feature=Value(dtype='int64', id=None), length=-1, large=True, id=None), 'D': Sequence(feature=Value(dtype='int64', id=None), length=-1, large=True, id=None), '__index_level_0__': Value(dtype='int64', id=None)}\r\nwith type\r\nstruct<0: int64, 1: int64, 2: int64, 3: int64, 4: int64, 5: int64, 6: int64, 7: int64, 8: int64, 9: int64, 10: int64, 11: int64, 12: int64, 13: int64, 14: int64, 15: int64, 16: int64, 17: int64, 18: int64, 19: int64, A: large_list<item: int64>, B: large_list<item: int64>, C: large_list<item: int64>, D: large_list<item: int64>, __index_level_0__: int64>\r\n```\r\n\r\ncode to reproduce is actually 2 separate scripts below.\r\n\r\ncreating test data:\r\n```\r\nimport pandas as pd\r\nimport numpy as np\r\n\r\ndf = pd.DataFrame(np.random.randint(0, 100000, size=(100000, 20)))\r\nfeatureVector = np.random.randint(0, 100000, size=(100000, 1000)).tolist()\r\n\r\ndf['A'] = featureVector\r\ndf['B'] = featureVector\r\ndf['C'] = featureVector\r\ndf['D'] = featureVector\r\n\r\ndf.to_parquet('data/train_data.parquet', engine='pyarrow')\r\n```\r\n\r\nloading data, converting to HF dataset, attempting to save to disk\r\n```\r\nimport datasets\r\nimport polars as pl\r\n\r\ndf = pl.read_parquet('data/train_data.parquet')\r\n\r\ndataset = datasets.Dataset.from_polars(df)\r\n\r\ndataset.save_to_disk(\"data/test.hf\")\r\n```\r\n\r\nIf this isn't the appropriate place to put this, let me know. Since it isn't merged yet I didn't think raising an issue was appropriate.",
"Thanks for your useful review comments, @dakotamurdock. \r\n\r\nI am investigating that issue to fix it in this PR.",
"Hi @albertvillanova thanks for your work! When is the fix planned to be released?\r\n\r\nI tested your feature branch and managed to load from a polars dataframe with the large_list type, persist to disk, load and convert it again. Also asserted that they are both equal.\r\n\r\n```\r\n> print(df[:3])\r\n\r\nshape: (3, 6)\r\n┌─────────────────┬─────────────────┬──────────┬────────────────┬─────────────────┬────────────────┐\r\n│ plain_text ┆ title ┆ language ┆ language_score ┆ plain_text_hash ┆ response_objec │\r\n│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ t │\r\n│ str ┆ str ┆ str ┆ f64 ┆ str ┆ --- │\r\n│ ┆ ┆ ┆ ┆ ┆ list[struct[3] │\r\n│ ┆ ┆ ┆ ┆ ┆ ] │\r\n╞═════════════════╪═════════════════╪══════════╪════════════════╪═════════════════╪════════════════╡\r\n│ Royal fans ┆ Prince Louis ┆ en ┆ 0.987661 ┆ 37e438dccb283d1 ┆ [{\"Prince Loui │\r\n│ delighted by ┆ delights crowd ┆ ┆ ┆ f3be2d9d4bb7ed3 ┆ s\",\"waves\",\"cr │\r\n│ Prince… ┆ wi… ┆ ┆ ┆ … ┆ ow… │\r\n│ There have been ┆ Reactions After ┆ en ┆ 0.991371 ┆ 37fafbb69dfcfa5 ┆ [{\"David │\r\n│ diverse reacti… ┆ Davido Alleged… ┆ ┆ ┆ 303d5e3e6917a35 ┆ Adedeji │\r\n│ ┆ ┆ ┆ ┆ … ┆ Adeleke\",\"is … │\r\n│ Betfred will ┆ Betfred to pay ┆ en ┆ 0.980579 ┆ 922e19e6f598e9b ┆ [{\"Betfred\",\"w │\r\n│ pay a £3.25 ┆ £3.25 million ┆ ┆ ┆ 14cdb6772829cbd ┆ ill │\r\n│ milli… ┆ f… ┆ ┆ ┆ … ┆ pay\",\"£3.25 … │\r\n└─────────────────┴─────────────────┴──────────┴────────────────┴─────────────────┴────────────────┘\r\n\r\n> Dataset.from_polars(df).save_to_disk('./test')\r\n\r\nSaving the dataset (1/1 shards): 100%|██████████| 14997/14997 [00:00<00:00, 225472.09 examples/s]\r\n\r\n> another_df = load_from_disk('./test').to_polars()\r\n> print(another_df[:3])\r\n\r\nshape: (3, 6)\r\n┌─────────────────┬─────────────────┬──────────┬────────────────┬─────────────────┬────────────────┐\r\n│ plain_text ┆ title ┆ language ┆ language_score ┆ plain_text_hash ┆ response_objec │\r\n│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ t │\r\n│ str ┆ str ┆ str ┆ f64 ┆ str ┆ --- │\r\n│ ┆ ┆ ┆ ┆ ┆ list[struct[3] │\r\n│ ┆ ┆ ┆ ┆ ┆ ] │\r\n╞═════════════════╪═════════════════╪══════════╪════════════════╪═════════════════╪════════════════╡\r\n│ Royal fans ┆ Prince Louis ┆ en ┆ 0.987661 ┆ 37e438dccb283d1 ┆ [{\"Prince Loui │\r\n│ delighted by ┆ delights crowd ┆ ┆ ┆ f3be2d9d4bb7ed3 ┆ s\",\"waves\",\"cr │\r\n│ Prince… ┆ wi… ┆ ┆ ┆ … ┆ ow… │\r\n│ There have been ┆ Reactions After ┆ en ┆ 0.991371 ┆ 37fafbb69dfcfa5 ┆ [{\"David │\r\n│ diverse reacti… ┆ Davido Alleged… ┆ ┆ ┆ 303d5e3e6917a35 ┆ Adedeji │\r\n│ ┆ ┆ ┆ ┆ … ┆ Adeleke\",\"is … │\r\n│ Betfred will ┆ Betfred to pay ┆ en ┆ 0.980579 ┆ 922e19e6f598e9b ┆ [{\"Betfred\",\"w │\r\n│ pay a £3.25 ┆ £3.25 million ┆ ┆ ┆ 14cdb6772829cbd ┆ ill │\r\n│ milli… ┆ f… ┆ ┆ ┆ … ┆ pay\",\"£3.25 … │\r\n└─────────────────┴─────────────────┴──────────┴────────────────┴─────────────────┴────────────────┘\r\n\r\n> another_df.equals(df)\r\n\r\nTrue\r\n```\r\n\r\nThis is indeed the error I was getting with datasets==2.19.1\r\n```\r\nDataset.from_polars(df).save_to_disk('./test')\r\nValueError: Arrow type large_list<item: struct<entity1: large_string, relationship: large_string, entity2: large_string>> does not have a datasets dtype equivalent.\r\n```",
"@EdoardoLuciani thanks for your feedback!\r\nI think we should make a new release soon: last one was on June 13.\r\nWhat do you think, @huggingface/datasets?\r\nThe only potential problem I see are the breaking changes once we remove all deprecated code...",
"Your issue was fixed, @dakotamurdock.",
"I am working in a big refactoring of the approach to support large_list: implement a new `LargeList` type instead of using `Sequence.large` attribute.",
"There are many feature-functions and most of them are not properly covered by tests.\r\n\r\nI am adding tests and fixing these feature-functions.",
"I think this PR is ready for review, @huggingface/datasets.",
"<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) | 0.005640 / 0.011353 (-0.005713) | 0.003926 / 0.011008 (-0.007083) | 0.063103 / 0.038508 (0.024595) | 0.032088 / 0.023109 (0.008979) | 0.238615 / 0.275898 (-0.037283) | 0.268379 / 0.323480 (-0.055101) | 0.003146 / 0.007986 (-0.004840) | 0.002813 / 0.004328 (-0.001516) | 0.049681 / 0.004250 (0.045431) | 0.044577 / 0.037052 (0.007525) | 0.249782 / 0.258489 (-0.008708) | 0.282548 / 0.293841 (-0.011293) | 0.029986 / 0.128546 (-0.098560) | 0.012474 / 0.075646 (-0.063172) | 0.203347 / 0.419271 (-0.215925) | 0.035950 / 0.043533 (-0.007583) | 0.243410 / 0.255139 (-0.011729) | 0.267056 / 0.283200 (-0.016143) | 0.022086 / 0.141683 (-0.119597) | 1.145513 / 1.452155 (-0.306641) | 1.207583 / 1.492716 (-0.285133) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.095584 / 0.018006 (0.077578) | 0.304264 / 0.000490 (0.303774) | 0.000215 / 0.000200 (0.000015) | 0.000043 / 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) | 0.019460 / 0.037411 (-0.017952) | 0.062268 / 0.014526 (0.047742) | 0.074943 / 0.176557 (-0.101613) | 0.121657 / 0.737135 (-0.615478) | 0.075930 / 0.296338 (-0.220408) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.288975 / 0.215209 (0.073766) | 2.869610 / 2.077655 (0.791955) | 1.491057 / 1.504120 (-0.013063) | 1.384160 / 1.541195 (-0.157035) | 1.380977 / 1.468490 (-0.087513) | 0.723181 / 4.584777 (-3.861596) | 2.397960 / 3.745712 (-1.347752) | 2.899919 / 5.269862 (-2.369942) | 1.878714 / 4.565676 (-2.686962) | 0.078162 / 0.424275 (-0.346113) | 0.005115 / 0.007607 (-0.002493) | 0.337599 / 0.226044 (0.111555) | 3.367450 / 2.268929 (1.098522) | 1.823745 / 55.444624 (-53.620880) | 1.540528 / 6.876477 (-5.335949) | 1.546146 / 2.142072 (-0.595927) | 0.796927 / 4.805227 (-4.008300) | 0.134389 / 6.500664 (-6.366275) | 0.042298 / 0.075469 (-0.033172) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.959687 / 1.841788 (-0.882101) | 11.505269 / 8.074308 (3.430961) | 9.631551 / 10.191392 (-0.559841) | 0.142301 / 0.680424 (-0.538123) | 0.013912 / 0.534201 (-0.520289) | 0.314940 / 0.579283 (-0.264343) | 0.263134 / 0.434364 (-0.171229) | 0.352966 / 0.540337 (-0.187372) | 0.440421 / 1.386936 (-0.946515) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005878 / 0.011353 (-0.005475) | 0.003866 / 0.011008 (-0.007142) | 0.051347 / 0.038508 (0.012839) | 0.032662 / 0.023109 (0.009553) | 0.270701 / 0.275898 (-0.005197) | 0.345277 / 0.323480 (0.021797) | 0.004485 / 0.007986 (-0.003501) | 0.002782 / 0.004328 (-0.001546) | 0.048302 / 0.004250 (0.044051) | 0.040355 / 0.037052 (0.003303) | 0.285196 / 0.258489 (0.026707) | 0.320339 / 0.293841 (0.026499) | 0.032937 / 0.128546 (-0.095610) | 0.012298 / 0.075646 (-0.063348) | 0.061579 / 0.419271 (-0.357692) | 0.034129 / 0.043533 (-0.009403) | 0.265985 / 0.255139 (0.010846) | 0.302066 / 0.283200 (0.018867) | 0.018812 / 0.141683 (-0.122871) | 1.175705 / 1.452155 (-0.276450) | 1.197207 / 1.492716 (-0.295510) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.096076 / 0.018006 (0.078070) | 0.312793 / 0.000490 (0.312303) | 0.000228 / 0.000200 (0.000028) | 0.000053 / 0.000054 (-0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022858 / 0.037411 (-0.014553) | 0.077160 / 0.014526 (0.062634) | 0.089742 / 0.176557 (-0.086815) | 0.130929 / 0.737135 (-0.606207) | 0.093431 / 0.296338 (-0.202907) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.298884 / 0.215209 (0.083675) | 2.961050 / 2.077655 (0.883395) | 1.620694 / 1.504120 (0.116574) | 1.499331 / 1.541195 (-0.041863) | 1.513118 / 1.468490 (0.044628) | 0.734738 / 4.584777 (-3.850039) | 0.972978 / 3.745712 (-2.772734) | 2.928172 / 5.269862 (-2.341690) | 1.903667 / 4.565676 (-2.662010) | 0.079207 / 0.424275 (-0.345068) | 0.005803 / 0.007607 (-0.001804) | 0.350144 / 0.226044 (0.124099) | 3.519456 / 2.268929 (1.250528) | 1.983809 / 55.444624 (-53.460815) | 1.690527 / 6.876477 (-5.185950) | 1.739301 / 2.142072 (-0.402772) | 0.802045 / 4.805227 (-4.003182) | 0.133041 / 6.500664 (-6.367623) | 0.042112 / 0.075469 (-0.033357) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.030056 / 1.841788 (-0.811731) | 12.077692 / 8.074308 (4.003384) | 9.988253 / 10.191392 (-0.203139) | 0.142745 / 0.680424 (-0.537679) | 0.015842 / 0.534201 (-0.518359) | 0.299055 / 0.579283 (-0.280228) | 0.123788 / 0.434364 (-0.310576) | 0.352782 / 0.540337 (-0.187555) | 0.451140 / 1.386936 (-0.935796) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0cf0be8906063d09456285be9c9f7ce5789726ae \"CML watermark\")\n"
] | "2024-07-02T09:52:52Z" | "2024-08-12T14:49:45Z" | "2024-08-12T14:43:45Z" | MEMBER | null | Allow Polars round trip by supporting pyarrow large list.
Fix #6834, fix #6984.
Supersede and close #4800, close #6835, close #6986. | {
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https://api.github.com/repos/huggingface/datasets/issues/7018 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7018/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7018/comments | https://api.github.com/repos/huggingface/datasets/issues/7018/events | https://github.com/huggingface/datasets/issues/7018 | 2,383,700,286 | I_kwDODunzps6OFGE- | 7,018 | `load_dataset` fails to load dataset saved by `save_to_disk` | {
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"In my case the error was:\r\n```\r\nValueError: You are trying to load a dataset that was saved using `save_to_disk`. Please use `load_from_disk` instead.\r\n```\r\nDid you try `load_from_disk`?",
"More generally, any reason there is no API consistency between save_to_disk and push_to_hub ? \r\n\r\nWould be nice to be able to save_to_disk and then upload manually to the hub and load_dataset (which works in some situations but not all)..."
] | "2024-07-01T12:19:19Z" | "2024-08-05T09:21:55Z" | null | NONE | null | ### Describe the bug
This code fails to load the dataset it just saved:
```python
from datasets import load_dataset
from transformers import AutoTokenizer
MODEL = "google-bert/bert-base-cased"
tokenizer = AutoTokenizer.from_pretrained(MODEL)
dataset = load_dataset("yelp_review_full")
def tokenize_function(examples):
return tokenizer(examples["text"], padding="max_length", truncation=True)
tokenized_datasets = dataset.map(tokenize_function, batched=True)
tokenized_datasets.save_to_disk("dataset")
tokenized_datasets = load_dataset("dataset/") # raises
```
It raises `ValueError: Couldn't infer the same data file format for all splits. Got {NamedSplit('train'): ('arrow', {}), NamedSplit('test'): ('json', {})}`.
I believe this bug is caused by the [logic that tries to infer dataset format](https://github.com/huggingface/datasets/blob/9af8dd3de7626183a9a9ec8973cebc672d690400/src/datasets/load.py#L556). It counts the most common file extension. However, a small dataset can fit in a single `.arrow` file and have two JSON metadata files, causing the format to be inferred as JSON:
```shell
$ ls -l dataset/test
-rw-r--r-- 1 sliedes sliedes 191498784 Jul 1 13:55 data-00000-of-00001.arrow
-rw-r--r-- 1 sliedes sliedes 1730 Jul 1 13:55 dataset_info.json
-rw-r--r-- 1 sliedes sliedes 249 Jul 1 13:55 state.json
```
### Steps to reproduce the bug
Execute the code above.
### Expected behavior
The dataset is loaded successfully.
### Environment info
- `datasets` version: 2.20.0
- Platform: Linux-6.9.3-arch1-1-x86_64-with-glibc2.39
- Python version: 3.12.4
- `huggingface_hub` version: 0.23.4
- PyArrow version: 16.1.0
- Pandas version: 2.2.2
- `fsspec` version: 2024.5.0
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https://api.github.com/repos/huggingface/datasets/issues/7017 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7017/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7017/comments | https://api.github.com/repos/huggingface/datasets/issues/7017/events | https://github.com/huggingface/datasets/pull/7017 | 2,383,647,419 | PR_kwDODunzps50D3gi | 7,017 | Support fsspec 2024.6.1 | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7017). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<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) | 0.005520 / 0.011353 (-0.005832) | 0.004216 / 0.011008 (-0.006792) | 0.063465 / 0.038508 (0.024957) | 0.032116 / 0.023109 (0.009007) | 0.242486 / 0.275898 (-0.033412) | 0.262554 / 0.323480 (-0.060925) | 0.004218 / 0.007986 (-0.003768) | 0.003264 / 0.004328 (-0.001064) | 0.050306 / 0.004250 (0.046056) | 0.044995 / 0.037052 (0.007942) | 0.257797 / 0.258489 (-0.000693) | 0.284595 / 0.293841 (-0.009246) | 0.030623 / 0.128546 (-0.097924) | 0.012245 / 0.075646 (-0.063401) | 0.205496 / 0.419271 (-0.213775) | 0.039327 / 0.043533 (-0.004206) | 0.246834 / 0.255139 (-0.008305) | 0.269296 / 0.283200 (-0.013903) | 0.017714 / 0.141683 (-0.123969) | 1.127246 / 1.452155 (-0.324909) | 1.172147 / 1.492716 (-0.320569) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.137621 / 0.018006 (0.119615) | 0.299843 / 0.000490 (0.299353) | 0.000248 / 0.000200 (0.000048) | 0.000051 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018968 / 0.037411 (-0.018443) | 0.062636 / 0.014526 (0.048111) | 0.074098 / 0.176557 (-0.102459) | 0.121139 / 0.737135 (-0.615996) | 0.075121 / 0.296338 (-0.221217) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.289907 / 0.215209 (0.074698) | 2.872250 / 2.077655 (0.794595) | 1.508635 / 1.504120 (0.004515) | 1.345356 / 1.541195 (-0.195839) | 1.361858 / 1.468490 (-0.106632) | 0.738961 / 4.584777 (-3.845816) | 2.414616 / 3.745712 (-1.331097) | 2.843464 / 5.269862 (-2.426398) | 1.953716 / 4.565676 (-2.611961) | 0.079063 / 0.424275 (-0.345212) | 0.005498 / 0.007607 (-0.002109) | 0.346211 / 0.226044 (0.120166) | 3.446294 / 2.268929 (1.177366) | 1.857191 / 55.444624 (-53.587433) | 1.536924 / 6.876477 (-5.339553) | 1.655782 / 2.142072 (-0.486290) | 0.800508 / 4.805227 (-4.004719) | 0.136116 / 6.500664 (-6.364548) | 0.042648 / 0.075469 (-0.032821) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.964286 / 1.841788 (-0.877501) | 11.574645 / 8.074308 (3.500336) | 9.351631 / 10.191392 (-0.839761) | 0.139693 / 0.680424 (-0.540731) | 0.014368 / 0.534201 (-0.519833) | 0.303953 / 0.579283 (-0.275330) | 0.263302 / 0.434364 (-0.171062) | 0.342436 / 0.540337 (-0.197901) | 0.457195 / 1.386936 (-0.929741) |\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) | 0.005526 / 0.011353 (-0.005827) | 0.003959 / 0.011008 (-0.007050) | 0.049979 / 0.038508 (0.011471) | 0.032695 / 0.023109 (0.009586) | 0.269461 / 0.275898 (-0.006437) | 0.296622 / 0.323480 (-0.026858) | 0.004410 / 0.007986 (-0.003576) | 0.002708 / 0.004328 (-0.001621) | 0.048413 / 0.004250 (0.044163) | 0.040567 / 0.037052 (0.003515) | 0.278854 / 0.258489 (0.020364) | 0.318839 / 0.293841 (0.024998) | 0.031228 / 0.128546 (-0.097318) | 0.012411 / 0.075646 (-0.063236) | 0.060077 / 0.419271 (-0.359194) | 0.033072 / 0.043533 (-0.010461) | 0.275281 / 0.255139 (0.020142) | 0.292588 / 0.283200 (0.009388) | 0.018218 / 0.141683 (-0.123465) | 1.124877 / 1.452155 (-0.327278) | 1.164880 / 1.492716 (-0.327836) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.095098 / 0.018006 (0.077092) | 0.298341 / 0.000490 (0.297851) | 0.000225 / 0.000200 (0.000025) | 0.000049 / 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) | 0.022502 / 0.037411 (-0.014909) | 0.076650 / 0.014526 (0.062124) | 0.088851 / 0.176557 (-0.087705) | 0.128261 / 0.737135 (-0.608875) | 0.089305 / 0.296338 (-0.207033) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.298704 / 0.215209 (0.083495) | 2.917605 / 2.077655 (0.839951) | 1.568964 / 1.504120 (0.064844) | 1.437668 / 1.541195 (-0.103527) | 1.458787 / 1.468490 (-0.009704) | 0.732347 / 4.584777 (-3.852430) | 0.960834 / 3.745712 (-2.784878) | 2.947899 / 5.269862 (-2.321963) | 1.885576 / 4.565676 (-2.680100) | 0.079093 / 0.424275 (-0.345182) | 0.005199 / 0.007607 (-0.002408) | 0.353754 / 0.226044 (0.127710) | 3.495197 / 2.268929 (1.226268) | 1.936840 / 55.444624 (-53.507785) | 1.622797 / 6.876477 (-5.253680) | 1.627132 / 2.142072 (-0.514940) | 0.804007 / 4.805227 (-4.001221) | 0.135990 / 6.500664 (-6.364674) | 0.041606 / 0.075469 (-0.033863) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.004860 / 1.841788 (-0.836928) | 12.027573 / 8.074308 (3.953265) | 10.478055 / 10.191392 (0.286663) | 0.143946 / 0.680424 (-0.536477) | 0.015538 / 0.534201 (-0.518663) | 0.302592 / 0.579283 (-0.276691) | 0.123177 / 0.434364 (-0.311187) | 0.340752 / 0.540337 (-0.199585) | 0.436536 / 1.386936 (-0.950400) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#100361d7ccae451a34c6bd9e48dee55d6a3c6006 \"CML watermark\")\n"
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"There is an open issue #2514 about this which also proposes solutions."
] | "2024-07-01T09:01:06Z" | "2024-07-20T06:51:58Z" | null | NONE | null | ### Feature request
`drop_duplicates` method for huggingface datasets (similiar in simplicity to the `pandas` one)
### Motivation
Ease of use
### Your contribution
I don't think i am good enough to help | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7015 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7015/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7015/comments | https://api.github.com/repos/huggingface/datasets/issues/7015/events | https://github.com/huggingface/datasets/pull/7015 | 2,383,151,220 | PR_kwDODunzps50CJuE | 7,015 | add split argument to Generator | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7015). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"@albertvillanova thanks for the review, please take a look",
"@albertvillanova please take a look",
"Thank you again! Your PR is 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) | 0.005267 / 0.011353 (-0.006086) | 0.003711 / 0.011008 (-0.007297) | 0.062288 / 0.038508 (0.023780) | 0.031357 / 0.023109 (0.008248) | 0.233592 / 0.275898 (-0.042306) | 0.257722 / 0.323480 (-0.065758) | 0.003124 / 0.007986 (-0.004861) | 0.003335 / 0.004328 (-0.000994) | 0.048594 / 0.004250 (0.044344) | 0.043853 / 0.037052 (0.006801) | 0.248589 / 0.258489 (-0.009900) | 0.278474 / 0.293841 (-0.015367) | 0.029573 / 0.128546 (-0.098973) | 0.011779 / 0.075646 (-0.063868) | 0.204989 / 0.419271 (-0.214282) | 0.035734 / 0.043533 (-0.007799) | 0.240064 / 0.255139 (-0.015075) | 0.263105 / 0.283200 (-0.020094) | 0.018764 / 0.141683 (-0.122919) | 1.115705 / 1.452155 (-0.336449) | 1.175457 / 1.492716 (-0.317260) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092664 / 0.018006 (0.074657) | 0.297893 / 0.000490 (0.297403) | 0.000217 / 0.000200 (0.000017) | 0.000047 / 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) | 0.019056 / 0.037411 (-0.018355) | 0.062472 / 0.014526 (0.047946) | 0.073462 / 0.176557 (-0.103094) | 0.119723 / 0.737135 (-0.617412) | 0.074420 / 0.296338 (-0.221919) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.283131 / 0.215209 (0.067922) | 2.776694 / 2.077655 (0.699039) | 1.455586 / 1.504120 (-0.048534) | 1.323902 / 1.541195 (-0.217293) | 1.333169 / 1.468490 (-0.135321) | 0.723921 / 4.584777 (-3.860856) | 2.385842 / 3.745712 (-1.359870) | 2.926843 / 5.269862 (-2.343018) | 1.896773 / 4.565676 (-2.668903) | 0.079754 / 0.424275 (-0.344521) | 0.005188 / 0.007607 (-0.002419) | 0.342466 / 0.226044 (0.116421) | 3.404204 / 2.268929 (1.135275) | 1.856575 / 55.444624 (-53.588049) | 1.554507 / 6.876477 (-5.321970) | 1.564065 / 2.142072 (-0.578007) | 0.810363 / 4.805227 (-3.994864) | 0.135537 / 6.500664 (-6.365127) | 0.041987 / 0.075469 (-0.033482) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.962288 / 1.841788 (-0.879500) | 11.310837 / 8.074308 (3.236529) | 9.630034 / 10.191392 (-0.561358) | 0.131108 / 0.680424 (-0.549316) | 0.015225 / 0.534201 (-0.518976) | 0.304211 / 0.579283 (-0.275072) | 0.272707 / 0.434364 (-0.161657) | 0.341550 / 0.540337 (-0.198787) | 0.444528 / 1.386936 (-0.942408) |\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) | 0.005665 / 0.011353 (-0.005688) | 0.003916 / 0.011008 (-0.007092) | 0.049946 / 0.038508 (0.011438) | 0.031760 / 0.023109 (0.008651) | 0.273826 / 0.275898 (-0.002072) | 0.300193 / 0.323480 (-0.023287) | 0.004350 / 0.007986 (-0.003635) | 0.002749 / 0.004328 (-0.001579) | 0.048451 / 0.004250 (0.044201) | 0.039798 / 0.037052 (0.002746) | 0.284570 / 0.258489 (0.026081) | 0.318855 / 0.293841 (0.025014) | 0.032724 / 0.128546 (-0.095822) | 0.012103 / 0.075646 (-0.063543) | 0.059857 / 0.419271 (-0.359414) | 0.034185 / 0.043533 (-0.009348) | 0.276079 / 0.255139 (0.020940) | 0.294070 / 0.283200 (0.010871) | 0.018168 / 0.141683 (-0.123515) | 1.149681 / 1.452155 (-0.302473) | 1.191349 / 1.492716 (-0.301367) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092676 / 0.018006 (0.074669) | 0.304971 / 0.000490 (0.304481) | 0.000203 / 0.000200 (0.000003) | 0.000050 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023110 / 0.037411 (-0.014301) | 0.079117 / 0.014526 (0.064591) | 0.087457 / 0.176557 (-0.089099) | 0.128295 / 0.737135 (-0.608840) | 0.089747 / 0.296338 (-0.206592) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.305158 / 0.215209 (0.089949) | 2.992277 / 2.077655 (0.914623) | 1.595369 / 1.504120 (0.091249) | 1.462955 / 1.541195 (-0.078240) | 1.476269 / 1.468490 (0.007779) | 0.731652 / 4.584777 (-3.853124) | 0.961053 / 3.745712 (-2.784659) | 2.800259 / 5.269862 (-2.469602) | 1.881249 / 4.565676 (-2.684428) | 0.079503 / 0.424275 (-0.344772) | 0.005252 / 0.007607 (-0.002355) | 0.354921 / 0.226044 (0.128877) | 3.495272 / 2.268929 (1.226343) | 1.956419 / 55.444624 (-53.488205) | 1.654941 / 6.876477 (-5.221536) | 1.782506 / 2.142072 (-0.359567) | 0.816487 / 4.805227 (-3.988741) | 0.135870 / 6.500664 (-6.364794) | 0.041114 / 0.075469 (-0.034355) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.050346 / 1.841788 (-0.791442) | 12.510129 / 8.074308 (4.435821) | 10.524835 / 10.191392 (0.333443) | 0.152388 / 0.680424 (-0.528036) | 0.016073 / 0.534201 (-0.518128) | 0.301956 / 0.579283 (-0.277327) | 0.126871 / 0.434364 (-0.307493) | 0.339554 / 0.540337 (-0.200783) | 0.435873 / 1.386936 (-0.951064) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ead089d949febdce415d79ef3802e188316c0b26 \"CML watermark\")\n"
] | "2024-07-01T08:09:25Z" | "2024-07-26T09:37:51Z" | "2024-07-26T09:31:56Z" | CONTRIBUTOR | null | ## Actual
When creating a multi-split dataset using generators like
```python
datasets.DatasetDict({
"val": datasets.Dataset.from_generator(
generator=generator_val,
features=features
),
"test": datasets.Dataset.from_generator(
generator=generator_test,
features=features,
)
})
```
It displays (for both test and val)
```
Generating train split
```
## Expected
I would like to be able to improve this behavior by doing
```python
datasets.DatasetDict({
"val": datasets.Dataset.from_generator(
generator=generator_val,
features=features,
split="val"
),
"test": datasets.Dataset.from_generator(
generator=generator_test,
features=features,
split="test"
)
})
```
It would display
```
Generating val split
```
and
```
Generating test split
```
## Proposal
Current PR is adding an explicit `split` argument and replace the implicit "train" split in the following classes/function :
* Generator
* from_generator
* AbstractDatasetInputStream
* GeneratorDatasetInputStream
Please share your feedbacks | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7014). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"The failing CI tests are unrelated to this PR.\r\n\r\nWe can see that now the integration tests on Windows finish in a reasonable amount of time, e.g. 8m 10s.",
"<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) | 0.005219 / 0.011353 (-0.006134) | 0.003825 / 0.011008 (-0.007183) | 0.063082 / 0.038508 (0.024574) | 0.031258 / 0.023109 (0.008149) | 0.232288 / 0.275898 (-0.043610) | 0.261140 / 0.323480 (-0.062340) | 0.003185 / 0.007986 (-0.004801) | 0.002807 / 0.004328 (-0.001522) | 0.049438 / 0.004250 (0.045188) | 0.045112 / 0.037052 (0.008059) | 0.245327 / 0.258489 (-0.013162) | 0.277941 / 0.293841 (-0.015900) | 0.029190 / 0.128546 (-0.099357) | 0.012071 / 0.075646 (-0.063575) | 0.204351 / 0.419271 (-0.214921) | 0.036546 / 0.043533 (-0.006987) | 0.235999 / 0.255139 (-0.019140) | 0.269069 / 0.283200 (-0.014131) | 0.019047 / 0.141683 (-0.122636) | 1.117213 / 1.452155 (-0.334941) | 1.202807 / 1.492716 (-0.289909) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.096680 / 0.018006 (0.078674) | 0.304513 / 0.000490 (0.304023) | 0.000211 / 0.000200 (0.000011) | 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) | 0.019526 / 0.037411 (-0.017885) | 0.062239 / 0.014526 (0.047713) | 0.073988 / 0.176557 (-0.102569) | 0.122156 / 0.737135 (-0.614980) | 0.075727 / 0.296338 (-0.220611) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.284125 / 0.215209 (0.068916) | 2.804235 / 2.077655 (0.726581) | 1.463729 / 1.504120 (-0.040390) | 1.337854 / 1.541195 (-0.203341) | 1.340435 / 1.468490 (-0.128055) | 0.711647 / 4.584777 (-3.873130) | 2.365194 / 3.745712 (-1.380518) | 2.839193 / 5.269862 (-2.430669) | 1.909730 / 4.565676 (-2.655947) | 0.077399 / 0.424275 (-0.346876) | 0.005432 / 0.007607 (-0.002175) | 0.332281 / 0.226044 (0.106236) | 3.301854 / 2.268929 (1.032925) | 1.836672 / 55.444624 (-53.607952) | 1.511144 / 6.876477 (-5.365333) | 1.624167 / 2.142072 (-0.517905) | 0.803453 / 4.805227 (-4.001775) | 0.132760 / 6.500664 (-6.367904) | 0.042323 / 0.075469 (-0.033146) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.951576 / 1.841788 (-0.890212) | 11.476809 / 8.074308 (3.402501) | 9.208285 / 10.191392 (-0.983107) | 0.131797 / 0.680424 (-0.548626) | 0.014362 / 0.534201 (-0.519839) | 0.316051 / 0.579283 (-0.263232) | 0.269250 / 0.434364 (-0.165114) | 0.366949 / 0.540337 (-0.173388) | 0.471047 / 1.386936 (-0.915889) |\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) | 0.005905 / 0.011353 (-0.005448) | 0.003892 / 0.011008 (-0.007116) | 0.050513 / 0.038508 (0.012005) | 0.030903 / 0.023109 (0.007794) | 0.268835 / 0.275898 (-0.007063) | 0.288825 / 0.323480 (-0.034655) | 0.004372 / 0.007986 (-0.003614) | 0.002805 / 0.004328 (-0.001523) | 0.048497 / 0.004250 (0.044246) | 0.040665 / 0.037052 (0.003613) | 0.279842 / 0.258489 (0.021352) | 0.310715 / 0.293841 (0.016874) | 0.032133 / 0.128546 (-0.096413) | 0.012288 / 0.075646 (-0.063358) | 0.059719 / 0.419271 (-0.359552) | 0.033825 / 0.043533 (-0.009708) | 0.264670 / 0.255139 (0.009531) | 0.283799 / 0.283200 (0.000599) | 0.017968 / 0.141683 (-0.123715) | 1.160578 / 1.452155 (-0.291577) | 1.198602 / 1.492716 (-0.294115) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094388 / 0.018006 (0.076382) | 0.301861 / 0.000490 (0.301371) | 0.000212 / 0.000200 (0.000012) | 0.000045 / 0.000054 (-0.000009) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022901 / 0.037411 (-0.014510) | 0.076816 / 0.014526 (0.062290) | 0.089203 / 0.176557 (-0.087354) | 0.129040 / 0.737135 (-0.608096) | 0.090758 / 0.296338 (-0.205580) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.301191 / 0.215209 (0.085982) | 2.962887 / 2.077655 (0.885232) | 1.607134 / 1.504120 (0.103014) | 1.477817 / 1.541195 (-0.063377) | 1.485984 / 1.468490 (0.017494) | 0.717358 / 4.584777 (-3.867419) | 0.976018 / 3.745712 (-2.769694) | 2.951509 / 5.269862 (-2.318352) | 1.910619 / 4.565676 (-2.655057) | 0.078579 / 0.424275 (-0.345697) | 0.005209 / 0.007607 (-0.002398) | 0.345287 / 0.226044 (0.119243) | 3.487012 / 2.268929 (1.218084) | 1.938104 / 55.444624 (-53.506521) | 1.639341 / 6.876477 (-5.237136) | 1.617874 / 2.142072 (-0.524198) | 0.793721 / 4.805227 (-4.011506) | 0.136834 / 6.500664 (-6.363830) | 0.041211 / 0.075469 (-0.034258) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.988106 / 1.841788 (-0.853682) | 12.035176 / 8.074308 (3.960868) | 10.594559 / 10.191392 (0.403167) | 0.149917 / 0.680424 (-0.530507) | 0.015913 / 0.534201 (-0.518288) | 0.307658 / 0.579283 (-0.271625) | 0.130645 / 0.434364 (-0.303719) | 0.348450 / 0.540337 (-0.191887) | 0.443559 / 1.386936 (-0.943377) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9af8dd3de7626183a9a9ec8973cebc672d690400 \"CML watermark\")\n"
] | "2024-07-01T06:45:35Z" | "2024-07-01T07:16:36Z" | "2024-07-01T07:10:27Z" | MEMBER | null | Skip faiss tests on Windows to avoid running CI for 360 minutes.
Fix #7013.
Revert once the underlying issue is fixed. | {
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] | null | [] | "2024-07-01T06:40:03Z" | "2024-07-01T07:10:28Z" | "2024-07-01T07:10:28Z" | MEMBER | null | Faiss tests on Windows make the CI run indefinitely until maximum execution time (360 minutes) is reached.
See: https://github.com/huggingface/datasets/actions/runs/9712659783
```
test (integration, windows-latest, deps-minimum)
The job running on runner GitHub Actions 60 has exceeded the maximum execution time of 360 minutes.
test (integration, windows-latest, deps-latest)
The job running on runner GitHub Actions 238 has exceeded the maximum execution time of 360 minutes.
```
```
____________________________ tests/test_search.py _____________________________
[gw1] win32 -- Python 3.8.10 C:\hostedtoolcache\windows\Python\3.8.10\x64\python.exe
worker 'gw1' crashed while running 'tests/test_search.py::IndexableDatasetTest::test_add_faiss_index'
____________________________ tests/test_search.py _____________________________
[gw2] win32 -- Python 3.8.10 C:\hostedtoolcache\windows\Python\3.8.10\x64\python.exe
worker 'gw2' crashed while running 'tests/test_search.py::IndexableDatasetTest::test_add_faiss_index'
```
```
tests/test_search.py::IndexableDatasetTest::test_add_faiss_index
[gw0] node down: Not properly terminated
[gw0] FAILED tests/test_search.py::IndexableDatasetTest::test_add_faiss_index
replacing crashed worker gw0
tests/test_search.py::IndexableDatasetTest::test_add_faiss_index
[gw1] node down: Not properly terminated
[gw1] FAILED tests/test_search.py::IndexableDatasetTest::test_add_faiss_index
replacing crashed worker gw1
tests/test_search.py::IndexableDatasetTest::test_add_faiss_index
[gw2] node down: Not properly terminated
[gw2] FAILED tests/test_search.py::IndexableDatasetTest::test_add_faiss_index
replacing crashed worker gw2
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7011). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<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) | 0.005589 / 0.011353 (-0.005764) | 0.003855 / 0.011008 (-0.007153) | 0.063445 / 0.038508 (0.024937) | 0.030815 / 0.023109 (0.007706) | 0.244052 / 0.275898 (-0.031846) | 0.269916 / 0.323480 (-0.053563) | 0.003130 / 0.007986 (-0.004856) | 0.003349 / 0.004328 (-0.000980) | 0.049338 / 0.004250 (0.045088) | 0.045314 / 0.037052 (0.008261) | 0.250646 / 0.258489 (-0.007844) | 0.295828 / 0.293841 (0.001987) | 0.029808 / 0.128546 (-0.098738) | 0.012299 / 0.075646 (-0.063347) | 0.204946 / 0.419271 (-0.214325) | 0.036387 / 0.043533 (-0.007146) | 0.244316 / 0.255139 (-0.010823) | 0.269308 / 0.283200 (-0.013892) | 0.019226 / 0.141683 (-0.122457) | 1.138739 / 1.452155 (-0.313416) | 1.155265 / 1.492716 (-0.337451) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094085 / 0.018006 (0.076078) | 0.299764 / 0.000490 (0.299275) | 0.000205 / 0.000200 (0.000005) | 0.000052 / 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) | 0.018361 / 0.037411 (-0.019050) | 0.062665 / 0.014526 (0.048139) | 0.075888 / 0.176557 (-0.100668) | 0.120915 / 0.737135 (-0.616221) | 0.075465 / 0.296338 (-0.220873) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.279698 / 0.215209 (0.064489) | 2.784544 / 2.077655 (0.706889) | 1.498441 / 1.504120 (-0.005679) | 1.379789 / 1.541195 (-0.161406) | 1.388480 / 1.468490 (-0.080011) | 0.724249 / 4.584777 (-3.860528) | 2.343139 / 3.745712 (-1.402573) | 2.816179 / 5.269862 (-2.453683) | 1.908737 / 4.565676 (-2.656940) | 0.077686 / 0.424275 (-0.346589) | 0.005444 / 0.007607 (-0.002163) | 0.344084 / 0.226044 (0.118039) | 3.367548 / 2.268929 (1.098619) | 1.849200 / 55.444624 (-53.595424) | 1.556390 / 6.876477 (-5.320087) | 1.672902 / 2.142072 (-0.469170) | 0.795457 / 4.805227 (-4.009770) | 0.133521 / 6.500664 (-6.367143) | 0.042883 / 0.075469 (-0.032586) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.959094 / 1.841788 (-0.882694) | 11.399783 / 8.074308 (3.325475) | 9.075784 / 10.191392 (-1.115608) | 0.142897 / 0.680424 (-0.537527) | 0.014765 / 0.534201 (-0.519436) | 0.302259 / 0.579283 (-0.277024) | 0.261148 / 0.434364 (-0.173216) | 0.340302 / 0.540337 (-0.200035) | 0.459203 / 1.386936 (-0.927733) |\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) | 0.005821 / 0.011353 (-0.005532) | 0.003964 / 0.011008 (-0.007044) | 0.049904 / 0.038508 (0.011396) | 0.031061 / 0.023109 (0.007952) | 0.270002 / 0.275898 (-0.005896) | 0.289489 / 0.323480 (-0.033991) | 0.004477 / 0.007986 (-0.003509) | 0.002800 / 0.004328 (-0.001528) | 0.048029 / 0.004250 (0.043779) | 0.040486 / 0.037052 (0.003434) | 0.278442 / 0.258489 (0.019953) | 0.312606 / 0.293841 (0.018765) | 0.032920 / 0.128546 (-0.095626) | 0.012572 / 0.075646 (-0.063075) | 0.060589 / 0.419271 (-0.358682) | 0.034147 / 0.043533 (-0.009386) | 0.275282 / 0.255139 (0.020143) | 0.314073 / 0.283200 (0.030873) | 0.017555 / 0.141683 (-0.124128) | 1.149974 / 1.452155 (-0.302181) | 1.183715 / 1.492716 (-0.309002) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.095616 / 0.018006 (0.077610) | 0.302101 / 0.000490 (0.301611) | 0.000201 / 0.000200 (0.000001) | 0.000051 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022245 / 0.037411 (-0.015166) | 0.076890 / 0.014526 (0.062364) | 0.088471 / 0.176557 (-0.088085) | 0.128364 / 0.737135 (-0.608771) | 0.089907 / 0.296338 (-0.206431) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.302662 / 0.215209 (0.087453) | 2.979054 / 2.077655 (0.901399) | 1.576534 / 1.504120 (0.072414) | 1.443784 / 1.541195 (-0.097410) | 1.476000 / 1.468490 (0.007510) | 0.740580 / 4.584777 (-3.844197) | 0.953349 / 3.745712 (-2.792363) | 2.925619 / 5.269862 (-2.344243) | 1.904701 / 4.565676 (-2.660975) | 0.078404 / 0.424275 (-0.345872) | 0.005179 / 0.007607 (-0.002429) | 0.357217 / 0.226044 (0.131173) | 3.494812 / 2.268929 (1.225884) | 1.927345 / 55.444624 (-53.517280) | 1.627162 / 6.876477 (-5.249315) | 1.676748 / 2.142072 (-0.465324) | 0.798826 / 4.805227 (-4.006401) | 0.133617 / 6.500664 (-6.367047) | 0.041229 / 0.075469 (-0.034240) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.017046 / 1.841788 (-0.824742) | 12.045942 / 8.074308 (3.971634) | 10.430383 / 10.191392 (0.238991) | 0.144497 / 0.680424 (-0.535926) | 0.015809 / 0.534201 (-0.518392) | 0.304701 / 0.579283 (-0.274582) | 0.126496 / 0.434364 (-0.307868) | 0.340308 / 0.540337 (-0.200030) | 0.434917 / 1.386936 (-0.952019) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#054e57a8468af9fff5b75c08d2d6adf3e05fa763 \"CML watermark\")\n"
] | "2024-06-28T07:28:32Z" | "2024-06-28T12:25:25Z" | "2024-06-28T12:19:28Z" | MEMBER | null | Re-enable raising error from huggingface-hub FutureWarning in tests, once that the fix in transformers
- https://github.com/huggingface/transformers/pull/31007
was just released yesterday in transformers-4.42.0: https://github.com/huggingface/transformers/releases/tag/v4.42.0
Fix #7010. | {
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] | null | [] | "2024-06-28T07:23:40Z" | "2024-06-28T12:19:30Z" | "2024-06-28T12:19:29Z" | MEMBER | null | Re-enable raising error from huggingface-hub FutureWarning in CI, which was disabled by PR:
- #6876
Note that this can only be done once transformers releases the fix:
- https://github.com/huggingface/transformers/pull/31007 | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7009). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<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) | 0.005481 / 0.011353 (-0.005872) | 0.003580 / 0.011008 (-0.007428) | 0.062682 / 0.038508 (0.024174) | 0.031125 / 0.023109 (0.008015) | 0.239443 / 0.275898 (-0.036455) | 0.262950 / 0.323480 (-0.060529) | 0.003129 / 0.007986 (-0.004857) | 0.003393 / 0.004328 (-0.000935) | 0.048765 / 0.004250 (0.044514) | 0.044363 / 0.037052 (0.007311) | 0.248632 / 0.258489 (-0.009857) | 0.285056 / 0.293841 (-0.008785) | 0.029674 / 0.128546 (-0.098872) | 0.011963 / 0.075646 (-0.063684) | 0.204122 / 0.419271 (-0.215150) | 0.035867 / 0.043533 (-0.007665) | 0.245422 / 0.255139 (-0.009717) | 0.267165 / 0.283200 (-0.016035) | 0.018556 / 0.141683 (-0.123127) | 1.132112 / 1.452155 (-0.320043) | 1.173512 / 1.492716 (-0.319204) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092749 / 0.018006 (0.074743) | 0.298946 / 0.000490 (0.298457) | 0.000211 / 0.000200 (0.000011) | 0.000044 / 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) | 0.019496 / 0.037411 (-0.017915) | 0.062209 / 0.014526 (0.047683) | 0.074656 / 0.176557 (-0.101901) | 0.121238 / 0.737135 (-0.615897) | 0.075810 / 0.296338 (-0.220528) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.278089 / 0.215209 (0.062880) | 2.725602 / 2.077655 (0.647948) | 1.413346 / 1.504120 (-0.090774) | 1.290352 / 1.541195 (-0.250843) | 1.306732 / 1.468490 (-0.161758) | 0.713945 / 4.584777 (-3.870832) | 2.380131 / 3.745712 (-1.365581) | 2.804548 / 5.269862 (-2.465314) | 1.896506 / 4.565676 (-2.669170) | 0.078303 / 0.424275 (-0.345972) | 0.005475 / 0.007607 (-0.002132) | 0.340162 / 0.226044 (0.114117) | 3.355732 / 2.268929 (1.086803) | 1.776012 / 55.444624 (-53.668613) | 1.507006 / 6.876477 (-5.369471) | 1.607234 / 2.142072 (-0.534838) | 0.796458 / 4.805227 (-4.008769) | 0.135888 / 6.500664 (-6.364776) | 0.042352 / 0.075469 (-0.033118) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.988337 / 1.841788 (-0.853450) | 11.299311 / 8.074308 (3.225003) | 9.166845 / 10.191392 (-1.024547) | 0.140351 / 0.680424 (-0.540073) | 0.013932 / 0.534201 (-0.520269) | 0.302157 / 0.579283 (-0.277126) | 0.259355 / 0.434364 (-0.175009) | 0.339850 / 0.540337 (-0.200488) | 0.465345 / 1.386936 (-0.921591) |\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) | 0.005707 / 0.011353 (-0.005646) | 0.003846 / 0.011008 (-0.007162) | 0.050100 / 0.038508 (0.011591) | 0.031810 / 0.023109 (0.008701) | 0.265120 / 0.275898 (-0.010778) | 0.286635 / 0.323480 (-0.036845) | 0.004329 / 0.007986 (-0.003657) | 0.002757 / 0.004328 (-0.001571) | 0.050864 / 0.004250 (0.046614) | 0.039872 / 0.037052 (0.002820) | 0.277675 / 0.258489 (0.019186) | 0.310251 / 0.293841 (0.016410) | 0.032458 / 0.128546 (-0.096088) | 0.012072 / 0.075646 (-0.063574) | 0.060539 / 0.419271 (-0.358733) | 0.033772 / 0.043533 (-0.009761) | 0.265992 / 0.255139 (0.010853) | 0.286152 / 0.283200 (0.002953) | 0.018210 / 0.141683 (-0.123473) | 1.151461 / 1.452155 (-0.300694) | 1.199998 / 1.492716 (-0.292718) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094109 / 0.018006 (0.076103) | 0.298190 / 0.000490 (0.297701) | 0.000199 / 0.000200 (-0.000001) | 0.000045 / 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) | 0.022431 / 0.037411 (-0.014980) | 0.076319 / 0.014526 (0.061794) | 0.090023 / 0.176557 (-0.086533) | 0.128189 / 0.737135 (-0.608946) | 0.089564 / 0.296338 (-0.206774) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.298887 / 0.215209 (0.083678) | 2.928580 / 2.077655 (0.850926) | 1.565379 / 1.504120 (0.061259) | 1.424704 / 1.541195 (-0.116490) | 1.446336 / 1.468490 (-0.022154) | 0.716348 / 4.584777 (-3.868429) | 0.967465 / 3.745712 (-2.778247) | 2.967318 / 5.269862 (-2.302544) | 1.918878 / 4.565676 (-2.646798) | 0.077167 / 0.424275 (-0.347108) | 0.005271 / 0.007607 (-0.002336) | 0.342376 / 0.226044 (0.116332) | 3.386044 / 2.268929 (1.117115) | 1.915308 / 55.444624 (-53.529316) | 1.612729 / 6.876477 (-5.263748) | 1.621278 / 2.142072 (-0.520794) | 0.804639 / 4.805227 (-4.000589) | 0.132596 / 6.500664 (-6.368069) | 0.041075 / 0.075469 (-0.034394) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.996521 / 1.841788 (-0.845267) | 12.328856 / 8.074308 (4.254548) | 10.585154 / 10.191392 (0.393762) | 0.131720 / 0.680424 (-0.548704) | 0.016777 / 0.534201 (-0.517424) | 0.300424 / 0.579283 (-0.278860) | 0.128526 / 0.434364 (-0.305838) | 0.339961 / 0.540337 (-0.200377) | 0.441661 / 1.386936 (-0.945275) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a16477ddf8f96e590e9597225a5d180cce343f26 \"CML watermark\")\n"
] | "2024-06-28T05:37:36Z" | "2024-06-28T07:17:26Z" | "2024-06-28T07:11:17Z" | MEMBER | null | Support ruff 0.5.0 in CI and revert:
- #7007
Fix #7008. | {
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Also revert:
- #7007 | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7007). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<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) | 0.005225 / 0.011353 (-0.006128) | 0.003856 / 0.011008 (-0.007152) | 0.063455 / 0.038508 (0.024947) | 0.030184 / 0.023109 (0.007075) | 0.248518 / 0.275898 (-0.027380) | 0.270596 / 0.323480 (-0.052884) | 0.003185 / 0.007986 (-0.004800) | 0.002739 / 0.004328 (-0.001590) | 0.049379 / 0.004250 (0.045129) | 0.043190 / 0.037052 (0.006138) | 0.257181 / 0.258489 (-0.001308) | 0.283385 / 0.293841 (-0.010456) | 0.029702 / 0.128546 (-0.098844) | 0.012022 / 0.075646 (-0.063624) | 0.204531 / 0.419271 (-0.214741) | 0.035621 / 0.043533 (-0.007912) | 0.257745 / 0.255139 (0.002606) | 0.269033 / 0.283200 (-0.014167) | 0.019283 / 0.141683 (-0.122400) | 1.152477 / 1.452155 (-0.299678) | 1.180929 / 1.492716 (-0.311788) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094520 / 0.018006 (0.076514) | 0.299383 / 0.000490 (0.298893) | 0.000224 / 0.000200 (0.000024) | 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) | 0.019267 / 0.037411 (-0.018145) | 0.062458 / 0.014526 (0.047933) | 0.075743 / 0.176557 (-0.100814) | 0.128564 / 0.737135 (-0.608572) | 0.075549 / 0.296338 (-0.220789) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.288809 / 0.215209 (0.073600) | 2.854469 / 2.077655 (0.776814) | 1.581731 / 1.504120 (0.077611) | 1.460196 / 1.541195 (-0.080999) | 1.485567 / 1.468490 (0.017077) | 0.708824 / 4.584777 (-3.875953) | 2.362389 / 3.745712 (-1.383323) | 2.980804 / 5.269862 (-2.289057) | 1.918788 / 4.565676 (-2.646888) | 0.088389 / 0.424275 (-0.335886) | 0.005266 / 0.007607 (-0.002341) | 0.348598 / 0.226044 (0.122554) | 3.443202 / 2.268929 (1.174273) | 1.979311 / 55.444624 (-53.465314) | 1.655774 / 6.876477 (-5.220702) | 1.685538 / 2.142072 (-0.456535) | 0.788695 / 4.805227 (-4.016532) | 0.138403 / 6.500664 (-6.362261) | 0.043288 / 0.075469 (-0.032181) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.975874 / 1.841788 (-0.865913) | 11.506991 / 8.074308 (3.432683) | 9.640619 / 10.191392 (-0.550773) | 0.131897 / 0.680424 (-0.548527) | 0.014912 / 0.534201 (-0.519289) | 0.304173 / 0.579283 (-0.275110) | 0.262483 / 0.434364 (-0.171881) | 0.342636 / 0.540337 (-0.197701) | 0.440337 / 1.386936 (-0.946599) |\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) | 0.005961 / 0.011353 (-0.005392) | 0.004023 / 0.011008 (-0.006985) | 0.050230 / 0.038508 (0.011722) | 0.033204 / 0.023109 (0.010095) | 0.263462 / 0.275898 (-0.012436) | 0.287517 / 0.323480 (-0.035963) | 0.004432 / 0.007986 (-0.003553) | 0.002938 / 0.004328 (-0.001390) | 0.049297 / 0.004250 (0.045047) | 0.041166 / 0.037052 (0.004113) | 0.279220 / 0.258489 (0.020731) | 0.312857 / 0.293841 (0.019016) | 0.032567 / 0.128546 (-0.095979) | 0.012566 / 0.075646 (-0.063080) | 0.060579 / 0.419271 (-0.358692) | 0.033760 / 0.043533 (-0.009773) | 0.264219 / 0.255139 (0.009080) | 0.282929 / 0.283200 (-0.000270) | 0.017434 / 0.141683 (-0.124248) | 1.148472 / 1.452155 (-0.303683) | 1.247434 / 1.492716 (-0.245282) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.004566 / 0.018006 (-0.013440) | 0.296110 / 0.000490 (0.295621) | 0.000219 / 0.000200 (0.000019) | 0.000051 / 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) | 0.022514 / 0.037411 (-0.014897) | 0.076554 / 0.014526 (0.062029) | 0.090427 / 0.176557 (-0.086130) | 0.128611 / 0.737135 (-0.608524) | 0.090748 / 0.296338 (-0.205590) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.315051 / 0.215209 (0.099842) | 3.099662 / 2.077655 (1.022007) | 1.706009 / 1.504120 (0.201889) | 1.574637 / 1.541195 (0.033442) | 1.592454 / 1.468490 (0.123964) | 0.724699 / 4.584777 (-3.860078) | 0.949954 / 3.745712 (-2.795758) | 2.818915 / 5.269862 (-2.450946) | 1.931290 / 4.565676 (-2.634386) | 0.079308 / 0.424275 (-0.344967) | 0.005414 / 0.007607 (-0.002193) | 0.373547 / 0.226044 (0.147503) | 3.742222 / 2.268929 (1.473293) | 2.076239 / 55.444624 (-53.368385) | 1.772359 / 6.876477 (-5.104118) | 1.894369 / 2.142072 (-0.247703) | 0.803650 / 4.805227 (-4.001578) | 0.136995 / 6.500664 (-6.363669) | 0.041565 / 0.075469 (-0.033905) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.989806 / 1.841788 (-0.851982) | 12.151497 / 8.074308 (4.077189) | 10.188075 / 10.191392 (-0.003317) | 0.141194 / 0.680424 (-0.539230) | 0.016351 / 0.534201 (-0.517850) | 0.303242 / 0.579283 (-0.276041) | 0.127446 / 0.434364 (-0.306918) | 0.339806 / 0.540337 (-0.200532) | 0.443527 / 1.386936 (-0.943409) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#dd631431cb73c3ca434dfd6b115a6c30c5a665a5 \"CML watermark\")\n"
] | "2024-06-28T05:09:17Z" | "2024-06-28T05:31:21Z" | "2024-06-28T05:25:17Z" | MEMBER | null | As a hotfix for CI, temporarily pin ruff upper version < 0.5.0.
Fix #7006.
Revert once root cause is fixed. | {
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See: https://github.com/huggingface/datasets/actions/runs/9707641618/job/26793170961?pr=6983
> src/datasets/features/features.py:844:12: E721 Use `is` and `is not` for type comparisons, or `isinstance()` for isinstance checks | {
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"Hi ! `data_dir=` is for directories, can you try using `data_files=` instead ?",
"If you are trying to load your image dataset from a local folder, you should replace \"data_dir=path/to/jsonl/metadata.jsonl\" with the real folder path in your computer.\r\n\r\nhttps://huggingface.co/docs/datasets/en/image_load#imagefolder",
"Ah yes. My bad. I was giving file name. I should have given the folder directory as the path. That solved my issue. Thank you @albertvillanova and @lhoestq. "
] | "2024-06-27T15:08:26Z" | "2024-06-28T09:56:19Z" | "2024-06-28T09:56:19Z" | NONE | null | ### Describe the bug
while trying to load custom dataset from jsonl file, I get the error: "metadata.jsonl doesn't contain any data files"
### Steps to reproduce the bug
This is my [metadata_v2.jsonl](https://github.com/user-attachments/files/16016011/metadata_v2.json) file. I have this file in the folder with all images mentioned in that json(l) file.
Through below mentioned command I am trying to load_dataset so that I can upload it as mentioned here on the [official website](https://huggingface.co/docs/datasets/en/image_dataset#upload-dataset-to-the-hub).
````
from datasets import load_dataset
dataset = load_dataset("imagefolder", data_dir="path/to/jsonl/metadata.jsonl")
````
error:
````
EmptyDatasetError Traceback (most recent call last)
Cell In[18], line 3
1 from datasets import load_dataset
----> 3 dataset = load_dataset("imagefolder",
4 data_dir="path/to/jsonl/file/metadata.jsonl")
5 dataset[0]["objects"]
File ~/anaconda3/envs/lvis/lib/python3.11/site-packages/datasets/load.py:2594, 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, trust_remote_code, **config_kwargs)
2589 verification_mode = VerificationMode(
2590 (verification_mode or VerificationMode.BASIC_CHECKS) if not save_infos else VerificationMode.ALL_CHECKS
2591 )
2593 # Create a dataset builder
-> 2594 builder_instance = load_dataset_builder(
2595 path=path,
2596 name=name,
2597 data_dir=data_dir,
2598 data_files=data_files,
2599 cache_dir=cache_dir,
2600 features=features,
2601 download_config=download_config,
2602 download_mode=download_mode,
2603 revision=revision,
2604 token=token,
2605 storage_options=storage_options,
2606 trust_remote_code=trust_remote_code,
2607 _require_default_config_name=name is None,
2608 **config_kwargs,
2609 )
2611 # Return iterable dataset in case of streaming
2612 if streaming:
File ~/anaconda3/envs/lvis/lib/python3.11/site-packages/datasets/load.py:2266, in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, token, use_auth_token, storage_options, trust_remote_code, _require_default_config_name, **config_kwargs)
2264 download_config = download_config.copy() if download_config else DownloadConfig()
2265 download_config.storage_options.update(storage_options)
-> 2266 dataset_module = dataset_module_factory(
2267 path,
2268 revision=revision,
2269 download_config=download_config,
2270 download_mode=download_mode,
2271 data_dir=data_dir,
2272 data_files=data_files,
2273 cache_dir=cache_dir,
2274 trust_remote_code=trust_remote_code,
2275 _require_default_config_name=_require_default_config_name,
2276 _require_custom_configs=bool(config_kwargs),
2277 )
2278 # Get dataset builder class from the processing script
2279 builder_kwargs = dataset_module.builder_kwargs
File ~/anaconda3/envs/lvis/lib/python3.11/site-packages/datasets/load.py:1805, in dataset_module_factory(path, revision, download_config, download_mode, dynamic_modules_path, data_dir, data_files, cache_dir, trust_remote_code, _require_default_config_name, _require_custom_configs, **download_kwargs)
1782 # We have several ways to get a dataset builder:
1783 #
1784 # - if path is the name of a packaged dataset module
(...)
1796
1797 # Try packaged
1798 if path in _PACKAGED_DATASETS_MODULES:
1799 return PackagedDatasetModuleFactory(
1800 path,
1801 data_dir=data_dir,
1802 data_files=data_files,
1803 download_config=download_config,
1804 download_mode=download_mode,
-> 1805 ).get_module()
1806 # Try locally
1807 elif path.endswith(filename):
File ~/anaconda3/envs/lvis/lib/python3.11/site-packages/datasets/load.py:1140, in PackagedDatasetModuleFactory.get_module(self)
1135 def get_module(self) -> DatasetModule:
1136 base_path = Path(self.data_dir or "").expanduser().resolve().as_posix()
1137 patterns = (
1138 sanitize_patterns(self.data_files)
1139 if self.data_files is not None
-> 1140 else get_data_patterns(base_path, download_config=self.download_config)
1141 )
1142 data_files = DataFilesDict.from_patterns(
1143 patterns,
1144 download_config=self.download_config,
1145 base_path=base_path,
1146 )
1147 supports_metadata = self.name in _MODULE_SUPPORTS_METADATA
File ~/anaconda3/envs/lvis/lib/python3.11/site-packages/datasets/data_files.py:503, in get_data_patterns(base_path, download_config)
501 return _get_data_files_patterns(resolver)
502 except FileNotFoundError:
--> 503 raise EmptyDatasetError(f"The directory at {base_path} doesn't contain any data files") from None
EmptyDatasetError: The directory at path/to/jsonl/file/metadata.jsonl doesn't contain any data files`
```
### Expected behavior
It should be able load the whole file in a format of "dataset" inside the dataset variable. But it gives error "The directory at "path/to/jsonl/metadata.jsonl" doesn't contain any data files."
### Environment info
I am using conda environment. | {
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https://api.github.com/repos/huggingface/datasets/issues/7004 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7004/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7004/comments | https://api.github.com/repos/huggingface/datasets/issues/7004/events | https://github.com/huggingface/datasets/pull/7004 | 2,376,064,264 | PR_kwDODunzps5zrIYR | 7,004 | Fix WebDatasets KeyError for user-defined Features when a field is missing in an example | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7004). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<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) | 0.005188 / 0.011353 (-0.006165) | 0.003812 / 0.011008 (-0.007196) | 0.062408 / 0.038508 (0.023900) | 0.030734 / 0.023109 (0.007625) | 0.236528 / 0.275898 (-0.039370) | 0.267684 / 0.323480 (-0.055796) | 0.003182 / 0.007986 (-0.004804) | 0.004009 / 0.004328 (-0.000319) | 0.048966 / 0.004250 (0.044715) | 0.045259 / 0.037052 (0.008207) | 0.250586 / 0.258489 (-0.007903) | 0.287079 / 0.293841 (-0.006762) | 0.029235 / 0.128546 (-0.099311) | 0.012216 / 0.075646 (-0.063431) | 0.203864 / 0.419271 (-0.215408) | 0.036324 / 0.043533 (-0.007209) | 0.245180 / 0.255139 (-0.009959) | 0.270327 / 0.283200 (-0.012872) | 0.018676 / 0.141683 (-0.123007) | 1.115568 / 1.452155 (-0.336586) | 1.183267 / 1.492716 (-0.309449) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094307 / 0.018006 (0.076301) | 0.299071 / 0.000490 (0.298581) | 0.000219 / 0.000200 (0.000019) | 0.000042 / 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) | 0.018336 / 0.037411 (-0.019076) | 0.062973 / 0.014526 (0.048447) | 0.074137 / 0.176557 (-0.102420) | 0.120553 / 0.737135 (-0.616582) | 0.075411 / 0.296338 (-0.220927) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.284751 / 0.215209 (0.069542) | 2.789294 / 2.077655 (0.711640) | 1.457789 / 1.504120 (-0.046331) | 1.339140 / 1.541195 (-0.202055) | 1.341685 / 1.468490 (-0.126805) | 0.714928 / 4.584777 (-3.869849) | 2.361197 / 3.745712 (-1.384516) | 2.791569 / 5.269862 (-2.478293) | 1.892261 / 4.565676 (-2.673416) | 0.077954 / 0.424275 (-0.346321) | 0.005454 / 0.007607 (-0.002153) | 0.350766 / 0.226044 (0.124721) | 3.416749 / 2.268929 (1.147820) | 1.835377 / 55.444624 (-53.609247) | 1.506456 / 6.876477 (-5.370020) | 1.642728 / 2.142072 (-0.499344) | 0.791543 / 4.805227 (-4.013684) | 0.133102 / 6.500664 (-6.367562) | 0.042572 / 0.075469 (-0.032897) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.977958 / 1.841788 (-0.863830) | 11.438271 / 8.074308 (3.363963) | 9.305719 / 10.191392 (-0.885673) | 0.141239 / 0.680424 (-0.539185) | 0.014330 / 0.534201 (-0.519871) | 0.302201 / 0.579283 (-0.277082) | 0.261688 / 0.434364 (-0.172676) | 0.338752 / 0.540337 (-0.201586) | 0.468466 / 1.386936 (-0.918470) |\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) | 0.005629 / 0.011353 (-0.005723) | 0.003997 / 0.011008 (-0.007011) | 0.050447 / 0.038508 (0.011939) | 0.031694 / 0.023109 (0.008585) | 0.277392 / 0.275898 (0.001494) | 0.290440 / 0.323480 (-0.033040) | 0.004403 / 0.007986 (-0.003583) | 0.002851 / 0.004328 (-0.001478) | 0.048593 / 0.004250 (0.044343) | 0.040622 / 0.037052 (0.003570) | 0.282640 / 0.258489 (0.024151) | 0.309390 / 0.293841 (0.015549) | 0.031453 / 0.128546 (-0.097094) | 0.012424 / 0.075646 (-0.063223) | 0.060311 / 0.419271 (-0.358960) | 0.033195 / 0.043533 (-0.010338) | 0.266867 / 0.255139 (0.011728) | 0.281966 / 0.283200 (-0.001234) | 0.018026 / 0.141683 (-0.123657) | 1.136273 / 1.452155 (-0.315882) | 1.141643 / 1.492716 (-0.351073) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.095011 / 0.018006 (0.077005) | 0.300571 / 0.000490 (0.300082) | 0.000220 / 0.000200 (0.000020) | 0.000044 / 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) | 0.022903 / 0.037411 (-0.014508) | 0.077130 / 0.014526 (0.062604) | 0.089576 / 0.176557 (-0.086980) | 0.127156 / 0.737135 (-0.609980) | 0.090008 / 0.296338 (-0.206331) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.289270 / 0.215209 (0.074061) | 2.848239 / 2.077655 (0.770585) | 1.546788 / 1.504120 (0.042668) | 1.417275 / 1.541195 (-0.123920) | 1.456214 / 1.468490 (-0.012276) | 0.716688 / 4.584777 (-3.868088) | 0.940242 / 3.745712 (-2.805470) | 2.911956 / 5.269862 (-2.357906) | 1.871358 / 4.565676 (-2.694318) | 0.077553 / 0.424275 (-0.346722) | 0.005279 / 0.007607 (-0.002328) | 0.343338 / 0.226044 (0.117294) | 3.368694 / 2.268929 (1.099766) | 1.896765 / 55.444624 (-53.547859) | 1.612414 / 6.876477 (-5.264063) | 1.615934 / 2.142072 (-0.526138) | 0.794016 / 4.805227 (-4.011212) | 0.131821 / 6.500664 (-6.368843) | 0.041495 / 0.075469 (-0.033975) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.003418 / 1.841788 (-0.838370) | 12.073906 / 8.074308 (3.999598) | 10.166291 / 10.191392 (-0.025101) | 0.131224 / 0.680424 (-0.549200) | 0.015246 / 0.534201 (-0.518955) | 0.299835 / 0.579283 (-0.279448) | 0.124308 / 0.434364 (-0.310056) | 0.336414 / 0.540337 (-0.203924) | 0.429569 / 1.386936 (-0.957367) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#83d28601fad73755b74314a9bc1e327005514d54 \"CML watermark\")\n",
"@lhoestq Thank you!"
] | "2024-06-26T18:58:05Z" | "2024-06-29T00:15:49Z" | "2024-06-28T09:30:12Z" | CONTRIBUTOR | null | Fixes: https://github.com/huggingface/datasets/issues/6900
Not sure if this needs any addition stuff before merging | {
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https://api.github.com/repos/huggingface/datasets/issues/7003 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7003/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7003/comments | https://api.github.com/repos/huggingface/datasets/issues/7003/events | https://github.com/huggingface/datasets/pull/7003 | 2,373,084,132 | PR_kwDODunzps5zhRAK | 7,003 | minor fix for bfloat16 | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7003). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<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) | 0.005633 / 0.011353 (-0.005720) | 0.004366 / 0.011008 (-0.006642) | 0.064081 / 0.038508 (0.025573) | 0.031790 / 0.023109 (0.008681) | 0.239270 / 0.275898 (-0.036628) | 0.267424 / 0.323480 (-0.056055) | 0.003229 / 0.007986 (-0.004756) | 0.002849 / 0.004328 (-0.001479) | 0.050147 / 0.004250 (0.045897) | 0.046119 / 0.037052 (0.009066) | 0.253506 / 0.258489 (-0.004983) | 0.280464 / 0.293841 (-0.013377) | 0.030561 / 0.128546 (-0.097985) | 0.012258 / 0.075646 (-0.063388) | 0.212222 / 0.419271 (-0.207049) | 0.036695 / 0.043533 (-0.006838) | 0.242141 / 0.255139 (-0.012998) | 0.263014 / 0.283200 (-0.020186) | 0.020008 / 0.141683 (-0.121675) | 1.103701 / 1.452155 (-0.348453) | 1.151641 / 1.492716 (-0.341076) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.095884 / 0.018006 (0.077878) | 0.300858 / 0.000490 (0.300368) | 0.000209 / 0.000200 (0.000009) | 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) | 0.018713 / 0.037411 (-0.018698) | 0.063659 / 0.014526 (0.049134) | 0.074588 / 0.176557 (-0.101968) | 0.120779 / 0.737135 (-0.616356) | 0.077768 / 0.296338 (-0.218570) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.281680 / 0.215209 (0.066471) | 2.754658 / 2.077655 (0.677003) | 1.454036 / 1.504120 (-0.050084) | 1.333153 / 1.541195 (-0.208042) | 1.383616 / 1.468490 (-0.084874) | 0.728933 / 4.584777 (-3.855844) | 2.374989 / 3.745712 (-1.370723) | 2.990824 / 5.269862 (-2.279038) | 1.899065 / 4.565676 (-2.666612) | 0.078657 / 0.424275 (-0.345619) | 0.005162 / 0.007607 (-0.002445) | 0.335883 / 0.226044 (0.109838) | 3.323047 / 2.268929 (1.054119) | 1.848290 / 55.444624 (-53.596335) | 1.519510 / 6.876477 (-5.356966) | 1.563608 / 2.142072 (-0.578465) | 0.807890 / 4.805227 (-3.997337) | 0.134517 / 6.500664 (-6.366147) | 0.042208 / 0.075469 (-0.033262) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.963634 / 1.841788 (-0.878154) | 11.617868 / 8.074308 (3.543560) | 9.804648 / 10.191392 (-0.386744) | 0.142311 / 0.680424 (-0.538113) | 0.013748 / 0.534201 (-0.520453) | 0.300309 / 0.579283 (-0.278974) | 0.268214 / 0.434364 (-0.166150) | 0.342406 / 0.540337 (-0.197931) | 0.430315 / 1.386936 (-0.956621) |\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) | 0.005533 / 0.011353 (-0.005820) | 0.004208 / 0.011008 (-0.006800) | 0.051732 / 0.038508 (0.013224) | 0.031296 / 0.023109 (0.008187) | 0.275091 / 0.275898 (-0.000807) | 0.296889 / 0.323480 (-0.026591) | 0.004363 / 0.007986 (-0.003623) | 0.002807 / 0.004328 (-0.001522) | 0.049727 / 0.004250 (0.045476) | 0.039798 / 0.037052 (0.002746) | 0.284379 / 0.258489 (0.025890) | 0.317281 / 0.293841 (0.023440) | 0.031286 / 0.128546 (-0.097261) | 0.012384 / 0.075646 (-0.063263) | 0.061619 / 0.419271 (-0.357652) | 0.032974 / 0.043533 (-0.010559) | 0.274313 / 0.255139 (0.019174) | 0.296142 / 0.283200 (0.012943) | 0.017391 / 0.141683 (-0.124291) | 1.148369 / 1.452155 (-0.303786) | 1.171539 / 1.492716 (-0.321177) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.097309 / 0.018006 (0.079302) | 0.304701 / 0.000490 (0.304212) | 0.000208 / 0.000200 (0.000008) | 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) | 0.022382 / 0.037411 (-0.015030) | 0.077000 / 0.014526 (0.062474) | 0.088165 / 0.176557 (-0.088392) | 0.129060 / 0.737135 (-0.608075) | 0.090128 / 0.296338 (-0.206211) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.285308 / 0.215209 (0.070099) | 2.816680 / 2.077655 (0.739025) | 1.542418 / 1.504120 (0.038298) | 1.418567 / 1.541195 (-0.122628) | 1.447018 / 1.468490 (-0.021472) | 0.737055 / 4.584777 (-3.847722) | 0.968285 / 3.745712 (-2.777427) | 2.880120 / 5.269862 (-2.389741) | 1.921813 / 4.565676 (-2.643864) | 0.079110 / 0.424275 (-0.345165) | 0.005826 / 0.007607 (-0.001781) | 0.336441 / 0.226044 (0.110397) | 3.326384 / 2.268929 (1.057456) | 1.929205 / 55.444624 (-53.515419) | 1.618215 / 6.876477 (-5.258261) | 1.769688 / 2.142072 (-0.372385) | 0.808009 / 4.805227 (-3.997219) | 0.136384 / 6.500664 (-6.364280) | 0.041332 / 0.075469 (-0.034137) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.010884 / 1.841788 (-0.830903) | 12.266118 / 8.074308 (4.191810) | 10.287424 / 10.191392 (0.096032) | 0.143172 / 0.680424 (-0.537251) | 0.015798 / 0.534201 (-0.518403) | 0.301604 / 0.579283 (-0.277679) | 0.131079 / 0.434364 (-0.303285) | 0.338396 / 0.540337 (-0.201941) | 0.460721 / 1.386936 (-0.926215) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1e1d31387aa594b2e745c8ed8964962c134d203d \"CML watermark\")\n"
] | "2024-06-25T16:10:04Z" | "2024-06-25T16:16:11Z" | "2024-06-25T16:10:10Z" | MEMBER | null | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7002 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7002/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7002/comments | https://api.github.com/repos/huggingface/datasets/issues/7002/events | https://github.com/huggingface/datasets/pull/7002 | 2,373,010,351 | PR_kwDODunzps5zhBld | 7,002 | Fix dump of bfloat16 torch tensor | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7002). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<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) | 0.005321 / 0.011353 (-0.006032) | 0.003495 / 0.011008 (-0.007514) | 0.065577 / 0.038508 (0.027069) | 0.030876 / 0.023109 (0.007767) | 0.255216 / 0.275898 (-0.020682) | 0.265111 / 0.323480 (-0.058368) | 0.003149 / 0.007986 (-0.004837) | 0.004062 / 0.004328 (-0.000267) | 0.051142 / 0.004250 (0.046891) | 0.042460 / 0.037052 (0.005408) | 0.270692 / 0.258489 (0.012203) | 0.284957 / 0.293841 (-0.008884) | 0.030143 / 0.128546 (-0.098403) | 0.012148 / 0.075646 (-0.063498) | 0.203706 / 0.419271 (-0.215565) | 0.035948 / 0.043533 (-0.007584) | 0.251391 / 0.255139 (-0.003748) | 0.270908 / 0.283200 (-0.012292) | 0.018496 / 0.141683 (-0.123187) | 1.118567 / 1.452155 (-0.333587) | 1.157695 / 1.492716 (-0.335021) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.135649 / 0.018006 (0.117643) | 0.281489 / 0.000490 (0.281000) | 0.000244 / 0.000200 (0.000044) | 0.000042 / 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) | 0.018700 / 0.037411 (-0.018711) | 0.062305 / 0.014526 (0.047779) | 0.074968 / 0.176557 (-0.101589) | 0.121490 / 0.737135 (-0.615645) | 0.075585 / 0.296338 (-0.220754) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.276929 / 0.215209 (0.061720) | 2.733543 / 2.077655 (0.655888) | 1.414585 / 1.504120 (-0.089535) | 1.301975 / 1.541195 (-0.239220) | 1.336698 / 1.468490 (-0.131792) | 0.720650 / 4.584777 (-3.864127) | 2.374796 / 3.745712 (-1.370917) | 2.866534 / 5.269862 (-2.403327) | 1.819607 / 4.565676 (-2.746069) | 0.077914 / 0.424275 (-0.346361) | 0.005146 / 0.007607 (-0.002461) | 0.331722 / 0.226044 (0.105678) | 3.290875 / 2.268929 (1.021946) | 1.799806 / 55.444624 (-53.644818) | 1.476816 / 6.876477 (-5.399660) | 1.511441 / 2.142072 (-0.630631) | 0.798043 / 4.805227 (-4.007185) | 0.134577 / 6.500664 (-6.366087) | 0.042055 / 0.075469 (-0.033415) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.967908 / 1.841788 (-0.873880) | 11.215688 / 8.074308 (3.141380) | 9.486403 / 10.191392 (-0.704989) | 0.141864 / 0.680424 (-0.538560) | 0.013462 / 0.534201 (-0.520739) | 0.302601 / 0.579283 (-0.276682) | 0.266870 / 0.434364 (-0.167494) | 0.336963 / 0.540337 (-0.203375) | 0.425374 / 1.386936 (-0.961562) |\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) | 0.005549 / 0.011353 (-0.005803) | 0.003464 / 0.011008 (-0.007544) | 0.051421 / 0.038508 (0.012913) | 0.032320 / 0.023109 (0.009211) | 0.269591 / 0.275898 (-0.006307) | 0.292015 / 0.323480 (-0.031465) | 0.004351 / 0.007986 (-0.003634) | 0.002772 / 0.004328 (-0.001556) | 0.048836 / 0.004250 (0.044586) | 0.039501 / 0.037052 (0.002449) | 0.282419 / 0.258489 (0.023930) | 0.312289 / 0.293841 (0.018448) | 0.031788 / 0.128546 (-0.096759) | 0.012074 / 0.075646 (-0.063572) | 0.060457 / 0.419271 (-0.358814) | 0.033106 / 0.043533 (-0.010427) | 0.270323 / 0.255139 (0.015184) | 0.287855 / 0.283200 (0.004655) | 0.017865 / 0.141683 (-0.123818) | 1.130406 / 1.452155 (-0.321749) | 1.178679 / 1.492716 (-0.314038) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093606 / 0.018006 (0.075600) | 0.297328 / 0.000490 (0.296838) | 0.000211 / 0.000200 (0.000011) | 0.000043 / 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) | 0.022498 / 0.037411 (-0.014913) | 0.076927 / 0.014526 (0.062401) | 0.088013 / 0.176557 (-0.088544) | 0.127279 / 0.737135 (-0.609857) | 0.089424 / 0.296338 (-0.206914) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.296441 / 0.215209 (0.081232) | 2.913051 / 2.077655 (0.835396) | 1.581816 / 1.504120 (0.077696) | 1.451575 / 1.541195 (-0.089620) | 1.458968 / 1.468490 (-0.009522) | 0.727191 / 4.584777 (-3.857586) | 0.954607 / 3.745712 (-2.791106) | 2.824357 / 5.269862 (-2.445505) | 1.886779 / 4.565676 (-2.678898) | 0.079397 / 0.424275 (-0.344878) | 0.005566 / 0.007607 (-0.002041) | 0.351655 / 0.226044 (0.125611) | 3.395790 / 2.268929 (1.126862) | 1.886238 / 55.444624 (-53.558387) | 1.615413 / 6.876477 (-5.261064) | 1.723922 / 2.142072 (-0.418150) | 0.807858 / 4.805227 (-3.997369) | 0.132998 / 6.500664 (-6.367667) | 0.040396 / 0.075469 (-0.035073) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.008527 / 1.841788 (-0.833261) | 11.736104 / 8.074308 (3.661796) | 10.283367 / 10.191392 (0.091975) | 0.141386 / 0.680424 (-0.539038) | 0.015722 / 0.534201 (-0.518479) | 0.301785 / 0.579283 (-0.277498) | 0.123073 / 0.434364 (-0.311291) | 0.340478 / 0.540337 (-0.199859) | 0.462936 / 1.386936 (-0.924000) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#bfb0a414d68e945addf95a9419a8314c372e19ba \"CML watermark\")\n"
] | "2024-06-25T15:38:09Z" | "2024-06-25T16:10:16Z" | "2024-06-25T15:51:52Z" | MEMBER | null | close https://github.com/huggingface/datasets/issues/7000 | {
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https://api.github.com/repos/huggingface/datasets/issues/7001 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7001/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7001/comments | https://api.github.com/repos/huggingface/datasets/issues/7001/events | https://github.com/huggingface/datasets/issues/7001 | 2,372,930,879 | I_kwDODunzps6NcA0_ | 7,001 | Datasetbuilder Local Download FileNotFoundError | {
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"Ok it seems the solution is to use the directory string without the trailing \"/\" which in my case as: \r\n\r\n`parquet_dir = \"~/data/Parquet\" `\r\n\r\nStill i think this is a weird behavior... "
] | "2024-06-25T15:02:34Z" | "2024-06-25T15:21:19Z" | null | NONE | null | ### Describe the bug
So I was trying to download a dataset and save it as parquet and I follow the [tutorial](https://huggingface.co/docs/datasets/filesystems#download-and-prepare-a-dataset-into-a-cloud-storage) of Huggingface. However, during the excution I face a FileNotFoundError.
I debug the code and it seems there is a bug there:
So first it creates a .incomplete folder and before moving its contents the following code deletes the directory
[Code](https://github.com/huggingface/datasets/blob/98fdc9e78e6d057ca66e58a37f49d6618aab8130/src/datasets/builder.py#L984)
hence as a result I face with:
``` FileNotFoundError: [Errno 2] No such file or directory: '~/data/Parquet/.incomplete '```
### Steps to reproduce the bug
```
from datasets import load_dataset_builder
from pathlib import Path
parquet_dir = "~/data/Parquet/"
Path(parquet_dir).mkdir(parents=True, exist_ok=True)
builder = load_dataset_builder(
"rotten_tomatoes",
)
builder.download_and_prepare(parquet_dir, file_format="parquet")
```
### Expected behavior
Downloads the files and saves as parquet
### Environment info
Ubuntu,
Python 3.10
```
datasets 2.19.1
``` | null | {
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https://api.github.com/repos/huggingface/datasets/issues/7000 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/7000/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/7000/comments | https://api.github.com/repos/huggingface/datasets/issues/7000/events | https://github.com/huggingface/datasets/issues/7000 | 2,372,887,585 | I_kwDODunzps6Nb2Qh | 7,000 | IterableDataset: Unsupported ScalarType BFloat16 | {
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"@lhoestq Thank you for merging #6607, but unfortunately the issue persists for `IterableDataset` :pensive: ",
"Hi ! I opened https://github.com/huggingface/datasets/pull/7002 to fix this bug",
"Amazing, thank you so much @lhoestq! :pray:"
] | "2024-06-25T14:43:26Z" | "2024-06-25T16:04:00Z" | "2024-06-25T15:51:53Z" | NONE | null | ### Describe the bug
`IterableDataset.from_generator` crashes when using BFloat16:
```
File "/usr/local/lib/python3.11/site-packages/datasets/utils/_dill.py", line 169, in _save_torchTensor
args = (obj.detach().cpu().numpy(),)
^^^^^^^^^^^^^^^^^^^^^^^^^^
TypeError: Got unsupported ScalarType BFloat16
```
### Steps to reproduce the bug
```python
import torch
from datasets import IterableDataset
def demo(x):
yield {"x": x}
x = torch.tensor([1.], dtype=torch.bfloat16)
dataset = IterableDataset.from_generator(
demo,
gen_kwargs=dict(x=x),
)
example = next(iter(dataset))
print(example)
```
### Expected behavior
Code sample should print:
```python
{'x': tensor([1.], dtype=torch.bfloat16)}
```
### Environment info
```
datasets==2.20.0
torch==2.2.2
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6999). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<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) | 0.005330 / 0.011353 (-0.006023) | 0.003946 / 0.011008 (-0.007062) | 0.063530 / 0.038508 (0.025022) | 0.030529 / 0.023109 (0.007419) | 0.239364 / 0.275898 (-0.036534) | 0.261683 / 0.323480 (-0.061797) | 0.003197 / 0.007986 (-0.004789) | 0.003485 / 0.004328 (-0.000844) | 0.049575 / 0.004250 (0.045325) | 0.046164 / 0.037052 (0.009112) | 0.246129 / 0.258489 (-0.012360) | 0.281365 / 0.293841 (-0.012476) | 0.029480 / 0.128546 (-0.099066) | 0.012450 / 0.075646 (-0.063196) | 0.203696 / 0.419271 (-0.215575) | 0.036539 / 0.043533 (-0.006994) | 0.241664 / 0.255139 (-0.013475) | 0.260930 / 0.283200 (-0.022270) | 0.019931 / 0.141683 (-0.121752) | 1.221075 / 1.452155 (-0.231080) | 1.246315 / 1.492716 (-0.246402) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.095061 / 0.018006 (0.077055) | 0.304773 / 0.000490 (0.304283) | 0.000208 / 0.000200 (0.000008) | 0.000050 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019032 / 0.037411 (-0.018380) | 0.062521 / 0.014526 (0.047995) | 0.075668 / 0.176557 (-0.100889) | 0.121634 / 0.737135 (-0.615501) | 0.075456 / 0.296338 (-0.220882) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.291721 / 0.215209 (0.076512) | 2.845445 / 2.077655 (0.767790) | 1.450971 / 1.504120 (-0.053149) | 1.334586 / 1.541195 (-0.206609) | 1.358095 / 1.468490 (-0.110396) | 0.729624 / 4.584777 (-3.855153) | 2.411504 / 3.745712 (-1.334208) | 2.858871 / 5.269862 (-2.410991) | 1.893074 / 4.565676 (-2.672603) | 0.079068 / 0.424275 (-0.345207) | 0.005476 / 0.007607 (-0.002131) | 0.329816 / 0.226044 (0.103771) | 3.305361 / 2.268929 (1.036432) | 1.799924 / 55.444624 (-53.644700) | 1.512130 / 6.876477 (-5.364347) | 1.635195 / 2.142072 (-0.506877) | 0.801486 / 4.805227 (-4.003741) | 0.134677 / 6.500664 (-6.365987) | 0.042266 / 0.075469 (-0.033203) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.969835 / 1.841788 (-0.871952) | 11.421833 / 8.074308 (3.347524) | 9.799120 / 10.191392 (-0.392272) | 0.144888 / 0.680424 (-0.535536) | 0.014191 / 0.534201 (-0.520010) | 0.301037 / 0.579283 (-0.278246) | 0.263329 / 0.434364 (-0.171034) | 0.403013 / 0.540337 (-0.137324) | 0.463805 / 1.386936 (-0.923131) |\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) | 0.005913 / 0.011353 (-0.005440) | 0.003890 / 0.011008 (-0.007118) | 0.049995 / 0.038508 (0.011487) | 0.032497 / 0.023109 (0.009387) | 0.269926 / 0.275898 (-0.005972) | 0.295567 / 0.323480 (-0.027913) | 0.004365 / 0.007986 (-0.003620) | 0.002818 / 0.004328 (-0.001510) | 0.049055 / 0.004250 (0.044805) | 0.040683 / 0.037052 (0.003630) | 0.283043 / 0.258489 (0.024554) | 0.321072 / 0.293841 (0.027232) | 0.032760 / 0.128546 (-0.095787) | 0.012370 / 0.075646 (-0.063277) | 0.061574 / 0.419271 (-0.357698) | 0.033714 / 0.043533 (-0.009819) | 0.276287 / 0.255139 (0.021148) | 0.290078 / 0.283200 (0.006878) | 0.017250 / 0.141683 (-0.124432) | 1.165291 / 1.452155 (-0.286863) | 1.213687 / 1.492716 (-0.279029) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.096122 / 0.018006 (0.078115) | 0.311954 / 0.000490 (0.311464) | 0.000213 / 0.000200 (0.000013) | 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) | 0.022142 / 0.037411 (-0.015270) | 0.076470 / 0.014526 (0.061945) | 0.088340 / 0.176557 (-0.088216) | 0.128594 / 0.737135 (-0.608542) | 0.089780 / 0.296338 (-0.206558) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.298129 / 0.215209 (0.082920) | 2.943735 / 2.077655 (0.866080) | 1.574351 / 1.504120 (0.070231) | 1.446688 / 1.541195 (-0.094506) | 1.477714 / 1.468490 (0.009223) | 0.722195 / 4.584777 (-3.862582) | 0.967675 / 3.745712 (-2.778037) | 2.803346 / 5.269862 (-2.466515) | 1.895882 / 4.565676 (-2.669794) | 0.079193 / 0.424275 (-0.345082) | 0.005250 / 0.007607 (-0.002357) | 0.350193 / 0.226044 (0.124149) | 3.514562 / 2.268929 (1.245634) | 1.962743 / 55.444624 (-53.481881) | 1.677308 / 6.876477 (-5.199169) | 1.811473 / 2.142072 (-0.330600) | 0.796234 / 4.805227 (-4.008993) | 0.131810 / 6.500664 (-6.368854) | 0.041301 / 0.075469 (-0.034168) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.030700 / 1.841788 (-0.811088) | 12.108809 / 8.074308 (4.034501) | 10.426112 / 10.191392 (0.234720) | 0.139829 / 0.680424 (-0.540595) | 0.015133 / 0.534201 (-0.519068) | 0.307782 / 0.579283 (-0.271501) | 0.130554 / 0.434364 (-0.303810) | 0.342728 / 0.540337 (-0.197610) | 0.435426 / 1.386936 (-0.951510) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9ddea80a0bca8dcf4ed5ca58dbeda3e309cf5a84 \"CML watermark\")\n"
] | "2024-06-25T09:06:16Z" | "2024-08-21T09:07:07Z" | "2024-08-21T09:01:18Z" | MEMBER | null | Remove tasks, as part of the 3.0 release. | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6998). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<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) | 0.005396 / 0.011353 (-0.005957) | 0.003974 / 0.011008 (-0.007034) | 0.063490 / 0.038508 (0.024982) | 0.030299 / 0.023109 (0.007189) | 0.244489 / 0.275898 (-0.031409) | 0.274116 / 0.323480 (-0.049364) | 0.003187 / 0.007986 (-0.004798) | 0.003433 / 0.004328 (-0.000896) | 0.049313 / 0.004250 (0.045062) | 0.043677 / 0.037052 (0.006624) | 0.260198 / 0.258489 (0.001709) | 0.283558 / 0.293841 (-0.010283) | 0.029728 / 0.128546 (-0.098819) | 0.011950 / 0.075646 (-0.063696) | 0.204371 / 0.419271 (-0.214901) | 0.035712 / 0.043533 (-0.007821) | 0.252715 / 0.255139 (-0.002424) | 0.268906 / 0.283200 (-0.014293) | 0.021153 / 0.141683 (-0.120529) | 1.125599 / 1.452155 (-0.326556) | 1.163122 / 1.492716 (-0.329594) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.095089 / 0.018006 (0.077083) | 0.298576 / 0.000490 (0.298086) | 0.000214 / 0.000200 (0.000014) | 0.000051 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018567 / 0.037411 (-0.018844) | 0.062337 / 0.014526 (0.047811) | 0.074231 / 0.176557 (-0.102326) | 0.120960 / 0.737135 (-0.616175) | 0.076124 / 0.296338 (-0.220215) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.286936 / 0.215209 (0.071727) | 2.816656 / 2.077655 (0.739001) | 1.486772 / 1.504120 (-0.017348) | 1.373289 / 1.541195 (-0.167905) | 1.392739 / 1.468490 (-0.075752) | 0.708091 / 4.584777 (-3.876686) | 2.410034 / 3.745712 (-1.335678) | 2.912701 / 5.269862 (-2.357161) | 1.850924 / 4.565676 (-2.714752) | 0.078896 / 0.424275 (-0.345380) | 0.005116 / 0.007607 (-0.002491) | 0.332275 / 0.226044 (0.106231) | 3.306562 / 2.268929 (1.037633) | 1.853051 / 55.444624 (-53.591573) | 1.556210 / 6.876477 (-5.320267) | 1.558892 / 2.142072 (-0.583181) | 0.789917 / 4.805227 (-4.015310) | 0.133683 / 6.500664 (-6.366981) | 0.042566 / 0.075469 (-0.032904) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.957050 / 1.841788 (-0.884738) | 11.401462 / 8.074308 (3.327154) | 9.782988 / 10.191392 (-0.408404) | 0.142127 / 0.680424 (-0.538296) | 0.014730 / 0.534201 (-0.519471) | 0.302647 / 0.579283 (-0.276636) | 0.264654 / 0.434364 (-0.169710) | 0.341340 / 0.540337 (-0.198998) | 0.425808 / 1.386936 (-0.961128) |\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) | 0.005679 / 0.011353 (-0.005674) | 0.003513 / 0.011008 (-0.007495) | 0.050135 / 0.038508 (0.011627) | 0.031614 / 0.023109 (0.008505) | 0.260064 / 0.275898 (-0.015834) | 0.285816 / 0.323480 (-0.037664) | 0.004428 / 0.007986 (-0.003558) | 0.002816 / 0.004328 (-0.001512) | 0.048441 / 0.004250 (0.044191) | 0.039622 / 0.037052 (0.002570) | 0.274940 / 0.258489 (0.016451) | 0.311837 / 0.293841 (0.017996) | 0.031439 / 0.128546 (-0.097107) | 0.012056 / 0.075646 (-0.063590) | 0.060109 / 0.419271 (-0.359163) | 0.033123 / 0.043533 (-0.010409) | 0.261563 / 0.255139 (0.006424) | 0.282640 / 0.283200 (-0.000560) | 0.017168 / 0.141683 (-0.124515) | 1.127859 / 1.452155 (-0.324295) | 1.182414 / 1.492716 (-0.310303) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.095517 / 0.018006 (0.077510) | 0.300578 / 0.000490 (0.300088) | 0.000212 / 0.000200 (0.000012) | 0.000044 / 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) | 0.022192 / 0.037411 (-0.015220) | 0.076617 / 0.014526 (0.062091) | 0.087405 / 0.176557 (-0.089151) | 0.127011 / 0.737135 (-0.610124) | 0.088706 / 0.296338 (-0.207632) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.294260 / 0.215209 (0.079051) | 2.872879 / 2.077655 (0.795224) | 1.531374 / 1.504120 (0.027254) | 1.399232 / 1.541195 (-0.141962) | 1.400708 / 1.468490 (-0.067782) | 0.714003 / 4.584777 (-3.870773) | 0.943144 / 3.745712 (-2.802568) | 2.833396 / 5.269862 (-2.436466) | 1.890570 / 4.565676 (-2.675106) | 0.077664 / 0.424275 (-0.346611) | 0.005651 / 0.007607 (-0.001956) | 0.349476 / 0.226044 (0.123431) | 3.405768 / 2.268929 (1.136840) | 1.869739 / 55.444624 (-53.574885) | 1.575293 / 6.876477 (-5.301184) | 1.692981 / 2.142072 (-0.449092) | 0.795363 / 4.805227 (-4.009865) | 0.131532 / 6.500664 (-6.369132) | 0.041183 / 0.075469 (-0.034286) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.000821 / 1.841788 (-0.840967) | 11.987795 / 8.074308 (3.913487) | 10.147652 / 10.191392 (-0.043740) | 0.141314 / 0.680424 (-0.539110) | 0.015506 / 0.534201 (-0.518695) | 0.305090 / 0.579283 (-0.274193) | 0.123403 / 0.434364 (-0.310960) | 0.346507 / 0.540337 (-0.193831) | 0.471318 / 1.386936 (-0.915618) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#186b560eb2393c7d1913f4b3e76e9e04a081e09b \"CML watermark\")\n"
] | "2024-06-25T07:59:44Z" | "2024-06-25T08:22:38Z" | "2024-06-25T08:13:42Z" | MEMBER | null | Fix tests using hf-internal-testing/librispeech_asr_dummy once that dataset has been converted to Parquet.
Fix #6997. | {
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```
FAILED tests/test_inspect.py::test_get_dataset_config_names[hf-internal-testing/librispeech_asr_dummy-expected4] - AssertionError: assert ['clean'] == ['clean', 'other']
Right contains one more item: 'other'
Full diff:
[
'clean',
- 'other',
]
FAILED tests/test_inspect.py::test_get_dataset_default_config_name[hf-internal-testing/librispeech_asr_dummy-None] - AssertionError: assert 'clean' is None
```
Note that repository was recently converted to Parquet: https://huggingface.co/datasets/hf-internal-testing/librispeech_asr_dummy/commit/5be91486e11a2d616f4ec5db8d3fd248585ac07a | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6996). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<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) | 0.005296 / 0.011353 (-0.006057) | 0.003991 / 0.011008 (-0.007017) | 0.063892 / 0.038508 (0.025384) | 0.031185 / 0.023109 (0.008076) | 0.248300 / 0.275898 (-0.027598) | 0.270326 / 0.323480 (-0.053154) | 0.004343 / 0.007986 (-0.003643) | 0.002735 / 0.004328 (-0.001594) | 0.049751 / 0.004250 (0.045501) | 0.045629 / 0.037052 (0.008577) | 0.257584 / 0.258489 (-0.000905) | 0.284697 / 0.293841 (-0.009144) | 0.029403 / 0.128546 (-0.099143) | 0.012155 / 0.075646 (-0.063491) | 0.215241 / 0.419271 (-0.204031) | 0.036258 / 0.043533 (-0.007275) | 0.246878 / 0.255139 (-0.008261) | 0.268728 / 0.283200 (-0.014472) | 0.018113 / 0.141683 (-0.123570) | 1.130733 / 1.452155 (-0.321422) | 1.205148 / 1.492716 (-0.287568) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.095196 / 0.018006 (0.077189) | 0.300741 / 0.000490 (0.300252) | 0.000220 / 0.000200 (0.000020) | 0.000043 / 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) | 0.018319 / 0.037411 (-0.019093) | 0.062766 / 0.014526 (0.048240) | 0.074748 / 0.176557 (-0.101809) | 0.122177 / 0.737135 (-0.614959) | 0.076652 / 0.296338 (-0.219687) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.284508 / 0.215209 (0.069299) | 2.838298 / 2.077655 (0.760643) | 1.480098 / 1.504120 (-0.024022) | 1.362882 / 1.541195 (-0.178313) | 1.389036 / 1.468490 (-0.079454) | 0.747485 / 4.584777 (-3.837292) | 2.385333 / 3.745712 (-1.360379) | 2.924148 / 5.269862 (-2.345713) | 1.869061 / 4.565676 (-2.696616) | 0.079909 / 0.424275 (-0.344366) | 0.005173 / 0.007607 (-0.002434) | 0.345694 / 0.226044 (0.119650) | 3.430648 / 2.268929 (1.161719) | 1.837108 / 55.444624 (-53.607516) | 1.528498 / 6.876477 (-5.347979) | 1.567128 / 2.142072 (-0.574944) | 0.804615 / 4.805227 (-4.000612) | 0.135361 / 6.500664 (-6.365303) | 0.042195 / 0.075469 (-0.033274) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.986240 / 1.841788 (-0.855548) | 11.428084 / 8.074308 (3.353776) | 9.168227 / 10.191392 (-1.023165) | 0.131917 / 0.680424 (-0.548507) | 0.014324 / 0.534201 (-0.519877) | 0.302188 / 0.579283 (-0.277095) | 0.263790 / 0.434364 (-0.170574) | 0.343799 / 0.540337 (-0.196539) | 0.428518 / 1.386936 (-0.958418) |\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) | 0.005734 / 0.011353 (-0.005618) | 0.003914 / 0.011008 (-0.007094) | 0.050105 / 0.038508 (0.011596) | 0.031748 / 0.023109 (0.008639) | 0.266392 / 0.275898 (-0.009506) | 0.301221 / 0.323480 (-0.022259) | 0.004408 / 0.007986 (-0.003578) | 0.002811 / 0.004328 (-0.001517) | 0.049103 / 0.004250 (0.044853) | 0.041030 / 0.037052 (0.003978) | 0.281003 / 0.258489 (0.022513) | 0.318086 / 0.293841 (0.024245) | 0.032695 / 0.128546 (-0.095852) | 0.012239 / 0.075646 (-0.063408) | 0.060387 / 0.419271 (-0.358885) | 0.034179 / 0.043533 (-0.009354) | 0.266020 / 0.255139 (0.010881) | 0.288551 / 0.283200 (0.005351) | 0.018778 / 0.141683 (-0.122905) | 1.214959 / 1.452155 (-0.237196) | 1.268269 / 1.492716 (-0.224447) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.095449 / 0.018006 (0.077443) | 0.305733 / 0.000490 (0.305243) | 0.000216 / 0.000200 (0.000016) | 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) | 0.022565 / 0.037411 (-0.014847) | 0.077266 / 0.014526 (0.062740) | 0.089345 / 0.176557 (-0.087212) | 0.128900 / 0.737135 (-0.608236) | 0.089746 / 0.296338 (-0.206593) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.298221 / 0.215209 (0.083012) | 2.957671 / 2.077655 (0.880016) | 1.584674 / 1.504120 (0.080554) | 1.456906 / 1.541195 (-0.084288) | 1.467609 / 1.468490 (-0.000881) | 0.718726 / 4.584777 (-3.866051) | 0.948157 / 3.745712 (-2.797555) | 2.953559 / 5.269862 (-2.316303) | 1.895182 / 4.565676 (-2.670494) | 0.078380 / 0.424275 (-0.345895) | 0.005640 / 0.007607 (-0.001968) | 0.352978 / 0.226044 (0.126933) | 3.436341 / 2.268929 (1.167413) | 1.962418 / 55.444624 (-53.482206) | 1.655444 / 6.876477 (-5.221033) | 1.680082 / 2.142072 (-0.461990) | 0.792920 / 4.805227 (-4.012307) | 0.133518 / 6.500664 (-6.367146) | 0.041123 / 0.075469 (-0.034346) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.022546 / 1.841788 (-0.819242) | 12.076711 / 8.074308 (4.002402) | 10.159920 / 10.191392 (-0.031472) | 0.143709 / 0.680424 (-0.536715) | 0.015499 / 0.534201 (-0.518702) | 0.302096 / 0.579283 (-0.277187) | 0.125202 / 0.434364 (-0.309162) | 0.349499 / 0.540337 (-0.190839) | 0.456019 / 1.386936 (-0.930917) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c3aaf2108e3fd77f92aed3b1dce0fd551daf9a0a \"CML watermark\")\n"
] | "2024-06-25T06:54:40Z" | "2024-08-21T09:42:52Z" | "2024-08-21T09:35:06Z" | MEMBER | null | Remove deprecated code, as part of the 3.0 release.
First merge:
- [x] #6983
- [x] #6987
- [x] #6999 | {
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"What is the version of your installed `huggingface-hub`:\r\n```python\r\nimport huggingface_hub\r\nprint(huggingface_hub.__version__)\r\n```\r\n\r\nIt seems you have a very old version of `huggingface-hub`, where `CommitInfo` was not still implemented. You need to update it:\r\n```\r\npip install -U huggingface-hub\r\n```\r\n\r\nNote that `CommitInfo` was implemented in huggingface-hub 0.10.0 and datasets requires \"huggingface-hub>=0.21.2\"",
"The version of my huggingface-hub is 0.23.4.",
"The error message says there is no CommitInfo in your installed huggingface-hub library:\r\n```\r\nImportError: cannot import name 'CommitInfo' from 'huggingface_hub' (D:\\Anaconda3\\envs\\CS224S\\Lib\\site-packages\\huggingface_hub_init_.py)\r\n```\r\n\r\nAnd this is implemented since version 0.10.0:\r\n- https://github.com/huggingface/huggingface_hub/pull/1066",
"I am getting the exact same issue when I `import datasets`. The version of my huggingface-hub is also 0.23.4. I dont see a solution in the comments. Not sure why is this issue closed?",
"I closed the issue because the problem is not related to the `datasets` library.\r\n\r\nThe problem is with your local Python environment: it seems corrupted. You could try to remove it and regenerate it again.",
"I have recreated my conda environment but still run into the same issue. Here is my environment:\r\n```\r\nconda create --name esm python=3.10\r\n conda activate esm\r\n conda install pytorch torchvision torchaudio pytorch-cuda=12.1 -c pytorch -c nvidia\r\n pip3 install -r requirements.txt\r\n```\r\nRequirements.txt\r\n```\r\naccelerate\r\ndatasets==2.20.0\r\npyfastx\r\ntransformers\r\nboto3\r\nhuggingface_hub==0.23.4\r\n```\r\n\r\nAnd then I get:\r\n```\r\n>>> import datasets\r\nTraceback (most recent call last):\r\n File \"<stdin>\", line 1, in <module>\r\n File \"/fsx/ubuntu/miniconda3/envs/esm2/lib/python3.10/site-packages/datasets/__init__.py\", line 17, in <module>\r\n from .arrow_dataset import Dataset\r\n File \"/fsx/ubuntu/miniconda3/envs/esm2/lib/python3.10/site-packages/datasets/arrow_dataset.py\", line 63, in <module>\r\n from huggingface_hub import (\r\nImportError: cannot import name 'CommitInfo' from 'huggingface_hub' (/fsx/ubuntu/miniconda3/envs/esm2/lib/python3.10/site-packages/huggingface_hub/__init__.py)\r\n>>>\r\n```\r\n\r\n",
"You can check:\r\n```\r\n>>> import huggingface_hub\r\n>>> print(huggingface_hub.__version__)\r\n```",
"This is what I see:\r\n```\r\n>>> import huggingface_hub\r\n>>> print(huggingface_hub.__version__)\r\n0.23.4\r\n```",
"Installing `chardet` makes it work for some reason"
] | "2024-06-24T17:07:22Z" | "2024-07-16T17:51:06Z" | "2024-06-25T06:11:37Z" | NONE | null | ### Describe the bug
I encountered an ImportError while trying to import `load_dataset` from the `datasets` module in Hugging Face. The error message indicates a problem with importing 'CommitInfo' from 'huggingface_hub'.
### Steps to reproduce the bug
1. pip install git+https://github.com/huggingface/datasets
2. from datasets import load_dataset
### Expected behavior
ImportError Traceback (most recent call last)
Cell In[7], [line 1](vscode-notebook-cell:?execution_count=7&line=1)
----> [1](vscode-notebook-cell:?execution_count=7&line=1) from datasets import load_dataset
[3](vscode-notebook-cell:?execution_count=7&line=3) train_set = load_dataset("mispeech/speechocean762", split="train")
[4](vscode-notebook-cell:?execution_count=7&line=4) test_set = load_dataset("mispeech/speechocean762", split="test")
File d:\Anaconda3\envs\CS224S\Lib\site-packages\datasets\__init__.py:[1](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/__init__.py:1)7
1 # Copyright 2020 The HuggingFace Datasets Authors and the TensorFlow Datasets Authors.
[2](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/__init__.py:2) #
[3](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/__init__.py:3) # Licensed under the Apache License, Version 2.0 (the "License");
(...)
[12](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/__init__.py:12) # See the License for the specific language governing permissions and
[13](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/__init__.py:13) # limitations under the License.
[15](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/__init__.py:15) __version__ = "2.20.1.dev0"
---> [17](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/__init__.py:17) from .arrow_dataset import Dataset
[18](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/__init__.py:18) from .arrow_reader import ReadInstruction
[19](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/__init__.py:19) from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
File d:\Anaconda3\envs\CS224S\Lib\site-packages\datasets\arrow_dataset.py:63
[61](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/arrow_dataset.py:61) import pyarrow.compute as pc
[62](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/arrow_dataset.py:62) from fsspec.core import url_to_fs
---> [63](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/arrow_dataset.py:63) from huggingface_hub import (
[64](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/arrow_dataset.py:64) CommitInfo,
[65](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/arrow_dataset.py:65) CommitOperationAdd,
...
[70](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/arrow_dataset.py:70) )
[71](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/arrow_dataset.py:71) from huggingface_hub.hf_api import RepoFile
[72](file:///D:/Anaconda3/envs/CS224S/Lib/site-packages/datasets/arrow_dataset.py:72) from multiprocess import Pool
ImportError: cannot import name 'CommitInfo' from 'huggingface_hub' (d:\Anaconda3\envs\CS224S\Lib\site-packages\huggingface_hub\__init__.py)
Output is truncated. View as a [scrollable element](command:cellOutput.enableScrolling?580889ab-0f61-4f37-9214-eaa2b3807f85) or open in a [text editor](command:workbench.action.openLargeOutput?580889ab-0f61-4f37-9214-eaa2b3807f85). Adjust cell output [settings](command:workbench.action.openSettings?%5B%22%40tag%3AnotebookOutputLayout%22%5D)...
### Environment info
Leo@DESKTOP-9NHUAMI MSYS /d/Anaconda3/envs/CS224S/Lib/site-packages/huggingface_hub
$ datasets-cli env
Traceback (most recent call last):
File "<frozen runpy>", line 198, in _run_module_as_main
File "<frozen runpy>", line 88, in _run_code
File "D:\Anaconda3\envs\CS224S\Scripts\datasets-cli.exe\__main__.py", line 4, in <module>
File "D:\Anaconda3\envs\CS224S\Lib\site-packages\datasets\__init__.py", line 17, in <module>
from .arrow_dataset import Dataset
File "D:\Anaconda3\envs\CS224S\Lib\site-packages\datasets\arrow_dataset.py", line 63, in <module>
from huggingface_hub import (
ImportError: cannot import name 'CommitInfo' from 'huggingface_hub' (D:\Anaconda3\envs\CS224S\Lib\site-packages\huggingface_hub\__init__.py)
(CS224S) | {
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"Sure~",
"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6994). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<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) | 0.005538 / 0.011353 (-0.005815) | 0.003997 / 0.011008 (-0.007011) | 0.063444 / 0.038508 (0.024935) | 0.032552 / 0.023109 (0.009442) | 0.266574 / 0.275898 (-0.009324) | 0.282841 / 0.323480 (-0.040639) | 0.004279 / 0.007986 (-0.003706) | 0.002788 / 0.004328 (-0.001540) | 0.049226 / 0.004250 (0.044976) | 0.044688 / 0.037052 (0.007636) | 0.275464 / 0.258489 (0.016975) | 0.305278 / 0.293841 (0.011437) | 0.030097 / 0.128546 (-0.098450) | 0.012237 / 0.075646 (-0.063410) | 0.205526 / 0.419271 (-0.213745) | 0.036145 / 0.043533 (-0.007388) | 0.267395 / 0.255139 (0.012256) | 0.289149 / 0.283200 (0.005949) | 0.019044 / 0.141683 (-0.122639) | 1.162294 / 1.452155 (-0.289861) | 1.183642 / 1.492716 (-0.309074) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.139125 / 0.018006 (0.121119) | 0.301743 / 0.000490 (0.301253) | 0.000260 / 0.000200 (0.000061) | 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) | 0.019494 / 0.037411 (-0.017917) | 0.063078 / 0.014526 (0.048552) | 0.076989 / 0.176557 (-0.099567) | 0.121363 / 0.737135 (-0.615773) | 0.080040 / 0.296338 (-0.216298) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.284401 / 0.215209 (0.069192) | 2.805397 / 2.077655 (0.727742) | 1.555609 / 1.504120 (0.051489) | 1.405662 / 1.541195 (-0.135533) | 1.459492 / 1.468490 (-0.008999) | 0.718376 / 4.584777 (-3.866401) | 2.395918 / 3.745712 (-1.349794) | 2.976753 / 5.269862 (-2.293108) | 1.883938 / 4.565676 (-2.681738) | 0.078867 / 0.424275 (-0.345408) | 0.005207 / 0.007607 (-0.002400) | 0.335178 / 0.226044 (0.109133) | 3.313414 / 2.268929 (1.044485) | 1.856929 / 55.444624 (-53.587696) | 1.565319 / 6.876477 (-5.311158) | 1.592723 / 2.142072 (-0.549350) | 0.793621 / 4.805227 (-4.011606) | 0.134208 / 6.500664 (-6.366456) | 0.042853 / 0.075469 (-0.032616) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.981553 / 1.841788 (-0.860235) | 11.810438 / 8.074308 (3.736130) | 9.529874 / 10.191392 (-0.661518) | 0.142216 / 0.680424 (-0.538207) | 0.014303 / 0.534201 (-0.519898) | 0.304600 / 0.579283 (-0.274684) | 0.261869 / 0.434364 (-0.172495) | 0.347301 / 0.540337 (-0.193036) | 0.437395 / 1.386936 (-0.949541) |\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) | 0.005881 / 0.011353 (-0.005472) | 0.004039 / 0.011008 (-0.006969) | 0.050241 / 0.038508 (0.011733) | 0.032670 / 0.023109 (0.009561) | 0.264940 / 0.275898 (-0.010959) | 0.287105 / 0.323480 (-0.036374) | 0.004844 / 0.007986 (-0.003142) | 0.002867 / 0.004328 (-0.001462) | 0.048083 / 0.004250 (0.043833) | 0.040965 / 0.037052 (0.003913) | 0.274390 / 0.258489 (0.015901) | 0.312107 / 0.293841 (0.018266) | 0.031714 / 0.128546 (-0.096832) | 0.012603 / 0.075646 (-0.063043) | 0.060698 / 0.419271 (-0.358573) | 0.033130 / 0.043533 (-0.010402) | 0.264444 / 0.255139 (0.009305) | 0.282797 / 0.283200 (-0.000403) | 0.027872 / 0.141683 (-0.113811) | 1.139026 / 1.452155 (-0.313129) | 1.181431 / 1.492716 (-0.311285) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.097314 / 0.018006 (0.079308) | 0.301326 / 0.000490 (0.300836) | 0.000215 / 0.000200 (0.000015) | 0.000049 / 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) | 0.023394 / 0.037411 (-0.014018) | 0.076270 / 0.014526 (0.061744) | 0.089065 / 0.176557 (-0.087491) | 0.129996 / 0.737135 (-0.607139) | 0.089642 / 0.296338 (-0.206697) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.295390 / 0.215209 (0.080181) | 2.877849 / 2.077655 (0.800194) | 1.537129 / 1.504120 (0.033009) | 1.409441 / 1.541195 (-0.131754) | 1.432468 / 1.468490 (-0.036023) | 0.718054 / 4.584777 (-3.866722) | 0.930872 / 3.745712 (-2.814841) | 2.841028 / 5.269862 (-2.428834) | 1.921990 / 4.565676 (-2.643686) | 0.077638 / 0.424275 (-0.346637) | 0.005494 / 0.007607 (-0.002113) | 0.336331 / 0.226044 (0.110287) | 3.330490 / 2.268929 (1.061561) | 1.887994 / 55.444624 (-53.556630) | 1.593332 / 6.876477 (-5.283144) | 1.726956 / 2.142072 (-0.415116) | 0.783612 / 4.805227 (-4.021615) | 0.129926 / 6.500664 (-6.370738) | 0.040792 / 0.075469 (-0.034677) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.980274 / 1.841788 (-0.861514) | 12.193871 / 8.074308 (4.119563) | 10.348934 / 10.191392 (0.157542) | 0.141584 / 0.680424 (-0.538840) | 0.015737 / 0.534201 (-0.518464) | 0.300725 / 0.579283 (-0.278558) | 0.127190 / 0.434364 (-0.307174) | 0.341142 / 0.540337 (-0.199196) | 0.459523 / 1.386936 (-0.927413) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#637246baf96f07b19b193ed101f34b65cb35cffb \"CML watermark\")\n"
] | "2024-06-24T15:07:47Z" | "2024-06-26T04:37:35Z" | "2024-06-25T16:19:17Z" | CONTRIBUTOR | null | Fix #6990. | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6993). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<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) | 0.005810 / 0.011353 (-0.005543) | 0.003984 / 0.011008 (-0.007024) | 0.064347 / 0.038508 (0.025839) | 0.031943 / 0.023109 (0.008834) | 0.252596 / 0.275898 (-0.023302) | 0.274032 / 0.323480 (-0.049448) | 0.003494 / 0.007986 (-0.004492) | 0.002817 / 0.004328 (-0.001511) | 0.050132 / 0.004250 (0.045881) | 0.048008 / 0.037052 (0.010955) | 0.249037 / 0.258489 (-0.009452) | 0.288526 / 0.293841 (-0.005315) | 0.031038 / 0.128546 (-0.097509) | 0.012542 / 0.075646 (-0.063104) | 0.205682 / 0.419271 (-0.213590) | 0.038022 / 0.043533 (-0.005511) | 0.259001 / 0.255139 (0.003862) | 0.267455 / 0.283200 (-0.015744) | 0.021980 / 0.141683 (-0.119703) | 1.123996 / 1.452155 (-0.328159) | 1.173801 / 1.492716 (-0.318915) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.102827 / 0.018006 (0.084821) | 0.317210 / 0.000490 (0.316720) | 0.000222 / 0.000200 (0.000022) | 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) | 0.019483 / 0.037411 (-0.017928) | 0.064098 / 0.014526 (0.049572) | 0.076219 / 0.176557 (-0.100337) | 0.122898 / 0.737135 (-0.614237) | 0.080657 / 0.296338 (-0.215681) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.278378 / 0.215209 (0.063169) | 2.792314 / 2.077655 (0.714659) | 1.516439 / 1.504120 (0.012319) | 1.374052 / 1.541195 (-0.167143) | 1.370848 / 1.468490 (-0.097642) | 0.756002 / 4.584777 (-3.828775) | 2.349581 / 3.745712 (-1.396131) | 2.994094 / 5.269862 (-2.275768) | 1.904242 / 4.565676 (-2.661435) | 0.078769 / 0.424275 (-0.345506) | 0.005103 / 0.007607 (-0.002505) | 0.336331 / 0.226044 (0.110287) | 3.329502 / 2.268929 (1.060574) | 1.863545 / 55.444624 (-53.581079) | 1.554690 / 6.876477 (-5.321787) | 1.588448 / 2.142072 (-0.553624) | 0.787322 / 4.805227 (-4.017905) | 0.138345 / 6.500664 (-6.362320) | 0.042228 / 0.075469 (-0.033241) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.968607 / 1.841788 (-0.873181) | 11.972076 / 8.074308 (3.897768) | 9.927608 / 10.191392 (-0.263784) | 0.141666 / 0.680424 (-0.538758) | 0.014591 / 0.534201 (-0.519610) | 0.301995 / 0.579283 (-0.277288) | 0.274360 / 0.434364 (-0.160004) | 0.338396 / 0.540337 (-0.201941) | 0.431081 / 1.386936 (-0.955855) |\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) | 0.006122 / 0.011353 (-0.005231) | 0.004201 / 0.011008 (-0.006807) | 0.050204 / 0.038508 (0.011695) | 0.033222 / 0.023109 (0.010113) | 0.274357 / 0.275898 (-0.001542) | 0.296238 / 0.323480 (-0.027242) | 0.004542 / 0.007986 (-0.003444) | 0.002880 / 0.004328 (-0.001449) | 0.049103 / 0.004250 (0.044852) | 0.042294 / 0.037052 (0.005242) | 0.286459 / 0.258489 (0.027970) | 0.324988 / 0.293841 (0.031147) | 0.032084 / 0.128546 (-0.096462) | 0.012329 / 0.075646 (-0.063318) | 0.060261 / 0.419271 (-0.359010) | 0.034130 / 0.043533 (-0.009403) | 0.271432 / 0.255139 (0.016293) | 0.306251 / 0.283200 (0.023051) | 0.019744 / 0.141683 (-0.121939) | 1.153483 / 1.452155 (-0.298672) | 1.209126 / 1.492716 (-0.283591) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.004737 / 0.018006 (-0.013270) | 0.313458 / 0.000490 (0.312968) | 0.000216 / 0.000200 (0.000017) | 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) | 0.022472 / 0.037411 (-0.014939) | 0.076725 / 0.014526 (0.062199) | 0.091356 / 0.176557 (-0.085201) | 0.132427 / 0.737135 (-0.604708) | 0.091072 / 0.296338 (-0.205266) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.294414 / 0.215209 (0.079205) | 2.913695 / 2.077655 (0.836040) | 1.567309 / 1.504120 (0.063189) | 1.448664 / 1.541195 (-0.092531) | 1.466386 / 1.468490 (-0.002105) | 0.718605 / 4.584777 (-3.866172) | 0.951963 / 3.745712 (-2.793749) | 2.812565 / 5.269862 (-2.457297) | 1.886483 / 4.565676 (-2.679193) | 0.077912 / 0.424275 (-0.346363) | 0.005371 / 0.007607 (-0.002236) | 0.349528 / 0.226044 (0.123484) | 3.431049 / 2.268929 (1.162121) | 1.920210 / 55.444624 (-53.524414) | 1.637927 / 6.876477 (-5.238549) | 1.767502 / 2.142072 (-0.374570) | 0.808672 / 4.805227 (-3.996555) | 0.134261 / 6.500664 (-6.366403) | 0.041295 / 0.075469 (-0.034174) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.023454 / 1.841788 (-0.818334) | 12.433731 / 8.074308 (4.359423) | 10.413191 / 10.191392 (0.221799) | 0.156813 / 0.680424 (-0.523611) | 0.015446 / 0.534201 (-0.518755) | 0.301935 / 0.579283 (-0.277348) | 0.133655 / 0.434364 (-0.300709) | 0.340296 / 0.540337 (-0.200041) | 0.466314 / 1.386936 (-0.920622) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6cf563fd57807e923a29ebbe327fecb4ef969877 \"CML watermark\")\n",
"Hi @lhoestq,\r\n\r\nI was confused by `legacy` prefix added to the [image data loading](https://huggingface.co/docs/datasets/main/en/image_dataset#legacy-loading-script) script section. I have a custom image dataset and have looked through the documentation to find something similar but can't find a good alternative What is now the recommend way to create a custom image dataset then? I want the HF format but will not host it on the hub.\r\n\r\nApologies in advance if this is the wrong place to ask such questions...",
"We stopped making new features for datasets with scripts for obvious security reasons, that's why they are marked as \"legacy\". What is blocking you from hosting on HF ?",
"Hi, thanks for the prompt answer :) I am working on proprietary datasets for the company where I am employed. We want to keep the data in-house but would like to investigate the use of the HF ecosystem.",
"I see ! Note that it's possible to have private repos on HF (+ dataset viewer) and you can even choose the storage region, if it can help"
] | "2024-06-24T14:45:28Z" | "2024-07-08T13:10:53Z" | "2024-06-27T09:31:21Z" | MEMBER | null | + mark as legacy in some parts of the docs since we'll not build new features for script datasets | {
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"Hi ! can you try updating `datasets` and `huggingface_hub` ?\r\n\r\n```\r\npip install -U datasets huggingface_hub\r\n```"
] | "2024-06-22T16:12:08Z" | "2024-06-25T15:43:05Z" | null | NONE | null | ### Describe the bug
I'm currently trying to stream data using dataset since the dataset is too big but it hangs indefinitely without loading the first batch. I use AIMOS which is a supercomputer that uses proxy to connect to the internet. I assume it has to do with the network configurations. I've already set up both HTTP_PROXY and HTTPS_PROXY. streaming = False works fine.
### Steps to reproduce the bug
use load_dataset with streaming = True in AIMOS
### Expected behavior
does not hang indefinitely and loads batches to start training run
### Environment info
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6991). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"@albertvillanova Any chance we could get this in before the next release? Everything depending on HuggingFace has their NumPy upgrade blocked.",
"The incompatible libraries are:\r\n- faiss-cpu 1.8.0.post1 requires numpy<2.0,>=1.0, but you have numpy 2.0.0 which is incompatible.\r\n- tensorflow 2.16.2 requires numpy<2.0.0,>=1.23.5; python_version <= \"3.11\", but you have numpy 2.0.0 which is incompatible.\r\n- transformers 4.42.3 requires numpy<2.0,>=1.17, but you have numpy 2.0.0 which is incompatible.",
"Why is it installing numpy 2 if the dependencies don't support it?",
"For me, I'm getting:\r\n```\r\n❯ uv pip install --system \"datasets[tests] @ .\"\r\nFound existing alias for \"uv pip install\". You should use: \"pipi\"\r\nResolved 119 packages in 934ms\r\n Built datasets @ file:///Users/neil/src/datasets\r\nPrepared 1 package in 1.28s\r\nUninstalled 1 package in 10ms\r\nInstalled 2 packages in 17ms\r\n - datasets==2.20.1.dev0 (from file:///Users/neil/src/datasets)\r\n + datasets==2.20.1.dev0 (from file:///Users/neil/src/datasets)\r\n + numpy==1.26.4\r\n```",
"Which version on Python do you have?",
"3.12.4 I'll try on 3.10 now.",
"Please, note that I obtained the previous incompatible libraries in my local environment, by forcing the update of numpy.",
"In the Python 3.10 CI, the situation is different:\r\n- for example, they install an older version of tensorflow (2.14.0), where probably the constraint on numpy was not yet implemented. See the details: https://github.com/huggingface/datasets/actions/runs/9879100332/job/27306903343?pr=6991\r\n```\r\n> uv pip install --system \"datasets[tests] @ .\"\r\n...\r\n + faiss-cpu==1.8.0\r\n...\r\n + numpy==2.0.0\r\n...\r\n + tensorflow==2.14.0\r\n```\r\n\r\nSee, CI installs:\r\n- faiss-cpu 1.8.0 instead of 1.8.0.post1\r\n- tensorflow 2.14.0 instead of 2.16.2\r\n- transformers 4.41.2 instead of 4.42.3",
"~~The main point is that we cannot support numpy 2.0 until tensorflow and faiss do.~~\r\n\r\nAlternatively, we should ignore/select tests depending on the installed versions.",
"> Alternatively, we should ignore/select tests depending on the installed versions.\r\n\r\nThat works.\r\n\r\nAlternatively, you could depend on tensorflow >= 2.16.2 (etc.) for the tests?",
"Yes, I was thinking of a workaround solution.\r\n\r\nThe issue I see is that our CI will not test numpy 2.0 indeed.",
"> The issue I see is that our CI will not test numpy 2.0 indeed.\r\n\r\nRight, that's the advantage of the test skipping you wanted, I see your point.\r\n\r\nThing is, it won't be long before tensorflow supports numpy 2.0, and then the situation is resolved and your tests test numpy 2.0. Do you really want to invest a lot of effort into testing numpy 2.0 for a few months benefit?",
"Without testing Numpy 2.0, we do not know if there are some other parts in the code broken.",
"> Without testing Numpy 2.0, we do not know if there are some other parts in the code broken.\r\n\r\nYes, you're right. I understand you're point, but you could say this for anything that your test dependencies don't support.\r\n\r\nI guess the solution is to write tests that don't depend on tensorflow, etc., but still use numpy. You could write some Jax tests for example.\r\n\r\nThat said, blocking numpy 2 isn't a good solution in my opinion. These dependencies are extremely late in supporting Numpy 2. They were supposed to be testing against preview releases over three months ago. I don't think the world should have to wait for them.",
"> I guess the solution is to write tests that don't depend on tensorflow, etc., but still use numpy.\r\nThat is my point. What we cannot do is just blindly support Numpy 2.0 without knowing its consequences. We need to test it:\r\n- to know if our core code works with it\r\n- to know what optional libraries are incompatible\r\n\r\nFor example, while testing locally, I have discovered that librosa is also incompatible with numpy-2.0, due to its dependency on soxr:\r\n- https://github.com/dofuuz/python-soxr/issues/28",
"While testing locally, I have also discovered that pytorch does not support Numpy 2.0 on Windows platforms:\r\n- https://github.com/pytorch/pytorch/issues/128860",
"I am adding Numpy 2.0 tests to your PR if you don't mind, before merging this PR.",
"Awesome, thank you! Please let me know if I need to do anything.",
"Now we test numpy 2.0 in the `test_py310_numpy2` CI tests: https://github.com/huggingface/datasets/actions/runs/9907254874/job/27370545495?pr=6991\r\n```\r\n + numpy==2.0.0\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) | 0.005709 / 0.011353 (-0.005643) | 0.003947 / 0.011008 (-0.007061) | 0.064407 / 0.038508 (0.025899) | 0.029903 / 0.023109 (0.006794) | 0.244838 / 0.275898 (-0.031060) | 0.268894 / 0.323480 (-0.054586) | 0.003200 / 0.007986 (-0.004786) | 0.002867 / 0.004328 (-0.001461) | 0.050016 / 0.004250 (0.045765) | 0.047682 / 0.037052 (0.010629) | 0.252186 / 0.258489 (-0.006303) | 0.292050 / 0.293841 (-0.001791) | 0.030277 / 0.128546 (-0.098270) | 0.012283 / 0.075646 (-0.063364) | 0.205875 / 0.419271 (-0.213397) | 0.037202 / 0.043533 (-0.006331) | 0.246045 / 0.255139 (-0.009094) | 0.272422 / 0.283200 (-0.010777) | 0.020572 / 0.141683 (-0.121111) | 1.114343 / 1.452155 (-0.337812) | 1.169909 / 1.492716 (-0.322808) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.096612 / 0.018006 (0.078605) | 0.303025 / 0.000490 (0.302535) | 0.000210 / 0.000200 (0.000010) | 0.000043 / 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) | 0.019292 / 0.037411 (-0.018119) | 0.062548 / 0.014526 (0.048023) | 0.076027 / 0.176557 (-0.100530) | 0.121752 / 0.737135 (-0.615383) | 0.076608 / 0.296338 (-0.219730) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.283900 / 0.215209 (0.068691) | 2.829829 / 2.077655 (0.752174) | 1.428934 / 1.504120 (-0.075186) | 1.316796 / 1.541195 (-0.224399) | 1.330012 / 1.468490 (-0.138478) | 0.702245 / 4.584777 (-3.882532) | 2.380454 / 3.745712 (-1.365259) | 2.882881 / 5.269862 (-2.386980) | 1.920345 / 4.565676 (-2.645332) | 0.077860 / 0.424275 (-0.346415) | 0.005295 / 0.007607 (-0.002312) | 0.336968 / 0.226044 (0.110924) | 3.327808 / 2.268929 (1.058879) | 1.781958 / 55.444624 (-53.662666) | 1.489412 / 6.876477 (-5.387065) | 1.634829 / 2.142072 (-0.507243) | 0.787985 / 4.805227 (-4.017243) | 0.134397 / 6.500664 (-6.366267) | 0.042906 / 0.075469 (-0.032563) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.967647 / 1.841788 (-0.874141) | 11.714541 / 8.074308 (3.640233) | 9.350228 / 10.191392 (-0.841164) | 0.142675 / 0.680424 (-0.537749) | 0.014609 / 0.534201 (-0.519592) | 0.301970 / 0.579283 (-0.277314) | 0.262350 / 0.434364 (-0.172014) | 0.342933 / 0.540337 (-0.197404) | 0.437321 / 1.386936 (-0.949615) |\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) | 0.005622 / 0.011353 (-0.005731) | 0.003958 / 0.011008 (-0.007050) | 0.050667 / 0.038508 (0.012159) | 0.032842 / 0.023109 (0.009733) | 0.252292 / 0.275898 (-0.023606) | 0.280602 / 0.323480 (-0.042878) | 0.004313 / 0.007986 (-0.003673) | 0.002870 / 0.004328 (-0.001458) | 0.049549 / 0.004250 (0.045299) | 0.040448 / 0.037052 (0.003396) | 0.270264 / 0.258489 (0.011775) | 0.302988 / 0.293841 (0.009147) | 0.030840 / 0.128546 (-0.097707) | 0.012131 / 0.075646 (-0.063515) | 0.060061 / 0.419271 (-0.359211) | 0.033025 / 0.043533 (-0.010507) | 0.251909 / 0.255139 (-0.003230) | 0.275511 / 0.283200 (-0.007689) | 0.018399 / 0.141683 (-0.123284) | 1.160744 / 1.452155 (-0.291411) | 1.188265 / 1.492716 (-0.304452) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.097719 / 0.018006 (0.079712) | 0.304389 / 0.000490 (0.303899) | 0.000217 / 0.000200 (0.000017) | 0.000045 / 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) | 0.022964 / 0.037411 (-0.014447) | 0.076897 / 0.014526 (0.062372) | 0.088930 / 0.176557 (-0.087626) | 0.128926 / 0.737135 (-0.608209) | 0.091049 / 0.296338 (-0.205290) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.285670 / 0.215209 (0.070461) | 2.806071 / 2.077655 (0.728416) | 1.527161 / 1.504120 (0.023041) | 1.410291 / 1.541195 (-0.130903) | 1.427071 / 1.468490 (-0.041419) | 0.705527 / 4.584777 (-3.879250) | 0.926915 / 3.745712 (-2.818797) | 2.893078 / 5.269862 (-2.376784) | 1.907113 / 4.565676 (-2.658564) | 0.077326 / 0.424275 (-0.346949) | 0.005182 / 0.007607 (-0.002425) | 0.332282 / 0.226044 (0.106237) | 3.312889 / 2.268929 (1.043960) | 1.853839 / 55.444624 (-53.590785) | 1.592013 / 6.876477 (-5.284464) | 1.620234 / 2.142072 (-0.521838) | 0.776894 / 4.805227 (-4.028333) | 0.132411 / 6.500664 (-6.368253) | 0.041430 / 0.075469 (-0.034039) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.003468 / 1.841788 (-0.838320) | 12.472251 / 8.074308 (4.397943) | 10.603243 / 10.191392 (0.411851) | 0.132561 / 0.680424 (-0.547863) | 0.015790 / 0.534201 (-0.518411) | 0.306724 / 0.579283 (-0.272559) | 0.125812 / 0.434364 (-0.308552) | 0.343782 / 0.540337 (-0.196555) | 0.445915 / 1.386936 (-0.941021) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#dfc2b1b14ab8f32730d2bc36c8016ecefbcbabd1 \"CML watermark\")\n"
] | "2024-06-22T09:19:53Z" | "2024-07-12T12:11:18Z" | "2024-07-12T12:04:53Z" | CONTRIBUTOR | null | Fixes https://github.com/huggingface/datasets/issues/6980 | {
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"ah yes good catch ! feel free to open a PR with your suggested fix"
] | "2024-06-21T14:25:26Z" | "2024-06-25T16:19:19Z" | "2024-06-25T16:19:19Z" | CONTRIBUTOR | null | ### Describe the bug
I'm trying to split `IterableDataset` by `split_dataset_by_node`.
But when doing split on a already split dataset, the resulting `rank` is greater than `world_size`.
### Steps to reproduce the bug
Here is the minimal code for reproduction:
```py
>>> from datasets import load_dataset
>>> from datasets.distributed import split_dataset_by_node
>>> dataset = load_dataset('fla-hub/slimpajama-test', split='train', streaming=True)
>>> dataset = split_dataset_by_node(dataset, 1, 32)
>>> dataset._distributed
DistributedConfig(rank=1, world_size=32)
>>> dataset = split_dataset_by_node(dataset, 1, 15)
>>> dataset._distributed
DistributedConfig(rank=481, world_size=480)
```
As you can see, the second rank 481 > 480, which is problematic.
### Expected behavior
I think this error comes from this line @lhoestq
https://github.com/huggingface/datasets/blob/a6ccf944e42c1a84de81bf326accab9999b86c90/src/datasets/iterable_dataset.py#L2943-L2944
We may need to obtain the rank first. Then the above code gives
```py
>>> dataset._distributed
DistributedConfig(rank=16, world_size=480)
```
### Environment info
datasets==2.20.0 | {
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} | [] | open | false | null | [] | null | [] | "2024-06-21T02:09:22Z" | "2024-06-21T02:12:55Z" | null | NONE | null | ### Describe the bug
- When reading dataset, a cache will be generated to the ~/. cache/huggingface/datasets directory
- When using .map and .filter operations, runtime cache will be generated to the /tmp/hf_datasets-* directory
- The default is to use the path of tempfile.tempdir
- If I modify this path to the NFS disk, an error will be reported, but the program will continue to run
- https://github.com/huggingface/datasets/blob/main/src/datasets/config.py#L257
```
Traceback (most recent call last):
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/process.py", line 315, in _bootstrap
self.run()
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/managers.py", line 616, in _run_server
server.serve_forever()
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/managers.py", line 182, in serve_forever
sys.exit(0)
SystemExit: 0
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/util.py", line 300, in _run_finalizers
finalizer()
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/util.py", line 224, in __call__
res = self._callback(*self._args, **self._kwargs)
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/util.py", line 133, in _remove_temp_dir
rmtree(tempdir)
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/shutil.py", line 718, in rmtree
_rmtree_safe_fd(fd, path, onerror)
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/shutil.py", line 675, in _rmtree_safe_fd
onerror(os.unlink, fullname, sys.exc_info())
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/shutil.py", line 673, in _rmtree_safe_fd
os.unlink(entry.name, dir_fd=topfd)
OSError: [Errno 16] Device or resource busy: '.nfs000000038330a012000030b4'
Traceback (most recent call last):
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/process.py", line 315, in _bootstrap
self.run()
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/managers.py", line 616, in _run_server
server.serve_forever()
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/managers.py", line 182, in serve_forever
sys.exit(0)
SystemExit: 0
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/util.py", line 300, in _run_finalizers
finalizer()
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/util.py", line 224, in __call__
res = self._callback(*self._args, **self._kwargs)
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/site-packages/multiprocess/util.py", line 133, in _remove_temp_dir
rmtree(tempdir)
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/shutil.py", line 718, in rmtree
_rmtree_safe_fd(fd, path, onerror)
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/shutil.py", line 675, in _rmtree_safe_fd
onerror(os.unlink, fullname, sys.exc_info())
File "/home/wzp/miniconda3/envs/dask/lib/python3.8/shutil.py", line 673, in _rmtree_safe_fd
os.unlink(entry.name, dir_fd=topfd)
OSError: [Errno 16] Device or resource busy: '.nfs0000000400064d4a000030e5'
```
### Steps to reproduce the bug
```
import os
import time
import tempfile
from datasets import load_dataset
def add_column(sample):
# print(type(sample))
# time.sleep(0.1)
sample['__ds__stats__'] = {'data': 123}
return sample
def filt_column(sample):
# print(type(sample))
if len(sample['content']) > 10:
return True
else:
return False
if __name__ == '__main__':
input_dir = '/mnt/temp/CN/small' # some json dataset
dataset = load_dataset('json', data_dir=input_dir)
temp_dir = '/media/release/release/temp/temp' # a nfs folder
os.makedirs(temp_dir, exist_ok=True)
# change huggingface-datasets runtime cache in nfs（default in /tmp）
tempfile.tempdir = temp_dir
aa = dataset.map(add_column, num_proc=64)
aa = aa.filter(filt_column, num_proc=64)
print(aa)
```
### Expected behavior
no error occur
### Environment info
datasets==2.18.0
ubuntu 20.04 | null | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6988). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"`Dataset` objects are not made to be subclassed, so I don't think going in that direction is a good idea. In particular there is absolutely no test to make sure it works well, and nothing in the internal has been made to anticipate this use case.\r\n\r\nI'd suggest to use a separate function to push changes to the Dataset card, and call it after `push_to_hub()`. This way people can also use a similar logic with other tools that `datasets`. You can also use composition instead of subclassing.",
"Would you consider an alternative where a Dataset instance carries a dataset card template which can be updated?\n\nI don't want to burden my users with having to call another method after `push_to_hub` themselves. If you're not a fan of the template approach above either, then I'll likely subclass `push_to_hub` to once again download the just-uploaded-but-empty dataset card, update it, and reupload it. It'll just be a bit more requests than necessary, but not a big deal overall.\n\n- Tom Aarsen ",
"Actually I find the idea of overriding `_create_dataset_card` better than implementing a templating logic. My main concern is that if we go in that direction we better make sure that subclasses of `Dataset` are working well. \r\n\r\nWell if it's been working fine on your side why not, but make sure you test correctly features that could not work because of subclassing (e.g. I'm pretty sure `map()` won't return your subclass of `Dataset`). Or at least the ones that matter for your lib.\r\n\r\nIf it sounds good to you I'm fine with merging your addition to let you override the dataset card.",
"> e.g. I'm pretty sure map() won't return your subclass of Dataset\r\n\r\nI understand that there's limitations such as this one. The subclass doesn't have to be robust - I'd just like some simple automatic dataset card generation options directly after generating the dataset. This can be removed if the user does additional steps before pushing the model, e.g. mapping, filtering, saving to disk and uploading the loaded dataset, etc.\r\n\r\n> If it sounds good to you I'm fine with merging your addition to let you override the dataset card.\r\n\r\nThat would be quite useful for me! I appreciate it.\r\n\r\nI'm not very sure what the test failures are caused by, I believe the only change in behaviour is that\r\n```python\r\n DatasetInfosDict({config_name: info_to_dump}).to_dataset_card_data(dataset_card_data)\r\n MetadataConfigs({config_name: metadata_config_to_dump}).to_dataset_card_data(dataset_card_data)\r\n```\r\nare not called when `dataset_card` was already defined. Unless these have side-effects other than updating `dataset_card_data`, it shouldn't be any different than `main`.\r\n\r\n- Tom Aarsen",
"Let's try to have this PR merged then !\r\n\r\nIMO your current implementation can be improved since you path both the dataset card data and the dataset card itself, which is redundant. Also I anticipate the failures in the CI to come from your default implementation which doesn't correspond to what it was doing before\r\n\r\n> Unless these have side-effects other than updating dataset_card_data, it shouldn't be any different than main.\r\n\r\nIndeed the dataset_card_data is the value from attribute of the dataset_card from a few lines before your changes, so yes it modifies the dataset_card object too."
] | "2024-06-20T10:47:57Z" | "2024-06-21T16:04:58Z" | null | MEMBER | null | Hello!
## Pull Request overview
* Move dataset card creation to method for easier overriding
## Details
It's common for me to fully automatically download, reformat, and upload a dataset (e.g. see https://huggingface.co/datasets?other=sentence-transformers), but one aspect that I cannot easily automate is the dataset card generation. This is because during `push_to_hub`, the dataset card is created in 3 lines of code in a much larger method. To automatically generate a dataset card, I need to either:
1. Subclass `Dataset`/`DatasetDict`, copy the entire `push_to_hub` method to override the ~3 lines used to generate the dataset card. This is not viable as the method is likely to change over time.
2. Use `push_to_hub` normally, then separately download the pushed (but empty) dataset card, update it, and reupload the modified dataset. This works fine, but prevents me from being able to return a `Dataset` to my users which will automatically use a nice dataset card.
So, in this PR I'm proposing to move the dataset generation into another method so that it can be overridden more easily. For example, imagine the following use case:
````python
import json
from typing import Any, Dict, Optional
from datasets import Dataset, load_dataset
from datasets.info import DatasetInfosDict, DatasetInfo
from datasets.utils.metadata import MetadataConfigs
from huggingface_hub import DatasetCardData, DatasetCard
TEMPLATE = r"""---
{dataset_card_data}
---
# Dataset Card for {source_dataset_name} with mined hard negatives
This dataset is a collection of {column_one}-{column_two}-negative triplets from the {source_dataset_name} dataset. See [{source_dataset_name}](https://huggingface.co/datasets/{source_dataset_id}) for additional information. This dataset can be used directly with Sentence Transformers to train embedding models.
## Mining Parameters
The negative samples have been mined using the following parameters:
- `range_min`: {range_min}, i.e. we skip the {range_min} most similar samples
- `range_max`: {range_max}, i.e. we only look at the top {range_max} most similar samples
- `margin`: {margin}, i.e. we require negative similarity + margin < positive similarity, so negative samples can't be more similar than the known true answer
- `sampling_strategy`: {sampling_strategy}, i.e. whether to randomly sample from the candidate negatives or take the "top" negatives
- `num_negatives`: {num_negatives}, i.e. we mine {num_negatives} negatives per question-answer pair
## Dataset Format
- Columns: {column_one}, {column_two}, negative
- Column types: str, str, str
- Example:
```python
{example}
```
"""
class HNMDataset(Dataset):
@classmethod
def from_dict(cls, *args, mining_kwargs: Dict[str, Any], **kwargs) -> "HNMDataset":
dataset = super().from_dict(*args, **kwargs)
dataset.mining_kwargs = mining_kwargs
return dataset
def _create_dataset_card(
self,
dataset_card_data: DatasetCardData,
dataset_card: Optional[DatasetCard],
config_name: str,
info_to_dump: DatasetInfo,
metadata_config_to_dump: MetadataConfigs,
) -> DatasetCard:
if dataset_card:
return dataset_card
DatasetInfosDict({config_name: info_to_dump}).to_dataset_card_data(dataset_card_data)
MetadataConfigs({config_name: metadata_config_to_dump}).to_dataset_card_data(dataset_card_data)
dataset_card_data.tags = ["sentence-transformers"]
dataset_name = self.mining_kwargs["source_dataset"].info.dataset_name
# Very messy, just as an example:
dataset_id = list(self.mining_kwargs["source_dataset"].info.download_checksums.keys())[0].removeprefix("hf://datasets/").split("@")[0]
content = TEMPLATE.format(**{
"dataset_card_data": str(dataset_card_data),
"source_dataset_name": dataset_name,
"source_dataset_id": dataset_id,
"range_min": self.mining_kwargs["range_min"],
"range_max": self.mining_kwargs["range_max"],
"margin": self.mining_kwargs["margin"],
"sampling_strategy": self.mining_kwargs["sampling_strategy"],
"num_negatives": self.mining_kwargs["num_negatives"],
"column_one": self.column_names[0],
"column_two": self.column_names[1],
"example": json.dumps(self[0], indent=4),
})
return DatasetCard(content)
source_dataset = load_dataset("sentence-transformers/gooaq", split="train[:100]")
dataset = HNMDataset.from_dict({
"query": source_dataset["question"],
"answer": source_dataset["answer"],
# "negative": ... <- In my case, this column would be 'mined' automatically with these parameters
}, mining_kwargs={
"range_min": 10,
"range_max": 20,
"max_score": 0.9,
"margin": 0.1,
"sampling_strategy": "random",
"num_negatives": 3,
"source_dataset": source_dataset,
})
dataset.push_to_hub("tomaarsen/mining_demo", private=True)
````
In this script, I've created a subclass which stores some additional information about how the dataset was generated. It's a bit hacky (e.g. setting a `mining_kwargs` parameter in `from_dict` that wasn't created in `__init__`, but that's just a consequence of how the `from_...` methods don't accept kwargs), but it allows me to create a "hard negatives mining" function that returns a dataset which people can use locally like normal, but if they choose to upload it, then it'll automatically include some information, e.g.: https://huggingface.co/datasets/tomaarsen/mining_demo
This allows others to actually find this dataset (e.g. via the `sentence-transformers` tag) and get an idea of the quality, source, etc. by looking at the model card.
## Note
I'm not fixed on this solution whatsoever: I am also completely fine with other solutions, e.g. a `dataset.set_dataset_card_creator` method that allows me to provide a function without even having to subclass anything. I'm open to all ideas :)
cc @albertvillanova @lhoestq
cc @LysandreJik
- Tom Aarsen | null | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6987). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<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) | 0.005931 / 0.011353 (-0.005422) | 0.004127 / 0.011008 (-0.006881) | 0.063854 / 0.038508 (0.025346) | 0.034687 / 0.023109 (0.011577) | 0.251397 / 0.275898 (-0.024501) | 0.280348 / 0.323480 (-0.043132) | 0.005008 / 0.007986 (-0.002977) | 0.002930 / 0.004328 (-0.001398) | 0.050703 / 0.004250 (0.046452) | 0.047109 / 0.037052 (0.010057) | 0.258525 / 0.258489 (0.000035) | 0.288759 / 0.293841 (-0.005081) | 0.030547 / 0.128546 (-0.097999) | 0.102184 / 0.075646 (0.026537) | 0.207934 / 0.419271 (-0.211338) | 0.036477 / 0.043533 (-0.007056) | 0.338160 / 0.255139 (0.083021) | 0.310735 / 0.283200 (0.027535) | 0.018637 / 0.141683 (-0.123045) | 1.228539 / 1.452155 (-0.223616) | 1.168004 / 1.492716 (-0.324713) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.098355 / 0.018006 (0.080348) | 0.302310 / 0.000490 (0.301820) | 0.000215 / 0.000200 (0.000015) | 0.000044 / 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) | 0.019607 / 0.037411 (-0.017804) | 0.063795 / 0.014526 (0.049269) | 0.075029 / 0.176557 (-0.101528) | 0.121293 / 0.737135 (-0.615842) | 0.076480 / 0.296338 (-0.219858) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.285285 / 0.215209 (0.070076) | 2.747455 / 2.077655 (0.669801) | 1.454190 / 1.504120 (-0.049929) | 1.330777 / 1.541195 (-0.210418) | 1.358292 / 1.468490 (-0.110198) | 0.724991 / 4.584777 (-3.859786) | 2.374889 / 3.745712 (-1.370823) | 2.985868 / 5.269862 (-2.283994) | 1.921521 / 4.565676 (-2.644156) | 0.078589 / 0.424275 (-0.345686) | 0.005104 / 0.007607 (-0.002503) | 0.333898 / 0.226044 (0.107853) | 3.317702 / 2.268929 (1.048773) | 1.887161 / 55.444624 (-53.557463) | 1.510700 / 6.876477 (-5.365777) | 1.544175 / 2.142072 (-0.597898) | 0.804262 / 4.805227 (-4.000965) | 0.134015 / 6.500664 (-6.366649) | 0.042819 / 0.075469 (-0.032650) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.012142 / 1.841788 (-0.829645) | 11.861780 / 8.074308 (3.787472) | 9.797285 / 10.191392 (-0.394107) | 0.142114 / 0.680424 (-0.538310) | 0.013984 / 0.534201 (-0.520217) | 0.302412 / 0.579283 (-0.276871) | 0.265060 / 0.434364 (-0.169304) | 0.337510 / 0.540337 (-0.202828) | 0.432197 / 1.386936 (-0.954739) |\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) | 0.003991 / 0.011008 (-0.007017) | 0.049874 / 0.038508 (0.011366) | 0.033771 / 0.023109 (0.010662) | 0.264789 / 0.275898 (-0.011109) | 0.287554 / 0.323480 (-0.035926) | 0.004341 / 0.007986 (-0.003644) | 0.002888 / 0.004328 (-0.001441) | 0.049383 / 0.004250 (0.045133) | 0.040757 / 0.037052 (0.003704) | 0.286067 / 0.258489 (0.027578) | 0.311105 / 0.293841 (0.017264) | 0.031482 / 0.128546 (-0.097064) | 0.012358 / 0.075646 (-0.063288) | 0.060298 / 0.419271 (-0.358973) | 0.033237 / 0.043533 (-0.010296) | 0.265804 / 0.255139 (0.010665) | 0.281273 / 0.283200 (-0.001927) | 0.017879 / 0.141683 (-0.123804) | 1.154059 / 1.452155 (-0.298096) | 1.156758 / 1.492716 (-0.335958) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.004677 / 0.018006 (-0.013329) | 0.300768 / 0.000490 (0.300278) | 0.000212 / 0.000200 (0.000013) | 0.000043 / 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) | 0.023032 / 0.037411 (-0.014379) | 0.077498 / 0.014526 (0.062973) | 0.089134 / 0.176557 (-0.087422) | 0.129691 / 0.737135 (-0.607444) | 0.091372 / 0.296338 (-0.204967) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.290823 / 0.215209 (0.075613) | 2.873159 / 2.077655 (0.795504) | 1.563361 / 1.504120 (0.059241) | 1.447048 / 1.541195 (-0.094147) | 1.490473 / 1.468490 (0.021983) | 0.715642 / 4.584777 (-3.869135) | 0.996223 / 3.745712 (-2.749489) | 2.861466 / 5.269862 (-2.408396) | 1.915581 / 4.565676 (-2.650096) | 0.077892 / 0.424275 (-0.346383) | 0.005463 / 0.007607 (-0.002144) | 0.339670 / 0.226044 (0.113626) | 3.412830 / 2.268929 (1.143902) | 1.908676 / 55.444624 (-53.535949) | 1.625358 / 6.876477 (-5.251119) | 1.769437 / 2.142072 (-0.372635) | 0.792505 / 4.805227 (-4.012722) | 0.133007 / 6.500664 (-6.367657) | 0.041305 / 0.075469 (-0.034164) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.986882 / 1.841788 (-0.854905) | 12.368101 / 8.074308 (4.293793) | 10.367439 / 10.191392 (0.176047) | 0.141248 / 0.680424 (-0.539176) | 0.016144 / 0.534201 (-0.518057) | 0.300962 / 0.579283 (-0.278321) | 0.126863 / 0.434364 (-0.307501) | 0.341107 / 0.540337 (-0.199230) | 0.439819 / 1.386936 (-0.947117) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b2754625d45e153bd9758af40e65e7545321fc2a \"CML watermark\")\n"
] | "2024-06-20T07:27:14Z" | "2024-06-26T19:41:55Z" | "2024-06-26T19:35:42Z" | MEMBER | null | Remove beam, as part of the 3.0 release. | {
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https://api.github.com/repos/huggingface/datasets/issues/6986 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6986/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6986/comments | https://api.github.com/repos/huggingface/datasets/issues/6986/events | https://github.com/huggingface/datasets/pull/6986 | 2,362,584,179 | PR_kwDODunzps5y-Zi0 | 6,986 | Add large_list type support in string_to_arrow | {
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"@albertvillanova @KennethEnevoldsen"
] | "2024-06-19T14:54:25Z" | "2024-08-12T14:43:48Z" | "2024-08-12T14:43:47Z" | NONE | null | add large_list type support in string_to_arrow() and _arrow_to_datasets_dtype() in features.py
Fix #6984
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"Please note that the error is raised just at import:\r\n```python\r\nimport pyarrow.parquet as pq\r\n```\r\n\r\nTherefore it must be caused by some problem with your pyarrow installation. I would recommend you uninstall and install pyarrow again.\r\n\r\nI also see that it seems you use conda to install pyarrow. Please note that pyarrow offers 3 different packages in conda-forge: https://arrow.apache.org/docs/python/install.html#using-conda\r\n```\r\nconda install -c conda-forge pyarrow\r\n```\r\n> While the pyarrow [conda-forge](https://conda-forge.org/) package is the right choice for most users, both a minimal and maximal variant of the package exist, either of which may be better for your use case. See [Differences between conda-forge packages](https://arrow.apache.org/docs/python/install.html#python-conda-differences).\r\n\r\nPlease, make sure you install the right one: I guess it is either `pyarrow` (or `pyarrow-all`).",
"I have same issue, please downgrade pyarrow==15.0.2, it seem datasets library need to be fix",
"It is not a problem with the `datasets` library: we support latest version of `pyarrow` and our Continuous Integration tests are using pyarrow 16.1.0 without any problem.\r\n\r\nThe error reported here is raised when importing pyarrow.parquet:\r\n```\r\n---> 29 import pyarrow.parquet as pq\r\n```\r\n```\r\nFile /opt/conda/lib/python3.10/site-packages/pyarrow/parquet/__init__.py:20\r\n 1 # Licensed to the Apache Software Foundation (ASF) under one\r\n 2 # or more contributor license agreements. See the NOTICE file\r\n 3 # distributed with this work for additional information\r\n (...)\r\n 17 \r\n 18 # flake8: noqa\r\n---> 20 from .core import *\r\n\r\nFile /opt/conda/lib/python3.10/site-packages/pyarrow/parquet/core.py:33\r\n 30 import pyarrow as pa\r\n 32 try:\r\n---> 33 import pyarrow._parquet as _parquet\r\n 34 except ImportError as exc:\r\n 35 raise ImportError(\r\n 36 \"The pyarrow installation is not built with support \"\r\n 37 f\"for the Parquet file format ({str(exc)})\"\r\n 38 ) from None\r\n\r\nFile /opt/conda/lib/python3.10/site-packages/pyarrow/_parquet.pyx:1, in init pyarrow._parquet()\r\n\r\nAttributeError: module 'pyarrow.lib' has no attribute 'ListViewType'\r\n```\r\n\r\nThis can only be explained if pyarrow was not properly installed. \r\n\r\nIf the user just installed `pyarrow-core` from conda-forge, then its parquet subpackage is not installed and cannot be imported. You can check pyarrow docs:\r\n- Differences between conda-forge packages: https://arrow.apache.org/docs/python/install.html#python-conda-differences\r\n> The `pyarrow-core` package includes the following functionality:\r\n> ...\r\n> The `pyarrow` package adds the following:\r\n> ...\r\n> Parquet (i.e., `pyarrow.parquet`)",
"I'm still seeing the same issue on datasets version 2.20.0. I installed pyarrow version 17.0.0 with `pip install`. Downgrading to pyarrow==15.0.2 also did not resolve the issue.",
"@RenaLu As of UTC time 07/27/2024 23:20:00, I hit the same issue and reinstalling `pyarrow==15.0.2` resolved the issue for me. You may want to check if your `pyarrow` is successfully downgraded.",
"I can confirm @albertvillanova's [analysis & suggestion](https://github.com/huggingface/datasets/issues/6985#issuecomment-2188022888) - `pip uninstall pyarrow` followed by `pip install pyarrow` solved it for me. \r\n\r\nI suspect this is because pyarrow was initially installed as a pandas extra `pandas[...,parquet,...]`, then pip-upgrading pyarrow resulted in the issue.\r\n\r\n@RenaLu did you uninstall pyarrow between changing versions?",
"After trying all the above combinations and failing, running the following in the notebook fixed the error!!\r\n`!conda install -c conda-forge -y datasets pyarrow libparquet`\r\nNote : Uninstall any existing dataset and pyarrow installations in the env before executing the above.",
"If on colab, remember to restart the runtime so the new pyarrow is imported. I also upgraded pip which is recommended in pyarrow's installation instructions.",
"fixed doing this: !pip install --upgrade datasets\r\n\r\n!pip show pyarrow\r\n!pip show datasets\r\n!pip uninstall -y pyarrow\r\n!pip install pyarrow --no-cache-dir\r\n!pip install pyarrow\r\n!pip install transformers\r\n!pip install --upgrade datasets\r\n!pip install datasets\r\n! pip install pyarrow\r\n! pip install pyarrow.parquet\r\n!pip install transformers\r\n\r\n# Import necessary libraries\r\nfrom datasets import load_dataset\r\nimport pyarrow.parquet as pq\r\nimport pyarrow.lib as lib\r\nimport pandas as pd\r\nfrom transformers import AutoTokenizer, AutoModelForSequenceClassification, Trainer, TrainingArguments\r\n",
"but now i cant run test, so i remove it, ERROR: Could not find a version that satisfies the requirement pyarrow.parquet (from versions: none)\r\nERROR: No matching distribution found for pyarrow.parquet will still running but will tell you this",
"I have the same question right now, python3.12 and transformers4.44.2, I have not fixed it"
] | "2024-06-19T13:22:28Z" | "2024-11-04T08:07:55Z" | "2024-06-25T05:40:51Z" | NONE | null | ### Describe the bug
I have been struggling with this for two days, any help would be appreciated. Python 3.10
```
from setfit import SetFitModel
from huggingface_hub import login
access_token_read = "cccxxxccc"
# Authenticate with the Hugging Face Hub
login(token=access_token_read)
# Load the models from the Hugging Face Hub
trainer_relv = SetFitModel.from_pretrained("snowdere/trainer_relevance")
trainer_trust = SetFitModel.from_pretrained("snowdere/trainer_trust")
trainer_sent = SetFitModel.from_pretrained("snowdere/trainer_sent")
trainer_topic = SetFitModel.from_pretrained("snowdere/trainer_topic")
```
```
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
Cell In[6], line 1
----> 1 from setfit import SetFitModel
2 from huggingface_hub import login
4 access_token_read = "ccsddsds"
File /opt/conda/lib/python3.10/site-packages/setfit/__init__.py:7
4 import os
5 import warnings
----> 7 from .data import get_templated_dataset, sample_dataset
8 from .model_card import SetFitModelCardData
9 from .modeling import SetFitHead, SetFitModel
File /opt/conda/lib/python3.10/site-packages/setfit/data.py:5
3 import pandas as pd
4 import torch
----> 5 from datasets import Dataset, DatasetDict, load_dataset
6 from torch.utils.data import Dataset as TorchDataset
8 from . import logging
File /opt/conda/lib/python3.10/site-packages/datasets/__init__.py:18
1 # ruff: noqa
2 # Copyright 2020 The HuggingFace Datasets Authors and the TensorFlow Datasets Authors.
3 #
(...)
13 # See the License for the specific language governing permissions and
14 # limitations under the License.
16 __version__ = "2.19.0"
---> 18 from .arrow_dataset import Dataset
19 from .arrow_reader import ReadInstruction
20 from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
File /opt/conda/lib/python3.10/site-packages/datasets/arrow_dataset.py:76
73 from tqdm.contrib.concurrent import thread_map
75 from . import config
---> 76 from .arrow_reader import ArrowReader
77 from .arrow_writer import ArrowWriter, OptimizedTypedSequence
78 from .data_files import sanitize_patterns
File /opt/conda/lib/python3.10/site-packages/datasets/arrow_reader.py:29
26 from typing import TYPE_CHECKING, List, Optional, Union
28 import pyarrow as pa
---> 29 import pyarrow.parquet as pq
30 from tqdm.contrib.concurrent import thread_map
32 from .download.download_config import DownloadConfig
File /opt/conda/lib/python3.10/site-packages/pyarrow/parquet/__init__.py:20
1 # Licensed to the Apache Software Foundation (ASF) under one
2 # or more contributor license agreements. See the NOTICE file
3 # distributed with this work for additional information
(...)
17
18 # flake8: noqa
---> 20 from .core import *
File /opt/conda/lib/python3.10/site-packages/pyarrow/parquet/core.py:33
30 import pyarrow as pa
32 try:
---> 33 import pyarrow._parquet as _parquet
34 except ImportError as exc:
35 raise ImportError(
36 "The pyarrow installation is not built with support "
37 f"for the Parquet file format ({str(exc)})"
38 ) from None
File /opt/conda/lib/python3.10/site-packages/pyarrow/_parquet.pyx:1, in init pyarrow._parquet()
AttributeError: module 'pyarrow.lib' has no attribute 'ListViewType'
```
setfit: 1.0.3
transformers: 4.41.2
lingua-language-detector: 2.0.2
polars: 0.20.31
lightning: None
google-cloud-bigquery: 3.24.0
shapely: 2.0.4
pyarrow: 16.0.0
### Steps to reproduce the bug
I have tried all version combinations for Dataset and Pyarrow, the all have the same error since a few days ago. This is accross multiple scripts I have.
### Expected behavior
Just ron normally.
### Environment info
3.10 | {
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"Hi ! Thanks for reporting :)\r\n\r\nWe don't support `large_list` yet, though it should be added to `Sequence` IMO (maybe with a parameter `large=True` ?)"
] | "2024-06-19T11:38:48Z" | "2024-08-12T14:43:46Z" | "2024-08-12T14:43:46Z" | NONE | null | ### Feature request
This returns error.
```python
from datasets import Dataset
dsdf = Dataset.from_dict({"x": [[1, 2], [3, 4, 5]], "y": ["a", "b"]})
Dataset.from_polars(dsdf.to_polars())
```
ValueError: Arrow type large_list<item: int64> does not have a datasets dtype equivalent.
### Motivation
When datasets contain Sequence data type, it will be converted to Arrow type large_list. However, the reverse (from large_list to Sequence) does not work.
### Your contribution
No | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6983). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<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) | 0.005566 / 0.011353 (-0.005787) | 0.003977 / 0.011008 (-0.007031) | 0.063250 / 0.038508 (0.024742) | 0.030907 / 0.023109 (0.007798) | 0.244989 / 0.275898 (-0.030909) | 0.272139 / 0.323480 (-0.051341) | 0.004332 / 0.007986 (-0.003653) | 0.002960 / 0.004328 (-0.001368) | 0.050147 / 0.004250 (0.045896) | 0.044740 / 0.037052 (0.007688) | 0.256947 / 0.258489 (-0.001542) | 0.290372 / 0.293841 (-0.003469) | 0.030444 / 0.128546 (-0.098102) | 0.012675 / 0.075646 (-0.062971) | 0.203852 / 0.419271 (-0.215420) | 0.036977 / 0.043533 (-0.006556) | 0.244401 / 0.255139 (-0.010738) | 0.270020 / 0.283200 (-0.013179) | 0.018177 / 0.141683 (-0.123506) | 1.122189 / 1.452155 (-0.329966) | 1.176688 / 1.492716 (-0.316028) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.100721 / 0.018006 (0.082715) | 0.311824 / 0.000490 (0.311335) | 0.000222 / 0.000200 (0.000022) | 0.000043 / 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) | 0.020039 / 0.037411 (-0.017373) | 0.062084 / 0.014526 (0.047558) | 0.074317 / 0.176557 (-0.102240) | 0.123935 / 0.737135 (-0.613200) | 0.076186 / 0.296338 (-0.220153) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.284827 / 0.215209 (0.069618) | 2.782727 / 2.077655 (0.705072) | 1.417624 / 1.504120 (-0.086496) | 1.294476 / 1.541195 (-0.246718) | 1.332658 / 1.468490 (-0.135832) | 0.724820 / 4.584777 (-3.859957) | 2.384546 / 3.745712 (-1.361166) | 2.866759 / 5.269862 (-2.403103) | 1.930756 / 4.565676 (-2.634921) | 0.083090 / 0.424275 (-0.341185) | 0.005566 / 0.007607 (-0.002041) | 0.340117 / 0.226044 (0.114072) | 3.342417 / 2.268929 (1.073488) | 1.807842 / 55.444624 (-53.636782) | 1.511647 / 6.876477 (-5.364830) | 1.653893 / 2.142072 (-0.488179) | 0.803983 / 4.805227 (-4.001244) | 0.136205 / 6.500664 (-6.364459) | 0.042815 / 0.075469 (-0.032654) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.962346 / 1.841788 (-0.879442) | 11.792239 / 8.074308 (3.717931) | 9.236256 / 10.191392 (-0.955136) | 0.143200 / 0.680424 (-0.537224) | 0.015050 / 0.534201 (-0.519151) | 0.304623 / 0.579283 (-0.274660) | 0.266417 / 0.434364 (-0.167947) | 0.341213 / 0.540337 (-0.199124) | 0.454258 / 1.386936 (-0.932678) |\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) | 0.005917 / 0.011353 (-0.005436) | 0.004005 / 0.011008 (-0.007003) | 0.049781 / 0.038508 (0.011273) | 0.033310 / 0.023109 (0.010200) | 0.271881 / 0.275898 (-0.004017) | 0.296855 / 0.323480 (-0.026625) | 0.004479 / 0.007986 (-0.003507) | 0.002818 / 0.004328 (-0.001510) | 0.048213 / 0.004250 (0.043962) | 0.043480 / 0.037052 (0.006428) | 0.285963 / 0.258489 (0.027473) | 0.317304 / 0.293841 (0.023463) | 0.031619 / 0.128546 (-0.096928) | 0.012312 / 0.075646 (-0.063335) | 0.059904 / 0.419271 (-0.359368) | 0.033152 / 0.043533 (-0.010381) | 0.274198 / 0.255139 (0.019059) | 0.290469 / 0.283200 (0.007269) | 0.019424 / 0.141683 (-0.122258) | 1.133669 / 1.452155 (-0.318485) | 1.194427 / 1.492716 (-0.298290) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.101561 / 0.018006 (0.083555) | 0.312617 / 0.000490 (0.312127) | 0.000216 / 0.000200 (0.000016) | 0.000045 / 0.000054 (-0.000009) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023705 / 0.037411 (-0.013706) | 0.076781 / 0.014526 (0.062255) | 0.089922 / 0.176557 (-0.086634) | 0.129182 / 0.737135 (-0.607953) | 0.092022 / 0.296338 (-0.204317) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.300977 / 0.215209 (0.085768) | 2.909088 / 2.077655 (0.831433) | 1.592821 / 1.504120 (0.088701) | 1.466627 / 1.541195 (-0.074568) | 1.497558 / 1.468490 (0.029068) | 0.720986 / 4.584777 (-3.863791) | 0.958039 / 3.745712 (-2.787673) | 3.023413 / 5.269862 (-2.246448) | 1.933245 / 4.565676 (-2.632432) | 0.080500 / 0.424275 (-0.343775) | 0.005243 / 0.007607 (-0.002364) | 0.361259 / 0.226044 (0.135215) | 3.447317 / 2.268929 (1.178389) | 1.938234 / 55.444624 (-53.506390) | 1.671563 / 6.876477 (-5.204913) | 1.674647 / 2.142072 (-0.467425) | 0.790606 / 4.805227 (-4.014621) | 0.133312 / 6.500664 (-6.367352) | 0.041241 / 0.075469 (-0.034228) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.996167 / 1.841788 (-0.845621) | 12.460877 / 8.074308 (4.386569) | 10.608415 / 10.191392 (0.417023) | 0.134076 / 0.680424 (-0.546348) | 0.016166 / 0.534201 (-0.518035) | 0.301218 / 0.579283 (-0.278065) | 0.128979 / 0.434364 (-0.305385) | 0.336453 / 0.540337 (-0.203884) | 0.435561 / 1.386936 (-0.951375) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#70e7355b7125fb792107ef5128ee3ad15cbec26c \"CML watermark\")\n"
] | "2024-06-19T09:08:55Z" | "2024-06-28T06:57:38Z" | "2024-06-28T06:51:30Z" | MEMBER | null | Remove all metrics, as part of the 3.0 release.
Note they are deprecated since 2.5.0 version. | {
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https://api.github.com/repos/huggingface/datasets/issues/6982 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6982/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6982/comments | https://api.github.com/repos/huggingface/datasets/issues/6982/events | https://github.com/huggingface/datasets/issues/6982 | 2,361,661,469 | I_kwDODunzps6MxBgd | 6,982 | cannot split dataset when using load_dataset | {
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"it seems the bug will happened in all windows system, I tried it in windows8.1, 10, 11 and all of them failed. But it won't happened in the Linux(Ubuntu and Centos7) and Mac (both my virtual and physical machine). I still don't know what the problem is. May be related to the path? I cannot run the split file in my windows server which created in Linux (even I replace the path in the arrow document)....work for it for a week but still cannot fix it .....upset",
"Have you properly logged in? Are you using the a valid token?\r\n\r\nNote that this dataset is gated and you must follow the right procedure to be able to access it. You can find more info in the docs: https://huggingface.co/docs/hub/datasets-gated#access-gated-datasets-as-a-user",
"> Have you properly logged in? Are you using the a valid token?\r\n> \r\n> Note that this dataset is gated and you must follow the right procedure to be able to access it. You can find more info in the docs: https://huggingface.co/docs/hub/datasets-gated#access-gated-datasets-as-a-user\r\n\r\nI finally found it what happened. It is not about the logging. When I copy the dataset from its original path (C:/Users/cybes/.cache/huggingface/datasets/downloads/extracted/XXX/cv-corpus-7.0-2021-07-21) to the desktop and load each tsv in it one by one , when I load the test spilt, the following warning occurs:\r\n\"ArrowInvalid: Failed to parse string: 'Benchmark' as a scalar of type double\"\r\n\r\nThen I manually deleted them in the \"segment\", the error won't happen anymore, even I replace the original path with these revised tsv and use the previous loading method (common_voice_train = load_dataset(\"mozilla-foundation/common_voice_7_0\", \"ja\", split=\"train\", trust_remote_code=True)). It can work properly."
] | "2024-06-19T08:07:16Z" | "2024-07-08T06:20:16Z" | "2024-07-08T06:20:16Z" | NONE | null | ### Describe the bug
when I use load_dataset methods to load mozilla-foundation/common_voice_7_0, it can successfully download and extracted the dataset but It cannot generating the arrow document,
This bug happened in my server, my laptop, so as #6906 , but it won't happen in the google colab. I work for it for days, even I load the datasets from local path, it can Generating train split and validation split but bug happen again in test split.
### Steps to reproduce the bug
from datasets import load_dataset, load_metric, Audio
common_voice_train = load_dataset("mozilla-foundation/common_voice_7_0", "ja", split="train", token=selftoken, trust_remote_code=True)
### Expected behavior
```
{
"name": "ValueError",
"message": "Instruction \"train\" corresponds to no data!",
"stack": "---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Cell In[2], line 3
1 from datasets import load_dataset, load_metric, Audio
----> 3 common_voice_train = load_dataset(\"mozilla-foundation/common_voice_7_0\", \"ja\", split=\"train\",token='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')#,trust_remote_code=True)#,streaming=True)
4 common_voice_test = load_dataset(\"mozilla-foundation/common_voice_7_0\", \"ja\", split=\"test\",token='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')#,trust_remote_code=True)#,streaming=True)
File c:\\Users\\cybes\\.conda\\envs\\ECoG\\lib\\site-packages\\datasets\\load.py:2626, 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, trust_remote_code, **config_kwargs)
2622 # Build dataset for splits
2623 keep_in_memory = (
2624 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size)
2625 )
-> 2626 ds = builder_instance.as_dataset(split=split, verification_mode=verification_mode, in_memory=keep_in_memory)
2627 # Rename and cast features to match task schema
2628 if task is not None:
2629 # To avoid issuing the same warning twice
File c:\\Users\\cybes\\.conda\\envs\\ECoG\\lib\\site-packages\\datasets\\builder.py:1266, in DatasetBuilder.as_dataset(self, split, run_post_process, verification_mode, ignore_verifications, in_memory)
1263 verification_mode = VerificationMode(verification_mode or VerificationMode.BASIC_CHECKS)
1265 # Create a dataset for each of the given splits
-> 1266 datasets = map_nested(
1267 partial(
1268 self._build_single_dataset,
1269 run_post_process=run_post_process,
1270 verification_mode=verification_mode,
1271 in_memory=in_memory,
1272 ),
1273 split,
1274 map_tuple=True,
1275 disable_tqdm=True,
1276 )
1277 if isinstance(datasets, dict):
1278 datasets = DatasetDict(datasets)
File c:\\Users\\cybes\\.conda\\envs\\ECoG\\lib\\site-packages\\datasets\\utils\\py_utils.py:484, in map_nested(function, data_struct, dict_only, map_list, map_tuple, map_numpy, num_proc, parallel_min_length, batched, batch_size, types, disable_tqdm, desc)
482 if batched:
483 data_struct = [data_struct]
--> 484 mapped = function(data_struct)
485 if batched:
486 mapped = mapped[0]
File c:\\Users\\cybes\\.conda\\envs\\ECoG\\lib\\site-packages\\datasets\\builder.py:1296, in DatasetBuilder._build_single_dataset(self, split, run_post_process, verification_mode, in_memory)
1293 split = Split(split)
1295 # Build base dataset
-> 1296 ds = self._as_dataset(
1297 split=split,
1298 in_memory=in_memory,
1299 )
1300 if run_post_process:
1301 for resource_file_name in self._post_processing_resources(split).values():
File c:\\Users\\cybes\\.conda\\envs\\ECoG\\lib\\site-packages\\datasets\\builder.py:1370, in DatasetBuilder._as_dataset(self, split, in_memory)
1368 if self._check_legacy_cache():
1369 dataset_name = self.name
-> 1370 dataset_kwargs = ArrowReader(cache_dir, self.info).read(
1371 name=dataset_name,
1372 instructions=split,
1373 split_infos=self.info.splits.values(),
1374 in_memory=in_memory,
1375 )
1376 fingerprint = self._get_dataset_fingerprint(split)
1377 return Dataset(fingerprint=fingerprint, **dataset_kwargs)
File c:\\Users\\cybes\\.conda\\envs\\ECoG\\lib\\site-packages\\datasets\\arrow_reader.py:256, in BaseReader.read(self, name, instructions, split_infos, in_memory)
254 msg = f'Instruction \"{instructions}\" corresponds to no data!'
255 #msg = f'Instruction \"{self._path}\",\"{name}\",\"{instructions}\",\"{split_infos}\" corresponds to no data!'
--> 256 raise ValueError(msg)
257 return self.read_files(files=files, original_instructions=instructions, in_memory=in_memory)
ValueError: Instruction \"train\" corresponds to no data!"
}
```
### Environment info
Environment:
python 3.9
windows 11 pro
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https://api.github.com/repos/huggingface/datasets/issues/6981 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6981/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6981/comments | https://api.github.com/repos/huggingface/datasets/issues/6981/events | https://github.com/huggingface/datasets/pull/6981 | 2,361,520,022 | PR_kwDODunzps5y6tnN | 6,981 | Update docs on trust_remote_code defaults to False | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6981). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<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) | 0.005578 / 0.011353 (-0.005775) | 0.003946 / 0.011008 (-0.007062) | 0.063317 / 0.038508 (0.024808) | 0.031878 / 0.023109 (0.008769) | 0.312571 / 0.275898 (0.036673) | 0.281415 / 0.323480 (-0.042065) | 0.004139 / 0.007986 (-0.003846) | 0.002730 / 0.004328 (-0.001598) | 0.049539 / 0.004250 (0.045289) | 0.045056 / 0.037052 (0.008003) | 0.263820 / 0.258489 (0.005330) | 0.297817 / 0.293841 (0.003976) | 0.029490 / 0.128546 (-0.099056) | 0.012467 / 0.075646 (-0.063179) | 0.204607 / 0.419271 (-0.214664) | 0.036305 / 0.043533 (-0.007228) | 0.244102 / 0.255139 (-0.011037) | 0.267855 / 0.283200 (-0.015345) | 0.019794 / 0.141683 (-0.121889) | 1.130784 / 1.452155 (-0.321371) | 1.172507 / 1.492716 (-0.320209) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092430 / 0.018006 (0.074424) | 0.296460 / 0.000490 (0.295970) | 0.000210 / 0.000200 (0.000010) | 0.000042 / 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) | 0.019467 / 0.037411 (-0.017944) | 0.062850 / 0.014526 (0.048324) | 0.074067 / 0.176557 (-0.102490) | 0.123280 / 0.737135 (-0.613856) | 0.077036 / 0.296338 (-0.219302) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.282687 / 0.215209 (0.067478) | 2.786715 / 2.077655 (0.709060) | 1.492028 / 1.504120 (-0.012092) | 1.373603 / 1.541195 (-0.167592) | 1.405004 / 1.468490 (-0.063486) | 0.714408 / 4.584777 (-3.870369) | 2.376785 / 3.745712 (-1.368927) | 2.916150 / 5.269862 (-2.353712) | 1.921184 / 4.565676 (-2.644493) | 0.078354 / 0.424275 (-0.345921) | 0.005236 / 0.007607 (-0.002371) | 0.334647 / 0.226044 (0.108603) | 3.262069 / 2.268929 (0.993140) | 1.858300 / 55.444624 (-53.586324) | 1.572968 / 6.876477 (-5.303509) | 1.659145 / 2.142072 (-0.482927) | 0.779546 / 4.805227 (-4.025681) | 0.132623 / 6.500664 (-6.368041) | 0.042423 / 0.075469 (-0.033046) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.985516 / 1.841788 (-0.856271) | 12.001321 / 8.074308 (3.927013) | 9.927011 / 10.191392 (-0.264381) | 0.142645 / 0.680424 (-0.537779) | 0.013808 / 0.534201 (-0.520393) | 0.303422 / 0.579283 (-0.275861) | 0.262666 / 0.434364 (-0.171698) | 0.339369 / 0.540337 (-0.200969) | 0.431028 / 1.386936 (-0.955908) |\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) | 0.005848 / 0.011353 (-0.005505) | 0.003971 / 0.011008 (-0.007037) | 0.050746 / 0.038508 (0.012238) | 0.031554 / 0.023109 (0.008445) | 0.277678 / 0.275898 (0.001780) | 0.300776 / 0.323480 (-0.022704) | 0.004428 / 0.007986 (-0.003558) | 0.002773 / 0.004328 (-0.001555) | 0.049882 / 0.004250 (0.045632) | 0.039833 / 0.037052 (0.002780) | 0.289143 / 0.258489 (0.030654) | 0.321425 / 0.293841 (0.027584) | 0.031701 / 0.128546 (-0.096845) | 0.012687 / 0.075646 (-0.062960) | 0.060650 / 0.419271 (-0.358621) | 0.033318 / 0.043533 (-0.010215) | 0.277019 / 0.255139 (0.021880) | 0.292345 / 0.283200 (0.009145) | 0.018520 / 0.141683 (-0.123163) | 1.143933 / 1.452155 (-0.308222) | 1.183913 / 1.492716 (-0.308803) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094467 / 0.018006 (0.076461) | 0.298822 / 0.000490 (0.298332) | 0.000201 / 0.000200 (0.000001) | 0.000045 / 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) | 0.022811 / 0.037411 (-0.014601) | 0.078084 / 0.014526 (0.063558) | 0.089079 / 0.176557 (-0.087477) | 0.130229 / 0.737135 (-0.606906) | 0.090851 / 0.296338 (-0.205487) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.294981 / 0.215209 (0.079772) | 2.908294 / 2.077655 (0.830639) | 1.591281 / 1.504120 (0.087161) | 1.446032 / 1.541195 (-0.095162) | 1.469441 / 1.468490 (0.000951) | 0.726477 / 4.584777 (-3.858300) | 0.983086 / 3.745712 (-2.762626) | 2.892715 / 5.269862 (-2.377147) | 1.974092 / 4.565676 (-2.591584) | 0.079500 / 0.424275 (-0.344775) | 0.005497 / 0.007607 (-0.002110) | 0.342220 / 0.226044 (0.116176) | 3.414508 / 2.268929 (1.145579) | 1.941550 / 55.444624 (-53.503074) | 1.645268 / 6.876477 (-5.231209) | 1.805909 / 2.142072 (-0.336163) | 0.814483 / 4.805227 (-3.990744) | 0.135867 / 6.500664 (-6.364797) | 0.041718 / 0.075469 (-0.033751) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.999751 / 1.841788 (-0.842036) | 12.488263 / 8.074308 (4.413954) | 10.867040 / 10.191392 (0.675648) | 0.143999 / 0.680424 (-0.536425) | 0.015496 / 0.534201 (-0.518705) | 0.302170 / 0.579283 (-0.277113) | 0.123753 / 0.434364 (-0.310611) | 0.340424 / 0.540337 (-0.199913) | 0.458339 / 1.386936 (-0.928597) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a6ccf944e42c1a84de81bf326accab9999b86c90 \"CML watermark\")\n"
] | "2024-06-19T07:12:21Z" | "2024-06-19T14:32:59Z" | "2024-06-19T14:26:37Z" | MEMBER | null | Update docs on trust_remote_code defaults to False.
The docs needed to be updated due to this PR:
- #6954 | {
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] | closed | false | null | [] | null | [] | "2024-06-18T23:30:22Z" | "2024-07-12T12:04:54Z" | "2024-07-12T12:04:53Z" | CONTRIBUTOR | null | ### Feature request
Support NumPy 2.0.
### Motivation
NumPy introduces the Array API, which bridges the gap between machine learning libraries. Many clients of HuggingFace are eager to start using the Array API.
Besides that, NumPy 2 provides a cleaner interface than NumPy 1.
### Tasks
NumPy 2.0 was released for testing so that libraries could ensure compatibility [since mid-March](https://github.com/numpy/numpy/issues/24300#issuecomment-1986815755). What needs to be done for HuggingFace to support Numpy 2?
- [x] Fix use of `array`: https://github.com/huggingface/datasets/pull/6976
- [ ] Remove [NumPy version limit](https://github.com/huggingface/datasets/pull/6975): https://github.com/huggingface/datasets/pull/6991 | {
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"Hello,\r\n\r\nHave you tried loading the dataset in streaming mode? [Documentation](https://huggingface.co/docs/datasets/v2.20.0/stream)\r\n\r\nThis way you wouldn't have to load it all. Also, let's be nice to Parquet, it's a really nice technology and we don't need to be mean :)",
"I have downloaded part of it, just want to know how to load part of it, stream mode is not work for me since my network (in china) not stable, I don't want do it all again and again.\r\n\r\nJust curious, doesn't there a way to load part of it?",
"Could you convert the IterableDataset to a Dataset after taking the first 100 rows with `.take`? This way, you would have a local copy of the first 100 rows on your system and thus won't need to download. Would that work?\r\n\r\nHere is a [SO question](https://stackoverflow.com/questions/76227219/can-i-convert-an-iterabledataset-to-dataset) detailing how to do the conversion.",
"I mean, the parquet is like:\r\n\r\n00000-0143554\r\n00001-0143554\r\n00002-0143554\r\n...\r\n00100-0143554\r\n...\r\n09100-0143554\r\n\r\nI just downloaded the first 9900 part of it. \r\n\r\nI can not load with load_dataset, it throw an error says my file is not same as parquet all amount.\r\n\r\nHow could I load the only I have? \r\n\r\n( I really don't want downlaod them all, cause, I don't need all, and pulus, its huge.... )\r\n\r\nAs I said, I have donwloaded about 9999... It's not about stream... I just wnat to konw how to load offline... part....",
"Hi, @lucasjinreal.\r\n\r\nI am not sure of understanding your issue. What is the error message and stack trace you get? What version of `datasets` are you using? Could you provide a reproducible example?\r\n\r\nWithout knowing all those details, I would naively say that you can load whatever number of Parquet files by using the \"parquet\" loader: https://huggingface.co/docs/datasets/loading#parquet\r\n```python\r\nds = load_dataset(\"parquet\", data_files=\"data/train-001*-of-00314.parquet\", split=\"train\")\r\n```",
"@albertvillanova Not sure you have tested with this or not, but I have tried,\r\n\r\nthe only error I got is it still laodding all parquet with a progress bar maxium to the whole number 014354, and it loads my 0 - 000999 part, then throws an error.\r\n\r\nSays Numinfo is not same.\r\n\r\nI am so confused,",
"Yes, my code snippet works.\n\nCould you copy-paste your code and the output? Otherwise we are not able to know what the issue is.",
"@albertvillanova Hi, thanks for the tracing of the issue.\r\n\r\nThis is the output:\r\n\r\n```\r\nython get_llava_recap_cc3m.py\r\nGenerating train split: 3%|███▋ | 101910/3199866 [00:16<08:30, 6065.67 examples/s]\r\nTraceback (most recent call last):\r\n File \"get_llava_recap_cc3m.py\", line 31, in <module>\r\n dataset = load_dataset(\"llava-recap-cc3m/\", data_files=\"data/train-0000*-of-00314.parquet\")\r\n File \"/usr/local/lib/python3.8/dist-packages/datasets/load.py\", line 2582, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"/usr/local/lib/python3.8/dist-packages/datasets/builder.py\", line 1005, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"/usr/local/lib/python3.8/dist-packages/datasets/builder.py\", line 1118, in _download_and_prepare\r\n verify_splits(self.info.splits, split_dict)\r\n File \"/usr/local/lib/python3.8/dist-packages/datasets/utils/info_utils.py\", line 101, in verify_splits\r\n raise NonMatchingSplitsSizesError(str(bad_splits))\r\ndatasets.utils.info_utils.NonMatchingSplitsSizesError: [{'expected': SplitInfo(name='train', num_bytes=156885281898.75, num_examples=3199866, shard_lengths=None, dataset_name=None), 'recorded': SplitInfo(name='train', num_bytes=4994080770, num_examples=101910, shard_lengths=[10191, 10291, 10291, 10291, 10291, 10191, 10191, 10291, 10291, 9591], dataset_name='llava-recap-cc3m')}]\r\n```\r\n\r\nthis is my code:\r\n\r\n```\r\ndataset = load_dataset(\"llava-recap-cc3m/\", data_files=\"data/train-0000*-of-00314.parquet\")\r\n```\r\n\r\nMy situation and requirements:\r\n\r\n00314 is all, but I downlaode about 150, half of it, as you can see, i used `0000*-of-00314.` which should be at most 99 file being loaded.\r\n\r\nBut it just fail.\r\n\r\nCan u understand my issue now?\r\n\r\nIf so, then **do not** suggest me with stream, Just want to know, is there a way to load part if it...... **and please don't say you can not replicate my issue when you have downloaded them all**, my english is not good, but I think all situations and all prerequists I have addressed already.\r\n\r\n",
"I see you did not use the \"parquet\" loader as I suggested in my code snippet above: https://github.com/huggingface/datasets/issues/6979#issuecomment-2182031415\r\nPlease try passing \"parquet\" instead of \"llava-recap-cc3m/\" to `load_dataset`, and the complete path to data files in `data_files`:\r\n```python\r\nload_dataset(\"parquet\", data_files=\"llava-recap-cc3m/data/train-001*-of-00314.parquet\")\r\n```",
"Let me explain that you get the error because of this content within the `dataset_info` YAML tag in the `llava-recap-cc3m/README.md`:\r\n```\r\n - name: train\r\n num_bytes: 156885281898.75\r\n num_examples: 3199866\r\n```\r\n\r\nBy default, if there is that content in the README file, `load_dataset` performs a basic check to verify it the generated number of examples matches the expected one and raises a `NonMatchingSplitsSizesError` if that is not the case. \r\n\r\nYou can avoid this basic check by passing `verification_mode=\"no_checks\"`:\r\n```python\r\nload_dataset(\"llava-recap-cc3m/\", data_files=\"data/train-0000*-of-00314.parquet\", verification_mode=\"no_checks\")\r\n```",
"And please, next time you have an issue, please fill the Bug template issue with all the necessary information: https://github.com/huggingface/datasets/issues/new?assignees=&labels=&projects=&template=bug-report.yml\r\n\r\nOtherwise it is very difficult for us to understand the underlying problem and to propose a pertinent solution.",
"thank u albert!\r\n\r\nIt solved my issue!"
] | "2024-06-18T15:44:16Z" | "2024-06-21T17:09:32Z" | "2024-06-21T13:32:50Z" | NONE | null | I have a HUGE dataset about 14TB, I unable to download all parquet all. I just take about 100 from it.
dataset = load_dataset("xx/", data_files="data/train-001*-of-00314.parquet")
How can I just using 000 - 100 from a 00314 from all partially?
I search whole net didn't found a solution, **this is stupid if they didn't support it, and I swear I wont using stupid parquet any more**
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6978). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<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) | 0.005144 / 0.011353 (-0.006209) | 0.003500 / 0.011008 (-0.007509) | 0.063670 / 0.038508 (0.025162) | 0.031793 / 0.023109 (0.008683) | 0.239611 / 0.275898 (-0.036287) | 0.276681 / 0.323480 (-0.046799) | 0.004148 / 0.007986 (-0.003838) | 0.002713 / 0.004328 (-0.001615) | 0.048832 / 0.004250 (0.044582) | 0.043066 / 0.037052 (0.006014) | 0.256835 / 0.258489 (-0.001655) | 0.292224 / 0.293841 (-0.001617) | 0.027530 / 0.128546 (-0.101017) | 0.010509 / 0.075646 (-0.065137) | 0.203370 / 0.419271 (-0.215901) | 0.035643 / 0.043533 (-0.007890) | 0.252161 / 0.255139 (-0.002978) | 0.271883 / 0.283200 (-0.011316) | 0.018658 / 0.141683 (-0.123024) | 1.081676 / 1.452155 (-0.370479) | 1.142146 / 1.492716 (-0.350571) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093484 / 0.018006 (0.075477) | 0.298607 / 0.000490 (0.298117) | 0.000220 / 0.000200 (0.000020) | 0.000044 / 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) | 0.019021 / 0.037411 (-0.018390) | 0.062471 / 0.014526 (0.047946) | 0.075393 / 0.176557 (-0.101163) | 0.121040 / 0.737135 (-0.616095) | 0.077613 / 0.296338 (-0.218726) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.294857 / 0.215209 (0.079648) | 2.931143 / 2.077655 (0.853489) | 1.510866 / 1.504120 (0.006746) | 1.379574 / 1.541195 (-0.161621) | 1.352358 / 1.468490 (-0.116133) | 0.561670 / 4.584777 (-4.023107) | 2.378434 / 3.745712 (-1.367278) | 2.713203 / 5.269862 (-2.556658) | 1.706416 / 4.565676 (-2.859260) | 0.062355 / 0.424275 (-0.361920) | 0.004971 / 0.007607 (-0.002636) | 0.336498 / 0.226044 (0.110453) | 3.316464 / 2.268929 (1.047535) | 1.833035 / 55.444624 (-53.611589) | 1.532808 / 6.876477 (-5.343668) | 1.537323 / 2.142072 (-0.604749) | 0.639430 / 4.805227 (-4.165798) | 0.115808 / 6.500664 (-6.384856) | 0.043545 / 0.075469 (-0.031924) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.974428 / 1.841788 (-0.867360) | 11.368914 / 8.074308 (3.294606) | 9.754488 / 10.191392 (-0.436904) | 0.146277 / 0.680424 (-0.534146) | 0.013917 / 0.534201 (-0.520284) | 0.286809 / 0.579283 (-0.292474) | 0.267144 / 0.434364 (-0.167219) | 0.326161 / 0.540337 (-0.214177) | 0.418059 / 1.386936 (-0.968877) |\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) | 0.005341 / 0.011353 (-0.006012) | 0.003460 / 0.011008 (-0.007548) | 0.050135 / 0.038508 (0.011627) | 0.032014 / 0.023109 (0.008905) | 0.259835 / 0.275898 (-0.016063) | 0.286275 / 0.323480 (-0.037205) | 0.004350 / 0.007986 (-0.003636) | 0.002800 / 0.004328 (-0.001529) | 0.049358 / 0.004250 (0.045107) | 0.040182 / 0.037052 (0.003130) | 0.278352 / 0.258489 (0.019863) | 0.307869 / 0.293841 (0.014028) | 0.029151 / 0.128546 (-0.099395) | 0.010091 / 0.075646 (-0.065555) | 0.058814 / 0.419271 (-0.360458) | 0.033150 / 0.043533 (-0.010383) | 0.263594 / 0.255139 (0.008455) | 0.284065 / 0.283200 (0.000866) | 0.017968 / 0.141683 (-0.123714) | 1.145605 / 1.452155 (-0.306550) | 1.196884 / 1.492716 (-0.295832) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094045 / 0.018006 (0.076039) | 0.299031 / 0.000490 (0.298541) | 0.000210 / 0.000200 (0.000011) | 0.000044 / 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) | 0.022510 / 0.037411 (-0.014901) | 0.077478 / 0.014526 (0.062953) | 0.087746 / 0.176557 (-0.088811) | 0.129311 / 0.737135 (-0.607825) | 0.089921 / 0.296338 (-0.206418) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.290279 / 0.215209 (0.075070) | 2.880725 / 2.077655 (0.803070) | 1.541262 / 1.504120 (0.037142) | 1.424475 / 1.541195 (-0.116719) | 1.436397 / 1.468490 (-0.032093) | 0.578237 / 4.584777 (-4.006540) | 0.965249 / 3.745712 (-2.780463) | 2.682534 / 5.269862 (-2.587327) | 1.732859 / 4.565676 (-2.832817) | 0.065523 / 0.424275 (-0.358752) | 0.005466 / 0.007607 (-0.002141) | 0.343985 / 0.226044 (0.117940) | 3.397463 / 2.268929 (1.128534) | 1.929370 / 55.444624 (-53.515255) | 1.605135 / 6.876477 (-5.271342) | 1.753926 / 2.142072 (-0.388146) | 0.659929 / 4.805227 (-4.145298) | 0.118093 / 6.500664 (-6.382571) | 0.041252 / 0.075469 (-0.034217) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.009177 / 1.841788 (-0.832610) | 11.959624 / 8.074308 (3.885316) | 10.484672 / 10.191392 (0.293280) | 0.142085 / 0.680424 (-0.538339) | 0.015955 / 0.534201 (-0.518245) | 0.283649 / 0.579283 (-0.295634) | 0.125681 / 0.434364 (-0.308683) | 0.320490 / 0.540337 (-0.219847) | 0.440353 / 1.386936 (-0.946583) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e47a746bcda4b97db2467542b76d3215b3569ff0 \"CML watermark\")\n",
"Maybe a patch release will be needed with this fix."
] | "2024-06-18T10:26:34Z" | "2024-06-19T06:23:24Z" | "2024-06-19T05:50:18Z" | MEMBER | null | A regression was introduced for pandas < 2.0.0 in PR:
- #6914
As described in pandas docs, the `dtype_backend` parameter was first added in pandas 2.0.0: https://pandas.pydata.org/docs/reference/api/pandas.read_json.html
This PR fixes the regression by passing (or not) the `dtype_backend` parameter depending on pandas version.
Maybe, in a future 3.0 `datasets` release, we could just require pandas > 2.0.
Reported by:
- #6977 | {
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"Thanks for reporting, @xiaoyaolangzhi.\r\n\r\nIndeed, we are currently requiring `pandas` >= 2.0.0.\r\n\r\nYou will need to update pandas in your local environment:\r\n```\r\npip install -U pandas\r\n``` ",
"Thank you very much."
] | "2024-06-18T08:41:01Z" | "2024-06-18T10:06:10Z" | "2024-06-18T10:06:09Z" | NONE | null | ### Describe the bug
```
load_dataset(path="json", data_files="./test.json")
```
```
Generating train split: 0 examples [00:00, ? examples/s]
Traceback (most recent call last):
File "/usr/local/lib/python3.10/dist-packages/datasets/packaged_modules/json/json.py", line 132, in _generate_tables
pa_table = paj.read_json(
File "pyarrow/_json.pyx", line 308, in pyarrow._json.read_json
File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: JSON parse error: Column() changed from object to array in row 0
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 1997, in _prepare_split_single
for _, table in generator:
File "/usr/local/lib/python3.10/dist-packages/datasets/packaged_modules/json/json.py", line 155, in _generate_tables
df = pd.read_json(f, dtype_backend="pyarrow")
File "/usr/local/lib/python3.10/dist-packages/pandas/util/_decorators.py", line 211, in wrapper
return func(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/pandas/util/_decorators.py", line 331, in wrapper
return func(*args, **kwargs)
TypeError: read_json() got an unexpected keyword argument 'dtype_backend'
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/app/t1.py", line 11, in <module>
load_dataset(path=data_path, data_files="./t2.json")
File "/usr/local/lib/python3.10/dist-packages/datasets/load.py", line 2616, in load_dataset
builder_instance.download_and_prepare(
File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 1029, in download_and_prepare
self._download_and_prepare(
File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 1124, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 1884, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File "/usr/local/lib/python3.10/dist-packages/datasets/builder.py", line 2040, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset
```
```
import pandas as pd
with open("./test.json", "r") as f:
df = pd.read_json(f, dtype_backend="pyarrow")
```
```
Traceback (most recent call last):
File "/app/t3.py", line 3, in <module>
df = pd.read_json(f, dtype_backend="pyarrow")
File "/usr/local/lib/python3.10/dist-packages/pandas/util/_decorators.py", line 211, in wrapper
return func(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/pandas/util/_decorators.py", line 331, in wrapper
return func(*args, **kwargs)
TypeError: read_json() got an unexpected keyword argument 'dtype_backend'
```
### Steps to reproduce the bug
.
### Expected behavior
.
### Environment info
```
datasets 2.20.0
pandas 1.5.3
``` | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6976). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<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) | 0.005361 / 0.011353 (-0.005992) | 0.003983 / 0.011008 (-0.007025) | 0.062865 / 0.038508 (0.024357) | 0.029880 / 0.023109 (0.006771) | 0.261465 / 0.275898 (-0.014433) | 0.269791 / 0.323480 (-0.053689) | 0.004198 / 0.007986 (-0.003788) | 0.002942 / 0.004328 (-0.001387) | 0.049002 / 0.004250 (0.044751) | 0.043232 / 0.037052 (0.006180) | 0.328774 / 0.258489 (0.070285) | 0.297308 / 0.293841 (0.003467) | 0.030552 / 0.128546 (-0.097994) | 0.012632 / 0.075646 (-0.063015) | 0.204156 / 0.419271 (-0.215116) | 0.036014 / 0.043533 (-0.007519) | 0.241224 / 0.255139 (-0.013915) | 0.268358 / 0.283200 (-0.014842) | 0.019227 / 0.141683 (-0.122456) | 1.114515 / 1.452155 (-0.337639) | 1.147029 / 1.492716 (-0.345688) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094925 / 0.018006 (0.076919) | 0.301548 / 0.000490 (0.301059) | 0.000211 / 0.000200 (0.000011) | 0.000043 / 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) | 0.018875 / 0.037411 (-0.018536) | 0.062824 / 0.014526 (0.048298) | 0.075657 / 0.176557 (-0.100900) | 0.121926 / 0.737135 (-0.615209) | 0.077102 / 0.296338 (-0.219236) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.286018 / 0.215209 (0.070808) | 2.832222 / 2.077655 (0.754567) | 1.462629 / 1.504120 (-0.041491) | 1.354746 / 1.541195 (-0.186449) | 1.339504 / 1.468490 (-0.128986) | 0.718381 / 4.584777 (-3.866396) | 2.401456 / 3.745712 (-1.344256) | 3.013518 / 5.269862 (-2.256343) | 1.944892 / 4.565676 (-2.620784) | 0.078793 / 0.424275 (-0.345482) | 0.005219 / 0.007607 (-0.002388) | 0.349551 / 0.226044 (0.123507) | 3.417844 / 2.268929 (1.148916) | 1.830669 / 55.444624 (-53.613956) | 1.502134 / 6.876477 (-5.374343) | 1.529242 / 2.142072 (-0.612830) | 0.793732 / 4.805227 (-4.011495) | 0.133571 / 6.500664 (-6.367093) | 0.042588 / 0.075469 (-0.032881) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.988167 / 1.841788 (-0.853620) | 11.926728 / 8.074308 (3.852420) | 9.806971 / 10.191392 (-0.384421) | 0.173951 / 0.680424 (-0.506473) | 0.015308 / 0.534201 (-0.518893) | 0.310768 / 0.579283 (-0.268515) | 0.268261 / 0.434364 (-0.166103) | 0.342962 / 0.540337 (-0.197375) | 0.431255 / 1.386936 (-0.955681) |\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) | 0.005680 / 0.011353 (-0.005673) | 0.004231 / 0.011008 (-0.006778) | 0.051009 / 0.038508 (0.012501) | 0.031431 / 0.023109 (0.008322) | 0.268582 / 0.275898 (-0.007316) | 0.287942 / 0.323480 (-0.035538) | 0.004442 / 0.007986 (-0.003543) | 0.002818 / 0.004328 (-0.001511) | 0.050241 / 0.004250 (0.045991) | 0.039933 / 0.037052 (0.002881) | 0.285814 / 0.258489 (0.027325) | 0.316082 / 0.293841 (0.022241) | 0.032416 / 0.128546 (-0.096130) | 0.012398 / 0.075646 (-0.063248) | 0.060779 / 0.419271 (-0.358493) | 0.033706 / 0.043533 (-0.009827) | 0.273915 / 0.255139 (0.018776) | 0.289752 / 0.283200 (0.006553) | 0.017859 / 0.141683 (-0.123824) | 1.150224 / 1.452155 (-0.301930) | 1.197467 / 1.492716 (-0.295250) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093810 / 0.018006 (0.075803) | 0.302529 / 0.000490 (0.302039) | 0.000221 / 0.000200 (0.000021) | 0.000047 / 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) | 0.022903 / 0.037411 (-0.014508) | 0.077445 / 0.014526 (0.062919) | 0.089335 / 0.176557 (-0.087222) | 0.130848 / 0.737135 (-0.606287) | 0.091106 / 0.296338 (-0.205232) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.294194 / 0.215209 (0.078985) | 2.886983 / 2.077655 (0.809328) | 1.557768 / 1.504120 (0.053648) | 1.424467 / 1.541195 (-0.116727) | 1.440625 / 1.468490 (-0.027865) | 0.724793 / 4.584777 (-3.859984) | 0.985216 / 3.745712 (-2.760496) | 2.856826 / 5.269862 (-2.413036) | 1.911638 / 4.565676 (-2.654039) | 0.080350 / 0.424275 (-0.343925) | 0.005616 / 0.007607 (-0.001991) | 0.348713 / 0.226044 (0.122668) | 3.414764 / 2.268929 (1.145835) | 1.925056 / 55.444624 (-53.519568) | 1.635752 / 6.876477 (-5.240725) | 1.761117 / 2.142072 (-0.380955) | 0.808309 / 4.805227 (-3.996918) | 0.136893 / 6.500664 (-6.363771) | 0.042116 / 0.075469 (-0.033354) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.004740 / 1.841788 (-0.837048) | 12.495859 / 8.074308 (4.421550) | 10.681233 / 10.191392 (0.489841) | 0.133320 / 0.680424 (-0.547104) | 0.015943 / 0.534201 (-0.518258) | 0.304869 / 0.579283 (-0.274414) | 0.128616 / 0.434364 (-0.305748) | 0.345930 / 0.540337 (-0.194407) | 0.457434 / 1.386936 (-0.929502) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#84d9dea52098c9403efb43d5b542dd6d45000bec \"CML watermark\")\n"
] | "2024-06-17T11:29:22Z" | "2024-06-19T14:30:32Z" | "2024-06-19T14:04:34Z" | CONTRIBUTOR | null | Following the conversion guide, copy=False is no longer required and will result in an error: https://numpy.org/devdocs/numpy_2_0_migration_guide.html#adapting-to-changes-in-the-copy-keyword.
The following fix should resolve the issue.
error found during testing on the MTEB repository e.g. [here](https://github.com/embeddings-benchmark/mteb/pull/938) | {
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"The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6975). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.",
"<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) | 0.005168 / 0.011353 (-0.006185) | 0.003720 / 0.011008 (-0.007288) | 0.063347 / 0.038508 (0.024839) | 0.031474 / 0.023109 (0.008364) | 0.243233 / 0.275898 (-0.032665) | 0.276695 / 0.323480 (-0.046785) | 0.004109 / 0.007986 (-0.003877) | 0.002689 / 0.004328 (-0.001639) | 0.049522 / 0.004250 (0.045271) | 0.043477 / 0.037052 (0.006425) | 0.258578 / 0.258489 (0.000088) | 0.288134 / 0.293841 (-0.005707) | 0.027836 / 0.128546 (-0.100710) | 0.010677 / 0.075646 (-0.064969) | 0.206412 / 0.419271 (-0.212860) | 0.036204 / 0.043533 (-0.007329) | 0.250588 / 0.255139 (-0.004551) | 0.272354 / 0.283200 (-0.010846) | 0.018359 / 0.141683 (-0.123324) | 1.118867 / 1.452155 (-0.333288) | 1.157318 / 1.492716 (-0.335399) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092927 / 0.018006 (0.074921) | 0.298252 / 0.000490 (0.297762) | 0.000228 / 0.000200 (0.000028) | 0.000042 / 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) | 0.018824 / 0.037411 (-0.018588) | 0.069304 / 0.014526 (0.054778) | 0.075094 / 0.176557 (-0.101462) | 0.122546 / 0.737135 (-0.614590) | 0.076453 / 0.296338 (-0.219885) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.287131 / 0.215209 (0.071922) | 2.838945 / 2.077655 (0.761291) | 1.473578 / 1.504120 (-0.030542) | 1.351214 / 1.541195 (-0.189981) | 1.354924 / 1.468490 (-0.113566) | 0.577092 / 4.584777 (-4.007685) | 2.348072 / 3.745712 (-1.397640) | 2.762130 / 5.269862 (-2.507732) | 1.725195 / 4.565676 (-2.840482) | 0.063596 / 0.424275 (-0.360679) | 0.004921 / 0.007607 (-0.002686) | 0.335422 / 0.226044 (0.109377) | 3.340398 / 2.268929 (1.071469) | 1.789390 / 55.444624 (-53.655234) | 1.516247 / 6.876477 (-5.360229) | 1.529653 / 2.142072 (-0.612420) | 0.643547 / 4.805227 (-4.161680) | 0.116491 / 6.500664 (-6.384173) | 0.042404 / 0.075469 (-0.033065) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.959839 / 1.841788 (-0.881948) | 11.269778 / 8.074308 (3.195470) | 9.574898 / 10.191392 (-0.616494) | 0.128979 / 0.680424 (-0.551444) | 0.013901 / 0.534201 (-0.520300) | 0.280778 / 0.579283 (-0.298505) | 0.256511 / 0.434364 (-0.177853) | 0.319361 / 0.540337 (-0.220977) | 0.411803 / 1.386936 (-0.975133) |\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) | 0.005453 / 0.011353 (-0.005899) | 0.003478 / 0.011008 (-0.007530) | 0.050055 / 0.038508 (0.011547) | 0.031415 / 0.023109 (0.008306) | 0.275057 / 0.275898 (-0.000841) | 0.296690 / 0.323480 (-0.026789) | 0.004253 / 0.007986 (-0.003732) | 0.002777 / 0.004328 (-0.001551) | 0.049553 / 0.004250 (0.045303) | 0.039843 / 0.037052 (0.002791) | 0.286938 / 0.258489 (0.028449) | 0.318579 / 0.293841 (0.024738) | 0.029773 / 0.128546 (-0.098774) | 0.010404 / 0.075646 (-0.065242) | 0.057915 / 0.419271 (-0.361356) | 0.033486 / 0.043533 (-0.010047) | 0.273293 / 0.255139 (0.018154) | 0.293155 / 0.283200 (0.009955) | 0.017843 / 0.141683 (-0.123839) | 1.131130 / 1.452155 (-0.321024) | 1.167412 / 1.492716 (-0.325304) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092553 / 0.018006 (0.074547) | 0.298888 / 0.000490 (0.298399) | 0.000201 / 0.000200 (0.000001) | 0.000043 / 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) | 0.022646 / 0.037411 (-0.014765) | 0.076921 / 0.014526 (0.062395) | 0.089238 / 0.176557 (-0.087318) | 0.128793 / 0.737135 (-0.608342) | 0.089190 / 0.296338 (-0.207148) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.292552 / 0.215209 (0.077343) | 2.884277 / 2.077655 (0.806622) | 1.568798 / 1.504120 (0.064678) | 1.441819 / 1.541195 (-0.099375) | 1.435766 / 1.468490 (-0.032724) | 0.572435 / 4.584777 (-4.012342) | 0.957387 / 3.745712 (-2.788326) | 2.650843 / 5.269862 (-2.619019) | 1.727424 / 4.565676 (-2.838252) | 0.063470 / 0.424275 (-0.360805) | 0.005314 / 0.007607 (-0.002293) | 0.345881 / 0.226044 (0.119836) | 3.395463 / 2.268929 (1.126535) | 1.921340 / 55.444624 (-53.523285) | 1.621563 / 6.876477 (-5.254914) | 1.742561 / 2.142072 (-0.399512) | 0.639948 / 4.805227 (-4.165279) | 0.116091 / 6.500664 (-6.384573) | 0.041218 / 0.075469 (-0.034251) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.991506 / 1.841788 (-0.850281) | 11.897462 / 8.074308 (3.823154) | 10.083008 / 10.191392 (-0.108384) | 0.140626 / 0.680424 (-0.539798) | 0.015454 / 0.534201 (-0.518747) | 0.283856 / 0.579283 (-0.295427) | 0.125935 / 0.434364 (-0.308429) | 0.323884 / 0.540337 (-0.216454) | 0.438348 / 1.386936 (-0.948588) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e59582adc7fcb53a86a8ca8eda7e04a4e7b25bd2 \"CML watermark\")\n"
] | "2024-06-17T10:36:54Z" | "2024-06-17T12:49:53Z" | "2024-06-17T12:43:56Z" | MEMBER | null | Set temporary numpy upper version < 2.0.0 to fix CI. See: https://github.com/huggingface/datasets/actions/runs/9546031216/job/26308072017
```
A module that was compiled using NumPy 1.x cannot be run in
NumPy 2.0.0 as it may crash. To support both 1.x and 2.x
versions of NumPy, modules must be compiled with NumPy 2.0.
Some module may need to rebuild instead e.g. with 'pybind11>=2.12'.
If you are a user of the module, the easiest solution will be to
downgrade to 'numpy<2' or try to upgrade the affected module.
We expect that some modules will need time to support NumPy 2.
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"add remove_unused_columns=False to training_args\r\nhttps://github.com/huggingface/datasets/issues/6535#issuecomment-1874024704",
"Closing this issue because it was a reported and fixed in transformers."
] | "2024-06-16T07:53:54Z" | "2024-07-01T11:25:40Z" | "2024-07-01T11:25:40Z" | NONE | null | ### Describe the bug
I am encountering an IndexError while training a T5-small model on the Squad dataset using the transformers and datasets libraries. The error occurs even with a minimal reproducible example, suggesting a potential bug or incompatibility.
### Steps to reproduce the bug
1.Install the required libraries: !pip install transformers datasets
2.Run the following code:
!pip install transformers datasets
import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, TrainingArguments, Trainer, DataCollatorWithPadding
# Load a small, publicly available dataset
from datasets import load_dataset
dataset = load_dataset("squad", split="train[:100]") # Use a small subset for testing
# Load a pre-trained model and tokenizer
model_name = "t5-small"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
# Define a basic data collator
data_collator = DataCollatorWithPadding(tokenizer=tokenizer)
# Define training arguments
training_args = TrainingArguments(
output_dir="./results",
per_device_train_batch_size=2,
num_train_epochs=1,
)
# Create a trainer
trainer = Trainer(
model=model,
args=training_args,
train_dataset=dataset,
data_collator=data_collator,
)
# Train the model
trainer.train()
### Expected behavior
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
[<ipython-input-23-f13a4b23c001>](https://localhost:8080/#) in <cell line: 34>()
32
33 # Train the model
---> 34 trainer.train()
10 frames
[/usr/local/lib/python3.10/dist-packages/datasets/formatting/formatting.py](https://localhost:8080/#) in _check_valid_index_key(key, size)
427 if isinstance(key, int):
428 if (key < 0 and key + size < 0) or (key >= size):
--> 429 raise IndexError(f"Invalid key: {key} is out of bounds for size {size}")
430 return
431 elif isinstance(key, slice):
IndexError: Invalid key: 42 is out of bounds for size 0
### Environment info
transformers version:4.41.2
datasets version:1.18.4
Python version:3.10.12
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