Split (2)

train · 2.4k rows

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https://api.github.com/repos/huggingface/datasets/issues/6460	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6460/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6460/comments	https://api.github.com/repos/huggingface/datasets/issues/6460/events	https://github.com/huggingface/datasets/issues/6460	2,017,433,899	I_kwDODunzps54P5kr	6,460	jsonlines files don't load with `load_dataset`	{ "avatar_url": "https://avatars.githubusercontent.com/u/41377532?v=4", "events_url": "https://api.github.com/users/serenalotreck/events{/privacy}", "followers_url": "https://api.github.com/users/serenalotreck/followers", "following_url": "https://api.github.com/users/serenalotreck/following{/other_user}", "gists_url": "https://api.github.com/users/serenalotreck/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/serenalotreck", "id": 41377532, "login": "serenalotreck", "node_id": "MDQ6VXNlcjQxMzc3NTMy", "organizations_url": "https://api.github.com/users/serenalotreck/orgs", "received_events_url": "https://api.github.com/users/serenalotreck/received_events", "repos_url": "https://api.github.com/users/serenalotreck/repos", "site_admin": false, "starred_url": "https://api.github.com/users/serenalotreck/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/serenalotreck/subscriptions", "type": "User", "url": "https://api.github.com/users/serenalotreck", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "Hi @serenalotreck,\r\n\r\nWe use Apache Arrow `pyarrow` to read jsonlines and it throws an error when trying to load your data files:\r\n```python\r\nIn [1]: import pyarrow as pa\r\n\r\nIn [2]: data = pa.json.read_json(\"train.jsonl\")\r\n---------------------------------------------------------------------------\r\nArrowInvalid Traceback (most recent call last)\r\n<ipython-input-14-e9b104832528> in <module>\r\n----> 1 data = pa.json.read_json(\"train.jsonl\")\r\n\r\n.../huggingface/datasets/venv/lib/python3.9/site-packages/pyarrow/_json.pyx in pyarrow._json.read_json()\r\n\r\n.../huggingface/datasets/venv/lib/python3.9/site-packages/pyarrow/error.pxi in pyarrow.lib.pyarrow_internal_check_status()\r\n\r\n.../huggingface/datasets/venv/lib/python3.9/site-packages/pyarrow/error.pxi in pyarrow.lib.check_status()\r\n\r\nArrowInvalid: JSON parse error: Column(/ner/[]/[]/[]) changed from number to string in row 0\r\n```\r\n\r\nI think it has to do with the data structure of the fields \"ner\" (and also \"relations\"):\r\n```json\r\n\"ner\": [\r\n [\r\n [0, 4, \"Biochemical_process\"], \r\n [15, 16, \"Protein\"]\r\n ], \r\n```\r\nArrow interprets this data structure as an array, an arrays contain just a single data type: \r\n- when reading sequentially, it finds first the `0` and infers that the data is of type `number`;\r\n- when it finds the string `\"Biochemical_process\"`, it cannot cast it to number and throws the `ArrowInvalid` error\r\n\r\nOne solution could be to change the data structure of your data files. Any other ideas, @huggingface/datasets ?", "Hi @albertvillanova, \r\n\r\nThanks for the explanation! To the best of my knowledge, arrays in a json [can contain multiple data types](https://docs.actian.com/ingres/11.2/index.html#page/SQLRef/Data_Types.htm), and I'm able to read these files with the `jsonlines` package. Is the requirement for arrays to only have one data type specific to PyArrow?\r\n\r\nI'd prefer to keep the data structure as is, since it's a specific input requirement for the models this data was generated for. Any thoughts on how to enable the use of `load_dataset` with this dataset would be great!", "Hi again @serenalotreck,\r\n\r\nYes, it is specific to PyArrow: as far as I know, Arrow does not support arrays with multiple data types.\r\n\r\nAs this is related specifically to your dataset structure (and not the `datasets` library), I have created a dedicated issue in your dataset page: https://huggingface.co/datasets/slotreck/pickle/discussions/1\r\n\r\nLet's continue the discussion there! :hugs: ", "> Hi again @serenalotreck,\r\n> \r\n> Yes, it is specific to PyArrow: as far as I know, Arrow does not support arrays with multiple data types.\r\n> \r\n> As this is related specifically to your dataset structure (and not the `datasets` library), I have created a dedicated issue in your dataset page: https://huggingface.co/datasets/slotreck/pickle/discussions/1\r\n> \r\n> Let's continue the discussion there! 🤗\r\n\r\nThis is really terrible. My JSONL format data is very simple, but I still report this error\r\n![image](https://github.com/huggingface/datasets/assets/58240629/e3fed922-ced4-406c-b5bc-90a4b891c4ee)\r\nThe error message is as follows：\r\n File \"pyarrow/_json.pyx\", line 290, in pyarrow._json.read_json\r\n File \"pyarrow/error.pxi\", line 144, in pyarrow.lib.pyarrow_internal_check_status\r\n File \"pyarrow/error.pxi\", line 100, in pyarrow.lib.check_status\r\npyarrow.lib.ArrowInvalid: JSON parse error: Column(/inputs) changed from string to number in row 208\r\n" ]	2023-11-29T21:20:11Z	2023-12-29T02:58:29Z	2023-12-05T13:30:53Z	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug While [the docs](https://huggingface.co/docs/datasets/upload_dataset#upload-dataset) seem to state that `.jsonl` is a supported extension for `datasets`, loading the dataset results in a `JSONDecodeError`. ### Steps to reproduce the bug Code: ``` from datasets import load_dataset dset = load_dataset('slotreck/pickle') ``` Traceback: ``` Downloading readme: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 925/925 [00:00<00:00, 3.11MB/s] Downloading and preparing dataset json/slotreck--pickle to /mnt/home/lotrecks/.cache/huggingface/datasets/slotreck___json/slotreck--pickle-0c311f36ed032b04/0.0.0/8bb11242116d547c741b2e8a1f18598ffdd40a1d4f2a2872c7a28b697434bc96... Downloading data: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 589k/589k [00:00<00:00, 18.9MB/s] Downloading data: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 104k/104k [00:00<00:00, 4.61MB/s] Downloading data: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 170k/170k [00:00<00:00, 7.71MB/s] Downloading data files: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 3/3 [00:00<00:00, 3.77it/s] Extracting data files: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 3/3 [00:00<00:00, 523.92it/s] Generating train split: 0 examples [00:00, ? examples/s]Failed to read file '/mnt/home/lotrecks/.cache/huggingface/datasets/downloads/6ec07bb2f279c9377036af6948532513fa8f48244c672d2644a2d7018ee5c9cb' with error <class 'pyarrow.lib.ArrowInvalid'>: JSON parse error: Column(/ner/[]/[]/[]) changed from number to string in row 0 Traceback (most recent call last): File "/mnt/home/lotrecks/anaconda3/envs/pickle/lib/python3.7/site-packages/datasets/packaged_modules/json/json.py", line 144, in _generate_tables dataset = json.load(f) File "/mnt/home/lotrecks/anaconda3/envs/pickle/lib/python3.7/json/__init__.py", line 296, in load parse_constant=parse_constant, object_pairs_hook=object_pairs_hook, kw) File "/mnt/home/lotrecks/anaconda3/envs/pickle/lib/python3.7/json/__init__.py", line 348, in loads return _default_decoder.decode(s) File "/mnt/home/lotrecks/anaconda3/envs/pickle/lib/python3.7/json/decoder.py", line 340, in decode raise JSONDecodeError("Extra data", s, end) json.decoder.JSONDecodeError: Extra data: line 2 column 1 (char 3086) During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/mnt/home/lotrecks/anaconda3/envs/pickle/lib/python3.7/site-packages/datasets/builder.py", line 1879, in _prepare_split_single for _, table in generator: File "/mnt/home/lotrecks/anaconda3/envs/pickle/lib/python3.7/site-packages/datasets/packaged_modules/json/json.py", line 147, in _generate_tables raise e File "/mnt/home/lotrecks/anaconda3/envs/pickle/lib/python3.7/site-packages/datasets/packaged_modules/json/json.py", line 122, in _generate_tables io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size) File "pyarrow/_json.pyx", line 259, in pyarrow._json.read_json File "pyarrow/error.pxi", line 144, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 100, in pyarrow.lib.check_status pyarrow.lib.ArrowInvalid: JSON parse error: Column(/ner/[]/[]/[]) changed from number to string in row 0 The above exception was the direct cause of the following exception: Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/mnt/home/lotrecks/anaconda3/envs/pickle/lib/python3.7/site-packages/datasets/load.py", line 1815, in load_dataset storage_options=storage_options, File "/mnt/home/lotrecks/anaconda3/envs/pickle/lib/python3.7/site-packages/datasets/builder.py", line 913, in download_and_prepare download_and_prepare_kwargs, File "/mnt/home/lotrecks/anaconda3/envs/pickle/lib/python3.7/site-packages/datasets/builder.py", line 1004, in _download_and_prepare self._prepare_split(split_generator, prepare_split_kwargs) File "/mnt/home/lotrecks/anaconda3/envs/pickle/lib/python3.7/site-packages/datasets/builder.py", line 1768, in _prepare_split gen_kwargs=gen_kwargs, job_id=job_id, _prepare_split_args File "/mnt/home/lotrecks/anaconda3/envs/pickle/lib/python3.7/site-packages/datasets/builder.py", line 1912, in _prepare_split_single raise DatasetGenerationError("An error occurred while generating the dataset") from e datasets.builder.DatasetGenerationError: An error occurred while generating the dataset ``` ### Expected behavior For the dataset to be loaded without error. ### Environment info - 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https://api.github.com/repos/huggingface/datasets/issues/4811	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/4811/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/4811/comments	https://api.github.com/repos/huggingface/datasets/issues/4811/events	https://github.com/huggingface/datasets/issues/4811	1,333,043,421	I_kwDODunzps5PdKDd	4,811	Bug in function validate_type for Python >= 3.9	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": 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]	null	[]	2022-08-09T10:25:21Z	2022-08-12T13:27:05Z	2022-08-12T13:27:05Z	MEMBER	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	## Describe the bug The function `validate_type` assumes that the type `typing.Optional[str]` is automatically transformed to `typing.Union[str, NoneType]`. ```python In [4]: typing.Optional[str] Out[4]: typing.Union[str, NoneType] ``` However, this is not the case for Python 3.9: ```python In [3]: typing.Optional[str] Out[3]: typing.Optional[str] ```	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/4811/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/4811/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/4552	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/4552/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/4552/comments	https://api.github.com/repos/huggingface/datasets/issues/4552/events	https://github.com/huggingface/datasets/pull/4552	1,282,615,646	PR_kwDODunzps46QSHV	4,552	Tell users to upload on the hub directly	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "Thanks ! I updated the two remaining files" ]	2022-06-23T15:47:52Z	2022-06-26T15:49:46Z	2022-06-26T15:39:11Z	MEMBER	null	null	null	As noted in https://github.com/huggingface/datasets/pull/4534, it is still not clear that it is recommended to add datasets on the Hugging Face Hub directly instead of GitHub, so I updated some docs. Moreover since users won't be able to get reviews from us on the Hub, I added a paragraph to tell users that they can open a discussion and tag `datasets` maintainers for reviews. Finally I removed the _previous good reasons_ to add a dataset on GitHub to only keep this one: > In some rare cases it makes more sense to open a PR on GitHub. For example when you are not the author of the dataset and there is no clear organization / namespace that you can put the dataset under. Does it sound good to you @albertvillanova @julien-c ?	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 3, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 3, "url": "https://api.github.com/repos/huggingface/datasets/issues/4552/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/4552/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/4552.diff", "html_url": "https://github.com/huggingface/datasets/pull/4552", "merged_at": "2022-06-26T15:39:11Z", "patch_url": "https://github.com/huggingface/datasets/pull/4552.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/4552" }
https://api.github.com/repos/huggingface/datasets/issues/5358	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5358/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5358/comments	https://api.github.com/repos/huggingface/datasets/issues/5358/events	https://github.com/huggingface/datasets/pull/5358	1,495,270,822	PR_kwDODunzps5FYBcq	5,358	Fix `fs.open` resource leaks	{ "avatar_url": "https://avatars.githubusercontent.com/u/297847?v=4", "events_url": "https://api.github.com/users/tkukurin/events{/privacy}", "followers_url": "https://api.github.com/users/tkukurin/followers", "following_url": "https://api.github.com/users/tkukurin/following{/other_user}", "gists_url": "https://api.github.com/users/tkukurin/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/tkukurin", "id": 297847, "login": "tkukurin", "node_id": "MDQ6VXNlcjI5Nzg0Nw==", "organizations_url": "https://api.github.com/users/tkukurin/orgs", "received_events_url": "https://api.github.com/users/tkukurin/received_events", "repos_url": "https://api.github.com/users/tkukurin/repos", "site_admin": false, "starred_url": "https://api.github.com/users/tkukurin/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/tkukurin/subscriptions", "type": "User", "url": "https://api.github.com/users/tkukurin", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "@mariosasko Sorry, I didn't check tests/style after doing a merge from the Git UI last week. Thx for fixing. \r\n\r\nFYI I'm getting \"Only those with [write access](https://docs.github.com/articles/what-are-the-different-access-permissions) to this repository can merge pull requests.\" so it seems somebody else needs to merge this.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008816 / 0.011353 (-0.002536) \| 0.004691 / 0.011008 (-0.006317) \| 0.100039 / 0.038508 (0.061531) \| 0.035422 / 0.023109 (0.012313) \| 0.312600 / 0.275898 (0.036702) \| 0.378684 / 0.323480 (0.055204) \| 0.007593 / 0.007986 (-0.000392) \| 0.005183 / 0.004328 (0.000855) \| 0.078040 / 0.004250 (0.073790) \| 0.041845 / 0.037052 (0.004793) \| 0.325251 / 0.258489 (0.066762) \| 0.363459 / 0.293841 (0.069618) \| 0.038006 / 0.128546 (-0.090540) \| 0.011911 / 0.075646 (-0.063735) \| 0.335020 / 0.419271 (-0.084251) \| 0.048765 / 0.043533 (0.005233) \| 0.305913 / 0.255139 (0.050774) \| 0.337620 / 0.283200 (0.054420) \| 0.101867 / 0.141683 (-0.039816) \| 1.450091 / 1.452155 (-0.002064) \| 1.437303 / 1.492716 (-0.055413) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.225650 / 0.018006 (0.207644) \| 0.492480 / 0.000490 (0.491990) \| 0.002857 / 0.000200 (0.002658) \| 0.000075 / 0.000054 (0.000020) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026231 / 0.037411 (-0.011180) \| 0.105479 / 0.014526 (0.090953) \| 0.118438 / 0.176557 (-0.058119) \| 0.167313 / 0.737135 (-0.569822) \| 0.119416 / 0.296338 (-0.176923) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.396233 / 0.215209 (0.181024) \| 3.943325 / 2.077655 (1.865671) \| 1.778864 / 1.504120 (0.274744) \| 1.587957 / 1.541195 (0.046763) \| 1.615404 / 1.468490 (0.146914) \| 0.709427 / 4.584777 (-3.875350) \| 3.823310 / 3.745712 (0.077598) \| 3.461376 / 5.269862 (-1.808486) \| 1.888330 / 4.565676 (-2.677346) \| 0.086910 / 0.424275 (-0.337365) \| 0.012215 / 0.007607 (0.004608) \| 0.504877 / 0.226044 (0.278833) \| 5.051513 / 2.268929 (2.782584) \| 2.249389 / 55.444624 (-53.195235) \| 1.890949 / 6.876477 (-4.985528) \| 2.015584 / 2.142072 (-0.126489) \| 0.862313 / 4.805227 (-3.942914) \| 0.166295 / 6.500664 (-6.334369) \| 0.061131 / 0.075469 (-0.014338) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.201804 / 1.841788 (-0.639984) \| 14.589425 / 8.074308 (6.515117) \| 13.855522 / 10.191392 (3.664130) \| 0.193406 / 0.680424 (-0.487018) \| 0.028614 / 0.534201 (-0.505587) \| 0.439857 / 0.579283 (-0.139426) \| 0.443330 / 0.434364 (0.008966) \| 0.514078 / 0.540337 (-0.026259) \| 0.608245 / 1.386936 (-0.778691) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007087 / 0.011353 (-0.004265) \| 0.005024 / 0.011008 (-0.005985) \| 0.096852 / 0.038508 (0.058344) \| 0.032870 / 0.023109 (0.009761) \| 0.397790 / 0.275898 (0.121892) \| 0.420717 / 0.323480 (0.097237) \| 0.005552 / 0.007986 (-0.002434) \| 0.003742 / 0.004328 (-0.000586) \| 0.074788 / 0.004250 (0.070537) \| 0.048030 / 0.037052 (0.010977) \| 0.398520 / 0.258489 (0.140031) \| 0.460919 / 0.293841 (0.167078) \| 0.037652 / 0.128546 (-0.090894) \| 0.012249 / 0.075646 (-0.063397) \| 0.333077 / 0.419271 (-0.086194) \| 0.052364 / 0.043533 (0.008831) \| 0.394358 / 0.255139 (0.139219) \| 0.414193 / 0.283200 (0.130994) \| 0.103569 / 0.141683 (-0.038114) \| 1.499208 / 1.452155 (0.047053) \| 1.619481 / 1.492716 (0.126764) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.229476 / 0.018006 (0.211470) \| 0.448670 / 0.000490 (0.448180) \| 0.000399 / 0.000200 (0.000199) \| 0.000056 / 0.000054 (0.000001) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027550 / 0.037411 (-0.009862) \| 0.109180 / 0.014526 (0.094654) \| 0.118372 / 0.176557 (-0.058185) \| 0.153136 / 0.737135 (-0.583999) \| 0.122689 / 0.296338 (-0.173650) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.445163 / 0.215209 (0.229954) \| 4.426350 / 2.077655 (2.348695) \| 2.194902 / 1.504120 (0.690782) \| 2.019049 / 1.541195 (0.477854) \| 2.032795 / 1.468490 (0.564305) \| 0.700752 / 4.584777 (-3.884025) \| 3.797616 / 3.745712 (0.051903) \| 2.046414 / 5.269862 (-3.223447) \| 1.345037 / 4.565676 (-3.220639) \| 0.085389 / 0.424275 (-0.338886) \| 0.012824 / 0.007607 (0.005217) \| 0.553875 / 0.226044 (0.327831) \| 5.550252 / 2.268929 (3.281323) \| 2.702822 / 55.444624 (-52.741803) \| 2.346257 / 6.876477 (-4.530220) \| 2.410772 / 2.142072 (0.268699) \| 0.848271 / 4.805227 (-3.956957) \| 0.170787 / 6.500664 (-6.329877) \| 0.064344 / 0.075469 (-0.011125) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.266222 / 1.841788 (-0.575566) \| 14.501194 / 8.074308 (6.426886) \| 13.413678 / 10.191392 (3.222286) \| 0.589048 / 0.680424 (-0.091375) \| 0.018246 / 0.534201 (-0.515955) \| 0.425221 / 0.579283 (-0.154062) \| 0.425900 / 0.434364 (-0.008464) \| 0.494023 / 0.540337 (-0.046314) \| 0.604324 / 1.386936 (-0.782612) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png \"CML watermark\")\n" ]	2022-12-13T22:35:51Z	2023-01-05T16:46:31Z	2023-01-05T15:59:51Z	CONTRIBUTOR	null	null	null	Invoking `{load,save}_from_dict` results in resource leak warnings, this should fix. Introduces no significant logic changes.	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5358/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5358/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/5358.diff", "html_url": "https://github.com/huggingface/datasets/pull/5358", "merged_at": "2023-01-05T15:59:51Z", "patch_url": "https://github.com/huggingface/datasets/pull/5358.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5358" }
https://api.github.com/repos/huggingface/datasets/issues/4588	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/4588/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/4588/comments	https://api.github.com/repos/huggingface/datasets/issues/4588/events	https://github.com/huggingface/datasets/pull/4588	1,287,368,751	PR_kwDODunzps46f2kF	4,588	Host head_qa data on the Hub and fix NonMatchingChecksumError	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "Hi @albertvillanova ! Thanks for the fix ;)\r\nCan I safely checkout from this branch to build `datasets` or it is preferable to wait until all CI tests pass?\r\nThanks 🙏 ", "@younesbelkada we have just merged this PR." ]	2022-06-28T13:39:28Z	2022-07-05T16:01:15Z	2022-07-05T15:49:52Z	MEMBER	null	null	null	This PR: - Hosts head_qa data on the Hub instead of Google Drive - Fixes NonMatchingChecksumError Fix https://huggingface.co/datasets/head_qa/discussions/1	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 1, "total_count": 1, "url": "https://api.github.com/repos/huggingface/datasets/issues/4588/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/4588/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/4588.diff", "html_url": "https://github.com/huggingface/datasets/pull/4588", "merged_at": "2022-07-05T15:49:52Z", "patch_url": "https://github.com/huggingface/datasets/pull/4588.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/4588" }
https://api.github.com/repos/huggingface/datasets/issues/7054	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/7054/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/7054/comments	https://api.github.com/repos/huggingface/datasets/issues/7054/events	https://github.com/huggingface/datasets/pull/7054	2,418,548,995	PR_kwDODunzps514T1f	7,054	Add batching to `IterableDataset`	{ "avatar_url": "https://avatars.githubusercontent.com/u/61876623?v=4", "events_url": "https://api.github.com/users/lappemic/events{/privacy}", "followers_url": "https://api.github.com/users/lappemic/followers", "following_url": "https://api.github.com/users/lappemic/following{/other_user}", "gists_url": "https://api.github.com/users/lappemic/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lappemic", "id": 61876623, "login": "lappemic", "node_id": "MDQ6VXNlcjYxODc2NjIz", "organizations_url": "https://api.github.com/users/lappemic/orgs", "received_events_url": "https://api.github.com/users/lappemic/received_events", "repos_url": "https://api.github.com/users/lappemic/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lappemic/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lappemic/subscriptions", "type": "User", "url": "https://api.github.com/users/lappemic", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "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	null	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	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/7054/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/7054/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/7054.diff", "html_url": "https://github.com/huggingface/datasets/pull/7054", "merged_at": "2024-07-23T10:34:28Z", "patch_url": "https://github.com/huggingface/datasets/pull/7054.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/7054" }
https://api.github.com/repos/huggingface/datasets/issues/6309	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6309/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6309/comments	https://api.github.com/repos/huggingface/datasets/issues/6309/events	https://github.com/huggingface/datasets/pull/6309	1,946,916,969	PR_kwDODunzps5c_YcX	6,309	Fix get_data_patterns for directories with the word data twice	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006461 / 0.011353 (-0.004891) \| 0.004035 / 0.011008 (-0.006973) \| 0.085037 / 0.038508 (0.046529) \| 0.072434 / 0.023109 (0.049325) \| 0.308565 / 0.275898 (0.032667) \| 0.330455 / 0.323480 (0.006975) \| 0.003782 / 0.007986 (-0.004204) \| 0.004363 / 0.004328 (0.000034) \| 0.065242 / 0.004250 (0.060991) \| 0.056111 / 0.037052 (0.019058) \| 0.318008 / 0.258489 (0.059519) \| 0.357904 / 0.293841 (0.064063) \| 0.030702 / 0.128546 (-0.097844) \| 0.008741 / 0.075646 (-0.066905) \| 0.287666 / 0.419271 (-0.131605) \| 0.052281 / 0.043533 (0.008748) \| 0.306894 / 0.255139 (0.051755) \| 0.335739 / 0.283200 (0.052540) \| 0.023712 / 0.141683 (-0.117971) \| 1.492304 / 1.452155 (0.040149) \| 1.544540 / 1.492716 (0.051823) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.299419 / 0.018006 (0.281413) \| 0.547195 / 0.000490 (0.546705) \| 0.011571 / 0.000200 (0.011371) \| 0.000223 / 0.000054 (0.000168) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028364 / 0.037411 (-0.009048) \| 0.081445 / 0.014526 (0.066919) \| 0.626670 / 0.176557 (0.450114) \| 0.159964 / 0.737135 (-0.577171) \| 0.100528 / 0.296338 (-0.195811) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.409915 / 0.215209 (0.194705) \| 4.108689 / 2.077655 (2.031034) \| 2.046247 / 1.504120 (0.542127) \| 1.851081 / 1.541195 (0.309887) \| 1.857857 / 1.468490 (0.389367) \| 0.493246 / 4.584777 (-4.091531) \| 3.581557 / 3.745712 (-0.164155) \| 3.456708 / 5.269862 (-1.813153) \| 2.051054 / 4.565676 (-2.514623) \| 0.057553 / 0.424275 (-0.366722) \| 0.007287 / 0.007607 (-0.000320) \| 0.493094 / 0.226044 (0.267050) \| 4.873051 / 2.268929 (2.604122) \| 2.515266 / 55.444624 (-52.929358) \| 2.144743 / 6.876477 (-4.731733) \| 2.159412 / 2.142072 (0.017340) \| 0.595627 / 4.805227 (-4.209601) \| 0.133773 / 6.500664 (-6.366891) \| 0.059965 / 0.075469 (-0.015504) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.259625 / 1.841788 (-0.582163) \| 19.030742 / 8.074308 (10.956434) \| 14.039246 / 10.191392 (3.847854) \| 0.168116 / 0.680424 (-0.512308) \| 0.018168 / 0.534201 (-0.516033) \| 0.391187 / 0.579283 (-0.188096) \| 0.420901 / 0.434364 (-0.013463) \| 0.465827 / 0.540337 (-0.074511) \| 0.718373 / 1.386936 (-0.668563) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006616 / 0.011353 (-0.004737) \| 0.004048 / 0.011008 (-0.006960) \| 0.064568 / 0.038508 (0.026060) \| 0.075933 / 0.023109 (0.052824) \| 0.396353 / 0.275898 (0.120455) \| 0.424159 / 0.323480 (0.100679) \| 0.005446 / 0.007986 (-0.002540) \| 0.003393 / 0.004328 (-0.000935) \| 0.064673 / 0.004250 (0.060422) \| 0.056983 / 0.037052 (0.019930) \| 0.402478 / 0.258489 (0.143989) \| 0.433240 / 0.293841 (0.139399) \| 0.032100 / 0.128546 (-0.096446) \| 0.008664 / 0.075646 (-0.066983) \| 0.070502 / 0.419271 (-0.348770) \| 0.047800 / 0.043533 (0.004267) \| 0.399506 / 0.255139 (0.144367) \| 0.418376 / 0.283200 (0.135176) \| 0.022654 / 0.141683 (-0.119029) \| 1.487280 / 1.452155 (0.035125) \| 1.543733 / 1.492716 (0.051017) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.317660 / 0.018006 (0.299654) \| 0.523922 / 0.000490 (0.523432) \| 0.007086 / 0.000200 (0.006886) \| 0.000109 / 0.000054 (0.000055) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032381 / 0.037411 (-0.005030) \| 0.091636 / 0.014526 (0.077110) \| 0.104743 / 0.176557 (-0.071814) \| 0.158793 / 0.737135 (-0.578342) \| 0.103164 / 0.296338 (-0.193175) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.434081 / 0.215209 (0.218872) \| 4.329448 / 2.077655 (2.251794) \| 2.335855 / 1.504120 (0.831735) \| 2.177513 / 1.541195 (0.636319) \| 2.205406 / 1.468490 (0.736916) \| 0.500117 / 4.584777 (-4.084660) \| 3.693715 / 3.745712 (-0.051997) \| 3.305803 / 5.269862 (-1.964059) \| 2.048283 / 4.565676 (-2.517394) \| 0.058301 / 0.424275 (-0.365974) \| 0.007196 / 0.007607 (-0.000411) \| 0.512917 / 0.226044 (0.286873) \| 5.129283 / 2.268929 (2.860355) \| 2.836200 / 55.444624 (-52.608425) \| 2.499022 / 6.876477 (-4.377455) \| 2.652305 / 2.142072 (0.510232) \| 0.604219 / 4.805227 (-4.201008) \| 0.137310 / 6.500664 (-6.363354) \| 0.060880 / 0.075469 (-0.014589) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.346948 / 1.841788 (-0.494839) \| 19.499516 / 8.074308 (11.425208) \| 14.701500 / 10.191392 (4.510108) \| 0.168626 / 0.680424 (-0.511798) \| 0.020002 / 0.534201 (-0.514199) \| 0.394729 / 0.579283 (-0.184554) \| 0.428323 / 0.434364 (-0.006040) \| 0.481202 / 0.540337 (-0.059136) \| 0.684768 / 1.386936 (-0.702169) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#fed9c07458afc73870e8ec9846bf1fc5cac0b378 \"CML watermark\")\n", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6309). All of your documentation changes will be reflected on that endpoint.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007033 / 0.011353 (-0.004320) \| 0.004411 / 0.011008 (-0.006597) \| 0.086146 / 0.038508 (0.047638) \| 0.086669 / 0.023109 (0.063560) \| 0.329145 / 0.275898 (0.053247) \| 0.348728 / 0.323480 (0.025248) \| 0.004404 / 0.007986 (-0.003582) \| 0.003656 / 0.004328 (-0.000673) \| 0.066120 / 0.004250 (0.061869) \| 0.059157 / 0.037052 (0.022105) \| 0.316537 / 0.258489 (0.058048) \| 0.369065 / 0.293841 (0.075224) \| 0.031921 / 0.128546 (-0.096625) \| 0.008877 / 0.075646 (-0.066770) \| 0.290068 / 0.419271 (-0.129204) \| 0.054007 / 0.043533 (0.010475) \| 0.308823 / 0.255139 (0.053684) \| 0.331189 / 0.283200 (0.047989) \| 0.027313 / 0.141683 (-0.114370) \| 1.486772 / 1.452155 (0.034617) \| 1.570359 / 1.492716 (0.077643) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.315991 / 0.018006 (0.297985) \| 0.577876 / 0.000490 (0.577386) \| 0.011207 / 0.000200 (0.011007) \| 0.000089 / 0.000054 (0.000035) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031753 / 0.037411 (-0.005658) \| 0.089270 / 0.014526 (0.074744) \| 0.102518 / 0.176557 (-0.074038) \| 0.160260 / 0.737135 (-0.576875) \| 0.103365 / 0.296338 (-0.192973) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.405789 / 0.215209 (0.190580) \| 4.052740 / 2.077655 (1.975085) \| 2.052076 / 1.504120 (0.547956) \| 1.873966 / 1.541195 (0.332771) \| 1.997156 / 1.468490 (0.528665) \| 0.494975 / 4.584777 (-4.089802) \| 3.600007 / 3.745712 (-0.145705) \| 3.626459 / 5.269862 (-1.643403) \| 2.176927 / 4.565676 (-2.388750) \| 0.057894 / 0.424275 (-0.366381) \| 0.007469 / 0.007607 (-0.000138) \| 0.487422 / 0.226044 (0.261377) \| 4.868744 / 2.268929 (2.599815) \| 2.528707 / 55.444624 (-52.915918) \| 2.149520 / 6.876477 (-4.726956) \| 2.275491 / 2.142072 (0.133419) \| 0.589112 / 4.805227 (-4.216115) \| 0.136644 / 6.500664 (-6.364020) \| 0.062144 / 0.075469 (-0.013325) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.286625 / 1.841788 (-0.555163) \| 20.528128 / 8.074308 (12.453819) \| 15.290866 / 10.191392 (5.099474) \| 0.168380 / 0.680424 (-0.512044) \| 0.018908 / 0.534201 (-0.515293) \| 0.397210 / 0.579283 (-0.182073) \| 0.426133 / 0.434364 (-0.008231) \| 0.471754 / 0.540337 (-0.068584) \| 0.653343 / 1.386936 (-0.733593) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007599 / 0.011353 (-0.003754) \| 0.004499 / 0.011008 (-0.006509) \| 0.066248 / 0.038508 (0.027740) \| 0.097704 / 0.023109 (0.074595) \| 0.414558 / 0.275898 (0.138660) \| 0.451088 / 0.323480 (0.127609) \| 0.005932 / 0.007986 (-0.002054) \| 0.003698 / 0.004328 (-0.000630) \| 0.065784 / 0.004250 (0.061534) \| 0.064777 / 0.037052 (0.027725) \| 0.443318 / 0.258489 (0.184829) \| 0.456896 / 0.293841 (0.163055) \| 0.033436 / 0.128546 (-0.095111) \| 0.008977 / 0.075646 (-0.066669) \| 0.072067 / 0.419271 (-0.347205) \| 0.049571 / 0.043533 (0.006038) \| 0.420325 / 0.255139 (0.165186) \| 0.443588 / 0.283200 (0.160388) \| 0.026723 / 0.141683 (-0.114960) \| 1.512566 / 1.452155 (0.060411) \| 1.647591 / 1.492716 (0.154875) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.326410 / 0.018006 (0.308404) \| 0.532878 / 0.000490 (0.532388) \| 0.006257 / 0.000200 (0.006057) \| 0.000104 / 0.000054 (0.000049) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.037289 / 0.037411 (-0.000122) \| 0.104940 / 0.014526 (0.090414) \| 0.113597 / 0.176557 (-0.062960) \| 0.170562 / 0.737135 (-0.566573) \| 0.114583 / 0.296338 (-0.181755) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.435530 / 0.215209 (0.220321) \| 4.332659 / 2.077655 (2.255005) \| 2.343576 / 1.504120 (0.839456) \| 2.190517 / 1.541195 (0.649322) \| 2.323101 / 1.468490 (0.854611) \| 0.493019 / 4.584777 (-4.091758) \| 3.686726 / 3.745712 (-0.058986) \| 3.437143 / 5.269862 (-1.832719) \| 2.167193 / 4.565676 (-2.398483) \| 0.059636 / 0.424275 (-0.364639) \| 0.007696 / 0.007607 (0.000089) \| 0.511159 / 0.226044 (0.285115) \| 5.119358 / 2.268929 (2.850429) \| 2.814934 / 55.444624 (-52.629690) \| 2.477871 / 6.876477 (-4.398606) \| 2.774473 / 2.142072 (0.632401) \| 0.590258 / 4.805227 (-4.214969) \| 0.135923 / 6.500664 (-6.364741) \| 0.062793 / 0.075469 (-0.012676) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.350192 / 1.841788 (-0.491596) \| 21.382135 / 8.074308 (13.307827) \| 16.024198 / 10.191392 (5.832806) \| 0.163623 / 0.680424 (-0.516801) \| 0.020749 / 0.534201 (-0.513452) \| 0.402578 / 0.579283 (-0.176705) \| 0.436569 / 0.434364 (0.002205) \| 0.477217 / 0.540337 (-0.063121) \| 0.682929 / 1.386936 (-0.704007) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#fa36173f2e8c6f266efd236933eff3a95af0382c \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006671 / 0.011353 (-0.004681) \| 0.004176 / 0.011008 (-0.006832) \| 0.084095 / 0.038508 (0.045587) \| 0.076345 / 0.023109 (0.053236) \| 0.341201 / 0.275898 (0.065303) \| 0.381920 / 0.323480 (0.058440) \| 0.005578 / 0.007986 (-0.002408) \| 0.003535 / 0.004328 (-0.000794) \| 0.065227 / 0.004250 (0.060976) \| 0.054983 / 0.037052 (0.017931) \| 0.345938 / 0.258489 (0.087449) \| 0.398708 / 0.293841 (0.104867) \| 0.031029 / 0.128546 (-0.097518) \| 0.008643 / 0.075646 (-0.067004) \| 0.287286 / 0.419271 (-0.131985) \| 0.052424 / 0.043533 (0.008892) \| 0.342914 / 0.255139 (0.087775) \| 0.366982 / 0.283200 (0.083782) \| 0.024511 / 0.141683 (-0.117172) \| 1.510575 / 1.452155 (0.058421) \| 1.593214 / 1.492716 (0.100497) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.272703 / 0.018006 (0.254697) \| 0.583235 / 0.000490 (0.582746) \| 0.008467 / 0.000200 (0.008267) \| 0.000295 / 0.000054 (0.000240) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029654 / 0.037411 (-0.007757) \| 0.085078 / 0.014526 (0.070552) \| 0.106391 / 0.176557 (-0.070165) \| 0.155790 / 0.737135 (-0.581345) \| 0.104835 / 0.296338 (-0.191503) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.408584 / 0.215209 (0.193375) \| 4.082557 / 2.077655 (2.004902) \| 2.054001 / 1.504120 (0.549881) \| 1.868470 / 1.541195 (0.327275) \| 1.950600 / 1.468490 (0.482110) \| 0.492572 / 4.584777 (-4.092205) \| 3.497105 / 3.745712 (-0.248607) \| 3.464596 / 5.269862 (-1.805265) \| 2.106399 / 4.565676 (-2.459278) \| 0.057413 / 0.424275 (-0.366862) \| 0.007449 / 0.007607 (-0.000158) \| 0.482900 / 0.226044 (0.256856) \| 4.844152 / 2.268929 (2.575223) \| 2.499930 / 55.444624 (-52.944695) \| 2.180396 / 6.876477 (-4.696081) \| 2.282830 / 2.142072 (0.140758) \| 0.581371 / 4.805227 (-4.223857) \| 0.134641 / 6.500664 (-6.366023) \| 0.063137 / 0.075469 (-0.012332) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.274291 / 1.841788 (-0.567496) \| 19.426189 / 8.074308 (11.351881) \| 14.292833 / 10.191392 (4.101441) \| 0.166321 / 0.680424 (-0.514102) \| 0.018419 / 0.534201 (-0.515782) \| 0.392433 / 0.579283 (-0.186850) \| 0.415128 / 0.434364 (-0.019236) \| 0.459274 / 0.540337 (-0.081063) \| 0.714668 / 1.386936 (-0.672268) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006740 / 0.011353 (-0.004613) \| 0.004283 / 0.011008 (-0.006725) \| 0.063845 / 0.038508 (0.025337) \| 0.077037 / 0.023109 (0.053927) \| 0.425103 / 0.275898 (0.149205) \| 0.445525 / 0.323480 (0.122046) \| 0.005755 / 0.007986 (-0.002230) \| 0.003589 / 0.004328 (-0.000739) \| 0.064515 / 0.004250 (0.060265) \| 0.057398 / 0.037052 (0.020346) \| 0.424781 / 0.258489 (0.166292) \| 0.452162 / 0.293841 (0.158321) \| 0.032164 / 0.128546 (-0.096382) \| 0.008660 / 0.075646 (-0.066986) \| 0.069873 / 0.419271 (-0.349399) \| 0.048100 / 0.043533 (0.004567) \| 0.409097 / 0.255139 (0.153958) \| 0.441533 / 0.283200 (0.158333) \| 0.024122 / 0.141683 (-0.117560) \| 1.503431 / 1.452155 (0.051277) \| 1.577518 / 1.492716 (0.084802) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.264433 / 0.018006 (0.246426) \| 0.553631 / 0.000490 (0.553141) \| 0.006354 / 0.000200 (0.006154) \| 0.000106 / 0.000054 (0.000051) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033259 / 0.037411 (-0.004152) \| 0.094908 / 0.014526 (0.080382) \| 0.108238 / 0.176557 (-0.068318) \| 0.161354 / 0.737135 (-0.575781) \| 0.109073 / 0.296338 (-0.187265) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.434450 / 0.215209 (0.219241) \| 4.347501 / 2.077655 (2.269847) \| 2.362225 / 1.504120 (0.858105) \| 2.189285 / 1.541195 (0.648090) \| 2.288797 / 1.468490 (0.820307) \| 0.487782 / 4.584777 (-4.096995) \| 3.598732 / 3.745712 (-0.146980) \| 3.343263 / 5.269862 (-1.926599) \| 2.086256 / 4.565676 (-2.479420) \| 0.057838 / 0.424275 (-0.366437) \| 0.007412 / 0.007607 (-0.000195) \| 0.510098 / 0.226044 (0.284054) \| 5.088743 / 2.268929 (2.819814) \| 2.809105 / 55.444624 (-52.635519) \| 2.476005 / 6.876477 (-4.400471) \| 2.753785 / 2.142072 (0.611712) \| 0.585045 / 4.805227 (-4.220182) \| 0.131162 / 6.500664 (-6.369502) \| 0.060431 / 0.075469 (-0.015038) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.342149 / 1.841788 (-0.499639) \| 20.602369 / 8.074308 (12.528061) \| 14.973301 / 10.191392 (4.781909) \| 0.151655 / 0.680424 (-0.528769) \| 0.020793 / 0.534201 (-0.513408) \| 0.401657 / 0.579283 (-0.177626) \| 0.419845 / 0.434364 (-0.014519) \| 0.467225 / 0.540337 (-0.073113) \| 0.672469 / 1.386936 (-0.714467) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#474beafbc1c2735ff4747f5675855583be2ede06 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007006 / 0.011353 (-0.004346) \| 0.004282 / 0.011008 (-0.006726) \| 0.085413 / 0.038508 (0.046905) \| 0.085148 / 0.023109 (0.062038) \| 0.336543 / 0.275898 (0.060645) \| 0.367959 / 0.323480 (0.044479) \| 0.004337 / 0.007986 (-0.003648) \| 0.004535 / 0.004328 (0.000207) \| 0.065379 / 0.004250 (0.061128) \| 0.059993 / 0.037052 (0.022941) \| 0.343162 / 0.258489 (0.084673) \| 0.383766 / 0.293841 (0.089925) \| 0.031520 / 0.128546 (-0.097026) \| 0.008605 / 0.075646 (-0.067042) \| 0.288620 / 0.419271 (-0.130651) \| 0.053617 / 0.043533 (0.010084) \| 0.339389 / 0.255139 (0.084250) \| 0.350842 / 0.283200 (0.067642) \| 0.027816 / 0.141683 (-0.113867) \| 1.505500 / 1.452155 (0.053346) \| 1.566511 / 1.492716 (0.073795) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.272203 / 0.018006 (0.254197) \| 0.569729 / 0.000490 (0.569240) \| 0.010061 / 0.000200 (0.009861) \| 0.000328 / 0.000054 (0.000273) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030015 / 0.037411 (-0.007396) \| 0.083991 / 0.014526 (0.069465) \| 0.099796 / 0.176557 (-0.076761) \| 0.159131 / 0.737135 (-0.578004) \| 0.099102 / 0.296338 (-0.197237) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.390076 / 0.215209 (0.174867) \| 3.897157 / 2.077655 (1.819502) \| 1.935912 / 1.504120 (0.431793) \| 1.815109 / 1.541195 (0.273915) \| 1.875041 / 1.468490 (0.406551) \| 0.482168 / 4.584777 (-4.102609) \| 3.556140 / 3.745712 (-0.189572) \| 3.528889 / 5.269862 (-1.740972) \| 2.132767 / 4.565676 (-2.432909) \| 0.057761 / 0.424275 (-0.366514) \| 0.007353 / 0.007607 (-0.000254) \| 0.464801 / 0.226044 (0.238757) \| 4.637301 / 2.268929 (2.368372) \| 2.362239 / 55.444624 (-53.082386) \| 2.049811 / 6.876477 (-4.826665) \| 2.143485 / 2.142072 (0.001412) \| 0.580929 / 4.805227 (-4.224299) \| 0.140252 / 6.500664 (-6.360412) \| 0.061352 / 0.075469 (-0.014117) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.257487 / 1.841788 (-0.584301) \| 19.453319 / 8.074308 (11.379011) \| 14.276332 / 10.191392 (4.084940) \| 0.166772 / 0.680424 (-0.513652) \| 0.018339 / 0.534201 (-0.515862) \| 0.393008 / 0.579283 (-0.186275) \| 0.420960 / 0.434364 (-0.013404) \| 0.464331 / 0.540337 (-0.076007) \| 0.717973 / 1.386936 (-0.668963) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007255 / 0.011353 (-0.004098) \| 0.004230 / 0.011008 (-0.006778) \| 0.065191 / 0.038508 (0.026683) \| 0.085765 / 0.023109 (0.062655) \| 0.412464 / 0.275898 (0.136566) \| 0.446067 / 0.323480 (0.122587) \| 0.005875 / 0.007986 (-0.002110) \| 0.003700 / 0.004328 (-0.000628) \| 0.065430 / 0.004250 (0.061179) \| 0.060284 / 0.037052 (0.023231) \| 0.419984 / 0.258489 (0.161495) \| 0.453779 / 0.293841 (0.159938) \| 0.032595 / 0.128546 (-0.095952) \| 0.008873 / 0.075646 (-0.066773) \| 0.072124 / 0.419271 (-0.347148) \| 0.048072 / 0.043533 (0.004539) \| 0.408725 / 0.255139 (0.153586) \| 0.432485 / 0.283200 (0.149285) \| 0.024662 / 0.141683 (-0.117021) \| 1.540434 / 1.452155 (0.088279) \| 1.624768 / 1.492716 (0.132051) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.253220 / 0.018006 (0.235214) \| 0.555469 / 0.000490 (0.554980) \| 0.007765 / 0.000200 (0.007565) \| 0.000101 / 0.000054 (0.000046) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032666 / 0.037411 (-0.004745) \| 0.094786 / 0.014526 (0.080260) \| 0.108219 / 0.176557 (-0.068337) \| 0.161546 / 0.737135 (-0.575589) \| 0.109828 / 0.296338 (-0.186510) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.437024 / 0.215209 (0.221815) \| 4.354065 / 2.077655 (2.276411) \| 2.336832 / 1.504120 (0.832713) \| 2.161959 / 1.541195 (0.620764) \| 2.257214 / 1.468490 (0.788724) \| 0.501576 / 4.584777 (-4.083201) \| 3.654292 / 3.745712 (-0.091420) \| 3.349504 / 5.269862 (-1.920357) \| 2.092998 / 4.565676 (-2.472679) \| 0.058740 / 0.424275 (-0.365535) \| 0.007420 / 0.007607 (-0.000187) \| 0.513443 / 0.226044 (0.287399) \| 5.151247 / 2.268929 (2.882319) \| 2.816036 / 55.444624 (-52.628589) \| 2.451863 / 6.876477 (-4.424613) \| 2.709908 / 2.142072 (0.567836) \| 0.597834 / 4.805227 (-4.207394) \| 0.136547 / 6.500664 (-6.364117) \| 0.062030 / 0.075469 (-0.013439) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.371412 / 1.841788 (-0.470375) \| 20.398981 / 8.074308 (12.324673) \| 14.932307 / 10.191392 (4.740915) \| 0.167796 / 0.680424 (-0.512628) \| 0.020740 / 0.534201 (-0.513461) \| 0.397162 / 0.579283 (-0.182121) \| 0.435493 / 0.434364 (0.001129) \| 0.477074 / 0.540337 (-0.063264) \| 0.697546 / 1.386936 (-0.689390) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#017cefbc832bfe662afd87d9d1241104bf67c53e \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007388 / 0.011353 (-0.003964) \| 0.004408 / 0.011008 (-0.006600) \| 0.098225 / 0.038508 (0.059717) \| 0.079368 / 0.023109 (0.056259) \| 0.381866 / 0.275898 (0.105968) \| 0.425942 / 0.323480 (0.102462) \| 0.005978 / 0.007986 (-0.002007) \| 0.003677 / 0.004328 (-0.000651) \| 0.075488 / 0.004250 (0.071238) \| 0.061725 / 0.037052 (0.024672) \| 0.389126 / 0.258489 (0.130637) \| 0.444099 / 0.293841 (0.150258) \| 0.036222 / 0.128546 (-0.092324) \| 0.009926 / 0.075646 (-0.065720) \| 0.336632 / 0.419271 (-0.082640) \| 0.060867 / 0.043533 (0.017335) \| 0.385437 / 0.255139 (0.130298) \| 0.416599 / 0.283200 (0.133399) \| 0.025118 / 0.141683 (-0.116565) \| 1.728073 / 1.452155 (0.275919) \| 1.847750 / 1.492716 (0.355033) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.263774 / 0.018006 (0.245768) \| 0.491242 / 0.000490 (0.490752) \| 0.013621 / 0.000200 (0.013421) \| 0.000333 / 0.000054 (0.000279) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032911 / 0.037411 (-0.004500) \| 0.095738 / 0.014526 (0.081212) \| 0.110482 / 0.176557 (-0.066075) \| 0.175533 / 0.737135 (-0.561603) \| 0.109240 / 0.296338 (-0.187098) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.453967 / 0.215209 (0.238758) \| 4.489384 / 2.077655 (2.411730) \| 2.185496 / 1.504120 (0.681376) \| 1.979126 / 1.541195 (0.437931) \| 2.016364 / 1.468490 (0.547874) \| 0.565539 / 4.584777 (-4.019238) \| 4.106561 / 3.745712 (0.360849) \| 3.906402 / 5.269862 (-1.363460) \| 2.342186 / 4.565676 (-2.223491) \| 0.067815 / 0.424275 (-0.356460) \| 0.008663 / 0.007607 (0.001056) \| 0.543841 / 0.226044 (0.317796) \| 5.433491 / 2.268929 (3.164563) \| 2.785723 / 55.444624 (-52.658901) \| 2.355716 / 6.876477 (-4.520760) \| 2.397563 / 2.142072 (0.255491) \| 0.682587 / 4.805227 (-4.122641) \| 0.156548 / 6.500664 (-6.344116) \| 0.070654 / 0.075469 (-0.004815) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.475183 / 1.841788 (-0.366605) \| 21.353030 / 8.074308 (13.278722) \| 15.938324 / 10.191392 (5.746932) \| 0.167010 / 0.680424 (-0.513413) \| 0.020931 / 0.534201 (-0.513270) \| 0.464376 / 0.579283 (-0.114907) \| 0.472546 / 0.434364 (0.038182) \| 0.544645 / 0.540337 (0.004308) \| 0.752940 / 1.386936 (-0.633996) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007359 / 0.011353 (-0.003994) \| 0.004276 / 0.011008 (-0.006732) \| 0.075345 / 0.038508 (0.036837) \| 0.080105 / 0.023109 (0.056995) \| 0.480456 / 0.275898 (0.204558) \| 0.514974 / 0.323480 (0.191494) \| 0.006087 / 0.007986 (-0.001899) \| 0.003717 / 0.004328 (-0.000611) \| 0.075067 / 0.004250 (0.070816) \| 0.063739 / 0.037052 (0.026686) \| 0.487569 / 0.258489 (0.229080) \| 0.530198 / 0.293841 (0.236357) \| 0.036056 / 0.128546 (-0.092491) \| 0.009606 / 0.075646 (-0.066041) \| 0.082343 / 0.419271 (-0.336929) \| 0.055488 / 0.043533 (0.011956) \| 0.484789 / 0.255139 (0.229650) \| 0.501918 / 0.283200 (0.218718) \| 0.025340 / 0.141683 (-0.116342) \| 1.784417 / 1.452155 (0.332262) \| 1.854202 / 1.492716 (0.361486) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.252476 / 0.018006 (0.234470) \| 0.484967 / 0.000490 (0.484478) \| 0.005471 / 0.000200 (0.005271) \| 0.000111 / 0.000054 (0.000057) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.037084 / 0.037411 (-0.000327) \| 0.106648 / 0.014526 (0.092122) \| 0.123393 / 0.176557 (-0.053164) \| 0.183088 / 0.737135 (-0.554047) \| 0.122572 / 0.296338 (-0.173767) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.516003 / 0.215209 (0.300793) \| 5.107748 / 2.077655 (3.030093) \| 2.778044 / 1.504120 (1.273924) \| 2.589944 / 1.541195 (1.048749) \| 2.649921 / 1.468490 (1.181431) \| 0.572783 / 4.584777 (-4.011994) \| 4.211331 / 3.745712 (0.465619) \| 3.738859 / 5.269862 (-1.531003) \| 2.331628 / 4.565676 (-2.234048) \| 0.067347 / 0.424275 (-0.356928) \| 0.008513 / 0.007607 (0.000905) \| 0.601056 / 0.226044 (0.375012) \| 5.990921 / 2.268929 (3.721992) \| 3.311544 / 55.444624 (-52.133081) \| 2.929850 / 6.876477 (-3.946627) \| 3.118741 / 2.142072 (0.976669) \| 0.685975 / 4.805227 (-4.119253) \| 0.155105 / 6.500664 (-6.345559) \| 0.069629 / 0.075469 (-0.005840) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.602367 / 1.841788 (-0.239421) \| 22.577072 / 8.074308 (14.502764) \| 17.049655 / 10.191392 (6.858263) \| 0.182412 / 0.680424 (-0.498011) \| 0.023137 / 0.534201 (-0.511064) \| 0.466988 / 0.579283 (-0.112295) \| 0.483887 / 0.434364 (0.049523) \| 0.556099 / 0.540337 (0.015761) \| 0.798332 / 1.386936 (-0.588604) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3e6d8318bd73a91852c22d14f1d788ac6dc8ae90 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009086 / 0.011353 (-0.002267) \| 0.004755 / 0.011008 (-0.006253) \| 0.128866 / 0.038508 (0.090358) \| 0.086099 / 0.023109 (0.062990) \| 0.378079 / 0.275898 (0.102181) \| 0.487431 / 0.323480 (0.163951) \| 0.004712 / 0.007986 (-0.003274) \| 0.003622 / 0.004328 (-0.000706) \| 0.081214 / 0.004250 (0.076963) \| 0.057226 / 0.037052 (0.020174) \| 0.407655 / 0.258489 (0.149166) \| 0.448630 / 0.293841 (0.154789) \| 0.049051 / 0.128546 (-0.079495) \| 0.014537 / 0.075646 (-0.061110) \| 0.467343 / 0.419271 (0.048071) \| 0.070482 / 0.043533 (0.026949) \| 0.379664 / 0.255139 (0.124525) \| 0.464181 / 0.283200 (0.180981) \| 0.039973 / 0.141683 (-0.101710) \| 1.731164 / 1.452155 (0.279010) \| 1.886895 / 1.492716 (0.394178) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.251327 / 0.018006 (0.233321) \| 0.502670 / 0.000490 (0.502180) \| 0.012183 / 0.000200 (0.011984) \| 0.000111 / 0.000054 (0.000057) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028892 / 0.037411 (-0.008519) \| 0.093789 / 0.014526 (0.079263) \| 0.104255 / 0.176557 (-0.072301) \| 0.170257 / 0.737135 (-0.566879) \| 0.115430 / 0.296338 (-0.180909) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.573745 / 0.215209 (0.358536) \| 5.873732 / 2.077655 (3.796077) \| 2.485188 / 1.504120 (0.981068) \| 2.018476 / 1.541195 (0.477282) \| 2.062765 / 1.468490 (0.594275) \| 0.913816 / 4.584777 (-3.670961) \| 5.362338 / 3.745712 (1.616626) \| 4.698758 / 5.269862 (-0.571103) \| 3.132973 / 4.565676 (-1.432703) \| 0.093594 / 0.424275 (-0.330681) \| 0.008359 / 0.007607 (0.000751) \| 0.693997 / 0.226044 (0.467953) \| 7.042645 / 2.268929 (4.773717) \| 3.196180 / 55.444624 (-52.248445) \| 2.384585 / 6.876477 (-4.491892) \| 2.301256 / 2.142072 (0.159183) \| 1.048025 / 4.805227 (-3.757202) \| 0.206931 / 6.500664 (-6.293733) \| 0.069401 / 0.075469 (-0.006068) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.598898 / 1.841788 (-0.242889) \| 22.963667 / 8.074308 (14.889359) \| 20.373688 / 10.191392 (10.182296) \| 0.239716 / 0.680424 (-0.440707) \| 0.040213 / 0.534201 (-0.493988) \| 0.503268 / 0.579283 (-0.076015) \| 0.630750 / 0.434364 (0.196386) \| 0.578007 / 0.540337 (0.037669) \| 0.789564 / 1.386936 (-0.597372) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009129 / 0.011353 (-0.002224) \| 0.005453 / 0.011008 (-0.005555) \| 0.101040 / 0.038508 (0.062532) \| 0.099172 / 0.023109 (0.076062) \| 0.508453 / 0.275898 (0.232555) \| 0.570858 / 0.323480 (0.247378) \| 0.006584 / 0.007986 (-0.001401) \| 0.003800 / 0.004328 (-0.000528) \| 0.094349 / 0.004250 (0.090098) \| 0.064642 / 0.037052 (0.027590) \| 0.563008 / 0.258489 (0.304518) \| 0.625560 / 0.293841 (0.331719) \| 0.050121 / 0.128546 (-0.078426) \| 0.014183 / 0.075646 (-0.061463) \| 0.106564 / 0.419271 (-0.312707) \| 0.061030 / 0.043533 (0.017498) \| 0.522311 / 0.255139 (0.267172) \| 0.598356 / 0.283200 (0.315156) \| 0.042008 / 0.141683 (-0.099675) \| 1.879999 / 1.452155 (0.427844) \| 1.963879 / 1.492716 (0.471162) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.270573 / 0.018006 (0.252567) \| 0.554356 / 0.000490 (0.553866) \| 0.008145 / 0.000200 (0.007945) \| 0.000218 / 0.000054 (0.000163) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031089 / 0.037411 (-0.006322) \| 0.099568 / 0.014526 (0.085043) \| 0.118304 / 0.176557 (-0.058253) \| 0.182991 / 0.737135 (-0.554144) \| 0.115874 / 0.296338 (-0.180465) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.615020 / 0.215209 (0.399811) \| 6.279740 / 2.077655 (4.202085) \| 2.882094 / 1.504120 (1.377974) \| 2.559265 / 1.541195 (1.018070) \| 2.639259 / 1.468490 (1.170769) \| 0.903727 / 4.584777 (-3.681050) \| 5.248555 / 3.745712 (1.502843) \| 4.817340 / 5.269862 (-0.452522) \| 3.056880 / 4.565676 (-1.508797) \| 0.096602 / 0.424275 (-0.327673) \| 0.008660 / 0.007607 (0.001053) \| 0.794347 / 0.226044 (0.568303) \| 7.625127 / 2.268929 (5.356198) \| 3.766826 / 55.444624 (-51.677798) \| 2.968254 / 6.876477 (-3.908223) \| 3.260595 / 2.142072 (1.118523) \| 1.066228 / 4.805227 (-3.739000) \| 0.207158 / 6.500664 (-6.293506) \| 0.076920 / 0.075469 (0.001451) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.741442 / 1.841788 (-0.100345) \| 23.499552 / 8.074308 (15.425244) \| 22.064966 / 10.191392 (11.873574) \| 0.239173 / 0.680424 (-0.441251) \| 0.032105 / 0.534201 (-0.502096) \| 0.484709 / 0.579283 (-0.094574) \| 0.583632 / 0.434364 (0.149268) \| 0.569018 / 0.540337 (0.028681) \| 0.815764 / 1.386936 (-0.571172) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3aeb078ba1afd713e901df43343c160877403d07 \"CML watermark\")\n" ]	2023-10-17T09:00:39Z	2023-10-18T14:01:52Z	2023-10-18T13:50:35Z	MEMBER	null	null	null	Before the fix, `get_data_patterns` inferred wrongly the split name for paths with the word "data" twice: - For the URL path: `hf://datasets/piuba-bigdata/articles_and_comments@f328d536425ae8fcac5d098c8408f437bffdd357/data/train-00001-of-00009.parquet` (note the org name `piuba-bigdata/` ending with `data/`) - The inferred split name was: `articles_and_comments@f328d536425ae8fcac5d098c8408f437bffdd357/data/train` instead of `train` This PR fixes this issue by passing the `base_path` (`hf://datasets/piuba-bigdata/articles_and_comments@f328d536425ae8fcac5d098c8408f437bffdd357`) to `_get_data_files_patterns` and prepending it to the regex split pattern (`data/{split}-[0-9][0-9][0-9][0-9][0-9]-of-[0-9][0-9][0-9][0-9][0-9].\\..`). Fix #6305. Fix https://huggingface.co/datasets/piuba-bigdata/articles_and_comments/discussions/1	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6309/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6309/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6309.diff", "html_url": "https://github.com/huggingface/datasets/pull/6309", "merged_at": "2023-10-18T13:50:35Z", "patch_url": "https://github.com/huggingface/datasets/pull/6309.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6309" }
https://api.github.com/repos/huggingface/datasets/issues/5649	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5649/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5649/comments	https://api.github.com/repos/huggingface/datasets/issues/5649/events	https://github.com/huggingface/datasets/issues/5649	1,630,173,460	I_kwDODunzps5hKnkU	5,649	The index column created with .to_sql() is dependent on the batch_size when writing	{ "avatar_url": "https://avatars.githubusercontent.com/u/45281?v=4", "events_url": "https://api.github.com/users/lsb/events{/privacy}", "followers_url": "https://api.github.com/users/lsb/followers", "following_url": "https://api.github.com/users/lsb/following{/other_user}", "gists_url": "https://api.github.com/users/lsb/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lsb", "id": 45281, "login": "lsb", "node_id": "MDQ6VXNlcjQ1Mjgx", "organizations_url": "https://api.github.com/users/lsb/orgs", "received_events_url": "https://api.github.com/users/lsb/received_events", "repos_url": "https://api.github.com/users/lsb/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lsb/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lsb/subscriptions", "type": "User", "url": "https://api.github.com/users/lsb", "user_view_type": "public" }	[]	closed	false	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	[ { "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" } ]	null	[ "Thanks for reporting, @lsb. \r\n\r\nWe are investigating it.\r\n\r\nOn the other hand, please note that in the next `datasets` release, the index will not be created by default (see #5583). If you would like to have it, you will need to explicitly pass `index=True`. ", "I think this is low enough priority for me to close this as Won't Fix. If I need any primary keys I can generate them beforehand. Feel free to reopen." ]	2023-03-18T05:25:17Z	2023-06-17T07:01:57Z	2023-06-17T07:01:57Z	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug It seems like the "index" column is designed to be unique? The values are only unique per batch. The SQL index is not a unique index. This can be a problem, for instance, when building a faiss index on a dataset and then trying to match up ids with a sql export. ### Steps to reproduce the bug ``` from datasets import Dataset import sqlite3 db = sqlite3.connect(":memory:") nice_numbers = Dataset.from_dict({"nice_number": range(101,106)}) nice_numbers.to_sql("nice1", db, batch_size=1) nice_numbers.to_sql("nice2", db, batch_size=2) print(db.execute("select * from nice1").fetchall()) # [(0, 101), (0, 102), (0, 103), (0, 104), (0, 105)] print(db.execute("select * from nice2").fetchall()) # [(0, 101), (1, 102), (0, 103), (1, 104), (0, 105)] ``` ### Expected behavior I expected the "index" column to be unique ### Environment info ``` % datasets-cli env Copy-and-paste the text below in your GitHub issue. - `datasets` version: 2.10.1 - Platform: macOS-13.2.1-arm64-arm-64bit - Python version: 3.9.6 - PyArrow version: 7.0.0 - Pandas version: 1.5.2 zsh: segmentation fault datasets-cli env ```	{ "avatar_url": "https://avatars.githubusercontent.com/u/45281?v=4", "events_url": "https://api.github.com/users/lsb/events{/privacy}", "followers_url": "https://api.github.com/users/lsb/followers", "following_url": "https://api.github.com/users/lsb/following{/other_user}", "gists_url": "https://api.github.com/users/lsb/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lsb", "id": 45281, "login": "lsb", "node_id": "MDQ6VXNlcjQ1Mjgx", "organizations_url": "https://api.github.com/users/lsb/orgs", "received_events_url": "https://api.github.com/users/lsb/received_events", "repos_url": "https://api.github.com/users/lsb/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lsb/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lsb/subscriptions", "type": "User", "url": "https://api.github.com/users/lsb", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5649/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5649/timeline	null	not_planned	null	null
https://api.github.com/repos/huggingface/datasets/issues/5862	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5862/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5862/comments	https://api.github.com/repos/huggingface/datasets/issues/5862/events	https://github.com/huggingface/datasets/issues/5862	1,710,140,646	I_kwDODunzps5l7qzm	5,862	IndexError: list index out of range with data hosted on Zenodo	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	[ { "color": "d73a4a", "default": true, "description": "Something isn't working", "id": 1935892857, "name": "bug", "node_id": "MDU6TGFiZWwxOTM1ODkyODU3", "url": "https://api.github.com/repos/huggingface/datasets/labels/bug" } ]	open	false	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	[ { "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" } ]	null	[ "This error is also raised when data is hosted on Google Drive:\r\n- https://huggingface.co/datasets/docred/discussions/5\r\n- https://huggingface.co/datasets/linnaeus/discussions/3\r\n- https://huggingface.co/datasets/poleval2019_mt/discussions/3\r\n- https://huggingface.co/datasets/reddit_tifu/discussions/2\r\n- https://huggingface.co/datasets/species_800/discussions/3\r\n- https://huggingface.co/datasets/wiki_lingua/discussions/1\r\n- https://huggingface.co/datasets/yoruba_text_c3/discussions/1" ]	2023-05-15T13:47:19Z	2023-09-25T12:09:51Z	null	MEMBER	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	The dataset viewer sometimes raises an `IndexError`: ``` IndexError: list index out of range ``` See: - huggingface/datasets-server#1151 - https://huggingface.co/datasets/reddit/discussions/5 - huggingface/datasets-server#1118 - https://huggingface.co/datasets/krr-oxford/OntoLAMA/discussions/1 - https://huggingface.co/datasets/hyperpartisan_news_detection/discussions/3 - https://huggingface.co/datasets/um005/discussions/2 - https://huggingface.co/datasets/tapaco/discussions/2 - https://huggingface.co/datasets/common_language/discussions/3 - https://huggingface.co/datasets/pass/discussions/1 After investigation: - This happens with data files hosted on Zenodo - Indeed, there is an underlying 429 HTTP error: Too Many Requests Note that some time ago, it also happened with data files hosted on Google Drive. See: - #4581 - #4580 The reason then was that there was a 403 HTTP error: Forbidden	null	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5862/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5862/timeline	null	null	null	null
https://api.github.com/repos/huggingface/datasets/issues/7167	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/7167/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/7167/comments	https://api.github.com/repos/huggingface/datasets/issues/7167/events	https://github.com/huggingface/datasets/issues/7167	2,546,708,014	I_kwDODunzps6Xy64u	7,167	Error Mapping on sd3, sdxl and upcoming flux controlnet training scripts in diffusers	{ "avatar_url": "https://avatars.githubusercontent.com/u/90132896?v=4", "events_url": "https://api.github.com/users/Night1099/events{/privacy}", "followers_url": "https://api.github.com/users/Night1099/followers", "following_url": "https://api.github.com/users/Night1099/following{/other_user}", "gists_url": "https://api.github.com/users/Night1099/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/Night1099", "id": 90132896, "login": "Night1099", "node_id": "MDQ6VXNlcjkwMTMyODk2", "organizations_url": "https://api.github.com/users/Night1099/orgs", "received_events_url": "https://api.github.com/users/Night1099/received_events", "repos_url": "https://api.github.com/users/Night1099/repos", "site_admin": false, "starred_url": "https://api.github.com/users/Night1099/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/Night1099/subscriptions", "type": "User", "url": "https://api.github.com/users/Night1099", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "this is happening on large datasets, if anyone happens upon this i was able to fix by changing\r\n\r\n```\r\ntrain_dataset = train_dataset.map(compute_embeddings_fn, batched=True, new_fingerprint=new_fingerprint)\r\n```\r\n\r\nto\r\n\r\n```\r\ntrain_dataset = train_dataset.map(compute_embeddings_fn, batched=True, batch_size=16, new_fingerprint=new_fingerprint)\r\n```" ]	2024-09-25T01:39:51Z	2024-09-30T05:28:15Z	2024-09-30T05:28:04Z	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug ``` Map: 6%\|██████ \| 8000/138120 [19:27<5:16:36, 6.85 examples/s] Traceback (most recent call last): File "/workspace/diffusers/examples/controlnet/train_controlnet_sd3.py", line 1416, in <module> main(args) File "/workspace/diffusers/examples/controlnet/train_controlnet_sd3.py", line 1132, in main train_dataset = train_dataset.map(compute_embeddings_fn, batched=True, new_fingerprint=new_fingerprint) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.11/dist-packages/datasets/arrow_dataset.py", line 560, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, args, kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.11/dist-packages/datasets/arrow_dataset.py", line 3035, in map for rank, done, content in Dataset._map_single(*dataset_kwargs): File "/usr/local/lib/python3.11/dist-packages/datasets/arrow_dataset.py", line 3461, in _map_single writer.write_batch(batch) File "/usr/local/lib/python3.11/dist-packages/datasets/arrow_writer.py", line 567, in write_batch self.write_table(pa_table, writer_batch_size) File "/usr/local/lib/python3.11/dist-packages/datasets/arrow_writer.py", line 579, in write_table pa_table = pa_table.combine_chunks() ^^^^^^^^^^^^^^^^^^^^^^^^^ File "pyarrow/table.pxi", line 4387, in pyarrow.lib.Table.combine_chunks File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status pyarrow.lib.ArrowInvalid: offset overflow while concatenating arrays Traceback (most recent call last): File "/usr/local/bin/accelerate", line 8, in <module> sys.exit(main()) ^^^^^^ File "/usr/local/lib/python3.11/dist-packages/accelerate/commands/accelerate_cli.py", line 48, in main args.func(args) File "/usr/local/lib/python3.11/dist-packages/accelerate/commands/launch.py", line 1174, in launch_command simple_launcher(args) File "/usr/local/lib/python3.11/dist-packages/accelerate/commands/launch.py", line 769, in simple_launcher ``` ### Steps to reproduce the bug The dataset has no problem training on sd1.5 controlnet train script ### Expected behavior Script not randomly erroing with error above ### Environment info - `datasets` version: 3.0.0 - Platform: Linux-6.5.0-44-generic-x86_64-with-glibc2.35 - Python version: 3.11.9 - `huggingface_hub` version: 0.25.1 - PyArrow version: 17.0.0 - Pandas version: 2.2.3 - `fsspec` version: 2024.6.1 training on A100	{ "avatar_url": "https://avatars.githubusercontent.com/u/90132896?v=4", "events_url": "https://api.github.com/users/Night1099/events{/privacy}", "followers_url": "https://api.github.com/users/Night1099/followers", "following_url": "https://api.github.com/users/Night1099/following{/other_user}", "gists_url": "https://api.github.com/users/Night1099/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/Night1099", "id": 90132896, "login": "Night1099", "node_id": "MDQ6VXNlcjkwMTMyODk2", "organizations_url": "https://api.github.com/users/Night1099/orgs", "received_events_url": "https://api.github.com/users/Night1099/received_events", "repos_url": "https://api.github.com/users/Night1099/repos", "site_admin": false, "starred_url": "https://api.github.com/users/Night1099/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/Night1099/subscriptions", "type": "User", "url": "https://api.github.com/users/Night1099", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/7167/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/7167/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/6032	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6032/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6032/comments	https://api.github.com/repos/huggingface/datasets/issues/6032/events	https://github.com/huggingface/datasets/issues/6032	1,804,358,679	I_kwDODunzps5rjFQX	6,032	DownloadConfig.proxies not work when load_dataset_builder calling HfApi.dataset_info	{ "avatar_url": "https://avatars.githubusercontent.com/u/138426806?v=4", "events_url": "https://api.github.com/users/codingl2k1/events{/privacy}", "followers_url": "https://api.github.com/users/codingl2k1/followers", "following_url": "https://api.github.com/users/codingl2k1/following{/other_user}", "gists_url": "https://api.github.com/users/codingl2k1/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/codingl2k1", "id": 138426806, "login": "codingl2k1", "node_id": "U_kgDOCEA5tg", "organizations_url": "https://api.github.com/users/codingl2k1/orgs", "received_events_url": "https://api.github.com/users/codingl2k1/received_events", "repos_url": "https://api.github.com/users/codingl2k1/repos", "site_admin": false, "starred_url": "https://api.github.com/users/codingl2k1/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/codingl2k1/subscriptions", "type": "User", "url": "https://api.github.com/users/codingl2k1", "user_view_type": "public" }	[]	open	false	null	[]	null	[ "`HfApi` comes from the `huggingface_hub` package. You can use [this](https://huggingface.co/docs/huggingface_hub/v0.16.3/en/package_reference/utilities#huggingface_hub.configure_http_backend) utility to change the `huggingface_hub`'s `Session` proxies (see the example).\r\n\r\nWe plan to implement https://github.com/huggingface/datasets/issues/5080 and make this behavior more consistent eventually.", "> this\r\n\r\nThanks. I will try `huggingface_hub.configure_http_backend` to change session's config.", "@mariosasko are you saying if I do the following:\r\n\r\n```\r\ndef backend_factory() -> requests.Session:\r\n session = requests.Session()\r\n session.proxies = {\r\n \"https\": \"127.0.0.1:8887\",\r\n \"http\": \"127.0.0.1:8887\",\r\n }\r\n session.verify = \"/etc/ssl/certs/ca-certificates.crt\"\r\n return session\r\n\r\n# Set it as the default session factory\r\nconfigure_http_backend(backend_factory=backend_factory)\r\n```\r\n\r\nwhich works nicely with transformer library:\r\n\r\n```\r\ndef download_gpt_2_model():\r\n tokenizer = GPT2Tokenizer.from_pretrained(\r\n \"gpt2\", force_download=True, resume_download=False\r\n )\r\n text = \"Replace me by any text you'd like.\"\r\n encoded_input = tokenizer(text, return_tensors=\"pt\")\r\n print(encoded_input)\r\n\r\n model = GPT2Model.from_pretrained(\r\n \"gpt2\", force_download=True, resume_download=False\r\n )\r\n output = model(**encoded_input)\r\n```\r\n\r\nshould work for datasets library as well ?\r\n\r\nIn my case if I just do:\r\n\r\n```\r\ndef download_sts12_sts_dataset():\r\n dataset = load_dataset(\r\n \"mteb/sts12-sts\",\r\n download_mode=\"force_redownload\",\r\n verification_mode=\"basic_checks\",\r\n revision=\"main\",\r\n )\r\n\r\n```\r\nI am getting:\r\n`ConnectionError: Couldn't reach https://huggingface.co/datasets/mteb/sts12-sts/resolve/main/dataset_infos.json (ConnectTimeout(MaxRetryError(\"HTTPSConnectionPool(host='huggingface.co', port=443): Max retries exceeded with url: /datasets/mteb/sts12-sts/resolve/main/dataset_infos.json (Caused by ConnectTimeoutError(<urllib3.connection.HTTPSConnection object at 0x7f429e87a3a0>, 'Connection to huggingface.co timed out. (connect timeout=100)'))\")))`\r\n\r\nwhich is typical when the proxy server is not defined. Looks like what is set in configure_http_backend(backend_factory=backend_factory) is ignore.\r\n\r\nIf I use env variable instead, it is working \r\n```\r\ndef download_sts12_sts_dataset():\r\n\r\n os.environ[\"https_proxy\"] = \"127.0.0.1:8887\"\r\n os.environ[\"http_proxy\"] = \"127.0.0.1:8887\"\r\n os.environ[\"REQUESTS_CA_BUNDLE\"] = \"/etc/ssl/certs/ca-certificates.crt\"\r\n\r\n dataset = load_dataset(\r\n \"mteb/sts12-sts\",\r\n download_mode=\"force_redownload\",\r\n verification_mode=\"basic_checks\",\r\n revision=\"main\",\r\n )\r\n```\r\n\r\nShould I add something ?\r\n\r\nI am using `huggingface_hub 0.15.1`, `datasets 2.13.0`, `transformers 4.30.2`", "`huggingface_hub.configure_http_backend` works for `transformers` because they only use the `huggingface_hub` lib for downloads. Our download logic is a bit more complex (e.g., we also support downloading non-Hub files), so we are not aligned with them yet. In the meantime, it's best to use the env vars.", "@mariosasko I fully understand that the logic for dataset is different. I see 2 issues with the current implementation of the env variables:\r\n\r\n- having the same https_proxy/http_prox/no_proxy env variables for all tools is not good in some case. For example I have 2 differents proxy server. In 2019 we had discussion with the Tensorflow teams and they recommended to do the following: TFDS_HTTP_PROXY, TFDS_HTTPS_PROXY ...\r\n- with recent version of requests, it is not possible to deactivate TLS interception (verify=false) by using env variable. This is useful to debug things and in some case TLS is not working and you need to ignore verifying the SSL certificate (probably not recommended) \r\n\r\nOne of the best way is to able to pass our requests.Session() directly\r\n```\r\nimport openai\r\nsession = requests.Session()\r\nsession.cert = CERT\r\nsession.verify = False\r\nopenai.requestssession = session\r\n```\r\n\r\nMy 2 cents in this discussion" ]	2023-07-14T07:22:55Z	2023-09-11T13:50:41Z	null	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug ```python download_config = DownloadConfig(proxies={'https': '<my proxy>'}) builder = load_dataset_builder(..., download_config=download_config) ``` But, when getting the dataset_info from HfApi, the http requests not using the proxies. ### Steps to reproduce the bug 1. Setup proxies in DownloadConfig. 2. Call `load_dataset_build` with download_config. 3. Inspect the call stack in HfApi.dataset_info. ![image](https://github.com/huggingface/datasets/assets/138426806/33e538a8-2e22-4e63-b634-343febe5324b) ### Expected behavior DownloadConfig.proxies works for getting dataset_info. ### Environment info https://github.com/huggingface/datasets/commit/406b2212263c0d33f267e35b917f410ff6b3bc00 Python 3.11.4	null	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6032/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6032/timeline	null	null	null	null
https://api.github.com/repos/huggingface/datasets/issues/6443	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6443/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6443/comments	https://api.github.com/repos/huggingface/datasets/issues/6443/events	https://github.com/huggingface/datasets/issues/6443	2,006,568,368	I_kwDODunzps53mc2w	6,443	Trouble loading files defined in YAML explicitly	{ "avatar_url": "https://avatars.githubusercontent.com/u/1676121?v=4", "events_url": "https://api.github.com/users/severo/events{/privacy}", "followers_url": "https://api.github.com/users/severo/followers", "following_url": "https://api.github.com/users/severo/following{/other_user}", "gists_url": "https://api.github.com/users/severo/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/severo", "id": 1676121, "login": "severo", "node_id": "MDQ6VXNlcjE2NzYxMjE=", "organizations_url": "https://api.github.com/users/severo/orgs", "received_events_url": "https://api.github.com/users/severo/received_events", "repos_url": "https://api.github.com/users/severo/repos", "site_admin": false, "starred_url": "https://api.github.com/users/severo/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/severo/subscriptions", "type": "User", "url": "https://api.github.com/users/severo", "user_view_type": "public" }	[ { "color": "d73a4a", "default": true, "description": "Something isn't working", "id": 1935892857, "name": "bug", "node_id": "MDU6TGFiZWwxOTM1ODkyODU3", "url": "https://api.github.com/repos/huggingface/datasets/labels/bug" } ]	open	false	null	[]	null	[ "There is a typo in one of the file names - `data/edf.csv` should be renamed to `data/def.csv` 🙂. ", "wow, I reviewed it twice to avoid being ashamed like that, but... I didn't notice the typo.\r\n\r\n---\r\n\r\nBesides this: do you think we would be able to improve the error message to make this clearer?" ]	2023-11-22T15:18:10Z	2023-11-23T09:06:20Z	null	COLLABORATOR	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	Look at https://huggingface.co/datasets/severo/doc-yaml-2 It's a reproduction of the example given in the docs at https://huggingface.co/docs/hub/datasets-manual-configuration ``` You can select multiple files per split using a list of paths: my_dataset_repository/ ├── README.md ├── data/ │ ├── abc.csv │ └── def.csv └── holdout/ └── ghi.csv --- configs: - config_name: default data_files: - split: train path: - "data/abc.csv" - "data/def.csv" - split: test path: "holdout/ghi.csv" --- ``` It raises the following error: ``` Error code: ConfigNamesError Exception: FileNotFoundError Message: Couldn't find a dataset script at /src/services/worker/severo/doc-yaml-2/doc-yaml-2.py or any data file in the same directory. Couldn't find 'severo/doc-yaml-2' on the Hugging Face Hub either: FileNotFoundError: Unable to find 'hf://datasets/severo/doc-yaml-2@938a0578fb4c6bc9da7d80b06a3ba39c2834b0c2/data/def.csv' with any supported extension ['.csv', '.tsv', '.json', '.jsonl', '.parquet', '.arrow', '.txt', '.blp', '.bmp', '.dib', '.bufr', '.cur', '.pcx', '.dcx', '.dds', '.ps', '.eps', '.fit', '.fits', '.fli', '.flc', '.ftc', '.ftu', '.gbr', '.gif', '.grib', '.h5', '.hdf', '.png', '.apng', '.jp2', '.j2k', '.jpc', '.jpf', '.jpx', '.j2c', '.icns', '.ico', '.im', '.iim', '.tif', '.tiff', '.jfif', '.jpe', '.jpg', '.jpeg', '.mpg', '.mpeg', '.msp', '.pcd', '.pxr', '.pbm', '.pgm', '.ppm', '.pnm', '.psd', '.bw', '.rgb', '.rgba', '.sgi', '.ras', '.tga', '.icb', '.vda', '.vst', '.webp', '.wmf', '.emf', '.xbm', '.xpm', '.BLP', '.BMP', '.DIB', '.BUFR', '.CUR', '.PCX', '.DCX', '.DDS', '.PS', '.EPS', '.FIT', '.FITS', '.FLI', '.FLC', '.FTC', '.FTU', '.GBR', '.GIF', '.GRIB', '.H5', '.HDF', '.PNG', '.APNG', '.JP2', '.J2K', '.JPC', '.JPF', '.JPX', '.J2C', '.ICNS', '.ICO', '.IM', '.IIM', '.TIF', '.TIFF', '.JFIF', '.JPE', '.JPG', '.JPEG', '.MPG', '.MPEG', '.MSP', '.PCD', '.PXR', '.PBM', '.PGM', '.PPM', '.PNM', '.PSD', '.BW', '.RGB', '.RGBA', '.SGI', '.RAS', '.TGA', '.ICB', '.VDA', '.VST', '.WEBP', '.WMF', '.EMF', '.XBM', '.XPM', '.aiff', '.au', '.avr', '.caf', '.flac', '.htk', '.svx', '.mat4', '.mat5', '.mpc2k', '.ogg', '.paf', '.pvf', '.raw', '.rf64', '.sd2', '.sds', '.ircam', '.voc', '.w64', '.wav', '.nist', '.wavex', '.wve', '.xi', '.mp3', '.opus', '.AIFF', '.AU', '.AVR', '.CAF', '.FLAC', '.HTK', '.SVX', '.MAT4', '.MAT5', '.MPC2K', '.OGG', '.PAF', '.PVF', '.RAW', '.RF64', '.SD2', '.SDS', '.IRCAM', '.VOC', '.W64', '.WAV', '.NIST', '.WAVEX', '.WVE', '.XI', '.MP3', '.OPUS', '.zip'] Traceback: Traceback (most recent call last): File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 65, in compute_config_names_response for config in sorted(get_dataset_config_names(path=dataset, token=hf_token)) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 351, in get_dataset_config_names dataset_module = dataset_module_factory( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1507, in dataset_module_factory raise FileNotFoundError( FileNotFoundError: Couldn't find a dataset script at /src/services/worker/severo/doc-yaml-2/doc-yaml-2.py or any data file in the same directory. Couldn't find 'severo/doc-yaml-2' on the Hugging Face Hub either: FileNotFoundError: Unable to find 'hf://datasets/severo/doc-yaml-2@938a0578fb4c6bc9da7d80b06a3ba39c2834b0c2/data/def.csv' with any supported extension ['.csv', '.tsv', '.json', '.jsonl', '.parquet', '.arrow', '.txt', '.blp', '.bmp', '.dib', '.bufr', '.cur', '.pcx', '.dcx', '.dds', '.ps', '.eps', '.fit', '.fits', '.fli', '.flc', '.ftc', '.ftu', '.gbr', '.gif', '.grib', '.h5', '.hdf', '.png', '.apng', '.jp2', '.j2k', '.jpc', '.jpf', '.jpx', '.j2c', '.icns', '.ico', '.im', '.iim', '.tif', '.tiff', '.jfif', '.jpe', '.jpg', '.jpeg', '.mpg', '.mpeg', '.msp', '.pcd', '.pxr', '.pbm', '.pgm', '.ppm', '.pnm', '.psd', '.bw', '.rgb', '.rgba', '.sgi', '.ras', '.tga', '.icb', '.vda', '.vst', '.webp', '.wmf', '.emf', '.xbm', '.xpm', '.BLP', '.BMP', '.DIB', '.BUFR', '.CUR', '.PCX', '.DCX', '.DDS', '.PS', '.EPS', '.FIT', '.FITS', '.FLI', '.FLC', '.FTC', '.FTU', '.GBR', '.GIF', '.GRIB', '.H5', '.HDF', '.PNG', '.APNG', '.JP2', '.J2K', '.JPC', '.JPF', '.JPX', '.J2C', '.ICNS', '.ICO', '.IM', '.IIM', '.TIF', '.TIFF', '.JFIF', '.JPE', '.JPG', '.JPEG', '.MPG', '.MPEG', '.MSP', '.PCD', '.PXR', '.PBM', '.PGM', '.PPM', '.PNM', '.PSD', '.BW', '.RGB', '.RGBA', '.SGI', '.RAS', '.TGA', '.ICB', '.VDA', '.VST', '.WEBP', '.WMF', '.EMF', '.XBM', '.XPM', '.aiff', '.au', '.avr', '.caf', '.flac', '.htk', '.svx', '.mat4', '.mat5', '.mpc2k', '.ogg', '.paf', '.pvf', '.raw', '.rf64', '.sd2', '.sds', '.ircam', '.voc', '.w64', '.wav', '.nist', '.wavex', '.wve', '.xi', '.mp3', '.opus', '.AIFF', '.AU', '.AVR', '.CAF', '.FLAC', '.HTK', '.SVX', '.MAT4', '.MAT5', '.MPC2K', '.OGG', '.PAF', '.PVF', '.RAW', '.RF64', '.SD2', '.SDS', '.IRCAM', '.VOC', '.W64', '.WAV', '.NIST', '.WAVEX', '.WVE', '.XI', '.MP3', '.OPUS', '.zip'] ```	null	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6443/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6443/timeline	null	null	null	null
https://api.github.com/repos/huggingface/datasets/issues/6977	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6977/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6977/comments	https://api.github.com/repos/huggingface/datasets/issues/6977/events	https://github.com/huggingface/datasets/issues/6977	2,359,295,045	I_kwDODunzps6Mn_xF	6,977	load json file error with v2.20.0	{ "avatar_url": "https://avatars.githubusercontent.com/u/15037766?v=4", "events_url": "https://api.github.com/users/xiaoyaolangzhi/events{/privacy}", "followers_url": "https://api.github.com/users/xiaoyaolangzhi/followers", "following_url": "https://api.github.com/users/xiaoyaolangzhi/following{/other_user}", "gists_url": "https://api.github.com/users/xiaoyaolangzhi/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/xiaoyaolangzhi", "id": 15037766, "login": "xiaoyaolangzhi", "node_id": "MDQ6VXNlcjE1MDM3NzY2", "organizations_url": "https://api.github.com/users/xiaoyaolangzhi/orgs", "received_events_url": "https://api.github.com/users/xiaoyaolangzhi/received_events", "repos_url": "https://api.github.com/users/xiaoyaolangzhi/repos", "site_admin": false, "starred_url": "https://api.github.com/users/xiaoyaolangzhi/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/xiaoyaolangzhi/subscriptions", "type": "User", "url": "https://api.github.com/users/xiaoyaolangzhi", "user_view_type": "public" }	[]	closed	false	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	[ { "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" } ]	null	[ "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	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### 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 ```	{ "avatar_url": "https://avatars.githubusercontent.com/u/15037766?v=4", "events_url": "https://api.github.com/users/xiaoyaolangzhi/events{/privacy}", "followers_url": "https://api.github.com/users/xiaoyaolangzhi/followers", "following_url": "https://api.github.com/users/xiaoyaolangzhi/following{/other_user}", "gists_url": "https://api.github.com/users/xiaoyaolangzhi/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/xiaoyaolangzhi", "id": 15037766, "login": "xiaoyaolangzhi", "node_id": "MDQ6VXNlcjE1MDM3NzY2", "organizations_url": "https://api.github.com/users/xiaoyaolangzhi/orgs", "received_events_url": "https://api.github.com/users/xiaoyaolangzhi/received_events", "repos_url": "https://api.github.com/users/xiaoyaolangzhi/repos", "site_admin": false, "starred_url": "https://api.github.com/users/xiaoyaolangzhi/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/xiaoyaolangzhi/subscriptions", "type": "User", "url": "https://api.github.com/users/xiaoyaolangzhi", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6977/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6977/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/5174	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5174/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5174/comments	https://api.github.com/repos/huggingface/datasets/issues/5174/events	https://github.com/huggingface/datasets/pull/5174	1,427,216,416	PR_kwDODunzps5Bv3rh	5,174	Preserve None in list type cast in PyArrow 10	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._" ]	2022-10-28T12:48:30Z	2022-10-28T13:15:33Z	2022-10-28T13:13:18Z	COLLABORATOR	null	null	null	The `ListArray` type in PyArrow 10.0.0 supports the `mask` parameter, which allows us to preserve Nones in nested lists in `cast` instead of replacing them with empty lists. Fix https://github.com/huggingface/datasets/issues/3676	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5174/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5174/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/5174.diff", "html_url": "https://github.com/huggingface/datasets/pull/5174", "merged_at": "2022-10-28T13:13:18Z", "patch_url": "https://github.com/huggingface/datasets/pull/5174.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5174" }
https://api.github.com/repos/huggingface/datasets/issues/6000	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6000/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6000/comments	https://api.github.com/repos/huggingface/datasets/issues/6000/events	https://github.com/huggingface/datasets/pull/6000	1,782,456,878	PR_kwDODunzps5UU_FB	6,000	Pin `joblib` to avoid `joblibspark` test failures	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006722 / 0.011353 (-0.004631) \| 0.004425 / 0.011008 (-0.006583) \| 0.100850 / 0.038508 (0.062341) \| 0.040816 / 0.023109 (0.017707) \| 0.348823 / 0.275898 (0.072925) \| 0.446285 / 0.323480 (0.122805) \| 0.005738 / 0.007986 (-0.002247) \| 0.003517 / 0.004328 (-0.000811) \| 0.078824 / 0.004250 (0.074574) \| 0.064695 / 0.037052 (0.027643) \| 0.389894 / 0.258489 (0.131405) \| 0.416107 / 0.293841 (0.122266) \| 0.028850 / 0.128546 (-0.099696) \| 0.009011 / 0.075646 (-0.066635) \| 0.323117 / 0.419271 (-0.096154) \| 0.049162 / 0.043533 (0.005629) \| 0.340144 / 0.255139 (0.085005) \| 0.382072 / 0.283200 (0.098872) \| 0.023160 / 0.141683 (-0.118523) \| 1.549218 / 1.452155 (0.097063) \| 1.581266 / 1.492716 (0.088550) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.293360 / 0.018006 (0.275353) \| 0.602189 / 0.000490 (0.601700) \| 0.004608 / 0.000200 (0.004408) \| 0.000082 / 0.000054 (0.000028) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028144 / 0.037411 (-0.009267) \| 0.107088 / 0.014526 (0.092562) \| 0.112188 / 0.176557 (-0.064369) \| 0.174669 / 0.737135 (-0.562466) \| 0.116359 / 0.296338 (-0.179980) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.422911 / 0.215209 (0.207702) \| 4.231524 / 2.077655 (2.153869) \| 1.906711 / 1.504120 (0.402591) \| 1.706841 / 1.541195 (0.165646) \| 1.792066 / 1.468490 (0.323576) \| 0.559221 / 4.584777 (-4.025556) \| 3.434280 / 3.745712 (-0.311433) \| 1.918714 / 5.269862 (-3.351148) \| 1.073070 / 4.565676 (-3.492606) \| 0.067891 / 0.424275 (-0.356384) \| 0.011927 / 0.007607 (0.004320) \| 0.530843 / 0.226044 (0.304799) \| 5.309213 / 2.268929 (3.040285) \| 2.439246 / 55.444624 (-53.005378) \| 2.101245 / 6.876477 (-4.775231) \| 2.177436 / 2.142072 (0.035363) \| 0.672150 / 4.805227 (-4.133077) \| 0.137571 / 6.500664 (-6.363093) \| 0.068343 / 0.075469 (-0.007126) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.265262 / 1.841788 (-0.576525) \| 14.988021 / 8.074308 (6.913713) \| 13.611677 / 10.191392 (3.420285) \| 0.171389 / 0.680424 (-0.509035) \| 0.017681 / 0.534201 (-0.516520) \| 0.377542 / 0.579283 (-0.201741) \| 0.399475 / 0.434364 (-0.034889) \| 0.469553 / 0.540337 (-0.070785) \| 0.561888 / 1.386936 (-0.825048) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006782 / 0.011353 (-0.004571) \| 0.004412 / 0.011008 (-0.006597) \| 0.078594 / 0.038508 (0.040086) \| 0.039930 / 0.023109 (0.016820) \| 0.371879 / 0.275898 (0.095981) \| 0.444910 / 0.323480 (0.121430) \| 0.005707 / 0.007986 (-0.002279) \| 0.003901 / 0.004328 (-0.000427) \| 0.080125 / 0.004250 (0.075875) \| 0.063977 / 0.037052 (0.026925) \| 0.382781 / 0.258489 (0.124292) \| 0.441791 / 0.293841 (0.147950) \| 0.030428 / 0.128546 (-0.098118) \| 0.009008 / 0.075646 (-0.066638) \| 0.084447 / 0.419271 (-0.334824) \| 0.044432 / 0.043533 (0.000899) \| 0.365686 / 0.255139 (0.110547) \| 0.394312 / 0.283200 (0.111113) \| 0.024508 / 0.141683 (-0.117175) \| 1.577020 / 1.452155 (0.124865) \| 1.630259 / 1.492716 (0.137543) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.307960 / 0.018006 (0.289953) \| 0.591473 / 0.000490 (0.590983) \| 0.008098 / 0.000200 (0.007898) \| 0.000110 / 0.000054 (0.000056) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029567 / 0.037411 (-0.007845) \| 0.112773 / 0.014526 (0.098247) \| 0.117362 / 0.176557 (-0.059194) \| 0.174293 / 0.737135 (-0.562843) \| 0.123156 / 0.296338 (-0.173182) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.457475 / 0.215209 (0.242266) \| 4.599067 / 2.077655 (2.521412) \| 2.262638 / 1.504120 (0.758518) \| 2.124943 / 1.541195 (0.583748) \| 2.339912 / 1.468490 (0.871422) \| 0.566264 / 4.584777 (-4.018513) \| 3.489261 / 3.745712 (-0.256451) \| 1.925151 / 5.269862 (-3.344711) \| 1.099389 / 4.565676 (-3.466287) \| 0.068232 / 0.424275 (-0.356043) \| 0.011660 / 0.007607 (0.004052) \| 0.571227 / 0.226044 (0.345183) \| 5.702059 / 2.268929 (3.433130) \| 2.837701 / 55.444624 (-52.606924) \| 2.605468 / 6.876477 (-4.271008) \| 2.818396 / 2.142072 (0.676323) \| 0.681856 / 4.805227 (-4.123371) \| 0.141401 / 6.500664 (-6.359263) \| 0.069728 / 0.075469 (-0.005741) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.354935 / 1.841788 (-0.486853) \| 15.437404 / 8.074308 (7.363095) \| 15.415193 / 10.191392 (5.223801) \| 0.153459 / 0.680424 (-0.526964) \| 0.017190 / 0.534201 (-0.517011) \| 0.367256 / 0.579283 (-0.212027) \| 0.392709 / 0.434364 (-0.041655) \| 0.426125 / 0.540337 (-0.114213) \| 0.522612 / 1.386936 (-0.864324) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#25ac13d8ab23e7d99252ce083a45e8333b6bbcdc \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009183 / 0.011353 (-0.002170) \| 0.005232 / 0.011008 (-0.005776) \| 0.120349 / 0.038508 (0.081841) \| 0.044715 / 0.023109 (0.021606) \| 0.361519 / 0.275898 (0.085621) \| 0.463702 / 0.323480 (0.140223) \| 0.005842 / 0.007986 (-0.002144) \| 0.004041 / 0.004328 (-0.000288) \| 0.096953 / 0.004250 (0.092703) \| 0.070593 / 0.037052 (0.033540) \| 0.409790 / 0.258489 (0.151301) \| 0.477452 / 0.293841 (0.183611) \| 0.045827 / 0.128546 (-0.082719) \| 0.014038 / 0.075646 (-0.061608) \| 0.421317 / 0.419271 (0.002045) \| 0.065276 / 0.043533 (0.021743) \| 0.360074 / 0.255139 (0.104935) \| 0.409147 / 0.283200 (0.125947) \| 0.032444 / 0.141683 (-0.109238) \| 1.739257 / 1.452155 (0.287102) \| 1.831408 / 1.492716 (0.338692) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.274852 / 0.018006 (0.256846) \| 0.596320 / 0.000490 (0.595830) \| 0.006399 / 0.000200 (0.006199) \| 0.000133 / 0.000054 (0.000079) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031400 / 0.037411 (-0.006012) \| 0.127052 / 0.014526 (0.112526) \| 0.134269 / 0.176557 (-0.042288) \| 0.225998 / 0.737135 (-0.511137) \| 0.150019 / 0.296338 (-0.146319) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.654202 / 0.215209 (0.438993) \| 6.216735 / 2.077655 (4.139081) \| 2.440214 / 1.504120 (0.936094) \| 2.150575 / 1.541195 (0.609380) \| 2.124790 / 1.468490 (0.656300) \| 0.923514 / 4.584777 (-3.661263) \| 5.556924 / 3.745712 (1.811212) \| 2.843886 / 5.269862 (-2.425975) \| 1.834232 / 4.565676 (-2.731444) \| 0.111735 / 0.424275 (-0.312540) \| 0.014823 / 0.007607 (0.007216) \| 0.820503 / 0.226044 (0.594459) \| 7.887737 / 2.268929 (5.618809) \| 3.120307 / 55.444624 (-52.324317) \| 2.405856 / 6.876477 (-4.470621) \| 2.411239 / 2.142072 (0.269167) \| 1.071283 / 4.805227 (-3.733944) \| 0.227738 / 6.500664 (-6.272926) \| 0.073516 / 0.075469 (-0.001953) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.531806 / 1.841788 (-0.309982) \| 18.547661 / 8.074308 (10.473353) \| 21.083922 / 10.191392 (10.892530) \| 0.241706 / 0.680424 (-0.438718) \| 0.034169 / 0.534201 (-0.500032) \| 0.497514 / 0.579283 (-0.081769) \| 0.599801 / 0.434364 (0.165437) \| 0.576465 / 0.540337 (0.036127) \| 0.673509 / 1.386936 (-0.713427) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007558 / 0.011353 (-0.003795) \| 0.005001 / 0.011008 (-0.006008) \| 0.093809 / 0.038508 (0.055301) \| 0.039792 / 0.023109 (0.016683) \| 0.456869 / 0.275898 (0.180971) \| 0.493370 / 0.323480 (0.169891) \| 0.005561 / 0.007986 (-0.002424) \| 0.003982 / 0.004328 (-0.000346) \| 0.085421 / 0.004250 (0.081170) \| 0.059817 / 0.037052 (0.022765) \| 0.468040 / 0.258489 (0.209550) \| 0.514853 / 0.293841 (0.221012) \| 0.044267 / 0.128546 (-0.084279) \| 0.012674 / 0.075646 (-0.062972) \| 0.098324 / 0.419271 (-0.320948) \| 0.056604 / 0.043533 (0.013071) \| 0.432200 / 0.255139 (0.177061) \| 0.459812 / 0.283200 (0.176612) \| 0.033872 / 0.141683 (-0.107811) \| 1.618576 / 1.452155 (0.166421) \| 1.676562 / 1.492716 (0.183846) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.230625 / 0.018006 (0.212619) \| 0.600558 / 0.000490 (0.600068) \| 0.003419 / 0.000200 (0.003219) \| 0.000113 / 0.000054 (0.000059) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026916 / 0.037411 (-0.010496) \| 0.103003 / 0.014526 (0.088478) \| 0.117078 / 0.176557 (-0.059478) \| 0.169359 / 0.737135 (-0.567776) \| 0.120305 / 0.296338 (-0.176034) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.616877 / 0.215209 (0.401668) \| 6.157232 / 2.077655 (4.079577) \| 2.869219 / 1.504120 (1.365099) \| 2.381410 / 1.541195 (0.840216) \| 2.417357 / 1.468490 (0.948867) \| 0.914947 / 4.584777 (-3.669830) \| 5.718526 / 3.745712 (1.972814) \| 2.757253 / 5.269862 (-2.512609) \| 1.794122 / 4.565676 (-2.771554) \| 0.108423 / 0.424275 (-0.315852) \| 0.013378 / 0.007607 (0.005771) \| 0.831067 / 0.226044 (0.605023) \| 8.478946 / 2.268929 (6.210018) \| 3.685937 / 55.444624 (-51.758687) \| 2.867472 / 6.876477 (-4.009005) \| 2.895975 / 2.142072 (0.753903) \| 1.137547 / 4.805227 (-3.667681) \| 0.213891 / 6.500664 (-6.286773) \| 0.075825 / 0.075469 (0.000356) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.621193 / 1.841788 (-0.220594) \| 17.322110 / 8.074308 (9.247802) \| 21.804016 / 10.191392 (11.612624) \| 0.243692 / 0.680424 (-0.436732) \| 0.030331 / 0.534201 (-0.503870) \| 0.492186 / 0.579283 (-0.087097) \| 0.632583 / 0.434364 (0.198219) \| 0.576265 / 0.540337 (0.035927) \| 0.713165 / 1.386936 (-0.673771) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a293ceb5aa41c4ae265c0e2aa9ada2d544466121 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008916 / 0.011353 (-0.002437) \| 0.004737 / 0.011008 (-0.006271) \| 0.134271 / 0.038508 (0.095763) \| 0.054472 / 0.023109 (0.031363) \| 0.380942 / 0.275898 (0.105044) \| 0.474138 / 0.323480 (0.150658) \| 0.007917 / 0.007986 (-0.000068) \| 0.003748 / 0.004328 (-0.000580) \| 0.092765 / 0.004250 (0.088515) \| 0.077873 / 0.037052 (0.040821) \| 0.397533 / 0.258489 (0.139043) \| 0.454737 / 0.293841 (0.160896) \| 0.039901 / 0.128546 (-0.088645) \| 0.010188 / 0.075646 (-0.065458) \| 0.447312 / 0.419271 (0.028040) \| 0.068684 / 0.043533 (0.025151) \| 0.371554 / 0.255139 (0.116415) \| 0.459655 / 0.283200 (0.176455) \| 0.027157 / 0.141683 (-0.114526) \| 1.874643 / 1.452155 (0.422488) \| 2.014800 / 1.492716 (0.522083) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.227079 / 0.018006 (0.209073) \| 0.483241 / 0.000490 (0.482751) \| 0.012404 / 0.000200 (0.012204) \| 0.000409 / 0.000054 (0.000354) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033135 / 0.037411 (-0.004277) \| 0.137782 / 0.014526 (0.123257) \| 0.142951 / 0.176557 (-0.033605) \| 0.209825 / 0.737135 (-0.527311) \| 0.152438 / 0.296338 (-0.143900) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.513066 / 0.215209 (0.297857) \| 5.122776 / 2.077655 (3.045121) \| 2.399270 / 1.504120 (0.895150) \| 2.180143 / 1.541195 (0.638949) \| 2.286395 / 1.468490 (0.817905) \| 0.641866 / 4.584777 (-3.942911) \| 4.694922 / 3.745712 (0.949210) \| 2.543390 / 5.269862 (-2.726472) \| 1.398592 / 4.565676 (-3.167084) \| 0.088662 / 0.424275 (-0.335613) \| 0.015854 / 0.007607 (0.008247) \| 0.688891 / 0.226044 (0.462847) \| 6.370148 / 2.268929 (4.101220) \| 2.949974 / 55.444624 (-52.494650) \| 2.538049 / 6.876477 (-4.338428) \| 2.699380 / 2.142072 (0.557308) \| 0.792670 / 4.805227 (-4.012557) \| 0.169126 / 6.500664 (-6.331538) \| 0.078511 / 0.075469 (0.003042) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.609119 / 1.841788 (-0.232669) \| 18.785069 / 8.074308 (10.710761) \| 16.670783 / 10.191392 (6.479391) \| 0.213081 / 0.680424 (-0.467343) \| 0.023904 / 0.534201 (-0.510296) \| 0.567720 / 0.579283 (-0.011564) \| 0.505806 / 0.434364 (0.071442) \| 0.649466 / 0.540337 (0.109129) \| 0.773174 / 1.386936 (-0.613762) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008036 / 0.011353 (-0.003317) \| 0.004808 / 0.011008 (-0.006201) \| 0.094316 / 0.038508 (0.055808) \| 0.056174 / 0.023109 (0.033065) \| 0.481618 / 0.275898 (0.205720) \| 0.565300 / 0.323480 (0.241820) \| 0.006339 / 0.007986 (-0.001646) \| 0.003950 / 0.004328 (-0.000379) \| 0.093389 / 0.004250 (0.089139) \| 0.076163 / 0.037052 (0.039111) \| 0.489013 / 0.258489 (0.230524) \| 0.565451 / 0.293841 (0.271611) \| 0.039392 / 0.128546 (-0.089155) \| 0.010553 / 0.075646 (-0.065093) \| 0.101406 / 0.419271 (-0.317865) \| 0.062355 / 0.043533 (0.018822) \| 0.470461 / 0.255139 (0.215322) \| 0.502574 / 0.283200 (0.219375) \| 0.030196 / 0.141683 (-0.111486) \| 1.893926 / 1.452155 (0.441771) \| 1.958902 / 1.492716 (0.466185) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.198074 / 0.018006 (0.180068) \| 0.476828 / 0.000490 (0.476338) \| 0.003457 / 0.000200 (0.003257) \| 0.000105 / 0.000054 (0.000051) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.037576 / 0.037411 (0.000165) \| 0.146663 / 0.014526 (0.132138) \| 0.152969 / 0.176557 (-0.023588) \| 0.218683 / 0.737135 (-0.518452) \| 0.161552 / 0.296338 (-0.134786) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.525988 / 0.215209 (0.310779) \| 5.234673 / 2.077655 (3.157018) \| 2.571668 / 1.504120 (1.067548) \| 2.339760 / 1.541195 (0.798565) \| 2.422886 / 1.468490 (0.954395) \| 0.651537 / 4.584777 (-3.933240) \| 4.811148 / 3.745712 (1.065436) \| 4.451165 / 5.269862 (-0.818697) \| 2.016283 / 4.565676 (-2.549394) \| 0.096393 / 0.424275 (-0.327882) \| 0.015222 / 0.007607 (0.007615) \| 0.739132 / 0.226044 (0.513087) \| 6.813327 / 2.268929 (4.544399) \| 3.169018 / 55.444624 (-52.275606) \| 2.783120 / 6.876477 (-4.093356) \| 2.918979 / 2.142072 (0.776907) \| 0.797476 / 4.805227 (-4.007751) \| 0.171038 / 6.500664 (-6.329626) \| 0.079878 / 0.075469 (0.004409) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.595082 / 1.841788 (-0.246705) \| 19.685844 / 8.074308 (11.611536) \| 17.518989 / 10.191392 (7.327597) \| 0.220015 / 0.680424 (-0.460409) \| 0.026351 / 0.534201 (-0.507850) \| 0.578977 / 0.579283 (-0.000306) \| 0.549564 / 0.434364 (0.115200) \| 0.667564 / 0.540337 (0.127227) \| 0.802121 / 1.386936 (-0.584815) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e9aee64766aaddfda60a735cfc93345aed64bdcf \"CML watermark\")\n" ]	2023-06-30T12:36:54Z	2023-06-30T13:17:05Z	2023-06-30T13:08:27Z	COLLABORATOR	null	null	null	`joblibspark` doesn't support the latest `joblib` release. See https://github.com/huggingface/datasets/actions/runs/5401870932/jobs/9812337078 for the errors	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6000/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6000/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6000.diff", "html_url": "https://github.com/huggingface/datasets/pull/6000", "merged_at": "2023-06-30T13:08:27Z", "patch_url": "https://github.com/huggingface/datasets/pull/6000.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6000" }
https://api.github.com/repos/huggingface/datasets/issues/5834	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5834/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5834/comments	https://api.github.com/repos/huggingface/datasets/issues/5834/events	https://github.com/huggingface/datasets/issues/5834	1,702,448,892	I_kwDODunzps5leU78	5,834	Is uint8 supported?	{ "avatar_url": "https://avatars.githubusercontent.com/u/17979572?v=4", "events_url": "https://api.github.com/users/ryokan0123/events{/privacy}", "followers_url": "https://api.github.com/users/ryokan0123/followers", "following_url": "https://api.github.com/users/ryokan0123/following{/other_user}", "gists_url": "https://api.github.com/users/ryokan0123/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/ryokan0123", "id": 17979572, "login": "ryokan0123", "node_id": "MDQ6VXNlcjE3OTc5NTcy", "organizations_url": "https://api.github.com/users/ryokan0123/orgs", "received_events_url": "https://api.github.com/users/ryokan0123/received_events", "repos_url": "https://api.github.com/users/ryokan0123/repos", "site_admin": false, "starred_url": "https://api.github.com/users/ryokan0123/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/ryokan0123/subscriptions", "type": "User", "url": "https://api.github.com/users/ryokan0123", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "Hi ! The numpy formatting detaults to int64 and float32 - but you can use uint8 using\r\n```python\r\nds = ds.with_format(\"numpy\", dtype=np.uint8)\r\n```", "Related to https://github.com/huggingface/datasets/issues/5517.", "Thank you!\r\nBy setting `ds.with_format(\"numpy\", dtype=np.uint8)`, the dataset returns the data in `uint8`.\r\n\r\nHowever, `with_format` and `set_format` seem to cast the data on-the-fly.\r\nI want to reduce the dataset size by using `uint8` instead of `int64` and I observe no difference between using `int64` and `uint8` for the vector.\r\nIs there any way to actually store the data in `uint8` and save the disk space and the downloading time when loaded from the hub?\r\n", "If the feature type is `Value(\"uint8\")` then it's written an uint8 on disk using the uint8 Arrow dtype.\r\n\r\ne.g.\r\n```python\r\nds = Dataset.from_dict({\"a\": range(10)}, features=Features({\"a\": Value(\"uint8\")}))\r\nds.data.nbytes\r\n# 10\r\n```", "Oh, I understand now.\r\nThe data was stored in `uint8` from the beginning (when the dataset returns `int64`).\r\n\r\nThank you for your time!\r\nMy question is fully resolved." ]	2023-05-09T17:31:13Z	2023-05-13T05:04:21Z	2023-05-13T05:04:21Z	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug I expect the dataset to store the data in the `uint8` data type, but it's returning `int64` instead. While I've found that `datasets` doesn't yet support float16 (https://github.com/huggingface/datasets/issues/4981), I'm wondering if this is the case for other data types as well. Is there a way to store vector data as `uint8` and then upload it to the hub? ### Steps to reproduce the bug ```python from datasets import Features, Dataset, Sequence, Value import numpy as np dataset = Dataset.from_dict( {"vector": [np.array([0, 1, 2], dtype=np.uint8)]}, features=Features({"vector": Sequence(Value("uint8"))}) ).with_format("numpy") print(dataset[0]["vector"].dtype) ``` ### Expected behavior Expected: `uint8` Actual: `int64` ### Environment info - `datasets` version: 2.12.0 - Platform: macOS-12.1-x86_64-i386-64bit - Python version: 3.8.12 - Huggingface_hub version: 0.12.1 - PyArrow version: 11.0.0 - Pandas version: 1.5.3	{ "avatar_url": "https://avatars.githubusercontent.com/u/17979572?v=4", "events_url": "https://api.github.com/users/ryokan0123/events{/privacy}", "followers_url": "https://api.github.com/users/ryokan0123/followers", "following_url": "https://api.github.com/users/ryokan0123/following{/other_user}", "gists_url": "https://api.github.com/users/ryokan0123/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/ryokan0123", "id": 17979572, "login": "ryokan0123", "node_id": "MDQ6VXNlcjE3OTc5NTcy", "organizations_url": "https://api.github.com/users/ryokan0123/orgs", "received_events_url": "https://api.github.com/users/ryokan0123/received_events", "repos_url": "https://api.github.com/users/ryokan0123/repos", "site_admin": false, "starred_url": "https://api.github.com/users/ryokan0123/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/ryokan0123/subscriptions", "type": "User", "url": "https://api.github.com/users/ryokan0123", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5834/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5834/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/5545	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5545/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5545/comments	https://api.github.com/repos/huggingface/datasets/issues/5545/events	https://github.com/huggingface/datasets/pull/5545	1,590,315,972	PR_kwDODunzps5KRKct	5,545	Added return methods for URL-references to the pushed dataset	{ "avatar_url": "https://avatars.githubusercontent.com/u/25269220?v=4", "events_url": "https://api.github.com/users/davidberenstein1957/events{/privacy}", "followers_url": "https://api.github.com/users/davidberenstein1957/followers", "following_url": "https://api.github.com/users/davidberenstein1957/following{/other_user}", "gists_url": "https://api.github.com/users/davidberenstein1957/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/davidberenstein1957", "id": 25269220, "login": "davidberenstein1957", "node_id": "MDQ6VXNlcjI1MjY5MjIw", "organizations_url": "https://api.github.com/users/davidberenstein1957/orgs", "received_events_url": "https://api.github.com/users/davidberenstein1957/received_events", "repos_url": "https://api.github.com/users/davidberenstein1957/repos", "site_admin": false, "starred_url": "https://api.github.com/users/davidberenstein1957/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/davidberenstein1957/subscriptions", "type": "User", "url": "https://api.github.com/users/davidberenstein1957", "user_view_type": "public" }	[]	open	false	null	[]	null	[ "Hi ! Maybe we'd need to align with `transformers` and other libraries that implement `push_to_hub` to agree on what it should return.\r\n\r\ne.g. in `transformers` the typing says it returns a string, but in practice it returns a `CommitInfo`.\r\n\r\nTherefore I'd not add an output to `push_to_hub` here unless we had a chance to discuss more broadly.\r\n\r\nAnyway in my opinion it should no just return the URL of the repository, but ideally the URL at the revision where the data were pushed", "Perhaps a mixin or something similar could be defined on the `hfh` side to ensure the `push_to_hub` API is aligned across our projects. \r\n\r\nPS: this would also mean that the PRs such as https://github.com/huggingface/datasets/pull/5528 would no longer be our responsibility\r\n\r\ncc @Wauplin ", "I agree, with universability and the idea is more about returning at least something that references where to find the uploaded file/model or otherwise. \r\n\r\nIdeally, the referenced PR would work.", "imo this would be a good use case to just use `huggingface_hub` and align to what we do there :)", "@mariosasko, can you give me some pointers to where I might help implementing this for the `huggingface-hub`?", "> @mariosasko: Perhaps a mixin or something similar could be defined on the hfh side to ensure the push_to_hub API is aligned across our projects.\r\n\r\n> @julien-c: imo this would be a good use case to just use huggingface_hub and align to what we do there :)\r\n\r\nI (finally) opened a PR to harmonize return types: https://github.com/huggingface/huggingface_hub/pull/1921. It should hopefully be shipped in next release later this week (:crossed_fingers:). " ]	2023-02-18T11:26:25Z	2023-12-18T16:57:56Z	null	NONE	null	null	null	Hi, I was missing the ability to easily open the pushed dataset and it seemed like a quick fix. Maybe we also want to log this info somewhere, but let me know if I need to add that too. Cheers, David	null	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5545/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5545/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/5545.diff", "html_url": "https://github.com/huggingface/datasets/pull/5545", "merged_at": null, "patch_url": "https://github.com/huggingface/datasets/pull/5545.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5545" }
https://api.github.com/repos/huggingface/datasets/issues/7453	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/7453/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/7453/comments	https://api.github.com/repos/huggingface/datasets/issues/7453/events	https://github.com/huggingface/datasets/pull/7453	2,920,719,503	PR_kwDODunzps6OsxR1	7,453	release: 3.4.0	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7453). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update." ]	2025-03-14T16:30:45Z	2025-03-14T16:38:10Z	2025-03-14T16:38:08Z	MEMBER	null	null	null	null	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/7453/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/7453/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/7453.diff", "html_url": "https://github.com/huggingface/datasets/pull/7453", "merged_at": "2025-03-14T16:38:08Z", "patch_url": "https://github.com/huggingface/datasets/pull/7453.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/7453" }
https://api.github.com/repos/huggingface/datasets/issues/6191	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6191/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6191/comments	https://api.github.com/repos/huggingface/datasets/issues/6191/events	https://github.com/huggingface/datasets/pull/6191	1,871,634,840	PR_kwDODunzps5ZCKmv	6,191	Add missing `revision` argument	{ "avatar_url": "https://avatars.githubusercontent.com/u/45557362?v=4", "events_url": "https://api.github.com/users/qgallouedec/events{/privacy}", "followers_url": "https://api.github.com/users/qgallouedec/followers", "following_url": "https://api.github.com/users/qgallouedec/following{/other_user}", "gists_url": "https://api.github.com/users/qgallouedec/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/qgallouedec", "id": 45557362, "login": "qgallouedec", "node_id": "MDQ6VXNlcjQ1NTU3MzYy", "organizations_url": "https://api.github.com/users/qgallouedec/orgs", "received_events_url": "https://api.github.com/users/qgallouedec/received_events", "repos_url": "https://api.github.com/users/qgallouedec/repos", "site_admin": false, "starred_url": "https://api.github.com/users/qgallouedec/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/qgallouedec/subscriptions", "type": "User", "url": "https://api.github.com/users/qgallouedec", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "I have found the same issue. Good fix. Should be merged as soon as possible.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006258 / 0.011353 (-0.005095) \| 0.003717 / 0.011008 (-0.007291) \| 0.079444 / 0.038508 (0.040936) \| 0.066318 / 0.023109 (0.043209) \| 0.310129 / 0.275898 (0.034231) \| 0.346948 / 0.323480 (0.023469) \| 0.003505 / 0.007986 (-0.004480) \| 0.002855 / 0.004328 (-0.001474) \| 0.062447 / 0.004250 (0.058197) \| 0.050191 / 0.037052 (0.013139) \| 0.314550 / 0.258489 (0.056061) \| 0.357883 / 0.293841 (0.064042) \| 0.027754 / 0.128546 (-0.100792) \| 0.008068 / 0.075646 (-0.067578) \| 0.262170 / 0.419271 (-0.157102) \| 0.045834 / 0.043533 (0.002301) \| 0.306938 / 0.255139 (0.051799) \| 0.339229 / 0.283200 (0.056030) \| 0.021188 / 0.141683 (-0.120495) \| 1.430904 / 1.452155 (-0.021251) \| 1.542038 / 1.492716 (0.049321) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.201232 / 0.018006 (0.183226) \| 0.432848 / 0.000490 (0.432358) \| 0.002403 / 0.000200 (0.002203) \| 0.000070 / 0.000054 (0.000015) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024068 / 0.037411 (-0.013344) \| 0.074077 / 0.014526 (0.059551) \| 0.083578 / 0.176557 (-0.092978) \| 0.144497 / 0.737135 (-0.592638) \| 0.085386 / 0.296338 (-0.210952) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.397912 / 0.215209 (0.182702) \| 3.940953 / 2.077655 (1.863299) \| 1.935914 / 1.504120 (0.431794) \| 1.753688 / 1.541195 (0.212493) \| 1.832916 / 1.468490 (0.364426) \| 0.503320 / 4.584777 (-4.081457) \| 3.068693 / 3.745712 (-0.677019) \| 2.867543 / 5.269862 (-2.402318) \| 1.876265 / 4.565676 (-2.689412) \| 0.057234 / 0.424275 (-0.367041) \| 0.006753 / 0.007607 (-0.000854) \| 0.468456 / 0.226044 (0.242411) \| 4.681671 / 2.268929 (2.412742) \| 2.445141 / 55.444624 (-52.999483) \| 2.182366 / 6.876477 (-4.694110) \| 2.399365 / 2.142072 (0.257293) \| 0.591880 / 4.805227 (-4.213347) \| 0.126176 / 6.500664 (-6.374488) \| 0.061488 / 0.075469 (-0.013982) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.244013 / 1.841788 (-0.597775) \| 18.534720 / 8.074308 (10.460412) \| 13.853267 / 10.191392 (3.661875) \| 0.154167 / 0.680424 (-0.526257) \| 0.016685 / 0.534201 (-0.517515) \| 0.331044 / 0.579283 (-0.248239) \| 0.341399 / 0.434364 (-0.092965) \| 0.378878 / 0.540337 (-0.161459) \| 0.535707 / 1.386936 (-0.851230) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006284 / 0.011353 (-0.005069) \| 0.003707 / 0.011008 (-0.007301) \| 0.062481 / 0.038508 (0.023973) \| 0.063342 / 0.023109 (0.040233) \| 0.445465 / 0.275898 (0.169567) \| 0.482021 / 0.323480 (0.158541) \| 0.004909 / 0.007986 (-0.003076) \| 0.002908 / 0.004328 (-0.001420) \| 0.063111 / 0.004250 (0.058860) \| 0.050197 / 0.037052 (0.013145) \| 0.453367 / 0.258489 (0.194878) \| 0.485249 / 0.293841 (0.191408) \| 0.028532 / 0.128546 (-0.100014) \| 0.008157 / 0.075646 (-0.067490) \| 0.068033 / 0.419271 (-0.351238) \| 0.041093 / 0.043533 (-0.002440) \| 0.446555 / 0.255139 (0.191416) \| 0.469103 / 0.283200 (0.185904) \| 0.019529 / 0.141683 (-0.122154) \| 1.503135 / 1.452155 (0.050980) \| 1.545819 / 1.492716 (0.053103) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.257274 / 0.018006 (0.239268) \| 0.418643 / 0.000490 (0.418153) \| 0.011604 / 0.000200 (0.011405) \| 0.000094 / 0.000054 (0.000040) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026286 / 0.037411 (-0.011125) \| 0.082459 / 0.014526 (0.067933) \| 0.090007 / 0.176557 (-0.086550) \| 0.144963 / 0.737135 (-0.592173) \| 0.093236 / 0.296338 (-0.203102) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.456331 / 0.215209 (0.241122) \| 4.559469 / 2.077655 (2.481814) \| 2.503452 / 1.504120 (0.999333) \| 2.326579 / 1.541195 (0.785384) \| 2.387551 / 1.468490 (0.919061) \| 0.508683 / 4.584777 (-4.076094) \| 3.071293 / 3.745712 (-0.674419) \| 2.872820 / 5.269862 (-2.397041) \| 1.891674 / 4.565676 (-2.674003) \| 0.058951 / 0.424275 (-0.365324) \| 0.006493 / 0.007607 (-0.001114) \| 0.526747 / 0.226044 (0.300703) \| 5.279985 / 2.268929 (3.011057) \| 2.986146 / 55.444624 (-52.458478) \| 2.603462 / 6.876477 (-4.273015) \| 2.766776 / 2.142072 (0.624704) \| 0.594685 / 4.805227 (-4.210542) \| 0.125174 / 6.500664 (-6.375490) \| 0.061430 / 0.075469 (-0.014039) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.350012 / 1.841788 (-0.491776) \| 18.991941 / 8.074308 (10.917633) \| 14.903483 / 10.191392 (4.712091) \| 0.145918 / 0.680424 (-0.534506) \| 0.017766 / 0.534201 (-0.516435) \| 0.335350 / 0.579283 (-0.243933) \| 0.357936 / 0.434364 (-0.076428) \| 0.392355 / 0.540337 (-0.147983) \| 0.545787 / 1.386936 (-0.841149) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#439e115d34a2d8737af719660c1b586ac32279dc \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005927 / 0.011353 (-0.005426) \| 0.003497 / 0.011008 (-0.007512) \| 0.079802 / 0.038508 (0.041294) \| 0.058994 / 0.023109 (0.035885) \| 0.309349 / 0.275898 (0.033451) \| 0.344876 / 0.323480 (0.021396) \| 0.004631 / 0.007986 (-0.003354) \| 0.002814 / 0.004328 (-0.001515) \| 0.062228 / 0.004250 (0.057978) \| 0.046001 / 0.037052 (0.008949) \| 0.312196 / 0.258489 (0.053707) \| 0.356283 / 0.293841 (0.062442) \| 0.027264 / 0.128546 (-0.101282) \| 0.007992 / 0.075646 (-0.067654) \| 0.260746 / 0.419271 (-0.158526) \| 0.045112 / 0.043533 (0.001579) \| 0.310463 / 0.255139 (0.055324) \| 0.336456 / 0.283200 (0.053256) \| 0.020364 / 0.141683 (-0.121319) \| 1.482159 / 1.452155 (0.030005) \| 1.541586 / 1.492716 (0.048870) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.185035 / 0.018006 (0.167028) \| 0.432104 / 0.000490 (0.431615) \| 0.002911 / 0.000200 (0.002711) \| 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) \| 0.023674 / 0.037411 (-0.013737) \| 0.072462 / 0.014526 (0.057936) \| 0.080154 / 0.176557 (-0.096402) \| 0.143022 / 0.737135 (-0.594114) \| 0.082909 / 0.296338 (-0.213430) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.436977 / 0.215209 (0.221768) \| 4.359633 / 2.077655 (2.281979) \| 2.321479 / 1.504120 (0.817359) \| 2.115277 / 1.541195 (0.574082) \| 2.172303 / 1.468490 (0.703813) \| 0.495735 / 4.584777 (-4.089042) \| 3.006773 / 3.745712 (-0.738939) \| 2.866560 / 5.269862 (-2.403302) \| 1.839339 / 4.565676 (-2.726337) \| 0.056925 / 0.424275 (-0.367350) \| 0.006777 / 0.007607 (-0.000830) \| 0.507217 / 0.226044 (0.281172) \| 5.064933 / 2.268929 (2.796004) \| 2.737542 / 55.444624 (-52.707082) \| 2.386227 / 6.876477 (-4.490250) \| 2.566375 / 2.142072 (0.424302) \| 0.582965 / 4.805227 (-4.222262) \| 0.124715 / 6.500664 (-6.375949) \| 0.061560 / 0.075469 (-0.013909) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.295684 / 1.841788 (-0.546103) \| 18.178345 / 8.074308 (10.104037) \| 13.795886 / 10.191392 (3.604494) \| 0.131464 / 0.680424 (-0.548960) \| 0.016808 / 0.534201 (-0.517393) \| 0.334190 / 0.579283 (-0.245093) \| 0.347358 / 0.434364 (-0.087006) \| 0.386198 / 0.540337 (-0.154139) \| 0.527807 / 1.386936 (-0.859129) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006118 / 0.011353 (-0.005235) \| 0.003634 / 0.011008 (-0.007374) \| 0.062117 / 0.038508 (0.023609) \| 0.061407 / 0.023109 (0.038298) \| 0.448047 / 0.275898 (0.172149) \| 0.483382 / 0.323480 (0.159902) \| 0.004849 / 0.007986 (-0.003137) \| 0.002859 / 0.004328 (-0.001469) \| 0.061714 / 0.004250 (0.057463) \| 0.047959 / 0.037052 (0.010907) \| 0.452038 / 0.258489 (0.193549) \| 0.485206 / 0.293841 (0.191365) \| 0.028254 / 0.128546 (-0.100292) \| 0.008055 / 0.075646 (-0.067591) \| 0.067752 / 0.419271 (-0.351519) \| 0.040355 / 0.043533 (-0.003178) \| 0.446986 / 0.255139 (0.191847) \| 0.472554 / 0.283200 (0.189354) \| 0.019461 / 0.141683 (-0.122222) \| 1.459048 / 1.452155 (0.006893) \| 1.497283 / 1.492716 (0.004566) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.241788 / 0.018006 (0.223782) \| 0.457352 / 0.000490 (0.456862) \| 0.003841 / 0.000200 (0.003641) \| 0.000081 / 0.000054 (0.000027) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026429 / 0.037411 (-0.010982) \| 0.081604 / 0.014526 (0.067078) \| 0.092881 / 0.176557 (-0.083675) \| 0.146057 / 0.737135 (-0.591078) \| 0.092987 / 0.296338 (-0.203352) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.456641 / 0.215209 (0.241432) \| 4.567853 / 2.077655 (2.490198) \| 2.491684 / 1.504120 (0.987564) \| 2.323647 / 1.541195 (0.782452) \| 2.387689 / 1.468490 (0.919198) \| 0.505114 / 4.584777 (-4.079663) \| 3.071615 / 3.745712 (-0.674098) \| 2.912391 / 5.269862 (-2.357471) \| 1.922350 / 4.565676 (-2.643326) \| 0.057785 / 0.424275 (-0.366490) \| 0.006642 / 0.007607 (-0.000965) \| 0.532463 / 0.226044 (0.306418) \| 5.344084 / 2.268929 (3.075155) \| 2.970182 / 55.444624 (-52.474442) \| 2.601733 / 6.876477 (-4.274744) \| 2.763803 / 2.142072 (0.621731) \| 0.596333 / 4.805227 (-4.208894) \| 0.127047 / 6.500664 (-6.373617) \| 0.062516 / 0.075469 (-0.012953) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.343206 / 1.841788 (-0.498581) \| 19.405215 / 8.074308 (11.330907) \| 15.406568 / 10.191392 (5.215176) \| 0.132328 / 0.680424 (-0.548096) \| 0.017882 / 0.534201 (-0.516318) \| 0.336393 / 0.579283 (-0.242890) \| 0.361989 / 0.434364 (-0.072375) \| 0.394336 / 0.540337 (-0.146001) \| 0.545166 / 1.386936 (-0.841770) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#439e115d34a2d8737af719660c1b586ac32279dc \"CML watermark\")\n" ]	2023-08-29T13:05:04Z	2023-09-04T06:38:17Z	2023-08-31T13:50:00Z	MEMBER	null	null	null	I've noticed that when you're not working on the main branch, there are sometimes errors in the files returned. After some investigation, I realized that the revision was not properly passed everywhere. This PR proposes a fix.	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	{ "+1": 1, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 1, "url": "https://api.github.com/repos/huggingface/datasets/issues/6191/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6191/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6191.diff", "html_url": "https://github.com/huggingface/datasets/pull/6191", "merged_at": "2023-08-31T13:50:00Z", "patch_url": "https://github.com/huggingface/datasets/pull/6191.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6191" }
https://api.github.com/repos/huggingface/datasets/issues/7136	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/7136/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/7136/comments	https://api.github.com/repos/huggingface/datasets/issues/7136/events	https://github.com/huggingface/datasets/pull/7136	2,506,115,857	PR_kwDODunzps56b9R-	7,136	Do not consume unnecessary memory during sharding	{ "avatar_url": "https://avatars.githubusercontent.com/u/12694897?v=4", "events_url": "https://api.github.com/users/janEbert/events{/privacy}", "followers_url": "https://api.github.com/users/janEbert/followers", "following_url": "https://api.github.com/users/janEbert/following{/other_user}", "gists_url": "https://api.github.com/users/janEbert/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/janEbert", "id": 12694897, "login": "janEbert", "node_id": "MDQ6VXNlcjEyNjk0ODk3", "organizations_url": "https://api.github.com/users/janEbert/orgs", "received_events_url": "https://api.github.com/users/janEbert/received_events", "repos_url": "https://api.github.com/users/janEbert/repos", "site_admin": false, "starred_url": "https://api.github.com/users/janEbert/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/janEbert/subscriptions", "type": "User", "url": "https://api.github.com/users/janEbert", "user_view_type": "public" }	[]	open	false	null	[]	null	[]	2024-09-04T19:26:06Z	2024-09-04T19:28:23Z	null	NONE	null	null	null	When sharding `IterableDataset`s, a temporary list is created that is then indexed. There is no need to create a temporary list of a potentially very large step/world size, with standard `islice` functionality, so we avoid it. ```shell pytest tests/test_distributed.py -k iterable ``` Runs successfully.	null	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/7136/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/7136/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/7136.diff", "html_url": "https://github.com/huggingface/datasets/pull/7136", "merged_at": null, "patch_url": "https://github.com/huggingface/datasets/pull/7136.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/7136" }
https://api.github.com/repos/huggingface/datasets/issues/4860	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/4860/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/4860/comments	https://api.github.com/repos/huggingface/datasets/issues/4860/events	https://github.com/huggingface/datasets/pull/4860	1,342,311,540	PR_kwDODunzps49WjEu	4,860	Add collection3 dataset	{ "avatar_url": "https://avatars.githubusercontent.com/u/16446994?v=4", "events_url": "https://api.github.com/users/pefimov/events{/privacy}", "followers_url": "https://api.github.com/users/pefimov/followers", "following_url": "https://api.github.com/users/pefimov/following{/other_user}", "gists_url": "https://api.github.com/users/pefimov/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/pefimov", "id": 16446994, "login": "pefimov", "node_id": "MDQ6VXNlcjE2NDQ2OTk0", "organizations_url": "https://api.github.com/users/pefimov/orgs", "received_events_url": "https://api.github.com/users/pefimov/received_events", "repos_url": "https://api.github.com/users/pefimov/repos", "site_admin": false, "starred_url": "https://api.github.com/users/pefimov/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/pefimov/subscriptions", "type": "User", "url": "https://api.github.com/users/pefimov", "user_view_type": "public" }	[ { "color": "ffffff", "default": true, "description": "This will not be worked on", "id": 1935892913, "name": "wontfix", "node_id": "MDU6TGFiZWwxOTM1ODkyOTEz", "url": "https://api.github.com/repos/huggingface/datasets/labels/wontfix" } ]	closed	false	null	[]	null	[ "Hi @pefimov. Thanks for you awesome work on this dataset contribution.\r\n\r\nHowever, now we are using the Hub to add new datasets, instead of this GitHub repo. \r\n\r\nYou could share this dataset under the appropriate Hub organization namespace. This way the dataset will be accessible using:\r\n```python\r\nds = load_dataset(\"<org_namespace>/collection3\")\r\n```\r\n\r\nYou have the procedure documented in our online docs: \r\n- [Create a dataset loading script](https://huggingface.co/docs/datasets/dataset_script)\r\n- [Share](https://huggingface.co/docs/datasets/share)\r\n\r\nMoreover, datasets shared on the Hub no longer need the dummy data files.\r\n\r\nPlease, feel free to ping me if you need any further guidance/support. ", "> However, now we are using the Hub to add new datasets, instead of this GitHub repo.\r\n> \r\n> You could share this dataset under the appropriate Hub organization namespace. This way the dataset will be accessible using:\r\n> \r\n> ```python\r\n> ds = load_dataset(\"<org_namespace>/collection3\")\r\n> ```\r\n> \r\nHi @albertvillanova . Thank you for your response.\r\n\r\nI thought that Collection3 is large and important dataset in Russian presented in 2016 but not represented in huggingface.\r\n\r\nAlso I am not related to authors or organisation of dataset", "The current policy of sharing datasets on the Hub instead of in this GitHub repo has no relation with the importance of the dataset: https://huggingface.co/docs/datasets/share#datasets-on-github-legacy \r\n> The distinction between a Hub dataset and a dataset from GitHub only comes from the legacy sharing workflow. It does not involve any ranking, decisioning, or opinion regarding the contents of the dataset itself.\r\n\r\nIt is not required to be an author/owner (or belong to the organization that is owner) of the dataset in order to share it on the Hub (as it was not the case when sharing them on this GitHub repo). \r\n\r\nIt is recommended to share it under an organization namespace that makes sense though. For this specific dataset, do you know of a clear organization under which it could be shared on the Hub? Maybe \"labinform\", or \"Information Research Laboratory\" or \"Lomonosov Moscow State University\"?\r\n\r\nIn cases like this, where the org is not evident, one possibility could be to contact the dataset owners/creators and ask them. According the publication paper, the authors are:\r\n- V.A. Mozharova\r\n- N.V. Loukachevitch\r\n\r\nI think maybe it would be worth contacting them.", "@pefimov I have contacted the authors (and put you in CC).", "Reply from the authors:\r\n> It is better to use name: Research Computing Center of Lomonosov Moscow State University (short name RCC-MSU)\r\n> https://rcc.msu.ru/en", "I have created the corresponding org namespace and dataset empty repository: https://huggingface.co/datasets/RCC-MSU/collection3\r\n\r\n@pefimov feel free to open a PR on the Hub if you are willing to do so: \r\n- Go to the Community tab on the repo: https://huggingface.co/datasets/RCC-MSU/collection3/discussions\r\n- And click: New pull request button\r\n\r\nDocs: [Pull requests and Discussions](https://huggingface.co/docs/hub/repositories-pull-requests-discussions) on the Hub", "Thanks" ]	2022-08-17T21:31:42Z	2022-08-23T20:02:45Z	2022-08-22T09:08:59Z	NONE	null	null	null	null	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/4860/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/4860/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/4860.diff", "html_url": "https://github.com/huggingface/datasets/pull/4860", "merged_at": null, "patch_url": "https://github.com/huggingface/datasets/pull/4860.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/4860" }
https://api.github.com/repos/huggingface/datasets/issues/6431	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6431/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6431/comments	https://api.github.com/repos/huggingface/datasets/issues/6431/events	https://github.com/huggingface/datasets/pull/6431	1,997,202,770	PR_kwDODunzps5fpfos	6,431	Create DatasetNotFoundError and DataFilesNotFoundError	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004459 / 0.011353 (-0.006894) \| 0.002883 / 0.011008 (-0.008125) \| 0.062434 / 0.038508 (0.023925) \| 0.030353 / 0.023109 (0.007244) \| 0.256696 / 0.275898 (-0.019202) \| 0.280557 / 0.323480 (-0.042923) \| 0.003903 / 0.007986 (-0.004083) \| 0.002424 / 0.004328 (-0.001905) \| 0.048509 / 0.004250 (0.044259) \| 0.043583 / 0.037052 (0.006531) \| 0.253900 / 0.258489 (-0.004590) \| 0.309146 / 0.293841 (0.015305) \| 0.023253 / 0.128546 (-0.105294) \| 0.007073 / 0.075646 (-0.068573) \| 0.204118 / 0.419271 (-0.215154) \| 0.056429 / 0.043533 (0.012897) \| 0.247331 / 0.255139 (-0.007808) \| 0.271581 / 0.283200 (-0.011619) \| 0.017021 / 0.141683 (-0.124662) \| 1.115057 / 1.452155 (-0.337098) \| 1.209947 / 1.492716 (-0.282770) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093141 / 0.018006 (0.075134) \| 0.295987 / 0.000490 (0.295497) \| 0.000221 / 0.000200 (0.000021) \| 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) \| 0.019182 / 0.037411 (-0.018230) \| 0.062049 / 0.014526 (0.047523) \| 0.073824 / 0.176557 (-0.102733) \| 0.120175 / 0.737135 (-0.616960) \| 0.074700 / 0.296338 (-0.221639) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.280036 / 0.215209 (0.064827) \| 2.731512 / 2.077655 (0.653857) \| 1.414606 / 1.504120 (-0.089514) \| 1.302433 / 1.541195 (-0.238761) \| 1.313012 / 1.468490 (-0.155478) \| 0.399722 / 4.584777 (-4.185055) \| 2.371249 / 3.745712 (-1.374463) \| 2.582520 / 5.269862 (-2.687342) \| 1.558505 / 4.565676 (-3.007171) \| 0.045765 / 0.424275 (-0.378510) \| 0.004748 / 0.007607 (-0.002859) \| 0.327623 / 0.226044 (0.101578) \| 3.258742 / 2.268929 (0.989814) \| 1.756798 / 55.444624 (-53.687826) \| 1.494551 / 6.876477 (-5.381925) \| 1.518161 / 2.142072 (-0.623911) \| 0.468560 / 4.805227 (-4.336667) \| 0.101034 / 6.500664 (-6.399630) \| 0.048259 / 0.075469 (-0.027210) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.938146 / 1.841788 (-0.903642) \| 11.636387 / 8.074308 (3.562078) \| 10.638909 / 10.191392 (0.447517) \| 0.128340 / 0.680424 (-0.552084) \| 0.015194 / 0.534201 (-0.519007) \| 0.275961 / 0.579283 (-0.303322) \| 0.264629 / 0.434364 (-0.169735) \| 0.308580 / 0.540337 (-0.231758) \| 0.433658 / 1.386936 (-0.953278) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004797 / 0.011353 (-0.006556) \| 0.002801 / 0.011008 (-0.008208) \| 0.048101 / 0.038508 (0.009593) \| 0.056406 / 0.023109 (0.033296) \| 0.274966 / 0.275898 (-0.000932) \| 0.298310 / 0.323480 (-0.025170) \| 0.004115 / 0.007986 (-0.003871) \| 0.002437 / 0.004328 (-0.001891) \| 0.047921 / 0.004250 (0.043671) \| 0.038812 / 0.037052 (0.001760) \| 0.279594 / 0.258489 (0.021105) \| 0.313703 / 0.293841 (0.019862) \| 0.024485 / 0.128546 (-0.104061) \| 0.007095 / 0.075646 (-0.068551) \| 0.053398 / 0.419271 (-0.365874) \| 0.032306 / 0.043533 (-0.011227) \| 0.278014 / 0.255139 (0.022875) \| 0.301156 / 0.283200 (0.017956) \| 0.017353 / 0.141683 (-0.124330) \| 1.150168 / 1.452155 (-0.301987) \| 1.190822 / 1.492716 (-0.301894) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092162 / 0.018006 (0.074156) \| 0.301031 / 0.000490 (0.300541) \| 0.000244 / 0.000200 (0.000044) \| 0.000062 / 0.000054 (0.000008) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.020918 / 0.037411 (-0.016494) \| 0.072030 / 0.014526 (0.057504) \| 0.081813 / 0.176557 (-0.094743) \| 0.120233 / 0.737135 (-0.616903) \| 0.082874 / 0.296338 (-0.213465) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.291659 / 0.215209 (0.076450) \| 2.841978 / 2.077655 (0.764323) \| 1.594207 / 1.504120 (0.090087) \| 1.473941 / 1.541195 (-0.067254) \| 1.514393 / 1.468490 (0.045903) \| 0.393393 / 4.584777 (-4.191384) \| 2.443663 / 3.745712 (-1.302050) \| 2.545747 / 5.269862 (-2.724114) \| 1.521130 / 4.565676 (-3.044546) \| 0.046246 / 0.424275 (-0.378030) \| 0.004826 / 0.007607 (-0.002781) \| 0.340909 / 0.226044 (0.114865) \| 3.319474 / 2.268929 (1.050546) \| 1.933110 / 55.444624 (-53.511515) \| 1.662463 / 6.876477 (-5.214014) \| 1.670331 / 2.142072 (-0.471742) \| 0.458062 / 4.805227 (-4.347165) \| 0.098397 / 6.500664 (-6.402267) \| 0.041339 / 0.075469 (-0.034130) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.973718 / 1.841788 (-0.868070) \| 12.095266 / 8.074308 (4.020957) \| 10.761212 / 10.191392 (0.569820) \| 0.142352 / 0.680424 (-0.538072) \| 0.015423 / 0.534201 (-0.518778) \| 0.270912 / 0.579283 (-0.308371) \| 0.276618 / 0.434364 (-0.157746) \| 0.309120 / 0.540337 (-0.231217) \| 0.415330 / 1.386936 (-0.971606) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#cf4ba6f0e2641056774c01f62984aef5de5d68f1 \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004676 / 0.011353 (-0.006677) \| 0.003101 / 0.011008 (-0.007907) \| 0.062260 / 0.038508 (0.023752) \| 0.030012 / 0.023109 (0.006903) \| 0.253704 / 0.275898 (-0.022194) \| 0.276404 / 0.323480 (-0.047075) \| 0.004060 / 0.007986 (-0.003926) \| 0.002467 / 0.004328 (-0.001861) \| 0.047921 / 0.004250 (0.043670) \| 0.045760 / 0.037052 (0.008708) \| 0.254529 / 0.258489 (-0.003960) \| 0.286283 / 0.293841 (-0.007558) \| 0.023301 / 0.128546 (-0.105246) \| 0.007407 / 0.075646 (-0.068239) \| 0.204541 / 0.419271 (-0.214730) \| 0.056387 / 0.043533 (0.012854) \| 0.252120 / 0.255139 (-0.003019) \| 0.275795 / 0.283200 (-0.007404) \| 0.018648 / 0.141683 (-0.123034) \| 1.113484 / 1.452155 (-0.338671) \| 1.168685 / 1.492716 (-0.324031) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.098286 / 0.018006 (0.080280) \| 0.304619 / 0.000490 (0.304129) \| 0.000225 / 0.000200 (0.000025) \| 0.000058 / 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) \| 0.019183 / 0.037411 (-0.018229) \| 0.062183 / 0.014526 (0.047657) \| 0.074288 / 0.176557 (-0.102269) \| 0.120576 / 0.737135 (-0.616560) \| 0.074833 / 0.296338 (-0.221505) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.280512 / 0.215209 (0.065303) \| 2.770052 / 2.077655 (0.692397) \| 1.471234 / 1.504120 (-0.032886) \| 1.352080 / 1.541195 (-0.189114) \| 1.374518 / 1.468490 (-0.093973) \| 0.407108 / 4.584777 (-4.177669) \| 2.400581 / 3.745712 (-1.345131) \| 2.677507 / 5.269862 (-2.592355) \| 1.578042 / 4.565676 (-2.987635) \| 0.048539 / 0.424275 (-0.375736) \| 0.004905 / 0.007607 (-0.002703) \| 0.346676 / 0.226044 (0.120631) \| 3.367732 / 2.268929 (1.098803) \| 1.844405 / 55.444624 (-53.600220) \| 1.576883 / 6.876477 (-5.299594) \| 1.666986 / 2.142072 (-0.475086) \| 0.495872 / 4.805227 (-4.309355) \| 0.103142 / 6.500664 (-6.397522) \| 0.044037 / 0.075469 (-0.031432) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.980865 / 1.841788 (-0.860923) \| 12.268525 / 8.074308 (4.194217) \| 10.756554 / 10.191392 (0.565162) \| 0.129954 / 0.680424 (-0.550470) \| 0.013864 / 0.534201 (-0.520337) \| 0.267653 / 0.579283 (-0.311630) \| 0.265120 / 0.434364 (-0.169244) \| 0.309050 / 0.540337 (-0.231288) \| 0.423877 / 1.386936 (-0.963059) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005074 / 0.011353 (-0.006279) \| 0.003001 / 0.011008 (-0.008007) \| 0.048271 / 0.038508 (0.009763) \| 0.061206 / 0.023109 (0.038097) \| 0.279268 / 0.275898 (0.003370) \| 0.302592 / 0.323480 (-0.020888) \| 0.004177 / 0.007986 (-0.003809) \| 0.002452 / 0.004328 (-0.001876) \| 0.048259 / 0.004250 (0.044009) \| 0.040032 / 0.037052 (0.002979) \| 0.281398 / 0.258489 (0.022909) \| 0.314121 / 0.293841 (0.020280) \| 0.025137 / 0.128546 (-0.103409) \| 0.007230 / 0.075646 (-0.068416) \| 0.054537 / 0.419271 (-0.364735) \| 0.033266 / 0.043533 (-0.010267) \| 0.277305 / 0.255139 (0.022166) \| 0.295993 / 0.283200 (0.012794) \| 0.019278 / 0.141683 (-0.122405) \| 1.131700 / 1.452155 (-0.320454) \| 1.183848 / 1.492716 (-0.308868) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092258 / 0.018006 (0.074251) \| 0.310668 / 0.000490 (0.310178) \| 0.000219 / 0.000200 (0.000019) \| 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) \| 0.021838 / 0.037411 (-0.015574) \| 0.071382 / 0.014526 (0.056857) \| 0.081389 / 0.176557 (-0.095168) \| 0.120389 / 0.737135 (-0.616746) \| 0.084135 / 0.296338 (-0.212203) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.291676 / 0.215209 (0.076467) \| 2.840623 / 2.077655 (0.762968) \| 1.565748 / 1.504120 (0.061628) \| 1.452529 / 1.541195 (-0.088666) \| 1.490633 / 1.468490 (0.022143) \| 0.402878 / 4.584777 (-4.181899) \| 2.486192 / 3.745712 (-1.259520) \| 2.520563 / 5.269862 (-2.749299) \| 1.518550 / 4.565676 (-3.047127) \| 0.047423 / 0.424275 (-0.376852) \| 0.004823 / 0.007607 (-0.002784) \| 0.353122 / 0.226044 (0.127078) \| 3.452136 / 2.268929 (1.183208) \| 1.973798 / 55.444624 (-53.470827) \| 1.669569 / 6.876477 (-5.206907) \| 1.654910 / 2.142072 (-0.487163) \| 0.486746 / 4.805227 (-4.318481) \| 0.097260 / 6.500664 (-6.403404) \| 0.040608 / 0.075469 (-0.034861) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.989705 / 1.841788 (-0.852083) \| 12.114386 / 8.074308 (4.040077) \| 11.284551 / 10.191392 (1.093159) \| 0.141408 / 0.680424 (-0.539016) \| 0.015275 / 0.534201 (-0.518926) \| 0.267407 / 0.579283 (-0.311877) \| 0.281007 / 0.434364 (-0.153357) \| 0.309617 / 0.540337 (-0.230720) \| 0.414033 / 1.386936 (-0.972903) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6f3f3e3feec9d7d4d36111401787eb7b5fd51836 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004888 / 0.011353 (-0.006465) \| 0.002775 / 0.011008 (-0.008233) \| 0.062000 / 0.038508 (0.023492) \| 0.050694 / 0.023109 (0.027584) \| 0.257063 / 0.275898 (-0.018835) \| 0.282743 / 0.323480 (-0.040736) \| 0.002862 / 0.007986 (-0.005124) \| 0.002305 / 0.004328 (-0.002023) \| 0.049549 / 0.004250 (0.045299) \| 0.038754 / 0.037052 (0.001701) \| 0.264047 / 0.258489 (0.005558) \| 0.310162 / 0.293841 (0.016321) \| 0.022901 / 0.128546 (-0.105645) \| 0.006894 / 0.075646 (-0.068752) \| 0.202467 / 0.419271 (-0.216805) \| 0.035901 / 0.043533 (-0.007631) \| 0.262344 / 0.255139 (0.007205) \| 0.285563 / 0.283200 (0.002364) \| 0.017070 / 0.141683 (-0.124613) \| 1.113972 / 1.452155 (-0.338182) \| 1.176261 / 1.492716 (-0.316455) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092912 / 0.018006 (0.074906) \| 0.302610 / 0.000490 (0.302120) \| 0.000204 / 0.000200 (0.000005) \| 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) \| 0.018232 / 0.037411 (-0.019179) \| 0.062367 / 0.014526 (0.047841) \| 0.074570 / 0.176557 (-0.101987) \| 0.120468 / 0.737135 (-0.616668) \| 0.075187 / 0.296338 (-0.221151) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.279760 / 0.215209 (0.064551) \| 2.715372 / 2.077655 (0.637717) \| 1.461636 / 1.504120 (-0.042484) \| 1.324220 / 1.541195 (-0.216975) \| 1.350724 / 1.468490 (-0.117766) \| 0.395648 / 4.584777 (-4.189129) \| 2.376548 / 3.745712 (-1.369164) \| 2.594662 / 5.269862 (-2.675200) \| 1.553528 / 4.565676 (-3.012148) \| 0.047875 / 0.424275 (-0.376400) \| 0.005287 / 0.007607 (-0.002321) \| 0.334734 / 0.226044 (0.108689) \| 3.294753 / 2.268929 (1.025825) \| 1.797901 / 55.444624 (-53.646724) \| 1.510907 / 6.876477 (-5.365570) \| 1.536070 / 2.142072 (-0.606003) \| 0.474672 / 4.805227 (-4.330555) \| 0.099323 / 6.500664 (-6.401341) \| 0.041703 / 0.075469 (-0.033766) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.947441 / 1.841788 (-0.894347) \| 11.451378 / 8.074308 (3.377070) \| 10.283213 / 10.191392 (0.091821) \| 0.131032 / 0.680424 (-0.549392) \| 0.014423 / 0.534201 (-0.519777) \| 0.272568 / 0.579283 (-0.306715) \| 0.267127 / 0.434364 (-0.167237) \| 0.307361 / 0.540337 (-0.232976) \| 0.403858 / 1.386936 (-0.983078) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004836 / 0.011353 (-0.006517) \| 0.002544 / 0.011008 (-0.008464) \| 0.047979 / 0.038508 (0.009471) \| 0.052211 / 0.023109 (0.029102) \| 0.273394 / 0.275898 (-0.002504) \| 0.291202 / 0.323480 (-0.032277) \| 0.004094 / 0.007986 (-0.003891) \| 0.002415 / 0.004328 (-0.001914) \| 0.048057 / 0.004250 (0.043807) \| 0.039756 / 0.037052 (0.002703) \| 0.277301 / 0.258489 (0.018812) \| 0.297626 / 0.293841 (0.003785) \| 0.024641 / 0.128546 (-0.103905) \| 0.006957 / 0.075646 (-0.068690) \| 0.053574 / 0.419271 (-0.365697) \| 0.036532 / 0.043533 (-0.007001) \| 0.273753 / 0.255139 (0.018614) \| 0.294254 / 0.283200 (0.011054) \| 0.022252 / 0.141683 (-0.119431) \| 1.128609 / 1.452155 (-0.323546) \| 1.217322 / 1.492716 (-0.275394) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091050 / 0.018006 (0.073044) \| 0.300089 / 0.000490 (0.299600) \| 0.000215 / 0.000200 (0.000015) \| 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) \| 0.021423 / 0.037411 (-0.015988) \| 0.069892 / 0.014526 (0.055366) \| 0.081125 / 0.176557 (-0.095432) \| 0.118725 / 0.737135 (-0.618411) \| 0.081357 / 0.296338 (-0.214981) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.295046 / 0.215209 (0.079837) \| 2.868813 / 2.077655 (0.791159) \| 1.579613 / 1.504120 (0.075493) \| 1.449308 / 1.541195 (-0.091887) \| 1.478804 / 1.468490 (0.010314) \| 0.416916 / 4.584777 (-4.167861) \| 2.461093 / 3.745712 (-1.284619) \| 2.449792 / 5.269862 (-2.820070) \| 1.573930 / 4.565676 (-2.991746) \| 0.046808 / 0.424275 (-0.377467) \| 0.004811 / 0.007607 (-0.002796) \| 0.352805 / 0.226044 (0.126761) \| 3.495034 / 2.268929 (1.226105) \| 1.952019 / 55.444624 (-53.492606) \| 1.642607 / 6.876477 (-5.233869) \| 1.775235 / 2.142072 (-0.366837) \| 0.482196 / 4.805227 (-4.323032) \| 0.099562 / 6.500664 (-6.401102) \| 0.040709 / 0.075469 (-0.034760) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.972750 / 1.841788 (-0.869038) \| 11.905172 / 8.074308 (3.830864) \| 10.613847 / 10.191392 (0.422455) \| 0.129892 / 0.680424 (-0.550532) \| 0.015611 / 0.534201 (-0.518590) \| 0.271884 / 0.579283 (-0.307400) \| 0.275270 / 0.434364 (-0.159094) \| 0.303213 / 0.540337 (-0.237125) \| 0.402338 / 1.386936 (-0.984598) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#bf8fa7ad7609ad34d4cc689f529ea606dd2560e0 \"CML watermark\")\n", "I think this PR can be merged.", "you already have an approval, feel free to merge!\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) \| 0.004826 / 0.011353 (-0.006527) \| 0.002979 / 0.011008 (-0.008029) \| 0.062055 / 0.038508 (0.023547) \| 0.056574 / 0.023109 (0.033465) \| 0.244342 / 0.275898 (-0.031556) \| 0.278040 / 0.323480 (-0.045439) \| 0.004020 / 0.007986 (-0.003965) \| 0.002474 / 0.004328 (-0.001855) \| 0.048451 / 0.004250 (0.044200) \| 0.038633 / 0.037052 (0.001580) \| 0.251389 / 0.258489 (-0.007100) \| 0.282739 / 0.293841 (-0.011102) \| 0.023298 / 0.128546 (-0.105248) \| 0.007513 / 0.075646 (-0.068134) \| 0.203014 / 0.419271 (-0.216257) \| 0.036216 / 0.043533 (-0.007317) \| 0.250988 / 0.255139 (-0.004151) \| 0.281228 / 0.283200 (-0.001972) \| 0.018259 / 0.141683 (-0.123424) \| 1.121200 / 1.452155 (-0.330955) \| 1.184298 / 1.492716 (-0.308419) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093730 / 0.018006 (0.075724) \| 0.301716 / 0.000490 (0.301226) \| 0.000223 / 0.000200 (0.000023) \| 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) \| 0.019238 / 0.037411 (-0.018173) \| 0.064329 / 0.014526 (0.049803) \| 0.075657 / 0.176557 (-0.100899) \| 0.122616 / 0.737135 (-0.614519) \| 0.077459 / 0.296338 (-0.218880) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.280153 / 0.215209 (0.064944) \| 2.715488 / 2.077655 (0.637833) \| 1.449666 / 1.504120 (-0.054454) \| 1.331903 / 1.541195 (-0.209292) \| 1.396200 / 1.468490 (-0.072290) \| 0.398861 / 4.584777 (-4.185916) \| 2.402814 / 3.745712 (-1.342898) \| 2.664033 / 5.269862 (-2.605829) \| 1.619589 / 4.565676 (-2.946088) \| 0.044798 / 0.424275 (-0.379477) \| 0.004989 / 0.007607 (-0.002618) \| 0.336822 / 0.226044 (0.110777) \| 3.245604 / 2.268929 (0.976676) \| 1.815633 / 55.444624 (-53.628991) \| 1.557975 / 6.876477 (-5.318501) \| 1.603655 / 2.142072 (-0.538417) \| 0.462980 / 4.805227 (-4.342247) \| 0.098340 / 6.500664 (-6.402324) \| 0.042750 / 0.075469 (-0.032719) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.973785 / 1.841788 (-0.868003) \| 12.379356 / 8.074308 (4.305048) \| 10.540164 / 10.191392 (0.348772) \| 0.144803 / 0.680424 (-0.535621) \| 0.013875 / 0.534201 (-0.520326) \| 0.270192 / 0.579283 (-0.309091) \| 0.264614 / 0.434364 (-0.169750) \| 0.313454 / 0.540337 (-0.226883) \| 0.402310 / 1.386936 (-0.984626) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004987 / 0.011353 (-0.006366) \| 0.003017 / 0.011008 (-0.007992) \| 0.048592 / 0.038508 (0.010084) \| 0.059370 / 0.023109 (0.036261) \| 0.277536 / 0.275898 (0.001638) \| 0.300592 / 0.323480 (-0.022888) \| 0.004870 / 0.007986 (-0.003115) \| 0.002452 / 0.004328 (-0.001876) \| 0.047972 / 0.004250 (0.043721) \| 0.042336 / 0.037052 (0.005283) \| 0.277570 / 0.258489 (0.019081) \| 0.304739 / 0.293841 (0.010898) \| 0.025313 / 0.128546 (-0.103233) \| 0.007219 / 0.075646 (-0.068427) \| 0.053967 / 0.419271 (-0.365304) \| 0.033314 / 0.043533 (-0.010219) \| 0.273908 / 0.255139 (0.018769) \| 0.291913 / 0.283200 (0.008713) \| 0.019440 / 0.141683 (-0.122243) \| 1.111047 / 1.452155 (-0.341107) \| 1.191276 / 1.492716 (-0.301440) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093985 / 0.018006 (0.075979) \| 0.303105 / 0.000490 (0.302615) \| 0.000235 / 0.000200 (0.000035) \| 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) \| 0.022226 / 0.037411 (-0.015186) \| 0.072151 / 0.014526 (0.057625) \| 0.081700 / 0.176557 (-0.094857) \| 0.121407 / 0.737135 (-0.615729) \| 0.083217 / 0.296338 (-0.213121) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.297286 / 0.215209 (0.082077) \| 2.913392 / 2.077655 (0.835738) \| 1.591758 / 1.504120 (0.087638) \| 1.463339 / 1.541195 (-0.077856) \| 1.495095 / 1.468490 (0.026605) \| 0.414341 / 4.584777 (-4.170436) \| 2.412438 / 3.745712 (-1.333275) \| 2.611452 / 5.269862 (-2.658410) \| 1.658545 / 4.565676 (-2.907132) \| 0.047269 / 0.424275 (-0.377007) \| 0.004872 / 0.007607 (-0.002735) \| 0.350746 / 0.226044 (0.124701) \| 3.491482 / 2.268929 (1.222554) \| 1.999009 / 55.444624 (-53.445616) \| 1.672862 / 6.876477 (-5.203615) \| 1.863095 / 2.142072 (-0.278977) \| 0.484746 / 4.805227 (-4.320481) \| 0.100774 / 6.500664 (-6.399890) \| 0.042519 / 0.075469 (-0.032950) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.984497 / 1.841788 (-0.857291) \| 12.972576 / 8.074308 (4.898268) \| 10.886021 / 10.191392 (0.694629) \| 0.141639 / 0.680424 (-0.538785) \| 0.015726 / 0.534201 (-0.518475) \| 0.284160 / 0.579283 (-0.295123) \| 0.291437 / 0.434364 (-0.142927) \| 0.314121 / 0.540337 (-0.226217) \| 0.420439 / 1.386936 (-0.966497) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#87ad7c7767b9cda62113c207f0ff42506a8f27c0 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004881 / 0.011353 (-0.006472) \| 0.002550 / 0.011008 (-0.008458) \| 0.062171 / 0.038508 (0.023663) \| 0.055341 / 0.023109 (0.032232) \| 0.243132 / 0.275898 (-0.032766) \| 0.265174 / 0.323480 (-0.058306) \| 0.002934 / 0.007986 (-0.005052) \| 0.002233 / 0.004328 (-0.002096) \| 0.049302 / 0.004250 (0.045052) \| 0.039491 / 0.037052 (0.002439) \| 0.252776 / 0.258489 (-0.005713) \| 0.280923 / 0.293841 (-0.012918) \| 0.022585 / 0.128546 (-0.105962) \| 0.006888 / 0.075646 (-0.068759) \| 0.202751 / 0.419271 (-0.216521) \| 0.035250 / 0.043533 (-0.008283) \| 0.251745 / 0.255139 (-0.003394) \| 0.267431 / 0.283200 (-0.015768) \| 0.019486 / 0.141683 (-0.122197) \| 1.161783 / 1.452155 (-0.290372) \| 1.194254 / 1.492716 (-0.298463) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.097772 / 0.018006 (0.079766) \| 0.309137 / 0.000490 (0.308647) \| 0.000225 / 0.000200 (0.000025) \| 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) \| 0.018719 / 0.037411 (-0.018693) \| 0.062211 / 0.014526 (0.047686) \| 0.074291 / 0.176557 (-0.102266) \| 0.119436 / 0.737135 (-0.617699) \| 0.075519 / 0.296338 (-0.220820) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.279778 / 0.215209 (0.064569) \| 2.730678 / 2.077655 (0.653023) \| 1.413922 / 1.504120 (-0.090198) \| 1.286747 / 1.541195 (-0.254447) \| 1.299835 / 1.468490 (-0.168656) \| 0.392516 / 4.584777 (-4.192261) \| 2.381816 / 3.745712 (-1.363896) \| 2.616944 / 5.269862 (-2.652918) \| 1.606152 / 4.565676 (-2.959525) \| 0.044867 / 0.424275 (-0.379408) \| 0.004915 / 0.007607 (-0.002692) \| 0.334078 / 0.226044 (0.108034) \| 3.388096 / 2.268929 (1.119167) \| 1.756666 / 55.444624 (-53.687958) \| 1.497211 / 6.876477 (-5.379266) \| 1.496787 / 2.142072 (-0.645285) \| 0.469145 / 4.805227 (-4.336082) \| 0.097821 / 6.500664 (-6.402843) \| 0.041850 / 0.075469 (-0.033619) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.956878 / 1.841788 (-0.884910) \| 11.520184 / 8.074308 (3.445875) \| 10.659216 / 10.191392 (0.467824) \| 0.143687 / 0.680424 (-0.536737) \| 0.014118 / 0.534201 (-0.520083) \| 0.270990 / 0.579283 (-0.308293) \| 0.270057 / 0.434364 (-0.164306) \| 0.311109 / 0.540337 (-0.229229) \| 0.407042 / 1.386936 (-0.979894) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004816 / 0.011353 (-0.006537) \| 0.002898 / 0.011008 (-0.008110) \| 0.048540 / 0.038508 (0.010032) \| 0.055286 / 0.023109 (0.032176) \| 0.279086 / 0.275898 (0.003187) \| 0.298950 / 0.323480 (-0.024529) \| 0.004090 / 0.007986 (-0.003896) \| 0.002497 / 0.004328 (-0.001832) \| 0.049160 / 0.004250 (0.044910) \| 0.040612 / 0.037052 (0.003560) \| 0.287832 / 0.258489 (0.029343) \| 0.305617 / 0.293841 (0.011776) \| 0.023936 / 0.128546 (-0.104610) \| 0.007565 / 0.075646 (-0.068081) \| 0.054037 / 0.419271 (-0.365235) \| 0.032389 / 0.043533 (-0.011144) \| 0.283031 / 0.255139 (0.027892) \| 0.295411 / 0.283200 (0.012212) \| 0.018466 / 0.141683 (-0.123217) \| 1.134660 / 1.452155 (-0.317495) \| 1.196212 / 1.492716 (-0.296504) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.099961 / 0.018006 (0.081955) \| 0.310831 / 0.000490 (0.310342) \| 0.000238 / 0.000200 (0.000038) \| 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) \| 0.021566 / 0.037411 (-0.015845) \| 0.070255 / 0.014526 (0.055729) \| 0.081221 / 0.176557 (-0.095336) \| 0.119404 / 0.737135 (-0.617732) \| 0.083005 / 0.296338 (-0.213333) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.302788 / 0.215209 (0.087579) \| 2.928876 / 2.077655 (0.851221) \| 1.601221 / 1.504120 (0.097101) \| 1.485147 / 1.541195 (-0.056047) \| 1.508698 / 1.468490 (0.040207) \| 0.402783 / 4.584777 (-4.181994) \| 2.432151 / 3.745712 (-1.313561) \| 2.476848 / 5.269862 (-2.793013) \| 1.585487 / 4.565676 (-2.980189) \| 0.045965 / 0.424275 (-0.378310) \| 0.004818 / 0.007607 (-0.002789) \| 0.354847 / 0.226044 (0.128803) \| 3.500670 / 2.268929 (1.231742) \| 1.951904 / 55.444624 (-53.492720) \| 1.675152 / 6.876477 (-5.201325) \| 1.795971 / 2.142072 (-0.346101) \| 0.470625 / 4.805227 (-4.334602) \| 0.126080 / 6.500664 (-6.374584) \| 0.040506 / 0.075469 (-0.034963) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.985251 / 1.841788 (-0.856536) \| 12.316710 / 8.074308 (4.242402) \| 10.674437 / 10.191392 (0.483045) \| 0.133622 / 0.680424 (-0.546802) \| 0.016756 / 0.534201 (-0.517445) \| 0.269318 / 0.579283 (-0.309965) \| 0.282258 / 0.434364 (-0.152106) \| 0.309941 / 0.540337 (-0.230396) \| 0.403189 / 1.386936 (-0.983747) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#08ceb927025575c453228cab31291b74043dba1a \"CML watermark\")\n", "I am merging this PR because we need it by `datasets-server`.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004935 / 0.011353 (-0.006418) \| 0.002643 / 0.011008 (-0.008365) \| 0.064449 / 0.038508 (0.025941) \| 0.053110 / 0.023109 (0.030001) \| 0.261576 / 0.275898 (-0.014322) \| 0.270866 / 0.323480 (-0.052614) \| 0.002895 / 0.007986 (-0.005091) \| 0.002349 / 0.004328 (-0.001979) \| 0.047620 / 0.004250 (0.043370) \| 0.038699 / 0.037052 (0.001647) \| 0.246663 / 0.258489 (-0.011826) \| 0.282021 / 0.293841 (-0.011820) \| 0.022807 / 0.128546 (-0.105739) \| 0.007242 / 0.075646 (-0.068404) \| 0.204236 / 0.419271 (-0.215035) \| 0.035429 / 0.043533 (-0.008104) \| 0.241684 / 0.255139 (-0.013455) \| 0.262343 / 0.283200 (-0.020857) \| 0.020036 / 0.141683 (-0.121647) \| 1.112687 / 1.452155 (-0.339467) \| 1.167086 / 1.492716 (-0.325630) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.107059 / 0.018006 (0.089053) \| 0.301036 / 0.000490 (0.300546) \| 0.000224 / 0.000200 (0.000024) \| 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) \| 0.018464 / 0.037411 (-0.018947) \| 0.063822 / 0.014526 (0.049296) \| 0.073562 / 0.176557 (-0.102994) \| 0.120136 / 0.737135 (-0.616999) \| 0.074934 / 0.296338 (-0.221405) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.275474 / 0.215209 (0.060265) \| 2.714239 / 2.077655 (0.636584) \| 1.455535 / 1.504120 (-0.048585) \| 1.336530 / 1.541195 (-0.204665) \| 1.359607 / 1.468490 (-0.108883) \| 0.396303 / 4.584777 (-4.188474) \| 2.366076 / 3.745712 (-1.379636) \| 2.600755 / 5.269862 (-2.669107) \| 1.572382 / 4.565676 (-2.993294) \| 0.045795 / 0.424275 (-0.378480) \| 0.004932 / 0.007607 (-0.002675) \| 0.332175 / 0.226044 (0.106130) \| 3.257843 / 2.268929 (0.988915) \| 1.799021 / 55.444624 (-53.645603) \| 1.532813 / 6.876477 (-5.343663) \| 1.552279 / 2.142072 (-0.589794) \| 0.471369 / 4.805227 (-4.333858) \| 0.098931 / 6.500664 (-6.401733) \| 0.042735 / 0.075469 (-0.032734) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.960779 / 1.841788 (-0.881009) \| 11.741631 / 8.074308 (3.667322) \| 10.355721 / 10.191392 (0.164329) \| 0.129025 / 0.680424 (-0.551399) \| 0.013794 / 0.534201 (-0.520407) \| 0.267268 / 0.579283 (-0.312015) \| 0.265582 / 0.434364 (-0.168782) \| 0.306242 / 0.540337 (-0.234095) \| 0.400367 / 1.386936 (-0.986569) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004966 / 0.011353 (-0.006387) \| 0.002846 / 0.011008 (-0.008163) \| 0.049104 / 0.038508 (0.010596) \| 0.055436 / 0.023109 (0.032327) \| 0.273892 / 0.275898 (-0.002006) \| 0.300207 / 0.323480 (-0.023273) \| 0.004017 / 0.007986 (-0.003969) \| 0.002465 / 0.004328 (-0.001863) \| 0.048088 / 0.004250 (0.043837) \| 0.040037 / 0.037052 (0.002984) \| 0.279918 / 0.258489 (0.021429) \| 0.305378 / 0.293841 (0.011537) \| 0.024326 / 0.128546 (-0.104220) \| 0.006992 / 0.075646 (-0.068654) \| 0.053545 / 0.419271 (-0.365726) \| 0.032312 / 0.043533 (-0.011221) \| 0.272899 / 0.255139 (0.017760) \| 0.289683 / 0.283200 (0.006483) \| 0.019121 / 0.141683 (-0.122562) \| 1.133296 / 1.452155 (-0.318858) \| 1.220989 / 1.492716 (-0.271728) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093193 / 0.018006 (0.075187) \| 0.307658 / 0.000490 (0.307168) \| 0.000224 / 0.000200 (0.000024) \| 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) \| 0.022906 / 0.037411 (-0.014506) \| 0.080931 / 0.014526 (0.066405) \| 0.081442 / 0.176557 (-0.095115) \| 0.121150 / 0.737135 (-0.615986) \| 0.083387 / 0.296338 (-0.212952) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.294979 / 0.215209 (0.079770) \| 2.900090 / 2.077655 (0.822435) \| 1.610061 / 1.504120 (0.105941) \| 1.455118 / 1.541195 (-0.086077) \| 1.456599 / 1.468490 (-0.011891) \| 0.397919 / 4.584777 (-4.186858) \| 2.421010 / 3.745712 (-1.324702) \| 2.486527 / 5.269862 (-2.783334) \| 1.573854 / 4.565676 (-2.991822) \| 0.046199 / 0.424275 (-0.378076) \| 0.004888 / 0.007607 (-0.002719) \| 0.342183 / 0.226044 (0.116139) \| 3.392068 / 2.268929 (1.123140) \| 1.963688 / 55.444624 (-53.480936) \| 1.667611 / 6.876477 (-5.208866) \| 1.833706 / 2.142072 (-0.308367) \| 0.509421 / 4.805227 (-4.295806) \| 0.099669 / 6.500664 (-6.400995) \| 0.041004 / 0.075469 (-0.034465) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.956314 / 1.841788 (-0.885474) \| 12.190194 / 8.074308 (4.115886) \| 10.417839 / 10.191392 (0.226447) \| 0.144139 / 0.680424 (-0.536285) \| 0.015841 / 0.534201 (-0.518359) \| 0.270436 / 0.579283 (-0.308847) \| 0.273952 / 0.434364 (-0.160412) \| 0.303018 / 0.540337 (-0.237319) \| 0.410163 / 1.386936 (-0.976773) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#aa8558fc7fe1f9f7675c7c5d21a14d1a19598296 \"CML watermark\")\n" ]	2023-11-16T16:02:55Z	2023-11-22T15:18:51Z	2023-11-22T15:12:33Z	MEMBER	null	null	null	Create `DatasetNotFoundError` and `DataFilesNotFoundError`. Fix #6397. CC: @severo	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6431/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6431/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6431.diff", "html_url": "https://github.com/huggingface/datasets/pull/6431", "merged_at": "2023-11-22T15:12:33Z", "patch_url": "https://github.com/huggingface/datasets/pull/6431.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6431" }
https://api.github.com/repos/huggingface/datasets/issues/7122	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/7122/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/7122/comments	https://api.github.com/repos/huggingface/datasets/issues/7122/events	https://github.com/huggingface/datasets/issues/7122	2,482,491,258	I_kwDODunzps6T9896	7,122	[interleave_dataset] sample batches from a single source at a time	{ "avatar_url": "https://avatars.githubusercontent.com/u/4197249?v=4", "events_url": "https://api.github.com/users/memray/events{/privacy}", "followers_url": "https://api.github.com/users/memray/followers", "following_url": "https://api.github.com/users/memray/following{/other_user}", "gists_url": "https://api.github.com/users/memray/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/memray", "id": 4197249, "login": "memray", "node_id": "MDQ6VXNlcjQxOTcyNDk=", "organizations_url": "https://api.github.com/users/memray/orgs", "received_events_url": "https://api.github.com/users/memray/received_events", "repos_url": "https://api.github.com/users/memray/repos", "site_admin": false, "starred_url": "https://api.github.com/users/memray/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/memray/subscriptions", "type": "User", "url": "https://api.github.com/users/memray", "user_view_type": "public" }	[ { "color": "a2eeef", "default": true, "description": "New feature or request", "id": 1935892871, "name": "enhancement", "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement" } ]	open	false	null	[]	null	[]	2024-08-23T07:21:15Z	2024-08-23T07:21:15Z	null	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Feature request interleave_dataset and [RandomlyCyclingMultiSourcesExamplesIterable](https://github.com/huggingface/datasets/blob/3813ce846e52824b38e53895810682f0a496a2e3/src/datasets/iterable_dataset.py#L816) enable us to sample data examples from different sources. But can we also sample batches in a similar manner (each batch only contains data from a single source)? ### Motivation Some recent research [[1](https://blog.salesforceairesearch.com/sfr-embedded-mistral/), [2](https://arxiv.org/pdf/2310.07554)] shows that source homogenous batching can be helpful for contrastive learning. Can we add a function called `RandomlyCyclingMultiSourcesBatchesIterable` to support this functionality? ### Your contribution I can contribute a PR. But I wonder what the best way is to test its correctness and robustness.	null	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/7122/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/7122/timeline	null	null	null	null
https://api.github.com/repos/huggingface/datasets/issues/7250	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/7250/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/7250/comments	https://api.github.com/repos/huggingface/datasets/issues/7250/events	https://github.com/huggingface/datasets/pull/7250	2,612,041,969	PR_kwDODunzps5_zDPS	7,250	Basic XML support (mostly copy pasted from text)	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7250). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update." ]	2024-10-24T16:14:50Z	2024-10-24T16:19:18Z	2024-10-24T16:19:16Z	MEMBER	null	null	null	enable the viewer for datasets like https://huggingface.co/datasets/FrancophonIA/e-calm (there will be more and more apparently)	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/7250/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/7250/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/7250.diff", "html_url": "https://github.com/huggingface/datasets/pull/7250", "merged_at": "2024-10-24T16:19:16Z", "patch_url": "https://github.com/huggingface/datasets/pull/7250.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/7250" }
https://api.github.com/repos/huggingface/datasets/issues/5990	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5990/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5990/comments	https://api.github.com/repos/huggingface/datasets/issues/5990/events	https://github.com/huggingface/datasets/issues/5990	1,774,389,854	I_kwDODunzps5pwwpe	5,990	Pushing a large dataset on the hub consistently hangs	{ "avatar_url": "https://avatars.githubusercontent.com/u/10792502?v=4", "events_url": "https://api.github.com/users/AntreasAntoniou/events{/privacy}", "followers_url": "https://api.github.com/users/AntreasAntoniou/followers", "following_url": "https://api.github.com/users/AntreasAntoniou/following{/other_user}", "gists_url": "https://api.github.com/users/AntreasAntoniou/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/AntreasAntoniou", "id": 10792502, "login": "AntreasAntoniou", "node_id": "MDQ6VXNlcjEwNzkyNTAy", "organizations_url": "https://api.github.com/users/AntreasAntoniou/orgs", "received_events_url": "https://api.github.com/users/AntreasAntoniou/received_events", "repos_url": "https://api.github.com/users/AntreasAntoniou/repos", "site_admin": false, "starred_url": "https://api.github.com/users/AntreasAntoniou/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/AntreasAntoniou/subscriptions", "type": "User", "url": "https://api.github.com/users/AntreasAntoniou", "user_view_type": "public" }	[ { "color": "d73a4a", "default": true, "description": "Something isn't working", "id": 1935892857, "name": "bug", "node_id": "MDU6TGFiZWwxOTM1ODkyODU3", "url": "https://api.github.com/repos/huggingface/datasets/labels/bug" } ]	open	false	null	[]	null	[ "Hi @AntreasAntoniou , sorry to know you are facing this issue. To help debugging it, could you tell me:\r\n- What is the total dataset size?\r\n- Is it always failing on the same shard or is the hanging problem happening randomly?\r\n- Were you able to save the dataset as parquet locally? This would help us determine if the problem comes from the upload or the file generation.\r\n\r\nI'm cc-ing @lhoestq who might have some insights from a `datasets` perspective.", "One trick that can also help is to check the traceback when you kill your python process: it will show where in the code it was hanging", "Right. So I did the trick @lhoestq suggested. Here is where things seem to hang\r\n\r\n```\r\nError while uploading 'data/train-00120-of-00195-466c2dbab2eb9989.parquet' to the Hub. \r\nPushing split train to the Hub. \r\nCreating parquet from Arrow format: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 3/3 [00:03<00:00, 1.15s/ba]\r\nUpload 1 LFS files: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:52<00:00, 52.12s/it]\r\nCreating parquet from Arrow format: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 3/3 [00:03<00:00, 1.08s/ba]\r\nUpload 1 LFS files: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:45<00:00, 45.54s/it]\r\nCreating parquet from Arrow format: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 3/3 [00:03<00:00, 1.08s/ba]\r\nCreating parquet from Arrow format: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 3/3 [00:03<00:00, 1.03s/ba^Upload 1 LFS files: 0%\| \| 0/1 [\r\n21:27:35<?, ?it/s] \r\nPushing dataset shards to the dataset hub: 63%\|█████████████████████████████████████████████████████████████▎ \| 122/195 [23:37:11<14:07:59, 696.98s/it]\r\n^CError in sys.excepthook: \r\nTraceback (most recent call last): \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/rich/console.py\", line 1699, in print \r\n extend(render(renderable, render_options)) \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/rich/console.py\", line 1335, in render \r\n yield from self.render(render_output, _options) \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/rich/console.py\", line 1331, in render \r\n for render_output in iter_render: \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/rich/constrain.py\", line 29, in __rich_console__ \r\n yield from console.render(self.renderable, child_options) \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/rich/console.py\", line 1331, in render \r\n for render_output in iter_render: \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/rich/panel.py\", line 220, in __rich_console__ \r\n lines = console.render_lines(renderable, child_options, style=style) \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/rich/console.py\", line 1371, in render_lines \r\n lines = list( \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/rich/segment.py\", line 292, in split_and_crop_lines \r\n for segment in segments: \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/rich/console.py\", line 1331, in render \r\n for render_output in iter_render: \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/rich/padding.py\", line 97, in __rich_console__ \r\n lines = console.render_lines( \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/rich/console.py\", line 1371, in render_lines \r\n lines = list( \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/rich/segment.py\", line 292, in split_and_crop_lines \r\n for segment in segments: \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/rich/console.py\", line 1335, in render \r\n yield from self.render(render_output, _options) \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/rich/console.py\", line 1331, in render \r\n for render_output in iter_render: \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/rich/syntax.py\", line 611, in __rich_console__ \r\n segments = Segments(self._get_syntax(console, options)) \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/rich/segment.py\", line 668, in __init__ \r\n self.segments = list(segments) \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/rich/syntax.py\", line 674, in _get_syntax \r\n lines: Union[List[Text], Lines] = text.split(\"\\n\", allow_blank=ends_on_nl) \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/rich/text.py\", line 1042, in split \r\n lines = Lines( \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/rich/containers.py\", line 70, in __init__ \r\n self._lines: List[\"Text\"] = list(lines) \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/rich/text.py\", line 1043, in <genexpr> \r\n line for line in self.divide(flatten_spans()) if line.plain != separator \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/rich/text.py\", line 385, in plain \r\n if len(self._text) != 1: \r\nKeyboardInterrupt \r\n \r\nOriginal exception was: \r\nTraceback (most recent call last): \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/tqdm/contrib/concurrent.py\", line 51, in _executor_map \r\n return list(tqdm_class(ex.map(fn, iterables, chunksize=chunksize), kwargs)) \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/tqdm/std.py\", line 1178, in __iter__ \r\n for obj in iterable: \r\n File \"/opt/conda/envs/main/lib/python3.10/concurrent/futures/_base.py\", line 621, in result_iterator \r\n yield _result_or_cancel(fs.pop()) \r\n File \"/opt/conda/envs/main/lib/python3.10/concurrent/futures/_base.py\", line 319, in _result_or_cancel \r\n return fut.result(timeout) \r\n File \"/opt/conda/envs/main/lib/python3.10/concurrent/futures/_base.py\", line 453, in result \r\n self._condition.wait(timeout) \r\n File \"/opt/conda/envs/main/lib/python3.10/threading.py\", line 320, in wait \r\n waiter.acquire() \r\nKeyboardInterrupt \r\n \r\nDuring handling of the above exception, another exception occurred: \r\n \r\nTraceback (most recent call last): \r\n File \"/TALI/tali/scripts/validate_dataset.py\", line 127, in <module> \r\n train_dataset.push_to_hub(repo_id=\"Antreas/TALI-base\", max_shard_size=\"5GB\") \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/datasets/dataset_dict.py\", line 1583, in push_to_hub \r\n repo_id, split, uploaded_size, dataset_nbytes, _, _ = self[split]._push_parquet_shards_to_hub( \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/datasets/arrow_dataset.py\", line 5275, in _push_parquet_shards_to_hub \r\n _retry( \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/datasets/utils/file_utils.py\", line 282, in _retry \r\n return func(func_args, *func_kwargs) \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py\", line 118, in _inner_fn \r\n return fn(args, *kwargs) \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/huggingface_hub/hf_api.py\", line 826, in _inner \r\n return fn(self, args, *kwargs) \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/huggingface_hub/hf_api.py\", line 3205, in upload_file \r\n commit_info = self.create_commit( \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py\", line 118, in _inner_fn \r\n return fn(args, *kwargs) \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/huggingface_hub/hf_api.py\", line 826, in _inner \r\n return fn(self, args, *kwargs) \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/huggingface_hub/hf_api.py\", line 2680, in create_commit \r\n upload_lfs_files( \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py\", line 118, in _inner_fn \r\n return fn(args, *kwargs) \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/huggingface_hub/_commit_api.py\", line 353, in upload_lfs_files \r\n thread_map( \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/tqdm/contrib/concurrent.py\", line 69, in thread_map \r\n return _executor_map(ThreadPoolExecutor, fn, iterables, *tqdm_kwargs) \r\n File \"/opt/conda/envs/main/lib/python3.10/site-packages/tqdm/contrib/concurrent.py\", line 49, in _executor_map \r\n with PoolExecutor(max_workers=max_workers, initializer=tqdm_class.set_lock, \r\n File \"/opt/conda/envs/main/lib/python3.10/concurrent/futures/_base.py\", line 649, in __exit__ \r\n self.shutdown(wait=True) \r\n File \"/opt/conda/envs/main/lib/python3.10/concurrent/futures/thread.py\", line 235, in shutdown \r\n t.join() \r\n File \"/opt/conda/envs/main/lib/python3.10/threading.py\", line 1096, in join \r\n self._wait_for_tstate_lock() \r\n File \"/opt/conda/envs/main/lib/python3.10/threading.py\", line 1116, in _wait_for_tstate_lock \r\n if lock.acquire(block, timeout): \r\nKeyboardInterrupt \r\n```", "@Wauplin \r\n\r\n>What is the total dataset size?\r\n\r\nThere are three variants, and the random hanging happens on all three. The sizes are 2TB, 1TB, and 200GB. \r\n\r\n>Is it always failing on the same shard or is the hanging problem happening randomly?\r\n\r\nIt seems to be very much random, as restarting can help move past the previous hang, only to find a new one, or not. \r\n\r\n>Were you able to save the dataset as parquet locally? This would help us determine if the problem comes from the upload or the file generation.\r\n\r\nYes. The dataset seems to be locally stored as parquet. ", "Hmm it looks like an issue with TQDM lock. Maybe you can try updating TQDM ?", "I am using the latest version of tqdm\r\n\r\n```\r\n⬢ [Docker] ❯ pip install tqdm --upgrade\r\nRequirement already satisfied: tqdm in /opt/conda/envs/main/lib/python3.10/site-packages (4.65.0)\r\nWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\r\n```", "I tried trying to catch the hanging issue in action again\r\n\r\n```\r\nPushing dataset shards to the dataset hub: 65%\|█████████████████████████████████████████████████████████████████▊ \| 127/195 [2:28:02<1:19:15, 69.94s/it] \r\nError while uploading 'data/train-00127-of-00195-3f8d036ade107c27.parquet' to the Hub. \r\nPushing split train to the Hub. \r\nPushing dataset shards to the dataset hub: 64%\|████████████████████████████████████████████████████████████████▏ \| 124/195 [2:06:10<1:12:14, 61.05s/it]C^[^C^C^C \r\n╭─────────────────────────────── Traceback (most recent call last) ────────────────────────────────╮ \r\n│ /TALI/tali/scripts/validate_dataset.py:127 in <module> │ \r\n│ │ \r\n│ 124 │ │ \r\n│ 125 │ while not succesful_competion: │ \r\n│ 126 │ │ try: │ \r\n│ ❱ 127 │ │ │ train_dataset.push_to_hub(repo_id=\"Antreas/TALI-base\", max_shard_size=\"5GB\") │ \r\n│ 128 │ │ │ succesful_competion = True │ \r\n│ 129 │ │ except Exception as e: │ \r\n│ 130 │ │ │ print(e) │ \r\n│ │ \r\n│ /opt/conda/envs/main/lib/python3.10/site-packages/datasets/dataset_dict.py:1583 in push_to_hub │ \r\n│ │ \r\n│ 1580 │ │ for split in self.keys(): │ \r\n│ 1581 │ │ │ logger.warning(f\"Pushing split {split} to the Hub.\") │ \r\n│ 1582 │ │ │ # The split=key needs to be removed before merging │ \r\n│ ❱ 1583 │ │ │ repo_id, split, uploaded_size, dataset_nbytes, _, _ = self[split]._push_parq │ \r\n│ 1584 │ │ │ │ repo_id, │ \r\n│ 1585 │ │ │ │ split=split, │ \r\n│ 1586 │ │ │ │ private=private, │ \r\n│ │ \r\n│ /opt/conda/envs/main/lib/python3.10/site-packages/datasets/arrow_dataset.py:5263 in │ \r\n│ _push_parquet_shards_to_hub │ \r\n│ │ \r\n│ 5260 │ │ │ \r\n│ 5261 │ │ uploaded_size = 0 │ \r\n│ 5262 │ │ shards_path_in_repo = [] │ \r\n│ ❱ 5263 │ │ for index, shard in logging.tqdm( │ \r\n│ 5264 │ │ │ enumerate(itertools.chain([first_shard], shards_iter)), │ \r\n│ 5265 │ │ │ desc=\"Pushing dataset shards to the dataset hub\", │ \r\n│ 5266 │ │ │ total=num_shards, │ \r\n│ │ \r\n│ /opt/conda/envs/main/lib/python3.10/site-packages/tqdm/std.py:1178 in __iter__ │ \r\n│ │ \r\n│ 1175 │ │ time = self._time │ \r\n│ 1176 │ │ │ \r\n│ 1177 │ │ try: │\r\n│ ❱ 1178 │ │ │ for obj in iterable: │\r\n│ 1179 │ │ │ │ yield obj │\r\n│ 1180 │ │ │ │ # Update and possibly print the progressbar. │\r\n│ 1181 │ │ │ │ # Note: does not call self.update(1) for speed optimisation. │\r\n│ │\r\n│ /opt/conda/envs/main/lib/python3.10/site-packages/datasets/arrow_dataset.py:5238 in │\r\n│ shards_with_embedded_external_files │\r\n│ │\r\n│ 5235 │ │ │ │ for shard in shards: │\r\n│ 5236 │ │ │ │ │ format = shard.format │\r\n│ 5237 │ │ │ │ │ shard = shard.with_format(\"arrow\") │\r\n│ ❱ 5238 │ │ │ │ │ shard = shard.map( │\r\n│ 5239 │ │ │ │ │ │ embed_table_storage, │\r\n│ 5240 │ │ │ │ │ │ batched=True, │\r\n│ 5241 │ │ │ │ │ │ batch_size=1000, │\r\n│ │\r\n│ /opt/conda/envs/main/lib/python3.10/site-packages/datasets/arrow_dataset.py:578 in wrapper │\r\n│ │\r\n│ 575 │ │ else: │\r\n│ 576 │ │ │ self: \"Dataset\" = kwargs.pop(\"self\") │\r\n│ 577 │ │ # apply actual function │\r\n│ ❱ 578 │ │ out: Union[\"Dataset\", \"DatasetDict\"] = func(self, args, *kwargs) │ \r\n│ 579 │ │ datasets: List[\"Dataset\"] = list(out.values()) if isinstance(out, dict) else [ou │ \r\n│ 580 │ │ for dataset in datasets: │ \r\n│ 581 │ │ │ # Remove task templates if a column mapping of the template is no longer val │ \r\n│ │ \r\n│ /opt/conda/envs/main/lib/python3.10/site-packages/datasets/arrow_dataset.py:543 in wrapper │ \r\n│ │ \r\n│ 540 │ │ │ \"output_all_columns\": self._output_all_columns, │ \r\n│ 541 │ │ } │ \r\n│ 542 │ │ # apply actual function │ \r\n│ ❱ 543 │ │ out: Union[\"Dataset\", \"DatasetDict\"] = func(self, args, kwargs) │ \r\n│ 544 │ │ datasets: List[\"Dataset\"] = list(out.values()) if isinstance(out, dict) else [ou │ \r\n│ 545 │ │ # re-apply format to the output │ \r\n│ 546 │ │ for dataset in datasets: │ \r\n│ │ \r\n│ /opt/conda/envs/main/lib/python3.10/site-packages/datasets/arrow_dataset.py:3073 in map │ \r\n│ │ \r\n│ 3070 │ │ │ │ │ leave=False, │ \r\n│ 3071 │ │ │ │ │ desc=desc or \"Map\", │ \r\n│ 3072 │ │ │ │ ) as pbar: │ \r\n│ ❱ 3073 │ │ │ │ │ for rank, done, content in Dataset._map_single(dataset_kwargs): │ \r\n│ 3074 │ │ │ │ │ │ if done: │ \r\n│ 3075 │ │ │ │ │ │ │ shards_done += 1 │ \r\n│ 3076 │ │ │ │ │ │ │ logger.debug(f\"Finished processing shard number {rank} of {n │ \r\n│ │ \r\n│ /opt/conda/envs/main/lib/python3.10/site-packages/datasets/arrow_dataset.py:3464 in _map_single │ \r\n│ │ \r\n│ 3461 │ │ │ │ │ │ │ │ buf_writer, writer, tmp_file = init_buffer_and_writer() │ \r\n│ 3462 │ │ │ │ │ │ │ │ stack.enter_context(writer) │ \r\n│ 3463 │ │ │ │ │ │ │ if isinstance(batch, pa.Table): │ \r\n│ ❱ 3464 │ │ │ │ │ │ │ │ writer.write_table(batch) │ \r\n│ 3465 │ │ │ │ │ │ │ else: │ \r\n│ 3466 │ │ │ │ │ │ │ │ writer.write_batch(batch) │ \r\n│ 3467 │ │ │ │ │ │ num_examples_progress_update += num_examples_in_batch │ \r\n│ │ \r\n│ /opt/conda/envs/main/lib/python3.10/site-packages/datasets/arrow_writer.py:567 in write_table │ \r\n│ │ \r\n│ 564 │ │ │ writer_batch_size = self.writer_batch_size │ \r\n│ 565 │ │ if self.pa_writer is None: │ \r\n│ 566 │ │ │ self._build_writer(inferred_schema=pa_table.schema) │ \r\n│ ❱ 567 │ │ pa_table = pa_table.combine_chunks() │ \r\n│ 568 │ │ pa_table = table_cast(pa_table, self._schema) │ \r\n│ 569 │ │ if self.embed_local_files: │ \r\n│ 570 │ │ │ pa_table = embed_table_storage(pa_table) │ \r\n╰──────────────────────────────────────────────────────────────────────────────────────────────────╯ \r\nKeyboardInterrupt \r\n```", "I'm on my phone so can't help that much. What I'd advice to do is to [save_to_disk](https://huggingface.co/docs/datasets/package_reference/main_classes#save_to_disk) if it's not already done and then upload the files/folder to the Hub separately. You can find what you need in the [upload guide](https://huggingface.co/docs/huggingface_hub/guides/upload). It might not help finding the exact issue for now but at least it can unblock you. ", "In your last stacktrace it interrupted while embedding external content - in case your dataset in made of images or audio files that live on your disk. Is it the case ?", "Yeah, the dataset has images, audio, video and text. ", "It's maybe related to https://github.com/apache/arrow/issues/34455: are you using ArrayND features ?\r\n\r\nAlso what's your `pyarrow` version ? Could you try updating to >= 12.0.1 ?", "I was using pyarrow == 12.0.0\r\n\r\nI am not explicitly using ArrayND features, unless the hub API automatically converts my files to such. ", "I have now updated to pyarrow == 12.0.1 and retrying", "You can also try to reduce the `max_shard_size` - Sometimes parquet has a hard time working with data bigger than 2GB", "So, updating the pyarrow seems to help. It can still throw errors here and there but I can retry when that happens. It's better than hanging. \r\n\r\nHowever, I am a bit confused about something. I have uploaded my datasets, but while earlier I could see all three sets, now I can only see 1. What's going on? \r\nhttps://huggingface.co/datasets/Antreas/TALI-base\r\n\r\nI have seen this happen before as well, so I deleted and reuploaded, but this dataset is way too large for me to do this. ", "It's a bug on our side, I'll update the dataset viewer ;)\r\n\r\nThanks for reporting !", "Apparently this happened because of bad modifications in the README.md split metadata.\r\n\r\nI fixed them in this PR: https://huggingface.co/datasets/Antreas/TALI-base/discussions/1", "@lhoestq It's a bit odd that when uploading a dataset, one set at a time \"train\", \"val\", \"test\", the push_to_hub function overwrites the readme and removes differently named sets from previous commits. i.e., you push \"val\", all is well. Then you push \"test\", and the \"val\" entry disappears from the readme, while the data remain intact. ", "Also, just found another related issue. One of the many that make things hang or fail when pushing to hub. \r\n\r\nIn the following code:\r\n\r\n```python\r\ntrain_generator = lambda: data_generator(\"train\", percentage=1.0)\r\n val_generator = lambda: data_generator(\"val\")\r\n test_generator = lambda: data_generator(\"test\")\r\n\r\n train_data = datasets.Dataset.from_generator(\r\n train_generator,\r\n num_proc=mp.cpu_count(),\r\n writer_batch_size=5000,\r\n cache_dir=tali_dataset_dir,\r\n )\r\n\r\n val_data = datasets.Dataset.from_generator(\r\n val_generator,\r\n writer_batch_size=5000,\r\n num_proc=mp.cpu_count(),\r\n cache_dir=tali_dataset_dir,\r\n )\r\n\r\n test_data = datasets.Dataset.from_generator(\r\n test_generator,\r\n writer_batch_size=5000,\r\n num_proc=mp.cpu_count(),\r\n cache_dir=tali_dataset_dir,\r\n )\r\n\r\n print(f\"Pushing TALI-large to hub\")\r\n\r\n dataset = datasets.DatasetDict(\r\n {\"train\": train_data, \"val\": val_data, \"test\": test_data}\r\n )\r\n succesful_competion = False\r\n\r\n while not succesful_competion:\r\n try:\r\n dataset.push_to_hub(repo_id=\"Antreas/TALI-large\", max_shard_size=\"2GB\")\r\n succesful_competion = True\r\n except Exception as e:\r\n print(e)\r\n ```\r\n \r\n \r\n Things keep failing in the push_to_repo step, at random places, with the following error:\r\n \r\n ```bash\r\n Pushing dataset shards to the dataset hub: 7%\|██████████▋ \| 67/950 [42:41<9:22:37, 38.23s/it]\r\nError while uploading 'data/train-00067-of-00950-a4d179ed5a593486.parquet' to the Hub.\r\nPushing split train to the Hub.\r\nCreating parquet from Arrow format: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 2/2 [00:01<00:00, 1.81ba/s]\r\nUpload 1 LFS files: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:11<00:00, 11.20s/it]\r\nCreating parquet from Arrow format: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 2/2 [00:00<00:00, 2.48ba/s]\r\nUpload 1 LFS files: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:15<00:00, 15.30s/it]\r\nCreating parquet from Arrow format: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 2/2 [00:00<00:00, 2.39ba/s]\r\nUpload 1 LFS files: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:11<00:00, 11.52s/it]\r\nCreating parquet from Arrow format: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 2/2 [00:00<00:00, 2.47ba/s]\r\nUpload 1 LFS files: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:10<00:00, 10.39s/it]\r\nCreating parquet from Arrow format: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 2/2 [00:00<00:00, 2.26ba/s]\r\nUpload 1 LFS files: 0%\| \| 0/1 [16:38<?, ?it/s]\r\nPushing dataset shards to the dataset hub: 7%\|███████████▎ \| 71/950 [44:37<9:12:28, 37.71s/it]\r\nError while uploading 'data/train-00071-of-00950-72bab6e5cb223aee.parquet' to the Hub.\r\nPushing split train to the Hub.\r\nCreating parquet from Arrow format: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 2/2 [00:00<00:00, 2.18ba/s]\r\nUpload 1 LFS files: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:10<00:00, 10.94s/it]\r\nCreating parquet from Arrow format: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 2/2 [00:00<00:00, 2.36ba/s]\r\nUpload 1 LFS files: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:10<00:00, 10.67s/it]\r\nCreating parquet from Arrow format: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 2/2 [00:00<00:00, 2.57ba/s]\r\nUpload 1 LFS files: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:10<00:00, 10.16s/it]\r\nCreating parquet from Arrow format: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 2/2 [00:00<00:00, 2.68ba/s]\r\nUpload 1 LFS files: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:09<00:00, 9.63s/it]\r\nCreating parquet from Arrow format: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 2/2 [00:00<00:00, 2.36ba/s]\r\nUpload 1 LFS files: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:10<00:00, 10.67s/it]\r\nCreating parquet from Arrow format: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 2/2 [00:00<00:00, 2.37ba/s]\r\nUpload 1 LFS files: 0%\| \| 0/1 [16:39<?, ?it/s]\r\nPushing dataset shards to the dataset hub: 8%\|████████████ \| 76/950 [46:21<8:53:08, 36.60s/it]\r\nError while uploading 'data/train-00076-of-00950-b90e4e3b433db179.parquet' to the Hub.\r\nPushing split train to the Hub.\r\nCreating parquet from Arrow format: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 2/2 [00:00<00:00, 2.21ba/s]\r\nUpload 1 LFS files: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:25<00:00, 25.40s/it]\r\nCreating parquet from Arrow format: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 2/2 [00:01<00:00, 1.56ba/s]\r\nUpload 1 LFS files: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:10<00:00, 10.40s/it]\r\nCreating parquet from Arrow format: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 2/2 [00:00<00:00, 2.49ba/s]\r\nUpload 1 LFS files: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:23<00:00, 23.53s/it]\r\nCreating parquet from Arrow format: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 2/2 [00:00<00:00, 2.27ba/s]\r\nUpload 1 LFS files: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:10<00:00, 10.25s/it]\r\nCreating parquet from Arrow format: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 2/2 [00:00<00:00, 2.42ba/s]\r\nUpload 1 LFS files: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:11<00:00, 11.03s/it]\r\nCreating parquet from Arrow format: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 2/2 [00:00<00:00, 2.39ba/s]\r\nUpload 1 LFS files: 0%\| \| 0/1 [16:39<?, ?it/s]\r\nPushing dataset shards to the dataset hub: 9%\|████████████▊ \| 81/950 [48:30<8:40:22, 35.93s/it]\r\nError while uploading 'data/train-00081-of-00950-84b0450a1df093a9.parquet' to the Hub.\r\nPushing split train to the Hub.\r\nCreating parquet from Arrow format: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 2/2 [00:00<00:00, 2.18ba/s]\r\nUpload 1 LFS files: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:11<00:00, 11.65s/it]\r\nCreating parquet from Arrow format: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 2/2 [00:01<00:00, 1.92ba/s]\r\nUpload 1 LFS files: 0%\| \| 0/1 [16:38<?, ?it/s]\r\nPushing dataset shards to the dataset hub: 9%\|█████████████ \| 82/950 [48:55<8:37:57, 35.80s/it]\r\nError while uploading 'data/train-00082-of-00950-0a1f52da35653e08.parquet' to the Hub.\r\nPushing split train to the Hub.\r\nCreating parquet from Arrow format: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 2/2 [00:00<00:00, 2.31ba/s]\r\nUpload 1 LFS files: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:26<00:00, 26.29s/it]\r\nCreating parquet from Arrow format: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 2/2 [00:00<00:00, 2.42ba/s]\r\nUpload 1 LFS files: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:10<00:00, 10.57s/it]\r\nCreating parquet from Arrow format: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 2/2 [00:00<00:00, 2.64ba/s]\r\nUpload 1 LFS files: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:10<00:00, 10.35s/it]\r\nCreating parquet from Arrow format: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 2/2 [00:00<00:00, 2.64ba/s]\r\nUpload 1 LFS files: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:11<00:00, 11.74s/it]\r\nCreating parquet from Arrow format: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 2/2 [00:00<00:00, 2.31ba/s]\r\nUpload 1 LFS files: 0%\| \| 0/1 [16:40<?, ?it/s]\r\nPushing dataset shards to the dataset hub: 9%\|█████████████▋ \| 86/950 [50:48<8:30:25, 35.45s/it]\r\nError while uploading 'data/train-00086-of-00950-e1cc80dd17191b20.parquet' to the Hub.\r\n```\r\n\r\nI have a while loop that forces retries, but it seems that the progress itself is randomly getting lost as well. Any ideas on how to improve this? It has been blocking me for way too long. \r\n\r\nShould I build the parquet manually and then push manually as well? If I do things manually, how can I ensure my dataset works properly with \"stream=True\"? \r\n\r\nThank you for your help and time. ", "> @lhoestq It's a bit odd that when uploading a dataset, one set at a time \"train\", \"val\", \"test\", the push_to_hub function overwrites the readme and removes differently named sets from previous commits. i.e., you push \"val\", all is well. Then you push \"test\", and the \"val\" entry disappears from the readme, while the data remain intact.\r\n\r\nHmm this shouldn't happen. What code did you run exactly ? Using which version of `datasets` ?", "> I have a while loop that forces retries, but it seems that the progress itself is randomly getting lost as well. Any ideas on how to improve this? It has been blocking me for way too long.\r\n\r\nCould you also print the cause of the error (`e.__cause__`) ? Or show the full stack trace when the error happens ?\r\nThis would give more details about why it failed and would help investigate.", "> Should I build the parquet manually and then push manually as well? If I do things manually, how can I ensure my dataset works properly with \"stream=True\"?\r\n\r\nParquet is supported out of the box ^^\r\n\r\nIf you want to make sure it works as expected you can try locally first:\r\n```python\r\nds = load_dataset(\"path/to/local\", streaming=True)\r\n```", "@lhoestq @AntreasAntoniou I transferred this issue to the `datasets` repository as the questions and answers are more related to this repo. Hope it can help other users find the bug and fixes more easily (like updating [tqdm](https://github.com/huggingface/datasets/issues/5990#issuecomment-1607120204) and [pyarrow](https://github.com/huggingface/datasets/issues/5990#issuecomment-1607120278) or [setting a lower `max_shard_size`](https://github.com/huggingface/datasets/issues/5990#issuecomment-1607120328)).\r\n\r\n~For the initial \"pushing large dataset consistently hangs\"-issue, I still think it's best to try to `save_to_disk` first and then upload it manually/with a script (see [upload_folder](https://huggingface.co/docs/huggingface_hub/guides/upload#upload-a-folder)). It's not the most satisfying solution but at least it would confirm from where the problem comes from.~\r\n\r\nEDIT: removed suggestion about saving to disk first (see https://github.com/huggingface/datasets/issues/5990#issuecomment-1607186914).", "> @lhoestq @AntreasAntoniou I transferred this issue to the datasets repository as the questions and answers are more related to this repo. Hope it can help other users find the bug and fixes more easily (like updating https://github.com/huggingface/datasets/issues/5990#issuecomment-1607120204 and https://github.com/huggingface/datasets/issues/5990#issuecomment-1607120278 or https://github.com/huggingface/datasets/issues/5990#issuecomment-1607120328).\r\n\r\nthanks :)\r\n\r\n> For the initial \"pushing large dataset consistently hangs\"-issue, I still think it's best to try to save_to_disk first and then upload it manually/with a script (see [upload_folder](https://huggingface.co/docs/huggingface_hub/guides/upload#upload-a-folder)). It's not the most satisfying solution but at least it would confirm from where the problem comes from.\r\n\r\nAs I've already said in other discussions, I would not recommend pushing files saved with `save_to_disk` to the Hub but save to parquet shards and upload them instead. The Hub does not support datasets saved with `save_to_disk`, which is meant for disk only.", "> As I've already said in other discussions, I would not recommend pushing files saved with save_to_disk to the Hub but save to parquet shards and upload them instead. The Hub does not support datasets saved with save_to_disk, which is meant for disk only.\r\n\r\nWell noted, thanks. That part was not clear to me :)", "Sorry for not replying in a few days, I was on leave. :) \r\n\r\nSo, here are more information as to the error that causes some of the delay\r\n\r\n```bash\r\nPushing Antreas/TALI-tiny to hub\r\nAttempting to push to hub\r\nPushing split train to the Hub.\r\nCreating parquet from Arrow format: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 6/6 [00:24<00:00, 4.06s/ba]\r\nCreating parquet from Arrow format: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 6/6 [00:24<00:00, 4.15s/ba]\r\nCreating parquet from Arrow format: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 6/6 [00:26<00:00, 4.45s/ba]\r\n/opt/conda/envs/main/lib/python3.10/site-packages/huggingface_hub/lfs.py:310: UserWarning: hf_transfer is enabled but does not support uploading from bytes or BinaryIO, falling back to regular upload\r\n warnings.warn(\r\nCreating parquet from Arrow format: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 6/6 [00:25<00:00, 4.26s/ba]\r\nCreating parquet from Arrow format: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 6/6 [00:27<00:00, 4.58s/ba]\r\nCreating parquet from Arrow format: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 6/6 [00:24<00:00, 4.10s/ba]\r\nPushing dataset shards to the dataset hub: 22%\|████████████████████████▎ \| 5/23 [52:23<3:08:37, 628.74s/it]\r\nException: Error while uploading 'data/train-00005-of-00023-e224d901fd65e062.parquet' to the Hub., with stacktrace: <traceback object at 0x7f745458d0c0>, and type: <class 'RuntimeError'>, and \r\ncause: HTTPSConnectionPool(host='s3.us-east-1.amazonaws.com', port=443): Max retries exceeded with url: \r\n/lfs.huggingface.co/repos/7c/d3/7cd385d9324302dc13e3986331d72d9be6fa0174c63dcfe0e08cd474f7f1e8b7/3415166ae28c0beccbbc692f38742b8dea2c197f5c805321104e888d21d7eb90?X-Amz-Algorithm=AWS4-HMAC-SHA256\r\n&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Credential=AKIA4N7VTDGO27GPWFUO%2F20230627%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20230627T003349Z&X-Amz-Expires=86400&X-Amz-Signature=5a12ff96f2\r\n91f644134170992a6628e5f3c4e7b2e7fc3e940b4378fe11ae5390&X-Amz-SignedHeaders=host&partNumber=1&uploadId=JSsK8r63XSF.VlKQx3Vf8OW4DEVp5YIIY7LPnuapNIegsxs5EHgM1p4u0.Nn6_wlPlQnvxm8HKMxZhczKE9KB74t0etB\r\noLcxqBIvsgey3uXBTZMAEGwU6y7CDUADiEIO&x-id=UploadPart (Caused by SSLError(SSLEOFError(8, 'EOF occurred in violation of protocol (_ssl.c:2426)')))\r\nPush failed, retrying\r\nAttempting to push to hub\r\nPushing split train to the Hub.\r\n```\r\n\r\nOne issue is that the uploading does not continue from the chunk it failed off. It often continues from a very old chunk. e.g. if it failed on chunk 192/250, it will continue from say 53/250, and this behaviour appears almost random. ", "Are you using a proxy of some sort ?", "I am using a kubernetes cluster built into a university VPN. ", "So, other than the random connection drops here and there, any idea why the progress does not continue where it left off?\r\n\r\n```bash\r\nPushing split train to the Hub.\r\nCreating parquet from Arrow format: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 28/28 [00:02<00:00, 10.79ba/s]\r\nCreating parquet from Arrow format: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 28/28 [00:02<00:00, 13.65ba/s]\r\nCreating parquet from Arrow format: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 28/28 [00:02<00:00, 13.39ba/s]\r\nCreating parquet from Arrow format: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 28/28 [00:02<00:00, 13.04ba/s]\r\nCreating parquet from Arrow format: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 28/28 [00:02<00:00, 13.52ba/s]\r\nCreating parquet from Arrow format: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 28/28 [00:02<00:00, 12.28ba/s]\r\nPushing dataset shards to the dataset hub: 20%\|██████████████████████ \| 75/381 [1:34:39<6:26:11, 75.72s/it]\r\nException: Error while uploading 'data/train-00075-of-00381-1614bc251b778766.parquet' to the Hub., with stacktrace: <traceback object at 0x7fab6d9a4980>, and type: <class 'RuntimeError'>, and \r\ncause: HTTPSConnectionPool(host='s3.us-east-1.amazonaws.com', port=443): Max retries exceeded with url: \r\n/lfs.huggingface.co/repos/3b/31/3b311464573d8d63b137fcd5b40af1e7a5b1306843c88e80372d0117157504e5/ed8dae933fb79ae1ef5fb1f698f5125d3e1c02977ac69438631f152bb3bfdd1e?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-\r\nAmz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Credential=AKIA4N7VTDGO27GPWFUO%2F20230629%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20230629T053004Z&X-Amz-Expires=86400&X-Amz-Signature=da2b26270edfd6d0\r\nd069c015a5a432031107a8664c3f0917717e5e40c688183c&X-Amz-SignedHeaders=host&partNumber=1&uploadId=2erWGHTh3ICqBLU_QvHfnygZ2tkMWbL0rEqpJdYohCKHUHnfwMjvoBIg0TI_KSGn4rSKxUxOyqSIzFUFSRSzixZeLeneaXJOw.Qx8\r\nzLKSV5xV7HRQDj4RBesNve6cSoo&x-id=UploadPart (Caused by SSLError(SSLEOFError(8, 'EOF occurred in violation of protocol (_ssl.c:2426)')))\r\nPush failed, retrying\r\nAttempting to push to hub\r\nPushing split train to the Hub.\r\nCreating parquet from Arrow format: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 28/28 [00:02<00:00, 12.09ba/s]\r\nCreating parquet from Arrow format: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 28/28 [00:02<00:00, 11.51ba/s]\r\nCreating parquet from Arrow format: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 28/28 [00:02<00:00, 10.77ba/s]\r\nPushing dataset shards to the dataset hub: 20%\|██████████████████████▋ \| 77/381 [1:32:50<6:06:34, 72.35s/it]\r\nException: Error while uploading 'data/train-00077-of-00381-368b2327a9908aab.parquet' to the Hub., with stacktrace: <traceback object at 0x7fab45b27f80>, and type: <class 'RuntimeError'>, and \r\ncause: HTTPSConnectionPool(host='s3.us-east-1.amazonaws.com', port=443): Max retries exceeded with url: \r\n/lfs.huggingface.co/repos/3b/31/3b311464573d8d63b137fcd5b40af1e7a5b1306843c88e80372d0117157504e5/9462ff2c5e61283b53b091984a22de2f41a2f6e37b681171e2eca4a998f979cb?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-\r\nAmz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Credential=AKIA4N7VTDGO27GPWFUO%2F20230629%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20230629T070510Z&X-Amz-Expires=86400&X-Amz-Signature=9ab8487b93d443cd\r\n21f05476405855d46051a0771b4986bbb20f770ded21b1a4&X-Amz-SignedHeaders=host&partNumber=1&uploadId=UiHX1B.DcoAO2QmIHpWpCuNPwhXU_o1dsTkTGPqZt1P51o9k0yz.EsFD9eKpQMwgAST3jOatRG78I_JWRBeLBDYYVNp8r0TpIdeSg\r\neUg8uwPZOCPw9y5mWOw8MWJrnBo&x-id=UploadPart (Caused by SSLError(SSLEOFError(8, 'EOF occurred in violation of protocol (_ssl.c:2426)')))\r\nPush failed, retrying\r\nAttempting to push to hub\r\nPushing split train to the Hub.\r\nPushing dataset shards to the dataset hub: 8%\|████████▋ \| 29/381 [27:39<5:50:03, 59.67s/it]\r\nMap: 36%\|████████████████████████████████████████████████████ \| 1000/2764 [00:35<00:34, 51.63 examples/Map: 72%\|████████████████████████████████████████████████████████████████████████████████████████████████████████▏ \| 2000/2764 [00:40<00:15, 49.06 examples/Map: 72%\|████████████████████████████████████████████████████████████████████████████████████████████████████████▏ \| 2000/2764 [00:55<00:15, 49.06 examples/Map: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 2764/2764 [00:56<00:00, 48.82 examples/Pushing dataset shards to the dataset hub: 8%\|████████▉ \| 30/381 [28:35<5:43:03, 58.64s/iPushing dataset shards to the dataset hub: 8%\|█████████▎ \| 31/381 [29:40<5:52:18, 60.40s/iPushing dataset shards to the dataset hub: 8%\|█████████▌ \| 32/381 [30:46<6:02:20, 62.29s/it] \r\nMap: 36%\|███████████████████████████████████████████████████▎ \r\n```\r\n\r\nThis is actually the issue that wastes the most time for me, and I need it fixed. Please advice on how I can go about it.\r\n\r\nNotice how the progress goes from \r\n\| 77/381 to 30/381", "If the any shard is missing on the Hub, it will re-upload it. It looks like the 30th shard was missing on the Hub in your case. \r\n\r\nIt also means that the other files up to the 77th that were successfully uploaded won't be uploaded again.\r\n\r\ncc @mariosasko who might know better" ]	2023-06-10T14:46:47Z	2025-02-15T09:29:10Z	null	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug Once I have locally built a large dataset that I want to push to hub, I use the recommended approach of .push_to_hub to get the dataset on the hub, and after pushing a few shards, it consistently hangs. This has happened over 40 times over the past week, and despite my best efforts to try and catch this happening and kill a process and restart, it seems to be extremely time wasting -- so I came to you to report this and to seek help. I already tried installing hf_transfer, but it doesn't support Byte file uploads so I uninstalled it. ### Reproduction ```python import multiprocessing as mp import pathlib from math import ceil import datasets import numpy as np from tqdm.auto import tqdm from tali.data.data import select_subtitles_between_timestamps from tali.utils import load_json tali_dataset_dir = "/data/" if __name__ == "__main__": full_dataset = datasets.load_dataset( "Antreas/TALI", num_proc=mp.cpu_count(), cache_dir=tali_dataset_dir ) def data_generator(set_name, percentage: float = 1.0): dataset = full_dataset[set_name] for item in tqdm(dataset): video_list = item["youtube_content_video"] video_list = np.random.choice( video_list, int(ceil(len(video_list) * percentage)) ) if len(video_list) == 0: continue captions = item["youtube_subtitle_text"] captions = select_subtitles_between_timestamps( subtitle_dict=load_json( captions.replace( "/data/", tali_dataset_dir, ) ), starting_timestamp=0, ending_timestamp=100000000, ) for video_path in video_list: temp_path = video_path.replace("/data/", tali_dataset_dir) video_path_actual: pathlib.Path = pathlib.Path(temp_path) if video_path_actual.exists(): item["youtube_content_video"] = open(video_path_actual, "rb").read() item["youtube_subtitle_text"] = captions yield item train_generator = lambda: data_generator("train", percentage=0.1) val_generator = lambda: data_generator("val") test_generator = lambda: data_generator("test") train_data = datasets.Dataset.from_generator( train_generator, num_proc=mp.cpu_count(), writer_batch_size=5000, cache_dir=tali_dataset_dir, ) val_data = datasets.Dataset.from_generator( val_generator, writer_batch_size=5000, num_proc=mp.cpu_count(), cache_dir=tali_dataset_dir, ) test_data = datasets.Dataset.from_generator( test_generator, writer_batch_size=5000, num_proc=mp.cpu_count(), cache_dir=tali_dataset_dir, ) dataset = datasets.DatasetDict( { "train": train_data, "val": val_data, "test": test_data, } ) succesful_competion = False while not succesful_competion: try: dataset.push_to_hub(repo_id="Antreas/TALI-small", max_shard_size="5GB") succesful_competion = True except Exception as e: print(e) ``` ### Logs ```shell Pushing dataset shards to the dataset hub: 33%\|██████████████████████████████████████▎ \| 7/21 [24:33<49:06, 210.45s/it] Error while uploading 'data/val-00007-of-00021-6b216a984af1a4c8.parquet' to the Hub. Pushing split train to the Hub. Resuming upload of the dataset shards. Pushing dataset shards to the dataset hub: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 46/46 [42:10<00:00, 55.01s/it] Pushing split val to the Hub. Resuming upload of the dataset shards. Creating parquet from Arrow format: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 3/3 [00:01<00:00, 1.55ba/s] Upload 1 LFS files: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:23<00:00, 23.51s/it] Creating parquet from Arrow format: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 3/3 [00:02<00:00, 1.39ba/s] Upload 1 LFS files: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:30<00:00, 30.19s/it] Creating parquet from Arrow format: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 3/3 [00:02<00:00, 1.28ba/s] Upload 1 LFS files: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:24<00:00, 24.08s/it] Creating parquet from Arrow format: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 3/3 [00:02<00:00, 1.42ba/s] Upload 1 LFS files: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:23<00:00, 23.97s/it] Creating parquet from Arrow format: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 3/3 [00:02<00:00, 1.49ba/s] Creating parquet from Arrow format: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 3/3 [00:02<00:00, 1.54ba/s^ Upload 1 LFS files: 0%\| \| 0/1 [04:42<?, ?it/s] Pushing dataset shards to the dataset hub: 52%\|████████████████████████████████████████████████████████████▏ \| 11/21 [17:23<15:48, 94.82s/it] That's where it got stuck ``` ### System info ```shell - huggingface_hub version: 0.15.1 - Platform: Linux-5.4.0-147-generic-x86_64-with-glibc2.35 - Python version: 3.10.11 - Running in iPython ?: No - Running in notebook ?: No - Running in Google Colab ?: No - Token path ?: /root/.cache/huggingface/token - Has saved token ?: True - Who am I ?: Antreas - Configured git credential helpers: store - FastAI: N/A - Tensorflow: N/A - Torch: 2.1.0.dev20230606+cu121 - Jinja2: 3.1.2 - Graphviz: N/A - Pydot: N/A - Pillow: 9.5.0 - hf_transfer: N/A - gradio: N/A - numpy: 1.24.3 - ENDPOINT: https://huggingface.co - HUGGINGFACE_HUB_CACHE: /root/.cache/huggingface/hub - HUGGINGFACE_ASSETS_CACHE: /root/.cache/huggingface/assets - HF_TOKEN_PATH: /root/.cache/huggingface/token - HF_HUB_OFFLINE: False - HF_HUB_DISABLE_TELEMETRY: False - HF_HUB_DISABLE_PROGRESS_BARS: None - HF_HUB_DISABLE_SYMLINKS_WARNING: False - HF_HUB_DISABLE_EXPERIMENTAL_WARNING: False - HF_HUB_DISABLE_IMPLICIT_TOKEN: False - HF_HUB_ENABLE_HF_TRANSFER: False ```	null	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5990/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5990/timeline	null	null	null	null
https://api.github.com/repos/huggingface/datasets/issues/5256	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5256/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5256/comments	https://api.github.com/repos/huggingface/datasets/issues/5256/events	https://github.com/huggingface/datasets/pull/5256	1,452,652,586	PR_kwDODunzps5DFDY0	5,256	fix wrong print	{ "avatar_url": "https://avatars.githubusercontent.com/u/7569098?v=4", "events_url": "https://api.github.com/users/WrRan/events{/privacy}", "followers_url": "https://api.github.com/users/WrRan/followers", "following_url": "https://api.github.com/users/WrRan/following{/other_user}", "gists_url": "https://api.github.com/users/WrRan/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/WrRan", "id": 7569098, "login": "WrRan", "node_id": "MDQ6VXNlcjc1NjkwOTg=", "organizations_url": "https://api.github.com/users/WrRan/orgs", "received_events_url": "https://api.github.com/users/WrRan/received_events", "repos_url": "https://api.github.com/users/WrRan/repos", "site_admin": false, "starred_url": "https://api.github.com/users/WrRan/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/WrRan/subscriptions", "type": "User", "url": "https://api.github.com/users/WrRan", "user_view_type": "public" }	[]	closed	false	null	[]	null	[]	2022-11-17T03:54:26Z	2022-11-18T11:05:32Z	2022-11-18T11:05:32Z	CONTRIBUTOR	null	null	null	print `encoded_dataset.column_names` not `dataset.column_names`	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5256/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5256/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/5256.diff", "html_url": "https://github.com/huggingface/datasets/pull/5256", "merged_at": "2022-11-18T11:05:32Z", "patch_url": "https://github.com/huggingface/datasets/pull/5256.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5256" }
https://api.github.com/repos/huggingface/datasets/issues/4678	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/4678/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/4678/comments	https://api.github.com/repos/huggingface/datasets/issues/4678/events	https://github.com/huggingface/datasets/issues/4678	1,303,741,432	I_kwDODunzps5NtYP4	4,678	Cant pass streaming dataset to dataloader after take()	{ "avatar_url": "https://avatars.githubusercontent.com/u/39166683?v=4", "events_url": "https://api.github.com/users/zankner/events{/privacy}", "followers_url": "https://api.github.com/users/zankner/followers", "following_url": "https://api.github.com/users/zankner/following{/other_user}", "gists_url": "https://api.github.com/users/zankner/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/zankner", "id": 39166683, "login": "zankner", "node_id": "MDQ6VXNlcjM5MTY2Njgz", "organizations_url": "https://api.github.com/users/zankner/orgs", "received_events_url": "https://api.github.com/users/zankner/received_events", "repos_url": "https://api.github.com/users/zankner/repos", "site_admin": false, "starred_url": "https://api.github.com/users/zankner/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/zankner/subscriptions", "type": "User", "url": "https://api.github.com/users/zankner", "user_view_type": "public" }	[ { "color": "d73a4a", "default": true, "description": "Something isn't working", "id": 1935892857, "name": "bug", "node_id": "MDU6TGFiZWwxOTM1ODkyODU3", "url": "https://api.github.com/repos/huggingface/datasets/labels/bug" } ]	open	false	null	[]	null	[ "Hi! Calling `take` on an iterable/streamable dataset makes it not possible to shard the dataset, which in turn disables multi-process loading (attempts to split the workload over the shards), so to go past this limitation, you can either use single-process loading in `DataLoader` (`num_workers=None`) or fetch the first `50_000/batch_size` batches in the loop." ]	2022-07-13T17:34:18Z	2022-07-14T13:07:21Z	null	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	## Describe the bug I am trying to pass a streaming version of c4 to a dataloader, but it can't be passed after I call `dataset.take(n)`. Some functions such as `shuffle()` can be applied without breaking the dataloader but not take. ## Steps to reproduce the bug ```python import datasets import torch dset = datasets.load_dataset(path='c4', name='en', split="train", streaming=True) dset = dset.take(50_000) dset = dset.with_format("torch") num_workers = 8 batch_size = 512 loader = torch.utils.data.DataLoader(dataset=dset, batch_size=batch_size, num_workers=num_workers) for batch in loader: ... ``` ## Expected results No error thrown when iterating over the dataloader ## Actual results Original Traceback (most recent call last): File "/usr/local/lib/python3.9/dist-packages/torch/utils/data/_utils/worker.py", line 287, in _worker_loop data = fetcher.fetch(index) File "/usr/local/lib/python3.9/dist-packages/torch/utils/data/_utils/fetch.py", line 32, in fetch data.append(next(self.dataset_iter)) File "/root/.local/lib/python3.9/site-packages/datasets/formatting/dataset_wrappers/torch_iterable_dataset.py", line 48, in __iter__ for key, example in self._iter_shard(shard_idx): File "/root/.local/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 586, in _iter_shard yield from ex_iterable.shard_data_sources(shard_idx) File "/root/.local/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 60, in shard_data_sources raise NotImplementedError(f"{type(self)} doesn't implement shard_data_sources yet") NotImplementedError: <class 'datasets.iterable_dataset.TakeExamplesIterable'> doesn't implement shard_data_sources yet ## Environment info - `datasets` version: 2.3.2 - Platform: Linux-5.4.0-120-generic-x86_64-with-glibc2.31 - Python version: 3.9.13 - PyArrow version: 8.0.0 - Pandas version: 1.4.3	null	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 1, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 1, "url": "https://api.github.com/repos/huggingface/datasets/issues/4678/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/4678/timeline	null	null	null	null
https://api.github.com/repos/huggingface/datasets/issues/6574	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6574/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6574/comments	https://api.github.com/repos/huggingface/datasets/issues/6574/events	https://github.com/huggingface/datasets/pull/6574	2,072,579,549	PR_kwDODunzps5jltBC	6,574	Fix tests based on datasets that used to have scripts	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6574). 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) \| 0.005447 / 0.011353 (-0.005906) \| 0.004030 / 0.011008 (-0.006978) \| 0.063770 / 0.038508 (0.025262) \| 0.032602 / 0.023109 (0.009493) \| 0.247722 / 0.275898 (-0.028176) \| 0.286507 / 0.323480 (-0.036973) \| 0.003035 / 0.007986 (-0.004951) \| 0.003638 / 0.004328 (-0.000690) \| 0.048790 / 0.004250 (0.044540) \| 0.045358 / 0.037052 (0.008306) \| 0.256308 / 0.258489 (-0.002181) \| 0.286601 / 0.293841 (-0.007239) \| 0.028644 / 0.128546 (-0.099903) \| 0.011149 / 0.075646 (-0.064497) \| 0.209796 / 0.419271 (-0.209475) \| 0.036737 / 0.043533 (-0.006796) \| 0.247427 / 0.255139 (-0.007712) \| 0.274564 / 0.283200 (-0.008636) \| 0.019717 / 0.141683 (-0.121966) \| 1.107423 / 1.452155 (-0.344732) \| 1.167830 / 1.492716 (-0.324886) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.095695 / 0.018006 (0.077688) \| 0.305675 / 0.000490 (0.305185) \| 0.000211 / 0.000200 (0.000011) \| 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) \| 0.018969 / 0.037411 (-0.018443) \| 0.063764 / 0.014526 (0.049239) \| 0.075831 / 0.176557 (-0.100726) \| 0.125340 / 0.737135 (-0.611795) \| 0.077585 / 0.296338 (-0.218753) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.280876 / 0.215209 (0.065667) \| 2.748107 / 2.077655 (0.670452) \| 1.452201 / 1.504120 (-0.051919) \| 1.328001 / 1.541195 (-0.213194) \| 1.415581 / 1.468490 (-0.052909) \| 0.568228 / 4.584777 (-4.016549) \| 2.410486 / 3.745712 (-1.335226) \| 2.975157 / 5.269862 (-2.294704) \| 1.854096 / 4.565676 (-2.711581) \| 0.063275 / 0.424275 (-0.361000) \| 0.005121 / 0.007607 (-0.002487) \| 0.340006 / 0.226044 (0.113961) \| 3.362404 / 2.268929 (1.093476) \| 1.803913 / 55.444624 (-53.640711) \| 1.540557 / 6.876477 (-5.335919) \| 1.629240 / 2.142072 (-0.512833) \| 0.653595 / 4.805227 (-4.151632) \| 0.119558 / 6.500664 (-6.381107) \| 0.044365 / 0.075469 (-0.031104) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.964557 / 1.841788 (-0.877231) \| 12.550303 / 8.074308 (4.475995) \| 10.261302 / 10.191392 (0.069910) \| 0.130834 / 0.680424 (-0.549589) \| 0.014458 / 0.534201 (-0.519743) \| 0.294833 / 0.579283 (-0.284450) \| 0.268141 / 0.434364 (-0.166223) \| 0.332492 / 0.540337 (-0.207845) \| 0.427835 / 1.386936 (-0.959101) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005577 / 0.011353 (-0.005776) \| 0.003823 / 0.011008 (-0.007185) \| 0.050815 / 0.038508 (0.012307) \| 0.031197 / 0.023109 (0.008088) \| 0.269869 / 0.275898 (-0.006029) \| 0.294371 / 0.323480 (-0.029109) \| 0.004153 / 0.007986 (-0.003833) \| 0.002884 / 0.004328 (-0.001445) \| 0.048985 / 0.004250 (0.044735) \| 0.047824 / 0.037052 (0.010772) \| 0.270062 / 0.258489 (0.011573) \| 0.306354 / 0.293841 (0.012514) \| 0.030614 / 0.128546 (-0.097932) \| 0.011209 / 0.075646 (-0.064438) \| 0.058943 / 0.419271 (-0.360329) \| 0.060824 / 0.043533 (0.017291) \| 0.273580 / 0.255139 (0.018441) \| 0.288375 / 0.283200 (0.005175) \| 0.022097 / 0.141683 (-0.119585) \| 1.159109 / 1.452155 (-0.293046) \| 1.201463 / 1.492716 (-0.291253) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093024 / 0.018006 (0.075018) \| 0.302838 / 0.000490 (0.302348) \| 0.000223 / 0.000200 (0.000023) \| 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) \| 0.022991 / 0.037411 (-0.014420) \| 0.081575 / 0.014526 (0.067050) \| 0.090134 / 0.176557 (-0.086423) \| 0.129506 / 0.737135 (-0.607629) \| 0.091747 / 0.296338 (-0.204592) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.294735 / 0.215209 (0.079525) \| 2.857557 / 2.077655 (0.779902) \| 1.590577 / 1.504120 (0.086457) \| 1.479404 / 1.541195 (-0.061790) \| 1.515746 / 1.468490 (0.047256) \| 0.579934 / 4.584777 (-4.004843) \| 2.462790 / 3.745712 (-1.282922) \| 2.944498 / 5.269862 (-2.325363) \| 1.836767 / 4.565676 (-2.728909) \| 0.064899 / 0.424275 (-0.359376) \| 0.005232 / 0.007607 (-0.002375) \| 0.349708 / 0.226044 (0.123664) \| 3.424801 / 2.268929 (1.155873) \| 1.945331 / 55.444624 (-53.499294) \| 1.688862 / 6.876477 (-5.187615) \| 1.712593 / 2.142072 (-0.429480) \| 0.665894 / 4.805227 (-4.139333) \| 0.121356 / 6.500664 (-6.379308) \| 0.046908 / 0.075469 (-0.028561) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.983507 / 1.841788 (-0.858280) \| 13.279790 / 8.074308 (5.205482) \| 11.623531 / 10.191392 (1.432139) \| 0.144567 / 0.680424 (-0.535857) \| 0.016253 / 0.534201 (-0.517948) \| 0.291842 / 0.579283 (-0.287441) \| 0.278389 / 0.434364 (-0.155975) \| 0.328971 / 0.540337 (-0.211366) \| 0.443204 / 1.386936 (-0.943732) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9fad0c69738434aec91b61d52c0450336f7535ed \"CML watermark\")\n" ]	2024-01-09T15:16:16Z	2024-01-09T16:11:33Z	2024-01-09T16:05:13Z	MEMBER	null	null	null	...now that `squad` and `paws` don't have a script anymore	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6574/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6574/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6574.diff", "html_url": "https://github.com/huggingface/datasets/pull/6574", "merged_at": "2024-01-09T16:05:13Z", "patch_url": "https://github.com/huggingface/datasets/pull/6574.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6574" }
https://api.github.com/repos/huggingface/datasets/issues/5941	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5941/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5941/comments	https://api.github.com/repos/huggingface/datasets/issues/5941/events	https://github.com/huggingface/datasets/issues/5941	1,751,838,897	I_kwDODunzps5oavCx	5,941	Load Data Sets Too Slow In Train Seq2seq Model	{ "avatar_url": "https://avatars.githubusercontent.com/u/19569322?v=4", "events_url": "https://api.github.com/users/xyx361100238/events{/privacy}", "followers_url": "https://api.github.com/users/xyx361100238/followers", "following_url": "https://api.github.com/users/xyx361100238/following{/other_user}", "gists_url": "https://api.github.com/users/xyx361100238/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/xyx361100238", "id": 19569322, "login": "xyx361100238", "node_id": "MDQ6VXNlcjE5NTY5MzIy", "organizations_url": "https://api.github.com/users/xyx361100238/orgs", "received_events_url": "https://api.github.com/users/xyx361100238/received_events", "repos_url": "https://api.github.com/users/xyx361100238/repos", "site_admin": false, "starred_url": "https://api.github.com/users/xyx361100238/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/xyx361100238/subscriptions", "type": "User", "url": "https://api.github.com/users/xyx361100238", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "Hi ! you can speed it up using multiprocessing by passing `num_proc=` to `load_dataset()`", "already did，but not useful for step Generating train split，it works in step \"Resolving data files\" & \"Downloading data files\" ", "@mariosasko some advice ， thanks！", "I met the same problem, terrible experience", "@mariosasko ", "We need more info about the issue to provide help. \r\n\r\nCan you interrupt the process (with `num_proc=None`) after the `load_dataset` call when the slowdown occurs? So we can know what part of the code is causing it.\r\n\r\nThe `audiofolder` \\ `imagefolder` with metadata is not performant for large datasets. Luckily, we can make them much faster if drop the nested metadata files feature (not that useful). I plan to work on this soon.\r\n\r\nIn the meantime, it's better to use `Dataset.from_generator` (requires replacing the `load_dataset` calls in the transformers script with `Dataset.from_generator`) or write a dataset loading script for large datasets.", "Can you interrupt the process (with num_proc=None) after the load_dataset call when the slowdown occurs? So we can know what part of the code is causing it.\r\n（I'll try this operation）\r\nThe audiofolder \\ imagefolder with metadata is not performant for large datasets. Luckily, we can make them much faster if drop the nested metadata files feature (not that useful). I plan to work on this soon.\r\n(My data is indeed a bit large, exceeding 10000 hours of audio data. Looking forward to your improvement work very much)\r\n\r\nIn the meantime, it's better to use Dataset.from_generator (requires replacing the load_dataset calls in the transformers script with Dataset.from_generator) or write a dataset loading script for large datasets.\r\n（I want to use Dataset.from_generator instead of load_dataset ，where can i found sample code to load audio&label dataset， I was to do asr task）", "Can you interrupt the process (with num_proc=None) after the load_dataset call when the slowdown occurs? So we can know what part of the code is causing it.\r\n================================================================================\r\nHere is the log：\r\n[load_dataset.log](https://github.com/huggingface/datasets/files/12169362/load_dataset.log)\r\n（The larger my training data, the slower it loads）\r\n![image](https://github.com/huggingface/datasets/assets/19569322/381b73e4-0a54-4240-b95e-cb8164584047)\r\n\r\n", "In the meantime, it's better to use Dataset.from_generator (requires replacing the load_dataset calls in the transformers script with Dataset.from_generator) or write a dataset loading script for large datasets.\r\n================================================================================\r\nI tried ‘Dataset. from_generator’ implements data loading, but the testing results show no improvement", "I have already solved this problem, referring to #5990 : read audio frist, then use data_generator to change format ." ]	2023-06-12T03:58:43Z	2023-08-15T02:52:22Z	2023-08-15T02:52:22Z	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug step 'Generating train split' in load_dataset is too slow： ![image](https://github.com/huggingface/datasets/assets/19569322/d9b08eee-95fe-4741-a346-b70416c948f8) ### Steps to reproduce the bug Data： own data，16K16B Mono wav Oficial Script:[ run_speech_recognition_seq2seq.py](https://github.com/huggingface/transformers/blob/main/examples/pytorch/speech-recognition/run_speech_recognition_seq2seq.py) Add Code： if data_args.data_path is not None: print(data_args.data_path) raw_datasets = load_dataset("audiofolder", data_dir=data_args.data_path, cache_dir=model_args.cache_dir) raw_datasets = raw_datasets.cast_column("audio", Audio(sampling_rate=16000)) raw_datasets = raw_datasets["train"].train_test_split(test_size=0.005, shuffle=True) （change cache_dir to other path ，ex:/DATA/cache） ### Expected behavior load data fast,at least 1000+ `Generating train split: 387875 examples [32:24:45, 1154.83 examples/s]` ### Environment info - `transformers` version: 4.28.0.dev0 - Platform: Linux-5.4.0-149-generic-x86_64-with-debian-bullseye-sid - Python version: 3.7.16 - Huggingface_hub version: 0.13.2 - PyTorch version (GPU?): 1.13.1+cu116 (True) - Tensorflow version (GPU?): not installed (NA) - Flax version (CPU?/GPU?/TPU?): not installed (NA) - Jax version: not installed - JaxLib version: not installed - Using GPU in script?: <fill in> - Using distributed or parallel set-up in script?: <fill in>	{ "avatar_url": "https://avatars.githubusercontent.com/u/19569322?v=4", "events_url": "https://api.github.com/users/xyx361100238/events{/privacy}", "followers_url": "https://api.github.com/users/xyx361100238/followers", "following_url": "https://api.github.com/users/xyx361100238/following{/other_user}", "gists_url": "https://api.github.com/users/xyx361100238/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/xyx361100238", "id": 19569322, "login": "xyx361100238", "node_id": "MDQ6VXNlcjE5NTY5MzIy", "organizations_url": "https://api.github.com/users/xyx361100238/orgs", "received_events_url": "https://api.github.com/users/xyx361100238/received_events", "repos_url": "https://api.github.com/users/xyx361100238/repos", "site_admin": false, "starred_url": "https://api.github.com/users/xyx361100238/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/xyx361100238/subscriptions", "type": "User", "url": "https://api.github.com/users/xyx361100238", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5941/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5941/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/6620	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6620/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6620/comments	https://api.github.com/repos/huggingface/datasets/issues/6620/events	https://github.com/huggingface/datasets/issues/6620	2,103,110,536	I_kwDODunzps59WuuI	6,620	wiki_dpr.py error (ID mismatch between lines {id} and vector {vec_id}	{ "avatar_url": "https://avatars.githubusercontent.com/u/101498700?v=4", "events_url": "https://api.github.com/users/kiehls90/events{/privacy}", "followers_url": "https://api.github.com/users/kiehls90/followers", "following_url": "https://api.github.com/users/kiehls90/following{/other_user}", "gists_url": "https://api.github.com/users/kiehls90/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/kiehls90", "id": 101498700, "login": "kiehls90", "node_id": "U_kgDOBgy_TA", "organizations_url": "https://api.github.com/users/kiehls90/orgs", "received_events_url": "https://api.github.com/users/kiehls90/received_events", "repos_url": "https://api.github.com/users/kiehls90/repos", "site_admin": false, "starred_url": "https://api.github.com/users/kiehls90/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/kiehls90/subscriptions", "type": "User", "url": "https://api.github.com/users/kiehls90", "user_view_type": "public" }	[]	closed	false	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	[ { "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" } ]	null	[ "Thanks for reporting, @kiehls90.\r\n\r\nAs this seems an issue with the specific \"wiki_dpr\" dataset, I am transferring the issue to the corresponding dataset page: https://huggingface.co/datasets/wiki_dpr/discussions/13" ]	2024-01-27T01:00:09Z	2024-02-06T09:40:19Z	2024-02-06T09:40:19Z	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug I'm trying to run a rag example, and the dataset is wiki_dpr. wiki_dpr download and extracting have been completed successfully. However, at the generating train split stage, an error from wiki_dpr.py keeps popping up. Especially in "_generate_examples" : 1. The following error occurs in the line id, text, title = line.strip().split("\t") ValueError: not enough values to unpack (expected 3, got 2) -> This part handles exceptions so that even if an error occurs, it passes. 2. ID mismatch between lines {id} and vector {vec_id} This error seems to occur at the line " assert int(id) == int(vec_id),". After I handled the exception in the split error, generating train split progressed to 80%, but an id mismatch error occurred at about the 16200000th vector id. Debugging is even more difficult because it takes a long time to download and split wiki_dpr. I need help. thank you in advance!! ### Steps to reproduce the bug Occurs in the generating train split step when running the rag example in the transformers repository. Specifically, it is an error in wiki_dpr.py. ### Expected behavior . ### Environment info python 3.8	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	{ "+1": 1, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 1, "url": "https://api.github.com/repos/huggingface/datasets/issues/6620/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6620/timeline	null	not_planned	null	null
https://api.github.com/repos/huggingface/datasets/issues/6552	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6552/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6552/comments	https://api.github.com/repos/huggingface/datasets/issues/6552/events	https://github.com/huggingface/datasets/issues/6552	2,063,157,187	I_kwDODunzps56-UfD	6,552	Loading a dataset from Google Colab hangs at "Resolving data files".	{ "avatar_url": "https://avatars.githubusercontent.com/u/99779?v=4", "events_url": "https://api.github.com/users/KelSolaar/events{/privacy}", "followers_url": "https://api.github.com/users/KelSolaar/followers", "following_url": "https://api.github.com/users/KelSolaar/following{/other_user}", "gists_url": "https://api.github.com/users/KelSolaar/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/KelSolaar", "id": 99779, "login": "KelSolaar", "node_id": "MDQ6VXNlcjk5Nzc5", "organizations_url": "https://api.github.com/users/KelSolaar/orgs", "received_events_url": "https://api.github.com/users/KelSolaar/received_events", "repos_url": "https://api.github.com/users/KelSolaar/repos", "site_admin": false, "starred_url": "https://api.github.com/users/KelSolaar/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/KelSolaar/subscriptions", "type": "User", "url": "https://api.github.com/users/KelSolaar", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "This bug comes from the `huggingface_hub` library, see: https://github.com/huggingface/huggingface_hub/issues/1952\r\n\r\nA fix is provided at https://github.com/huggingface/huggingface_hub/pull/1953. Feel free to install `huggingface_hub` from this PR, or wait for it to be merged and the new version of `huggingface_hub` to be released", "Thanks!" ]	2024-01-03T02:18:17Z	2024-01-08T10:09:04Z	2024-01-08T10:09:04Z	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug Hello, I'm trying to load a dataset from Google Colab but the process hangs at `Resolving data files`: ![image](https://github.com/huggingface/datasets/assets/99779/7175ad85-e571-46ed-9f87-92653985777d) It is happening when the `_get_origin_metadata` definition is invoked: ```python def _get_origin_metadata( data_files: List[str], max_workers=64, download_config: Optional[DownloadConfig] = None, ) -> Tuple[str]: return thread_map( partial(_get_single_origin_metadata, download_config=download_config), data_files, max_workers=max_workers, tqdm_class=hf_tqdm, desc="Resolving data files", disable=len(data_files) <= 16, ``` The thread is then stuck at `waiter.acquire()` in the builtin `threading.py` file. I can load the dataset just fine on my machine. Cheers, Thomas ### Steps to reproduce the bug In Google Colab: ```python !pip install datasets from datasets import load_dataset dataset = load_dataset("colour-science/color-checker-detection-dataset") ``` ### Expected behavior The dataset should be loaded. ### Environment info - `datasets` version: 2.16.1 - Platform: Linux-6.1.58+-x86_64-with-glibc2.35 - Python version: 3.10.12 - `huggingface_hub` version: 0.20.1 - PyArrow version: 10.0.1 - Pandas version: 1.5.3 - `fsspec` version: 2023.6.0	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6552/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6552/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/7469	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/7469/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/7469/comments	https://api.github.com/repos/huggingface/datasets/issues/7469/events	https://github.com/huggingface/datasets/issues/7469	2,936,606,080	I_kwDODunzps6vCQ2A	7,469	Custom split name with the web interface	{ "avatar_url": "https://avatars.githubusercontent.com/u/15141326?v=4", "events_url": "https://api.github.com/users/vince62s/events{/privacy}", "followers_url": "https://api.github.com/users/vince62s/followers", "following_url": "https://api.github.com/users/vince62s/following{/other_user}", "gists_url": "https://api.github.com/users/vince62s/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/vince62s", "id": 15141326, "login": "vince62s", "node_id": "MDQ6VXNlcjE1MTQxMzI2", "organizations_url": "https://api.github.com/users/vince62s/orgs", "received_events_url": "https://api.github.com/users/vince62s/received_events", "repos_url": "https://api.github.com/users/vince62s/repos", "site_admin": false, "starred_url": "https://api.github.com/users/vince62s/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/vince62s/subscriptions", "type": "User", "url": "https://api.github.com/users/vince62s", "user_view_type": "public" }	[]	closed	false	null	[]	null	[]	2025-03-20T20:45:59Z	2025-03-21T07:20:37Z	2025-03-21T07:20:37Z	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug According the doc here: https://huggingface.co/docs/hub/datasets-file-names-and-splits#custom-split-name it should infer the split name from the subdir of data or the beg of the name of the files in data. When doing this manually through web upload it does not work. it uses "train" as a unique split. example: https://huggingface.co/datasets/eole-nlp/estimator_chatml ### Steps to reproduce the bug follow the link above ### Expected behavior there should be two splits "mlqe" and "1720_da" ### Environment info website	{ "avatar_url": "https://avatars.githubusercontent.com/u/15141326?v=4", "events_url": "https://api.github.com/users/vince62s/events{/privacy}", "followers_url": "https://api.github.com/users/vince62s/followers", "following_url": "https://api.github.com/users/vince62s/following{/other_user}", "gists_url": "https://api.github.com/users/vince62s/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/vince62s", "id": 15141326, "login": "vince62s", "node_id": "MDQ6VXNlcjE1MTQxMzI2", "organizations_url": "https://api.github.com/users/vince62s/orgs", "received_events_url": "https://api.github.com/users/vince62s/received_events", "repos_url": "https://api.github.com/users/vince62s/repos", "site_admin": false, "starred_url": "https://api.github.com/users/vince62s/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/vince62s/subscriptions", "type": "User", "url": "https://api.github.com/users/vince62s", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/7469/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/7469/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/5475	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5475/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5475/comments	https://api.github.com/repos/huggingface/datasets/issues/5475/events	https://github.com/huggingface/datasets/issues/5475	1,559,030,149	I_kwDODunzps5c7OmF	5,475	Dataset scan time is much slower than using native arrow	{ "avatar_url": "https://avatars.githubusercontent.com/u/121845112?v=4", "events_url": "https://api.github.com/users/jonny-cyberhaven/events{/privacy}", "followers_url": "https://api.github.com/users/jonny-cyberhaven/followers", "following_url": "https://api.github.com/users/jonny-cyberhaven/following{/other_user}", "gists_url": "https://api.github.com/users/jonny-cyberhaven/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/jonny-cyberhaven", "id": 121845112, "login": "jonny-cyberhaven", "node_id": "U_kgDOB0M1eA", "organizations_url": "https://api.github.com/users/jonny-cyberhaven/orgs", "received_events_url": "https://api.github.com/users/jonny-cyberhaven/received_events", "repos_url": "https://api.github.com/users/jonny-cyberhaven/repos", "site_admin": false, "starred_url": "https://api.github.com/users/jonny-cyberhaven/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/jonny-cyberhaven/subscriptions", "type": "User", "url": "https://api.github.com/users/jonny-cyberhaven", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "Hi ! In your code you only iterate on the Arrow buffers - you don't actually load the data as python objects. For a fair comparison, you can modify your code using:\r\n```diff\r\n- for _ in range(0, len(table), bsz):\r\n- _ = {k:table[k][_ : _ + bsz] for k in cols}\r\n+ for _ in range(0, len(table), bsz):\r\n+ _ = {k:table[k][_ : _ + bsz].to_pylist() for k in cols}\r\n```\r\n\r\nI re-ran your code and got a speed ratio of 1.00x and 1.02x", "Ah I see, datasets is implicitly making this conversion. Thanks for pointing that out!\r\n\r\nIf it's not too much, I would also suggest updating some of your docs with the same `.to_pylist()` conversion in the code snippet that follows [here](https://huggingface.co/course/chapter5/4?fw=pt#:~:text=let%E2%80%99s%20run%20a%20little%20speed%20test%20by%20iterating%20over%20all%20the%20elements%20in%20the%20PubMed%20Abstracts%20dataset%3A).", "This code snippet shows `datasets` code that reads the Arrow data as python objects already, there is no need to add to_pylist. Or were you thinking about something else ?" ]	2023-01-27T01:32:25Z	2023-01-30T16:17:11Z	2023-01-30T16:17:11Z	CONTRIBUTOR	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug I'm basically running the same scanning experiment from the tutorials https://huggingface.co/course/chapter5/4?fw=pt except now I'm comparing to a native pyarrow version. I'm finding that the native pyarrow approach is much faster (2 orders of magnitude). Is there something I'm missing that explains this phenomenon? ### Steps to reproduce the bug https://colab.research.google.com/drive/11EtHDaGAf1DKCpvYnAPJUW-LFfAcDzHY?usp=sharing ### Expected behavior I expect scan times to be on par with using pyarrow directly. ### Environment info standard colab environment	{ "avatar_url": "https://avatars.githubusercontent.com/u/121845112?v=4", "events_url": "https://api.github.com/users/jonny-cyberhaven/events{/privacy}", "followers_url": "https://api.github.com/users/jonny-cyberhaven/followers", "following_url": "https://api.github.com/users/jonny-cyberhaven/following{/other_user}", "gists_url": "https://api.github.com/users/jonny-cyberhaven/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/jonny-cyberhaven", "id": 121845112, "login": "jonny-cyberhaven", "node_id": "U_kgDOB0M1eA", "organizations_url": "https://api.github.com/users/jonny-cyberhaven/orgs", "received_events_url": "https://api.github.com/users/jonny-cyberhaven/received_events", "repos_url": "https://api.github.com/users/jonny-cyberhaven/repos", "site_admin": false, "starred_url": "https://api.github.com/users/jonny-cyberhaven/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/jonny-cyberhaven/subscriptions", "type": "User", "url": "https://api.github.com/users/jonny-cyberhaven", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5475/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5475/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/7529	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/7529/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/7529/comments	https://api.github.com/repos/huggingface/datasets/issues/7529/events	https://github.com/huggingface/datasets/issues/7529	3,007,118,969	I_kwDODunzps6zPP55	7,529	audio folder builder cannot detect custom split name	{ "avatar_url": "https://avatars.githubusercontent.com/u/37548991?v=4", "events_url": "https://api.github.com/users/phineas-pta/events{/privacy}", "followers_url": "https://api.github.com/users/phineas-pta/followers", "following_url": "https://api.github.com/users/phineas-pta/following{/other_user}", "gists_url": "https://api.github.com/users/phineas-pta/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/phineas-pta", "id": 37548991, "login": "phineas-pta", "node_id": "MDQ6VXNlcjM3NTQ4OTkx", "organizations_url": "https://api.github.com/users/phineas-pta/orgs", "received_events_url": "https://api.github.com/users/phineas-pta/received_events", "repos_url": "https://api.github.com/users/phineas-pta/repos", "site_admin": false, "starred_url": "https://api.github.com/users/phineas-pta/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/phineas-pta/subscriptions", "type": "User", "url": "https://api.github.com/users/phineas-pta", "user_view_type": "public" }	[]	open	false	null	[]	null	[]	2025-04-20T16:53:21Z	2025-04-20T16:53:21Z	null	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug when using audio folder builder (`load_dataset("audiofolder", data_dir="/path/to/folder")`), it cannot detect custom split name other than train/validation/test ### Steps to reproduce the bug i have the following folder structure ``` my_dataset/ ├── train/ │ ├── lorem.wav │ ├── … │ └── metadata.csv ├── test/ │ ├── ipsum.wav │ ├── … │ └── metadata.csv ├── validation/ │ ├── dolor.wav │ ├── … │ └── metadata.csv └── custom/ ├── sit.wav ├── … └── metadata.csv ``` using `ds = load_dataset("audiofolder", data_dir="/path/to/my_dataset")` ### Expected behavior i got `ds` with only 3 splits train/validation/test, whenever i rename train/validation/test folder it also disappear if i re-create `ds` ### Environment info - `datasets` version: 3.5.0 - Platform: Windows-11-10.0.26100-SP0 - Python version: 3.12.8 - `huggingface_hub` version: 0.30.2 - PyArrow version: 18.1.0 - Pandas version: 2.2.3 - `fsspec` version: 2024.9.0	null	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/7529/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/7529/timeline	null	null	null	null
https://api.github.com/repos/huggingface/datasets/issues/6118	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6118/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6118/comments	https://api.github.com/repos/huggingface/datasets/issues/6118/events	https://github.com/huggingface/datasets/issues/6118	1,835,940,417	I_kwDODunzps5tbjpB	6,118	IterableDataset.from_generator() fails with pickle error when provided a generator or iterator	{ "avatar_url": "https://avatars.githubusercontent.com/u/1281051?v=4", "events_url": "https://api.github.com/users/finkga/events{/privacy}", "followers_url": "https://api.github.com/users/finkga/followers", "following_url": "https://api.github.com/users/finkga/following{/other_user}", "gists_url": "https://api.github.com/users/finkga/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/finkga", "id": 1281051, "login": "finkga", "node_id": "MDQ6VXNlcjEyODEwNTE=", "organizations_url": "https://api.github.com/users/finkga/orgs", "received_events_url": "https://api.github.com/users/finkga/received_events", "repos_url": "https://api.github.com/users/finkga/repos", "site_admin": false, "starred_url": "https://api.github.com/users/finkga/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/finkga/subscriptions", "type": "User", "url": "https://api.github.com/users/finkga", "user_view_type": "public" }	[]	open	false	null	[]	null	[ "Hi! `IterableDataset.from_generator` expects a generator function, not the object (to be consistent with `Dataset.from_generator`).\r\n\r\nYou can fix the above snippet as follows:\r\n```python\r\ntrain_dataset = IterableDataset.from_generator(line_generator, fn_kwargs={\"files\": model_training_files})\r\n```", "to anyone reaching this issue, the argument is `gen_kwargs`:\r\n```py\r\ntrain_dataset = IterableDataset.from_generator(line_generator, gen_kwargs={\"files\": model_training_files})\r\n```", "This still fails, for both Dataset and IterableDataset\r\n\r\n```python\r\n records = [1, 2, 3]\r\n\r\n gen = ({\"row\": str(x)} for x in records)\r\n\r\n dataset = IterableDataset.from_generator(generator=gen)\r\n ```\r\n\r\nEdit: gen_kwargs must be picklable, it can't be an iterator even if you are not doing multiprocessing, the same goes for included namespace variables." ]	2023-08-04T01:45:04Z	2024-12-18T18:30:57Z	null	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug Description Providing a generator in an instantiation of IterableDataset.from_generator() fails with `TypeError: cannot pickle 'generator' object` when the generator argument is supplied with a generator. Code example ``` def line_generator(files: List[Path]): if isinstance(files, str): files = [Path(files)] for file in files: if isinstance(file, str): file = Path(file) yield from open(file,'r').readlines() ... model_training_files = ['file1.txt', 'file2.txt', 'file3.txt'] train_dataset = IterableDataset.from_generator(generator=line_generator(model_training_files)) ``` Traceback Traceback (most recent call last): File "/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.9/lib/python3.9/contextlib.py", line 135, in __exit__ self.gen.throw(type, value, traceback) File "/Users/d3p692/code/clem_bert/venv/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 691, in _no_cache_fields yield File "/Users/d3p692/code/clem_bert/venv/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 701, in dumps dump(obj, file) File "/Users/d3p692/code/clem_bert/venv/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 676, in dump Pickler(file, recurse=True).dump(obj) File "/Users/d3p692/code/clem_bert/venv/lib/python3.9/site-packages/dill/_dill.py", line 394, in dump StockPickler.dump(self, obj) File "/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.9/lib/python3.9/pickle.py", line 487, in dump self.save(obj) File "/Users/d3p692/code/clem_bert/venv/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 666, in save dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id) File "/Users/d3p692/code/clem_bert/venv/lib/python3.9/site-packages/dill/_dill.py", line 388, in save StockPickler.save(self, obj, save_persistent_id) File "/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.9/lib/python3.9/pickle.py", line 560, in save f(self, obj) # Call unbound method with explicit self File "/Users/d3p692/code/clem_bert/venv/lib/python3.9/site-packages/dill/_dill.py", line 1186, in save_module_dict StockPickler.save_dict(pickler, obj) File "/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.9/lib/python3.9/pickle.py", line 971, in save_dict self._batch_setitems(obj.items()) File "/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.9/lib/python3.9/pickle.py", line 997, in _batch_setitems save(v) File "/Users/d3p692/code/clem_bert/venv/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 666, in save dill.Pickler.save(self, obj, save_persistent_id=save_persistent_id) File "/Users/d3p692/code/clem_bert/venv/lib/python3.9/site-packages/dill/_dill.py", line 388, in save StockPickler.save(self, obj, save_persistent_id) File "/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.9/lib/python3.9/pickle.py", line 578, in save rv = reduce(self.proto) TypeError: cannot pickle 'generator' object ### Steps to reproduce the bug 1. Create a set of text files to iterate over. 2. Create a generator that returns the lines in each file until all files are exhausted. 3. Instantiate the dataset over the generator by instantiating an IterableDataset.from_generator(). 4. Wait for the explosion. ### Expected behavior I would expect that since the function claims to accept a generator that there would be no crash. Instead, I would expect the dataset to return all the lines in the files as queued up in the `line_generator()` function. ### Environment info datasets.__version__ == '2.13.1' Python 3.9.6 Platform: Darwin WE35261 22.5.0 Darwin Kernel Version 22.5.0: Thu Jun 8 22:22:22 PDT 2023; root:xnu-8796.121.3~7/RELEASE_X86_64 x86_64	null	{ "+1": 1, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 1, "url": "https://api.github.com/repos/huggingface/datasets/issues/6118/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6118/timeline	null	null	null	null
https://api.github.com/repos/huggingface/datasets/issues/7451	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/7451/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/7451/comments	https://api.github.com/repos/huggingface/datasets/issues/7451/events	https://github.com/huggingface/datasets/pull/7451	2,919,835,663	PR_kwDODunzps6OpwDz	7,451	Fix resuming after `ds.set_epoch(new_epoch)`	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7451). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update." ]	2025-03-14T10:31:25Z	2025-03-14T10:50:11Z	2025-03-14T10:50:09Z	MEMBER	null	null	null	close https://github.com/huggingface/datasets/issues/7447	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/7451/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/7451/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/7451.diff", "html_url": "https://github.com/huggingface/datasets/pull/7451", "merged_at": "2025-03-14T10:50:09Z", "patch_url": "https://github.com/huggingface/datasets/pull/7451.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/7451" }
https://api.github.com/repos/huggingface/datasets/issues/6819	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6819/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6819/comments	https://api.github.com/repos/huggingface/datasets/issues/6819/events	https://github.com/huggingface/datasets/issues/6819	2,248,043,797	I_kwDODunzps6F_m0V	6,819	Give more details in `DataFilesNotFoundError` when getting the config names	{ "avatar_url": "https://avatars.githubusercontent.com/u/1676121?v=4", "events_url": "https://api.github.com/users/severo/events{/privacy}", "followers_url": "https://api.github.com/users/severo/followers", "following_url": "https://api.github.com/users/severo/following{/other_user}", "gists_url": "https://api.github.com/users/severo/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/severo", "id": 1676121, "login": "severo", "node_id": "MDQ6VXNlcjE2NzYxMjE=", "organizations_url": "https://api.github.com/users/severo/orgs", "received_events_url": "https://api.github.com/users/severo/received_events", "repos_url": "https://api.github.com/users/severo/repos", "site_admin": false, "starred_url": "https://api.github.com/users/severo/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/severo/subscriptions", "type": "User", "url": "https://api.github.com/users/severo", "user_view_type": "public" }	[ { "color": "a2eeef", "default": true, "description": "New feature or request", "id": 1935892871, "name": "enhancement", "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement" } ]	open	false	null	[]	null	[]	2024-04-17T11:19:47Z	2024-04-17T11:19:47Z	null	COLLABORATOR	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Feature request After https://huggingface.co/datasets/cis-lmu/Glot500/commit/39060e01272ff228cc0ce1d31ae53789cacae8c3, the dataset viewer gives the following error: ``` { "error": "Cannot get the config names for the dataset.", "cause_exception": "DataFilesNotFoundError", "cause_message": "No (supported) data files found in cis-lmu/Glot500", "cause_traceback": [ "Traceback (most recent call last):\n", " File \"/src/services/worker/src/worker/job_runners/dataset/config_names.py\", line 73, in compute_config_names_response\n config_names = get_dataset_config_names(\n", " File \"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py\", line 347, in get_dataset_config_names\n dataset_module = dataset_module_factory(\n", " File \"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py\", line 1873, in dataset_module_factory\n raise e1 from None\n", " File \"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py\", line 1854, in dataset_module_factory\n return HubDatasetModuleFactoryWithoutScript(\n", " File \"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py\", line 1245, in get_module\n module_name, default_builder_kwargs = infer_module_for_data_files(\n", " File \"/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py\", line 595, in infer_module_for_data_files\n raise DataFilesNotFoundError(\"No (supported) data files found\" + (f\" in {path}\" if path else \"\"))\n", "datasets.exceptions.DataFilesNotFoundError: No (supported) data files found in cis-lmu/Glot500\n" ] } ``` because the deleted files were still listed in the README, see https://huggingface.co/datasets/cis-lmu/Glot500/discussions/4 Ideally, the error message would include the name of the first configuration with missing files, to help the user understand how to fix it. Here, it would tell that configuration `aze_Ethi` has no supported data files, instead of telling that the `cis-lmu/Glot500` dataset has no supported data files (which is not true). ### Motivation Giving more detail in the error would help the Datasets Hub users to debug why the dataset viewer does not work. ### Your contribution Not sure how to best fix this, as there are a lot of loops on the dataset configs in the traceback methods. "maybe" it would be easier to handle if the code was completely isolating each config.	null	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6819/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6819/timeline	null	null	null	null
https://api.github.com/repos/huggingface/datasets/issues/4550	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/4550/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/4550/comments	https://api.github.com/repos/huggingface/datasets/issues/4550/events	https://github.com/huggingface/datasets/issues/4550	1,282,374,441	I_kwDODunzps5Mb3sp	4,550	imdb source error	{ "avatar_url": "https://avatars.githubusercontent.com/u/20128202?v=4", "events_url": "https://api.github.com/users/Muhtasham/events{/privacy}", "followers_url": "https://api.github.com/users/Muhtasham/followers", "following_url": "https://api.github.com/users/Muhtasham/following{/other_user}", "gists_url": "https://api.github.com/users/Muhtasham/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/Muhtasham", "id": 20128202, "login": "Muhtasham", "node_id": "MDQ6VXNlcjIwMTI4MjAy", "organizations_url": "https://api.github.com/users/Muhtasham/orgs", "received_events_url": "https://api.github.com/users/Muhtasham/received_events", "repos_url": "https://api.github.com/users/Muhtasham/repos", "site_admin": false, "starred_url": "https://api.github.com/users/Muhtasham/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/Muhtasham/subscriptions", "type": "User", "url": "https://api.github.com/users/Muhtasham", "user_view_type": "public" }	[ { "color": "d73a4a", "default": true, "description": "Something isn't working", "id": 1935892857, "name": "bug", "node_id": "MDU6TGFiZWwxOTM1ODkyODU3", "url": "https://api.github.com/repos/huggingface/datasets/labels/bug" } ]	closed	false	null	[]	null	[ "Thanks for reporting, @Muhtasham.\r\n\r\nIndeed IMDB dataset is not accessible from yesterday, because the data is hosted on the data owners servers at Stanford (http://ai.stanford.edu/) and these are down due to a power outage originated by a fire: https://twitter.com/StanfordAILab/status/1539472302399623170?s=20&t=1HU1hrtaXprtn14U61P55w\r\n\r\nAs a temporary workaroud, you can load the IMDB dataset with this tweak:\r\n```python\r\nds = load_dataset(\"imdb\", revision=\"tmp-fix-imdb\")\r\n```\r\n" ]	2022-06-23T13:02:52Z	2022-06-23T13:47:05Z	2022-06-23T13:47:04Z	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	## Describe the bug imdb dataset not loading ## Steps to reproduce the bug ```python from datasets import load_dataset dataset = load_dataset("imdb") ``` ## Expected results ## Actual results ```bash 06/23/2022 14:45:18 - INFO - datasets.builder - Dataset not on Hf google storage. Downloading and preparing it from source 06/23/2022 14:46:34 - INFO - datasets.utils.file_utils - HEAD request to http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz timed out, retrying... [1.0] ..... ConnectionError: Couldn't reach http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz (ConnectTimeout(MaxRetryError("HTTPConnectionPool(host='ai.stanford.edu', port=80): Max retries exceeded with url: /~amaas/data/sentiment/aclImdb_v1.tar.gz (Caused by ConnectTimeoutError(<urllib3.connection.HTTPConnection object at 0x7f2d750cf690>, 'Connection to ai.stanford.edu timed out. (connect timeout=100)'))"))) ``` ## Environment info - `datasets` version: 2.3.2 - Platform: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic - Python version: 3.7.13 - PyArrow version: 6.0.1 - Pandas version: 1.3.5	{ "avatar_url": "https://avatars.githubusercontent.com/u/20128202?v=4", "events_url": "https://api.github.com/users/Muhtasham/events{/privacy}", "followers_url": "https://api.github.com/users/Muhtasham/followers", "following_url": "https://api.github.com/users/Muhtasham/following{/other_user}", "gists_url": "https://api.github.com/users/Muhtasham/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/Muhtasham", "id": 20128202, "login": "Muhtasham", "node_id": "MDQ6VXNlcjIwMTI4MjAy", "organizations_url": "https://api.github.com/users/Muhtasham/orgs", "received_events_url": "https://api.github.com/users/Muhtasham/received_events", "repos_url": "https://api.github.com/users/Muhtasham/repos", "site_admin": false, "starred_url": "https://api.github.com/users/Muhtasham/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/Muhtasham/subscriptions", "type": "User", "url": "https://api.github.com/users/Muhtasham", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/4550/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/4550/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/7200	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/7200/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/7200/comments	https://api.github.com/repos/huggingface/datasets/issues/7200/events	https://github.com/huggingface/datasets/pull/7200	2,567,921,694	PR_kwDODunzps59sgRd	7,200	Fix the environment variable for huggingface cache	{ "avatar_url": "https://avatars.githubusercontent.com/u/989899?v=4", "events_url": "https://api.github.com/users/torotoki/events{/privacy}", "followers_url": "https://api.github.com/users/torotoki/followers", "following_url": "https://api.github.com/users/torotoki/following{/other_user}", "gists_url": "https://api.github.com/users/torotoki/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/torotoki", "id": 989899, "login": "torotoki", "node_id": "MDQ6VXNlcjk4OTg5OQ==", "organizations_url": "https://api.github.com/users/torotoki/orgs", "received_events_url": "https://api.github.com/users/torotoki/received_events", "repos_url": "https://api.github.com/users/torotoki/repos", "site_admin": false, "starred_url": "https://api.github.com/users/torotoki/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/torotoki/subscriptions", "type": "User", "url": "https://api.github.com/users/torotoki", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "Hi ! yes now `datasets` uses `huggingface_hub` to download and cache files from the HF Hub so you need to use `HF_HOME` (or manually `HF_HUB_CACHE` and `HF_DATASETS_CACHE` if you want to separate HF Hub cached files and cached datasets Arrow files)\r\n\r\nSo in your change I guess it needs to be `HF_HOME` instead of `HF_CACHE` ?", "Thank you for your comment. You are right. I am sorry for my mistake, I fixed it.", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7200). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "I just had this issue, and needed to move the setting the env code in the python file to top, before the import of the lib \r\nie. \r\n```python\r\nimport os\r\nLOCAL_DISK_MOUNT = '/mnt/data'\r\n\r\nos.environ['HF_HOME'] = f'{LOCAL_DISK_MOUNT}/hf_cache/'\r\nos.environ['HF_DATASETS_CACHE'] = f'{LOCAL_DISK_MOUNT}/datasets/'\r\n\r\nfrom datasets import load_dataset\r\nfrom datasets import load_dataset_builder\r\nfrom psutil._common import bytes2human\r\n\r\n\r\n```" ]	2024-10-05T11:54:35Z	2024-10-30T23:10:27Z	2024-10-08T15:45:18Z	CONTRIBUTOR	null	null	null	Resolve #6256. As far as I tested, `HF_DATASETS_CACHE` was ignored and I could not specify the cache directory at all except for the default one by this environment variable. `HF_HOME` has worked. Perhaps the recent change on file downloading by `huggingface_hub` could affect this bug. In my testing, I could not specify the cache directory even by `load_dataset("dataset_name" cache_dir="...")`. It might be another issue. I also welcome any advice to solve this issue.	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/7200/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/7200/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/7200.diff", "html_url": "https://github.com/huggingface/datasets/pull/7200", "merged_at": "2024-10-08T15:45:17Z", "patch_url": "https://github.com/huggingface/datasets/pull/7200.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/7200" }
https://api.github.com/repos/huggingface/datasets/issues/5555	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5555/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5555/comments	https://api.github.com/repos/huggingface/datasets/issues/5555/events	https://github.com/huggingface/datasets/issues/5555	1,592,469,938	I_kwDODunzps5e6ymy	5,555	`.shuffle` throwing error `ValueError: Protocol not known: parent`	{ "avatar_url": "https://avatars.githubusercontent.com/u/10768588?v=4", "events_url": "https://api.github.com/users/prabhakar267/events{/privacy}", "followers_url": "https://api.github.com/users/prabhakar267/followers", "following_url": "https://api.github.com/users/prabhakar267/following{/other_user}", "gists_url": "https://api.github.com/users/prabhakar267/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/prabhakar267", "id": 10768588, "login": "prabhakar267", "node_id": "MDQ6VXNlcjEwNzY4NTg4", "organizations_url": "https://api.github.com/users/prabhakar267/orgs", "received_events_url": "https://api.github.com/users/prabhakar267/received_events", "repos_url": "https://api.github.com/users/prabhakar267/repos", "site_admin": false, "starred_url": "https://api.github.com/users/prabhakar267/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/prabhakar267/subscriptions", "type": "User", "url": "https://api.github.com/users/prabhakar267", "user_view_type": "public" }	[]	open	false	null	[]	null	[ "Hi ! The indices mapping is written in the same cachedirectory as your dataset.\r\n\r\nCan you run this to show your current cache directory ?\r\n```python\r\nprint(train_dataset.cache_files)\r\n```", "```\r\n[{'filename': '.../train/dataset.arrow'}, {'filename': '.../train/dataset.arrow'}]\r\n```\r\n\r\nThese are the actual paths where `.hf` files are stored. ", "I'm not aware of any `.hf` file ? What are you referring to ?\r\n\r\nAlso the error says \"Protocol unknown: parent\". Is there a chance you may have ended up with a path that contains this string `parent://` ?", "I figured out why the issue was occuring but don't know the long-term fix.\r\nThe dataset I was trying to shuffle was loaded from a saved file which had `::` delimiter in filename. When I try with the exact same file without `::` in filename, it works as expected.\r\nQuick fix is to not use colons in filename. But if this is expected behaviour, this should be clearly stated in the documentation.\r\nThanks for help @lhoestq " ]	2023-02-20T21:33:45Z	2023-02-27T09:23:34Z	null	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug ``` --------------------------------------------------------------------------- ValueError Traceback (most recent call last) Cell In [16], line 1 ----> 1 train_dataset = train_dataset.shuffle() File /opt/conda/envs/pytorch/lib/python3.9/site-packages/datasets/arrow_dataset.py:551, in transmit_format.<locals>.wrapper(args, kwargs) 544 self_format = { 545 "type": self._format_type, 546 "format_kwargs": self._format_kwargs, 547 "columns": self._format_columns, 548 "output_all_columns": self._output_all_columns, 549 } 550 # apply actual function --> 551 out: Union["Dataset", "DatasetDict"] = func(self, args, *kwargs) 552 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out] 553 # re-apply format to the output File /opt/conda/envs/pytorch/lib/python3.9/site-packages/datasets/fingerprint.py:480, in fingerprint_transform.<locals>._fingerprint.<locals>.wrapper(args, *kwargs) 476 validate_fingerprint(kwargs[fingerprint_name]) 478 # Call actual function --> 480 out = func(self, args, *kwargs) 482 # Update fingerprint of in-place transforms + update in-place history of transforms 484 if inplace: # update after calling func so that the fingerprint doesn't change if the function fails File /opt/conda/envs/pytorch/lib/python3.9/site-packages/datasets/arrow_dataset.py:3616, in Dataset.shuffle(self, seed, generator, keep_in_memory, load_from_cache_file, indices_cache_file_name, writer_batch_size, new_fingerprint) 3610 return self._new_dataset_with_indices( 3611 fingerprint=new_fingerprint, indices_cache_file_name=indices_cache_file_name 3612 ) 3614 permutation = generator.permutation(len(self)) -> 3616 return self.select( 3617 indices=permutation, 3618 keep_in_memory=keep_in_memory, 3619 indices_cache_file_name=indices_cache_file_name if not keep_in_memory else None, 3620 writer_batch_size=writer_batch_size, 3621 new_fingerprint=new_fingerprint, 3622 ) File /opt/conda/envs/pytorch/lib/python3.9/site-packages/datasets/arrow_dataset.py:551, in transmit_format.<locals>.wrapper(args, *kwargs) 544 self_format = { 545 "type": self._format_type, 546 "format_kwargs": self._format_kwargs, 547 "columns": self._format_columns, 548 "output_all_columns": self._output_all_columns, 549 } 550 # apply actual function --> 551 out: Union["Dataset", "DatasetDict"] = func(self, args, *kwargs) 552 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out] 553 # re-apply format to the output File /opt/conda/envs/pytorch/lib/python3.9/site-packages/datasets/fingerprint.py:480, in fingerprint_transform.<locals>._fingerprint.<locals>.wrapper(args, *kwargs) 476 validate_fingerprint(kwargs[fingerprint_name]) 478 # Call actual function --> 480 out = func(self, args, *kwargs) 482 # Update fingerprint of in-place transforms + update in-place history of transforms 484 if inplace: # update after calling func so that the fingerprint doesn't change if the function fails File /opt/conda/envs/pytorch/lib/python3.9/site-packages/datasets/arrow_dataset.py:3266, in Dataset.select(self, indices, keep_in_memory, indices_cache_file_name, writer_batch_size, new_fingerprint) 3263 return self._select_contiguous(start, length, new_fingerprint=new_fingerprint) 3265 # If not contiguous, we need to create a new indices mapping -> 3266 return self._select_with_indices_mapping( 3267 indices, 3268 keep_in_memory=keep_in_memory, 3269 indices_cache_file_name=indices_cache_file_name, 3270 writer_batch_size=writer_batch_size, 3271 new_fingerprint=new_fingerprint, 3272 ) File /opt/conda/envs/pytorch/lib/python3.9/site-packages/datasets/arrow_dataset.py:551, in transmit_format.<locals>.wrapper(args, *kwargs) 544 self_format = { 545 "type": self._format_type, 546 "format_kwargs": self._format_kwargs, 547 "columns": self._format_columns, 548 "output_all_columns": self._output_all_columns, 549 } 550 # apply actual function --> 551 out: Union["Dataset", "DatasetDict"] = func(self, args, *kwargs) 552 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out] 553 # re-apply format to the output File /opt/conda/envs/pytorch/lib/python3.9/site-packages/datasets/fingerprint.py:480, in fingerprint_transform.<locals>._fingerprint.<locals>.wrapper(args, *kwargs) 476 validate_fingerprint(kwargs[fingerprint_name]) 478 # Call actual function --> 480 out = func(self, args, **kwargs) 482 # Update fingerprint of in-place transforms + update in-place history of transforms 484 if inplace: # update after calling func so that the fingerprint doesn't change if the function fails File /opt/conda/envs/pytorch/lib/python3.9/site-packages/datasets/arrow_dataset.py:3389, in Dataset._select_with_indices_mapping(self, indices, keep_in_memory, indices_cache_file_name, writer_batch_size, new_fingerprint) 3387 logger.info(f"Caching indices mapping at {indices_cache_file_name}") 3388 tmp_file = tempfile.NamedTemporaryFile("wb", dir=os.path.dirname(indices_cache_file_name), delete=False) -> 3389 writer = ArrowWriter( 3390 path=tmp_file.name, writer_batch_size=writer_batch_size, fingerprint=new_fingerprint, unit="indices" 3391 ) 3393 indices = indices if isinstance(indices, list) else list(indices) 3395 size = len(self) File /opt/conda/envs/pytorch/lib/python3.9/site-packages/datasets/arrow_writer.py:315, in ArrowWriter.__init__(self, schema, features, path, stream, fingerprint, writer_batch_size, hash_salt, check_duplicates, disable_nullable, update_features, with_metadata, unit, embed_local_files, storage_options) 312 self._disable_nullable = disable_nullable 314 if stream is None: --> 315 fs_token_paths = fsspec.get_fs_token_paths(path, storage_options=storage_options) 316 self._fs: fsspec.AbstractFileSystem = fs_token_paths[0] 317 self._path = ( 318 fs_token_paths[2][0] 319 if not is_remote_filesystem(self._fs) 320 else self._fs.unstrip_protocol(fs_token_paths[2][0]) 321 ) File /opt/conda/envs/pytorch/lib/python3.9/site-packages/fsspec/core.py:593, in get_fs_token_paths(urlpath, mode, num, name_function, storage_options, protocol, expand) 591 else: 592 urlpath = stringify_path(urlpath) --> 593 chain = _un_chain(urlpath, storage_options or {}) 594 if len(chain) > 1: 595 inkwargs = {} File /opt/conda/envs/pytorch/lib/python3.9/site-packages/fsspec/core.py:330, in _un_chain(path, kwargs) 328 for bit in reversed(bits): 329 protocol = split_protocol(bit)[0] or "file" --> 330 cls = get_filesystem_class(protocol) 331 extra_kwargs = cls._get_kwargs_from_urls(bit) 332 kws = kwargs.get(protocol, {}) File /opt/conda/envs/pytorch/lib/python3.9/site-packages/fsspec/registry.py:240, in get_filesystem_class(protocol) 238 if protocol not in registry: 239 if protocol not in known_implementations: --> 240 raise ValueError("Protocol not known: %s" % protocol) 241 bit = known_implementations[protocol] 242 try: ValueError: Protocol not known: parent ``` This is what the `train_dataset` object looks like ``` Dataset({ features: ['label', 'input_ids', 'attention_mask'], num_rows: 364166 }) ``` ### Steps to reproduce the bug The `train_dataset` obj is created by concatenating two datasets And then shuffle is called, but it throws the mentioned error. ### Expected behavior Should shuffle the dataset properly. ### Environment info - `datasets` version: 2.6.1 - Platform: Linux-5.15.0-1022-aws-x86_64-with-glibc2.31 - Python version: 3.9.13 - PyArrow version: 10.0.0 - Pandas version: 1.4.4	null	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5555/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5555/timeline	null	null	null	null
https://api.github.com/repos/huggingface/datasets/issues/6765	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6765/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6765/comments	https://api.github.com/repos/huggingface/datasets/issues/6765/events	https://github.com/huggingface/datasets/issues/6765	2,215,933,515	I_kwDODunzps6EFHZL	6,765	Compatibility issue between s3fs, fsspec, and datasets	{ "avatar_url": "https://avatars.githubusercontent.com/u/33383515?v=4", "events_url": "https://api.github.com/users/njbrake/events{/privacy}", "followers_url": "https://api.github.com/users/njbrake/followers", "following_url": "https://api.github.com/users/njbrake/following{/other_user}", "gists_url": "https://api.github.com/users/njbrake/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/njbrake", "id": 33383515, "login": "njbrake", "node_id": "MDQ6VXNlcjMzMzgzNTE1", "organizations_url": "https://api.github.com/users/njbrake/orgs", "received_events_url": "https://api.github.com/users/njbrake/received_events", "repos_url": "https://api.github.com/users/njbrake/repos", "site_admin": false, "starred_url": "https://api.github.com/users/njbrake/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/njbrake/subscriptions", "type": "User", "url": "https://api.github.com/users/njbrake", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "Hi! Instead of running `pip install` separately for each package, you should pass all the packages to a single `pip install` call (in this case, `pip install datasets s3fs`) to let `pip` properly resolve their versions.", "> Hi! Instead of running `pip install` separately for each package, you should pass all the packages to a single `pip install` call (in this case, `pip install datasets s3fs`) to let `pip` properly resolve their versions.\r\n\r\nThanks so much! My inexperience with pip is showing 😆 🙈 ", "> Hi! Instead of running `pip install` separately for each package, you should pass all the packages to a single `pip install` call (in this case, `pip install datasets s3fs`) to let `pip` properly resolve their versions.\r\n\r\nyou are awesome bro", "Hey, the suggestion by @mariosasko unfortunately only address this issue via pip. The original message was about poetry and I am still facing a dependency conflict with that.\r\n\r\nThe following command complains first about `fsspec` (`... no versions of fsspec match ...`) and then I get an error.\r\n\r\nCommand:\r\n`poetry add datasets s3fs` \r\n\r\nError: \r\n` ... your project ... depends on both datasets (^3.1.0) and s3fs (^2024.10.0), version solving failed`\r\n\r\nInstalling first `s3fs` and then the rest of the huggingface libraries, like `datasets`, also did not help." ]	2024-03-29T19:57:24Z	2024-11-12T14:50:48Z	2024-04-03T14:33:12Z	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug Here is the full error stack when installing: ``` ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts. datasets 2.18.0 requires fsspec[http]<=2024.2.0,>=2023.1.0, but you have fsspec 2024.3.1 which is incompatible. Successfully installed aiobotocore-2.12.1 aioitertools-0.11.0 botocore-1.34.51 fsspec-2024.3.1 jmespath-1.0.1 s3fs-2024.3.1 urllib3-2.0.7 wrapt-1.16.0 ``` When I install with pip, pip allows this error to exist while still installing s3fs, but this error breaks poetry, since poetry will refuse to install s3fs because of the dependency conflict. Maybe I'm missing something so maybe it's not a bug but some mistake on my end? Any input would be helpful. Thanks! ### Steps to reproduce the bug 1. conda create -n tmp python=3.10 -y 2. conda activate tmp 3. pip install datasets 4. pip install s3fs ### Expected behavior I would expect there to be no error. ### Environment info MacOS (ARM), Python3.10, conda 23.11.0.	{ "avatar_url": "https://avatars.githubusercontent.com/u/33383515?v=4", "events_url": "https://api.github.com/users/njbrake/events{/privacy}", "followers_url": "https://api.github.com/users/njbrake/followers", "following_url": "https://api.github.com/users/njbrake/following{/other_user}", "gists_url": "https://api.github.com/users/njbrake/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/njbrake", "id": 33383515, "login": "njbrake", "node_id": "MDQ6VXNlcjMzMzgzNTE1", "organizations_url": "https://api.github.com/users/njbrake/orgs", "received_events_url": "https://api.github.com/users/njbrake/received_events", "repos_url": "https://api.github.com/users/njbrake/repos", "site_admin": false, "starred_url": "https://api.github.com/users/njbrake/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/njbrake/subscriptions", "type": "User", "url": "https://api.github.com/users/njbrake", "user_view_type": "public" }	{ "+1": 1, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 1, "url": "https://api.github.com/repos/huggingface/datasets/issues/6765/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6765/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/6079	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6079/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6079/comments	https://api.github.com/repos/huggingface/datasets/issues/6079/events	https://github.com/huggingface/datasets/issues/6079	1,822,597,471	I_kwDODunzps5soqFf	6,079	Iterating over DataLoader based on HF datasets is stuck forever	{ "avatar_url": "https://avatars.githubusercontent.com/u/5454868?v=4", "events_url": "https://api.github.com/users/arindamsarkar93/events{/privacy}", "followers_url": "https://api.github.com/users/arindamsarkar93/followers", "following_url": "https://api.github.com/users/arindamsarkar93/following{/other_user}", "gists_url": "https://api.github.com/users/arindamsarkar93/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/arindamsarkar93", "id": 5454868, "login": "arindamsarkar93", "node_id": "MDQ6VXNlcjU0NTQ4Njg=", "organizations_url": "https://api.github.com/users/arindamsarkar93/orgs", "received_events_url": "https://api.github.com/users/arindamsarkar93/received_events", "repos_url": "https://api.github.com/users/arindamsarkar93/repos", "site_admin": false, "starred_url": "https://api.github.com/users/arindamsarkar93/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/arindamsarkar93/subscriptions", "type": "User", "url": "https://api.github.com/users/arindamsarkar93", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "When the process starts to hang, can you interrupt it with CTRL + C and paste the error stack trace here? ", "Thanks @mariosasko for your prompt response, here's the stack trace:\r\n\r\n```\r\nKeyboardInterrupt Traceback (most recent call last)\r\nCell In[12], line 4\r\n 2 t = time.time()\r\n 3 iter_ = 0\r\n----> 4 for batch in train_dataloader:\r\n 5 #batch_proc = streaming_obj.collect_streaming_data_batch(batch)\r\n 6 iter_ += 1\r\n 8 if iter_ == 1:\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/torch/utils/data/dataloader.py:634, in _BaseDataLoaderIter.__next__(self)\r\n 631 if self._sampler_iter is None:\r\n 632 # TODO(https://github.com/pytorch/pytorch/issues/76750)\r\n 633 self._reset() # type: ignore[call-arg]\r\n--> 634 data = self._next_data()\r\n 635 self._num_yielded += 1\r\n 636 if self._dataset_kind == _DatasetKind.Iterable and \\\r\n 637 self._IterableDataset_len_called is not None and \\\r\n 638 self._num_yielded > self._IterableDataset_len_called:\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/torch/utils/data/dataloader.py:678, in _SingleProcessDataLoaderIter._next_data(self)\r\n 676 def _next_data(self):\r\n 677 index = self._next_index() # may raise StopIteration\r\n--> 678 data = self._dataset_fetcher.fetch(index) # may raise StopIteration\r\n 679 if self._pin_memory:\r\n 680 data = _utils.pin_memory.pin_memory(data, self._pin_memory_device)\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py:32, in _IterableDatasetFetcher.fetch(self, possibly_batched_index)\r\n 30 for _ in possibly_batched_index:\r\n 31 try:\r\n---> 32 data.append(next(self.dataset_iter))\r\n 33 except StopIteration:\r\n 34 self.ended = True\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/datasets/iterable_dataset.py:1353, in IterableDataset.__iter__(self)\r\n 1350 yield formatter.format_row(pa_table)\r\n 1351 return\r\n-> 1353 for key, example in ex_iterable:\r\n 1354 if self.features:\r\n 1355 # `IterableDataset` automatically fills missing columns with None.\r\n 1356 # This is done with `_apply_feature_types_on_example`.\r\n 1357 example = _apply_feature_types_on_example(\r\n 1358 example, self.features, token_per_repo_id=self._token_per_repo_id\r\n 1359 )\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/datasets/iterable_dataset.py:956, in BufferShuffledExamplesIterable.__iter__(self)\r\n 954 # this is the shuffle buffer that we keep in memory\r\n 955 mem_buffer = []\r\n--> 956 for x in self.ex_iterable:\r\n 957 if len(mem_buffer) == buffer_size: # if the buffer is full, pick and example from it\r\n 958 i = next(indices_iterator)\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/datasets/iterable_dataset.py:296, in ShuffledDataSourcesArrowExamplesIterable.__iter__(self)\r\n 294 for key, pa_table in self.generate_tables_fn(kwargs_with_shuffled_shards):\r\n 295 for pa_subtable in pa_table.to_reader(max_chunksize=config.ARROW_READER_BATCH_SIZE_IN_DATASET_ITER):\r\n--> 296 formatted_batch = formatter.format_batch(pa_subtable)\r\n 297 for example in _batch_to_examples(formatted_batch):\r\n 298 yield key, example\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/datasets/formatting/formatting.py:448, in PythonFormatter.format_batch(self, pa_table)\r\n 446 if self.lazy:\r\n 447 return LazyBatch(pa_table, self)\r\n--> 448 batch = self.python_arrow_extractor().extract_batch(pa_table)\r\n 449 batch = self.python_features_decoder.decode_batch(batch)\r\n 450 return batch\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/datasets/formatting/formatting.py:150, in PythonArrowExtractor.extract_batch(self, pa_table)\r\n 149 def extract_batch(self, pa_table: pa.Table) -> dict:\r\n--> 150 return pa_table.to_pydict()\r\n\r\nKeyboardInterrupt: \r\n```\r\n", "Update: If i let it run, it eventually fails with:\r\n\r\n```\r\nRuntimeError Traceback (most recent call last)\r\nCell In[16], line 4\r\n 2 t = time.time()\r\n 3 iter_ = 0\r\n----> 4 for batch in train_dataloader:\r\n 5 #batch_proc = streaming_obj.collect_streaming_data_batch(batch)\r\n 6 iter_ += 1\r\n 8 if iter_ == 1:\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/torch/utils/data/dataloader.py:634, in _BaseDataLoaderIter.__next__(self)\r\n 631 if self._sampler_iter is None:\r\n 632 # TODO(https://github.com/pytorch/pytorch/issues/76750)\r\n 633 self._reset() # type: ignore[call-arg]\r\n--> 634 data = self._next_data()\r\n 635 self._num_yielded += 1\r\n 636 if self._dataset_kind == _DatasetKind.Iterable and \\\r\n 637 self._IterableDataset_len_called is not None and \\\r\n 638 self._num_yielded > self._IterableDataset_len_called:\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/torch/utils/data/dataloader.py:678, in _SingleProcessDataLoaderIter._next_data(self)\r\n 676 def _next_data(self):\r\n 677 index = self._next_index() # may raise StopIteration\r\n--> 678 data = self._dataset_fetcher.fetch(index) # may raise StopIteration\r\n 679 if self._pin_memory:\r\n 680 data = _utils.pin_memory.pin_memory(data, self._pin_memory_device)\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py:32, in _IterableDatasetFetcher.fetch(self, possibly_batched_index)\r\n 30 for _ in possibly_batched_index:\r\n 31 try:\r\n---> 32 data.append(next(self.dataset_iter))\r\n 33 except StopIteration:\r\n 34 self.ended = True\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/datasets/iterable_dataset.py:1360, in IterableDataset.__iter__(self)\r\n 1354 if self.features:\r\n 1355 # `IterableDataset` automatically fills missing columns with None.\r\n 1356 # This is done with `_apply_feature_types_on_example`.\r\n 1357 example = _apply_feature_types_on_example(\r\n 1358 example, self.features, token_per_repo_id=self._token_per_repo_id\r\n 1359 )\r\n-> 1360 yield format_dict(example) if format_dict else example\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/datasets/formatting/torch_formatter.py:85, in TorchFormatter.recursive_tensorize(self, data_struct)\r\n 84 def recursive_tensorize(self, data_struct: dict):\r\n---> 85 return map_nested(self._recursive_tensorize, data_struct, map_list=False)\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/datasets/utils/py_utils.py:463, in map_nested(function, data_struct, dict_only, map_list, map_tuple, map_numpy, num_proc, parallel_min_length, types, disable_tqdm, desc)\r\n 461 num_proc = 1\r\n 462 if num_proc != -1 and num_proc <= 1 or len(iterable) < parallel_min_length:\r\n--> 463 mapped = [\r\n 464 _single_map_nested((function, obj, types, None, True, None))\r\n 465 for obj in logging.tqdm(iterable, disable=disable_tqdm, desc=desc)\r\n 466 ]\r\n 467 else:\r\n 468 mapped = parallel_map(function, iterable, num_proc, types, disable_tqdm, desc, _single_map_nested)\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/datasets/utils/py_utils.py:464, in <listcomp>(.0)\r\n 461 num_proc = 1\r\n 462 if num_proc != -1 and num_proc <= 1 or len(iterable) < parallel_min_length:\r\n 463 mapped = [\r\n--> 464 _single_map_nested((function, obj, types, None, True, None))\r\n 465 for obj in logging.tqdm(iterable, disable=disable_tqdm, desc=desc)\r\n 466 ]\r\n 467 else:\r\n 468 mapped = parallel_map(function, iterable, num_proc, types, disable_tqdm, desc, _single_map_nested)\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/datasets/utils/py_utils.py:366, in _single_map_nested(args)\r\n 364 # Singleton first to spare some computation\r\n 365 if not isinstance(data_struct, dict) and not isinstance(data_struct, types):\r\n--> 366 return function(data_struct)\r\n 368 # Reduce logging to keep things readable in multiprocessing with tqdm\r\n 369 if rank is not None and logging.get_verbosity() < logging.WARNING:\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/datasets/formatting/torch_formatter.py:82, in TorchFormatter._recursive_tensorize(self, data_struct)\r\n 80 elif isinstance(data_struct, (list, tuple)):\r\n 81 return self._consolidate([self.recursive_tensorize(substruct) for substruct in data_struct])\r\n---> 82 return self._tensorize(data_struct)\r\n\r\nFile ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/datasets/formatting/torch_formatter.py:68, in TorchFormatter._tensorize(self, value)\r\n 66 if isinstance(value, PIL.Image.Image):\r\n 67 value = np.asarray(value)\r\n---> 68 return torch.tensor(value, {default_dtype, self.torch_tensor_kwargs})\r\n\r\nRuntimeError: Could not infer dtype of decimal.Decimal\r\n```", "PyTorch tensors cannot store `Decimal` objects. Casting the column with decimals to `float` should fix the issue.", "I already have cast in collate_fn, in which I perform .astype(float) for each numerical field.\r\nOn the same instance, I installed a conda env with python 3.6, and this works well.\r\n\r\nSample:\r\n\r\n```\r\ndef streaming_data_collate_fn(batch):\r\n df = pd.DataFrame.from_dict(batch)\r\n feat_vals = torch.FloatTensor(np.nan_to_num(np.array(df[feats].astype(float))))\r\n\r\n```", "`collate_fn` is applied after the `torch` formatting step, so I think the only option when working with an `IterableDataset` is to remove the `with_format` call and perform the conversion from Python values to PyTorch tensors in `collate_fn`. The standard `Dataset` supports `with_format(\"numpy\")`, which should make this conversion faster.", "Thanks! \r\nPython 3.10 conda-env: After replacing with_format(\"torch\") with with_format(\"numpy\"), the error went away. However, it was still taking over 2 minutes to load a very small batch of 64 samples with num_workers set to 32. Once I removed with_format call altogether, it is finishing in 11 seconds.\r\n\r\nPython 3.6 based conda-env: When I switch the kernel , neither of the above work, and with_format(\"torch\") is the only thing that works, and executes in 1.6 seconds.\r\n\r\nI feel something else is also amiss here.", "Can you share the `datasets` and `torch` versions installed in these conda envs?\r\n\r\n> Once I removed with_format call altogether, it is finishing in 11 seconds.\r\n\r\nHmm, that's surprising. What are your dataset's `.features`?", "Python 3.6: \r\ndatasets.__version__ 2.4.0\r\ntorch.__version__ 1.10.1+cu102\r\n\r\nPython 3.10:\r\ndatasets.__version__ 2.14.0\r\ntorch.__version__ 2.0.0\r\n\r\nAnonymized features are of the form (subset shown here):\r\n{\r\n'string_feature_i': Value(dtype='string', id=None),\r\n'numerical_feature_i': Value(dtype='decimal128(38, 0)', id=None),\r\n'numerical_feature_series_i': Sequence(feature=Value(dtype='float64', id=None), length=-1, id=None),\r\n}\r\n\r\n\r\nThere is no output from .features in python 3.6 kernel BTW.", "One more thing, in python 3.10 based kernel, interestingly increasing num_workers seem to be increasing the runtime of iterating I was trying out. In python 3.10 kernel execution, I do not even see multiple CPU cores spiking unlike in 3.6.\r\n\r\n512 batch size on 32 workers executes in 2.4 seconds on python 3.6 kernel, while it takes ~118 seconds on 3.10!", "Update: It seems the latency part is more of a multiprocessing issue with torch and some host specific issue, and I had to scourge through relevant pytorch issues, when I stumbled across these threads:\r\n1. https://github.com/pytorch/pytorch/issues/102494\r\n2. https://github.com/pytorch/pytorch/issues/102269\r\n3. https://github.com/pytorch/pytorch/issues/99625\r\n\r\nOut of the suggested solutions, the one that worked in my case was:\r\n```\r\nos.environ['KMP_AFFINITY'] = \"disabled\"\r\n```\r\nIt is working for now, though I have no clue why, just I hope it does not get stuck when I do actual model training, will update by tomorrow.\r\n\r\n\r\n", "I'm facing a similar situation in the local VS Code. \r\n\r\nDatasets version 2.14.4\r\nTorch 2.0.1+cu118\r\n\r\nSame code runs without issues in Colab\r\n\r\n```\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"Supermaxman/esa-hubble\", streaming=True)\r\nsample = next(iter(dataset[\"train\"]))\r\n```\r\n\r\nis stuck for minutes. If I interrupt, I get\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nKeyboardInterrupt Traceback (most recent call last)\r\nCell In[5], line 5\r\n 1 from datasets import load_dataset\r\n 3 dataset = load_dataset(\"Supermaxman/esa-hubble\", streaming=True)\r\n----> 5 sample = next(iter(dataset[\"train\"]))\r\n 6 print(sample[\"text\"])\r\n 7 sample[\"image\"]\r\n\r\nFile [~/miniconda3/envs/book/lib/python3.10/site-packages/datasets/iterable_dataset.py:1353](https://file+.vscode-resource.vscode-cdn.net/home/osanseviero/Desktop/workspace/genai/nbs/~/miniconda3/envs/book/lib/python3.10/site-packages/datasets/iterable_dataset.py:1353), in IterableDataset.__iter__(self)\r\n 1350 yield formatter.format_row(pa_table)\r\n 1351 return\r\n-> 1353 for key, example in ex_iterable:\r\n 1354 if self.features:\r\n 1355 # `IterableDataset` automatically fills missing columns with None.\r\n 1356 # This is done with `_apply_feature_types_on_example`.\r\n 1357 example = _apply_feature_types_on_example(\r\n 1358 example, self.features, token_per_repo_id=self._token_per_repo_id\r\n 1359 )\r\n\r\nFile [~/miniconda3/envs/book/lib/python3.10/site-packages/datasets/iterable_dataset.py:255](https://file+.vscode-resource.vscode-cdn.net/home/osanseviero/Desktop/workspace/genai/nbs/~/miniconda3/envs/book/lib/python3.10/site-packages/datasets/iterable_dataset.py:255), in ArrowExamplesIterable.__iter__(self)\r\n 253 def __iter__(self):\r\n 254 formatter = PythonFormatter()\r\n--> 255 for key, pa_table in self.generate_tables_fn(**self.kwargs):\r\n 256 for pa_subtable in pa_table.to_reader(max_chunksize=config.ARROW_READER_BATCH_SIZE_IN_DATASET_ITER):\r\n...\r\n-> 1130 return self._sslobj.read(len, buffer)\r\n 1131 else:\r\n 1132 return self._sslobj.read(len)\r\n```", "@osanseviero I assume the `self._sslobj.read(len, buffer)` line comes from the built-in `ssl` module, so this probably has something to do with your network. Please open a new issue with the full stack trace in case you haven't resolved this yet.", "Thank you reporting this and sharing the solution, I ran into this as well!", "Ran into same issue after upgrading to pytorch-2.0. Disabling KMP_AFFINITY as mentioned above worked for me. Thanks!\r\n" ]	2023-07-26T14:52:37Z	2024-02-07T17:46:52Z	2023-07-30T14:09:06Z	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug I am using Amazon Sagemaker notebook (Amazon Linux 2) with python 3.10 based Conda environment. I have a dataset in parquet format locally. When I try to iterate over it, the loader is stuck forever. Note that the same code is working for python 3.6 based conda environment seamlessly. What should be my next steps here? ### Steps to reproduce the bug ``` train_dataset = load_dataset( "parquet", data_files = {'train': tr_data_path + '*.parquet'}, split = 'train', collate_fn = streaming_data_collate_fn, streaming = True ).with_format('torch') train_dataloader = DataLoader(train_dataset, batch_size = 2, num_workers = 0) t = time.time() iter_ = 0 for batch in train_dataloader: iter_ += 1 if iter_ == 1000: break print (time.time() - t) ``` ### Expected behavior The snippet should work normally and load the next batch of data. ### Environment info datasets: '2.14.0' pyarrow: '12.0.0' torch: '2.0.0' Python: 3.10.10 \| packaged by conda-forge \| (main, Mar 24 2023, 20:08:06) [GCC 11.3.0] !uname -r 5.10.178-162.673.amzn2.x86_64	{ "avatar_url": "https://avatars.githubusercontent.com/u/5454868?v=4", "events_url": "https://api.github.com/users/arindamsarkar93/events{/privacy}", "followers_url": "https://api.github.com/users/arindamsarkar93/followers", "following_url": "https://api.github.com/users/arindamsarkar93/following{/other_user}", "gists_url": "https://api.github.com/users/arindamsarkar93/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/arindamsarkar93", "id": 5454868, "login": "arindamsarkar93", "node_id": "MDQ6VXNlcjU0NTQ4Njg=", "organizations_url": "https://api.github.com/users/arindamsarkar93/orgs", "received_events_url": "https://api.github.com/users/arindamsarkar93/received_events", "repos_url": "https://api.github.com/users/arindamsarkar93/repos", "site_admin": false, "starred_url": "https://api.github.com/users/arindamsarkar93/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/arindamsarkar93/subscriptions", "type": "User", "url": "https://api.github.com/users/arindamsarkar93", "user_view_type": "public" }	{ "+1": 1, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 1, "url": "https://api.github.com/repos/huggingface/datasets/issues/6079/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6079/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/6719	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6719/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6719/comments	https://api.github.com/repos/huggingface/datasets/issues/6719/events	https://github.com/huggingface/datasets/issues/6719	2,169,585,727	I_kwDODunzps6BUUA_	6,719	Is there any way to solve hanging of IterableDataset using split by node + filtering during inference	{ "avatar_url": "https://avatars.githubusercontent.com/u/8136905?v=4", "events_url": "https://api.github.com/users/ssharpe42/events{/privacy}", "followers_url": "https://api.github.com/users/ssharpe42/followers", "following_url": "https://api.github.com/users/ssharpe42/following{/other_user}", "gists_url": "https://api.github.com/users/ssharpe42/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/ssharpe42", "id": 8136905, "login": "ssharpe42", "node_id": "MDQ6VXNlcjgxMzY5MDU=", "organizations_url": "https://api.github.com/users/ssharpe42/orgs", "received_events_url": "https://api.github.com/users/ssharpe42/received_events", "repos_url": "https://api.github.com/users/ssharpe42/repos", "site_admin": false, "starred_url": "https://api.github.com/users/ssharpe42/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/ssharpe42/subscriptions", "type": "User", "url": "https://api.github.com/users/ssharpe42", "user_view_type": "public" }	[]	open	false	null	[]	null	[]	2024-03-05T15:55:13Z	2024-03-05T15:55:13Z	null	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug I am using an iterable dataset in a multi-node setup, trying to do training/inference while filtering the data on the fly. I usually do not use `split_dataset_by_node` but it is very slow using the IterableDatasetShard in `accelerate` and `transformers`. When I filter after applying `split_dataset_by_node`, it results in shards that are not equal sizes due to unequal samples filtered from each one. The distributed process hangs when trying to accomplish this. Is there any way to resolve this or is it impossible to implement? ### Steps to reproduce the bug Here is a toy example of what I am trying to do that reproduces the behavior ``` # torchrun --nproc-per-node 2 file.py import os import pandas as pd import torch from accelerate import Accelerator from datasets import Features, Value, load_dataset from datasets.distributed import split_dataset_by_node from torch.utils.data import DataLoader accelerator = Accelerator(device_placement=True, dispatch_batches=False) if accelerator.is_main_process: if not os.path.exists("scratch_data"): os.mkdir("scratch_data") n_shards = 4 for i in range(n_shards): df = pd.DataFrame({"id": list(range(10 * i, 10 * (i + 1)))}) df.to_parquet(f"scratch_data/shard_{i}.parquet") world_size = accelerator.num_processes local_rank = accelerator.process_index def collate_fn(examples): input_ids = [] for example in examples: input_ids.append(example["id"]) return torch.LongTensor(input_ids) dataset = load_dataset( "parquet", data_dir="scratch_data", split="train", streaming=True ) dataset = ( split_dataset_by_node(dataset, rank=local_rank, world_size=world_size) .filter(lambda x: x["id"] < 35) .shuffle(seed=42, buffer_size=100) ) batch_size = 2 train_dataloader = DataLoader( dataset, batch_size=batch_size, collate_fn=collate_fn, num_workers=2 ) for x in train_dataloader: x = x.to(accelerator.device) print({"rank": local_rank, "id": x}) y = accelerator.gather_for_metrics(x) if accelerator.is_main_process: print("gathered", y) ``` ### Expected behavior Is there any way to continue training/inference on the GPUs that have remaining data left without waiting for the others? Is it impossible to filter when ### Environment info - `datasets` version: 2.18.0 - Platform: Linux-5.10.209-198.812.amzn2.x86_64-x86_64-with-glibc2.31 - Python version: 3.10.13 - `huggingface_hub` version: 0.21.3 - PyArrow version: 15.0.0 - Pandas version: 2.2.1 - `fsspec` version: 2023.6.0	null	{ "+1": 4, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 4, "url": "https://api.github.com/repos/huggingface/datasets/issues/6719/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6719/timeline	null	null	null	null
https://api.github.com/repos/huggingface/datasets/issues/5608	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5608/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5608/comments	https://api.github.com/repos/huggingface/datasets/issues/5608/events	https://github.com/huggingface/datasets/issues/5608	1,609,996,563	I_kwDODunzps5f9pkT	5,608	audiofolder only creates dataset of 13 rows (files) when the data folder it's reading from has 20,000 mp3 files.	{ "avatar_url": "https://avatars.githubusercontent.com/u/107211437?v=4", "events_url": "https://api.github.com/users/jcho19/events{/privacy}", "followers_url": "https://api.github.com/users/jcho19/followers", "following_url": "https://api.github.com/users/jcho19/following{/other_user}", "gists_url": "https://api.github.com/users/jcho19/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/jcho19", "id": 107211437, "login": "jcho19", "node_id": "U_kgDOBmPqrQ", "organizations_url": "https://api.github.com/users/jcho19/orgs", "received_events_url": "https://api.github.com/users/jcho19/received_events", "repos_url": "https://api.github.com/users/jcho19/repos", "site_admin": false, "starred_url": "https://api.github.com/users/jcho19/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/jcho19/subscriptions", "type": "User", "url": "https://api.github.com/users/jcho19", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "Hi!\r\n\r\n> naming convention of mp3 files\r\n\r\nYes, this could be the problem. MP3 files should end with `.mp3`/`.MP3` to be recognized as audio files.\r\n\r\nIf the file names are not the culprit, can you paste the audio folder's directory structure to help us reproduce the error (e.g., by running the `tree \"x\"` command)?", "Hi! I'm sorry, I don't want to reveal my entire dataset, but here's a snippet (all of the mp3 files below are some of the ones not being recognized by audiofolder. Also, for another dataset, audiofolder loaded zero mp3 files because \"train\" was in the name of one of the mp3 files. \r\nmy_dataset\r\n├── data\r\n│ ├── VHA_Innovation_Stories_-_Day_2-123.mp3\r\n│ ├── VHA_Innovation_Stories_-_Day_2-124.mp3\r\n│ ├── ASSOCIATION_OF_GENERAL_PRACTITIONERS_OF_JAMAICA_NEPHROLOGY_CONFERENCE_-_JULY_3,_2022-93.mp3\r\n│ ├── ASSOCIATION_OF_GENERAL_PRACTITIONERS_OF_JAMAICA_NEPHROLOGY_CONFERENCE_-_JULY_3,_2022-94.mp3\r\n│ ├── ASSOCIATION_OF_GENERAL_PRACTITIONERS_OF_JAMAICA_NEPHROLOGY_CONFERENCE_-_JULY_3,_2022-95.mp3\r\n│ ├── Your_Impact\\357\\274\\232_Neurosurgery_equipment-5.mp3\r\n│ └── Your_Impact\\357\\274\\232_Neurosurgery_equipment-6.mp3\r\n└── metadata.csv\r\n\r\nHere's a few of the 13 files recognized by the dataset:\r\nBritish_Heart_Foundation_-_Your_guide_to_a_Coronary_Angiogram,_a_test_for_heart_disease-1.mp3\r\nBritish_Heart_Foundation_-_Your_guide_to_a_Coronary_Angiogram,_a_test_for_heart_disease-2.mp3\r\nBritish_Heart_Foundation_-_Your_guide_to_a_Coronary_Angiogram,_a_test_for_heart_disease-3.mp3\r\nIVP_⧸_IVU_test_Procedure_for_Kidneys_intravenous_pyelogram_-_medical_radiology_X-ray_ivp-1.mp3\r\nIVP_⧸_IVU_test_Procedure_for_Kidneys_intravenous_pyelogram_-_medical_radiology_X-ray_ivp-2.mp3" ]	2023-03-05T00:14:45Z	2023-03-12T00:02:57Z	2023-03-12T00:02:57Z	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug x = load_dataset("audiofolder", data_dir="x") When running this, x is a dataset of 13 rows (files) when it should be 20,000 rows (files) as the data_dir "x" has 20,000 mp3 files. Does anyone know what could possibly cause this (naming convention of mp3 files, etc.) ### Steps to reproduce the bug x = load_dataset("audiofolder", data_dir="x") ### Expected behavior x = load_dataset("audiofolder", data_dir="x") should create a dataset of 20,000 rows (files). ### Environment info - `datasets` version: 2.9.0 - Platform: Linux-3.10.0-1160.80.1.el7.x86_64-x86_64-with-glibc2.17 - Python version: 3.9.16 - PyArrow version: 11.0.0 - Pandas version: 1.5.3	{ "avatar_url": "https://avatars.githubusercontent.com/u/107211437?v=4", "events_url": "https://api.github.com/users/jcho19/events{/privacy}", "followers_url": "https://api.github.com/users/jcho19/followers", "following_url": "https://api.github.com/users/jcho19/following{/other_user}", "gists_url": "https://api.github.com/users/jcho19/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/jcho19", "id": 107211437, "login": "jcho19", "node_id": "U_kgDOBmPqrQ", "organizations_url": "https://api.github.com/users/jcho19/orgs", "received_events_url": "https://api.github.com/users/jcho19/received_events", "repos_url": "https://api.github.com/users/jcho19/repos", "site_admin": false, "starred_url": "https://api.github.com/users/jcho19/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/jcho19/subscriptions", "type": "User", "url": "https://api.github.com/users/jcho19", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5608/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5608/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/4596	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/4596/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/4596/comments	https://api.github.com/repos/huggingface/datasets/issues/4596/events	https://github.com/huggingface/datasets/issues/4596	1,288,381,735	I_kwDODunzps5MyyUn	4,596	Dataset Viewer issue for universal_dependencies	{ "avatar_url": "https://avatars.githubusercontent.com/u/16034009?v=4", "events_url": "https://api.github.com/users/Jordy-VL/events{/privacy}", "followers_url": "https://api.github.com/users/Jordy-VL/followers", "following_url": "https://api.github.com/users/Jordy-VL/following{/other_user}", "gists_url": "https://api.github.com/users/Jordy-VL/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/Jordy-VL", "id": 16034009, "login": "Jordy-VL", "node_id": "MDQ6VXNlcjE2MDM0MDA5", "organizations_url": "https://api.github.com/users/Jordy-VL/orgs", "received_events_url": "https://api.github.com/users/Jordy-VL/received_events", "repos_url": "https://api.github.com/users/Jordy-VL/repos", "site_admin": false, "starred_url": "https://api.github.com/users/Jordy-VL/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/Jordy-VL/subscriptions", "type": "User", "url": "https://api.github.com/users/Jordy-VL", "user_view_type": "public" }	[ { "color": "E5583E", "default": false, "description": "Related to the dataset viewer on huggingface.co", "id": 3470211881, "name": "dataset-viewer", "node_id": "LA_kwDODunzps7O1zsp", "url": "https://api.github.com/repos/huggingface/datasets/labels/dataset-viewer" } ]	closed	false	{ "avatar_url": "https://avatars.githubusercontent.com/u/1676121?v=4", "events_url": "https://api.github.com/users/severo/events{/privacy}", "followers_url": "https://api.github.com/users/severo/followers", "following_url": "https://api.github.com/users/severo/following{/other_user}", "gists_url": "https://api.github.com/users/severo/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/severo", "id": 1676121, "login": "severo", "node_id": "MDQ6VXNlcjE2NzYxMjE=", "organizations_url": "https://api.github.com/users/severo/orgs", "received_events_url": "https://api.github.com/users/severo/received_events", "repos_url": "https://api.github.com/users/severo/repos", "site_admin": false, "starred_url": "https://api.github.com/users/severo/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/severo/subscriptions", "type": "User", "url": "https://api.github.com/users/severo", "user_view_type": "public" }	[ { "avatar_url": "https://avatars.githubusercontent.com/u/1676121?v=4", "events_url": "https://api.github.com/users/severo/events{/privacy}", "followers_url": "https://api.github.com/users/severo/followers", "following_url": "https://api.github.com/users/severo/following{/other_user}", "gists_url": "https://api.github.com/users/severo/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/severo", "id": 1676121, "login": "severo", "node_id": "MDQ6VXNlcjE2NzYxMjE=", "organizations_url": "https://api.github.com/users/severo/orgs", "received_events_url": "https://api.github.com/users/severo/received_events", "repos_url": "https://api.github.com/users/severo/repos", "site_admin": false, "starred_url": "https://api.github.com/users/severo/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/severo/subscriptions", "type": "User", "url": "https://api.github.com/users/severo", "user_view_type": "public" } ]	null	[ "Thanks, looking at it!", "Finally fixed! We updated the dataset viewer and it fixed the issue.\r\n\r\nhttps://huggingface.co/datasets/universal_dependencies/viewer/aqz_tudet/train\r\n\r\n<img width=\"1561\" alt=\"Capture d’écran 2022-09-07 à 13 29 18\" src=\"https://user-images.githubusercontent.com/1676121/188867795-4f7dd438-d4f2-46cd-8a92-20a37fb2d6bc.png\">\r\n" ]	2022-06-29T08:50:29Z	2022-09-07T11:29:28Z	2022-09-07T11:29:27Z	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Link https://huggingface.co/datasets/universal_dependencies ### Description invalid json response body at https://datasets-server.huggingface.co/splits?dataset=universal_dependencies reason: Unexpected token I in JSON at position 0 ### Owner _No response_	{ "avatar_url": "https://avatars.githubusercontent.com/u/1676121?v=4", "events_url": "https://api.github.com/users/severo/events{/privacy}", "followers_url": "https://api.github.com/users/severo/followers", "following_url": "https://api.github.com/users/severo/following{/other_user}", "gists_url": "https://api.github.com/users/severo/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/severo", "id": 1676121, "login": "severo", "node_id": "MDQ6VXNlcjE2NzYxMjE=", "organizations_url": "https://api.github.com/users/severo/orgs", "received_events_url": "https://api.github.com/users/severo/received_events", "repos_url": "https://api.github.com/users/severo/repos", "site_admin": false, "starred_url": "https://api.github.com/users/severo/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/severo/subscriptions", "type": "User", "url": "https://api.github.com/users/severo", "user_view_type": "public" }	{ "+1": 2, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 2, "url": "https://api.github.com/repos/huggingface/datasets/issues/4596/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/4596/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/7466	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/7466/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/7466/comments	https://api.github.com/repos/huggingface/datasets/issues/7466/events	https://github.com/huggingface/datasets/pull/7466	2,928,661,327	PR_kwDODunzps6PHQyp	7,466	Fix local pdf loading	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7466). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update." ]	2025-03-18T14:09:06Z	2025-03-18T14:11:52Z	2025-03-18T14:09:21Z	MEMBER	null	null	null	fir this error when accessing a local pdf ``` File ~/.pyenv/versions/3.12.2/envs/hf-datasets/lib/python3.12/site-packages/pdfminer/psparser.py:220, in PSBaseParser.seek(self, pos) 218 """Seeks the parser to the given position.""" 219 log.debug("seek: %r", pos) --> 220 self.fp.seek(pos) 221 # reset the status for nextline() 222 self.bufpos = pos ValueError: seek of closed file ```	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/7466/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/7466/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/7466.diff", "html_url": "https://github.com/huggingface/datasets/pull/7466", "merged_at": "2025-03-18T14:09:21Z", "patch_url": "https://github.com/huggingface/datasets/pull/7466.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/7466" }
https://api.github.com/repos/huggingface/datasets/issues/6648	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6648/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6648/comments	https://api.github.com/repos/huggingface/datasets/issues/6648/events	https://github.com/huggingface/datasets/pull/6648	2,124,813,589	PR_kwDODunzps5mW1MA	6,648	Document usage of hfh cli instead of git	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6648). 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) \| 0.004951 / 0.011353 (-0.006402) \| 0.003187 / 0.011008 (-0.007821) \| 0.062959 / 0.038508 (0.024451) \| 0.028037 / 0.023109 (0.004928) \| 0.241374 / 0.275898 (-0.034524) \| 0.262792 / 0.323480 (-0.060688) \| 0.004132 / 0.007986 (-0.003854) \| 0.002766 / 0.004328 (-0.001563) \| 0.051416 / 0.004250 (0.047165) \| 0.040957 / 0.037052 (0.003904) \| 0.260760 / 0.258489 (0.002271) \| 0.282018 / 0.293841 (-0.011823) \| 0.027689 / 0.128546 (-0.100857) \| 0.010433 / 0.075646 (-0.065214) \| 0.211598 / 0.419271 (-0.207674) \| 0.035447 / 0.043533 (-0.008086) \| 0.244333 / 0.255139 (-0.010806) \| 0.263192 / 0.283200 (-0.020008) \| 0.016816 / 0.141683 (-0.124867) \| 1.103188 / 1.452155 (-0.348967) \| 1.179093 / 1.492716 (-0.313623) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092412 / 0.018006 (0.074406) \| 0.301226 / 0.000490 (0.300736) \| 0.000208 / 0.000200 (0.000008) \| 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) \| 0.018146 / 0.037411 (-0.019265) \| 0.061447 / 0.014526 (0.046921) \| 0.072162 / 0.176557 (-0.104394) \| 0.118965 / 0.737135 (-0.618170) \| 0.073756 / 0.296338 (-0.222583) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.285361 / 0.215209 (0.070152) \| 2.776928 / 2.077655 (0.699273) \| 1.506859 / 1.504120 (0.002739) \| 1.379119 / 1.541195 (-0.162075) \| 1.401798 / 1.468490 (-0.066692) \| 0.572512 / 4.584777 (-4.012265) \| 2.403793 / 3.745712 (-1.341919) \| 2.740496 / 5.269862 (-2.529366) \| 1.714611 / 4.565676 (-2.851065) \| 0.063496 / 0.424275 (-0.360780) \| 0.005009 / 0.007607 (-0.002598) \| 0.342438 / 0.226044 (0.116393) \| 3.368129 / 2.268929 (1.099200) \| 1.831200 / 55.444624 (-53.613424) \| 1.553611 / 6.876477 (-5.322866) \| 1.578116 / 2.142072 (-0.563956) \| 0.653034 / 4.805227 (-4.152193) \| 0.117724 / 6.500664 (-6.382940) \| 0.041188 / 0.075469 (-0.034282) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.972520 / 1.841788 (-0.869268) \| 11.186297 / 8.074308 (3.111989) \| 9.485829 / 10.191392 (-0.705563) \| 0.139715 / 0.680424 (-0.540708) \| 0.013705 / 0.534201 (-0.520496) \| 0.287384 / 0.579283 (-0.291899) \| 0.266784 / 0.434364 (-0.167580) \| 0.320789 / 0.540337 (-0.219548) \| 0.417484 / 1.386936 (-0.969452) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005570 / 0.011353 (-0.005783) \| 0.003416 / 0.011008 (-0.007592) \| 0.051160 / 0.038508 (0.012652) \| 0.031082 / 0.023109 (0.007973) \| 0.279336 / 0.275898 (0.003438) \| 0.300529 / 0.323480 (-0.022951) \| 0.004320 / 0.007986 (-0.003666) \| 0.002781 / 0.004328 (-0.001548) \| 0.049642 / 0.004250 (0.045391) \| 0.044379 / 0.037052 (0.007327) \| 0.293797 / 0.258489 (0.035308) \| 0.317844 / 0.293841 (0.024003) \| 0.049697 / 0.128546 (-0.078849) \| 0.010624 / 0.075646 (-0.065023) \| 0.058834 / 0.419271 (-0.360437) \| 0.033869 / 0.043533 (-0.009664) \| 0.280547 / 0.255139 (0.025408) \| 0.300685 / 0.283200 (0.017486) \| 0.017010 / 0.141683 (-0.124673) \| 1.172277 / 1.452155 (-0.279878) \| 1.205359 / 1.492716 (-0.287358) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092914 / 0.018006 (0.074907) \| 0.303561 / 0.000490 (0.303071) \| 0.000219 / 0.000200 (0.000019) \| 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) \| 0.022379 / 0.037411 (-0.015032) \| 0.075460 / 0.014526 (0.060934) \| 0.085795 / 0.176557 (-0.090762) \| 0.124776 / 0.737135 (-0.612360) \| 0.088260 / 0.296338 (-0.208079) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.302873 / 0.215209 (0.087664) \| 2.936173 / 2.077655 (0.858519) \| 1.589251 / 1.504120 (0.085131) \| 1.477552 / 1.541195 (-0.063643) \| 1.479322 / 1.468490 (0.010832) \| 0.570481 / 4.584777 (-4.014296) \| 2.434137 / 3.745712 (-1.311575) \| 2.774012 / 5.269862 (-2.495849) \| 1.718103 / 4.565676 (-2.847574) \| 0.061951 / 0.424275 (-0.362324) \| 0.004992 / 0.007607 (-0.002615) \| 0.352250 / 0.226044 (0.126205) \| 3.457417 / 2.268929 (1.188488) \| 1.934587 / 55.444624 (-53.510037) \| 1.646904 / 6.876477 (-5.229573) \| 1.669429 / 2.142072 (-0.472643) \| 0.649665 / 4.805227 (-4.155562) \| 0.116630 / 6.500664 (-6.384034) \| 0.040669 / 0.075469 (-0.034800) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.011488 / 1.841788 (-0.830300) \| 11.866394 / 8.074308 (3.792086) \| 10.144588 / 10.191392 (-0.046804) \| 0.129931 / 0.680424 (-0.550493) \| 0.014885 / 0.534201 (-0.519316) \| 0.287463 / 0.579283 (-0.291821) \| 0.280754 / 0.434364 (-0.153610) \| 0.330139 / 0.540337 (-0.210199) \| 0.414653 / 1.386936 (-0.972283) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#585275b8deaebd1bdcbd3725fa63172395791c73 \"CML watermark\")\n" ]	2024-02-08T10:24:56Z	2024-02-08T13:57:41Z	2024-02-08T13:51:39Z	MEMBER	null	null	null	(basically the same content as the hfh upload docs, but adapted for datasets)	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6648/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6648/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6648.diff", "html_url": "https://github.com/huggingface/datasets/pull/6648", "merged_at": "2024-02-08T13:51:39Z", "patch_url": "https://github.com/huggingface/datasets/pull/6648.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6648" }
https://api.github.com/repos/huggingface/datasets/issues/6685	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6685/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6685/comments	https://api.github.com/repos/huggingface/datasets/issues/6685/events	https://github.com/huggingface/datasets/pull/6685	2,145,570,006	PR_kwDODunzps5ndZQa	6,685	Updated Quickstart Notebook link	{ "avatar_url": "https://avatars.githubusercontent.com/u/55932554?v=4", "events_url": "https://api.github.com/users/Codeblockz/events{/privacy}", "followers_url": "https://api.github.com/users/Codeblockz/followers", "following_url": "https://api.github.com/users/Codeblockz/following{/other_user}", "gists_url": "https://api.github.com/users/Codeblockz/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/Codeblockz", "id": 55932554, "login": "Codeblockz", "node_id": "MDQ6VXNlcjU1OTMyNTU0", "organizations_url": "https://api.github.com/users/Codeblockz/orgs", "received_events_url": "https://api.github.com/users/Codeblockz/received_events", "repos_url": "https://api.github.com/users/Codeblockz/repos", "site_admin": false, "starred_url": "https://api.github.com/users/Codeblockz/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/Codeblockz/subscriptions", "type": "User", "url": "https://api.github.com/users/Codeblockz", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6685). 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) \| 0.005386 / 0.011353 (-0.005967) \| 0.003707 / 0.011008 (-0.007301) \| 0.062661 / 0.038508 (0.024153) \| 0.029058 / 0.023109 (0.005949) \| 0.249669 / 0.275898 (-0.026230) \| 0.280996 / 0.323480 (-0.042484) \| 0.004041 / 0.007986 (-0.003945) \| 0.002713 / 0.004328 (-0.001616) \| 0.047914 / 0.004250 (0.043664) \| 0.042014 / 0.037052 (0.004961) \| 0.265209 / 0.258489 (0.006720) \| 0.297320 / 0.293841 (0.003479) \| 0.028323 / 0.128546 (-0.100223) \| 0.010844 / 0.075646 (-0.064802) \| 0.205895 / 0.419271 (-0.213377) \| 0.035997 / 0.043533 (-0.007536) \| 0.245069 / 0.255139 (-0.010070) \| 0.266159 / 0.283200 (-0.017040) \| 0.017590 / 0.141683 (-0.124093) \| 1.132046 / 1.452155 (-0.320109) \| 1.177496 / 1.492716 (-0.315220) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.105441 / 0.018006 (0.087435) \| 0.301321 / 0.000490 (0.300831) \| 0.000211 / 0.000200 (0.000011) \| 0.000064 / 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) \| 0.018687 / 0.037411 (-0.018724) \| 0.061221 / 0.014526 (0.046695) \| 0.072556 / 0.176557 (-0.104001) \| 0.119641 / 0.737135 (-0.617495) \| 0.073781 / 0.296338 (-0.222557) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.284564 / 0.215209 (0.069354) \| 2.795786 / 2.077655 (0.718131) \| 1.437059 / 1.504120 (-0.067061) \| 1.309319 / 1.541195 (-0.231876) \| 1.315849 / 1.468490 (-0.152641) \| 0.578571 / 4.584777 (-4.006206) \| 2.350754 / 3.745712 (-1.394958) \| 2.758499 / 5.269862 (-2.511362) \| 1.705545 / 4.565676 (-2.860131) \| 0.063660 / 0.424275 (-0.360615) \| 0.005506 / 0.007607 (-0.002101) \| 0.334915 / 0.226044 (0.108871) \| 3.295922 / 2.268929 (1.026994) \| 1.796513 / 55.444624 (-53.648111) \| 1.488113 / 6.876477 (-5.388364) \| 1.523042 / 2.142072 (-0.619031) \| 0.648169 / 4.805227 (-4.157058) \| 0.119321 / 6.500664 (-6.381343) \| 0.041932 / 0.075469 (-0.033537) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.982432 / 1.841788 (-0.859356) \| 11.344780 / 8.074308 (3.270472) \| 9.627219 / 10.191392 (-0.564173) \| 0.142590 / 0.680424 (-0.537834) \| 0.013899 / 0.534201 (-0.520302) \| 0.286335 / 0.579283 (-0.292948) \| 0.266552 / 0.434364 (-0.167812) \| 0.320361 / 0.540337 (-0.219977) \| 0.420303 / 1.386936 (-0.966633) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005251 / 0.011353 (-0.006102) \| 0.003515 / 0.011008 (-0.007494) \| 0.049344 / 0.038508 (0.010836) \| 0.032055 / 0.023109 (0.008945) \| 0.280653 / 0.275898 (0.004755) \| 0.303989 / 0.323480 (-0.019491) \| 0.004402 / 0.007986 (-0.003584) \| 0.002758 / 0.004328 (-0.001570) \| 0.050947 / 0.004250 (0.046697) \| 0.044405 / 0.037052 (0.007353) \| 0.292856 / 0.258489 (0.034367) \| 0.325307 / 0.293841 (0.031466) \| 0.047720 / 0.128546 (-0.080827) \| 0.010589 / 0.075646 (-0.065057) \| 0.057728 / 0.419271 (-0.361543) \| 0.033842 / 0.043533 (-0.009691) \| 0.285443 / 0.255139 (0.030304) \| 0.300013 / 0.283200 (0.016814) \| 0.017444 / 0.141683 (-0.124238) \| 1.152880 / 1.452155 (-0.299275) \| 1.200670 / 1.492716 (-0.292046) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092355 / 0.018006 (0.074349) \| 0.307907 / 0.000490 (0.307418) \| 0.000226 / 0.000200 (0.000026) \| 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) \| 0.021624 / 0.037411 (-0.015787) \| 0.075855 / 0.014526 (0.061329) \| 0.087109 / 0.176557 (-0.089447) \| 0.124859 / 0.737135 (-0.612276) \| 0.088933 / 0.296338 (-0.207406) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.294213 / 0.215209 (0.079004) \| 2.893146 / 2.077655 (0.815491) \| 1.595061 / 1.504120 (0.090942) \| 1.480959 / 1.541195 (-0.060236) \| 1.528277 / 1.468490 (0.059787) \| 0.570273 / 4.584777 (-4.014504) \| 2.412948 / 3.745712 (-1.332764) \| 2.675009 / 5.269862 (-2.594852) \| 1.724005 / 4.565676 (-2.841671) \| 0.063359 / 0.424275 (-0.360916) \| 0.005008 / 0.007607 (-0.002599) \| 0.346570 / 0.226044 (0.120526) \| 3.456566 / 2.268929 (1.187637) \| 1.973109 / 55.444624 (-53.471515) \| 1.657562 / 6.876477 (-5.218915) \| 1.790086 / 2.142072 (-0.351986) \| 0.655277 / 4.805227 (-4.149950) \| 0.117985 / 6.500664 (-6.382679) \| 0.041128 / 0.075469 (-0.034342) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.001428 / 1.841788 (-0.840360) \| 11.953458 / 8.074308 (3.879150) \| 10.188439 / 10.191392 (-0.002953) \| 0.140863 / 0.680424 (-0.539561) \| 0.015278 / 0.534201 (-0.518923) \| 0.288193 / 0.579283 (-0.291090) \| 0.281732 / 0.434364 (-0.152632) \| 0.328034 / 0.540337 (-0.212304) \| 0.414571 / 1.386936 (-0.972365) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#531f35e688f81ec6b4c9044856a89a6b48142bd8 \"CML watermark\")\n" ]	2024-02-21T01:04:18Z	2024-03-12T21:31:04Z	2024-02-25T18:48:08Z	CONTRIBUTOR	null	null	null	Fixed Quickstart Notebook Link in the [Overview notebook](https://github.com/huggingface/datasets/blob/main/notebooks/Overview.ipynb)	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6685/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6685/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6685.diff", "html_url": "https://github.com/huggingface/datasets/pull/6685", "merged_at": "2024-02-25T18:48:08Z", "patch_url": "https://github.com/huggingface/datasets/pull/6685.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6685" }
https://api.github.com/repos/huggingface/datasets/issues/7063	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/7063/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/7063/comments	https://api.github.com/repos/huggingface/datasets/issues/7063/events	https://github.com/huggingface/datasets/issues/7063	2,424,488,648	I_kwDODunzps6QgsLI	7,063	Add `batch` method to `Dataset`	{ "avatar_url": "https://avatars.githubusercontent.com/u/61876623?v=4", "events_url": "https://api.github.com/users/lappemic/events{/privacy}", "followers_url": "https://api.github.com/users/lappemic/followers", "following_url": "https://api.github.com/users/lappemic/following{/other_user}", "gists_url": "https://api.github.com/users/lappemic/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lappemic", "id": 61876623, "login": "lappemic", "node_id": "MDQ6VXNlcjYxODc2NjIz", "organizations_url": "https://api.github.com/users/lappemic/orgs", "received_events_url": "https://api.github.com/users/lappemic/received_events", "repos_url": "https://api.github.com/users/lappemic/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lappemic/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lappemic/subscriptions", "type": "User", "url": "https://api.github.com/users/lappemic", "user_view_type": "public" }	[ { "color": "a2eeef", "default": true, "description": "New feature or request", "id": 1935892871, "name": "enhancement", "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement" } ]	closed	false	null	[]	null	[]	2024-07-23T07:36:59Z	2024-07-25T13:45:21Z	2024-07-25T13:45:21Z	CONTRIBUTOR	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### 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.	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/7063/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/7063/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/7501	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/7501/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/7501/comments	https://api.github.com/repos/huggingface/datasets/issues/7501/events	https://github.com/huggingface/datasets/issues/7501	2,976,721,014	I_kwDODunzps6xbSh2	7,501	Nested Feature raises ArrowNotImplementedError: Unsupported cast using function cast_struct	{ "avatar_url": "https://avatars.githubusercontent.com/u/26623948?v=4", "events_url": "https://api.github.com/users/yaner-here/events{/privacy}", "followers_url": "https://api.github.com/users/yaner-here/followers", "following_url": "https://api.github.com/users/yaner-here/following{/other_user}", "gists_url": "https://api.github.com/users/yaner-here/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/yaner-here", "id": 26623948, "login": "yaner-here", "node_id": "MDQ6VXNlcjI2NjIzOTQ4", "organizations_url": "https://api.github.com/users/yaner-here/orgs", "received_events_url": "https://api.github.com/users/yaner-here/received_events", "repos_url": "https://api.github.com/users/yaner-here/repos", "site_admin": false, "starred_url": "https://api.github.com/users/yaner-here/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/yaner-here/subscriptions", "type": "User", "url": "https://api.github.com/users/yaner-here", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "Solved by the default `load_dataset(features)` parameters. Do not use `Sequence` for the `list` in `list[any]` json schema, just simply use `[]`. For example, `\"b\": Sequence({...})` fails but `\"b\": [{...}]` works fine." ]	2025-04-07T12:35:39Z	2025-04-07T12:43:04Z	2025-04-07T12:43:03Z	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug `datasets.Features` seems to be unable to handle json file that contains fields of `list[dict]`. ### Steps to reproduce the bug ```json // test.json {"a": 1, "b": [{"c": 2, "d": 3}, {"c": 4, "d": 5}]} {"a": 5, "b": [{"c": 7, "d": 8}, {"c": 9, "d": 10}]} ``` ```python import json from datasets import Dataset, Features, Value, Sequence, load_dataset annotation_feature = Features({ "a": Value("int32"), "b": Sequence({ "c": Value("int32"), "d": Value("int32"), }), }) annotation_dataset = load_dataset( "json", data_files="test.json", features=annotation_feature ) ``` ``` ArrowNotImplementedError: Unsupported cast from list<item: struct<c: int32, d: int32>> to struct using function cast_struct The above exception was the direct cause of the following exception: DatasetGenerationError Traceback (most recent call last) Cell In[46], line 11 2 from datasets import Dataset, Features, Value, Sequence, load_dataset 4 annotation_feature = Features({ 5 "a": Value("int32"), 6 "b": Sequence({ (...) 9 }), 10 }) ---> 11 annotation_dataset = load_dataset( 12 "json", 13 data_files="test.json", 14 features=annotation_feature 15 ) ``` ### Expected behavior A `datasets.Datasets` instance should be initialized. ### Environment info - `datasets` version: 3.5.0 - Platform: Linux-6.11.0-21-generic-x86_64-with-glibc2.39 - Python version: 3.11.11 - `huggingface_hub` version: 0.30.1 - PyArrow version: 19.0.1 - Pandas version: 2.2.3 - `fsspec` version: 2024.12.0	{ "avatar_url": "https://avatars.githubusercontent.com/u/26623948?v=4", "events_url": "https://api.github.com/users/yaner-here/events{/privacy}", "followers_url": "https://api.github.com/users/yaner-here/followers", "following_url": "https://api.github.com/users/yaner-here/following{/other_user}", "gists_url": "https://api.github.com/users/yaner-here/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/yaner-here", "id": 26623948, "login": "yaner-here", "node_id": "MDQ6VXNlcjI2NjIzOTQ4", "organizations_url": "https://api.github.com/users/yaner-here/orgs", "received_events_url": "https://api.github.com/users/yaner-here/received_events", "repos_url": "https://api.github.com/users/yaner-here/repos", "site_admin": false, "starred_url": "https://api.github.com/users/yaner-here/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/yaner-here/subscriptions", "type": "User", "url": "https://api.github.com/users/yaner-here", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/7501/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/7501/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/4938	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/4938/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/4938/comments	https://api.github.com/repos/huggingface/datasets/issues/4938/events	https://github.com/huggingface/datasets/pull/4938	1,363,429,228	PR_kwDODunzps4-coaB	4,938	Remove main branch rename notice	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._" ]	2022-09-06T15:03:05Z	2022-09-06T16:46:11Z	2022-09-06T16:43:53Z	MEMBER	null	null	null	We added a notice in README.md to show that we renamed the master branch to main, but we can remove it now (it's been 2 months) I also unpinned the github issue about the branch renaming	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/4938/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/4938/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/4938.diff", "html_url": "https://github.com/huggingface/datasets/pull/4938", "merged_at": "2022-09-06T16:43:53Z", "patch_url": "https://github.com/huggingface/datasets/pull/4938.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/4938" }
https://api.github.com/repos/huggingface/datasets/issues/6970	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6970/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6970/comments	https://api.github.com/repos/huggingface/datasets/issues/6970/events	https://github.com/huggingface/datasets/pull/6970	2,351,380,029	PR_kwDODunzps5yYF37	6,970	Set dev version	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6970). 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) \| 0.005450 / 0.011353 (-0.005902) \| 0.003911 / 0.011008 (-0.007098) \| 0.063467 / 0.038508 (0.024959) \| 0.031029 / 0.023109 (0.007920) \| 0.247916 / 0.275898 (-0.027982) \| 0.274737 / 0.323480 (-0.048743) \| 0.003255 / 0.007986 (-0.004731) \| 0.002842 / 0.004328 (-0.001487) \| 0.049617 / 0.004250 (0.045366) \| 0.046689 / 0.037052 (0.009637) \| 0.255152 / 0.258489 (-0.003337) \| 0.288630 / 0.293841 (-0.005211) \| 0.028174 / 0.128546 (-0.100372) \| 0.010773 / 0.075646 (-0.064873) \| 0.202119 / 0.419271 (-0.217153) \| 0.035914 / 0.043533 (-0.007619) \| 0.248197 / 0.255139 (-0.006942) \| 0.273508 / 0.283200 (-0.009691) \| 0.020626 / 0.141683 (-0.121057) \| 1.125668 / 1.452155 (-0.326487) \| 1.156678 / 1.492716 (-0.336038) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.098294 / 0.018006 (0.080288) \| 0.306661 / 0.000490 (0.306172) \| 0.000227 / 0.000200 (0.000027) \| 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) \| 0.019118 / 0.037411 (-0.018293) \| 0.063086 / 0.014526 (0.048560) \| 0.077735 / 0.176557 (-0.098822) \| 0.123159 / 0.737135 (-0.613976) \| 0.077228 / 0.296338 (-0.219111) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.280031 / 0.215209 (0.064822) \| 2.762524 / 2.077655 (0.684870) \| 1.444571 / 1.504120 (-0.059549) \| 1.330590 / 1.541195 (-0.210604) \| 1.371937 / 1.468490 (-0.096553) \| 0.563847 / 4.584777 (-4.020930) \| 2.369908 / 3.745712 (-1.375804) \| 2.827441 / 5.269862 (-2.442420) \| 1.749864 / 4.565676 (-2.815812) \| 0.063996 / 0.424275 (-0.360279) \| 0.005060 / 0.007607 (-0.002547) \| 0.326067 / 0.226044 (0.100023) \| 3.270170 / 2.268929 (1.001242) \| 1.785164 / 55.444624 (-53.659460) \| 1.560432 / 6.876477 (-5.316045) \| 1.587005 / 2.142072 (-0.555068) \| 0.645714 / 4.805227 (-4.159513) \| 0.119975 / 6.500664 (-6.380689) \| 0.043962 / 0.075469 (-0.031507) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.979003 / 1.841788 (-0.862785) \| 11.988701 / 8.074308 (3.914393) \| 9.788564 / 10.191392 (-0.402828) \| 0.142644 / 0.680424 (-0.537780) \| 0.014924 / 0.534201 (-0.519277) \| 0.285942 / 0.579283 (-0.293341) \| 0.264086 / 0.434364 (-0.170278) \| 0.343360 / 0.540337 (-0.196977) \| 0.413467 / 1.386936 (-0.973469) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated 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 (-0.005535) \| 0.003726 / 0.011008 (-0.007283) \| 0.050936 / 0.038508 (0.012428) \| 0.032000 / 0.023109 (0.008890) \| 0.273282 / 0.275898 (-0.002616) \| 0.293889 / 0.323480 (-0.029591) \| 0.004287 / 0.007986 (-0.003699) \| 0.002797 / 0.004328 (-0.001531) \| 0.049088 / 0.004250 (0.044838) \| 0.040235 / 0.037052 (0.003183) \| 0.280240 / 0.258489 (0.021751) \| 0.315749 / 0.293841 (0.021908) \| 0.029986 / 0.128546 (-0.098560) \| 0.010440 / 0.075646 (-0.065206) \| 0.058935 / 0.419271 (-0.360336) \| 0.033198 / 0.043533 (-0.010335) \| 0.274321 / 0.255139 (0.019182) \| 0.288039 / 0.283200 (0.004840) \| 0.018865 / 0.141683 (-0.122818) \| 1.114915 / 1.452155 (-0.337240) \| 1.180548 / 1.492716 (-0.312169) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.095028 / 0.018006 (0.077022) \| 0.304797 / 0.000490 (0.304307) \| 0.000221 / 0.000200 (0.000021) \| 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) \| 0.022556 / 0.037411 (-0.014855) \| 0.076839 / 0.014526 (0.062313) \| 0.090255 / 0.176557 (-0.086302) \| 0.128748 / 0.737135 (-0.608387) \| 0.091718 / 0.296338 (-0.204621) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.296061 / 0.215209 (0.080852) \| 2.851376 / 2.077655 (0.773722) \| 1.548084 / 1.504120 (0.043964) \| 1.428589 / 1.541195 (-0.112606) \| 1.467244 / 1.468490 (-0.001246) \| 0.583533 / 4.584777 (-4.001244) \| 0.967436 / 3.745712 (-2.778277) \| 2.774775 / 5.269862 (-2.495087) \| 1.800435 / 4.565676 (-2.765242) \| 0.063998 / 0.424275 (-0.360277) \| 0.005420 / 0.007607 (-0.002187) \| 0.346353 / 0.226044 (0.120308) \| 3.383885 / 2.268929 (1.114956) \| 1.902914 / 55.444624 (-53.541710) \| 1.599545 / 6.876477 (-5.276932) \| 1.772754 / 2.142072 (-0.369318) \| 0.651804 / 4.805227 (-4.153423) \| 0.120380 / 6.500664 (-6.380284) \| 0.043311 / 0.075469 (-0.032159) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.004414 / 1.841788 (-0.837374) \| 12.356077 / 8.074308 (4.281769) \| 10.513420 / 10.191392 (0.322028) \| 0.132419 / 0.680424 (-0.548005) \| 0.015470 / 0.534201 (-0.518731) \| 0.284883 / 0.579283 (-0.294400) \| 0.130763 / 0.434364 (-0.303601) \| 0.320068 / 0.540337 (-0.220270) \| 0.430284 / 1.386936 (-0.956652) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#574791e0a0cf57ba761f679a054b9e89e4a3ee22 \"CML watermark\")\n" ]	2024-06-13T14:59:45Z	2024-06-13T15:06:18Z	2024-06-13T14:59:56Z	MEMBER	null	null	null	null	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6970/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6970/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6970.diff", "html_url": "https://github.com/huggingface/datasets/pull/6970", "merged_at": "2024-06-13T14:59:56Z", "patch_url": "https://github.com/huggingface/datasets/pull/6970.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6970" }
https://api.github.com/repos/huggingface/datasets/issues/6082	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6082/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6082/comments	https://api.github.com/repos/huggingface/datasets/issues/6082/events	https://github.com/huggingface/datasets/pull/6082	1,824,819,672	PR_kwDODunzps5WkdIn	6,082	Release: 2.14.1	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6082). All of your documentation changes will be reflected on that endpoint.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007215 / 0.011353 (-0.004138) \| 0.004101 / 0.011008 (-0.006907) \| 0.085884 / 0.038508 (0.047376) \| 0.085375 / 0.023109 (0.062266) \| 0.351610 / 0.275898 (0.075712) \| 0.399284 / 0.323480 (0.075804) \| 0.005598 / 0.007986 (-0.002388) \| 0.003405 / 0.004328 (-0.000923) \| 0.064906 / 0.004250 (0.060656) \| 0.059000 / 0.037052 (0.021948) \| 0.354589 / 0.258489 (0.096100) \| 0.406070 / 0.293841 (0.112229) \| 0.031627 / 0.128546 (-0.096919) \| 0.008597 / 0.075646 (-0.067049) \| 0.291050 / 0.419271 (-0.128221) \| 0.054120 / 0.043533 (0.010587) \| 0.366242 / 0.255139 (0.111103) \| 0.375975 / 0.283200 (0.092776) \| 0.025608 / 0.141683 (-0.116074) \| 1.473514 / 1.452155 (0.021359) \| 1.543226 / 1.492716 (0.050510) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.198068 / 0.018006 (0.180062) \| 0.450583 / 0.000490 (0.450093) \| 0.005368 / 0.000200 (0.005168) \| 0.000102 / 0.000054 (0.000047) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028323 / 0.037411 (-0.009089) \| 0.089058 / 0.014526 (0.074533) \| 0.097718 / 0.176557 (-0.078839) \| 0.154546 / 0.737135 (-0.582590) \| 0.098224 / 0.296338 (-0.198115) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.386292 / 0.215209 (0.171083) \| 3.846222 / 2.077655 (1.768567) \| 1.858695 / 1.504120 (0.354575) \| 1.685885 / 1.541195 (0.144690) \| 1.790727 / 1.468490 (0.322237) \| 0.486771 / 4.584777 (-4.098006) \| 3.658363 / 3.745712 (-0.087349) \| 5.345236 / 5.269862 (0.075374) \| 3.215942 / 4.565676 (-1.349734) \| 0.057580 / 0.424275 (-0.366695) \| 0.007382 / 0.007607 (-0.000225) \| 0.464174 / 0.226044 (0.238129) \| 4.640848 / 2.268929 (2.371920) \| 2.383152 / 55.444624 (-53.061472) \| 2.013288 / 6.876477 (-4.863188) \| 2.244142 / 2.142072 (0.102069) \| 0.585408 / 4.805227 (-4.219819) \| 0.134698 / 6.500664 (-6.365966) \| 0.060641 / 0.075469 (-0.014828) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.258414 / 1.841788 (-0.583374) \| 19.825848 / 8.074308 (11.751540) \| 14.644025 / 10.191392 (4.452633) \| 0.169198 / 0.680424 (-0.511226) \| 0.018180 / 0.534201 (-0.516021) \| 0.395100 / 0.579283 (-0.184183) \| 0.411543 / 0.434364 (-0.022821) \| 0.463364 / 0.540337 (-0.076973) \| 0.628613 / 1.386936 (-0.758323) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006860 / 0.011353 (-0.004493) \| 0.003981 / 0.011008 (-0.007027) \| 0.065589 / 0.038508 (0.027081) \| 0.082460 / 0.023109 (0.059350) \| 0.362980 / 0.275898 (0.087082) \| 0.394837 / 0.323480 (0.071357) \| 0.005298 / 0.007986 (-0.002688) \| 0.003372 / 0.004328 (-0.000957) \| 0.064918 / 0.004250 (0.060667) \| 0.058033 / 0.037052 (0.020981) \| 0.367259 / 0.258489 (0.108770) \| 0.403122 / 0.293841 (0.109281) \| 0.031566 / 0.128546 (-0.096980) \| 0.008583 / 0.075646 (-0.067063) \| 0.071287 / 0.419271 (-0.347984) \| 0.049586 / 0.043533 (0.006053) \| 0.359252 / 0.255139 (0.104113) \| 0.378519 / 0.283200 (0.095319) \| 0.023412 / 0.141683 (-0.118271) \| 1.494522 / 1.452155 (0.042367) \| 1.559176 / 1.492716 (0.066460) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.228396 / 0.018006 (0.210390) \| 0.441865 / 0.000490 (0.441375) \| 0.000395 / 0.000200 (0.000195) \| 0.000054 / 0.000054 (-0.000000) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031169 / 0.037411 (-0.006242) \| 0.093427 / 0.014526 (0.078901) \| 0.100673 / 0.176557 (-0.075883) \| 0.152817 / 0.737135 (-0.584319) \| 0.102226 / 0.296338 (-0.194112) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.437032 / 0.215209 (0.221823) \| 4.376078 / 2.077655 (2.298423) \| 2.346928 / 1.504120 (0.842808) \| 2.168573 / 1.541195 (0.627378) \| 2.261024 / 1.468490 (0.792534) \| 0.497080 / 4.584777 (-4.087697) \| 3.594402 / 3.745712 (-0.151310) \| 5.090361 / 5.269862 (-0.179501) \| 3.034750 / 4.565676 (-1.530927) \| 0.058538 / 0.424275 (-0.365737) \| 0.007892 / 0.007607 (0.000285) \| 0.517643 / 0.226044 (0.291598) \| 5.173174 / 2.268929 (2.904246) \| 2.825917 / 55.444624 (-52.618708) \| 2.542593 / 6.876477 (-4.333884) \| 2.716290 / 2.142072 (0.574218) \| 0.598253 / 4.805227 (-4.206974) \| 0.135610 / 6.500664 (-6.365054) \| 0.062113 / 0.075469 (-0.013356) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.389554 / 1.841788 (-0.452233) \| 20.412868 / 8.074308 (12.338560) \| 14.539988 / 10.191392 (4.348596) \| 0.162046 / 0.680424 (-0.518378) \| 0.018508 / 0.534201 (-0.515693) \| 0.398840 / 0.579283 (-0.180443) \| 0.400902 / 0.434364 (-0.033462) \| 0.463647 / 0.540337 (-0.076691) \| 0.612921 / 1.386936 (-0.774015) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#45bef1810d9341ba4cb27547d748fddb97843792 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005943 / 0.011353 (-0.005410) \| 0.003582 / 0.011008 (-0.007426) \| 0.080030 / 0.038508 (0.041522) \| 0.057458 / 0.023109 (0.034349) \| 0.390783 / 0.275898 (0.114885) \| 0.430926 / 0.323480 (0.107446) \| 0.003207 / 0.007986 (-0.004778) \| 0.003592 / 0.004328 (-0.000737) \| 0.062468 / 0.004250 (0.058217) \| 0.046739 / 0.037052 (0.009687) \| 0.394343 / 0.258489 (0.135854) \| 0.435912 / 0.293841 (0.142071) \| 0.026812 / 0.128546 (-0.101734) \| 0.007954 / 0.075646 (-0.067692) \| 0.261415 / 0.419271 (-0.157857) \| 0.044665 / 0.043533 (0.001132) \| 0.403454 / 0.255139 (0.148315) \| 0.418946 / 0.283200 (0.135747) \| 0.022247 / 0.141683 (-0.119436) \| 1.456387 / 1.452155 (0.004232) \| 1.508234 / 1.492716 (0.015518) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.182487 / 0.018006 (0.164480) \| 0.416343 / 0.000490 (0.415854) \| 0.001404 / 0.000200 (0.001204) \| 0.000062 / 0.000054 (0.000007) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023643 / 0.037411 (-0.013768) \| 0.071798 / 0.014526 (0.057272) \| 0.083623 / 0.176557 (-0.092933) \| 0.146023 / 0.737135 (-0.591112) \| 0.083094 / 0.296338 (-0.213245) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.417484 / 0.215209 (0.202275) \| 4.157393 / 2.077655 (2.079738) \| 1.950438 / 1.504120 (0.446318) \| 1.766639 / 1.541195 (0.225444) \| 1.807382 / 1.468490 (0.338892) \| 0.496061 / 4.584777 (-4.088716) \| 2.975001 / 3.745712 (-0.770711) \| 3.340608 / 5.269862 (-1.929254) \| 2.236293 / 4.565676 (-2.329384) \| 0.056946 / 0.424275 (-0.367329) \| 0.006506 / 0.007607 (-0.001101) \| 0.480377 / 0.226044 (0.254332) \| 4.788525 / 2.268929 (2.519597) \| 2.430139 / 55.444624 (-53.014485) \| 2.154145 / 6.876477 (-4.722332) \| 2.321623 / 2.142072 (0.179551) \| 0.584040 / 4.805227 (-4.221188) \| 0.124508 / 6.500664 (-6.376156) \| 0.060828 / 0.075469 (-0.014641) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.201641 / 1.841788 (-0.640146) \| 18.066232 / 8.074308 (9.991924) \| 14.022304 / 10.191392 (3.830912) \| 0.146573 / 0.680424 (-0.533850) \| 0.016892 / 0.534201 (-0.517308) \| 0.333259 / 0.579283 (-0.246024) \| 0.357795 / 0.434364 (-0.076568) \| 0.391265 / 0.540337 (-0.149072) \| 0.551378 / 1.386936 (-0.835558) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005706 / 0.011353 (-0.005647) \| 0.003448 / 0.011008 (-0.007560) \| 0.063146 / 0.038508 (0.024638) \| 0.056292 / 0.023109 (0.033183) \| 0.355533 / 0.275898 (0.079635) \| 0.394996 / 0.323480 (0.071517) \| 0.004270 / 0.007986 (-0.003716) \| 0.002790 / 0.004328 (-0.001538) \| 0.063033 / 0.004250 (0.058783) \| 0.044684 / 0.037052 (0.007631) \| 0.370621 / 0.258489 (0.112132) \| 0.401074 / 0.293841 (0.107233) \| 0.026737 / 0.128546 (-0.101809) \| 0.007872 / 0.075646 (-0.067774) \| 0.068815 / 0.419271 (-0.350457) \| 0.040976 / 0.043533 (-0.002557) \| 0.370733 / 0.255139 (0.115594) \| 0.387418 / 0.283200 (0.104218) \| 0.018854 / 0.141683 (-0.122829) \| 1.479834 / 1.452155 (0.027680) \| 1.536388 / 1.492716 (0.043672) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.222125 / 0.018006 (0.204119) \| 0.408007 / 0.000490 (0.407517) \| 0.000367 / 0.000200 (0.000167) \| 0.000055 / 0.000054 (0.000001) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025100 / 0.037411 (-0.012311) \| 0.076617 / 0.014526 (0.062091) \| 0.088311 / 0.176557 (-0.088246) \| 0.143785 / 0.737135 (-0.593350) \| 0.088349 / 0.296338 (-0.207989) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.419246 / 0.215209 (0.204037) \| 4.172413 / 2.077655 (2.094759) \| 2.199355 / 1.504120 (0.695235) \| 2.025158 / 1.541195 (0.483963) \| 2.074491 / 1.468490 (0.606001) \| 0.495893 / 4.584777 (-4.088884) \| 2.998858 / 3.745712 (-0.746854) \| 2.770531 / 5.269862 (-2.499331) \| 1.817497 / 4.565676 (-2.748179) \| 0.057317 / 0.424275 (-0.366958) \| 0.006723 / 0.007607 (-0.000884) \| 0.491062 / 0.226044 (0.265017) \| 4.906155 / 2.268929 (2.637226) \| 2.654916 / 55.444624 (-52.789708) \| 2.299873 / 6.876477 (-4.576604) \| 2.451438 / 2.142072 (0.309366) \| 0.585048 / 4.805227 (-4.220179) \| 0.124778 / 6.500664 (-6.375886) \| 0.062067 / 0.075469 (-0.013402) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.298239 / 1.841788 (-0.543549) \| 18.090238 / 8.074308 (10.015930) \| 13.822568 / 10.191392 (3.631176) \| 0.130560 / 0.680424 (-0.549864) \| 0.016662 / 0.534201 (-0.517539) \| 0.333337 / 0.579283 (-0.245946) \| 0.348493 / 0.434364 (-0.085871) \| 0.386049 / 0.540337 (-0.154289) \| 0.511156 / 1.386936 (-0.875780) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#029956a347b0306cd27f693e12cf9a82acf4ef80 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006014 / 0.011353 (-0.005339) \| 0.003623 / 0.011008 (-0.007385) \| 0.080500 / 0.038508 (0.041992) \| 0.057713 / 0.023109 (0.034603) \| 0.325976 / 0.275898 (0.050078) \| 0.359986 / 0.323480 (0.036506) \| 0.004709 / 0.007986 (-0.003277) \| 0.002933 / 0.004328 (-0.001395) \| 0.063457 / 0.004250 (0.059207) \| 0.047514 / 0.037052 (0.010462) \| 0.331629 / 0.258489 (0.073140) \| 0.382048 / 0.293841 (0.088207) \| 0.026949 / 0.128546 (-0.101597) \| 0.008043 / 0.075646 (-0.067604) \| 0.262152 / 0.419271 (-0.157119) \| 0.045271 / 0.043533 (0.001738) \| 0.333355 / 0.255139 (0.078216) \| 0.347996 / 0.283200 (0.064796) \| 0.020814 / 0.141683 (-0.120868) \| 1.460723 / 1.452155 (0.008568) \| 1.488845 / 1.492716 (-0.003872) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.193735 / 0.018006 (0.175728) \| 0.431433 / 0.000490 (0.430943) \| 0.002494 / 0.000200 (0.002294) \| 0.000066 / 0.000054 (0.000012) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023762 / 0.037411 (-0.013650) \| 0.072680 / 0.014526 (0.058154) \| 0.081687 / 0.176557 (-0.094869) \| 0.143224 / 0.737135 (-0.593911) \| 0.083083 / 0.296338 (-0.213255) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.397393 / 0.215209 (0.182184) \| 3.954643 / 2.077655 (1.876989) \| 1.950038 / 1.504120 (0.445919) \| 1.760551 / 1.541195 (0.219357) \| 1.871165 / 1.468490 (0.402675) \| 0.508645 / 4.584777 (-4.076132) \| 3.114379 / 3.745712 (-0.631333) \| 3.474554 / 5.269862 (-1.795307) \| 2.090126 / 4.565676 (-2.475551) \| 0.058008 / 0.424275 (-0.366267) \| 0.006465 / 0.007607 (-0.001142) \| 0.475009 / 0.226044 (0.248965) \| 4.767981 / 2.268929 (2.499052) \| 2.372050 / 55.444624 (-53.072574) \| 2.038094 / 6.876477 (-4.838383) \| 2.072819 / 2.142072 (-0.069253) \| 0.591913 / 4.805227 (-4.213314) \| 0.125002 / 6.500664 (-6.375662) \| 0.060055 / 0.075469 (-0.015414) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.234171 / 1.841788 (-0.607617) \| 18.121476 / 8.074308 (10.047168) \| 13.727313 / 10.191392 (3.535921) \| 0.136021 / 0.680424 (-0.544402) \| 0.016505 / 0.534201 (-0.517696) \| 0.331400 / 0.579283 (-0.247883) \| 0.346019 / 0.434364 (-0.088345) \| 0.378985 / 0.540337 (-0.161353) \| 0.522606 / 1.386936 (-0.864330) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006035 / 0.011353 (-0.005318) \| 0.003584 / 0.011008 (-0.007425) \| 0.061953 / 0.038508 (0.023445) \| 0.059416 / 0.023109 (0.036307) \| 0.359380 / 0.275898 (0.083482) \| 0.396842 / 0.323480 (0.073363) \| 0.004716 / 0.007986 (-0.003269) \| 0.002825 / 0.004328 (-0.001504) \| 0.061697 / 0.004250 (0.057447) \| 0.049009 / 0.037052 (0.011956) \| 0.363099 / 0.258489 (0.104610) \| 0.403672 / 0.293841 (0.109831) \| 0.027722 / 0.128546 (-0.100824) \| 0.007966 / 0.075646 (-0.067680) \| 0.067455 / 0.419271 (-0.351816) \| 0.042530 / 0.043533 (-0.001003) \| 0.361257 / 0.255139 (0.106118) \| 0.388957 / 0.283200 (0.105758) \| 0.021845 / 0.141683 (-0.119838) \| 1.431989 / 1.452155 (-0.020166) \| 1.503131 / 1.492716 (0.010415) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.241493 / 0.018006 (0.223487) \| 0.429319 / 0.000490 (0.428829) \| 0.002604 / 0.000200 (0.002404) \| 0.000074 / 0.000054 (0.000019) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026227 / 0.037411 (-0.011184) \| 0.077177 / 0.014526 (0.062651) \| 0.085840 / 0.176557 (-0.090717) \| 0.142280 / 0.737135 (-0.594855) \| 0.088465 / 0.296338 (-0.207873) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.434912 / 0.215209 (0.219703) \| 4.339664 / 2.077655 (2.262009) \| 2.242495 / 1.504120 (0.738375) \| 2.091353 / 1.541195 (0.550159) \| 2.161425 / 1.468490 (0.692935) \| 0.501647 / 4.584777 (-4.083130) \| 3.075326 / 3.745712 (-0.670386) \| 4.091557 / 5.269862 (-1.178304) \| 2.776425 / 4.565676 (-1.789251) \| 0.057338 / 0.424275 (-0.366937) \| 0.006767 / 0.007607 (-0.000840) \| 0.506882 / 0.226044 (0.280837) \| 5.059074 / 2.268929 (2.790146) \| 2.706665 / 55.444624 (-52.737959) \| 2.370253 / 6.876477 (-4.506224) \| 2.505421 / 2.142072 (0.363348) \| 0.590289 / 4.805227 (-4.214938) \| 0.125990 / 6.500664 (-6.374674) \| 0.062778 / 0.075469 (-0.012691) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.361287 / 1.841788 (-0.480501) \| 18.500726 / 8.074308 (10.426418) \| 13.844459 / 10.191392 (3.653067) \| 0.144416 / 0.680424 (-0.536008) \| 0.016987 / 0.534201 (-0.517214) \| 0.336237 / 0.579283 (-0.243046) \| 0.357116 / 0.434364 (-0.077248) \| 0.402062 / 0.540337 (-0.138275) \| 0.543066 / 1.386936 (-0.843870) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#029956a347b0306cd27f693e12cf9a82acf4ef80 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007559 / 0.011353 (-0.003794) \| 0.004379 / 0.011008 (-0.006629) \| 0.089702 / 0.038508 (0.051194) \| 0.065104 / 0.023109 (0.041995) \| 0.362016 / 0.275898 (0.086118) \| 0.376768 / 0.323480 (0.053288) \| 0.006538 / 0.007986 (-0.001447) \| 0.004167 / 0.004328 (-0.000161) \| 0.074138 / 0.004250 (0.069888) \| 0.052753 / 0.037052 (0.015701) \| 0.366367 / 0.258489 (0.107878) \| 0.389121 / 0.293841 (0.095280) \| 0.042820 / 0.128546 (-0.085727) \| 0.012560 / 0.075646 (-0.063086) \| 0.359235 / 0.419271 (-0.060037) \| 0.074250 / 0.043533 (0.030718) \| 0.384051 / 0.255139 (0.128912) \| 0.385450 / 0.283200 (0.102250) \| 0.046270 / 0.141683 (-0.095413) \| 1.593275 / 1.452155 (0.141120) \| 1.704207 / 1.492716 (0.211490) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.249390 / 0.018006 (0.231384) \| 0.614347 / 0.000490 (0.613857) \| 0.012641 / 0.000200 (0.012441) \| 0.000126 / 0.000054 (0.000072) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.029099 / 0.037411 (-0.008312) \| 0.090966 / 0.014526 (0.076440) \| 0.102273 / 0.176557 (-0.074284) \| 0.167564 / 0.737135 (-0.569571) \| 0.106118 / 0.296338 (-0.190220) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.536122 / 0.215209 (0.320913) \| 5.448464 / 2.077655 (3.370809) \| 2.461977 / 1.504120 (0.957857) \| 2.081506 / 1.541195 (0.540311) \| 2.091509 / 1.468490 (0.623019) \| 0.810307 / 4.584777 (-3.774470) \| 5.161304 / 3.745712 (1.415592) \| 4.525070 / 5.269862 (-0.744792) \| 2.886313 / 4.565676 (-1.679363) \| 0.093992 / 0.424275 (-0.330283) \| 0.008516 / 0.007607 (0.000909) \| 0.691978 / 0.226044 (0.465934) \| 6.834665 / 2.268929 (4.565737) \| 3.284355 / 55.444624 (-52.160270) \| 2.496803 / 6.876477 (-4.379674) \| 2.814387 / 2.142072 (0.672315) \| 0.985300 / 4.805227 (-3.819928) \| 0.210343 / 6.500664 (-6.290321) \| 0.075459 / 0.075469 (-0.000010) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.436073 / 1.841788 (-0.405714) \| 22.722401 / 8.074308 (14.648093) \| 19.988521 / 10.191392 (9.797129) \| 0.229757 / 0.680424 (-0.450667) \| 0.029672 / 0.534201 (-0.504529) \| 0.479914 / 0.579283 (-0.099369) \| 0.605106 / 0.434364 (0.170743) \| 0.511668 / 0.540337 (-0.028670) \| 0.800281 / 1.386936 (-0.586655) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008665 / 0.011353 (-0.002688) \| 0.006009 / 0.011008 (-0.004999) \| 0.073377 / 0.038508 (0.034869) \| 0.077188 / 0.023109 (0.054079) \| 0.451422 / 0.275898 (0.175524) \| 0.484640 / 0.323480 (0.161160) \| 0.006266 / 0.007986 (-0.001719) \| 0.004129 / 0.004328 (-0.000200) \| 0.063102 / 0.004250 (0.058851) \| 0.064653 / 0.037052 (0.027601) \| 0.439521 / 0.258489 (0.181032) \| 0.458964 / 0.293841 (0.165123) \| 0.046018 / 0.128546 (-0.082528) \| 0.014109 / 0.075646 (-0.061537) \| 0.095727 / 0.419271 (-0.323544) \| 0.070133 / 0.043533 (0.026600) \| 0.440143 / 0.255139 (0.185004) \| 0.502468 / 0.283200 (0.219269) \| 0.034582 / 0.141683 (-0.107101) \| 1.656282 / 1.452155 (0.204127) \| 1.784641 / 1.492716 (0.291925) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.303111 / 0.018006 (0.285105) \| 0.599194 / 0.000490 (0.598705) \| 0.000411 / 0.000200 (0.000211) \| 0.000073 / 0.000054 (0.000018) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033061 / 0.037411 (-0.004350) \| 0.096073 / 0.014526 (0.081548) \| 0.095347 / 0.176557 (-0.081209) \| 0.161004 / 0.737135 (-0.576131) \| 0.111544 / 0.296338 (-0.184794) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.615695 / 0.215209 (0.400486) \| 5.794243 / 2.077655 (3.716588) \| 2.594720 / 1.504120 (1.090600) \| 2.566255 / 1.541195 (1.025060) \| 2.573653 / 1.468490 (1.105163) \| 0.873653 / 4.584777 (-3.711124) \| 5.353323 / 3.745712 (1.607611) \| 4.604974 / 5.269862 (-0.664887) \| 2.901282 / 4.565676 (-1.664394) \| 0.099614 / 0.424275 (-0.324661) \| 0.010368 / 0.007607 (0.002761) \| 0.775490 / 0.226044 (0.549446) \| 7.245449 / 2.268929 (4.976520) \| 3.740165 / 55.444624 (-51.704459) \| 2.986132 / 6.876477 (-3.890345) \| 3.092510 / 2.142072 (0.950438) \| 1.022461 / 4.805227 (-3.782766) \| 0.212137 / 6.500664 (-6.288527) \| 0.084534 / 0.075469 (0.009065) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.687983 / 1.841788 (-0.153805) \| 23.491808 / 8.074308 (15.417500) \| 20.722165 / 10.191392 (10.530773) \| 0.231011 / 0.680424 (-0.449413) \| 0.028309 / 0.534201 (-0.505892) \| 0.436911 / 0.579283 (-0.142372) \| 0.583126 / 0.434364 (0.148762) \| 0.559712 / 0.540337 (0.019374) \| 0.820645 / 1.386936 (-0.566291) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#029956a347b0306cd27f693e12cf9a82acf4ef80 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006538 / 0.011353 (-0.004815) \| 0.003952 / 0.011008 (-0.007056) \| 0.084183 / 0.038508 (0.045675) \| 0.070616 / 0.023109 (0.047507) \| 0.320491 / 0.275898 (0.044593) \| 0.352021 / 0.323480 (0.028541) \| 0.005330 / 0.007986 (-0.002656) \| 0.003400 / 0.004328 (-0.000928) \| 0.066392 / 0.004250 (0.062141) \| 0.052529 / 0.037052 (0.015477) \| 0.329581 / 0.258489 (0.071092) \| 0.374437 / 0.293841 (0.080596) \| 0.031379 / 0.128546 (-0.097167) \| 0.008576 / 0.075646 (-0.067070) \| 0.288621 / 0.419271 (-0.130650) \| 0.052748 / 0.043533 (0.009215) \| 0.319911 / 0.255139 (0.064772) \| 0.358169 / 0.283200 (0.074970) \| 0.023128 / 0.141683 (-0.118555) \| 1.479578 / 1.452155 (0.027424) \| 1.566351 / 1.492716 (0.073635) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.217616 / 0.018006 (0.199610) \| 0.471546 / 0.000490 (0.471056) \| 0.003880 / 0.000200 (0.003680) \| 0.000085 / 0.000054 (0.000031) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027716 / 0.037411 (-0.009696) \| 0.081718 / 0.014526 (0.067192) \| 0.095457 / 0.176557 (-0.081100) \| 0.150746 / 0.737135 (-0.586389) \| 0.096061 / 0.296338 (-0.200277) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.406811 / 0.215209 (0.191602) \| 4.062757 / 2.077655 (1.985103) \| 2.060658 / 1.504120 (0.556538) \| 1.870944 / 1.541195 (0.329749) \| 1.908984 / 1.468490 (0.440493) \| 0.489053 / 4.584777 (-4.095724) \| 3.571038 / 3.745712 (-0.174674) \| 3.255351 / 5.269862 (-2.014511) \| 2.007078 / 4.565676 (-2.558599) \| 0.057078 / 0.424275 (-0.367197) \| 0.007240 / 0.007607 (-0.000367) \| 0.485641 / 0.226044 (0.259596) \| 4.841657 / 2.268929 (2.572729) \| 2.569676 / 55.444624 (-52.874949) \| 2.151119 / 6.876477 (-4.725357) \| 2.330337 / 2.142072 (0.188265) \| 0.581721 / 4.805227 (-4.223506) \| 0.132591 / 6.500664 (-6.368073) \| 0.060491 / 0.075469 (-0.014978) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.237699 / 1.841788 (-0.604089) \| 19.460306 / 8.074308 (11.385998) \| 14.123006 / 10.191392 (3.931614) \| 0.155669 / 0.680424 (-0.524754) \| 0.018385 / 0.534201 (-0.515816) \| 0.393330 / 0.579283 (-0.185953) \| 0.408890 / 0.434364 (-0.025474) \| 0.457348 / 0.540337 (-0.082989) \| 0.640293 / 1.386936 (-0.746643) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006582 / 0.011353 (-0.004771) \| 0.003950 / 0.011008 (-0.007059) \| 0.064636 / 0.038508 (0.026128) \| 0.077651 / 0.023109 (0.054541) \| 0.365505 / 0.275898 (0.089607) \| 0.393370 / 0.323480 (0.069890) \| 0.005466 / 0.007986 (-0.002520) \| 0.003314 / 0.004328 (-0.001014) \| 0.064960 / 0.004250 (0.060710) \| 0.057355 / 0.037052 (0.020302) \| 0.377773 / 0.258489 (0.119284) \| 0.408394 / 0.293841 (0.114553) \| 0.031698 / 0.128546 (-0.096848) \| 0.008575 / 0.075646 (-0.067071) \| 0.070390 / 0.419271 (-0.348881) \| 0.050035 / 0.043533 (0.006502) \| 0.360461 / 0.255139 (0.105323) \| 0.384862 / 0.283200 (0.101662) \| 0.025380 / 0.141683 (-0.116303) \| 1.484429 / 1.452155 (0.032275) \| 1.542944 / 1.492716 (0.050227) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.190193 / 0.018006 (0.172187) \| 0.468996 / 0.000490 (0.468506) \| 0.003012 / 0.000200 (0.002812) \| 0.000091 / 0.000054 (0.000036) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031488 / 0.037411 (-0.005923) \| 0.088673 / 0.014526 (0.074147) \| 0.101886 / 0.176557 (-0.074670) \| 0.156774 / 0.737135 (-0.580361) \| 0.102818 / 0.296338 (-0.193520) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.428019 / 0.215209 (0.212810) \| 4.271369 / 2.077655 (2.193714) \| 2.271530 / 1.504120 (0.767410) \| 2.085172 / 1.541195 (0.543977) \| 2.143439 / 1.468490 (0.674949) \| 0.493468 / 4.584777 (-4.091309) \| 3.569030 / 3.745712 (-0.176683) \| 4.777962 / 5.269862 (-0.491900) \| 2.872115 / 4.565676 (-1.693562) \| 0.058200 / 0.424275 (-0.366075) \| 0.007657 / 0.007607 (0.000050) \| 0.502874 / 0.226044 (0.276830) \| 5.026721 / 2.268929 (2.757792) \| 2.734301 / 55.444624 (-52.710324) \| 2.396072 / 6.876477 (-4.480405) \| 2.574322 / 2.142072 (0.432249) \| 0.593855 / 4.805227 (-4.211373) \| 0.135134 / 6.500664 (-6.365530) \| 0.061491 / 0.075469 (-0.013978) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.320522 / 1.841788 (-0.521265) \| 19.933221 / 8.074308 (11.858912) \| 14.055921 / 10.191392 (3.864529) \| 0.149620 / 0.680424 (-0.530804) \| 0.018590 / 0.534201 (-0.515611) \| 0.399550 / 0.579283 (-0.179733) \| 0.410463 / 0.434364 (-0.023901) \| 0.469872 / 0.540337 (-0.070465) \| 0.616481 / 1.386936 (-0.770455) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#029956a347b0306cd27f693e12cf9a82acf4ef80 \"CML watermark\")\n" ]	2023-07-27T17:05:54Z	2023-07-31T06:32:16Z	2023-07-27T17:08:38Z	MEMBER	null	null	null	null	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6082/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6082/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6082.diff", "html_url": "https://github.com/huggingface/datasets/pull/6082", "merged_at": "2023-07-27T17:08:38Z", "patch_url": "https://github.com/huggingface/datasets/pull/6082.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6082" }
https://api.github.com/repos/huggingface/datasets/issues/6548	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6548/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6548/comments	https://api.github.com/repos/huggingface/datasets/issues/6548/events	https://github.com/huggingface/datasets/issues/6548	2,061,047,984	I_kwDODunzps562Riw	6,548	Skip if a dataset has issues	{ "avatar_url": "https://avatars.githubusercontent.com/u/143214684?v=4", "events_url": "https://api.github.com/users/hadianasliwa/events{/privacy}", "followers_url": "https://api.github.com/users/hadianasliwa/followers", "following_url": "https://api.github.com/users/hadianasliwa/following{/other_user}", "gists_url": "https://api.github.com/users/hadianasliwa/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/hadianasliwa", "id": 143214684, "login": "hadianasliwa", "node_id": "U_kgDOCIlIXA", "organizations_url": "https://api.github.com/users/hadianasliwa/orgs", "received_events_url": "https://api.github.com/users/hadianasliwa/received_events", "repos_url": "https://api.github.com/users/hadianasliwa/repos", "site_admin": false, "starred_url": "https://api.github.com/users/hadianasliwa/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/hadianasliwa/subscriptions", "type": "User", "url": "https://api.github.com/users/hadianasliwa", "user_view_type": "public" }	[]	open	false	null	[]	null	[ "It looks like a transient DNS issue. It should work fine now if you try again.\r\n\r\nThere is no parameter in load_dataset to skip failed downloads. In your case it would have skipped every single subsequent download until the DNS issue was resolved anyway." ]	2023-12-31T12:41:26Z	2024-01-02T10:33:17Z	null	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug Hello everyone, I'm using load_datasets from huggingface to download the datasets and I'm facing an issue, the download starts but it reaches some state and then fails with the following error: Couldn't reach https://huggingface.co/datasets/wikimedia/wikipedia/resolve/4cb9b0d719291f1a10f96f67d609c5d442980dc9/20231101.ext/train-00000-of-00001.parquet Failed to resolve \'huggingface.co\' ([Errno -3] Temporary failure in name resolution)"))'))) ![image](https://github.com/huggingface/datasets/assets/143214684/8847d9cb-529e-4eda-9c76-282713dfa3af) so I was wondering is there a parameter to be passed to load_dataset() to skip files that can't be downloaded?? ### Steps to reproduce the bug Parameter to be passed to load_dataset() of huggingface to skip files that can't be downloaded?? ### Expected behavior load_dataset() finishes without error ### Environment info None	null	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6548/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6548/timeline	null	null	null	null
https://api.github.com/repos/huggingface/datasets/issues/7029	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/7029/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/7029/comments	https://api.github.com/repos/huggingface/datasets/issues/7029/events	https://github.com/huggingface/datasets/issues/7029	2,391,366,696	I_kwDODunzps6OiVwo	7,029	load_dataset on AWS lambda throws OSError(30, 'Read-only file system') error	{ "avatar_url": "https://avatars.githubusercontent.com/u/171606538?v=4", "events_url": "https://api.github.com/users/sugam-nexusflow/events{/privacy}", "followers_url": "https://api.github.com/users/sugam-nexusflow/followers", "following_url": "https://api.github.com/users/sugam-nexusflow/following{/other_user}", "gists_url": "https://api.github.com/users/sugam-nexusflow/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/sugam-nexusflow", "id": 171606538, "login": "sugam-nexusflow", "node_id": "U_kgDOCjqCCg", "organizations_url": "https://api.github.com/users/sugam-nexusflow/orgs", "received_events_url": "https://api.github.com/users/sugam-nexusflow/received_events", "repos_url": "https://api.github.com/users/sugam-nexusflow/repos", "site_admin": false, "starred_url": "https://api.github.com/users/sugam-nexusflow/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/sugam-nexusflow/subscriptions", "type": "User", "url": "https://api.github.com/users/sugam-nexusflow", "user_view_type": "public" }	[]	open	false	null	[]	null	[ "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	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### 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 fsspec version: 2023.10.0	null	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/7029/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/7029/timeline	null	null	null	null
https://api.github.com/repos/huggingface/datasets/issues/6961	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6961/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6961/comments	https://api.github.com/repos/huggingface/datasets/issues/6961/events	https://github.com/huggingface/datasets/issues/6961	2,342,022,418	I_kwDODunzps6LmG0S	6,961	Manual downloads should count as downloads	{ "avatar_url": "https://avatars.githubusercontent.com/u/8473183?v=4", "events_url": "https://api.github.com/users/umarbutler/events{/privacy}", "followers_url": "https://api.github.com/users/umarbutler/followers", "following_url": "https://api.github.com/users/umarbutler/following{/other_user}", "gists_url": "https://api.github.com/users/umarbutler/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/umarbutler", "id": 8473183, "login": "umarbutler", "node_id": "MDQ6VXNlcjg0NzMxODM=", "organizations_url": "https://api.github.com/users/umarbutler/orgs", "received_events_url": "https://api.github.com/users/umarbutler/received_events", "repos_url": "https://api.github.com/users/umarbutler/repos", "site_admin": false, "starred_url": "https://api.github.com/users/umarbutler/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/umarbutler/subscriptions", "type": "User", "url": "https://api.github.com/users/umarbutler", "user_view_type": "public" }	[ { "color": "a2eeef", "default": true, "description": "New feature or request", "id": 1935892871, "name": "enhancement", "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement" } ]	open	false	null	[]	null	[ "We're unlikely to add more features/support for datasets with python loading scripts, which include datasets with manual download. Sorry for the inconvenience" ]	2024-06-09T04:52:06Z	2024-06-13T16:05:00Z	null	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Feature request I would like to request that manual downloads of data files from Hugging Face dataset repositories count as downloads of a dataset. According to the documentation for the Hugging Face Hub, that is currently not the case: https://huggingface.co/docs/hub/en/datasets-download-stats ### Motivation This would ensure that downloads are accurately reported to end users. ### Your contribution N/A	null	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6961/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6961/timeline	null	null	null	null
https://api.github.com/repos/huggingface/datasets/issues/5287	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5287/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5287/comments	https://api.github.com/repos/huggingface/datasets/issues/5287/events	https://github.com/huggingface/datasets/pull/5287	1,461,971,889	PR_kwDODunzps5Dkttf	5,287	Fix methods using `IterableDataset.map` that lead to `features=None`	{ "avatar_url": "https://avatars.githubusercontent.com/u/36760800?v=4", "events_url": "https://api.github.com/users/alvarobartt/events{/privacy}", "followers_url": "https://api.github.com/users/alvarobartt/followers", "following_url": "https://api.github.com/users/alvarobartt/following{/other_user}", "gists_url": "https://api.github.com/users/alvarobartt/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/alvarobartt", "id": 36760800, "login": "alvarobartt", "node_id": "MDQ6VXNlcjM2NzYwODAw", "organizations_url": "https://api.github.com/users/alvarobartt/orgs", "received_events_url": "https://api.github.com/users/alvarobartt/received_events", "repos_url": "https://api.github.com/users/alvarobartt/repos", "site_admin": false, "starred_url": "https://api.github.com/users/alvarobartt/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/alvarobartt/subscriptions", "type": "User", "url": "https://api.github.com/users/alvarobartt", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "_The documentation is not available anymore as the PR was closed or merged._", "Maybe other options are:\r\n* Keep the `info.features` to `None` if those were initially `None`\r\n* Infer the features with pre-fetching just if the `info.features` is `None`\r\n* If the `info.features` are there, make sure that after `map` features is not `None`", "Hi @lhoestq something that's still not clear to me is: should we infer the features always when applying a `map` if those are initially `None`, or just assume that if the features are initially `None` those should be left that way unless the user specifically sets those (or during iter)?\r\n\r\nIn this PR I'm using `from datasets.iterable_dataset import _infer_features_from_batch` to infer the features when those are `None` using pre-fetch of `self._head()`, but I'm not sure if that's the expected behavior.\r\n\r\nThanks in advance for your help!", "Also, the PR still has some more work to do, but probably the most relevant thing to fix right now is that the `features` are being set to `None` in the functions `IterableDataset.rename_column`, `IterableDataset.rename_columns`, and `IterableDataset.remove_columns` when the `features` originally had a value. So once that's fixed maybe we can focus on improving the current `map`'s behavior, so as to avoid this from happening also when the user uses `map` directly and not through the functions mentioned above.", "> Cool thank you ! Resolving the features can be expensive sometimes, so maybe we don't resolve the features and we can just rename/remove columns if the features are known (i.e. if they're not None). What do you think ?\r\n\r\nThanks for the feedback! Makes sense to me 👍🏻 I'll commit the comments now!", "Already done @lhoestq, feel free to merge whenever you want! Also before merging, can you please link the following issues https://github.com/huggingface/datasets/issues/3888, https://github.com/huggingface/datasets/issues/5245, and https://github.com/huggingface/datasets/issues/5284, so that those are closed upon merge? Thanks!" ]	2022-11-23T15:33:25Z	2022-11-28T15:43:14Z	2022-11-28T12:53:22Z	MEMBER	null	null	null	As currently `IterableDataset.map` is setting the `info.features` to `None` every time as we don't know the output of the dataset in advance, `IterableDataset` methods such as `rename_column`, `rename_columns`, and `remove_columns`. that internally use `map` lead to the features being `None`. This PR is related to #3888, #5245, and #5284 ## ✅ Current solution The code in this PR is basically making sure that if the features were there since the beginning and a `rename_column`/`rename_columns` happens, those are kept and the rename is applied to the `Features` too. Also, if the features were not there before applying `rename_column`, `rename_columns` or `remove_columns`, a batch is prefetched and the features are being inferred (that could potentially be part of `IterableDataset.__init__` in case the `info.features` value is `None`). ## 💡 Ideas Some ideas were proposed in https://github.com/huggingface/datasets/issues/3888, but probably the most consistent solution even though it may take some time is to actually do the type inferencing during the `IterableDataset.__init__` in case the provided `info.features` is `None`, otherwise, we can just use the provided features. Additionally, as mentioned at https://github.com/huggingface/datasets/issues/3888, we could also include a `features` parameter to the `map` function, but that's probably more tedious. Also thanks to @lhoestq for sharing some ideas in both https://github.com/huggingface/datasets/issues/3888 and https://github.com/huggingface/datasets/issues/5245 :hugs:	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5287/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5287/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/5287.diff", "html_url": "https://github.com/huggingface/datasets/pull/5287", "merged_at": "2022-11-28T12:53:22Z", "patch_url": "https://github.com/huggingface/datasets/pull/5287.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5287" }
https://api.github.com/repos/huggingface/datasets/issues/5628	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5628/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5628/comments	https://api.github.com/repos/huggingface/datasets/issues/5628/events	https://github.com/huggingface/datasets/pull/5628	1,619,641,810	PR_kwDODunzps5LzVKi	5,628	add kwargs to index search	{ "avatar_url": "https://avatars.githubusercontent.com/u/55560583?v=4", "events_url": "https://api.github.com/users/SaulLu/events{/privacy}", "followers_url": "https://api.github.com/users/SaulLu/followers", "following_url": "https://api.github.com/users/SaulLu/following{/other_user}", "gists_url": "https://api.github.com/users/SaulLu/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/SaulLu", "id": 55560583, "login": "SaulLu", "node_id": "MDQ6VXNlcjU1NTYwNTgz", "organizations_url": "https://api.github.com/users/SaulLu/orgs", "received_events_url": "https://api.github.com/users/SaulLu/received_events", "repos_url": "https://api.github.com/users/SaulLu/repos", "site_admin": false, "starred_url": "https://api.github.com/users/SaulLu/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/SaulLu/subscriptions", "type": "User", "url": "https://api.github.com/users/SaulLu", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._" ]	2023-03-10T21:24:58Z	2023-03-15T14:48:47Z	2023-03-15T14:46:04Z	CONTRIBUTOR	null	null	null	This PR proposes to add kwargs to index search methods. This is particularly useful for setting the timeout of a query on elasticsearch. A typical use case would be: ```python dset.add_elasticsearch_index("filename", es_client=es_client) scores, examples = dset.get_nearest_examples("filename", "my_name-train_29", request_timeout=60) ```	{ "avatar_url": "https://avatars.githubusercontent.com/u/55560583?v=4", "events_url": "https://api.github.com/users/SaulLu/events{/privacy}", "followers_url": "https://api.github.com/users/SaulLu/followers", "following_url": "https://api.github.com/users/SaulLu/following{/other_user}", "gists_url": "https://api.github.com/users/SaulLu/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/SaulLu", "id": 55560583, "login": "SaulLu", "node_id": "MDQ6VXNlcjU1NTYwNTgz", "organizations_url": "https://api.github.com/users/SaulLu/orgs", "received_events_url": "https://api.github.com/users/SaulLu/received_events", "repos_url": "https://api.github.com/users/SaulLu/repos", "site_admin": false, "starred_url": "https://api.github.com/users/SaulLu/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/SaulLu/subscriptions", "type": "User", "url": "https://api.github.com/users/SaulLu", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 1, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 1, "url": "https://api.github.com/repos/huggingface/datasets/issues/5628/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5628/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/5628.diff", "html_url": "https://github.com/huggingface/datasets/pull/5628", "merged_at": "2023-03-15T14:46:04Z", "patch_url": "https://github.com/huggingface/datasets/pull/5628.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5628" }
https://api.github.com/repos/huggingface/datasets/issues/4761	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/4761/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/4761/comments	https://api.github.com/repos/huggingface/datasets/issues/4761/events	https://github.com/huggingface/datasets/issues/4761	1,321,068,411	I_kwDODunzps5Oved7	4,761	parallel searching in multi-gpu setting using faiss	{ "avatar_url": "https://avatars.githubusercontent.com/u/48146603?v=4", "events_url": "https://api.github.com/users/Jiaxin-Wen/events{/privacy}", "followers_url": "https://api.github.com/users/Jiaxin-Wen/followers", "following_url": "https://api.github.com/users/Jiaxin-Wen/following{/other_user}", "gists_url": "https://api.github.com/users/Jiaxin-Wen/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/Jiaxin-Wen", "id": 48146603, "login": "Jiaxin-Wen", "node_id": "MDQ6VXNlcjQ4MTQ2NjAz", "organizations_url": "https://api.github.com/users/Jiaxin-Wen/orgs", "received_events_url": "https://api.github.com/users/Jiaxin-Wen/received_events", "repos_url": "https://api.github.com/users/Jiaxin-Wen/repos", "site_admin": false, "starred_url": "https://api.github.com/users/Jiaxin-Wen/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/Jiaxin-Wen/subscriptions", "type": "User", "url": "https://api.github.com/users/Jiaxin-Wen", "user_view_type": "public" }	[]	open	false	null	[]	null	[ "And I don't see any speed up when increasing the number of GPUs while calling `get_nearest_examples_batch`.", "Hi ! Yes search_batch uses FAISS search which happens in parallel across the GPUs\r\n\r\n> And I don't see any speed up when increasing the number of GPUs while calling get_nearest_examples_batch.\r\n\r\nThat's unexpected, can you share the code you're running ?", "here is the code snippet\r\n\r\n```python\r\n\r\n# add faiss index\r\nsource_dataset = load_dataset(source_path)\r\nqueries = load_dataset(query_path)\r\ngpu = [0,1,2,3]\r\nsource_dataset.add_faiss_index(\r\n \"embedding\",\r\n device=gpu,\r\n )\r\n\r\n\r\n# batch query\r\nbatch_size = 32\r\nfor i in tqdm(range(0, len(queries), batch_size)):\r\n if i + batch_size >= len(queries):\r\n batched_queries = queries[i:]\r\n else:\r\n batched_queries = queries[i:i+batch_size]\r\n\r\n batched_query_embeddings = np.stack([i for i in batched_queries['embedding']], axis=0)\r\n scores, candidates = source_dataset.get_nearest_examples_batch(\r\n \"embedding\",\r\n batched_query_embeddings,\r\n k=5\r\n )\r\n```", "My version of datasets is `2.4.1.dev0`.", "The code looks all good to me, do you see all the GPUs being utilized ? What version of faiss are you using ?", "I can see the memory usage of all the GPUs.\r\nMy version of `faiss-gpu` is `1.7.2`", "It looks all good to me then ^^ though you said you didn't experienced speed improvements by adding more GPUs ? What size is your source dataset and what time differences did you experience ?", "query set: 1e6\r\nsource dataset: 1e6\r\nembedding size: 768\r\nindex: Flat\r\ntopk: 20\r\nGPU: V100\r\n\r\nThe time taken to traverse the query set once is about 1.5h, which is almost not influenced by the value of query batch size or the number of GPUs according to my experiments.", "Hmmm the number of GPUs should divide the time, something is going wrong. Can you check that adding more GPU does divide the memory used per GPU ? Maybe it can be worth looking at similar issues in the FAISS repository or create a noew issue over there to understand what's going on", "> Can you check that adding more GPU does divide the memory used per GPU \r\n\r\nThe memory used per GPU is unchanged while adding more GPU. Is this unexpected?\r\n\r\nI used to think that every GPU loads all the source vectors and the data parallelism is at the query level. 😆 ", "> I used to think that every GPU loads all the source vectors and the data parallelism is at the query level. 😆\r\n\r\nOh indeed that's possible, I wasn't sure. Anyway you can check that calling get_nearest_examples_batch simply calls search under the hood: \r\n\r\nhttps://github.com/huggingface/datasets/blob/f90f71fbbb33889fe75a3ffc101cdf16a88a3453/src/datasets/search.py#L375", "Here is a runnable script. \r\nMulti-GPU searching still does not work in my experiments.\r\n\r\n\r\n```python\r\nimport os\r\nfrom tqdm import tqdm\r\nimport numpy as np\r\nimport datasets\r\nfrom datasets import Dataset\r\n\r\nclass DPRSelector:\r\n\r\n def __init__(self, source, target, index_name, gpu=None):\r\n self.source = source\r\n self.target = target\r\n self.index_name = index_name\r\n\r\n cache_path = 'embedding.faiss'\r\n\r\n if not os.path.exists(cache_path):\r\n self.source.add_faiss_index(\r\n column=\"embedding\",\r\n index_name=index_name,\r\n device=gpu,\r\n )\r\n self.source.save_faiss_index(index_name, cache_path)\r\n else:\r\n self.source.load_faiss_index(\r\n index_name,\r\n cache_path,\r\n device=gpu\r\n )\r\n print('index builded!')\r\n\r\n def build_dataset(self, top_k, batch_size):\r\n print('start search')\r\n\r\n for i in tqdm(range(0, len(self.target), batch_size)):\r\n if i + batch_size >= len(self.target):\r\n batched_queries = self.target[i:]\r\n else:\r\n batched_queries = self.target[i:i+batch_size]\r\n\r\n\r\n batched_query_embeddings = np.stack([i for i in batched_queries['embedding']], axis=0)\r\n search_res = self.source.get_nearest_examples_batch(\r\n self.index_name,\r\n batched_query_embeddings,\r\n k=top_k\r\n )\r\n \r\n print('finish search')\r\n\r\n\r\ndef get_pseudo_dataset():\r\n pseudo_dict = {\"embedding\": np.zeros((1000000, 768), dtype=np.float32)}\r\n print('generate pseudo data')\r\n\r\n dataset = Dataset.from_dict(pseudo_dict)\r\n def list_to_array(data):\r\n return {\"embedding\": [np.array(vector, dtype=np.float32) for vector in data[\"embedding\"]]} \r\n dataset.set_transform(list_to_array, columns='embedding', output_all_columns=True)\r\n\r\n print('build dataset')\r\n return dataset\r\n\r\n\r\n\r\nif __name__==\"__main__\":\r\n\r\n np.random.seed(42)\r\n\r\n\r\n source_dataset = get_pseudo_dataset()\r\n target_dataset = get_pseudo_dataset()\r\n\r\n gpu = [0,1,2,3,4,5,6,7]\r\n selector = DPRSelector(source_dataset, target_dataset, \"embedding\", gpu=gpu)\r\n\r\n selector.build_dataset(top_k=20, batch_size=32)\r\n```", "@lhoestq Hi, could you please test the code above if you have time? 😄 ", "Maybe @albertvillanova you can take a look ? I won't be available in the following days", "@albertvillanova Hi, can you help with this issue?", "Hi @xwwwwww I'm investigating it, but I'm not an expert in Faiss. In principle, it is weird that your code does not work properly because it seems right...", "Have you tried passing `gpu=-1` and check if there is a speedup?", "> Have you tried passing `gpu=-1` and check if there is a speedup?\r\n\r\nyes, there is a speed up using GPU compared with CPU. ", "When passing `device=-1`, ALL existing GPUs are used (multi GPU): this is the maximum speedup you can get. To know the number of total GPUs:\r\n```\r\nimport faiss\r\n\r\nngpus = faiss.get_num_gpus()\r\nprint(ngpus)\r\n```\r\n\r\nWhen passing a list of integers to `device`, then only that number of GPUs are used (multi GPU as well)\r\n- the speedup should be proportional (more or less) to the ratio of the number of elements passed to `device` over `ngpus`\r\n- if this is not the case, then there is an issue in the implementation of this use case (however, I have reviewed the code and in principle I can't find any evident bug)\r\n\r\nWhen passing a positive integer to `device`, then only a single GPU is used.\r\n- this time should be more or less proportional to the time when passing `device=-1` over `ngpus`", "Thanks for your help!\r\nHave you run the code and replicated the same experimental results (i.e., no speedup while increasing the number of GPUs)?", "@albertvillanova @lhoestq Sorry for the bother, is there any progress on this issue? 😃 ", "I can confirm `add_faiss_index` calls `index = faiss.index_cpu_to_gpus_list(index, gpus=list(device))`.\r\n\r\nCould this be an issue with your environment ? Could you try running with 1 and 8 GPUs with a code similar to[ this one from the FAISS examples](https://github.com/facebookresearch/faiss/blob/main/tutorial/python/5-Multiple-GPUs.py) but using `gpu_index = faiss.index_cpu_to_gpus_list(cpu_index, gpus=list(device))`, and see if the speed changes ?", "Hi, I test the FAISS example and the speed indeed changes. I set `nb=1000000`, `nq=1000000` and `d=64`\r\n\r\n\| num GPUS \| time cost \|\r\n\| -------- \| --------- \|\r\n\| 1 \| 28.53 \|\r\n\| 5 \| 7.16 \|\r\n\r\n\r\n\r\n", "Ok the benchmark is great, not sure why it doesn't speed up the index in your case though. You can try running the benchmark with the same settings as your actual dataset\r\n```\r\nquery set: 1e6\r\nsource dataset: 1e6\r\nembedding size: 768\r\nindex: Flat\r\ntopk: 20\r\nGPU: V100\r\n```\r\n\r\nNote that you can still pass a FAISS index you built yourself to a dataset using https://huggingface.co/docs/datasets/v2.4.0/en/package_reference/main_classes#datasets.Dataset.add_faiss_index_from_external_arrays", "> Here is a runnable script. Multi-GPU searching still does not work in my experiments.\r\n> \r\n> ```python\r\n> import os\r\n> from tqdm import tqdm\r\n> import numpy as np\r\n> import datasets\r\n> from datasets import Dataset\r\n> \r\n> class DPRSelector:\r\n> \r\n> def __init__(self, source, target, index_name, gpu=None):\r\n> self.source = source\r\n> self.target = target\r\n> self.index_name = index_name\r\n> \r\n> cache_path = 'embedding.faiss'\r\n> \r\n> if not os.path.exists(cache_path):\r\n> self.source.add_faiss_index(\r\n> column=\"embedding\",\r\n> index_name=index_name,\r\n> device=gpu,\r\n> )\r\n> self.source.save_faiss_index(index_name, cache_path)\r\n> else:\r\n> self.source.load_faiss_index(\r\n> index_name,\r\n> cache_path,\r\n> device=gpu\r\n> )\r\n> print('index builded!')\r\n> \r\n> def build_dataset(self, top_k, batch_size):\r\n> print('start search')\r\n> \r\n> for i in tqdm(range(0, len(self.target), batch_size)):\r\n> if i + batch_size >= len(self.target):\r\n> batched_queries = self.target[i:]\r\n> else:\r\n> batched_queries = self.target[i:i+batch_size]\r\n> \r\n> \r\n> batched_query_embeddings = np.stack([i for i in batched_queries['embedding']], axis=0)\r\n> search_res = self.source.get_nearest_examples_batch(\r\n> self.index_name,\r\n> batched_query_embeddings,\r\n> k=top_k\r\n> )\r\n> \r\n> print('finish search')\r\n> \r\n> \r\n> def get_pseudo_dataset():\r\n> pseudo_dict = {\"embedding\": np.zeros((1000000, 768), dtype=np.float32)}\r\n> print('generate pseudo data')\r\n> \r\n> dataset = Dataset.from_dict(pseudo_dict)\r\n> def list_to_array(data):\r\n> return {\"embedding\": [np.array(vector, dtype=np.float32) for vector in data[\"embedding\"]]} \r\n> dataset.set_transform(list_to_array, columns='embedding', output_all_columns=True)\r\n> \r\n> print('build dataset')\r\n> return dataset\r\n> \r\n> \r\n> \r\n> if __name__==\"__main__\":\r\n> \r\n> np.random.seed(42)\r\n> \r\n> \r\n> source_dataset = get_pseudo_dataset()\r\n> target_dataset = get_pseudo_dataset()\r\n> \r\n> gpu = [0,1,2,3,4,5,6,7]\r\n> selector = DPRSelector(source_dataset, target_dataset, \"embedding\", gpu=gpu)\r\n> \r\n> selector.build_dataset(top_k=20, batch_size=32)\r\n> ```\r\n\r\nBy the way, have you run this toy example and replicated my experiment results? I think it is a more direct way to figure this out :)", "Hi,\r\n\r\nI have a similar question and would like to know if there's any progress in this issue. \r\n\r\n`dataset.add_faiss_index(column=\"embedding\")`, this takes around 5minutes to add the index.\r\n\r\n`dataset.add_faiss_index(column=\"embedding\", device=-1)`, this ran for more than 10minutes and still didn't complete execution. \r\n\r\nNow, I don't understand why that's the case as I expected for GPU the indexing should be faster" ]	2022-07-28T14:57:03Z	2023-07-21T02:07:10Z	null	CONTRIBUTOR	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	While I notice that `add_faiss_index` has supported assigning multiple GPUs, I am still confused about how it works. Does the `search-batch` function automatically parallelizes the input queries to different gpus?https://github.com/huggingface/datasets/blob/d76599bdd4d186b2e7c4f468b05766016055a0a5/src/datasets/search.py#L360	null	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/4761/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/4761/timeline	null	null	null	null
https://api.github.com/repos/huggingface/datasets/issues/5541	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5541/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5541/comments	https://api.github.com/repos/huggingface/datasets/issues/5541/events	https://github.com/huggingface/datasets/issues/5541	1,588,633,555	I_kwDODunzps5esJ_T	5,541	Flattening indices in selected datasets is extremely inefficient	{ "avatar_url": "https://avatars.githubusercontent.com/u/6591505?v=4", "events_url": "https://api.github.com/users/marioga/events{/privacy}", "followers_url": "https://api.github.com/users/marioga/followers", "following_url": "https://api.github.com/users/marioga/following{/other_user}", "gists_url": "https://api.github.com/users/marioga/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/marioga", "id": 6591505, "login": "marioga", "node_id": "MDQ6VXNlcjY1OTE1MDU=", "organizations_url": "https://api.github.com/users/marioga/orgs", "received_events_url": "https://api.github.com/users/marioga/received_events", "repos_url": "https://api.github.com/users/marioga/repos", "site_admin": false, "starred_url": "https://api.github.com/users/marioga/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/marioga/subscriptions", "type": "User", "url": "https://api.github.com/users/marioga", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "Running the script above on the branch https://github.com/huggingface/datasets/pull/5542 results in the expected behaviour:\r\n```\r\nNum chunks for original ds: 1\r\nOriginal ds save/load\r\nsave_to_disk -- RAM memory used: 0.671875 MB -- Total time: 0.255265 s\r\nload_from_disk -- RAM memory used: 42.796875 MB -- Total time: 0.014899 s\r\nNum chunks for original ds after reloading: 5000\r\n\r\nNum chunks for selected ds: 1\r\nflatten_indices -- RAM memory used: 42.546875 MB -- Total time: 23.735089 s\r\nNum chunks for selected ds after flattening: 5000\r\n\r\nSelected ds save/load\r\nsave_to_disk -- RAM memory used: 0.0 MB -- Total time: 0.287112 s\r\nload_from_disk -- RAM memory used: 38.84375 MB -- Total time: 0.014772 s\r\nNum chunks for selected ds after reloading: 5000\r\n```", "Wouahouh super cool @marioga thanks a lot!", "We just released `datasets==2.10.0` with this big improvement, thanks again @marioga " ]	2023-02-17T01:52:24Z	2023-02-22T13:15:20Z	2023-02-17T11:12:33Z	CONTRIBUTOR	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug If we perform a `select` (or `shuffle`, `train_test_split`, etc.) operation on a dataset , we end up with a dataset with an `indices_table`. Currently, flattening such dataset consumes a lot of memory and the resulting flat dataset contains ChunkedArrays with as many chunks as there are rows. This is extremely inefficient and slows down the operations on the flat dataset, e.g., saving/loading the dataset to disk becomes really slow. Perhaps more importantly, loading the dataset back from disk basically loads the whole table into RAM, as it cannot take advantage of memory mapping. ### Steps to reproduce the bug The following script reproduces the issue: ```python import gc import os import psutil import tempfile import time from datasets import Dataset DATASET_SIZE = 5000000 def profile(func): def wrapper(args, kwargs): mem_before = psutil.Process(os.getpid()).memory_info().rss / (1024 1024) start = time.time() # Run function here out = func(args, kwargs) end = time.time() mem_after = psutil.Process(os.getpid()).memory_info().rss / (1024 1024) print(f"{func.__name__} -- RAM memory used: {mem_after - mem_before} MB -- Total time: {end - start:.6f} s") return out return wrapper def main(): ds = Dataset.from_list([{'col': i} for i in range(DATASET_SIZE)]) print(f"Num chunks for original ds: {ds.data['col'].num_chunks}") with tempfile.TemporaryDirectory() as tmpdir: path1 = os.path.join(tmpdir, 'ds1') print("Original ds save/load") profile(ds.save_to_disk)(path1) ds_loaded = profile(Dataset.load_from_disk)(path1) print(f"Num chunks for original ds after reloading: {ds_loaded.data['col'].num_chunks}") print("") ds_select = ds.select(reversed(range(len(ds)))) print(f"Num chunks for selected ds: {ds_select.data['col'].num_chunks}") del ds del ds_loaded gc.collect() # This would happen anyway when we call save_to_disk ds_select = profile(ds_select.flatten_indices)() print(f"Num chunks for selected ds after flattening: {ds_select.data['col'].num_chunks}") print("") path2 = os.path.join(tmpdir, 'ds2') print("Selected ds save/load") profile(ds_select.save_to_disk)(path2) del ds_select gc.collect() ds_select_loaded = profile(Dataset.load_from_disk)(path2) print(f"Num chunks for selected ds after reloading: {ds_select_loaded.data['col'].num_chunks}") if __name__ == '__main__': main() ``` Sample result: ``` Num chunks for original ds: 1 Original ds save/load save_to_disk -- RAM memory used: 0.515625 MB -- Total time: 0.253888 s load_from_disk -- RAM memory used: 42.765625 MB -- Total time: 0.015176 s Num chunks for original ds after reloading: 5000 Num chunks for selected ds: 1 flatten_indices -- RAM memory used: 4852.609375 MB -- Total time: 46.116774 s Num chunks for selected ds after flattening: 5000000 Selected ds save/load save_to_disk -- RAM memory used: 1326.65625 MB -- Total time: 42.309825 s load_from_disk -- RAM memory used: 2085.953125 MB -- Total time: 11.659137 s Num chunks for selected ds after reloading: 5000000 ``` ### Expected behavior Saving/loading the dataset should be much faster and consume almost no extra memory thanks to pyarrow memory mapping. ### Environment info - `datasets` version: 2.9.1.dev0 - Platform: macOS-13.1-arm64-arm-64bit - Python version: 3.10.8 - PyArrow version: 11.0.0 - Pandas version: 1.5.3	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5541/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5541/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/5323	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5323/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5323/comments	https://api.github.com/repos/huggingface/datasets/issues/5323/events	https://github.com/huggingface/datasets/issues/5323	1,471,518,803	I_kwDODunzps5XtZhT	5,323	Duplicated Keys in Taskmaster-2 Dataset	{ "avatar_url": "https://avatars.githubusercontent.com/u/52380283?v=4", "events_url": "https://api.github.com/users/liaeh/events{/privacy}", "followers_url": "https://api.github.com/users/liaeh/followers", "following_url": "https://api.github.com/users/liaeh/following{/other_user}", "gists_url": "https://api.github.com/users/liaeh/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/liaeh", "id": 52380283, "login": "liaeh", "node_id": "MDQ6VXNlcjUyMzgwMjgz", "organizations_url": "https://api.github.com/users/liaeh/orgs", "received_events_url": "https://api.github.com/users/liaeh/received_events", "repos_url": "https://api.github.com/users/liaeh/repos", "site_admin": false, "starred_url": "https://api.github.com/users/liaeh/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/liaeh/subscriptions", "type": "User", "url": "https://api.github.com/users/liaeh", "user_view_type": "public" }	[]	closed	false	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	[ { "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" } ]	null	[ "Thanks for reporting, @liaeh.\r\n\r\nWe are having a look at it. ", "I have transferred the discussion to the Community tab of the dataset: https://huggingface.co/datasets/taskmaster2/discussions/1" ]	2022-12-01T15:31:06Z	2022-12-01T16:26:06Z	2022-12-01T16:26:06Z	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug Loading certain splits () of the taskmaster-2 dataset fails because of a DuplicatedKeysError. This occurs for the following domains: `'hotels', 'movies', 'music', 'sports'`. The domains `'flights', 'food-ordering', 'restaurant-search'` load fine. Output: ### Steps to reproduce the bug ``` from datasets import load_dataset dataset = load_dataset("taskmaster2", "music") ``` Output: ``` --------------------------------------------------------------------------- DuplicatedKeysError Traceback (most recent call last) File ~/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py:1532, in GeneratorBasedBuilder._prepare_split_single(self, arg) [1531](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=1530) example = self.info.features.encode_example(record) if self.info.features is not None else record -> [1532](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=1531) writer.write(example, key) [1533](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=1532) num_examples_progress_update += 1 File ~/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/arrow_writer.py:475, in ArrowWriter.write(self, example, key, writer_batch_size) [474](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/arrow_writer.py?line=473) if self._check_duplicates: --> [475](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/arrow_writer.py?line=474) self.check_duplicate_keys() [476](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/arrow_writer.py?line=475) # Re-intializing to empty list for next batch File ~/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/arrow_writer.py:492, in ArrowWriter.check_duplicate_keys(self) [486](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/arrow_writer.py?line=485) duplicate_key_indices = [ [487](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/arrow_writer.py?line=486) str(self._num_examples + index) [488](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/arrow_writer.py?line=487) for index, (duplicate_hash, _) in enumerate(self.hkey_record) [489](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/arrow_writer.py?line=488) if duplicate_hash == hash [490](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/arrow_writer.py?line=489) ] --> [492](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/arrow_writer.py?line=491) raise DuplicatedKeysError(key, duplicate_key_indices) [493](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/arrow_writer.py?line=492) else: DuplicatedKeysError: Found multiple examples generated with the same key The examples at index 858, 859 have the key dlg-89174425-d57a-4db7-a92b-165c3bff6735 During handling of the above exception, another exception occurred: DuplicatedKeysError Traceback (most recent call last) File ~/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py:1541, in GeneratorBasedBuilder._prepare_split_single(self, arg) [1540](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=1539) num_shards = shard_id + 1 -> [1541](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=1540) num_examples, num_bytes = writer.finalize() [1542](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=1541) writer.close() File ~/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/arrow_writer.py:563, in ArrowWriter.finalize(self, close_stream) [562](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/arrow_writer.py?line=561) if self._check_duplicates: --> [563](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/arrow_writer.py?line=562) self.check_duplicate_keys() [564](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/arrow_writer.py?line=563) # Re-intializing to empty list for next batch File ~/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/arrow_writer.py:492, in ArrowWriter.check_duplicate_keys(self) [486](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/arrow_writer.py?line=485) duplicate_key_indices = [ [487](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/arrow_writer.py?line=486) str(self._num_examples + index) [488](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/arrow_writer.py?line=487) for index, (duplicate_hash, _) in enumerate(self.hkey_record) [489](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/arrow_writer.py?line=488) if duplicate_hash == hash [490](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/arrow_writer.py?line=489) ] --> [492](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/arrow_writer.py?line=491) raise DuplicatedKeysError(key, duplicate_key_indices) [493](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/arrow_writer.py?line=492) else: DuplicatedKeysError: Found multiple examples generated with the same key The examples at index 858, 859 have the key dlg-89174425-d57a-4db7-a92b-165c3bff6735 The above exception was the direct cause of the following exception: DatasetGenerationError Traceback (most recent call last) Cell In[23], line 1 ----> 1 dataset = load_dataset("taskmaster2", "music") File ~/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/load.py:1741, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, num_proc, config_kwargs) [1738](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/load.py?line=1737) try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES [1740](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/load.py?line=1739) # Download and prepare data -> [1741](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/load.py?line=1740) builder_instance.download_and_prepare( [1742](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/load.py?line=1741) download_config=download_config, [1743](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/load.py?line=1742) download_mode=download_mode, [1744](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/load.py?line=1743) ignore_verifications=ignore_verifications, [1745](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/load.py?line=1744) try_from_hf_gcs=try_from_hf_gcs, [1746](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/load.py?line=1745) use_auth_token=use_auth_token, [1747](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/load.py?line=1746) num_proc=num_proc, [1748](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/load.py?line=1747) ) [1750](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/load.py?line=1749) # Build dataset for splits [1751](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/load.py?line=1750) keep_in_memory = ( [1752](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/load.py?line=1751) keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size) [1753](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/load.py?line=1752) ) File ~/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py:822, in DatasetBuilder.download_and_prepare(self, output_dir, download_config, download_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, file_format, max_shard_size, num_proc, storage_options, download_and_prepare_kwargs) [820](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=819) if num_proc is not None: [821](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=820) prepare_split_kwargs["num_proc"] = num_proc --> [822](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=821) self._download_and_prepare( [823](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=822) dl_manager=dl_manager, [824](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=823) verify_infos=verify_infos, [825](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=824) prepare_split_kwargs, [826](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=825) download_and_prepare_kwargs, [827](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=826) ) [828](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=827) # Sync info [829](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=828) self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values()) File ~/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py:1555, in GeneratorBasedBuilder._download_and_prepare(self, dl_manager, verify_infos, prepare_splits_kwargs) [1554](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=1553) def _download_and_prepare(self, dl_manager, verify_infos, prepare_splits_kwargs): -> [1555](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=1554) super()._download_and_prepare( [1556](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=1555) dl_manager, verify_infos, check_duplicate_keys=verify_infos, prepare_splits_kwargs [1557](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=1556) ) File ~/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py:913, in DatasetBuilder._download_and_prepare(self, dl_manager, verify_infos, prepare_split_kwargs) [909](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=908) split_dict.add(split_generator.split_info) [911](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=910) try: [912](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=911) # Prepare split will record examples associated to the split --> [913](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=912) self._prepare_split(split_generator, prepare_split_kwargs) [914](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=913) except OSError as e: [915](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=914) raise OSError( [916](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=915) "Cannot find data file. " [917](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=916) + (self.manual_download_instructions or "") [918](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=917) + "\nOriginal error:\n" [919](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=918) + str(e) [920](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=919) ) from None File ~/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py:1396, in GeneratorBasedBuilder._prepare_split(self, split_generator, check_duplicate_keys, file_format, num_proc, max_shard_size) [1394](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=1393) gen_kwargs = split_generator.gen_kwargs [1395](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=1394) job_id = 0 -> [1396](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=1395) for job_id, done, content in self._prepare_split_single( [1397](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=1396) {"gen_kwargs": gen_kwargs, "job_id": job_id, _prepare_split_args} [1398](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=1397) ): [1399](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=1398) if done: [1400](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=1399) result = content File ~/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py:1550, in GeneratorBasedBuilder._prepare_split_single(self, arg) [1548](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=1547) if isinstance(e, SchemaInferenceError) and e.__context__ is not None: [1549](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=1548) e = e.__context__ -> [1550](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=1549) raise DatasetGenerationError("An error occurred while generating the dataset") from e [1552](file:///home/user/repos/tts-dataset/tts-dataset/venv/lib/python3.9/site-packages/datasets/builder.py?line=1551) yield job_id, True, (total_num_examples, total_num_bytes, writer._features, num_shards, shard_lengths) DatasetGenerationError: An error occurred while generating the dataset ``` ### Expected behavior Loads the dataset ### Environment info - `datasets` version: 2.7.1 - Platform: Linux-5.13.0-40-generic-x86_64-with-glibc2.31 - Python version: 3.9.7 - PyArrow version: 10.0.1 - Pandas version: 1.5.2	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5323/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5323/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/7248	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/7248/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/7248/comments	https://api.github.com/repos/huggingface/datasets/issues/7248/events	https://github.com/huggingface/datasets/issues/7248	2,609,926,089	I_kwDODunzps6bkE_J	7,248	ModuleNotFoundError: No module named 'datasets.tasks'	{ "avatar_url": "https://avatars.githubusercontent.com/u/93593941?v=4", "events_url": "https://api.github.com/users/shoowadoo/events{/privacy}", "followers_url": "https://api.github.com/users/shoowadoo/followers", "following_url": "https://api.github.com/users/shoowadoo/following{/other_user}", "gists_url": "https://api.github.com/users/shoowadoo/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/shoowadoo", "id": 93593941, "login": "shoowadoo", "node_id": "U_kgDOBZQhVQ", "organizations_url": "https://api.github.com/users/shoowadoo/orgs", "received_events_url": "https://api.github.com/users/shoowadoo/received_events", "repos_url": "https://api.github.com/users/shoowadoo/repos", "site_admin": false, "starred_url": "https://api.github.com/users/shoowadoo/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/shoowadoo/subscriptions", "type": "User", "url": "https://api.github.com/users/shoowadoo", "user_view_type": "public" }	[]	open	false	null	[]	null	[ "tasks was removed in v3: #6999 \r\n\r\nI also don't see why TextClassification is imported, since it's not used after. So the fix is simple: delete this line.", "I opened https://huggingface.co/datasets/knowledgator/events_classification_biotech/discussions/7 to remove the line, hopefully the dataset owner will merge it soon" ]	2024-10-23T21:58:25Z	2024-10-24T17:00:19Z	null	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug --------------------------------------------------------------------------- ModuleNotFoundError Traceback (most recent call last) [<ipython-input-9-13b5f31bd391>](https://bcb6shpazyu-496ff2e9c6d22116-0-colab.googleusercontent.com/outputframe.html?vrz=colab_20241022-060119_RC00_688494744#) in <cell line: 1>() ----> 1 dataset = load_dataset('knowledgator/events_classification_biotech') 11 frames [/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://bcb6shpazyu-496ff2e9c6d22116-0-colab.googleusercontent.com/outputframe.html?vrz=colab_20241022-060119_RC00_688494744#) in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, keep_in_memory, save_infos, revision, token, streaming, num_proc, storage_options, trust_remote_code, config_kwargs) 2130 2131 # Create a dataset builder -> 2132 builder_instance = load_dataset_builder( 2133 path=path, 2134 name=name, [/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://bcb6shpazyu-496ff2e9c6d22116-0-colab.googleusercontent.com/outputframe.html?vrz=colab_20241022-060119_RC00_688494744#) in load_dataset_builder(path, name, data_dir, data_files, cache_dir, features, download_config, download_mode, revision, token, storage_options, trust_remote_code, _require_default_config_name, config_kwargs) 1886 raise ValueError(error_msg) 1887 -> 1888 builder_cls = get_dataset_builder_class(dataset_module, dataset_name=dataset_name) 1889 # Instantiate the dataset builder 1890 builder_instance: DatasetBuilder = builder_cls( [/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://bcb6shpazyu-496ff2e9c6d22116-0-colab.googleusercontent.com/outputframe.html?vrz=colab_20241022-060119_RC00_688494744#) in get_dataset_builder_class(dataset_module, dataset_name) 246 dataset_module.importable_file_path 247 ) if dataset_module.importable_file_path else nullcontext(): --> 248 builder_cls = import_main_class(dataset_module.module_path) 249 if dataset_module.builder_configs_parameters.builder_configs: 250 dataset_name = dataset_name or dataset_module.builder_kwargs.get("dataset_name") [/usr/local/lib/python3.10/dist-packages/datasets/load.py](https://bcb6shpazyu-496ff2e9c6d22116-0-colab.googleusercontent.com/outputframe.html?vrz=colab_20241022-060119_RC00_688494744#) in import_main_class(module_path) 167 def import_main_class(module_path) -> Optional[Type[DatasetBuilder]]: 168 """Import a module at module_path and return its main class: a DatasetBuilder""" --> 169 module = importlib.import_module(module_path) 170 # Find the main class in our imported module 171 module_main_cls = None [/usr/lib/python3.10/importlib/__init__.py](https://bcb6shpazyu-496ff2e9c6d22116-0-colab.googleusercontent.com/outputframe.html?vrz=colab_20241022-060119_RC00_688494744#) in import_module(name, package) 124 break 125 level += 1 --> 126 return _bootstrap._gcd_import(name[level:], package, level) 127 128 /usr/lib/python3.10/importlib/_bootstrap.py in _gcd_import(name, package, level) /usr/lib/python3.10/importlib/_bootstrap.py in _find_and_load(name, import_) /usr/lib/python3.10/importlib/_bootstrap.py in _find_and_load_unlocked(name, import_) /usr/lib/python3.10/importlib/_bootstrap.py in _load_unlocked(spec) /usr/lib/python3.10/importlib/_bootstrap_external.py in exec_module(self, module) /usr/lib/python3.10/importlib/_bootstrap.py in _call_with_frames_removed(f, args, *kwds) [~/.cache/huggingface/modules/datasets_modules/datasets/knowledgator--events_classification_biotech/9c8086d498c3104de3a3c5b6640837e18ccd829dcaca49f1cdffe3eb5c4a6361/events_classification_biotech.py](https://bcb6shpazyu-496ff2e9c6d22116-0-colab.googleusercontent.com/outputframe.html?vrz=colab_20241022-060119_RC00_688494744#) in <module> 1 import datasets 2 from datasets import load_dataset ----> 3 from datasets.tasks import TextClassification 4 5 DESCRIPTION = """ ModuleNotFoundError: No module named 'datasets.tasks' --------------------------------------------------------------------------- NOTE: If your import is failing due to a missing package, you can manually install dependencies using either !pip or !apt. To view examples of installing some common dependencies, click the "Open Examples" button below. --------------------------------------------------------------------------- ### Steps to reproduce the bug !pip install datasets from datasets import load_dataset dataset = load_dataset('knowledgator/events_classification_biotech') ### Expected behavior no ModuleNotFoundError ### Environment info google colab	null	{ "+1": 1, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 1, "url": "https://api.github.com/repos/huggingface/datasets/issues/7248/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/7248/timeline	null	null	null	null
https://api.github.com/repos/huggingface/datasets/issues/4867	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/4867/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/4867/comments	https://api.github.com/repos/huggingface/datasets/issues/4867/events	https://github.com/huggingface/datasets/pull/4867	1,344,982,646	PR_kwDODunzps49fZle	4,867	Complete tags of superglue dataset card	{ "avatar_url": "https://avatars.githubusercontent.com/u/17963619?v=4", "events_url": "https://api.github.com/users/richarddwang/events{/privacy}", "followers_url": "https://api.github.com/users/richarddwang/followers", "following_url": "https://api.github.com/users/richarddwang/following{/other_user}", "gists_url": "https://api.github.com/users/richarddwang/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/richarddwang", "id": 17963619, "login": "richarddwang", "node_id": "MDQ6VXNlcjE3OTYzNjE5", "organizations_url": "https://api.github.com/users/richarddwang/orgs", "received_events_url": "https://api.github.com/users/richarddwang/received_events", "repos_url": "https://api.github.com/users/richarddwang/repos", "site_admin": false, "starred_url": "https://api.github.com/users/richarddwang/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/richarddwang/subscriptions", "type": "User", "url": "https://api.github.com/users/richarddwang", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._" ]	2022-08-19T23:44:39Z	2022-08-22T09:14:03Z	2022-08-22T08:58:31Z	CONTRIBUTOR	null	null	null	Related to #4479 .	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/4867/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/4867/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/4867.diff", "html_url": "https://github.com/huggingface/datasets/pull/4867", "merged_at": "2022-08-22T08:58:31Z", "patch_url": "https://github.com/huggingface/datasets/pull/4867.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/4867" }
https://api.github.com/repos/huggingface/datasets/issues/4703	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/4703/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/4703/comments	https://api.github.com/repos/huggingface/datasets/issues/4703/events	https://github.com/huggingface/datasets/pull/4703	1,307,844,097	PR_kwDODunzps47kABf	4,703	Make cast in `from_pandas` more robust	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._" ]	2022-07-18T11:55:49Z	2022-07-22T11:17:42Z	2022-07-22T11:05:24Z	COLLABORATOR	null	null	null	Make the cast in `from_pandas` more robust (as it was done for the packaged modules in https://github.com/huggingface/datasets/pull/4364) This should be useful in situations like [this one](https://discuss.huggingface.co/t/loading-custom-audio-dataset-and-fine-tuning-model/8836/4).	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/4703/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/4703/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/4703.diff", "html_url": "https://github.com/huggingface/datasets/pull/4703", "merged_at": "2022-07-22T11:05:24Z", "patch_url": "https://github.com/huggingface/datasets/pull/4703.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/4703" }
https://api.github.com/repos/huggingface/datasets/issues/7408	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/7408/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/7408/comments	https://api.github.com/repos/huggingface/datasets/issues/7408/events	https://github.com/huggingface/datasets/pull/7408	2,858,012,313	PR_kwDODunzps6Ld_-m	7,408	Fix filter speed regression	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7408). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update." ]	2025-02-17T14:25:32Z	2025-02-17T14:28:48Z	2025-02-17T14:28:46Z	MEMBER	null	null	null	close https://github.com/huggingface/datasets/issues/7404	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/7408/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/7408/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/7408.diff", "html_url": "https://github.com/huggingface/datasets/pull/7408", "merged_at": "2025-02-17T14:28:46Z", "patch_url": "https://github.com/huggingface/datasets/pull/7408.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/7408" }
https://api.github.com/repos/huggingface/datasets/issues/6221	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6221/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6221/comments	https://api.github.com/repos/huggingface/datasets/issues/6221/events	https://github.com/huggingface/datasets/issues/6221	1,884,324,631	I_kwDODunzps5wUIMX	6,221	Support saving datasets with custom formatting	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	[]	open	false	null	[]	null	[ "Not a fan of pickling this sort of stuff either.\r\nNote that users can also share the code in their dataset documentation." ]	2023-09-06T16:03:32Z	2023-09-06T18:32:07Z	null	COLLABORATOR	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	Requested in https://discuss.huggingface.co/t/using-set-transform-on-a-dataset-leads-to-an-exception/53036. I am not sure if supporting this is the best idea for the following reasons: >For this to work, we would have to pickle a custom transform, which means the transform and the objects it references need to be serializable. Also, deserializing these bytes would make `load_from_disk` unsafe, so I'm not sure this is a good idea. @lhoestq WDYT?	null	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6221/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6221/timeline	null	null	null	null
https://api.github.com/repos/huggingface/datasets/issues/5216	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5216/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5216/comments	https://api.github.com/repos/huggingface/datasets/issues/5216/events	https://github.com/huggingface/datasets/issues/5216	1,441,041,947	I_kwDODunzps5V5I4b	5,216	save_elasticsearch_index	{ "avatar_url": "https://avatars.githubusercontent.com/u/12739718?v=4", "events_url": "https://api.github.com/users/amobash2/events{/privacy}", "followers_url": "https://api.github.com/users/amobash2/followers", "following_url": "https://api.github.com/users/amobash2/following{/other_user}", "gists_url": "https://api.github.com/users/amobash2/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/amobash2", "id": 12739718, "login": "amobash2", "node_id": "MDQ6VXNlcjEyNzM5NzE4", "organizations_url": "https://api.github.com/users/amobash2/orgs", "received_events_url": "https://api.github.com/users/amobash2/received_events", "repos_url": "https://api.github.com/users/amobash2/repos", "site_admin": false, "starred_url": "https://api.github.com/users/amobash2/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/amobash2/subscriptions", "type": "User", "url": "https://api.github.com/users/amobash2", "user_view_type": "public" }	[]	open	false	null	[]	null	[ "Hi ! I think there exist tools to dump and reload an index in your elastic search but I'm not super familiar with it.\r\n\r\nAnyway after reloading an index in elastic search you can call `ds.load_elasticsearch_index` which will connect the index to the dataset without re-indexing" ]	2022-11-08T23:06:52Z	2022-11-09T13:16:45Z	null	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	Hi, I am new to Dataset and elasticsearch. I was wondering is there any equivalent approach to save elasticsearch index as of save_faiss_index locally for later use, to remove the need to re-index a dataset?	null	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5216/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5216/timeline	null	null	null	null
https://api.github.com/repos/huggingface/datasets/issues/4544	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/4544/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/4544/comments	https://api.github.com/repos/huggingface/datasets/issues/4544/events	https://github.com/huggingface/datasets/issues/4544	1,280,500,340	I_kwDODunzps5MUuJ0	4,544	[CI] seqeval installation fails sometimes on python 3.6	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	[]	closed	false	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	[ { "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" } ]	null	[]	2022-06-22T16:35:23Z	2022-06-23T10:13:44Z	2022-06-23T10:13:44Z	MEMBER	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	The CI sometimes fails to install seqeval, which cause the `seqeval` metric tests to fail. The installation fails because of this error: ``` Collecting seqeval Downloading seqeval-1.2.2.tar.gz (43 kB) \|███████▌ \| 10 kB 42.1 MB/s eta 0:00:01 \|███████████████ \| 20 kB 53.3 MB/s eta 0:00:01 \|██████████████████████▌ \| 30 kB 67.2 MB/s eta 0:00:01 \|██████████████████████████████ \| 40 kB 76.1 MB/s eta 0:00:01 \|████████████████████████████████\| 43 kB 10.0 MB/s Preparing metadata (setup.py) ... - error ERROR: Command errored out with exit status 1: command: /home/circleci/.pyenv/versions/3.6.15/bin/python3.6 -c 'import io, os, sys, setuptools, tokenize; sys.argv[0] = '"'"'/tmp/pip-install-1l96tbyj/seqeval_b31086f711d84743abe6905d2aa9dade/setup.py'"'"'; __file__='"'"'/tmp/pip-install-1l96tbyj/seqeval_b31086f711d84743abe6905d2aa9dade/setup.py'"'"';f = getattr(tokenize, '"'"'open'"'"', open)(__file__) if os.path.exists(__file__) else io.StringIO('"'"'from setuptools import setup; setup()'"'"');code = f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' egg_info --egg-base /tmp/pip-pip-egg-info-pf54_vqy cwd: /tmp/pip-install-1l96tbyj/seqeval_b31086f711d84743abe6905d2aa9dade/ Complete output (22 lines): Traceback (most recent call last): File "<string>", line 1, in <module> File "/tmp/pip-install-1l96tbyj/seqeval_b31086f711d84743abe6905d2aa9dade/setup.py", line 56, in <module> 'Programming Language :: Python :: Implementation :: PyPy' File "/home/circleci/.pyenv/versions/3.6.15/lib/python3.6/site-packages/setuptools/__init__.py", line 143, in setup return distutils.core.setup(**attrs) File "/home/circleci/.pyenv/versions/3.6.15/lib/python3.6/distutils/core.py", line 108, in setup _setup_distribution = dist = klass(attrs) File "/home/circleci/.pyenv/versions/3.6.15/lib/python3.6/site-packages/setuptools/dist.py", line 442, in __init__ k: v for k, v in attrs.items() File "/home/circleci/.pyenv/versions/3.6.15/lib/python3.6/distutils/dist.py", line 281, in __init__ self.finalize_options() File "/home/circleci/.pyenv/versions/3.6.15/lib/python3.6/site-packages/setuptools/dist.py", line 601, in finalize_options ep.load()(self, ep.name, value) File "/home/circleci/.pyenv/versions/3.6.15/lib/python3.6/site-packages/pkg_resources/__init__.py", line 2346, in load return self.resolve() File "/home/circleci/.pyenv/versions/3.6.15/lib/python3.6/site-packages/pkg_resources/__init__.py", line 2352, in resolve module = __import__(self.module_name, fromlist=['__name__'], level=0) File "/tmp/pip-install-1l96tbyj/seqeval_b31086f711d84743abe6905d2aa9dade/.eggs/setuptools_scm-7.0.2-py3.6.egg/setuptools_scm/__init__.py", line 5 from __future__ import annotations ^ SyntaxError: future feature annotations is not defined ---------------------------------------- WARNING: Discarding https://files.pythonhosted.org/packages/9d/2d/233c79d5b4e5ab1dbf111242299153f3caddddbb691219f363ad55ce783d/seqeval-1.2.2.tar.gz#sha256=f28e97c3ab96d6fcd32b648f6438ff2e09cfba87f05939da9b3970713ec56e6f (from https://pypi.org/simple/seqeval/). Command errored out with exit status 1: python setup.py egg_info Check the logs for full command output. ``` for example in https://app.circleci.com/pipelines/github/huggingface/datasets/12665/workflows/93878eb9-a923-4b35-b2e7-c5e9b22f10ad/jobs/75300 Here is a diff of the pip install logs until the error is reached: https://www.diffchecker.com/VkQDLeQT This could be caused by the latest updates of setuptools-scm	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/4544/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/4544/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/7005	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/7005/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/7005/comments	https://api.github.com/repos/huggingface/datasets/issues/7005/events	https://github.com/huggingface/datasets/issues/7005	2,378,424,349	I_kwDODunzps6Nw-Ad	7,005	EmptyDatasetError: The directory at /metadata.jsonl doesn't contain any data files	{ "avatar_url": "https://avatars.githubusercontent.com/u/117731544?v=4", "events_url": "https://api.github.com/users/Aki1991/events{/privacy}", "followers_url": "https://api.github.com/users/Aki1991/followers", "following_url": "https://api.github.com/users/Aki1991/following{/other_user}", "gists_url": "https://api.github.com/users/Aki1991/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/Aki1991", "id": 117731544, "login": "Aki1991", "node_id": "U_kgDOBwRw2A", "organizations_url": "https://api.github.com/users/Aki1991/orgs", "received_events_url": "https://api.github.com/users/Aki1991/received_events", "repos_url": "https://api.github.com/users/Aki1991/repos", "site_admin": false, "starred_url": "https://api.github.com/users/Aki1991/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/Aki1991/subscriptions", "type": "User", "url": "https://api.github.com/users/Aki1991", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "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	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### 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.	{ "avatar_url": "https://avatars.githubusercontent.com/u/117731544?v=4", "events_url": "https://api.github.com/users/Aki1991/events{/privacy}", "followers_url": "https://api.github.com/users/Aki1991/followers", "following_url": "https://api.github.com/users/Aki1991/following{/other_user}", "gists_url": "https://api.github.com/users/Aki1991/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/Aki1991", "id": 117731544, "login": "Aki1991", "node_id": "U_kgDOBwRw2A", "organizations_url": "https://api.github.com/users/Aki1991/orgs", "received_events_url": "https://api.github.com/users/Aki1991/received_events", "repos_url": "https://api.github.com/users/Aki1991/repos", "site_admin": false, "starred_url": "https://api.github.com/users/Aki1991/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/Aki1991/subscriptions", "type": "User", "url": "https://api.github.com/users/Aki1991", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/7005/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/7005/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/7269	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/7269/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/7269/comments	https://api.github.com/repos/huggingface/datasets/issues/7269/events	https://github.com/huggingface/datasets/issues/7269	2,626,873,843	I_kwDODunzps6ckunz	7,269	Memory leak when streaming	{ "avatar_url": "https://avatars.githubusercontent.com/u/64205064?v=4", "events_url": "https://api.github.com/users/Jourdelune/events{/privacy}", "followers_url": "https://api.github.com/users/Jourdelune/followers", "following_url": "https://api.github.com/users/Jourdelune/following{/other_user}", "gists_url": "https://api.github.com/users/Jourdelune/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/Jourdelune", "id": 64205064, "login": "Jourdelune", "node_id": "MDQ6VXNlcjY0MjA1MDY0", "organizations_url": "https://api.github.com/users/Jourdelune/orgs", "received_events_url": "https://api.github.com/users/Jourdelune/received_events", "repos_url": "https://api.github.com/users/Jourdelune/repos", "site_admin": false, "starred_url": "https://api.github.com/users/Jourdelune/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/Jourdelune/subscriptions", "type": "User", "url": "https://api.github.com/users/Jourdelune", "user_view_type": "public" }	[]	open	false	null	[]	null	[ "I seem to have encountered the same problem when loading non streaming datasets. load_from_disk. Causing hundreds of GB of memory, but the dataset actually only has 50GB", "FYI when streaming parquet data, only one row group per worker is loaded in memory at a time.\r\n\r\nBtw for datasets of embeddings you can surely optimize your RAM by reading the data as torch tensors directly instead of the default python lists\r\n\r\n```python\r\nfrom datasets import load_dataset\r\nfrom torch.utils.data import DataLoader\r\n\r\ndataset = load_dataset(\"WaveGenAI/dataset\", streaming=True).with_format(\"torch\")\r\n\r\ndataloader = DataLoader(dataset[\"train\"], num_workers=3)\r\n```" ]	2024-10-31T13:33:52Z	2024-11-18T11:46:07Z	null	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug I try to use a dataset with streaming=True, the issue I have is that the RAM usage becomes higher and higher until it is no longer sustainable. I understand that huggingface store data in ram during the streaming, and more worker in dataloader there are, more a lot of shard will be stored in ram, but the issue I have is that the ram usage is not constant. So after each new shard loaded, the ram usage will be higher and higher. ### Steps to reproduce the bug You can run this code and see you ram usage, after each shard of 255 examples, your ram usage will be extended. ```py from datasets import load_dataset from torch.utils.data import DataLoader dataset = load_dataset("WaveGenAI/dataset", streaming=True) dataloader = DataLoader(dataset["train"], num_workers=3) for i, data in enumerate(dataloader): print(i, end="\r") ``` ### Expected behavior The Ram usage should be always the same (just 3 shards loaded in the ram). ### Environment info - `datasets` version: 3.0.1 - Platform: Linux-6.10.5-arch1-1-x86_64-with-glibc2.40 - Python version: 3.12.4 - `huggingface_hub` version: 0.26.0 - PyArrow version: 17.0.0 - Pandas version: 2.2.3 - `fsspec` version: 2024.6.1	null	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/7269/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/7269/timeline	null	null	null	null
https://api.github.com/repos/huggingface/datasets/issues/7345	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/7345/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/7345/comments	https://api.github.com/repos/huggingface/datasets/issues/7345/events	https://github.com/huggingface/datasets/issues/7345	2,758,585,709	I_kwDODunzps6kbK1t	7,345	Different behaviour of IterableDataset.map vs Dataset.map with remove_columns	{ "avatar_url": "https://avatars.githubusercontent.com/u/12157034?v=4", "events_url": "https://api.github.com/users/vttrifonov/events{/privacy}", "followers_url": "https://api.github.com/users/vttrifonov/followers", "following_url": "https://api.github.com/users/vttrifonov/following{/other_user}", "gists_url": "https://api.github.com/users/vttrifonov/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/vttrifonov", "id": 12157034, "login": "vttrifonov", "node_id": "MDQ6VXNlcjEyMTU3MDM0", "organizations_url": "https://api.github.com/users/vttrifonov/orgs", "received_events_url": "https://api.github.com/users/vttrifonov/received_events", "repos_url": "https://api.github.com/users/vttrifonov/repos", "site_admin": false, "starred_url": "https://api.github.com/users/vttrifonov/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/vttrifonov/subscriptions", "type": "User", "url": "https://api.github.com/users/vttrifonov", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "Good catch ! Do you think you can open a PR to fix this issue ?" ]	2024-12-25T07:36:48Z	2025-01-07T11:56:42Z	2025-01-07T11:56:42Z	CONTRIBUTOR	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug The following code ```python import datasets as hf ds1 = hf.Dataset.from_list([{'i': i} for i in [0,1]]) #ds1 = ds1.to_iterable_dataset() ds2 = ds1.map( lambda i: {'i': i+1}, input_columns = ['i'], remove_columns = ['i'] ) list(ds2) ``` produces ```python [{'i': 1}, {'i': 2}] ``` as expected. If the line that converts `ds1` to iterable is uncommented so that the `ds2` is a map of an `IterableDataset`, the result is ```python [{},{}] ``` I expected the output to be the same as before. It seems that in the second case the removed column is not added back into the output. The issue seems to be [here](https://github.com/huggingface/datasets/blob/6c6a82a573f946c4a81069f56446caed15cee9c2/src/datasets/iterable_dataset.py#L1093): the columns are removed after the mapping which is not what we want (or what the [documentation says](https://github.com/huggingface/datasets/blob/6c6a82a573f946c4a81069f56446caed15cee9c2/src/datasets/iterable_dataset.py#L2370)) because we want the columns removed from the transformed example but then added if the map produced them. This is `datasets==3.2.0` and `python==3.10` ### Steps to reproduce the bug see above ### Expected behavior see above ### Environment info see above	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/7345/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/7345/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/6365	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6365/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6365/comments	https://api.github.com/repos/huggingface/datasets/issues/6365/events	https://github.com/huggingface/datasets/issues/6365	1,970,140,392	I_kwDODunzps51bfTo	6,365	Parquet size grows exponential for categorical data	{ "avatar_url": "https://avatars.githubusercontent.com/u/82567957?v=4", "events_url": "https://api.github.com/users/aseganti/events{/privacy}", "followers_url": "https://api.github.com/users/aseganti/followers", "following_url": "https://api.github.com/users/aseganti/following{/other_user}", "gists_url": "https://api.github.com/users/aseganti/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/aseganti", "id": 82567957, "login": "aseganti", "node_id": "MDQ6VXNlcjgyNTY3OTU3", "organizations_url": "https://api.github.com/users/aseganti/orgs", "received_events_url": "https://api.github.com/users/aseganti/received_events", "repos_url": "https://api.github.com/users/aseganti/repos", "site_admin": false, "starred_url": "https://api.github.com/users/aseganti/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/aseganti/subscriptions", "type": "User", "url": "https://api.github.com/users/aseganti", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "Wrong repo." ]	2023-10-31T10:29:02Z	2023-10-31T10:49:17Z	2023-10-31T10:49:17Z	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug It seems that when saving a data frame with a categorical column inside the size can grow exponentially. This seems to happen because when we save the categorical data to parquet, we are saving the data + all the categories existing in the original data. This happens even when the categories are not present in the original data. ### Steps to reproduce the bug To reproduce the bug, it is enough to run this script: ``` import pandas as pd import os if __name__ == "__main__": for n in [10, 1e2, 1e3, 1e4, 1e5]: for n_col in [1, 10, 100, 1000, 10000]: input = pd.DataFrame([{"{i}": f"{i}_cat" for col in range(n_col)} for i in range(int(n))]) input.iloc[0:100].to_parquet("a.parquet") for col in input.columns: input[col] = input[col].astype("category") input.iloc[0:100].to_parquet("b.parquet") a_size_mb = os.stat("a.parquet").st_size / (1024 * 1024) b_size_mb = os.stat("b.parquet").st_size / (1024 * 1024) print(f"{n} {n_col} {a_size_mb} {b_size_mb} {100*b_size_mb/a_size_mb:.2f}") ``` That produces this output: <img width="464" alt="Screenshot 2023-10-31 at 11 25 25" src="https://github.com/huggingface/datasets/assets/82567957/2b8a9284-7f9e-4c10-a006-0a27236ebd15"> ### Expected behavior In my opinion either: 1. The two file should have (almost) the same size 2. There should be warning telling the user that such difference in size is possible ### Environment info Python 3.8.18 pandas==2.0.3 numpy==1.24.4	{ "avatar_url": "https://avatars.githubusercontent.com/u/82567957?v=4", "events_url": "https://api.github.com/users/aseganti/events{/privacy}", "followers_url": "https://api.github.com/users/aseganti/followers", "following_url": "https://api.github.com/users/aseganti/following{/other_user}", "gists_url": "https://api.github.com/users/aseganti/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/aseganti", "id": 82567957, "login": "aseganti", "node_id": "MDQ6VXNlcjgyNTY3OTU3", "organizations_url": "https://api.github.com/users/aseganti/orgs", "received_events_url": "https://api.github.com/users/aseganti/received_events", "repos_url": "https://api.github.com/users/aseganti/repos", "site_admin": false, "starred_url": "https://api.github.com/users/aseganti/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/aseganti/subscriptions", "type": "User", "url": "https://api.github.com/users/aseganti", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6365/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6365/timeline	null	not_planned	null	null
https://api.github.com/repos/huggingface/datasets/issues/5924	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5924/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5924/comments	https://api.github.com/repos/huggingface/datasets/issues/5924/events	https://github.com/huggingface/datasets/pull/5924	1,738,889,236	PR_kwDODunzps5SCiFv	5,924	Add parallel module using joblib for Spark	{ "avatar_url": "https://avatars.githubusercontent.com/u/12763339?v=4", "events_url": "https://api.github.com/users/es94129/events{/privacy}", "followers_url": "https://api.github.com/users/es94129/followers", "following_url": "https://api.github.com/users/es94129/following{/other_user}", "gists_url": "https://api.github.com/users/es94129/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/es94129", "id": 12763339, "login": "es94129", "node_id": "MDQ6VXNlcjEyNzYzMzM5", "organizations_url": "https://api.github.com/users/es94129/orgs", "received_events_url": "https://api.github.com/users/es94129/received_events", "repos_url": "https://api.github.com/users/es94129/repos", "site_admin": false, "starred_url": "https://api.github.com/users/es94129/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/es94129/subscriptions", "type": "User", "url": "https://api.github.com/users/es94129", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "Hi @lhoestq, I added the `parallel` part according to the discussion we had. Could you take a look to see if this is aligned with your proposal?\r\n\r\nMeanwhile I'm working on adding a `parallel_backend` parameter to `load_datasets` so that it can be used like:\r\n```python\r\nwith parallel_backend('spark', steps=['downloading']) as backend:\r\n ds = load_dataset(..., parallel_backend=backend)\r\n```\r\nwhere `parallel_backend` is a `ParallelBackend` class.", "_The documentation is not available anymore as the PR was closed or merged._", "@lhoestq Thanks for the comments!\r\nWith your suggestion, no changes made to `load_dataset` and I validated that downloading with spark is working now with this:\r\n```py\r\nwith parallel_backend('spark', steps=[\"download\"]):\r\n dataset = load_dataset(..., num_proc=2)\r\n```", "@lhoestq Can a maintainer help trigger the tests again?\r\n> One idea is to decorate the download method to set the current global step to \"download\", and then only use joblib if the current step is one of the steps provided in parallel_backend.\r\n\r\nYes I think this is doable in a subsequent PR.\r\nFor throwing `NotImplementedError` I also think it can be done in a subsequent PR, because I'm not sure if `Dataset.map` is the only function that a user would expect to run using `with parallel_backend`.", "Just triggered the tests :)\r\n\r\n> Yes I think this is doable in a subsequent PR.\r\nFor throwing NotImplementedError I also think it can be done in a subsequent PR, because I'm not sure if Dataset.map is the only function that a user would expect to run using with parallel_backend.\r\n\r\nI think any Dataset method that has a `num_proc` argument: Dataset.map (the other methods like filter or cast or based on map), and later we can see for the to_xxx methods (to_csv, to_parquet, etc.)", "Hi maintainers, I've just addressed most of the comments, please take another look, thank you.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008422 / 0.011353 (-0.002931) \| 0.005658 / 0.011008 (-0.005350) \| 0.135372 / 0.038508 (0.096864) \| 0.044766 / 0.023109 (0.021657) \| 0.417876 / 0.275898 (0.141978) \| 0.462785 / 0.323480 (0.139305) \| 0.005485 / 0.007986 (-0.002501) \| 0.005640 / 0.004328 (0.001311) \| 0.105020 / 0.004250 (0.100770) \| 0.049114 / 0.037052 (0.012062) \| 0.490450 / 0.258489 (0.231961) \| 0.467693 / 0.293841 (0.173852) \| 0.050929 / 0.128546 (-0.077617) \| 0.014644 / 0.075646 (-0.061002) \| 0.452373 / 0.419271 (0.033101) \| 0.074897 / 0.043533 (0.031364) \| 0.425816 / 0.255139 (0.170677) \| 0.420415 / 0.283200 (0.137215) \| 0.134121 / 0.141683 (-0.007561) \| 1.927744 / 1.452155 (0.475589) \| 2.014417 / 1.492716 (0.521701) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.254811 / 0.018006 (0.236805) \| 0.550011 / 0.000490 (0.549521) \| 0.004913 / 0.000200 (0.004714) \| 0.000117 / 0.000054 (0.000062) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032644 / 0.037411 (-0.004768) \| 0.135672 / 0.014526 (0.121146) \| 0.158984 / 0.176557 (-0.017572) \| 0.218267 / 0.737135 (-0.518869) \| 0.150348 / 0.296338 (-0.145991) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.625723 / 0.215209 (0.410514) \| 6.247559 / 2.077655 (4.169905) \| 2.626785 / 1.504120 (1.122666) \| 2.195224 / 1.541195 (0.654030) \| 2.232140 / 1.468490 (0.763650) \| 0.943082 / 4.584777 (-3.641695) \| 5.799262 / 3.745712 (2.053550) \| 2.849411 / 5.269862 (-2.420450) \| 1.744160 / 4.565676 (-2.821516) \| 0.119056 / 0.424275 (-0.305219) \| 0.014233 / 0.007607 (0.006626) \| 0.795238 / 0.226044 (0.569194) \| 7.569586 / 2.268929 (5.300657) \| 3.179481 / 55.444624 (-52.265143) \| 2.519772 / 6.876477 (-4.356704) \| 2.714570 / 2.142072 (0.572498) \| 1.107197 / 4.805227 (-3.698030) \| 0.229986 / 6.500664 (-6.270678) \| 0.087993 / 0.075469 (0.012524) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.535610 / 1.841788 (-0.306178) \| 18.639369 / 8.074308 (10.565061) \| 21.081844 / 10.191392 (10.890452) \| 0.253247 / 0.680424 (-0.427177) \| 0.026711 / 0.534201 (-0.507490) \| 0.503790 / 0.579283 (-0.075493) \| 0.600124 / 0.434364 (0.165760) \| 0.617944 / 0.540337 (0.077607) \| 0.766947 / 1.386936 (-0.619989) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007885 / 0.011353 (-0.003468) \| 0.004761 / 0.011008 (-0.006248) \| 0.097995 / 0.038508 (0.059487) \| 0.033624 / 0.023109 (0.010515) \| 0.504307 / 0.275898 (0.228409) \| 0.534803 / 0.323480 (0.211323) \| 0.006048 / 0.007986 (-0.001937) \| 0.005042 / 0.004328 (0.000714) \| 0.102288 / 0.004250 (0.098038) \| 0.048695 / 0.037052 (0.011643) \| 0.559086 / 0.258489 (0.300597) \| 0.553233 / 0.293841 (0.259392) \| 0.044596 / 0.128546 (-0.083950) \| 0.013696 / 0.075646 (-0.061950) \| 0.109875 / 0.419271 (-0.309397) \| 0.059993 / 0.043533 (0.016460) \| 0.485579 / 0.255139 (0.230440) \| 0.519835 / 0.283200 (0.236635) \| 0.123504 / 0.141683 (-0.018179) \| 1.820506 / 1.452155 (0.368351) \| 1.963448 / 1.492716 (0.470732) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.292663 / 0.018006 (0.274656) \| 0.557783 / 0.000490 (0.557293) \| 0.001330 / 0.000200 (0.001130) \| 0.000112 / 0.000054 (0.000057) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.036890 / 0.037411 (-0.000522) \| 0.140373 / 0.014526 (0.125847) \| 0.140176 / 0.176557 (-0.036381) \| 0.237378 / 0.737135 (-0.499757) \| 0.160186 / 0.296338 (-0.136152) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.673599 / 0.215209 (0.458390) \| 6.510280 / 2.077655 (4.432625) \| 2.981617 / 1.504120 (1.477497) \| 2.684664 / 1.541195 (1.143469) \| 2.760471 / 1.468490 (1.291981) \| 0.975413 / 4.584777 (-3.609364) \| 5.708933 / 3.745712 (1.963220) \| 2.772069 / 5.269862 (-2.497793) \| 1.763627 / 4.565676 (-2.802049) \| 0.111632 / 0.424275 (-0.312643) \| 0.013223 / 0.007607 (0.005616) \| 0.791545 / 0.226044 (0.565500) \| 8.063287 / 2.268929 (5.794359) \| 3.671920 / 55.444624 (-51.772704) \| 3.057248 / 6.876477 (-3.819229) \| 3.083569 / 2.142072 (0.941497) \| 1.118136 / 4.805227 (-3.687092) \| 0.214655 / 6.500664 (-6.286009) \| 0.083074 / 0.075469 (0.007605) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.761731 / 1.841788 (-0.080056) \| 18.874200 / 8.074308 (10.799892) \| 22.383693 / 10.191392 (12.192301) \| 0.240292 / 0.680424 (-0.440132) \| 0.028850 / 0.534201 (-0.505351) \| 0.557334 / 0.579283 (-0.021949) \| 0.627732 / 0.434364 (0.193369) \| 0.634484 / 0.540337 (0.094146) \| 0.767372 / 1.386936 (-0.619564) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#accaaf2e69fbb5dc5e50229d2eb1591b8ad982b6 \"CML watermark\")\n" ]	2023-06-02T22:25:25Z	2023-06-14T10:25:10Z	2023-06-14T10:15:46Z	CONTRIBUTOR	null	null	null	Discussion in https://github.com/huggingface/datasets/issues/5798	{ "avatar_url": 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0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5924/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5924/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/5924.diff", "html_url": "https://github.com/huggingface/datasets/pull/5924", "merged_at": "2023-06-14T10:15:46Z", "patch_url": "https://github.com/huggingface/datasets/pull/5924.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5924" }
https://api.github.com/repos/huggingface/datasets/issues/7194	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/7194/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/7194/comments	https://api.github.com/repos/huggingface/datasets/issues/7194/events	https://github.com/huggingface/datasets/issues/7194	2,563,364,199	I_kwDODunzps6YydVn	7,194	datasets.exceptions.DatasetNotFoundError for private dataset	{ "avatar_url": "https://avatars.githubusercontent.com/u/20212179?v=4", "events_url": "https://api.github.com/users/kdutia/events{/privacy}", "followers_url": "https://api.github.com/users/kdutia/followers", "following_url": "https://api.github.com/users/kdutia/following{/other_user}", "gists_url": "https://api.github.com/users/kdutia/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/kdutia", "id": 20212179, "login": "kdutia", "node_id": "MDQ6VXNlcjIwMjEyMTc5", "organizations_url": "https://api.github.com/users/kdutia/orgs", "received_events_url": "https://api.github.com/users/kdutia/received_events", "repos_url": "https://api.github.com/users/kdutia/repos", "site_admin": false, "starred_url": "https://api.github.com/users/kdutia/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/kdutia/subscriptions", "type": "User", "url": "https://api.github.com/users/kdutia", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "Actually there is no such dataset available, that is why you are getting that error.", "Fixed with @kdutia in Slack chat. Generating a new token fixed this issue. " ]	2024-10-03T07:49:36Z	2024-10-03T10:09:28Z	2024-10-03T10:09:28Z	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug The following Python code tries to download a private dataset and fails with the error `datasets.exceptions.DatasetNotFoundError: Dataset 'ClimatePolicyRadar/all-document-text-data-weekly' doesn't exist on the Hub or cannot be accessed.`. Downloading a public dataset doesn't work. ``` py from datasets import load_dataset _ = load_dataset("ClimatePolicyRadar/all-document-text-data-weekly") ``` This seems to be just an issue with my machine config as the code above works with a colleague's machine. So far I have tried: - logging back out and in from the Huggingface CLI using `huggingface-cli logout` - manually removing the token cache at `/Users/kalyan/.cache/huggingface/token` (found using `huggingface-cli env`) - manually passing a token in `load_dataset` My output of `huggingface-cli whoami`: ``` kdutia orgs: ClimatePolicyRadar ``` ### Steps to reproduce the bug ``` python Python 3.12.2 (main, Feb 6 2024, 20:19:44) [Clang 15.0.0 (clang-1500.1.0.2.5)] on darwin Type "help", "copyright", "credits" or "license" for more information. >>> from datasets import load_dataset >>> _ = load_dataset("ClimatePolicyRadar/all-document-text-data-weekly") Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/Users/kalyan/Library/Caches/pypoetry/virtualenvs/open-data-cnKQNmjn-py3.12/lib/python3.12/site-packages/datasets/load.py", line 2074, in load_dataset builder_instance = load_dataset_builder( ^^^^^^^^^^^^^^^^^^^^^ File "/Users/kalyan/Library/Caches/pypoetry/virtualenvs/open-data-cnKQNmjn-py3.12/lib/python3.12/site-packages/datasets/load.py", line 1795, in load_dataset_builder dataset_module = dataset_module_factory( ^^^^^^^^^^^^^^^^^^^^^^^ File "/Users/kalyan/Library/Caches/pypoetry/virtualenvs/open-data-cnKQNmjn-py3.12/lib/python3.12/site-packages/datasets/load.py", line 1659, in dataset_module_factory raise e1 from None File "/Users/kalyan/Library/Caches/pypoetry/virtualenvs/open-data-cnKQNmjn-py3.12/lib/python3.12/site-packages/datasets/load.py", line 1597, in dataset_module_factory raise DatasetNotFoundError(f"Dataset '{path}' doesn't exist on the Hub or cannot be accessed.") from e datasets.exceptions.DatasetNotFoundError: Dataset 'ClimatePolicyRadar/all-document-text-data-weekly' doesn't exist on the Hub or cannot be accessed. >>> ``` ### Expected behavior The dataset downloads successfully. ### Environment info From `huggingface-cli env`: ``` - huggingface_hub version: 0.25.1 - Platform: macOS-14.2.1-arm64-arm-64bit - Python version: 3.12.2 - Running in iPython ?: No - Running in notebook ?: No - Running in Google Colab ?: No - Running in Google Colab Enterprise ?: No - Token path ?: /Users/kalyan/.cache/huggingface/token - Has saved token ?: True - Who am I ?: kdutia - Configured git credential helpers: osxkeychain - FastAI: N/A - Tensorflow: N/A - Torch: N/A - Jinja2: 3.1.4 - Graphviz: N/A - keras: N/A - Pydot: N/A - Pillow: N/A - hf_transfer: N/A - gradio: N/A - tensorboard: N/A - numpy: 2.1.1 - pydantic: N/A - aiohttp: 3.10.8 - ENDPOINT: https://huggingface.co - HF_HUB_CACHE: /Users/kalyan/.cache/huggingface/hub - HF_ASSETS_CACHE: /Users/kalyan/.cache/huggingface/assets - HF_TOKEN_PATH: /Users/kalyan/.cache/huggingface/token - HF_HUB_OFFLINE: False - HF_HUB_DISABLE_TELEMETRY: False - HF_HUB_DISABLE_PROGRESS_BARS: None - HF_HUB_DISABLE_SYMLINKS_WARNING: False - HF_HUB_DISABLE_EXPERIMENTAL_WARNING: False - HF_HUB_DISABLE_IMPLICIT_TOKEN: False - HF_HUB_ENABLE_HF_TRANSFER: False - HF_HUB_ETAG_TIMEOUT: 10 - HF_HUB_DOWNLOAD_TIMEOUT: 10 ``` from `datasets-cli env`: ``` - `datasets` version: 3.0.1 - Platform: macOS-14.2.1-arm64-arm-64bit - Python version: 3.12.2 - `huggingface_hub` version: 0.25.1 - PyArrow version: 17.0.0 - Pandas version: 2.2.3 - `fsspec` version: 2024.6.1 ```	{ "avatar_url": "https://avatars.githubusercontent.com/u/8995957?v=4", "events_url": "https://api.github.com/users/davanstrien/events{/privacy}", "followers_url": "https://api.github.com/users/davanstrien/followers", "following_url": "https://api.github.com/users/davanstrien/following{/other_user}", "gists_url": "https://api.github.com/users/davanstrien/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/davanstrien", "id": 8995957, "login": "davanstrien", "node_id": "MDQ6VXNlcjg5OTU5NTc=", "organizations_url": "https://api.github.com/users/davanstrien/orgs", "received_events_url": "https://api.github.com/users/davanstrien/received_events", "repos_url": "https://api.github.com/users/davanstrien/repos", "site_admin": false, "starred_url": "https://api.github.com/users/davanstrien/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/davanstrien/subscriptions", "type": "User", "url": "https://api.github.com/users/davanstrien", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/7194/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/7194/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/6546	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6546/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6546/comments	https://api.github.com/repos/huggingface/datasets/issues/6546/events	https://github.com/huggingface/datasets/pull/6546	2,060,796,369	PR_kwDODunzps5i-Jgv	6,546	Release: 2.16.1	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6546). 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) \| 0.005415 / 0.011353 (-0.005938) \| 0.003733 / 0.011008 (-0.007275) \| 0.064178 / 0.038508 (0.025670) \| 0.033162 / 0.023109 (0.010053) \| 0.249799 / 0.275898 (-0.026099) \| 0.274875 / 0.323480 (-0.048605) \| 0.002977 / 0.007986 (-0.005009) \| 0.002696 / 0.004328 (-0.001633) \| 0.050042 / 0.004250 (0.045792) \| 0.047127 / 0.037052 (0.010074) \| 0.250865 / 0.258489 (-0.007624) \| 0.289758 / 0.293841 (-0.004083) \| 0.028007 / 0.128546 (-0.100539) \| 0.010671 / 0.075646 (-0.064975) \| 0.207123 / 0.419271 (-0.212148) \| 0.036403 / 0.043533 (-0.007130) \| 0.261527 / 0.255139 (0.006388) \| 0.277277 / 0.283200 (-0.005922) \| 0.019418 / 0.141683 (-0.122264) \| 1.118019 / 1.452155 (-0.334136) \| 1.180254 / 1.492716 (-0.312462) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004604 / 0.018006 (-0.013402) \| 0.308129 / 0.000490 (0.307639) \| 0.000202 / 0.000200 (0.000002) \| 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) \| 0.018400 / 0.037411 (-0.019011) \| 0.060777 / 0.014526 (0.046251) \| 0.073059 / 0.176557 (-0.103498) \| 0.119677 / 0.737135 (-0.617458) \| 0.074076 / 0.296338 (-0.222263) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.275353 / 0.215209 (0.060144) \| 2.694079 / 2.077655 (0.616424) \| 1.419670 / 1.504120 (-0.084450) \| 1.302079 / 1.541195 (-0.239116) \| 1.342077 / 1.468490 (-0.126413) \| 0.549794 / 4.584777 (-4.034983) \| 2.377149 / 3.745712 (-1.368563) \| 2.800362 / 5.269862 (-2.469500) \| 1.728152 / 4.565676 (-2.837524) \| 0.061774 / 0.424275 (-0.362501) \| 0.004898 / 0.007607 (-0.002709) \| 0.330996 / 0.226044 (0.104952) \| 3.262010 / 2.268929 (0.993082) \| 1.761106 / 55.444624 (-53.683518) \| 1.489783 / 6.876477 (-5.386694) \| 1.532470 / 2.142072 (-0.609602) \| 0.648814 / 4.805227 (-4.156414) \| 0.116893 / 6.500664 (-6.383771) \| 0.042167 / 0.075469 (-0.033303) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.937679 / 1.841788 (-0.904109) \| 11.621632 / 8.074308 (3.547324) \| 10.226177 / 10.191392 (0.034785) \| 0.129242 / 0.680424 (-0.551182) \| 0.014884 / 0.534201 (-0.519317) \| 0.287619 / 0.579283 (-0.291664) \| 0.261677 / 0.434364 (-0.172687) \| 0.336361 / 0.540337 (-0.203976) \| 0.426461 / 1.386936 (-0.960475) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005246 / 0.011353 (-0.006106) \| 0.003533 / 0.011008 (-0.007475) \| 0.051691 / 0.038508 (0.013182) \| 0.031551 / 0.023109 (0.008442) \| 0.297884 / 0.275898 (0.021986) \| 0.323100 / 0.323480 (-0.000380) \| 0.004101 / 0.007986 (-0.003884) \| 0.002668 / 0.004328 (-0.001661) \| 0.048764 / 0.004250 (0.044513) \| 0.045429 / 0.037052 (0.008377) \| 0.300107 / 0.258489 (0.041618) \| 0.335650 / 0.293841 (0.041809) \| 0.030061 / 0.128546 (-0.098485) \| 0.010878 / 0.075646 (-0.064768) \| 0.058561 / 0.419271 (-0.360710) \| 0.052829 / 0.043533 (0.009296) \| 0.302704 / 0.255139 (0.047565) \| 0.320527 / 0.283200 (0.037327) \| 0.018995 / 0.141683 (-0.122688) \| 1.144050 / 1.452155 (-0.308105) \| 1.255275 / 1.492716 (-0.237441) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092708 / 0.018006 (0.074701) \| 0.305204 / 0.000490 (0.304714) \| 0.000224 / 0.000200 (0.000024) \| 0.000055 / 0.000054 (0.000000) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021607 / 0.037411 (-0.015805) \| 0.075938 / 0.014526 (0.061412) \| 0.090864 / 0.176557 (-0.085693) \| 0.128248 / 0.737135 (-0.608887) \| 0.090322 / 0.296338 (-0.206017) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.302095 / 0.215209 (0.086886) \| 2.925686 / 2.077655 (0.848032) \| 1.617767 / 1.504120 (0.113648) \| 1.477975 / 1.541195 (-0.063220) \| 1.508576 / 1.468490 (0.040086) \| 0.574376 / 4.584777 (-4.010401) \| 2.467483 / 3.745712 (-1.278229) \| 2.832500 / 5.269862 (-2.437362) \| 1.765233 / 4.565676 (-2.800443) \| 0.064105 / 0.424275 (-0.360170) \| 0.005090 / 0.007607 (-0.002517) \| 0.349819 / 0.226044 (0.123774) \| 3.468916 / 2.268929 (1.199987) \| 1.946499 / 55.444624 (-53.498126) \| 1.684369 / 6.876477 (-5.192107) \| 1.711036 / 2.142072 (-0.431036) \| 0.650153 / 4.805227 (-4.155075) \| 0.116598 / 6.500664 (-6.384066) \| 0.041213 / 0.075469 (-0.034256) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.990842 / 1.841788 (-0.850946) \| 12.348468 / 8.074308 (4.274160) \| 11.174441 / 10.191392 (0.983049) \| 0.140950 / 0.680424 (-0.539473) \| 0.016100 / 0.534201 (-0.518101) \| 0.286486 / 0.579283 (-0.292797) \| 0.282054 / 0.434364 (-0.152310) \| 0.324261 / 0.540337 (-0.216076) \| 0.420717 / 1.386936 (-0.966219) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7b2bcd76457de720454c3ac304f2ed5c6f40acaa \"CML watermark\")\n" ]	2023-12-30T16:44:51Z	2023-12-30T16:52:07Z	2023-12-30T16:45:52Z	MEMBER	null	null	null	null	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6546/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6546/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6546.diff", "html_url": "https://github.com/huggingface/datasets/pull/6546", "merged_at": "2023-12-30T16:45:52Z", "patch_url": "https://github.com/huggingface/datasets/pull/6546.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6546" }
https://api.github.com/repos/huggingface/datasets/issues/5623	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5623/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5623/comments	https://api.github.com/repos/huggingface/datasets/issues/5623/events	https://github.com/huggingface/datasets/pull/5623	1,616,712,665	PR_kwDODunzps5Lpb4q	5,623	Remove set_access_token usage + fail tests if FutureWarning	{ "avatar_url": "https://avatars.githubusercontent.com/u/11801849?v=4", "events_url": "https://api.github.com/users/Wauplin/events{/privacy}", "followers_url": "https://api.github.com/users/Wauplin/followers", "following_url": "https://api.github.com/users/Wauplin/following{/other_user}", "gists_url": "https://api.github.com/users/Wauplin/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/Wauplin", "id": 11801849, "login": "Wauplin", "node_id": "MDQ6VXNlcjExODAxODQ5", "organizations_url": "https://api.github.com/users/Wauplin/orgs", "received_events_url": "https://api.github.com/users/Wauplin/received_events", "repos_url": "https://api.github.com/users/Wauplin/repos", "site_admin": false, "starred_url": "https://api.github.com/users/Wauplin/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/Wauplin/subscriptions", "type": "User", "url": "https://api.github.com/users/Wauplin", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008505 / 0.011353 (-0.002848) \| 0.004445 / 0.011008 (-0.006563) \| 0.102197 / 0.038508 (0.063689) \| 0.029886 / 0.023109 (0.006776) \| 0.305387 / 0.275898 (0.029489) \| 0.355986 / 0.323480 (0.032507) \| 0.006814 / 0.007986 (-0.001172) \| 0.003298 / 0.004328 (-0.001030) \| 0.079204 / 0.004250 (0.074954) \| 0.035618 / 0.037052 (-0.001434) \| 0.320430 / 0.258489 (0.061941) \| 0.353330 / 0.293841 (0.059490) \| 0.033280 / 0.128546 (-0.095266) \| 0.011300 / 0.075646 (-0.064347) \| 0.324627 / 0.419271 (-0.094644) \| 0.040405 / 0.043533 (-0.003128) \| 0.308760 / 0.255139 (0.053621) \| 0.331885 / 0.283200 (0.048685) \| 0.084605 / 0.141683 (-0.057077) \| 1.576598 / 1.452155 (0.124443) \| 1.530694 / 1.492716 (0.037977) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.191142 / 0.018006 (0.173136) \| 0.404042 / 0.000490 (0.403552) \| 0.001185 / 0.000200 (0.000985) \| 0.000074 / 0.000054 (0.000019) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022889 / 0.037411 (-0.014523) \| 0.095862 / 0.014526 (0.081336) \| 0.104382 / 0.176557 (-0.072175) \| 0.139407 / 0.737135 (-0.597728) \| 0.106813 / 0.296338 (-0.189525) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.419083 / 0.215209 (0.203874) \| 4.188702 / 2.077655 (2.111047) \| 1.897854 / 1.504120 (0.393734) \| 1.689544 / 1.541195 (0.148350) \| 1.714032 / 1.468490 (0.245542) \| 0.695541 / 4.584777 (-3.889236) \| 3.370584 / 3.745712 (-0.375128) \| 3.205549 / 5.269862 (-2.064313) \| 1.641202 / 4.565676 (-2.924474) \| 0.081849 / 0.424275 (-0.342426) \| 0.012043 / 0.007607 (0.004436) \| 0.529618 / 0.226044 (0.303574) \| 5.314167 / 2.268929 (3.045238) \| 2.357271 / 55.444624 (-53.087353) \| 1.979684 / 6.876477 (-4.896793) \| 2.030057 / 2.142072 (-0.112015) \| 0.813013 / 4.805227 (-3.992214) \| 0.150165 / 6.500664 (-6.350499) \| 0.064595 / 0.075469 (-0.010874) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.237824 / 1.841788 (-0.603964) \| 13.552178 / 8.074308 (5.477870) \| 14.089433 / 10.191392 (3.898041) \| 0.149325 / 0.680424 (-0.531099) \| 0.028543 / 0.534201 (-0.505658) \| 0.396848 / 0.579283 (-0.182435) \| 0.396230 / 0.434364 (-0.038134) \| 0.466317 / 0.540337 (-0.074021) \| 0.539579 / 1.386936 (-0.847357) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006224 / 0.011353 (-0.005128) \| 0.004429 / 0.011008 (-0.006579) \| 0.075740 / 0.038508 (0.037232) \| 0.026717 / 0.023109 (0.003608) \| 0.341685 / 0.275898 (0.065787) \| 0.383671 / 0.323480 (0.060191) \| 0.004682 / 0.007986 (-0.003304) \| 0.004681 / 0.004328 (0.000352) \| 0.076638 / 0.004250 (0.072387) \| 0.034577 / 0.037052 (-0.002476) \| 0.341160 / 0.258489 (0.082671) \| 0.407590 / 0.293841 (0.113749) \| 0.031121 / 0.128546 (-0.097425) \| 0.011479 / 0.075646 (-0.064167) \| 0.085299 / 0.419271 (-0.333973) \| 0.042005 / 0.043533 (-0.001528) \| 0.339682 / 0.255139 (0.084543) \| 0.377669 / 0.283200 (0.094469) \| 0.087751 / 0.141683 (-0.053932) \| 1.523910 / 1.452155 (0.071756) \| 1.607487 / 1.492716 (0.114771) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.225605 / 0.018006 (0.207599) \| 0.395851 / 0.000490 (0.395361) \| 0.004404 / 0.000200 (0.004204) \| 0.000082 / 0.000054 (0.000028) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.024489 / 0.037411 (-0.012922) \| 0.099813 / 0.014526 (0.085287) \| 0.107392 / 0.176557 (-0.069165) \| 0.139567 / 0.737135 (-0.597568) \| 0.110080 / 0.296338 (-0.186258) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.449051 / 0.215209 (0.233841) \| 4.463098 / 2.077655 (2.385443) \| 2.122548 / 1.504120 (0.618428) \| 1.913863 / 1.541195 (0.372669) \| 1.963988 / 1.468490 (0.495498) \| 0.698442 / 4.584777 (-3.886335) \| 3.330425 / 3.745712 (-0.415287) \| 1.867843 / 5.269862 (-3.402019) \| 1.163740 / 4.565676 (-3.401937) \| 0.083209 / 0.424275 (-0.341066) \| 0.012594 / 0.007607 (0.004987) \| 0.547074 / 0.226044 (0.321030) \| 5.474779 / 2.268929 (3.205851) \| 2.548025 / 55.444624 (-52.896599) \| 2.202435 / 6.876477 (-4.674041) \| 2.220330 / 2.142072 (0.078257) \| 0.810104 / 4.805227 (-3.995124) \| 0.151141 / 6.500664 (-6.349523) \| 0.066204 / 0.075469 (-0.009265) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.272075 / 1.841788 (-0.569712) \| 13.749523 / 8.074308 (5.675215) \| 14.270974 / 10.191392 (4.079582) \| 0.141285 / 0.680424 (-0.539139) \| 0.016526 / 0.534201 (-0.517675) \| 0.393175 / 0.579283 (-0.186109) \| 0.391577 / 0.434364 (-0.042787) \| 0.492824 / 0.540337 (-0.047513) \| 0.580069 / 1.386936 (-0.806867) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1cda14136c9f79c763c17d49b77eabfb233fbb35 \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008901 / 0.011353 (-0.002452) \| 0.005017 / 0.011008 (-0.005991) \| 0.099340 / 0.038508 (0.060832) \| 0.034218 / 0.023109 (0.011109) \| 0.295927 / 0.275898 (0.020029) \| 0.330087 / 0.323480 (0.006607) \| 0.008041 / 0.007986 (0.000056) \| 0.005013 / 0.004328 (0.000685) \| 0.074255 / 0.004250 (0.070004) \| 0.049634 / 0.037052 (0.012582) \| 0.299972 / 0.258489 (0.041483) \| 0.349879 / 0.293841 (0.056038) \| 0.038500 / 0.128546 (-0.090047) \| 0.011980 / 0.075646 (-0.063666) \| 0.332408 / 0.419271 (-0.086863) \| 0.048385 / 0.043533 (0.004852) \| 0.300393 / 0.255139 (0.045254) \| 0.316972 / 0.283200 (0.033772) \| 0.101674 / 0.141683 (-0.040009) \| 1.424300 / 1.452155 (-0.027854) \| 1.520658 / 1.492716 (0.027942) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.270084 / 0.018006 (0.252078) \| 0.538612 / 0.000490 (0.538123) \| 0.004439 / 0.000200 (0.004240) \| 0.000089 / 0.000054 (0.000035) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026841 / 0.037411 (-0.010570) \| 0.106454 / 0.014526 (0.091928) \| 0.118371 / 0.176557 (-0.058186) \| 0.155545 / 0.737135 (-0.581590) \| 0.125119 / 0.296338 (-0.171220) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.395794 / 0.215209 (0.180585) \| 3.958195 / 2.077655 (1.880540) \| 1.789010 / 1.504120 (0.284890) \| 1.601380 / 1.541195 (0.060186) \| 1.641062 / 1.468490 (0.172572) \| 0.679547 / 4.584777 (-3.905230) \| 3.778018 / 3.745712 (0.032306) \| 2.101232 / 5.269862 (-3.168630) \| 1.463932 / 4.565676 (-3.101745) \| 0.083639 / 0.424275 (-0.340636) \| 0.012339 / 0.007607 (0.004732) \| 0.498708 / 0.226044 (0.272663) \| 4.995178 / 2.268929 (2.726249) \| 2.272650 / 55.444624 (-53.171975) \| 1.907879 / 6.876477 (-4.968598) \| 2.012666 / 2.142072 (-0.129407) \| 0.829564 / 4.805227 (-3.975663) \| 0.165049 / 6.500664 (-6.335615) \| 0.062291 / 0.075469 (-0.013178) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.193977 / 1.841788 (-0.647811) \| 14.816939 / 8.074308 (6.742631) \| 14.369729 / 10.191392 (4.178337) \| 0.156339 / 0.680424 (-0.524084) \| 0.029151 / 0.534201 (-0.505050) \| 0.449362 / 0.579283 (-0.129921) \| 0.451895 / 0.434364 (0.017531) \| 0.520324 / 0.540337 (-0.020013) \| 0.610716 / 1.386936 (-0.776220) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007145 / 0.011353 (-0.004207) \| 0.005299 / 0.011008 (-0.005710) \| 0.074216 / 0.038508 (0.035708) \| 0.033015 / 0.023109 (0.009906) \| 0.337117 / 0.275898 (0.061219) \| 0.367161 / 0.323480 (0.043682) \| 0.005898 / 0.007986 (-0.002088) \| 0.005283 / 0.004328 (0.000955) \| 0.073795 / 0.004250 (0.069544) \| 0.049253 / 0.037052 (0.012201) \| 0.343327 / 0.258489 (0.084838) \| 0.396417 / 0.293841 (0.102576) \| 0.037162 / 0.128546 (-0.091384) \| 0.012456 / 0.075646 (-0.063191) \| 0.086668 / 0.419271 (-0.332604) \| 0.049937 / 0.043533 (0.006404) \| 0.335138 / 0.255139 (0.079999) \| 0.358111 / 0.283200 (0.074912) \| 0.107328 / 0.141683 (-0.034355) \| 1.482290 / 1.452155 (0.030135) \| 1.557872 / 1.492716 (0.065156) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.343759 / 0.018006 (0.325752) \| 0.542697 / 0.000490 (0.542207) \| 0.025943 / 0.000200 (0.025743) \| 0.000264 / 0.000054 (0.000209) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.028469 / 0.037411 (-0.008943) \| 0.108620 / 0.014526 (0.094094) \| 0.123667 / 0.176557 (-0.052890) \| 0.168829 / 0.737135 (-0.568306) \| 0.125875 / 0.296338 (-0.170464) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.424640 / 0.215209 (0.209431) \| 4.227611 / 2.077655 (2.149956) \| 2.003605 / 1.504120 (0.499486) \| 1.810696 / 1.541195 (0.269501) \| 1.882700 / 1.468490 (0.414210) \| 0.701361 / 4.584777 (-3.883416) \| 3.808054 / 3.745712 (0.062342) \| 3.234896 / 5.269862 (-2.034966) \| 1.872195 / 4.565676 (-2.693482) \| 0.088102 / 0.424275 (-0.336173) \| 0.012810 / 0.007607 (0.005203) \| 0.551855 / 0.226044 (0.325810) \| 5.245654 / 2.268929 (2.976725) \| 2.557123 / 55.444624 (-52.887502) \| 2.238897 / 6.876477 (-4.637580) \| 2.256260 / 2.142072 (0.114187) \| 0.849804 / 4.805227 (-3.955424) \| 0.170557 / 6.500664 (-6.330107) \| 0.064718 / 0.075469 (-0.010751) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.271701 / 1.841788 (-0.570087) \| 14.925010 / 8.074308 (6.850702) \| 14.966948 / 10.191392 (4.775556) \| 0.162966 / 0.680424 (-0.517458) \| 0.017618 / 0.534201 (-0.516583) \| 0.433484 / 0.579283 (-0.145799) \| 0.430047 / 0.434364 (-0.004316) \| 0.537356 / 0.540337 (-0.002981) \| 0.639237 / 1.386936 (-0.747699) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#aba888cb4d225b1a05596f52258a079bda98df70 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.012054 / 0.011353 (0.000702) \| 0.005923 / 0.011008 (-0.005085) \| 0.129531 / 0.038508 (0.091023) \| 0.036283 / 0.023109 (0.013173) \| 0.374406 / 0.275898 (0.098508) \| 0.452538 / 0.323480 (0.129058) \| 0.009419 / 0.007986 (0.001434) \| 0.004783 / 0.004328 (0.000454) \| 0.095292 / 0.004250 (0.091042) \| 0.041290 / 0.037052 (0.004238) \| 0.403940 / 0.258489 (0.145451) \| 0.443091 / 0.293841 (0.149250) \| 0.054635 / 0.128546 (-0.073911) \| 0.019062 / 0.075646 (-0.056584) \| 0.417053 / 0.419271 (-0.002218) \| 0.060865 / 0.043533 (0.017332) \| 0.378535 / 0.255139 (0.123396) \| 0.401036 / 0.283200 (0.117836) \| 0.122959 / 0.141683 (-0.018724) \| 1.768517 / 1.452155 (0.316362) \| 1.794700 / 1.492716 (0.301984) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.246529 / 0.018006 (0.228523) \| 0.576887 / 0.000490 (0.576397) \| 0.005031 / 0.000200 (0.004831) \| 0.000125 / 0.000054 (0.000070) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027363 / 0.037411 (-0.010049) \| 0.119037 / 0.014526 (0.104511) \| 0.148109 / 0.176557 (-0.028447) \| 0.179370 / 0.737135 (-0.557765) \| 0.145105 / 0.296338 (-0.151234) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.588748 / 0.215209 (0.373539) \| 5.934433 / 2.077655 (3.856778) \| 2.549811 / 1.504120 (1.045691) \| 2.234616 / 1.541195 (0.693421) \| 2.268002 / 1.468490 (0.799512) \| 1.154643 / 4.584777 (-3.430134) \| 5.333935 / 3.745712 (1.588223) \| 2.971065 / 5.269862 (-2.298796) \| 2.131427 / 4.565676 (-2.434250) \| 0.127737 / 0.424275 (-0.296538) \| 0.014699 / 0.007607 (0.007091) \| 0.735160 / 0.226044 (0.509115) \| 7.403838 / 2.268929 (5.134909) \| 3.298169 / 55.444624 (-52.146455) \| 2.661285 / 6.876477 (-4.215192) \| 2.688877 / 2.142072 (0.546805) \| 1.344110 / 4.805227 (-3.461118) \| 0.242016 / 6.500664 (-6.258648) \| 0.077418 / 0.075469 (0.001948) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.566426 / 1.841788 (-0.275362) \| 17.144308 / 8.074308 (9.070000) \| 19.360598 / 10.191392 (9.169206) \| 0.238554 / 0.680424 (-0.441870) \| 0.044946 / 0.534201 (-0.489255) \| 0.554183 / 0.579283 (-0.025100) \| 0.630175 / 0.434364 (0.195811) \| 0.630319 / 0.540337 (0.089982) \| 0.745060 / 1.386936 (-0.641876) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009255 / 0.011353 (-0.002098) \| 0.006951 / 0.011008 (-0.004057) \| 0.092021 / 0.038508 (0.053513) \| 0.035588 / 0.023109 (0.012479) \| 0.415564 / 0.275898 (0.139666) \| 0.446393 / 0.323480 (0.122913) \| 0.006532 / 0.007986 (-0.001453) \| 0.005099 / 0.004328 (0.000771) \| 0.094801 / 0.004250 (0.090550) \| 0.044926 / 0.037052 (0.007874) \| 0.439125 / 0.258489 (0.180636) \| 0.473004 / 0.293841 (0.179163) \| 0.057025 / 0.128546 (-0.071522) \| 0.018711 / 0.075646 (-0.056935) \| 0.110844 / 0.419271 (-0.308427) \| 0.058347 / 0.043533 (0.014814) \| 0.435721 / 0.255139 (0.180583) \| 0.434624 / 0.283200 (0.151424) \| 0.114505 / 0.141683 (-0.027178) \| 1.722379 / 1.452155 (0.270225) \| 1.775836 / 1.492716 (0.283120) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.275893 / 0.018006 (0.257887) \| 0.552590 / 0.000490 (0.552100) \| 0.007919 / 0.000200 (0.007719) \| 0.000122 / 0.000054 (0.000068) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030003 / 0.037411 (-0.007408) \| 0.130145 / 0.014526 (0.115619) \| 0.131878 / 0.176557 (-0.044678) \| 0.194693 / 0.737135 (-0.542442) \| 0.137689 / 0.296338 (-0.158650) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.619591 / 0.215209 (0.404382) \| 6.324095 / 2.077655 (4.246441) \| 2.756563 / 1.504120 (1.252444) \| 2.384744 / 1.541195 (0.843549) \| 2.450407 / 1.468490 (0.981917) \| 1.235391 / 4.584777 (-3.349386) \| 5.535383 / 3.745712 (1.789671) \| 4.831927 / 5.269862 (-0.437934) \| 2.757158 / 4.565676 (-1.808519) \| 0.133980 / 0.424275 (-0.290295) \| 0.014965 / 0.007607 (0.007358) \| 0.731423 / 0.226044 (0.505379) \| 7.401850 / 2.268929 (5.132921) \| 3.346585 / 55.444624 (-52.098039) \| 2.705523 / 6.876477 (-4.170953) \| 2.637397 / 2.142072 (0.495324) \| 1.347745 / 4.805227 (-3.457482) \| 0.248658 / 6.500664 (-6.252006) \| 0.077427 / 0.075469 (0.001958) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.520860 / 1.841788 (-0.320928) \| 17.153000 / 8.074308 (9.078692) \| 19.051393 / 10.191392 (8.860001) \| 0.236840 / 0.680424 (-0.443584) \| 0.026638 / 0.534201 (-0.507563) \| 0.518417 / 0.579283 (-0.060866) \| 0.607555 / 0.434364 (0.173191) \| 0.637381 / 0.540337 (0.097044) \| 0.767109 / 1.386936 (-0.619827) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5ee291f2c5e68a782c82f916e250d470a7e285e7 \"CML watermark\")\n", "Great, I merged it. Thanks for the review :)", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006711 / 0.011353 (-0.004641) \| 0.004472 / 0.011008 (-0.006536) \| 0.099581 / 0.038508 (0.061073) \| 0.028036 / 0.023109 (0.004927) \| 0.301197 / 0.275898 (0.025298) \| 0.339341 / 0.323480 (0.015861) \| 0.005107 / 0.007986 (-0.002879) \| 0.003312 / 0.004328 (-0.001017) \| 0.075823 / 0.004250 (0.071573) \| 0.040861 / 0.037052 (0.003809) \| 0.303407 / 0.258489 (0.044918) \| 0.350717 / 0.293841 (0.056876) \| 0.031657 / 0.128546 (-0.096889) \| 0.011627 / 0.075646 (-0.064020) \| 0.325465 / 0.419271 (-0.093806) \| 0.052671 / 0.043533 (0.009138) \| 0.301953 / 0.255139 (0.046814) \| 0.327164 / 0.283200 (0.043964) \| 0.091264 / 0.141683 (-0.050419) \| 1.508947 / 1.452155 (0.056792) \| 1.605685 / 1.492716 (0.112968) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.202977 / 0.018006 (0.184971) \| 0.400602 / 0.000490 (0.400112) \| 0.003253 / 0.000200 (0.003053) \| 0.000080 / 0.000054 (0.000025) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022453 / 0.037411 (-0.014958) \| 0.098633 / 0.014526 (0.084107) \| 0.105996 / 0.176557 (-0.070561) \| 0.162428 / 0.737135 (-0.574707) \| 0.107139 / 0.296338 (-0.189199) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.453061 / 0.215209 (0.237852) \| 4.530844 / 2.077655 (2.453190) \| 2.286394 / 1.504120 (0.782274) \| 2.076479 / 1.541195 (0.535284) \| 2.143730 / 1.468490 (0.675240) \| 0.702540 / 4.584777 (-3.882237) \| 3.442688 / 3.745712 (-0.303024) \| 1.874429 / 5.269862 (-3.395433) \| 1.172331 / 4.565676 (-3.393346) \| 0.083643 / 0.424275 (-0.340632) \| 0.012519 / 0.007607 (0.004911) \| 0.556859 / 0.226044 (0.330814) \| 5.582843 / 2.268929 (3.313915) \| 2.753734 / 55.444624 (-52.690890) \| 2.415771 / 6.876477 (-4.460705) \| 2.531428 / 2.142072 (0.389356) \| 0.813005 / 4.805227 (-3.992222) \| 0.153322 / 6.500664 (-6.347343) \| 0.068061 / 0.075469 (-0.007408) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.180481 / 1.841788 (-0.661306) \| 13.623933 / 8.074308 (5.549625) \| 14.431288 / 10.191392 (4.239896) \| 0.127580 / 0.680424 (-0.552844) \| 0.016714 / 0.534201 (-0.517487) \| 0.394236 / 0.579283 (-0.185047) \| 0.381718 / 0.434364 (-0.052646) \| 0.486749 / 0.540337 (-0.053589) \| 0.565939 / 1.386936 (-0.820997) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006720 / 0.011353 (-0.004633) \| 0.004518 / 0.011008 (-0.006491) \| 0.076819 / 0.038508 (0.038311) \| 0.027272 / 0.023109 (0.004163) \| 0.340890 / 0.275898 (0.064992) \| 0.381435 / 0.323480 (0.057955) \| 0.004980 / 0.007986 (-0.003005) \| 0.003382 / 0.004328 (-0.000947) \| 0.076368 / 0.004250 (0.072117) \| 0.037365 / 0.037052 (0.000313) \| 0.341484 / 0.258489 (0.082995) \| 0.388917 / 0.293841 (0.095076) \| 0.032004 / 0.128546 (-0.096543) \| 0.011612 / 0.075646 (-0.064034) \| 0.084929 / 0.419271 (-0.334342) \| 0.041861 / 0.043533 (-0.001671) \| 0.350392 / 0.255139 (0.095253) \| 0.369745 / 0.283200 (0.086546) \| 0.088301 / 0.141683 (-0.053382) \| 1.587296 / 1.452155 (0.135141) \| 1.629761 / 1.492716 (0.137045) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.174825 / 0.018006 (0.156818) \| 0.414371 / 0.000490 (0.413881) \| 0.001595 / 0.000200 (0.001395) \| 0.000078 / 0.000054 (0.000023) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025403 / 0.037411 (-0.012009) \| 0.099593 / 0.014526 (0.085067) \| 0.108819 / 0.176557 (-0.067738) \| 0.161613 / 0.737135 (-0.575523) \| 0.112302 / 0.296338 (-0.184037) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.439234 / 0.215209 (0.224024) \| 4.389073 / 2.077655 (2.311418) \| 2.063215 / 1.504120 (0.559095) \| 1.852550 / 1.541195 (0.311356) \| 1.920014 / 1.468490 (0.451524) \| 0.710255 / 4.584777 (-3.874522) \| 3.430549 / 3.745712 (-0.315164) \| 1.886072 / 5.269862 (-3.383790) \| 1.177490 / 4.565676 (-3.388186) \| 0.084877 / 0.424275 (-0.339398) \| 0.012894 / 0.007607 (0.005287) \| 0.544950 / 0.226044 (0.318906) \| 5.467347 / 2.268929 (3.198419) \| 2.508169 / 55.444624 (-52.936455) \| 2.167756 / 6.876477 (-4.708721) \| 2.212817 / 2.142072 (0.070744) \| 0.824762 / 4.805227 (-3.980465) \| 0.154387 / 6.500664 (-6.346277) \| 0.068535 / 0.075469 (-0.006934) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.284165 / 1.841788 (-0.557623) \| 14.153006 / 8.074308 (6.078697) \| 14.152569 / 10.191392 (3.961177) \| 0.130083 / 0.680424 (-0.550341) \| 0.016556 / 0.534201 (-0.517645) \| 0.383828 / 0.579283 (-0.195455) \| 0.388241 / 0.434364 (-0.046123) \| 0.477982 / 0.540337 (-0.062355) \| 0.565583 / 1.386936 (-0.821353) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f1e7442d34a059ff377437381542cc762feab057 \"CML watermark\")\n" ]	2023-03-09T08:46:01Z	2023-03-09T15:39:00Z	2023-03-09T15:31:59Z	CONTRIBUTOR	null	null	null	`set_access_token` is deprecated and will be removed in `huggingface_hub>=0.14`. This PR removes it from the tests (it was not used in `datasets` source code itself). FYI, it was not needed since `set_access_token` was just setting git credentials and `datasets` doesn't seem to use git anywhere. In the future, use `set_git_credential` if needed. It is a git-credential-agnostic helper, i.e. you can store your git token in `git-credential-cache`, `git-credential-store`, `osxkeychain`, etc. The legacy `set_access_token` could only set in `git-credential-store` no matter the user preference. (for context, I found out about this while working on https://github.com/huggingface/huggingface_hub/pull/1381) --- In addition to this, I have added ``` filterwarnings = error::FutureWarning:huggingface_hub* ``` to the `setup.cfg` config file to fail on future warnings from `huggingface_hub`. In `hfh`'s CI we trigger on FutureWarning from any package but it's less robust (any package update leads can lead to a failure). No obligation to keep it like that (I can remove it if you prefer) but I think it's a good idea in order to track future FutureWarnings. FYI, in `huggingface_hub` tests we use `-Werror::FutureWarning --log-cli-level=INFO -sv --durations=0` - FutureWarning are processed as error - verbose mode / INFO logs (and above) are captured for easier debugging in github report - track each test duration, just to see where we can improve. We have a quite long CI (~10min) so it helped improve that.	{ "avatar_url": "https://avatars.githubusercontent.com/u/11801849?v=4", "events_url": "https://api.github.com/users/Wauplin/events{/privacy}", "followers_url": "https://api.github.com/users/Wauplin/followers", "following_url": "https://api.github.com/users/Wauplin/following{/other_user}", "gists_url": "https://api.github.com/users/Wauplin/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/Wauplin", "id": 11801849, "login": "Wauplin", "node_id": "MDQ6VXNlcjExODAxODQ5", "organizations_url": "https://api.github.com/users/Wauplin/orgs", "received_events_url": "https://api.github.com/users/Wauplin/received_events", "repos_url": "https://api.github.com/users/Wauplin/repos", "site_admin": false, "starred_url": "https://api.github.com/users/Wauplin/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/Wauplin/subscriptions", "type": "User", "url": "https://api.github.com/users/Wauplin", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5623/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5623/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/5623.diff", "html_url": "https://github.com/huggingface/datasets/pull/5623", "merged_at": "2023-03-09T15:31:58Z", "patch_url": "https://github.com/huggingface/datasets/pull/5623.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5623" }
https://api.github.com/repos/huggingface/datasets/issues/6298	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6298/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6298/comments	https://api.github.com/repos/huggingface/datasets/issues/6298/events	https://github.com/huggingface/datasets/pull/6298	1,938,797,389	PR_kwDODunzps5ckg6j	6,298	Doc readme improvements	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006761 / 0.011353 (-0.004592) \| 0.004307 / 0.011008 (-0.006701) \| 0.084682 / 0.038508 (0.046174) \| 0.083994 / 0.023109 (0.060885) \| 0.316612 / 0.275898 (0.040714) \| 0.346157 / 0.323480 (0.022678) \| 0.004490 / 0.007986 (-0.003495) \| 0.003699 / 0.004328 (-0.000629) \| 0.066144 / 0.004250 (0.061894) \| 0.057958 / 0.037052 (0.020906) \| 0.319018 / 0.258489 (0.060529) \| 0.367597 / 0.293841 (0.073756) \| 0.031146 / 0.128546 (-0.097401) \| 0.008814 / 0.075646 (-0.066832) \| 0.290971 / 0.419271 (-0.128301) \| 0.052769 / 0.043533 (0.009236) \| 0.313125 / 0.255139 (0.057986) \| 0.330473 / 0.283200 (0.047273) \| 0.025922 / 0.141683 (-0.115760) \| 1.494989 / 1.452155 (0.042834) \| 1.556140 / 1.492716 (0.063423) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.310580 / 0.018006 (0.292574) \| 0.563600 / 0.000490 (0.563110) \| 0.012344 / 0.000200 (0.012144) \| 0.000382 / 0.000054 (0.000328) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031468 / 0.037411 (-0.005943) \| 0.084856 / 0.014526 (0.070331) \| 0.101371 / 0.176557 (-0.075186) \| 0.158735 / 0.737135 (-0.578400) \| 0.102451 / 0.296338 (-0.193888) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.402288 / 0.215209 (0.187079) \| 4.001351 / 2.077655 (1.923696) \| 2.022710 / 1.504120 (0.518590) \| 1.850236 / 1.541195 (0.309041) \| 1.946779 / 1.468490 (0.478289) \| 0.485828 / 4.584777 (-4.098949) \| 3.584925 / 3.745712 (-0.160787) \| 3.400815 / 5.269862 (-1.869046) \| 2.123187 / 4.565676 (-2.442490) \| 0.057373 / 0.424275 (-0.366902) \| 0.007383 / 0.007607 (-0.000224) \| 0.479773 / 0.226044 (0.253729) \| 4.805342 / 2.268929 (2.536414) \| 2.530151 / 55.444624 (-52.914473) \| 2.190136 / 6.876477 (-4.686341) \| 2.463666 / 2.142072 (0.321593) \| 0.583512 / 4.805227 (-4.221715) \| 0.134205 / 6.500664 (-6.366459) \| 0.062021 / 0.075469 (-0.013448) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.239532 / 1.841788 (-0.602255) \| 20.252941 / 8.074308 (12.178633) \| 14.265697 / 10.191392 (4.074305) \| 0.158745 / 0.680424 (-0.521679) \| 0.018605 / 0.534201 (-0.515596) \| 0.394246 / 0.579283 (-0.185037) \| 0.415260 / 0.434364 (-0.019104) \| 0.462636 / 0.540337 (-0.077701) \| 0.645318 / 1.386936 (-0.741618) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007063 / 0.011353 (-0.004290) \| 0.004388 / 0.011008 (-0.006621) \| 0.064997 / 0.038508 (0.026489) \| 0.085135 / 0.023109 (0.062026) \| 0.424349 / 0.275898 (0.148451) \| 0.456033 / 0.323480 (0.132553) \| 0.005745 / 0.007986 (-0.002241) \| 0.003705 / 0.004328 (-0.000624) \| 0.065835 / 0.004250 (0.061585) \| 0.058366 / 0.037052 (0.021314) \| 0.421654 / 0.258489 (0.163165) \| 0.460334 / 0.293841 (0.166493) \| 0.032828 / 0.128546 (-0.095718) \| 0.008974 / 0.075646 (-0.066673) \| 0.072524 / 0.419271 (-0.346747) \| 0.048558 / 0.043533 (0.005025) \| 0.413546 / 0.255139 (0.158407) \| 0.435765 / 0.283200 (0.152565) \| 0.023754 / 0.141683 (-0.117929) \| 1.476884 / 1.452155 (0.024730) \| 1.560011 / 1.492716 (0.067294) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.318279 / 0.018006 (0.300272) \| 0.544990 / 0.000490 (0.544501) \| 0.007118 / 0.000200 (0.006918) \| 0.000097 / 0.000054 (0.000043) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033352 / 0.037411 (-0.004059) \| 0.092921 / 0.014526 (0.078395) \| 0.109028 / 0.176557 (-0.067528) \| 0.161433 / 0.737135 (-0.575703) \| 0.108445 / 0.296338 (-0.187893) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.438925 / 0.215209 (0.223716) \| 4.400714 / 2.077655 (2.323059) \| 2.403727 / 1.504120 (0.899607) \| 2.236472 / 1.541195 (0.695277) \| 2.319219 / 1.468490 (0.850729) \| 0.490159 / 4.584777 (-4.094618) \| 3.647474 / 3.745712 (-0.098238) \| 3.433144 / 5.269862 (-1.836718) \| 2.145367 / 4.565676 (-2.420310) \| 0.057994 / 0.424275 (-0.366281) \| 0.007452 / 0.007607 (-0.000155) \| 0.513808 / 0.226044 (0.287763) \| 5.130792 / 2.268929 (2.861863) \| 2.861691 / 55.444624 (-52.582934) \| 2.473292 / 6.876477 (-4.403185) \| 2.756445 / 2.142072 (0.614372) \| 0.586783 / 4.805227 (-4.218444) \| 0.134170 / 6.500664 (-6.366494) \| 0.061149 / 0.075469 (-0.014320) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.350144 / 1.841788 (-0.491644) \| 21.003528 / 8.074308 (12.929220) \| 15.174314 / 10.191392 (4.982922) \| 0.186535 / 0.680424 (-0.493888) \| 0.020821 / 0.534201 (-0.513380) \| 0.399210 / 0.579283 (-0.180073) \| 0.431942 / 0.434364 (-0.002422) \| 0.475395 / 0.540337 (-0.064942) \| 0.677457 / 1.386936 (-0.709479) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6aa5fc278324a253eab43ad1bc048e822e4ae5c7 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007062 / 0.011353 (-0.004291) \| 0.004299 / 0.011008 (-0.006710) \| 0.086019 / 0.038508 (0.047511) \| 0.085166 / 0.023109 (0.062057) \| 0.355804 / 0.275898 (0.079906) \| 0.381056 / 0.323480 (0.057577) \| 0.005500 / 0.007986 (-0.002486) \| 0.003496 / 0.004328 (-0.000833) \| 0.064866 / 0.004250 (0.060615) \| 0.057399 / 0.037052 (0.020346) \| 0.357914 / 0.258489 (0.099425) \| 0.395387 / 0.293841 (0.101546) \| 0.031763 / 0.128546 (-0.096784) \| 0.008665 / 0.075646 (-0.066981) \| 0.290097 / 0.419271 (-0.129175) \| 0.053297 / 0.043533 (0.009765) \| 0.355659 / 0.255139 (0.100520) \| 0.378232 / 0.283200 (0.095032) \| 0.026015 / 0.141683 (-0.115668) \| 1.437121 / 1.452155 (-0.015034) \| 1.538798 / 1.492716 (0.046082) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.243518 / 0.018006 (0.225511) \| 0.461361 / 0.000490 (0.460871) \| 0.009529 / 0.000200 (0.009329) \| 0.000473 / 0.000054 (0.000419) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030379 / 0.037411 (-0.007032) \| 0.089851 / 0.014526 (0.075325) \| 0.098278 / 0.176557 (-0.078278) \| 0.157077 / 0.737135 (-0.580058) \| 0.098997 / 0.296338 (-0.197341) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.382415 / 0.215209 (0.167206) \| 3.801964 / 2.077655 (1.724309) \| 1.887680 / 1.504120 (0.383560) \| 1.775903 / 1.541195 (0.234709) \| 1.851338 / 1.468490 (0.382848) \| 0.483616 / 4.584777 (-4.101161) \| 3.612977 / 3.745712 (-0.132736) \| 3.397700 / 5.269862 (-1.872162) \| 2.114572 / 4.565676 (-2.451105) \| 0.057250 / 0.424275 (-0.367025) \| 0.007362 / 0.007607 (-0.000245) \| 0.456873 / 0.226044 (0.230829) \| 4.567319 / 2.268929 (2.298391) \| 2.399476 / 55.444624 (-53.045148) \| 2.054542 / 6.876477 (-4.821935) \| 2.343432 / 2.142072 (0.201359) \| 0.582319 / 4.805227 (-4.222908) \| 0.134045 / 6.500664 (-6.366619) \| 0.062726 / 0.075469 (-0.012743) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.283390 / 1.841788 (-0.558398) \| 20.358511 / 8.074308 (12.284202) \| 14.933989 / 10.191392 (4.742597) \| 0.164960 / 0.680424 (-0.515464) \| 0.018625 / 0.534201 (-0.515576) \| 0.394087 / 0.579283 (-0.185196) \| 0.416761 / 0.434364 (-0.017603) \| 0.466669 / 0.540337 (-0.073669) \| 0.643161 / 1.386936 (-0.743775) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007141 / 0.011353 (-0.004212) \| 0.004185 / 0.011008 (-0.006824) \| 0.066097 / 0.038508 (0.027588) \| 0.088436 / 0.023109 (0.065327) \| 0.401189 / 0.275898 (0.125291) \| 0.440402 / 0.323480 (0.116922) \| 0.005729 / 0.007986 (-0.002257) \| 0.003527 / 0.004328 (-0.000801) \| 0.065278 / 0.004250 (0.061027) \| 0.060866 / 0.037052 (0.023813) \| 0.407035 / 0.258489 (0.148546) \| 0.443923 / 0.293841 (0.150083) \| 0.032922 / 0.128546 (-0.095625) \| 0.008739 / 0.075646 (-0.066907) \| 0.071800 / 0.419271 (-0.347472) \| 0.048994 / 0.043533 (0.005461) \| 0.403736 / 0.255139 (0.148597) \| 0.419566 / 0.283200 (0.136366) \| 0.025369 / 0.141683 (-0.116314) \| 1.474980 / 1.452155 (0.022825) \| 1.553500 / 1.492716 (0.060784) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.225224 / 0.018006 (0.207218) \| 0.462891 / 0.000490 (0.462401) \| 0.006958 / 0.000200 (0.006758) \| 0.000163 / 0.000054 (0.000108) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034431 / 0.037411 (-0.002980) \| 0.100021 / 0.014526 (0.085495) \| 0.108339 / 0.176557 (-0.068217) \| 0.162762 / 0.737135 (-0.574374) \| 0.108951 / 0.296338 (-0.187388) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.435966 / 0.215209 (0.220757) \| 4.351744 / 2.077655 (2.274089) \| 2.372307 / 1.504120 (0.868187) \| 2.192146 / 1.541195 (0.650951) \| 2.326839 / 1.468490 (0.858349) \| 0.488292 / 4.584777 (-4.096485) \| 3.745227 / 3.745712 (-0.000485) \| 3.456306 / 5.269862 (-1.813556) \| 2.159771 / 4.565676 (-2.405906) \| 0.057953 / 0.424275 (-0.366322) \| 0.007469 / 0.007607 (-0.000138) \| 0.515116 / 0.226044 (0.289071) \| 5.162871 / 2.268929 (2.893942) \| 2.850336 / 55.444624 (-52.594288) \| 2.514700 / 6.876477 (-4.361777) \| 2.748843 / 2.142072 (0.606770) \| 0.587687 / 4.805227 (-4.217540) \| 0.134333 / 6.500664 (-6.366331) \| 0.062097 / 0.075469 (-0.013372) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.377082 / 1.841788 (-0.464705) \| 21.103127 / 8.074308 (13.028819) \| 15.325275 / 10.191392 (5.133883) \| 0.166225 / 0.680424 (-0.514199) \| 0.020472 / 0.534201 (-0.513729) \| 0.395866 / 0.579283 (-0.183417) \| 0.444964 / 0.434364 (0.010600) \| 0.475367 / 0.540337 (-0.064970) \| 0.693325 / 1.386936 (-0.693611) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#79b5bbbd52ffd90dd958c05b333d7c90a03756cc \"CML watermark\")\n" ]	2023-10-11T21:51:12Z	2023-10-12T12:47:15Z	2023-10-12T12:38:19Z	COLLABORATOR	null	null	null	Changes in the doc READMe: * adds two new sections (to be aligned with `transformers` and `hfh`): "Previewing the documentation" and "Writing documentation examples" * replaces the mentions of `transformers` with `datasets` * fixes some dead links	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": 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"rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6298/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6298/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6298.diff", "html_url": "https://github.com/huggingface/datasets/pull/6298", "merged_at": "2023-10-12T12:38:19Z", "patch_url": "https://github.com/huggingface/datasets/pull/6298.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6298" }
https://api.github.com/repos/huggingface/datasets/issues/6516	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6516/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6516/comments	https://api.github.com/repos/huggingface/datasets/issues/6516/events	https://github.com/huggingface/datasets/pull/6516	2,050,033,322	PR_kwDODunzps5icYX0	6,516	Support huggingface-hub pre-releases	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6516). 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) \| 0.005309 / 0.011353 (-0.006044) \| 0.003231 / 0.011008 (-0.007777) \| 0.062690 / 0.038508 (0.024182) \| 0.050811 / 0.023109 (0.027701) \| 0.258319 / 0.275898 (-0.017579) \| 0.275977 / 0.323480 (-0.047503) \| 0.002842 / 0.007986 (-0.005143) \| 0.002606 / 0.004328 (-0.001723) \| 0.048672 / 0.004250 (0.044421) \| 0.038730 / 0.037052 (0.001677) \| 0.258531 / 0.258489 (0.000042) \| 0.289327 / 0.293841 (-0.004514) \| 0.027994 / 0.128546 (-0.100552) \| 0.010446 / 0.075646 (-0.065200) \| 0.207152 / 0.419271 (-0.212119) \| 0.035839 / 0.043533 (-0.007693) \| 0.258416 / 0.255139 (0.003277) \| 0.274348 / 0.283200 (-0.008851) \| 0.019661 / 0.141683 (-0.122022) \| 1.103688 / 1.452155 (-0.348466) \| 1.207711 / 1.492716 (-0.285006) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.090693 / 0.018006 (0.072687) \| 0.300648 / 0.000490 (0.300158) \| 0.000215 / 0.000200 (0.000015) \| 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) \| 0.018589 / 0.037411 (-0.018822) \| 0.061056 / 0.014526 (0.046530) \| 0.074512 / 0.176557 (-0.102044) \| 0.121260 / 0.737135 (-0.615875) \| 0.073111 / 0.296338 (-0.223227) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.285811 / 0.215209 (0.070602) \| 2.785081 / 2.077655 (0.707426) \| 1.469493 / 1.504120 (-0.034627) \| 1.346389 / 1.541195 (-0.194806) \| 1.391866 / 1.468490 (-0.076624) \| 0.567304 / 4.584777 (-4.017473) \| 2.407150 / 3.745712 (-1.338562) \| 2.809915 / 5.269862 (-2.459946) \| 1.741185 / 4.565676 (-2.824491) \| 0.063073 / 0.424275 (-0.361202) \| 0.004974 / 0.007607 (-0.002633) \| 0.336431 / 0.226044 (0.110386) \| 3.331371 / 2.268929 (1.062443) \| 1.841466 / 55.444624 (-53.603159) \| 1.559065 / 6.876477 (-5.317411) \| 1.585033 / 2.142072 (-0.557039) \| 0.647469 / 4.805227 (-4.157759) \| 0.117488 / 6.500664 (-6.383176) \| 0.042535 / 0.075469 (-0.032934) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.936409 / 1.841788 (-0.905379) \| 11.301514 / 8.074308 (3.227206) \| 10.500465 / 10.191392 (0.309073) \| 0.131316 / 0.680424 (-0.549107) \| 0.014007 / 0.534201 (-0.520194) \| 0.286932 / 0.579283 (-0.292351) \| 0.263516 / 0.434364 (-0.170848) \| 0.340883 / 0.540337 (-0.199454) \| 0.443589 / 1.386936 (-0.943347) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005204 / 0.011353 (-0.006149) \| 0.003472 / 0.011008 (-0.007536) \| 0.049235 / 0.038508 (0.010727) \| 0.050668 / 0.023109 (0.027559) \| 0.270198 / 0.275898 (-0.005700) \| 0.293942 / 0.323480 (-0.029538) \| 0.003964 / 0.007986 (-0.004022) \| 0.002596 / 0.004328 (-0.001733) \| 0.048654 / 0.004250 (0.044404) \| 0.039411 / 0.037052 (0.002358) \| 0.271938 / 0.258489 (0.013449) \| 0.304308 / 0.293841 (0.010467) \| 0.029042 / 0.128546 (-0.099504) \| 0.010414 / 0.075646 (-0.065232) \| 0.058273 / 0.419271 (-0.360999) \| 0.032507 / 0.043533 (-0.011025) \| 0.271671 / 0.255139 (0.016532) \| 0.289850 / 0.283200 (0.006650) \| 0.017292 / 0.141683 (-0.124391) \| 1.126160 / 1.452155 (-0.325995) \| 1.177365 / 1.492716 (-0.315351) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091158 / 0.018006 (0.073152) \| 0.299143 / 0.000490 (0.298653) \| 0.000217 / 0.000200 (0.000017) \| 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) \| 0.022558 / 0.037411 (-0.014853) \| 0.076139 / 0.014526 (0.061613) \| 0.088344 / 0.176557 (-0.088212) \| 0.126640 / 0.737135 (-0.610495) \| 0.089736 / 0.296338 (-0.206602) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.295351 / 0.215209 (0.080142) \| 2.895779 / 2.077655 (0.818125) \| 1.585886 / 1.504120 (0.081766) \| 1.458601 / 1.541195 (-0.082594) \| 1.468880 / 1.468490 (0.000390) \| 0.554686 / 4.584777 (-4.030091) \| 2.466276 / 3.745712 (-1.279437) \| 2.741938 / 5.269862 (-2.527924) \| 1.711793 / 4.565676 (-2.853883) \| 0.062928 / 0.424275 (-0.361347) \| 0.005177 / 0.007607 (-0.002430) \| 0.343908 / 0.226044 (0.117863) \| 3.393360 / 2.268929 (1.124431) \| 1.928800 / 55.444624 (-53.515824) \| 1.652181 / 6.876477 (-5.224296) \| 1.643667 / 2.142072 (-0.498405) \| 0.632829 / 4.805227 (-4.172398) \| 0.114583 / 6.500664 (-6.386081) \| 0.041248 / 0.075469 (-0.034221) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.986196 / 1.841788 (-0.855592) \| 12.006772 / 8.074308 (3.932464) \| 10.522661 / 10.191392 (0.331269) \| 0.133710 / 0.680424 (-0.546713) \| 0.016714 / 0.534201 (-0.517487) \| 0.286502 / 0.579283 (-0.292781) \| 0.280090 / 0.434364 (-0.154273) \| 0.326063 / 0.540337 (-0.214275) \| 0.548485 / 1.386936 (-0.838452) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3f149204a2a5948287adcade5e90707aa5207a92 \"CML watermark\")\n" ]	2023-12-20T07:52:29Z	2023-12-20T08:51:34Z	2023-12-20T08:44:44Z	MEMBER	null	null	null	Support `huggingface-hub` pre-releases. This way we will have our CI green when testing `huggingface-hub` release candidates. See: https://github.com/huggingface/datasets/tree/ci-test-huggingface-hub-v0.20.0.rc1 Close #6513.	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	{ "+1": 2, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 2, "url": "https://api.github.com/repos/huggingface/datasets/issues/6516/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6516/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6516.diff", "html_url": "https://github.com/huggingface/datasets/pull/6516", "merged_at": "2023-12-20T08:44:44Z", "patch_url": "https://github.com/huggingface/datasets/pull/6516.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6516" }
https://api.github.com/repos/huggingface/datasets/issues/6286	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6286/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6286/comments	https://api.github.com/repos/huggingface/datasets/issues/6286/events	https://github.com/huggingface/datasets/pull/6286	1,932,640,128	PR_kwDODunzps5cPKNK	6,286	Create DefunctDatasetError	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009157 / 0.011353 (-0.002195) \| 0.004275 / 0.011008 (-0.006734) \| 0.099341 / 0.038508 (0.060833) \| 0.080634 / 0.023109 (0.057525) \| 0.373598 / 0.275898 (0.097700) \| 0.445048 / 0.323480 (0.121568) \| 0.006541 / 0.007986 (-0.001444) \| 0.003550 / 0.004328 (-0.000779) \| 0.071034 / 0.004250 (0.066784) \| 0.062637 / 0.037052 (0.025585) \| 0.379110 / 0.258489 (0.120621) \| 0.447896 / 0.293841 (0.154055) \| 0.047739 / 0.128546 (-0.080807) \| 0.012575 / 0.075646 (-0.063071) \| 0.332314 / 0.419271 (-0.086957) \| 0.065500 / 0.043533 (0.021967) \| 0.365919 / 0.255139 (0.110780) \| 0.438611 / 0.283200 (0.155412) \| 0.034243 / 0.141683 (-0.107440) \| 1.628034 / 1.452155 (0.175880) \| 1.802970 / 1.492716 (0.310253) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.224528 / 0.018006 (0.206522) \| 0.482094 / 0.000490 (0.481604) \| 0.012752 / 0.000200 (0.012552) \| 0.000570 / 0.000054 (0.000515) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025456 / 0.037411 (-0.011956) \| 0.082281 / 0.014526 (0.067756) \| 0.100050 / 0.176557 (-0.076506) \| 0.156931 / 0.737135 (-0.580204) \| 0.108229 / 0.296338 (-0.188110) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.560688 / 0.215209 (0.345479) \| 5.171711 / 2.077655 (3.094056) \| 2.273178 / 1.504120 (0.769058) \| 1.948158 / 1.541195 (0.406963) \| 1.879744 / 1.468490 (0.411254) \| 0.789216 / 4.584777 (-3.795561) \| 4.529370 / 3.745712 (0.783658) \| 4.008743 / 5.269862 (-1.261118) \| 2.633555 / 4.565676 (-1.932121) \| 0.085411 / 0.424275 (-0.338864) \| 0.007256 / 0.007607 (-0.000351) \| 0.623254 / 0.226044 (0.397209) \| 6.327256 / 2.268929 (4.058327) \| 2.911787 / 55.444624 (-52.532837) \| 2.240610 / 6.876477 (-4.635867) \| 2.352811 / 2.142072 (0.210738) \| 0.930114 / 4.805227 (-3.875114) \| 0.185028 / 6.500664 (-6.315636) \| 0.062115 / 0.075469 (-0.013354) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.394261 / 1.841788 (-0.447527) \| 19.689376 / 8.074308 (11.615067) \| 17.242289 / 10.191392 (7.050897) \| 0.209122 / 0.680424 (-0.471302) \| 0.027205 / 0.534201 (-0.506996) \| 0.408613 / 0.579283 (-0.170670) \| 0.503836 / 0.434364 (0.069472) \| 0.485179 / 0.540337 (-0.055158) \| 0.674333 / 1.386936 (-0.712603) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007506 / 0.011353 (-0.003847) \| 0.004683 / 0.011008 (-0.006325) \| 0.067584 / 0.038508 (0.029076) \| 0.065635 / 0.023109 (0.042525) \| 0.458814 / 0.275898 (0.182916) \| 0.477549 / 0.323480 (0.154069) \| 0.005212 / 0.007986 (-0.002774) \| 0.003393 / 0.004328 (-0.000936) \| 0.075307 / 0.004250 (0.071057) \| 0.051989 / 0.037052 (0.014937) \| 0.484229 / 0.258489 (0.225740) \| 0.470889 / 0.293841 (0.177048) \| 0.043528 / 0.128546 (-0.085018) \| 0.014685 / 0.075646 (-0.060962) \| 0.084199 / 0.419271 (-0.335073) \| 0.053970 / 0.043533 (0.010437) \| 0.432362 / 0.255139 (0.177223) \| 0.467472 / 0.283200 (0.184272) \| 0.031109 / 0.141683 (-0.110574) \| 1.525938 / 1.452155 (0.073784) \| 1.631993 / 1.492716 (0.139276) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.200196 / 0.018006 (0.182190) \| 0.479316 / 0.000490 (0.478827) \| 0.010146 / 0.000200 (0.009947) \| 0.000118 / 0.000054 (0.000063) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027911 / 0.037411 (-0.009500) \| 0.089720 / 0.014526 (0.075194) \| 0.097000 / 0.176557 (-0.079557) \| 0.157549 / 0.737135 (-0.579587) \| 0.098247 / 0.296338 (-0.198092) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.581401 / 0.215209 (0.366192) \| 5.703829 / 2.077655 (3.626174) \| 2.688272 / 1.504120 (1.184152) \| 2.321691 / 1.541195 (0.780496) \| 2.355987 / 1.468490 (0.887497) \| 0.759109 / 4.584777 (-3.825668) \| 4.711288 / 3.745712 (0.965576) \| 4.093019 / 5.269862 (-1.176843) \| 2.648240 / 4.565676 (-1.917437) \| 0.087839 / 0.424275 (-0.336436) \| 0.007060 / 0.007607 (-0.000547) \| 0.702783 / 0.226044 (0.476739) \| 6.986924 / 2.268929 (4.717996) \| 3.365970 / 55.444624 (-52.078654) \| 2.670876 / 6.876477 (-4.205600) \| 2.776431 / 2.142072 (0.634358) \| 0.920005 / 4.805227 (-3.885222) \| 0.197521 / 6.500664 (-6.303143) \| 0.069974 / 0.075469 (-0.005495) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.596947 / 1.841788 (-0.244841) \| 20.606007 / 8.074308 (12.531699) \| 18.437425 / 10.191392 (8.246033) \| 0.222445 / 0.680424 (-0.457978) \| 0.028610 / 0.534201 (-0.505591) \| 0.419748 / 0.579283 (-0.159535) \| 0.513409 / 0.434364 (0.079045) \| 0.487517 / 0.540337 (-0.052820) \| 0.706637 / 1.386936 (-0.680299) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d664439eb82d62889c21c5236a5869dae75ae779 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007744 / 0.011353 (-0.003609) \| 0.004678 / 0.011008 (-0.006330) \| 0.101243 / 0.038508 (0.062735) \| 0.085653 / 0.023109 (0.062543) \| 0.383772 / 0.275898 (0.107874) \| 0.422151 / 0.323480 (0.098671) \| 0.004566 / 0.007986 (-0.003419) \| 0.003900 / 0.004328 (-0.000429) \| 0.077778 / 0.004250 (0.073528) \| 0.063761 / 0.037052 (0.026709) \| 0.385505 / 0.258489 (0.127016) \| 0.436186 / 0.293841 (0.142345) \| 0.036172 / 0.128546 (-0.092374) \| 0.009935 / 0.075646 (-0.065711) \| 0.341434 / 0.419271 (-0.077837) \| 0.061866 / 0.043533 (0.018333) \| 0.385020 / 0.255139 (0.129881) \| 0.399455 / 0.283200 (0.116256) \| 0.029324 / 0.141683 (-0.112358) \| 1.784749 / 1.452155 (0.332594) \| 1.845926 / 1.492716 (0.353209) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.266322 / 0.018006 (0.248316) \| 0.508708 / 0.000490 (0.508218) \| 0.013680 / 0.000200 (0.013480) \| 0.000868 / 0.000054 (0.000814) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033887 / 0.037411 (-0.003525) \| 0.096709 / 0.014526 (0.082183) \| 0.109472 / 0.176557 (-0.067084) \| 0.174422 / 0.737135 (-0.562713) \| 0.110830 / 0.296338 (-0.185509) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.457533 / 0.215209 (0.242324) \| 4.615229 / 2.077655 (2.537575) \| 2.418820 / 1.504120 (0.914700) \| 2.181079 / 1.541195 (0.639884) \| 2.229164 / 1.468490 (0.760674) \| 0.554861 / 4.584777 (-4.029916) \| 4.323787 / 3.745712 (0.578075) \| 3.769396 / 5.269862 (-1.500466) \| 2.376850 / 4.565676 (-2.188826) \| 0.065030 / 0.424275 (-0.359245) \| 0.008397 / 0.007607 (0.000790) \| 0.541109 / 0.226044 (0.315065) \| 5.477540 / 2.268929 (3.208612) \| 2.957049 / 55.444624 (-52.487576) \| 2.511732 / 6.876477 (-4.364744) \| 2.703953 / 2.142072 (0.561881) \| 0.660822 / 4.805227 (-4.144405) \| 0.147035 / 6.500664 (-6.353630) \| 0.066045 / 0.075469 (-0.009424) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.526481 / 1.841788 (-0.315307) \| 22.020256 / 8.074308 (13.945948) \| 16.854566 / 10.191392 (6.663174) \| 0.192958 / 0.680424 (-0.487466) \| 0.021505 / 0.534201 (-0.512696) \| 0.462867 / 0.579283 (-0.116416) \| 0.514813 / 0.434364 (0.080449) \| 0.546147 / 0.540337 (0.005809) \| 0.767853 / 1.386936 (-0.619083) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007770 / 0.011353 (-0.003583) \| 0.004671 / 0.011008 (-0.006337) \| 0.080862 / 0.038508 (0.042354) \| 0.087049 / 0.023109 (0.063940) \| 0.479497 / 0.275898 (0.203599) \| 0.559787 / 0.323480 (0.236307) \| 0.007168 / 0.007986 (-0.000818) \| 0.003829 / 0.004328 (-0.000500) \| 0.079018 / 0.004250 (0.074768) \| 0.067359 / 0.037052 (0.030307) \| 0.516140 / 0.258489 (0.257651) \| 0.547000 / 0.293841 (0.253159) \| 0.037955 / 0.128546 (-0.090591) \| 0.010007 / 0.075646 (-0.065639) \| 0.087673 / 0.419271 (-0.331598) \| 0.059309 / 0.043533 (0.015777) \| 0.473920 / 0.255139 (0.218781) \| 0.529216 / 0.283200 (0.246017) \| 0.028236 / 0.141683 (-0.113447) \| 1.771127 / 1.452155 (0.318972) \| 1.918878 / 1.492716 (0.426162) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.242010 / 0.018006 (0.224004) \| 0.494944 / 0.000490 (0.494454) \| 0.006319 / 0.000200 (0.006119) \| 0.000111 / 0.000054 (0.000056) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.039220 / 0.037411 (0.001809) \| 0.113805 / 0.014526 (0.099279) \| 0.125704 / 0.176557 (-0.050853) \| 0.189198 / 0.737135 (-0.547937) \| 0.126334 / 0.296338 (-0.170004) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.502226 / 0.215209 (0.287017) \| 5.039133 / 2.077655 (2.961478) \| 2.782352 / 1.504120 (1.278232) \| 2.587654 / 1.541195 (1.046460) \| 2.692588 / 1.468490 (1.224098) \| 0.585672 / 4.584777 (-3.999105) \| 4.553078 / 3.745712 (0.807366) \| 3.864739 / 5.269862 (-1.405123) \| 2.536109 / 4.565676 (-2.029567) \| 0.069567 / 0.424275 (-0.354708) \| 0.008749 / 0.007607 (0.001142) \| 0.620645 / 0.226044 (0.394601) \| 6.247286 / 2.268929 (3.978357) \| 3.345293 / 55.444624 (-52.099332) \| 2.873970 / 6.876477 (-4.002507) \| 3.123190 / 2.142072 (0.981118) \| 0.687391 / 4.805227 (-4.117837) \| 0.159046 / 6.500664 (-6.341618) \| 0.071019 / 0.075469 (-0.004450) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.728724 / 1.841788 (-0.113064) \| 22.828390 / 8.074308 (14.754082) \| 17.305225 / 10.191392 (7.113833) \| 0.176571 / 0.680424 (-0.503853) \| 0.023837 / 0.534201 (-0.510364) \| 0.467935 / 0.579283 (-0.111348) \| 0.503701 / 0.434364 (0.069337) \| 0.558140 / 0.540337 (0.017803) \| 0.789326 / 1.386936 (-0.597610) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7d357eb4b499cd530c3f4e626f2825a50ee6c8aa \"CML watermark\")\n" ]	2023-10-09T09:23:23Z	2023-10-10T07:13:22Z	2023-10-10T07:03:04Z	MEMBER	null	null	null	Create `DefunctDatasetError` as a specific error to be raised when a dataset is defunct and no longer accessible. See Hub discussion: https://huggingface.co/datasets/the_pile_books3/discussions/7#6523c13a94f3a1a2092d251b	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6286/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6286/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6286.diff", "html_url": "https://github.com/huggingface/datasets/pull/6286", "merged_at": "2023-10-10T07:03:04Z", "patch_url": "https://github.com/huggingface/datasets/pull/6286.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6286" }
https://api.github.com/repos/huggingface/datasets/issues/7494	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/7494/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/7494/comments	https://api.github.com/repos/huggingface/datasets/issues/7494/events	https://github.com/huggingface/datasets/issues/7494	2,965,347,685	I_kwDODunzps6wv51l	7,494	Broken links in pdf loading documentation	{ "avatar_url": "https://avatars.githubusercontent.com/u/75789232?v=4", "events_url": "https://api.github.com/users/VyoJ/events{/privacy}", "followers_url": "https://api.github.com/users/VyoJ/followers", "following_url": "https://api.github.com/users/VyoJ/following{/other_user}", "gists_url": "https://api.github.com/users/VyoJ/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/VyoJ", "id": 75789232, "login": "VyoJ", "node_id": "MDQ6VXNlcjc1Nzg5MjMy", "organizations_url": "https://api.github.com/users/VyoJ/orgs", "received_events_url": "https://api.github.com/users/VyoJ/received_events", "repos_url": "https://api.github.com/users/VyoJ/repos", "site_admin": false, "starred_url": "https://api.github.com/users/VyoJ/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/VyoJ/subscriptions", "type": "User", "url": "https://api.github.com/users/VyoJ", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "thanks for reporting ! I fixed the links, the docs will be updated in the next release" ]	2025-04-02T06:45:22Z	2025-04-15T13:36:25Z	2025-04-15T13:36:04Z	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug Hi, just a couple of small issues I ran into while reading the docs for [loading pdf data](https://huggingface.co/docs/datasets/main/en/document_load): 1. The link for the [`Create a pdf dataset`](https://huggingface.co/docs/datasets/main/en/document_load#pdffolder) points to https://huggingface.co/docs/datasets/main/en/pdf_dataset instead of https://huggingface.co/docs/datasets/main/en/document_dataset and hence gives a 404 error. 2. At the top of the page, it's mentioned that to work with pdf datasets we need to have the `pdfplumber` package installed but the link to its installation guide points to `pytorch/vision` [installation instructions](https://github.com/pytorch/vision#installation) instead of `pdfplumber`'s [guide](https://github.com/jsvine/pdfplumber#installation) I love the work on enabling pdf dataset support and these small tweaks would help everyone navigate the docs better. Thanks! ### Steps to reproduce the bug The issue is on the [Load Document Data](https://huggingface.co/docs/datasets/main/en/document_load) page of the datasets docs. ### Expected behavior 1. For solving the first issue, I went through the [source .mdx code](https://github.com/huggingface/datasets/blob/main/docs/source/document_load.mdx?plain=1#L188) of the datasets docs and found that the link is pointing to `./pdf_dataset` instead of `./document_dataset` 2. For the second issue, I went through the [source .mdx code](https://github.com/huggingface/datasets/blob/main/docs/source/document_load.mdx?plain=1#L13) of the datasets docs and found that the link is `pytorch/vision` [installation instructions](https://github.com/pytorch/vision#installation) instead of `pdfplumber`'s [guide](https://github.com/jsvine/pdfplumber#installation) Just replacing these two links should fix the bugs ### Environment info datasets v3.5.0 (main at the time of writing)	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/7494/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/7494/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/6950	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6950/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6950/comments	https://api.github.com/repos/huggingface/datasets/issues/6950/events	https://github.com/huggingface/datasets/issues/6950	2,333,005,974	I_kwDODunzps6LDtiW	6,950	`Dataset.with_format` behaves inconsistently with documentation	{ "avatar_url": "https://avatars.githubusercontent.com/u/42494185?v=4", "events_url": "https://api.github.com/users/iansheng/events{/privacy}", "followers_url": "https://api.github.com/users/iansheng/followers", "following_url": "https://api.github.com/users/iansheng/following{/other_user}", "gists_url": "https://api.github.com/users/iansheng/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/iansheng", "id": 42494185, "login": "iansheng", "node_id": "MDQ6VXNlcjQyNDk0MTg1", "organizations_url": "https://api.github.com/users/iansheng/orgs", "received_events_url": "https://api.github.com/users/iansheng/received_events", "repos_url": "https://api.github.com/users/iansheng/repos", "site_admin": false, "starred_url": "https://api.github.com/users/iansheng/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/iansheng/subscriptions", "type": "User", "url": "https://api.github.com/users/iansheng", "user_view_type": "public" }	[ { "color": "0075ca", "default": true, "description": "Improvements or additions to documentation", "id": 1935892861, "name": "documentation", "node_id": "MDU6TGFiZWwxOTM1ODkyODYx", "url": "https://api.github.com/repos/huggingface/datasets/labels/documentation" } ]	closed	false	null	[]	null	[ "Hi ! It seems the documentation was outdated in this paragraph\r\n\r\nI fixed it here: https://github.com/huggingface/datasets/pull/6956", "Fixed." ]	2024-06-04T09:18:32Z	2024-06-25T08:05:49Z	2024-06-25T08:05:49Z	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug The actual behavior of the interface `Dataset.with_format` is inconsistent with the documentation. https://huggingface.co/docs/datasets/use_with_pytorch#n-dimensional-arrays https://huggingface.co/docs/datasets/v2.19.0/en/use_with_tensorflow#n-dimensional-arrays > If your dataset consists of N-dimensional arrays, you will see that by default they are considered as nested lists. > In particular, a PyTorch formatted dataset outputs nested lists instead of a single tensor. > A TensorFlow formatted dataset outputs a RaggedTensor instead of a single tensor. But I get a single tensor by default, which is inconsistent with the description. Actually the current behavior seems more reasonable to me. Therefore, the document needs to be modified. ### Steps to reproduce the bug ```python >>> from datasets import Dataset >>> data = [[[1, 2],[3, 4]],[[5, 6],[7, 8]]] >>> ds = Dataset.from_dict({"data": data}) >>> ds = ds.with_format("torch") >>> ds[0] {'data': tensor([[1, 2], [3, 4]])} >>> ds = ds.with_format("tf") >>> ds[0] {'data': <tf.Tensor: shape=(2, 2), dtype=int64, numpy= array([[1, 2], [3, 4]])>} ``` ### Expected behavior ```python >>> from datasets import Dataset >>> data = [[[1, 2],[3, 4]],[[5, 6],[7, 8]]] >>> ds = Dataset.from_dict({"data": data}) >>> ds = ds.with_format("torch") >>> ds[0] {'data': [tensor([1, 2]), tensor([3, 4])]} >>> ds = ds.with_format("tf") >>> ds[0] {'data': <tf.RaggedTensor [[1, 2], [3, 4]]>} ``` ### Environment info datasets==2.19.1 torch==2.1.0 tensorflow==2.13.1	{ "avatar_url": "https://avatars.githubusercontent.com/u/42494185?v=4", "events_url": "https://api.github.com/users/iansheng/events{/privacy}", "followers_url": "https://api.github.com/users/iansheng/followers", "following_url": "https://api.github.com/users/iansheng/following{/other_user}", "gists_url": "https://api.github.com/users/iansheng/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/iansheng", "id": 42494185, "login": "iansheng", "node_id": "MDQ6VXNlcjQyNDk0MTg1", "organizations_url": "https://api.github.com/users/iansheng/orgs", "received_events_url": "https://api.github.com/users/iansheng/received_events", "repos_url": "https://api.github.com/users/iansheng/repos", "site_admin": false, "starred_url": "https://api.github.com/users/iansheng/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/iansheng/subscriptions", "type": "User", "url": "https://api.github.com/users/iansheng", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6950/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6950/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/6487	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6487/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6487/comments	https://api.github.com/repos/huggingface/datasets/issues/6487/events	https://github.com/huggingface/datasets/pull/6487	2,035,424,254	PR_kwDODunzps5hqyfV	6,487	Update builder hash with info	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6487). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "Closing this one in favor of https://github.com/huggingface/datasets/pull/6458/commits/565c294fc12bc547730a023a610ed4f92313d8fb in https://github.com/huggingface/datasets/pull/6458" ]	2023-12-11T11:09:16Z	2024-01-11T06:35:07Z	2023-12-11T11:41:34Z	MEMBER	null	null	null	Currently if you change the `dataset_info` of a dataset (e.g. in the YAML part of the README.md), the cache ignores this change. This is problematic because you want to regenerate a dataset if you change the features or the split sizes for example (e.g. after push_to_hub) Ideally we should take the resolved files into account as well but this will be for another PR	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6487/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6487/timeline	null	null	1	{ "diff_url": "https://github.com/huggingface/datasets/pull/6487.diff", "html_url": "https://github.com/huggingface/datasets/pull/6487", "merged_at": null, "patch_url": "https://github.com/huggingface/datasets/pull/6487.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6487" }
https://api.github.com/repos/huggingface/datasets/issues/5095	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5095/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5095/comments	https://api.github.com/repos/huggingface/datasets/issues/5095/events	https://github.com/huggingface/datasets/pull/5095	1,403,221,408	PR_kwDODunzps5Afzsq	5,095	Fix tutorial (#5093)	{ "avatar_url": "https://avatars.githubusercontent.com/u/9295277?v=4", "events_url": "https://api.github.com/users/riccardobucco/events{/privacy}", "followers_url": "https://api.github.com/users/riccardobucco/followers", "following_url": "https://api.github.com/users/riccardobucco/following{/other_user}", "gists_url": "https://api.github.com/users/riccardobucco/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/riccardobucco", "id": 9295277, "login": "riccardobucco", "node_id": "MDQ6VXNlcjkyOTUyNzc=", "organizations_url": "https://api.github.com/users/riccardobucco/orgs", "received_events_url": "https://api.github.com/users/riccardobucco/received_events", "repos_url": "https://api.github.com/users/riccardobucco/repos", "site_admin": false, "starred_url": "https://api.github.com/users/riccardobucco/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/riccardobucco/subscriptions", "type": "User", "url": "https://api.github.com/users/riccardobucco", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "Oops I merged without linking to the hacktoberfest issue - not sure if it counts in this case\r\n\r\nsorry about that..\r\n\r\nNext time you can just mention \"Close #XXXX\" in your issue to link it", "It should :) (the `hacktoberfest` repo topic is all that matters)" ]	2022-10-10T13:55:15Z	2022-10-10T17:50:52Z	2022-10-10T15:32:20Z	CONTRIBUTOR	null	null	null	Close #5093	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5095/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5095/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/5095.diff", "html_url": "https://github.com/huggingface/datasets/pull/5095", "merged_at": "2022-10-10T15:32:20Z", "patch_url": "https://github.com/huggingface/datasets/pull/5095.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5095" }
https://api.github.com/repos/huggingface/datasets/issues/4565	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/4565/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/4565/comments	https://api.github.com/repos/huggingface/datasets/issues/4565/events	https://github.com/huggingface/datasets/issues/4565	1,284,141,666	I_kwDODunzps5MinJi	4,565	Add UFSC OCPap dataset	{ "avatar_url": "https://avatars.githubusercontent.com/u/20444345?v=4", "events_url": "https://api.github.com/users/johnnv1/events{/privacy}", "followers_url": "https://api.github.com/users/johnnv1/followers", "following_url": "https://api.github.com/users/johnnv1/following{/other_user}", "gists_url": "https://api.github.com/users/johnnv1/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/johnnv1", "id": 20444345, "login": "johnnv1", "node_id": "MDQ6VXNlcjIwNDQ0MzQ1", "organizations_url": "https://api.github.com/users/johnnv1/orgs", "received_events_url": "https://api.github.com/users/johnnv1/received_events", "repos_url": "https://api.github.com/users/johnnv1/repos", "site_admin": false, "starred_url": "https://api.github.com/users/johnnv1/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/johnnv1/subscriptions", "type": "User", "url": "https://api.github.com/users/johnnv1", "user_view_type": "public" }	[ { "color": "e99695", "default": false, "description": "Requesting to add a new dataset", "id": 2067376369, "name": "dataset request", "node_id": "MDU6TGFiZWwyMDY3Mzc2MzY5", "url": "https://api.github.com/repos/huggingface/datasets/labels/dataset%20request" } ]	closed	false	null	[]	null	[ "I will add this directly on the hub (same as #4486)—in https://huggingface.co/lapix" ]	2022-06-24T20:07:54Z	2022-07-06T19:03:02Z	2022-07-06T19:03:02Z	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	## Adding a Dataset - Name: UFSC OCPap: Papanicolaou Stained Oral Cytology Dataset (v4) - Description: The UFSC OCPap dataset comprises 9,797 labeled images of 1200x1600 pixels acquired from 5 slides of cancer diagnosed and 3 healthy of oral brush samples, from distinct patients. - Paper: https://dx.doi.org/10.2139/ssrn.4119212 - Data: https://data.mendeley.com/datasets/dr7ydy9xbk/1 - Motivation: real data of pap stained oral cytology samples Instructions to add a new dataset can be found [here](https://github.com/huggingface/datasets/blob/master/ADD_NEW_DATASET.md).	{ "avatar_url": "https://avatars.githubusercontent.com/u/20444345?v=4", "events_url": "https://api.github.com/users/johnnv1/events{/privacy}", "followers_url": "https://api.github.com/users/johnnv1/followers", "following_url": "https://api.github.com/users/johnnv1/following{/other_user}", "gists_url": "https://api.github.com/users/johnnv1/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/johnnv1", "id": 20444345, "login": "johnnv1", "node_id": "MDQ6VXNlcjIwNDQ0MzQ1", "organizations_url": "https://api.github.com/users/johnnv1/orgs", "received_events_url": "https://api.github.com/users/johnnv1/received_events", "repos_url": "https://api.github.com/users/johnnv1/repos", "site_admin": false, "starred_url": "https://api.github.com/users/johnnv1/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/johnnv1/subscriptions", "type": "User", "url": "https://api.github.com/users/johnnv1", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/4565/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/4565/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/5058	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5058/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5058/comments	https://api.github.com/repos/huggingface/datasets/issues/5058/events	https://github.com/huggingface/datasets/pull/5058	1,394,962,424	PR_kwDODunzps5AEVWn	5,058	Mark CI tests as xfail when 502 error	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._" ]	2022-10-03T15:53:55Z	2022-10-04T10:03:23Z	2022-10-04T10:01:23Z	MEMBER	null	null	null	To make CI more robust, we could mark as xfail when the Hub raises a 502 error (besides 500 error): - FAILED tests/test_upstream_hub.py::TestPushToHub::test_push_dataset_to_hub_skip_identical_files - https://github.com/huggingface/datasets/actions/runs/3174626525/jobs/5171672431 ``` > raise HTTPError(http_error_msg, response=self) E requests.exceptions.HTTPError: 502 Server Error: Bad Gateway for url: https://hub-ci.huggingface.co/datasets/__DUMMY_TRANSFORMERS_USER__/test-16648055339047.git/info/lfs/objects/batch ``` - FAILED tests/test_upstream_hub.py::TestPushToHub::test_push_dataset_dict_to_hub_overwrite_files - https://github.com/huggingface/datasets/actions/runs/3145587033/jobs/5113074889 ``` > raise HTTPError(http_error_msg, response=self) E requests.exceptions.HTTPError: 502 Server Error: Bad Gateway for url: https://hub-ci.huggingface.co/datasets/__DUMMY_TRANSFORMERS_USER__/test-16643866807322.git/info/lfs/objects/verify ``` Currently, we mark as xfail when 500 error: - #4845	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5058/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5058/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/5058.diff", "html_url": "https://github.com/huggingface/datasets/pull/5058", "merged_at": "2022-10-04T10:01:23Z", "patch_url": "https://github.com/huggingface/datasets/pull/5058.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5058" }
https://api.github.com/repos/huggingface/datasets/issues/6251	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6251/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6251/comments	https://api.github.com/repos/huggingface/datasets/issues/6251/events	https://github.com/huggingface/datasets/pull/6251	1,904,418,426	PR_kwDODunzps5awQsy	6,251	Support streaming datasets with pyarrow.parquet.read_table	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "This function reads an entire Arrow table in one go, which is not ideal memory-wise, so I don't think we should encourage using this function, considering we want to keep RAM usage as low as possible in the streaming mode. \r\n\r\n(Note that Parquet files are compressed, meaning the loaded table can be significantly larger than the size in Parquet.)\r\n\r\nInstead, we should suggest the authors to use:\r\n```python\r\nwith open(doc_path, \"rb\") as f:\r\n parquet_file = pq.ParquetFile(f)\r\n for batch in parquet_file.iter_batches():\r\n pa_table = pa.Table.from_batches([batch])\r\n yield idx, pa_table\r\n idx += 1\r\n```", "@mariosasko I think the potential problem you evoke is independent of whether or not we support streaming mode:\r\n- if the user's script with `read_table` works in non-streaming mode, it will also work in streaming mode after this PR\r\n\r\nIn fact, what we should suggest instead is to follow the scriptless approach, so that our `parquet` packaged module is used, with all the optimizations implemented. But this approach is not possible in all cases, and some use cases need to implement a script. And if they have small Parquet files and use `read_table`, I think we should support streaming.\r\n\r\nIn summary, let me clarify the goal and the scope of this PR:\r\n- a user needs using a loading script\r\n- their files are small enough so that they use `read_table`\r\n- their loading script works in non-streaming mode\r\n- therefore, this PR allows loading their dataset in streaming mode as well", "Yes, the no-script approach with metadata configs makes the most sense.\r\n\r\n> their files are small enough so that they use read_table\r\n\r\nSome of the Parquet files in that repo are larger than 1 GB ...\r\n\r\nAlso, I'd wait for more instances of people using the `read_table` function on the Hub before merging this PR.", "@mariosasko, yes, this solution is not specifically for the \"uonlp/CulturaX\" dataset, but for other use cases as I explained above: indeed, they finally removed the use of `read_table` because their data files are too large.\r\n\r\n> Also, I'd wait for more instances of people using the `read_table` function on the Hub before merging this PR.\r\n\r\nDo you know how many datasets are currently using `read_table`?", "> Do you know how many datasets are currently using read_table?\r\n\r\nZero (based on the script that checks the script contents of the public Hub datasets). ", "I see... Thanks! :hugs: ", "@mariosasko thanks for pointing the script! :hugs: \r\n\r\nHowever, I have found some Hub datasets that are using `read_table`, e.g.:\r\n- https://huggingface.co/datasets/jglaser/protein_ligand_contacts\r\n- https://huggingface.co/datasets/AresEkb/prof_standards_sbert_large_mt_nlu_ru\r\n- https://huggingface.co/datasets/victorcosta/pt_legislation\r\n- https://huggingface.co/datasets/jglaser/binding_affinity\r\n- https://huggingface.co/datasets/jglaser/pdbbind_complexes\r\n- https://huggingface.co/datasets/victorcosta/ria_pt__proems_format", "I'm merging this PR as discussed in private.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.008267 / 0.011353 (-0.003086) \| 0.005813 / 0.011008 (-0.005195) \| 0.108802 / 0.038508 (0.070294) \| 0.093996 / 0.023109 (0.070886) \| 0.403115 / 0.275898 (0.127217) \| 0.457299 / 0.323480 (0.133819) \| 0.006277 / 0.007986 (-0.001709) \| 0.004701 / 0.004328 (0.000373) \| 0.080700 / 0.004250 (0.076449) \| 0.077906 / 0.037052 (0.040854) \| 0.409972 / 0.258489 (0.151483) \| 0.477707 / 0.293841 (0.183867) \| 0.041816 / 0.128546 (-0.086731) \| 0.011250 / 0.075646 (-0.064397) \| 0.390634 / 0.419271 (-0.028637) \| 0.065361 / 0.043533 (0.021828) \| 0.404501 / 0.255139 (0.149362) \| 0.448162 / 0.283200 (0.164962) \| 0.032823 / 0.141683 (-0.108860) \| 1.899892 / 1.452155 (0.447737) \| 2.044561 / 1.492716 (0.551844) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.241093 / 0.018006 (0.223086) \| 0.482111 / 0.000490 (0.481622) \| 0.005505 / 0.000200 (0.005305) \| 0.000094 / 0.000054 (0.000039) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034861 / 0.037411 (-0.002551) \| 0.109296 / 0.014526 (0.094770) \| 0.127594 / 0.176557 (-0.048962) \| 0.191815 / 0.737135 (-0.545320) \| 0.122630 / 0.296338 (-0.173709) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.452194 / 0.215209 (0.236985) \| 4.486200 / 2.077655 (2.408545) \| 2.155635 / 1.504120 (0.651515) \| 2.004569 / 1.541195 (0.463374) \| 2.142570 / 1.468490 (0.674080) \| 0.561488 / 4.584777 (-4.023289) \| 4.381102 / 3.745712 (0.635390) \| 3.914920 / 5.269862 (-1.354942) \| 2.474271 / 4.565676 (-2.091406) \| 0.067528 / 0.424275 (-0.356747) \| 0.008723 / 0.007607 (0.001116) \| 0.536077 / 0.226044 (0.310033) \| 5.342050 / 2.268929 (3.073122) \| 2.735747 / 55.444624 (-52.708877) \| 2.353938 / 6.876477 (-4.522539) \| 2.442878 / 2.142072 (0.300805) \| 0.685404 / 4.805227 (-4.119823) \| 0.156657 / 6.500664 (-6.344007) \| 0.071714 / 0.075469 (-0.003755) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.562852 / 1.841788 (-0.278935) \| 24.538203 / 8.074308 (16.463895) \| 16.857777 / 10.191392 (6.666385) \| 0.184221 / 0.680424 (-0.496203) \| 0.021688 / 0.534201 (-0.512513) \| 0.470700 / 0.579283 (-0.108583) \| 0.470593 / 0.434364 (0.036229) \| 0.645066 / 0.540337 (0.104729) \| 0.756075 / 1.386936 (-0.630861) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009486 / 0.011353 (-0.001867) \| 0.004694 / 0.011008 (-0.006314) \| 0.080216 / 0.038508 (0.041708) \| 0.093479 / 0.023109 (0.070369) \| 0.537353 / 0.275898 (0.261455) \| 0.551631 / 0.323480 (0.228151) \| 0.007373 / 0.007986 (-0.000613) \| 0.004044 / 0.004328 (-0.000285) \| 0.075301 / 0.004250 (0.071051) \| 0.069408 / 0.037052 (0.032355) \| 0.527962 / 0.258489 (0.269473) \| 0.559423 / 0.293841 (0.265582) \| 0.039351 / 0.128546 (-0.089195) \| 0.010801 / 0.075646 (-0.064845) \| 0.092803 / 0.419271 (-0.326468) \| 0.058876 / 0.043533 (0.015343) \| 0.513742 / 0.255139 (0.258603) \| 0.574666 / 0.283200 (0.291466) \| 0.030277 / 0.141683 (-0.111406) \| 1.884936 / 1.452155 (0.432782) \| 2.008260 / 1.492716 (0.515543) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.242162 / 0.018006 (0.224156) \| 0.467400 / 0.000490 (0.466910) \| 0.005348 / 0.000200 (0.005148) \| 0.000103 / 0.000054 (0.000049) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.038022 / 0.037411 (0.000611) \| 0.108239 / 0.014526 (0.093713) \| 0.121514 / 0.176557 (-0.055042) \| 0.184951 / 0.737135 (-0.552184) \| 0.123138 / 0.296338 (-0.173200) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.558587 / 0.215209 (0.343377) \| 5.740312 / 2.077655 (3.662657) \| 3.172164 / 1.504120 (1.668044) \| 2.852908 / 1.541195 (1.311713) \| 2.894435 / 1.468490 (1.425945) \| 0.586399 / 4.584777 (-3.998378) \| 4.498342 / 3.745712 (0.752630) \| 4.000569 / 5.269862 (-1.269292) \| 2.610988 / 4.565676 (-1.954688) \| 0.068415 / 0.424275 (-0.355860) \| 0.008602 / 0.007607 (0.000994) \| 0.614731 / 0.226044 (0.388686) \| 6.068158 / 2.268929 (3.799229) \| 3.301070 / 55.444624 (-52.143554) \| 2.868034 / 6.876477 (-4.008443) \| 2.959072 / 2.142072 (0.816999) \| 0.684174 / 4.805227 (-4.121053) \| 0.154099 / 6.500664 (-6.346565) \| 0.070641 / 0.075469 (-0.004828) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.835667 / 1.841788 (-0.006120) \| 24.981645 / 8.074308 (16.907337) \| 17.218517 / 10.191392 (7.027125) \| 0.197055 / 0.680424 (-0.483368) \| 0.025465 / 0.534201 (-0.508736) \| 0.523498 / 0.579283 (-0.055785) \| 0.528268 / 0.434364 (0.093904) \| 0.599518 / 0.540337 (0.059180) \| 0.887206 / 1.386936 (-0.499730) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#dd786d3b8dc94f1ab717327e88f65879b525091d \"CML watermark\")\n" ]	2023-09-20T08:07:02Z	2023-09-27T06:37:03Z	2023-09-27T06:26:24Z	MEMBER	null	null	null	Support streaming datasets with `pyarrow.parquet.read_table`. See: https://huggingface.co/datasets/uonlp/CulturaX/discussions/2 CC: @AndreaFrancis	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6251/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6251/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6251.diff", "html_url": "https://github.com/huggingface/datasets/pull/6251", "merged_at": "2023-09-27T06:26:24Z", "patch_url": "https://github.com/huggingface/datasets/pull/6251.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6251" }
https://api.github.com/repos/huggingface/datasets/issues/7111	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/7111/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/7111/comments	https://api.github.com/repos/huggingface/datasets/issues/7111/events	https://github.com/huggingface/datasets/issues/7111	2,474,915,845	I_kwDODunzps6ThDgF	7,111	CI is broken for numpy-2: Failed to fetch wheel: llvmlite==0.34.0	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	[]	closed	false	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	[ { "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" } ]	null	[ "Note that the CI before was using:\r\n- llvmlite: 0.43.0\r\n- numba: 0.60.0\r\n\r\nNow it tries to use:\r\n- llvmlite: 0.34.0\r\n- numba: 0.51.2", "The issue is because numba-0.60.0 pins numpy<2.1 and `uv` tries to install latest numpy-2.1.0 with an old numba-0.51.0 version (and llvmlite-0.34.0). See discussion in their repo:\r\n- https://github.com/numba/numba/issues/9708\r\n\r\nLatest numpy-2.1.0 will be supported by the next numba-0.61.0 release in September.\r\n\r\nNote that our CI requires numba with the \"audio\" extra:\r\n- librosa > numba" ]	2024-08-20T07:27:28Z	2024-08-21T05:05:36Z	2024-08-20T09:02:36Z	MEMBER	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	Ci is broken with error `Failed to fetch wheel: llvmlite==0.34.0`: https://github.com/huggingface/datasets/actions/runs/10466825281/job/28984414269 ``` Run uv pip install --system "datasets[tests_numpy2] @ ." Resolved 150 packages in 4.42s error: Failed to prepare distributions Caused by: Failed to fetch wheel: llvmlite==0.34.0 Caused by: Build backend failed to build wheel through `build_wheel()` with exit status: 1 --- stdout: running bdist_wheel /home/runner/.cache/uv/builds-v0/.tmpcyKh8S/bin/python /home/runner/.cache/uv/built-wheels-v3/pypi/llvmlite/0.34.0/wrk1bNwq1gleSiznvrSEZ/llvmlite-0.34.0.tar.gz/ffi/build.py LLVM version... --- stderr: Traceback (most recent call last): File "/home/runner/.cache/uv/built-wheels-v3/pypi/llvmlite/0.34.0/wrk1bNwq1gleSiznvrSEZ/llvmlite-0.34.0.tar.gz/ffi/build.py", line 105, in main_posix out = subprocess.check_output([llvm_config, '--version']) File "/opt/hostedtoolcache/Python/3.10.14/x64/lib/python3.10/subprocess.py", line 421, in check_output return run(popenargs, stdout=PIPE, timeout=timeout, check=True, File "/opt/hostedtoolcache/Python/3.10.14/x64/lib/python3.10/subprocess.py", line 503, in run with Popen(popenargs, **kwargs) as process: File "/opt/hostedtoolcache/Python/3.10.14/x64/lib/python3.10/subprocess.py", line 971, in __init__ self._execute_child(args, executable, preexec_fn, close_fds, File "/opt/hostedtoolcache/Python/3.10.14/x64/lib/python3.10/subprocess.py", line 1863, in _execute_child raise child_exception_type(errno_num, err_msg, err_filename) FileNotFoundError: [Errno 2] No such file or directory: 'llvm-config' During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/home/runner/.cache/uv/built-wheels-v3/pypi/llvmlite/0.34.0/wrk1bNwq1gleSiznvrSEZ/llvmlite-0.34.0.tar.gz/ffi/build.py", line 191, in <module> main() File "/home/runner/.cache/uv/built-wheels-v3/pypi/llvmlite/0.34.0/wrk1bNwq1gleSiznvrSEZ/llvmlite-0.34.0.tar.gz/ffi/build.py", line 181, in main main_posix('linux', '.so') File "/home/runner/.cache/uv/built-wheels-v3/pypi/llvmlite/0.34.0/wrk1bNwq1gleSiznvrSEZ/llvmlite-0.34.0.tar.gz/ffi/build.py", line 107, in main_posix raise RuntimeError("%s failed executing, please point LLVM_CONFIG " RuntimeError: llvm-config failed executing, please point LLVM_CONFIG to the path for llvm-config error: command '/home/runner/.cache/uv/builds-v0/.tmpcyKh8S/bin/python' failed with exit code 1 ```	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/7111/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/7111/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/6826	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6826/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6826/comments	https://api.github.com/repos/huggingface/datasets/issues/6826/events	https://github.com/huggingface/datasets/pull/6826	2,252,445,242	PR_kwDODunzps5tJMZh	6,826	Set dev version	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6826). 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) \| 0.004893 / 0.011353 (-0.006460) \| 0.003238 / 0.011008 (-0.007771) \| 0.063143 / 0.038508 (0.024635) \| 0.029770 / 0.023109 (0.006661) \| 0.229052 / 0.275898 (-0.046846) \| 0.254534 / 0.323480 (-0.068945) \| 0.003083 / 0.007986 (-0.004903) \| 0.002615 / 0.004328 (-0.001714) \| 0.049684 / 0.004250 (0.045434) \| 0.043745 / 0.037052 (0.006693) \| 0.248985 / 0.258489 (-0.009504) \| 0.275957 / 0.293841 (-0.017884) \| 0.027323 / 0.128546 (-0.101223) \| 0.010372 / 0.075646 (-0.065275) \| 0.206494 / 0.419271 (-0.212778) \| 0.035230 / 0.043533 (-0.008303) \| 0.234235 / 0.255139 (-0.020904) \| 0.252395 / 0.283200 (-0.030805) \| 0.019442 / 0.141683 (-0.122240) \| 1.130677 / 1.452155 (-0.321478) \| 1.161721 / 1.492716 (-0.330996) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091659 / 0.018006 (0.073653) \| 0.301323 / 0.000490 (0.300833) \| 0.000212 / 0.000200 (0.000012) \| 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) \| 0.018360 / 0.037411 (-0.019051) \| 0.061101 / 0.014526 (0.046575) \| 0.072383 / 0.176557 (-0.104174) \| 0.117656 / 0.737135 (-0.619479) \| 0.073903 / 0.296338 (-0.222436) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.272768 / 0.215209 (0.057558) \| 2.655714 / 2.077655 (0.578059) \| 1.446254 / 1.504120 (-0.057866) \| 1.330543 / 1.541195 (-0.210652) \| 1.352527 / 1.468490 (-0.115964) \| 0.561428 / 4.584777 (-4.023349) \| 2.368182 / 3.745712 (-1.377530) \| 2.746508 / 5.269862 (-2.523353) \| 1.713972 / 4.565676 (-2.851705) \| 0.062046 / 0.424275 (-0.362229) \| 0.005427 / 0.007607 (-0.002180) \| 0.321652 / 0.226044 (0.095607) \| 3.181812 / 2.268929 (0.912883) \| 1.766778 / 55.444624 (-53.677846) \| 1.492502 / 6.876477 (-5.383975) \| 1.534658 / 2.142072 (-0.607415) \| 0.640372 / 4.805227 (-4.164856) \| 0.118180 / 6.500664 (-6.382484) \| 0.042698 / 0.075469 (-0.032771) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.993262 / 1.841788 (-0.848525) \| 11.512827 / 8.074308 (3.438518) \| 9.602140 / 10.191392 (-0.589252) \| 0.144723 / 0.680424 (-0.535701) \| 0.014122 / 0.534201 (-0.520079) \| 0.302211 / 0.579283 (-0.277072) \| 0.268026 / 0.434364 (-0.166338) \| 0.326524 / 0.540337 (-0.213813) \| 0.423781 / 1.386936 (-0.963155) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005388 / 0.011353 (-0.005965) \| 0.003535 / 0.011008 (-0.007473) \| 0.050139 / 0.038508 (0.011631) \| 0.031813 / 0.023109 (0.008704) \| 0.269501 / 0.275898 (-0.006397) \| 0.294355 / 0.323480 (-0.029125) \| 0.004128 / 0.007986 (-0.003858) \| 0.002684 / 0.004328 (-0.001644) \| 0.049295 / 0.004250 (0.045045) \| 0.040129 / 0.037052 (0.003077) \| 0.282406 / 0.258489 (0.023917) \| 0.309822 / 0.293841 (0.015981) \| 0.028506 / 0.128546 (-0.100040) \| 0.010434 / 0.075646 (-0.065213) \| 0.057890 / 0.419271 (-0.361382) \| 0.032487 / 0.043533 (-0.011046) \| 0.270631 / 0.255139 (0.015492) \| 0.288734 / 0.283200 (0.005534) \| 0.018710 / 0.141683 (-0.122973) \| 1.151571 / 1.452155 (-0.300583) \| 1.195222 / 1.492716 (-0.297494) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.090939 / 0.018006 (0.072932) \| 0.300278 / 0.000490 (0.299788) \| 0.000202 / 0.000200 (0.000002) \| 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) \| 0.022036 / 0.037411 (-0.015376) \| 0.075131 / 0.014526 (0.060605) \| 0.087775 / 0.176557 (-0.088782) \| 0.125719 / 0.737135 (-0.611416) \| 0.088491 / 0.296338 (-0.207848) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.300363 / 0.215209 (0.085154) \| 2.931852 / 2.077655 (0.854197) \| 1.633688 / 1.504120 (0.129568) \| 1.512641 / 1.541195 (-0.028554) \| 1.527703 / 1.468490 (0.059213) \| 0.572781 / 4.584777 (-4.011996) \| 2.445950 / 3.745712 (-1.299762) \| 2.883667 / 5.269862 (-2.386195) \| 1.761396 / 4.565676 (-2.804280) \| 0.064422 / 0.424275 (-0.359853) \| 0.005332 / 0.007607 (-0.002275) \| 0.346730 / 0.226044 (0.120686) \| 3.443815 / 2.268929 (1.174886) \| 1.988677 / 55.444624 (-53.455948) \| 1.707688 / 6.876477 (-5.168789) \| 1.694216 / 2.142072 (-0.447856) \| 0.634834 / 4.805227 (-4.170393) \| 0.115044 / 6.500664 (-6.385620) \| 0.040853 / 0.075469 (-0.034616) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.009382 / 1.841788 (-0.832405) \| 12.327511 / 8.074308 (4.253203) \| 10.123296 / 10.191392 (-0.068097) \| 0.130770 / 0.680424 (-0.549654) \| 0.015548 / 0.534201 (-0.518653) \| 0.286650 / 0.579283 (-0.292633) \| 0.270267 / 0.434364 (-0.164097) \| 0.333485 / 0.540337 (-0.206852) \| 0.428288 / 1.386936 (-0.958648) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f96e74d5c633cd5435dd526adb4a74631eb05c43 \"CML watermark\")\n" ]	2024-04-19T08:51:42Z	2024-04-19T09:05:25Z	2024-04-19T08:52:14Z	MEMBER	null	null	null	null	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6826/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6826/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6826.diff", "html_url": "https://github.com/huggingface/datasets/pull/6826", "merged_at": "2024-04-19T08:52:13Z", "patch_url": "https://github.com/huggingface/datasets/pull/6826.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6826" }
https://api.github.com/repos/huggingface/datasets/issues/4861	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/4861/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/4861/comments	https://api.github.com/repos/huggingface/datasets/issues/4861/events	https://github.com/huggingface/datasets/issues/4861	1,343,260,220	I_kwDODunzps5QEIY8	4,861	Using disk for memory with the method `from_dict`	{ "avatar_url": "https://avatars.githubusercontent.com/u/44556846?v=4", "events_url": "https://api.github.com/users/HugoLaurencon/events{/privacy}", "followers_url": "https://api.github.com/users/HugoLaurencon/followers", "following_url": "https://api.github.com/users/HugoLaurencon/following{/other_user}", "gists_url": "https://api.github.com/users/HugoLaurencon/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/HugoLaurencon", "id": 44556846, "login": "HugoLaurencon", "node_id": "MDQ6VXNlcjQ0NTU2ODQ2", "organizations_url": "https://api.github.com/users/HugoLaurencon/orgs", "received_events_url": "https://api.github.com/users/HugoLaurencon/received_events", "repos_url": "https://api.github.com/users/HugoLaurencon/repos", "site_admin": false, "starred_url": "https://api.github.com/users/HugoLaurencon/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/HugoLaurencon/subscriptions", "type": "User", "url": "https://api.github.com/users/HugoLaurencon", "user_view_type": "public" }	[ { "color": "a2eeef", "default": true, "description": "New feature or request", "id": 1935892871, "name": "enhancement", "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement" } ]	open	false	null	[]	null	[ "This issue was also causing an OOM in @nateraw 's workflow and shows again that behavior is confusing - we should definitely switch to using the disk IMO" ]	2022-08-18T15:18:18Z	2023-01-26T18:36:28Z	null	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	Is your feature request related to a problem? Please describe. I start with an empty dataset. In a loop, at each iteration, I create a new dataset with the method `from_dict` (based on some data I load) and I concatenate this new dataset with the one at the previous iteration. After some iterations, I have an OOM error. Describe the solution you'd like The method `from_dict` loads the data in RAM. It could be good to add an option to use the disk instead. Describe alternatives you've considered To solve the problem, I have to do an intermediate step where I save the new datasets at each iteration with `save_to_disk`. Once it's done, I open them all and concatenate them.	null	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/4861/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/4861/timeline	null	null	null	null
https://api.github.com/repos/huggingface/datasets/issues/5597	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5597/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5597/comments	https://api.github.com/repos/huggingface/datasets/issues/5597/events	https://github.com/huggingface/datasets/issues/5597	1,604,928,721	I_kwDODunzps5fqUTR	5,597	in-place dataset update	{ "avatar_url": "https://avatars.githubusercontent.com/u/3585459?v=4", "events_url": "https://api.github.com/users/speedcell4/events{/privacy}", "followers_url": "https://api.github.com/users/speedcell4/followers", "following_url": "https://api.github.com/users/speedcell4/following{/other_user}", "gists_url": "https://api.github.com/users/speedcell4/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/speedcell4", "id": 3585459, "login": "speedcell4", "node_id": "MDQ6VXNlcjM1ODU0NTk=", "organizations_url": "https://api.github.com/users/speedcell4/orgs", "received_events_url": "https://api.github.com/users/speedcell4/received_events", "repos_url": "https://api.github.com/users/speedcell4/repos", "site_admin": false, "starred_url": "https://api.github.com/users/speedcell4/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/speedcell4/subscriptions", "type": "User", "url": "https://api.github.com/users/speedcell4", "user_view_type": "public" }	[ { "color": "ffffff", "default": true, "description": "This will not be worked on", "id": 1935892913, "name": "wontfix", "node_id": "MDU6TGFiZWwxOTM1ODkyOTEz", "url": "https://api.github.com/repos/huggingface/datasets/labels/wontfix" } ]	closed	false	null	[]	null	[ "We won't support in-place modifications since `datasets` is based on the Apache Arrow format which doesn't support in-place modifications.\r\n\r\nIn your case the old dataset is garbage collected pretty quickly so you won't have memory issues.\r\n\r\nNote that datasets loaded from disk (memory mapped) are not loaded in memory, and therefore the new dataset actually use the same buffers as the old one.", "Thank you for your detailed reply.\r\n\r\n> In your case the old dataset is garbage collected pretty quickly so you won't have memory issues.\r\n\r\nI understand this, but it still copies the old dataset to create the new one, is this correct? So maybe it is not memory-consuming, but time-consuming?", "Indeed, and because of that it is more efficient to add multiple rows at once instead of one by one, using `concatenate_datasets` for example." ]	2023-03-01T12:58:18Z	2023-03-02T13:30:41Z	2023-03-02T03:47:00Z	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Motivation For the circumstance that I creat an empty `Dataset` and keep appending new rows into it, I found that it leads to creating a new dataset at each call. It looks quite memory-consuming. I just wonder if there is any more efficient way to do this. ```python from datasets import Dataset ds = Dataset.from_list([]) ds.add_item({'a': [1, 2, 3], 'b': 4}) print(ds) >>> Dataset({ >>> features: [], >>> num_rows: 0 >>> }) ds = ds.add_item({'a': [1, 2, 3], 'b': 4}) print(ds) >>> Dataset({ >>> features: ['a', 'b'], >>> num_rows: 1 >>> }) ``` ### Feature request Call for in-place dataset update functions, that update the existing `Dataset` in place without creating a new copy. The interface is supposed to keep the same style as PyTorch, such as the in-place version of a `function` is named `function_`. For example, the in-pace version of `add_item`, i.e., `add_item_`, immediately updates the `Dataset`. ```python from datasets import Dataset ds = Dataset.from_list([]) ds.add_item({'a': [1, 2, 3], 'b': 4}) print(ds) >>> Dataset({ >>> features: [], >>> num_rows: 0 >>> }) ds.add_item_({'a': [1, 2, 3], 'b': 4}) print(ds) >>> Dataset({ >>> features: ['a', 'b'], >>> num_rows: 1 >>> }) ``` ### Related Functions * `.map` * `.filter` * `.add_item`	{ "avatar_url": "https://avatars.githubusercontent.com/u/3585459?v=4", "events_url": "https://api.github.com/users/speedcell4/events{/privacy}", "followers_url": "https://api.github.com/users/speedcell4/followers", "following_url": "https://api.github.com/users/speedcell4/following{/other_user}", "gists_url": "https://api.github.com/users/speedcell4/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/speedcell4", "id": 3585459, "login": "speedcell4", "node_id": "MDQ6VXNlcjM1ODU0NTk=", "organizations_url": "https://api.github.com/users/speedcell4/orgs", "received_events_url": "https://api.github.com/users/speedcell4/received_events", "repos_url": "https://api.github.com/users/speedcell4/repos", "site_admin": false, "starred_url": "https://api.github.com/users/speedcell4/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/speedcell4/subscriptions", "type": "User", "url": "https://api.github.com/users/speedcell4", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5597/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5597/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/4579	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/4579/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/4579/comments	https://api.github.com/repos/huggingface/datasets/issues/4579/events	https://github.com/huggingface/datasets/pull/4579	1,286,106,285	PR_kwDODunzps46bo2h	4,579	Support streaming cfq dataset	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "@lhoestq I've been refactoring a little the code:\r\n- Use less RAM by loading only the required samples: only if its index is in the splits file\r\n- Start yielding \"earlier\" in streaming mode: for each `split_idx`:\r\n - either yield from buffer\r\n - or iterate over samples and either yield or buffer the sample\r\n \r\n The speed gain obviously depends on how the indexes are sorted in the split file:\r\n - Best case: indices are [1, 2, 3]\r\n - Worst case (no speed gain): indices are [3, 1, 2] or [3, 2, 1]\r\n\r\nLet me know what you think.", "I have to update the dummy data so that it aligns with the real data (inside the archive, the samples file `dataset.json` is the last member).", "There is an issue when testing `test_load_dataset_cfq` with dummy data:\r\n- `MockDownloadManager.iter_archive` yields FIRST `'cfq/dataset.json'`\r\n- [`Streaming`]`DownloadManager.iter_archive` yields LAST `'cfq/dataset.json'` when using real data tar.gz archive\r\n\r\nNote that this issue arises only with dummy data: loading the real dataset works smoothly for all configurations: I recreated the `dataset_infos.json` file to check it (it generated the same file).", "This PR should be merged first:\r\n- #4611", "Impressive, thank you ! :o \r\n\r\nfeel free to merge master into this branch, now that the files order is respected. You can merge if the CI is green :)" ]	2022-06-27T17:11:23Z	2022-07-04T19:35:01Z	2022-07-04T19:23:57Z	MEMBER	null	null	null	Support streaming cfq dataset.	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/4579/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/4579/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/4579.diff", "html_url": "https://github.com/huggingface/datasets/pull/4579", "merged_at": "2022-07-04T19:23:57Z", "patch_url": "https://github.com/huggingface/datasets/pull/4579.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/4579" }
https://api.github.com/repos/huggingface/datasets/issues/6049	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6049/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6049/comments	https://api.github.com/repos/huggingface/datasets/issues/6049/events	https://github.com/huggingface/datasets/pull/6049	1,810,378,706	PR_kwDODunzps5Vz1pd	6,049	Update `ruff` version in pre-commit config	{ "avatar_url": "https://avatars.githubusercontent.com/u/16348744?v=4", "events_url": "https://api.github.com/users/polinaeterna/events{/privacy}", "followers_url": "https://api.github.com/users/polinaeterna/followers", "following_url": "https://api.github.com/users/polinaeterna/following{/other_user}", "gists_url": "https://api.github.com/users/polinaeterna/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/polinaeterna", "id": 16348744, "login": "polinaeterna", "node_id": "MDQ6VXNlcjE2MzQ4NzQ0", "organizations_url": "https://api.github.com/users/polinaeterna/orgs", "received_events_url": "https://api.github.com/users/polinaeterna/received_events", "repos_url": "https://api.github.com/users/polinaeterna/repos", "site_admin": false, "starred_url": "https://api.github.com/users/polinaeterna/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/polinaeterna/subscriptions", "type": "User", "url": "https://api.github.com/users/polinaeterna", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6049). All of your documentation changes will be reflected on that endpoint.", "I've updated the `ruff`'s pre-commit version as part of https://github.com/huggingface/datasets/pull/6434, so feel free to close this PR." ]	2023-07-18T17:13:50Z	2023-12-01T14:26:19Z	2023-12-01T14:26:19Z	CONTRIBUTOR	null	null	null	so that it corresponds to the one that is being run in CI	{ "avatar_url": "https://avatars.githubusercontent.com/u/16348744?v=4", "events_url": "https://api.github.com/users/polinaeterna/events{/privacy}", "followers_url": "https://api.github.com/users/polinaeterna/followers", "following_url": "https://api.github.com/users/polinaeterna/following{/other_user}", "gists_url": "https://api.github.com/users/polinaeterna/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/polinaeterna", "id": 16348744, "login": "polinaeterna", "node_id": "MDQ6VXNlcjE2MzQ4NzQ0", "organizations_url": "https://api.github.com/users/polinaeterna/orgs", "received_events_url": "https://api.github.com/users/polinaeterna/received_events", "repos_url": "https://api.github.com/users/polinaeterna/repos", "site_admin": false, "starred_url": "https://api.github.com/users/polinaeterna/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/polinaeterna/subscriptions", "type": "User", "url": "https://api.github.com/users/polinaeterna", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6049/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6049/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6049.diff", "html_url": "https://github.com/huggingface/datasets/pull/6049", "merged_at": null, "patch_url": "https://github.com/huggingface/datasets/pull/6049.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6049" }
https://api.github.com/repos/huggingface/datasets/issues/5270	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5270/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5270/comments	https://api.github.com/repos/huggingface/datasets/issues/5270/events	https://github.com/huggingface/datasets/issues/5270	1,456,508,990	I_kwDODunzps5W0JA-	5,270	When len(_URLS) > 16, download will hang	{ "avatar_url": "https://avatars.githubusercontent.com/u/32936898?v=4", "events_url": "https://api.github.com/users/Freed-Wu/events{/privacy}", "followers_url": "https://api.github.com/users/Freed-Wu/followers", "following_url": "https://api.github.com/users/Freed-Wu/following{/other_user}", "gists_url": "https://api.github.com/users/Freed-Wu/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/Freed-Wu", "id": 32936898, "login": "Freed-Wu", "node_id": "MDQ6VXNlcjMyOTM2ODk4", "organizations_url": "https://api.github.com/users/Freed-Wu/orgs", "received_events_url": "https://api.github.com/users/Freed-Wu/received_events", "repos_url": "https://api.github.com/users/Freed-Wu/repos", "site_admin": false, "starred_url": "https://api.github.com/users/Freed-Wu/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/Freed-Wu/subscriptions", "type": "User", "url": "https://api.github.com/users/Freed-Wu", "user_view_type": "public" }	[]	open	false	null	[]	null	[ "It can fix the bug temporarily.\r\n```python\r\nfrom datasets import DownloadConfig\r\nconfig = DownloadConfig(num_proc=8)\r\nIn [5]: dataset = load_dataset('Freed-Wu/kodak', split='test', download_config=config)\r\nDownloading and preparing dataset kodak/default to /home/wzy/.cache/huggingface/datasets/Freed-Wu___kodak/default/0.0.1/6cf51f2b3d686d24a33fe86945f9e16802def212325f9345cf3cbb1b9f5f4a57...\r\nDownloading data files #4: 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100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 3/3 [00:13<00:00, 4.63s/obj]\r\nExtracting data files #0: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 3/3 [00:00<00:00, 1407.17obj/s]\r\nExtracting data files #1: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 3/3 [00:00<00:00, 1325.91obj/s]\r\nExtracting data files #3: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 3/3 [00:00<00:00, 1524.46obj/s]\r\nExtracting data files #2: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 3/3 [00:00<00:00, 1404.66obj/s]\r\nExtracting data files #4: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 3/3 [00:00<00:00, 1538.63obj/s]\r\nExtracting data files #6: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 3/3 [00:00<00:00, 1711.73obj/s]\r\nExtracting data files #7: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 3/3 [00:00<00:00, 2144.33obj/s]\r\nExtracting data files #5: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 3/3 [00:00<00:00, 1964.85obj/s]\r\nDataset kodak downloaded and prepared to /home/wzy/.cache/huggingface/datasets/Freed-Wu___kodak/default/0.0.1/6cf51f2b3d686d24a33fe86945f9e16802def212325f9345cf3cbb1b9f5f4a57. Subsequent calls will reuse this data.\r\n```", "Thanks for reporting ! This sounds like an issue with python multiprocessing. If we switch to multithreading for the downloads it should be much more robust - let me know if this is something you'd like to contribute, I'd be happy to help and give you some pointers", "> an issue with python multiprocessing\r\n\r\nIf it is an issue with multiprocessing, should we report it to upstream?", "Debugging this would require quite some work in my opinion, and I've often failed to make reproducible examples, since it's pretty correlated to one's environment + hardware. So I wouldn't spend too much time on this unless we manage to reproduce this on another machine consistently.\r\n\r\nInstead I'd encourage a more pragmatic fix that is: not create tons of processes (on regular machines it may slow things down anyway), and instead use multithreading by default.", "I am not expert of python. I hear about python has GIL, which result in multi processing is worse than multi threading. So I am not sure if this change makes sense?\r\n\r\nAnd if this is a bug of multi processing, why not report to upstream and let them fix? And even if change it to multi threading, how can we make sure it can truly fix this problem?", "Just my 2c. No offense.", "> Just my 2c. No offense.\r\n\r\nsure np ^^\r\n\r\n> I hear about python has GIL, which result in multi processing is worse than multi threading. So I am not sure if this change makes sense?\r\n\r\nHere the bottleneck speed is the bandwidth used to download the files. When downloading, the GIL is released, so multithreading gives the same speed as multiprocessing.\r\n\r\n> And if this is a bug of multi processing, why not report to upstream and let them fix?\r\n\r\nUsually to fix a bug it's important to be able to reproduce it. This way you can share it, experiment with it, and then make sure it's fixed. Here I'm afraid it's not easy to reproduce. Though I think that spawning too many processes for your machine can lead to this kind of issues.\r\n\r\n> And even if change it to multi threading, how can we make sure it can truly fix this problem?\r\n\r\nMultithreading is more robust in python because IIRC there are less locks involved which are often the cause of code hanging for no reason." ]	2022-11-19T14:27:41Z	2022-11-21T15:27:16Z	null	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug ```python In [9]: dataset = load_dataset('Freed-Wu/kodak', split='test') Downloading: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 2.53k/2.53k [00:00<00:00, 1.88MB/s] [11/19/22 22:16:21] WARNING Using custom data configuration default builder.py:379 Downloading and preparing dataset kodak/default to /home/wzy/.cache/huggingface/datasets/Freed-Wu___kodak/default/0.0.1/bd1cc3434212e3e654f7e16ad618f8a1470b5982b086c91b1d6bc7187183c6e9... Downloading: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 531k/531k [00:02<00:00, 239kB/s] #10: 100%\|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:04<00:00, 4.06s/obj] Downloading: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 534k/534k [00:02<00:00, 193kB/s] #14: 100%\|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:04<00:00, 4.37s/obj] Downloading: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 692k/692k [00:02<00:00, 269kB/s] #12: 100%\|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:04<00:00, 4.44s/obj] Downloading: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 566k/566k [00:02<00:00, 210kB/s] #5: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:04<00:00, 4.53s/obj] Downloading: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 613k/613k [00:02<00:00, 235kB/s] #13: 100%\|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:04<00:00, 4.53s/obj] Downloading: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 786k/786k [00:02<00:00, 342kB/s] #3: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:04<00:00, 4.60s/obj] Downloading: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 619k/619k [00:02<00:00, 254kB/s] #4: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:04<00:00, 4.68s/obj] Downloading: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 737k/737k [00:02<00:00, 271kB/s] Downloading: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 788k/788k [00:02<00:00, 285kB/s] #6: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:05<00:00, 5.04s/obj] Downloading: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 618k/618k [00:04<00:00, 153kB/s] #0: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 2/2 [00:11<00:00, 5.69s/obj] ^CProcess ForkPoolWorker-47: Process ForkPoolWorker-46: Process ForkPoolWorker-36: Process ForkPoolWorker-38:██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1/1 [00:05<00:00, 5.04s/obj] Process ForkPoolWorker-37: Process ForkPoolWorker-45: Process ForkPoolWorker-39: Process ForkPoolWorker-43: Process ForkPoolWorker-33: Process ForkPoolWorker-18: Traceback (most recent call last): Traceback (most recent call last): Traceback (most recent call last): Traceback (most recent call last): Traceback (most recent call last): File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap self.run() File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap self.run() File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap self.run() File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run self._target(self._args, self._kwargs) File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap self.run() File "/usr/lib/python3.10/multiprocessing/pool.py", line 114, in worker task = get() File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap self.run() File "/usr/lib/python3.10/multiprocessing/queues.py", line 364, in get with self._rlock: File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run self._target(self._args, *self._kwargs) File "/usr/lib/python3.10/multiprocessing/synchronize.py", line 95, in __enter__ return self._semlock.__enter__() File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run self._target(self._args, *self._kwargs) File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run self._target(self._args, *self._kwargs) File "/usr/lib/python3.10/multiprocessing/pool.py", line 114, in worker task = get() File "/usr/lib/python3.10/multiprocessing/pool.py", line 114, in worker task = get() File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run self._target(self._args, *self._kwargs) File "/usr/lib/python3.10/multiprocessing/queues.py", line 364, in get with self._rlock: File "/usr/lib/python3.10/multiprocessing/pool.py", line 114, in worker task = get() File "/usr/lib/python3.10/multiprocessing/queues.py", line 364, in get with self._rlock: KeyboardInterrupt File "/usr/lib/python3.10/multiprocessing/synchronize.py", line 95, in __enter__ return self._semlock.__enter__() Traceback (most recent call last): Traceback (most recent call last): Traceback (most recent call last): KeyboardInterrupt File "/usr/lib/python3.10/multiprocessing/pool.py", line 114, in worker task = get() File "/usr/lib/python3.10/multiprocessing/queues.py", line 364, in get with self._rlock: File "/usr/lib/python3.10/multiprocessing/queues.py", line 364, in get with self._rlock: File "/usr/lib/python3.10/multiprocessing/synchronize.py", line 95, in __enter__ return self._semlock.__enter__() File "/usr/lib/python3.10/multiprocessing/synchronize.py", line 95, in __enter__ return self._semlock.__enter__() KeyboardInterrupt File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap self.run() File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap self.run() KeyboardInterrupt File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap self.run() File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run self._target(self._args, *self._kwargs) File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run self._target(self._args, *self._kwargs) File "/usr/lib/python3.10/multiprocessing/pool.py", line 114, in worker task = get() File "/usr/lib/python3.10/multiprocessing/synchronize.py", line 95, in __enter__ return self._semlock.__enter__() File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run self._target(self._args, *self._kwargs) File "/usr/lib/python3.10/multiprocessing/pool.py", line 114, in worker task = get() File "/usr/lib/python3.10/multiprocessing/pool.py", line 114, in worker task = get() File "/usr/lib/python3.10/multiprocessing/queues.py", line 364, in get with self._rlock: File "/usr/lib/python3.10/multiprocessing/queues.py", line 365, in get res = self._reader.recv_bytes() File "/usr/lib/python3.10/multiprocessing/queues.py", line 364, in get with self._rlock: File "/usr/lib/python3.10/multiprocessing/synchronize.py", line 95, in __enter__ return self._semlock.__enter__() KeyboardInterrupt File "/usr/lib/python3.10/multiprocessing/synchronize.py", line 95, in __enter__ return self._semlock.__enter__() File "/usr/lib/python3.10/multiprocessing/connection.py", line 221, in recv_bytes buf = self._recv_bytes(maxlength) KeyboardInterrupt KeyboardInterrupt File "/usr/lib/python3.10/multiprocessing/connection.py", line 419, in _recv_bytes buf = self._recv(4) File "/usr/lib/python3.10/multiprocessing/connection.py", line 384, in _recv chunk = read(handle, remaining) KeyboardInterrupt Traceback (most recent call last): File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap self.run() File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run self._target(self._args, *self._kwargs) File "/usr/lib/python3.10/multiprocessing/pool.py", line 114, in worker task = get() File "/usr/lib/python3.10/multiprocessing/queues.py", line 364, in get with self._rlock: File "/usr/lib/python3.10/multiprocessing/synchronize.py", line 95, in __enter__ return self._semlock.__enter__() KeyboardInterrupt Process ForkPoolWorker-20: Process ForkPoolWorker-44: Process ForkPoolWorker-22: Traceback (most recent call last): File "/usr/lib/python3.10/site-packages/urllib3/util/connection.py", line 85, in create_connection sock.connect(sa) ConnectionRefusedError: [Errno 111] Connection refused During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap self.run() File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run self._target(self._args, *self._kwargs) File "/usr/lib/python3.10/multiprocessing/pool.py", line 125, in worker result = (True, func(args, *kwds)) File "/usr/lib/python3.10/multiprocessing/pool.py", line 48, in mapstar return list(map(args)) File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 215, in _single_map_nested mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar] File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 215, in <listcomp> mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar] File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 197, in _single_map_nested return function(data_struct) File "/usr/lib/python3.10/site-packages/datasets/utils/download_manager.py", line 217, in _download return cached_path(url_or_filename, download_config=download_config) File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 298, in cached_path output_path = get_from_cache( File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 561, in get_from_cache response = http_head( File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 476, in http_head response = _request_with_retry( File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 405, in _request_with_retry response = requests.request(method=method.upper(), url=url, timeout=timeout, params) File "/usr/lib/python3.10/site-packages/requests/api.py", line 59, in request return session.request(method=method, url=url, kwargs) File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 587, in request resp = self.send(prep, send_kwargs) File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 701, in send r = adapter.send(request, kwargs) File "/usr/lib/python3.10/site-packages/requests/adapters.py", line 489, in send resp = conn.urlopen( File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 703, in urlopen httplib_response = self._make_request( File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 386, in _make_request self._validate_conn(conn) File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 1042, in _validate_conn conn.connect() File "/usr/lib/python3.10/site-packages/urllib3/connection.py", line 358, in connect self.sock = conn = self._new_conn() File "/usr/lib/python3.10/site-packages/urllib3/connection.py", line 174, in _new_conn conn = connection.create_connection( File "/usr/lib/python3.10/site-packages/urllib3/util/connection.py", line 85, in create_connection sock.connect(sa) KeyboardInterrupt #1: 0%\| \| 0/2 [03:00<?, ?obj/s] Traceback (most recent call last): Traceback (most recent call last): File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap self.run() File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run self._target(self._args, self._kwargs) File "/usr/lib/python3.10/multiprocessing/pool.py", line 125, in worker result = (True, func(args, *kwds)) File "/usr/lib/python3.10/multiprocessing/pool.py", line 48, in mapstar return list(map(args)) File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 215, in _single_map_nested mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar] File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 215, in <listcomp> mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar] File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 197, in _single_map_nested return function(data_struct) File "/usr/lib/python3.10/site-packages/datasets/utils/download_manager.py", line 217, in _download return cached_path(url_or_filename, download_config=download_config) File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 298, in cached_path output_path = get_from_cache( File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 659, in get_from_cache http_get( File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 442, in http_get response = _request_with_retry( File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 405, in _request_with_retry response = requests.request(method=method.upper(), url=url, timeout=timeout, params) File "/usr/lib/python3.10/site-packages/requests/api.py", line 59, in request return session.request(method=method, url=url, kwargs) File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 587, in request resp = self.send(prep, send_kwargs) File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 701, in send r = adapter.send(request, kwargs) File "/usr/lib/python3.10/site-packages/requests/adapters.py", line 489, in send resp = conn.urlopen( File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 703, in urlopen httplib_response = self._make_request( File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 386, in _make_request self._validate_conn(conn) File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap self.run() File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 1042, in _validate_conn conn.connect() File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run self._target(self._args, self._kwargs) File "/usr/lib/python3.10/site-packages/urllib3/connection.py", line 358, in connect self.sock = conn = self._new_conn() File "/usr/lib/python3.10/multiprocessing/pool.py", line 125, in worker result = (True, func(args, *kwds)) File "/usr/lib/python3.10/site-packages/urllib3/connection.py", line 174, in _new_conn conn = connection.create_connection( File "/usr/lib/python3.10/multiprocessing/pool.py", line 48, in mapstar return list(map(args)) File "/usr/lib/python3.10/site-packages/urllib3/util/connection.py", line 72, in create_connection for res in socket.getaddrinfo(host, port, family, socket.SOCK_STREAM): File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 215, in _single_map_nested mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar] File "/usr/lib/python3.10/socket.py", line 955, in getaddrinfo for res in _socket.getaddrinfo(host, port, family, type, proto, flags): File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 215, in <listcomp> mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar] File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 197, in _single_map_nested return function(data_struct) File "/usr/lib/python3.10/site-packages/datasets/utils/download_manager.py", line 217, in _download return cached_path(url_or_filename, download_config=download_config) KeyboardInterrupt File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 298, in cached_path output_path = get_from_cache( File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 561, in get_from_cache response = http_head( File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 476, in http_head response = _request_with_retry( File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 405, in _request_with_retry response = requests.request(method=method.upper(), url=url, timeout=timeout, params) File "/usr/lib/python3.10/site-packages/requests/api.py", line 59, in request return session.request(method=method, url=url, kwargs) File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 587, in request resp = self.send(prep, send_kwargs) File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 701, in send r = adapter.send(request, kwargs) File "/usr/lib/python3.10/site-packages/requests/adapters.py", line 489, in send resp = conn.urlopen( File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 703, in urlopen httplib_response = self._make_request( File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 386, in _make_request self._validate_conn(conn) File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 1042, in _validate_conn conn.connect() File "/usr/lib/python3.10/site-packages/urllib3/connection.py", line 358, in connect self.sock = conn = self._new_conn() File "/usr/lib/python3.10/site-packages/urllib3/connection.py", line 174, in _new_conn conn = connection.create_connection( File "/usr/lib/python3.10/site-packages/urllib3/util/connection.py", line 72, in create_connection for res in socket.getaddrinfo(host, port, family, socket.SOCK_STREAM): File "/usr/lib/python3.10/socket.py", line 955, in getaddrinfo for res in _socket.getaddrinfo(host, port, family, type, proto, flags): KeyboardInterrupt #3: 0%\| \| 0/2 [03:00<?, ?obj/s] #11: 0%\| \| 0/1 [00:49<?, ?obj/s] Traceback (most recent call last): File "/usr/lib/python3.10/site-packages/urllib3/util/connection.py", line 85, in create_connection sock.connect(sa) ConnectionRefusedError: [Errno 111] Connection refused During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap self.run() File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run self._target(self._args, self._kwargs) File "/usr/lib/python3.10/multiprocessing/pool.py", line 125, in worker result = (True, func(args, *kwds)) File "/usr/lib/python3.10/multiprocessing/pool.py", line 48, in mapstar return list(map(args)) File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 215, in _single_map_nested mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar] File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 215, in <listcomp> mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar] File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 197, in _single_map_nested return function(data_struct) File "/usr/lib/python3.10/site-packages/datasets/utils/download_manager.py", line 217, in _download return cached_path(url_or_filename, download_config=download_config) File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 298, in cached_path output_path = get_from_cache( File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 561, in get_from_cache response = http_head( File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 476, in http_head response = _request_with_retry( File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 405, in _request_with_retry response = requests.request(method=method.upper(), url=url, timeout=timeout, params) File "/usr/lib/python3.10/site-packages/requests/api.py", line 59, in request return session.request(method=method, url=url, kwargs) File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 587, in request resp = self.send(prep, send_kwargs) File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 723, in send history = [resp for resp in gen] File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 723, in <listcomp> history = [resp for resp in gen] File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 266, in resolve_redirects resp = self.send( File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 701, in send r = adapter.send(request, kwargs) File "/usr/lib/python3.10/site-packages/requests/adapters.py", line 489, in send resp = conn.urlopen( File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 703, in urlopen httplib_response = self._make_request( File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 386, in _make_request self._validate_conn(conn) File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 1042, in _validate_conn conn.connect() File "/usr/lib/python3.10/site-packages/urllib3/connection.py", line 358, in connect self.sock = conn = self._new_conn() File "/usr/lib/python3.10/site-packages/urllib3/connection.py", line 174, in _new_conn conn = connection.create_connection( File "/usr/lib/python3.10/site-packages/urllib3/util/connection.py", line 85, in create_connection sock.connect(sa) KeyboardInterrupt #5: 0%\| \| 0/1 [03:00<?, ?obj/s] KeyboardInterrupt Process ForkPoolWorker-42: Traceback (most recent call last): File "/usr/lib/python3.10/multiprocessing/process.py", line 314, in _bootstrap self.run() File "/usr/lib/python3.10/multiprocessing/process.py", line 108, in run self._target(self._args, self._kwargs) File "/usr/lib/python3.10/multiprocessing/pool.py", line 125, in worker result = (True, func(args, *kwds)) File "/usr/lib/python3.10/multiprocessing/pool.py", line 48, in mapstar return list(map(args)) File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 215, in _single_map_nested mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar] File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 215, in <listcomp> mapped = [_single_map_nested((function, v, types, None, True)) for v in pbar] File "/usr/lib/python3.10/site-packages/datasets/utils/py_utils.py", line 197, in _single_map_nested return function(data_struct) File "/usr/lib/python3.10/site-packages/datasets/utils/download_manager.py", line 217, in _download return cached_path(url_or_filename, download_config=download_config) File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 298, in cached_path output_path = get_from_cache( File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 561, in get_from_cache response = http_head( File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 476, in http_head response = _request_with_retry( File "/usr/lib/python3.10/site-packages/datasets/utils/file_utils.py", line 405, in _request_with_retry response = requests.request(method=method.upper(), url=url, timeout=timeout, params) File "/usr/lib/python3.10/site-packages/requests/api.py", line 59, in request return session.request(method=method, url=url, kwargs) File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 587, in request resp = self.send(prep, send_kwargs) File "/usr/lib/python3.10/site-packages/requests/sessions.py", line 701, in send r = adapter.send(request, kwargs) File "/usr/lib/python3.10/site-packages/requests/adapters.py", line 489, in send resp = conn.urlopen( File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 703, in urlopen httplib_response = self._make_request( File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 386, in _make_request self._validate_conn(conn) File "/usr/lib/python3.10/site-packages/urllib3/connectionpool.py", line 1042, in _validate_conn conn.connect() File "/usr/lib/python3.10/site-packages/urllib3/connection.py", line 358, in connect self.sock = conn = self._new_conn() File "/usr/lib/python3.10/site-packages/urllib3/connection.py", line 174, in _new_conn conn = connection.create_connection( File "/usr/lib/python3.10/site-packages/urllib3/util/connection.py", line 72, in create_connection for res in socket.getaddrinfo(host, port, family, socket.SOCK_STREAM): File "/usr/lib/python3.10/socket.py", line 955, in getaddrinfo for res in _socket.getaddrinfo(host, port, family, type, proto, flags): KeyboardInterrupt #9: 0%\| \| 0/1 [00:51<?, ?obj/s] ``` ### Steps to reproduce the bug ```python """Kodak. Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ import datasets NUMBER = 17 _DESCRIPTION = """\ The pictures below link to lossless, true color (24 bits per pixel, aka "full color") images. It is my understanding they have been released by the Eastman Kodak Company for unrestricted usage. Many sites use them as a standard test suite for compression testing, etc. Prior to this site, they were only available in the Sun Raster format via ftp. This meant that the images could not be previewed before downloading. Since their release, however, the lossless PNG format has been incorporated into all the major browsers. Since PNG supports 24-bit lossless color (which GIF and JPEG do not), it is now possible to offer this browser-friendly access to the images. """ _HOMEPAGE = "https://r0k.us/graphics/kodak/" _LICENSE = "GPLv3" _URLS = [ f"https://github.com/MohamedBakrAli/Kodak-Lossless-True-Color-Image-Suite/raw/master/PhotoCD_PCD0992/{i}.png" for i in range(1, 1 + NUMBER) ] class Kodak(datasets.GeneratorBasedBuilder): """Kodak datasets.""" VERSION = datasets.Version("0.0.1") def _info(self): features = datasets.Features( { "image": datasets.Image(), } ) return datasets.DatasetInfo( description=_DESCRIPTION, features=features, homepage=_HOMEPAGE, license=_LICENSE, ) def _split_generators(self, dl_manager): """Return SplitGenerators.""" file_paths = dl_manager.download_and_extract(_URLS) return [ datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={ "file_paths": file_paths, }, ), ] def _generate_examples(self, file_paths): """Yield examples.""" for file_path in file_paths: yield file_path, {"image": file_path} ``` ### Expected behavior When `len(_URLS) < 16`, it works. ```python In [3]: dataset = load_dataset('Freed-Wu/kodak', split='test') Downloading: 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 2.53k/2.53k [00:00<00:00, 3.02MB/s] [11/19/22 22:04:28] WARNING Using custom data configuration default builder.py:379 Downloading and preparing dataset kodak/default to /home/wzy/.cache/huggingface/datasets/Freed-Wu___kodak/default/0.0.1/d26017602a592b5bfa7e008127cdf9dec5af220c9068005f1b4eda036031f475... Downloading: 100%\|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 593k/593k [00:00<00:00, 2.88MB/s] Downloading: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 621k/621k [00:03<00:00, 166kB/s] Downloading: 100%\|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 531k/531k [00:01<00:00, 366kB/s] 100%\|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 16/16 [00:13<00:00, 1.18it/s] 100%\|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 16/16 [00:00<00:00, 3832.38it/s] Dataset kodak downloaded and prepared to /home/wzy/.cache/huggingface/datasets/Freed-Wu___kodak/default/0.0.1/d26017602a592b5bfa7e008127cdf9dec5af220c9068005f1b4eda036031f475. Subsequent calls will reuse this data. ``` ### Environment info - `datasets` version: 2.7.0 - Platform: Linux-6.0.8-arch1-1-x86_64-with-glibc2.36 - Python version: 3.10.8 - PyArrow version: 9.0.0 - Pandas version: 1.4.4	null	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/5270/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/5270/timeline	null	null	null	null
https://api.github.com/repos/huggingface/datasets/issues/7172	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/7172/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/7172/comments	https://api.github.com/repos/huggingface/datasets/issues/7172/events	https://github.com/huggingface/datasets/pull/7172	2,549,781,691	PR_kwDODunzps58wNQ7	7,172	Add torchdata as a regular test dependency	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_7172). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update." ]	2024-09-26T07:45:55Z	2024-09-26T08:12:12Z	2024-09-26T08:05:40Z	MEMBER	null	null	null	Add `torchdata` as a regular test dependency. Note that previously, `torchdata` was installed from their repo and current main branch (0.10.0.dev) requires Python>=3.9. Also note they made a recent release: 0.8.0 on Jul 31, 2024. Fix #7171.	{ "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/albertvillanova", "id": 8515462, "login": "albertvillanova", "node_id": "MDQ6VXNlcjg1MTU0NjI=", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "repos_url": "https://api.github.com/users/albertvillanova/repos", "site_admin": false, "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "type": "User", "url": "https://api.github.com/users/albertvillanova", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/7172/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/7172/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/7172.diff", "html_url": "https://github.com/huggingface/datasets/pull/7172", "merged_at": "2024-09-26T08:05:40Z", "patch_url": "https://github.com/huggingface/datasets/pull/7172.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/7172" }
https://api.github.com/repos/huggingface/datasets/issues/5899	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/5899/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/5899/comments	https://api.github.com/repos/huggingface/datasets/issues/5899/events	https://github.com/huggingface/datasets/pull/5899	1,726,279,011	PR_kwDODunzps5RXods	5,899	canonicalize data dir in config ID hash	{ "avatar_url": "https://avatars.githubusercontent.com/u/5044802?v=4", "events_url": "https://api.github.com/users/kylrth/events{/privacy}", "followers_url": "https://api.github.com/users/kylrth/followers", "following_url": "https://api.github.com/users/kylrth/following{/other_user}", "gists_url": "https://api.github.com/users/kylrth/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/kylrth", "id": 5044802, "login": "kylrth", "node_id": "MDQ6VXNlcjUwNDQ4MDI=", "organizations_url": "https://api.github.com/users/kylrth/orgs", "received_events_url": "https://api.github.com/users/kylrth/received_events", "repos_url": "https://api.github.com/users/kylrth/repos", "site_admin": false, "starred_url": "https://api.github.com/users/kylrth/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/kylrth/subscriptions", "type": "User", "url": "https://api.github.com/users/kylrth", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009137 / 0.011353 (-0.002216) \| 0.006119 / 0.011008 (-0.004889) \| 0.136530 / 0.038508 (0.098022) \| 0.038434 / 0.023109 (0.015325) \| 0.427900 / 0.275898 (0.152002) \| 0.449757 / 0.323480 (0.126277) \| 0.007673 / 0.007986 (-0.000313) \| 0.007147 / 0.004328 (0.002818) \| 0.108029 / 0.004250 (0.103778) \| 0.055072 / 0.037052 (0.018020) \| 0.439245 / 0.258489 (0.180756) \| 0.477285 / 0.293841 (0.183444) \| 0.044838 / 0.128546 (-0.083708) \| 0.020814 / 0.075646 (-0.054832) \| 0.436098 / 0.419271 (0.016826) \| 0.067459 / 0.043533 (0.023926) \| 0.427470 / 0.255139 (0.172331) \| 0.443260 / 0.283200 (0.160060) \| 0.125466 / 0.141683 (-0.016216) \| 1.996756 / 1.452155 (0.544601) \| 2.100679 / 1.492716 (0.607962) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.278407 / 0.018006 (0.260401) \| 0.625855 / 0.000490 (0.625365) \| 0.005544 / 0.000200 (0.005344) \| 0.000107 / 0.000054 (0.000053) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033495 / 0.037411 (-0.003916) \| 0.134718 / 0.014526 (0.120192) \| 0.150151 / 0.176557 (-0.026406) \| 0.221385 / 0.737135 (-0.515751) \| 0.150932 / 0.296338 (-0.145406) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.668845 / 0.215209 (0.453636) \| 6.678436 / 2.077655 (4.600781) \| 2.714074 / 1.504120 (1.209954) \| 2.275784 / 1.541195 (0.734589) \| 2.332852 / 1.468490 (0.864361) \| 1.014877 / 4.584777 (-3.569900) \| 6.086455 / 3.745712 (2.340743) \| 2.990029 / 5.269862 (-2.279832) \| 1.862236 / 4.565676 (-2.703441) \| 0.122179 / 0.424275 (-0.302096) \| 0.015706 / 0.007607 (0.008099) \| 0.873473 / 0.226044 (0.647429) \| 8.580109 / 2.268929 (6.311180) \| 3.458360 / 55.444624 (-51.986264) \| 2.738801 / 6.876477 (-4.137676) \| 2.918428 / 2.142072 (0.776356) \| 1.224910 / 4.805227 (-3.580317) \| 0.243006 / 6.500664 (-6.257658) \| 0.087121 / 0.075469 (0.011652) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.757802 / 1.841788 (-0.083986) \| 19.447999 / 8.074308 (11.373691) \| 24.518157 / 10.191392 (14.326765) \| 0.245013 / 0.680424 (-0.435411) \| 0.032290 / 0.534201 (-0.501911) \| 0.542043 / 0.579283 (-0.037240) \| 0.708154 / 0.434364 (0.273790) \| 0.660584 / 0.540337 (0.120247) \| 0.794868 / 1.386936 (-0.592068) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.009496 / 0.011353 (-0.001857) \| 0.005842 / 0.011008 (-0.005166) \| 0.112813 / 0.038508 (0.074305) \| 0.039120 / 0.023109 (0.016011) \| 0.489717 / 0.275898 (0.213819) \| 0.532586 / 0.323480 (0.209107) \| 0.007681 / 0.007986 (-0.000304) \| 0.005337 / 0.004328 (0.001009) \| 0.107244 / 0.004250 (0.102994) \| 0.056847 / 0.037052 (0.019794) \| 0.499447 / 0.258489 (0.240958) \| 0.548995 / 0.293841 (0.255154) \| 0.058047 / 0.128546 (-0.070499) \| 0.015468 / 0.075646 (-0.060179) \| 0.124600 / 0.419271 (-0.294671) \| 0.060940 / 0.043533 (0.017407) \| 0.488370 / 0.255139 (0.233231) \| 0.518540 / 0.283200 (0.235341) \| 0.124147 / 0.141683 (-0.017536) \| 1.902922 / 1.452155 (0.450767) \| 2.033519 / 1.492716 (0.540803) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.319527 / 0.018006 (0.301521) \| 0.629641 / 0.000490 (0.629152) \| 0.000721 / 0.000200 (0.000521) \| 0.000101 / 0.000054 (0.000046) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.033150 / 0.037411 (-0.004262) \| 0.134250 / 0.014526 (0.119724) \| 0.161273 / 0.176557 (-0.015283) \| 0.211471 / 0.737135 (-0.525664) \| 0.155326 / 0.296338 (-0.141012) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.705244 / 0.215209 (0.490035) \| 7.043040 / 2.077655 (4.965386) \| 3.308948 / 1.504120 (1.804828) \| 2.885050 / 1.541195 (1.343855) \| 2.810260 / 1.468490 (1.341770) \| 1.027095 / 4.584777 (-3.557682) \| 6.111398 / 3.745712 (2.365686) \| 5.385545 / 5.269862 (0.115684) \| 2.521668 / 4.565676 (-2.044009) \| 0.122419 / 0.424275 (-0.301856) \| 0.016376 / 0.007607 (0.008768) \| 0.830856 / 0.226044 (0.604811) \| 8.952199 / 2.268929 (6.683271) \| 4.207875 / 55.444624 (-51.236749) \| 3.346624 / 6.876477 (-3.529853) \| 3.395316 / 2.142072 (1.253244) \| 1.351816 / 4.805227 (-3.453411) \| 0.303056 / 6.500664 (-6.197608) \| 0.098713 / 0.075469 (0.023244) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.841903 / 1.841788 (0.000116) \| 20.472125 / 8.074308 (12.397817) \| 23.433200 / 10.191392 (13.241808) \| 0.242599 / 0.680424 (-0.437825) \| 0.030701 / 0.534201 (-0.503500) \| 0.541614 / 0.579283 (-0.037669) \| 0.657827 / 0.434364 (0.223463) \| 0.652448 / 0.540337 (0.112111) \| 0.773743 / 1.386936 (-0.613193) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#02ee418831aba68d0be93227bce8b3f42ef8980f \"CML watermark\")\n" ]	2023-05-25T18:17:10Z	2023-06-02T16:02:15Z	2023-06-02T15:52:04Z	CONTRIBUTOR	null	null	null	fixes #5871 The second commit is optional but improves readability.	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": 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https://api.github.com/repos/huggingface/datasets/issues/7498	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/7498/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/7498/comments	https://api.github.com/repos/huggingface/datasets/issues/7498/events	https://github.com/huggingface/datasets/issues/7498	2,969,218,273	I_kwDODunzps6w-qzh	7,498	Extreme memory bandwidth.	{ "avatar_url": "https://avatars.githubusercontent.com/u/185079645?v=4", "events_url": "https://api.github.com/users/J0SZ/events{/privacy}", "followers_url": "https://api.github.com/users/J0SZ/followers", "following_url": "https://api.github.com/users/J0SZ/following{/other_user}", "gists_url": "https://api.github.com/users/J0SZ/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/J0SZ", "id": 185079645, "login": "J0SZ", "node_id": "U_kgDOCwgXXQ", "organizations_url": "https://api.github.com/users/J0SZ/orgs", "received_events_url": "https://api.github.com/users/J0SZ/received_events", "repos_url": "https://api.github.com/users/J0SZ/repos", "site_admin": false, "starred_url": "https://api.github.com/users/J0SZ/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/J0SZ/subscriptions", "type": "User", "url": "https://api.github.com/users/J0SZ", "user_view_type": "public" }	[]	open	false	null	[]	null	[]	2025-04-03T11:09:08Z	2025-04-03T11:11:22Z	null	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug When I use hf datasets on 4 GPU with 40 workers I get some extreme memory bandwidth of constant ~3GB/s. However, if I wrap the dataset in `IterableDataset`, this issue is gone and the data also loads way faster (4x faster training on 1 worker). It seems like the workers don't share memory and basically duplicate the data 4x40. ### Steps to reproduce the bug Trainer arguments: ``` dataloader_pin_memory=True, dataloader_num_workers=40, dataloader_prefetch_factor=2, dataloader_persistent_workers=True, ``` Call trainer: ``` trainer = Trainer( model=model, args=train_args, train_dataset=load_from_disk('..').with_fromat('torch'), ) ``` The dataset has 600GB and consists of 1225 files. ### Expected behavior The optimal bandwidth should be 100MB/s to keep up with GPU. ### Environment info Linux Python 3.11 datasets==3.2.0	null	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/7498/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/7498/timeline	null	null	null	null
https://api.github.com/repos/huggingface/datasets/issues/4919	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/4919/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/4919/comments	https://api.github.com/repos/huggingface/datasets/issues/4919/events	https://github.com/huggingface/datasets/pull/4919	1,357,441,599	PR_kwDODunzps4-IxDZ	4,919	feat: improve error message on Keys mismatch. closes #4917	{ "avatar_url": "https://avatars.githubusercontent.com/u/25532159?v=4", "events_url": "https://api.github.com/users/PaulLerner/events{/privacy}", "followers_url": "https://api.github.com/users/PaulLerner/followers", "following_url": "https://api.github.com/users/PaulLerner/following{/other_user}", "gists_url": "https://api.github.com/users/PaulLerner/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/PaulLerner", "id": 25532159, "login": "PaulLerner", "node_id": "MDQ6VXNlcjI1NTMyMTU5", "organizations_url": "https://api.github.com/users/PaulLerner/orgs", "received_events_url": "https://api.github.com/users/PaulLerner/received_events", "repos_url": "https://api.github.com/users/PaulLerner/repos", "site_admin": false, "starred_url": "https://api.github.com/users/PaulLerner/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/PaulLerner/subscriptions", "type": "User", "url": "https://api.github.com/users/PaulLerner", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "We are having an unrelated issue that makes several tests fail. We are working on that. Once fixed, you will be able to merge the main branch into this, so that you get the fix and the tests pass..." ]	2022-08-31T14:41:36Z	2022-09-05T08:46:01Z	2022-09-05T08:43:33Z	CONTRIBUTOR	null	null	null	Hi @lhoestq what do you think? Let me give you a code sample: ```py >>> import datasets >>> foo = datasets.Dataset.from_dict({'foo':[0,1], 'bar':[2,3]}) >>> foo.save_to_disk('foo') # edit foo/dataset_info.json e.g. rename the 'foo' feature to 'baz' >>> datasets.load_from_disk('foo') --------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-2-4863e606b330> in <module> ----> 1 datasets.load_from_disk('foo') ~/code/datasets/src/datasets/load.py in load_from_disk(dataset_path, fs, keep_in_memory) 1851 raise FileNotFoundError(f"Directory {dataset_path} not found") 1852 if fs.isfile(Path(dest_dataset_path, config.DATASET_INFO_FILENAME).as_posix()): -> 1853 return Dataset.load_from_disk(dataset_path, fs, keep_in_memory=keep_in_memory) 1854 elif fs.isfile(Path(dest_dataset_path, config.DATASETDICT_JSON_FILENAME).as_posix()): 1855 return DatasetDict.load_from_disk(dataset_path, fs, keep_in_memory=keep_in_memory) ~/code/datasets/src/datasets/arrow_dataset.py in load_from_disk(dataset_path, fs, keep_in_memory) 1230 info=dataset_info, 1231 split=split, -> 1232 fingerprint=state["_fingerprint"], 1233 ) 1234 ~/code/datasets/src/datasets/arrow_dataset.py in __init__(self, arrow_table, info, split, indices_table, fingerprint) 687 self.info.features = inferred_features 688 else: # make sure the nested columns are in the right order --> 689 self.info.features = self.info.features.reorder_fields_as(inferred_features) 690 691 # Infer fingerprint if None ~/code/datasets/src/datasets/features/features.py in reorder_fields_as(self, other) 1771 return source 1772 -> 1773 return Features(recursive_reorder(self, other)) 1774 1775 def flatten(self, max_depth=16) -> "Features": ~/code/datasets/src/datasets/features/features.py in recursive_reorder(source, target, stack) 1760 f"{source.keys()-target.keys()} are missing from dataset.arrow " 1761 f"and {target.keys()-source.keys()} are missing from dataset_info.json"+stack_position) -> 1762 raise ValueError(message) 1763 return {key: recursive_reorder(source[key], target[key], stack + f".{key}") for key in target} 1764 elif isinstance(source, list): ValueError: Keys mismatch: between {'baz': Value(dtype='int64', id=None), 'bar': Value(dtype='int64', id=None)} (dataset_info.json) and {'foo': Value(dtype='int64', id=None), 'bar': Value(dtype='int64', id=None)} (inferred from dataset.arrow). {'baz'} are missing from dataset.arrow and {'foo'} are missing from dataset_info.json ```	{ "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/lhoestq", "id": 42851186, "login": "lhoestq", "node_id": "MDQ6VXNlcjQyODUxMTg2", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "repos_url": "https://api.github.com/users/lhoestq/repos", "site_admin": false, "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "type": "User", "url": "https://api.github.com/users/lhoestq", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/4919/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/4919/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/4919.diff", "html_url": "https://github.com/huggingface/datasets/pull/4919", "merged_at": "2022-09-05T08:43:33Z", "patch_url": "https://github.com/huggingface/datasets/pull/4919.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/4919" }
https://api.github.com/repos/huggingface/datasets/issues/6005	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6005/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6005/comments	https://api.github.com/repos/huggingface/datasets/issues/6005/events	https://github.com/huggingface/datasets/pull/6005	1,788,103,576	PR_kwDODunzps5UoJ91	6,005	Drop Python 3.7 support	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006152 / 0.011353 (-0.005200) \| 0.003916 / 0.011008 (-0.007092) \| 0.097355 / 0.038508 (0.058847) \| 0.037228 / 0.023109 (0.014119) \| 0.315753 / 0.275898 (0.039855) \| 0.387949 / 0.323480 (0.064470) \| 0.004804 / 0.007986 (-0.003181) \| 0.002975 / 0.004328 (-0.001353) \| 0.076932 / 0.004250 (0.072682) \| 0.053497 / 0.037052 (0.016445) \| 0.331143 / 0.258489 (0.072654) \| 0.388347 / 0.293841 (0.094506) \| 0.027535 / 0.128546 (-0.101011) \| 0.008509 / 0.075646 (-0.067137) \| 0.312639 / 0.419271 (-0.106632) \| 0.047212 / 0.043533 (0.003679) \| 0.316875 / 0.255139 (0.061736) \| 0.352191 / 0.283200 (0.068992) \| 0.021380 / 0.141683 (-0.120303) \| 1.541401 / 1.452155 (0.089247) \| 1.519420 / 1.492716 (0.026704) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.206332 / 0.018006 (0.188326) \| 0.412252 / 0.000490 (0.411762) \| 0.005119 / 0.000200 (0.004919) \| 0.000077 / 0.000054 (0.000022) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023856 / 0.037411 (-0.013556) \| 0.098216 / 0.014526 (0.083691) \| 0.106553 / 0.176557 (-0.070003) \| 0.168767 / 0.737135 (-0.568369) \| 0.109244 / 0.296338 (-0.187094) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.457580 / 0.215209 (0.242371) \| 4.583246 / 2.077655 (2.505591) \| 2.296356 / 1.504120 (0.792236) \| 2.096216 / 1.541195 (0.555021) \| 2.159086 / 1.468490 (0.690596) \| 0.557905 / 4.584777 (-4.026872) \| 3.345910 / 3.745712 (-0.399802) \| 1.767436 / 5.269862 (-3.502426) \| 1.021583 / 4.565676 (-3.544094) \| 0.067265 / 0.424275 (-0.357011) \| 0.011411 / 0.007607 (0.003804) \| 0.559841 / 0.226044 (0.333797) \| 5.586892 / 2.268929 (3.317963) \| 2.735520 / 55.444624 (-52.709104) \| 2.429393 / 6.876477 (-4.447084) \| 2.544901 / 2.142072 (0.402829) \| 0.667603 / 4.805227 (-4.137625) \| 0.136244 / 6.500664 (-6.364421) \| 0.066961 / 0.075469 (-0.008508) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.206529 / 1.841788 (-0.635259) \| 13.988306 / 8.074308 (5.913998) \| 13.481813 / 10.191392 (3.290421) \| 0.161901 / 0.680424 (-0.518523) \| 0.016850 / 0.534201 (-0.517351) \| 0.367657 / 0.579283 (-0.211626) \| 0.393343 / 0.434364 (-0.041021) \| 0.465288 / 0.540337 (-0.075050) \| 0.559888 / 1.386936 (-0.827048) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005956 / 0.011353 (-0.005397) \| 0.003734 / 0.011008 (-0.007274) \| 0.077841 / 0.038508 (0.039333) \| 0.036532 / 0.023109 (0.013422) \| 0.438923 / 0.275898 (0.163025) \| 0.490133 / 0.323480 (0.166653) \| 0.004651 / 0.007986 (-0.003335) \| 0.002881 / 0.004328 (-0.001448) \| 0.077868 / 0.004250 (0.073618) \| 0.051700 / 0.037052 (0.014647) \| 0.448018 / 0.258489 (0.189529) \| 0.500304 / 0.293841 (0.206464) \| 0.029051 / 0.128546 (-0.099496) \| 0.008498 / 0.075646 (-0.067148) \| 0.082932 / 0.419271 (-0.336339) \| 0.043665 / 0.043533 (0.000132) \| 0.431613 / 0.255139 (0.176474) \| 0.458749 / 0.283200 (0.175549) \| 0.021951 / 0.141683 (-0.119731) \| 1.556043 / 1.452155 (0.103888) \| 1.588391 / 1.492716 (0.095675) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.220674 / 0.018006 (0.202667) \| 0.415408 / 0.000490 (0.414918) \| 0.002613 / 0.000200 (0.002413) \| 0.000075 / 0.000054 (0.000020) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025548 / 0.037411 (-0.011863) \| 0.103633 / 0.014526 (0.089107) \| 0.115193 / 0.176557 (-0.061364) \| 0.163971 / 0.737135 (-0.573164) \| 0.114754 / 0.296338 (-0.181585) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.456823 / 0.215209 (0.241614) \| 4.569950 / 2.077655 (2.492296) \| 2.196339 / 1.504120 (0.692219) \| 1.985822 / 1.541195 (0.444628) \| 2.044083 / 1.468490 (0.575593) \| 0.567919 / 4.584777 (-4.016858) \| 3.397515 / 3.745712 (-0.348197) \| 1.741087 / 5.269862 (-3.528775) \| 1.041237 / 4.565676 (-3.524440) \| 0.068963 / 0.424275 (-0.355313) \| 0.011677 / 0.007607 (0.004070) \| 0.565010 / 0.226044 (0.338966) \| 5.625886 / 2.268929 (3.356957) \| 2.670658 / 55.444624 (-52.773967) \| 2.300279 / 6.876477 (-4.576198) \| 2.392178 / 2.142072 (0.250106) \| 0.680226 / 4.805227 (-4.125001) \| 0.139119 / 6.500664 (-6.361545) \| 0.067953 / 0.075469 (-0.007516) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.303280 / 1.841788 (-0.538507) \| 14.458686 / 8.074308 (6.384378) \| 14.409369 / 10.191392 (4.217977) \| 0.144581 / 0.680424 (-0.535843) \| 0.016634 / 0.534201 (-0.517567) \| 0.364607 / 0.579283 (-0.214676) \| 0.394521 / 0.434364 (-0.039843) \| 0.433417 / 0.540337 (-0.106921) \| 0.527127 / 1.386936 (-0.859809) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#04a36f9546484dceadb84a133c1a460281d018f8 \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006245 / 0.011353 (-0.005108) \| 0.003871 / 0.011008 (-0.007138) \| 0.098823 / 0.038508 (0.060315) \| 0.039853 / 0.023109 (0.016744) \| 0.314989 / 0.275898 (0.039091) \| 0.376733 / 0.323480 (0.053254) \| 0.004754 / 0.007986 (-0.003232) \| 0.002971 / 0.004328 (-0.001357) \| 0.078451 / 0.004250 (0.074201) \| 0.053160 / 0.037052 (0.016107) \| 0.324443 / 0.258489 (0.065954) \| 0.361488 / 0.293841 (0.067647) \| 0.027942 / 0.128546 (-0.100604) \| 0.008535 / 0.075646 (-0.067111) \| 0.315526 / 0.419271 (-0.103745) \| 0.045706 / 0.043533 (0.002174) \| 0.329614 / 0.255139 (0.074475) \| 0.336339 / 0.283200 (0.053139) \| 0.021278 / 0.141683 (-0.120405) \| 1.529710 / 1.452155 (0.077555) \| 1.566833 / 1.492716 (0.074116) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.215263 / 0.018006 (0.197257) \| 0.440320 / 0.000490 (0.439830) \| 0.002627 / 0.000200 (0.002427) \| 0.000075 / 0.000054 (0.000021) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023971 / 0.037411 (-0.013441) \| 0.100549 / 0.014526 (0.086023) \| 0.106995 / 0.176557 (-0.069561) \| 0.169630 / 0.737135 (-0.567505) \| 0.111614 / 0.296338 (-0.184724) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.424911 / 0.215209 (0.209702) \| 4.246920 / 2.077655 (2.169266) \| 1.923321 / 1.504120 (0.419202) \| 1.714795 / 1.541195 (0.173600) \| 1.772906 / 1.468490 (0.304416) \| 0.554676 / 4.584777 (-4.030101) \| 3.478896 / 3.745712 (-0.266816) \| 2.800494 / 5.269862 (-2.469368) \| 1.382630 / 4.565676 (-3.183047) \| 0.067271 / 0.424275 (-0.357004) \| 0.010967 / 0.007607 (0.003360) \| 0.526769 / 0.226044 (0.300725) \| 5.288564 / 2.268929 (3.019636) \| 2.337459 / 55.444624 (-53.107165) \| 1.999975 / 6.876477 (-4.876502) \| 2.102680 / 2.142072 (-0.039392) \| 0.672181 / 4.805227 (-4.133046) \| 0.135097 / 6.500664 (-6.365567) \| 0.066950 / 0.075469 (-0.008519) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.264365 / 1.841788 (-0.577423) \| 14.282440 / 8.074308 (6.208132) \| 14.220200 / 10.191392 (4.028808) \| 0.139055 / 0.680424 (-0.541369) \| 0.016681 / 0.534201 (-0.517520) \| 0.367936 / 0.579283 (-0.211348) \| 0.393959 / 0.434364 (-0.040404) \| 0.424438 / 0.540337 (-0.115900) \| 0.508065 / 1.386936 (-0.878872) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006514 / 0.011353 (-0.004839) \| 0.003890 / 0.011008 (-0.007118) \| 0.078871 / 0.038508 (0.040363) \| 0.038080 / 0.023109 (0.014971) \| 0.358282 / 0.275898 (0.082384) \| 0.430654 / 0.323480 (0.107174) \| 0.005712 / 0.007986 (-0.002273) \| 0.003030 / 0.004328 (-0.001299) \| 0.078636 / 0.004250 (0.074386) \| 0.057771 / 0.037052 (0.020719) \| 0.368814 / 0.258489 (0.110325) \| 0.437047 / 0.293841 (0.143206) \| 0.029470 / 0.128546 (-0.099076) \| 0.008523 / 0.075646 (-0.067124) \| 0.083334 / 0.419271 (-0.335938) \| 0.044505 / 0.043533 (0.000972) \| 0.357484 / 0.255139 (0.102345) \| 0.393839 / 0.283200 (0.110639) \| 0.023340 / 0.141683 (-0.118343) \| 1.561033 / 1.452155 (0.108878) \| 1.595560 / 1.492716 (0.102844) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.204149 / 0.018006 (0.186143) \| 0.442747 / 0.000490 (0.442257) \| 0.003105 / 0.000200 (0.002905) \| 0.000085 / 0.000054 (0.000030) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.027002 / 0.037411 (-0.010409) \| 0.105595 / 0.014526 (0.091070) \| 0.108695 / 0.176557 (-0.067861) \| 0.163182 / 0.737135 (-0.573953) \| 0.114999 / 0.296338 (-0.181339) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.483713 / 0.215209 (0.268504) \| 4.836063 / 2.077655 (2.758409) \| 2.488072 / 1.504120 (0.983952) \| 2.289556 / 1.541195 (0.748361) \| 2.342912 / 1.468490 (0.874422) \| 0.565937 / 4.584777 (-4.018840) \| 3.479085 / 3.745712 (-0.266627) \| 1.770922 / 5.269862 (-3.498940) \| 1.046084 / 4.565676 (-3.519592) \| 0.067857 / 0.424275 (-0.356418) \| 0.011283 / 0.007607 (0.003676) \| 0.592966 / 0.226044 (0.366921) \| 5.932842 / 2.268929 (3.663914) \| 2.956252 / 55.444624 (-52.488372) \| 2.602704 / 6.876477 (-4.273772) \| 2.715625 / 2.142072 (0.573552) \| 0.674299 / 4.805227 (-4.130929) \| 0.136039 / 6.500664 (-6.364625) \| 0.067629 / 0.075469 (-0.007840) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.333734 / 1.841788 (-0.508054) \| 14.561943 / 8.074308 (6.487634) \| 14.455385 / 10.191392 (4.263993) \| 0.132020 / 0.680424 (-0.548404) \| 0.016893 / 0.534201 (-0.517308) \| 0.367146 / 0.579283 (-0.212137) \| 0.399623 / 0.434364 (-0.034741) \| 0.432658 / 0.540337 (-0.107680) \| 0.530475 / 1.386936 (-0.856461) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#18da5adb22b2b403b8d8ae673192746d2ed7e9f9 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006045 / 0.011353 (-0.005308) \| 0.003906 / 0.011008 (-0.007103) \| 0.097558 / 0.038508 (0.059050) \| 0.038827 / 0.023109 (0.015718) \| 0.393564 / 0.275898 (0.117666) \| 0.442459 / 0.323480 (0.118980) \| 0.004792 / 0.007986 (-0.003194) \| 0.002984 / 0.004328 (-0.001345) \| 0.076419 / 0.004250 (0.072169) \| 0.053606 / 0.037052 (0.016554) \| 0.409743 / 0.258489 (0.151254) \| 0.445753 / 0.293841 (0.151912) \| 0.027753 / 0.128546 (-0.100793) \| 0.008428 / 0.075646 (-0.067219) \| 0.310267 / 0.419271 (-0.109004) \| 0.057582 / 0.043533 (0.014049) \| 0.396624 / 0.255139 (0.141485) \| 0.416288 / 0.283200 (0.133089) \| 0.029048 / 0.141683 (-0.112635) \| 1.495362 / 1.452155 (0.043207) \| 1.546331 / 1.492716 (0.053615) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.203832 / 0.018006 (0.185826) \| 0.423649 / 0.000490 (0.423160) \| 0.004533 / 0.000200 (0.004333) \| 0.000076 / 0.000054 (0.000022) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023084 / 0.037411 (-0.014328) \| 0.100503 / 0.014526 (0.085977) \| 0.105058 / 0.176557 (-0.071499) \| 0.168506 / 0.737135 (-0.568629) \| 0.112019 / 0.296338 (-0.184320) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.425877 / 0.215209 (0.210668) \| 4.251278 / 2.077655 (2.173624) \| 1.931339 / 1.504120 (0.427219) \| 1.730578 / 1.541195 (0.189383) \| 1.750637 / 1.468490 (0.282147) \| 0.559307 / 4.584777 (-4.025470) \| 3.461665 / 3.745712 (-0.284047) \| 2.826959 / 5.269862 (-2.442903) \| 1.418448 / 4.565676 (-3.147229) \| 0.067881 / 0.424275 (-0.356394) \| 0.011394 / 0.007607 (0.003787) \| 0.533226 / 0.226044 (0.307181) \| 5.341849 / 2.268929 (3.072921) \| 2.367832 / 55.444624 (-53.076792) \| 2.027240 / 6.876477 (-4.849236) \| 2.095852 / 2.142072 (-0.046220) \| 0.673790 / 4.805227 (-4.131437) \| 0.136044 / 6.500664 (-6.364620) \| 0.066350 / 0.075469 (-0.009119) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.203740 / 1.841788 (-0.638048) \| 13.720879 / 8.074308 (5.646571) \| 13.405939 / 10.191392 (3.214547) \| 0.146792 / 0.680424 (-0.533632) \| 0.016844 / 0.534201 (-0.517357) \| 0.373455 / 0.579283 (-0.205828) \| 0.394596 / 0.434364 (-0.039768) \| 0.464715 / 0.540337 (-0.075623) \| 0.558931 / 1.386936 (-0.828005) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006118 / 0.011353 (-0.005235) \| 0.003817 / 0.011008 (-0.007191) \| 0.077494 / 0.038508 (0.038985) \| 0.037507 / 0.023109 (0.014398) \| 0.387030 / 0.275898 (0.111132) \| 0.437352 / 0.323480 (0.113872) \| 0.004810 / 0.007986 (-0.003176) \| 0.002935 / 0.004328 (-0.001394) \| 0.077143 / 0.004250 (0.072892) \| 0.053986 / 0.037052 (0.016933) \| 0.393164 / 0.258489 (0.134675) \| 0.449603 / 0.293841 (0.155762) \| 0.029303 / 0.128546 (-0.099244) \| 0.008481 / 0.075646 (-0.067165) \| 0.083363 / 0.419271 (-0.335908) \| 0.043877 / 0.043533 (0.000344) \| 0.378175 / 0.255139 (0.123036) \| 0.403996 / 0.283200 (0.120797) \| 0.021688 / 0.141683 (-0.119995) \| 1.541606 / 1.452155 (0.089452) \| 1.552996 / 1.492716 (0.060280) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.236759 / 0.018006 (0.218752) \| 0.416221 / 0.000490 (0.415732) \| 0.000862 / 0.000200 (0.000662) \| 0.000070 / 0.000054 (0.000016) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025543 / 0.037411 (-0.011868) \| 0.101731 / 0.014526 (0.087206) \| 0.108482 / 0.176557 (-0.068075) \| 0.160290 / 0.737135 (-0.576845) \| 0.111392 / 0.296338 (-0.184946) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.457767 / 0.215209 (0.242558) \| 4.565976 / 2.077655 (2.488321) \| 2.245413 / 1.504120 (0.741294) \| 2.031458 / 1.541195 (0.490264) \| 2.073193 / 1.468490 (0.604702) \| 0.560461 / 4.584777 (-4.024316) \| 3.422536 / 3.745712 (-0.323176) \| 2.977017 / 5.269862 (-2.292845) \| 1.377021 / 4.565676 (-3.188655) \| 0.068444 / 0.424275 (-0.355831) \| 0.011036 / 0.007607 (0.003429) \| 0.571501 / 0.226044 (0.345456) \| 5.702652 / 2.268929 (3.433723) \| 2.727132 / 55.444624 (-52.717492) \| 2.399269 / 6.876477 (-4.477208) \| 2.574281 / 2.142072 (0.432208) \| 0.682600 / 4.805227 (-4.122627) \| 0.136943 / 6.500664 (-6.363722) \| 0.067126 / 0.075469 (-0.008343) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.322196 / 1.841788 (-0.519592) \| 14.239509 / 8.074308 (6.165201) \| 14.235779 / 10.191392 (4.044387) \| 0.148262 / 0.680424 (-0.532162) \| 0.016566 / 0.534201 (-0.517635) \| 0.364034 / 0.579283 (-0.215249) \| 0.399157 / 0.434364 (-0.035207) \| 0.426348 / 0.540337 (-0.113990) \| 0.520804 / 1.386936 (-0.866132) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8f57aae06bd325d76cb70cb774450f3a66f169cf \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007808 / 0.011353 (-0.003545) \| 0.004706 / 0.011008 (-0.006303) \| 0.100530 / 0.038508 (0.062022) \| 0.052052 / 0.023109 (0.028943) \| 0.419300 / 0.275898 (0.143402) \| 0.488451 / 0.323480 (0.164971) \| 0.006350 / 0.007986 (-0.001636) \| 0.003875 / 0.004328 (-0.000453) \| 0.076489 / 0.004250 (0.072238) \| 0.077554 / 0.037052 (0.040502) \| 0.435863 / 0.258489 (0.177373) \| 0.483241 / 0.293841 (0.189400) \| 0.037518 / 0.128546 (-0.091028) \| 0.009857 / 0.075646 (-0.065789) \| 0.340933 / 0.419271 (-0.078339) \| 0.087046 / 0.043533 (0.043514) \| 0.410721 / 0.255139 (0.155582) \| 0.428995 / 0.283200 (0.145795) \| 0.041701 / 0.141683 (-0.099982) \| 1.821017 / 1.452155 (0.368862) \| 1.837021 / 1.492716 (0.344305) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.228444 / 0.018006 (0.210438) \| 0.480446 / 0.000490 (0.479956) \| 0.004963 / 0.000200 (0.004763) \| 0.000101 / 0.000054 (0.000046) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.032485 / 0.037411 (-0.004926) \| 0.096500 / 0.014526 (0.081974) \| 0.111547 / 0.176557 (-0.065010) \| 0.178842 / 0.737135 (-0.558294) \| 0.111099 / 0.296338 (-0.185240) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.467159 / 0.215209 (0.251950) \| 4.701676 / 2.077655 (2.624021) \| 2.390560 / 1.504120 (0.886440) \| 2.197722 / 1.541195 (0.656528) \| 2.264705 / 1.468490 (0.796215) \| 0.568667 / 4.584777 (-4.016110) \| 4.200724 / 3.745712 (0.455012) \| 3.777625 / 5.269862 (-1.492236) \| 2.372451 / 4.565676 (-2.193225) \| 0.067562 / 0.424275 (-0.356714) \| 0.008947 / 0.007607 (0.001340) \| 0.556910 / 0.226044 (0.330865) \| 5.528927 / 2.268929 (3.259998) \| 2.902780 / 55.444624 (-52.541844) \| 2.507933 / 6.876477 (-4.368544) \| 2.734627 / 2.142072 (0.592554) \| 0.683305 / 4.805227 (-4.121922) \| 0.158288 / 6.500664 (-6.342376) \| 0.071252 / 0.075469 (-0.004217) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.487502 / 1.841788 (-0.354286) \| 22.193341 / 8.074308 (14.119033) \| 15.922607 / 10.191392 (5.731215) \| 0.172189 / 0.680424 (-0.508235) \| 0.021502 / 0.534201 (-0.512699) \| 0.471198 / 0.579283 (-0.108085) \| 0.475979 / 0.434364 (0.041615) \| 0.544675 / 0.540337 (0.004338) \| 0.756102 / 1.386936 (-0.630834) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007635 / 0.011353 (-0.003717) \| 0.004614 / 0.011008 (-0.006394) \| 0.075852 / 0.038508 (0.037344) \| 0.049700 / 0.023109 (0.026591) \| 0.425957 / 0.275898 (0.150059) \| 0.512590 / 0.323480 (0.189110) \| 0.006921 / 0.007986 (-0.001065) \| 0.003714 / 0.004328 (-0.000615) \| 0.075536 / 0.004250 (0.071286) \| 0.070206 / 0.037052 (0.033153) \| 0.455706 / 0.258489 (0.197217) \| 0.512231 / 0.293841 (0.218390) \| 0.036685 / 0.128546 (-0.091861) \| 0.009793 / 0.075646 (-0.065853) \| 0.084208 / 0.419271 (-0.335064) \| 0.065262 / 0.043533 (0.021729) \| 0.423761 / 0.255139 (0.168622) \| 0.456791 / 0.283200 (0.173591) \| 0.044539 / 0.141683 (-0.097144) \| 1.797029 / 1.452155 (0.344874) \| 1.864124 / 1.492716 (0.371408) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.366840 / 0.018006 (0.348834) \| 0.479254 / 0.000490 (0.478765) \| 0.070383 / 0.000200 (0.070183) \| 0.000762 / 0.000054 (0.000707) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.034233 / 0.037411 (-0.003178) \| 0.103140 / 0.014526 (0.088614) \| 0.117099 / 0.176557 (-0.059457) \| 0.178532 / 0.737135 (-0.558603) \| 0.120092 / 0.296338 (-0.176247) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.492993 / 0.215209 (0.277784) \| 4.878776 / 2.077655 (2.801121) \| 2.566666 / 1.504120 (1.062547) \| 2.356383 / 1.541195 (0.815188) \| 2.454723 / 1.468490 (0.986233) \| 0.571432 / 4.584777 (-4.013345) \| 4.240554 / 3.745712 (0.494842) \| 7.509259 / 5.269862 (2.239398) \| 4.040294 / 4.565676 (-0.525382) \| 0.067409 / 0.424275 (-0.356866) \| 0.008657 / 0.007607 (0.001050) \| 0.585751 / 0.226044 (0.359707) \| 5.967668 / 2.268929 (3.698739) \| 3.195573 / 55.444624 (-52.249052) \| 2.839772 / 6.876477 (-4.036704) \| 2.806319 / 2.142072 (0.664246) \| 0.681502 / 4.805227 (-4.123725) \| 0.158673 / 6.500664 (-6.341991) \| 0.073224 / 0.075469 (-0.002245) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.623335 / 1.841788 (-0.218453) \| 22.490806 / 8.074308 (14.416498) \| 16.762435 / 10.191392 (6.571043) \| 0.180961 / 0.680424 (-0.499463) \| 0.022716 / 0.534201 (-0.511485) \| 0.472910 / 0.579283 (-0.106373) \| 0.471616 / 0.434364 (0.037252) \| 0.548192 / 0.540337 (0.007854) \| 0.734357 / 1.386936 (-0.652579) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c0498b47a00153d4730352b6595fc51ab054fb95 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005858 / 0.011353 (-0.005495) \| 0.003512 / 0.011008 (-0.007497) \| 0.079739 / 0.038508 (0.041231) \| 0.057736 / 0.023109 (0.034627) \| 0.317640 / 0.275898 (0.041742) \| 0.354157 / 0.323480 (0.030677) \| 0.004772 / 0.007986 (-0.003214) \| 0.002824 / 0.004328 (-0.001504) \| 0.063288 / 0.004250 (0.059037) \| 0.049542 / 0.037052 (0.012489) \| 0.323974 / 0.258489 (0.065485) \| 0.372149 / 0.293841 (0.078308) \| 0.026841 / 0.128546 (-0.101705) \| 0.007846 / 0.075646 (-0.067800) \| 0.262546 / 0.419271 (-0.156725) \| 0.051952 / 0.043533 (0.008420) \| 0.319439 / 0.255139 (0.064300) \| 0.343862 / 0.283200 (0.060663) \| 0.027021 / 0.141683 (-0.114662) \| 1.445211 / 1.452155 (-0.006944) \| 1.485006 / 1.492716 (-0.007711) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.183174 / 0.018006 (0.165167) \| 0.422794 / 0.000490 (0.422304) \| 0.004148 / 0.000200 (0.003948) \| 0.000067 / 0.000054 (0.000012) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.023037 / 0.037411 (-0.014374) \| 0.071300 / 0.014526 (0.056775) \| 0.083022 / 0.176557 (-0.093535) \| 0.146215 / 0.737135 (-0.590920) \| 0.082549 / 0.296338 (-0.213789) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.422846 / 0.215209 (0.207637) \| 4.215280 / 2.077655 (2.137626) \| 2.256802 / 1.504120 (0.752682) \| 2.056867 / 1.541195 (0.515673) \| 2.102478 / 1.468490 (0.633988) \| 0.497552 / 4.584777 (-4.087225) \| 3.049716 / 3.745712 (-0.695996) \| 4.209227 / 5.269862 (-1.060635) \| 2.599947 / 4.565676 (-1.965730) \| 0.059131 / 0.424275 (-0.365144) \| 0.006459 / 0.007607 (-0.001148) \| 0.495047 / 0.226044 (0.269003) \| 4.952332 / 2.268929 (2.683404) \| 2.675260 / 55.444624 (-52.769365) \| 2.333223 / 6.876477 (-4.543254) \| 2.449573 / 2.142072 (0.307500) \| 0.583420 / 4.805227 (-4.221807) \| 0.125140 / 6.500664 (-6.375524) \| 0.060209 / 0.075469 (-0.015260) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.215033 / 1.841788 (-0.626755) \| 18.101107 / 8.074308 (10.026799) \| 13.489222 / 10.191392 (3.297830) \| 0.147122 / 0.680424 (-0.533302) \| 0.016567 / 0.534201 (-0.517634) \| 0.329909 / 0.579283 (-0.249374) \| 0.340952 / 0.434364 (-0.093412) \| 0.379166 / 0.540337 (-0.161172) \| 0.510767 / 1.386936 (-0.876169) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005942 / 0.011353 (-0.005411) \| 0.003628 / 0.011008 (-0.007380) \| 0.061975 / 0.038508 (0.023467) \| 0.058331 / 0.023109 (0.035221) \| 0.393277 / 0.275898 (0.117379) \| 0.410740 / 0.323480 (0.087261) \| 0.004546 / 0.007986 (-0.003440) \| 0.002826 / 0.004328 (-0.001503) \| 0.062216 / 0.004250 (0.057966) \| 0.049801 / 0.037052 (0.012748) \| 0.394070 / 0.258489 (0.135581) \| 0.414407 / 0.293841 (0.120566) \| 0.027161 / 0.128546 (-0.101385) \| 0.007901 / 0.075646 (-0.067746) \| 0.066778 / 0.419271 (-0.352493) \| 0.041354 / 0.043533 (-0.002179) \| 0.379432 / 0.255139 (0.124293) \| 0.402966 / 0.283200 (0.119766) \| 0.020279 / 0.141683 (-0.121404) \| 1.416986 / 1.452155 (-0.035169) \| 1.474335 / 1.492716 (-0.018382) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.226147 / 0.018006 (0.208140) \| 0.404361 / 0.000490 (0.403871) \| 0.000358 / 0.000200 (0.000158) \| 0.000054 / 0.000054 (-0.000000) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.025105 / 0.037411 (-0.012306) \| 0.075849 / 0.014526 (0.061323) \| 0.084781 / 0.176557 (-0.091775) \| 0.137415 / 0.737135 (-0.599720) \| 0.086288 / 0.296338 (-0.210051) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.445925 / 0.215209 (0.230716) \| 4.453478 / 2.077655 (2.375823) \| 2.419048 / 1.504120 (0.914928) \| 2.246363 / 1.541195 (0.705168) \| 2.304022 / 1.468490 (0.835532) \| 0.499132 / 4.584777 (-4.085645) \| 3.001336 / 3.745712 (-0.744376) \| 2.902593 / 5.269862 (-2.367269) \| 1.819843 / 4.565676 (-2.745834) \| 0.057210 / 0.424275 (-0.367065) \| 0.006338 / 0.007607 (-0.001269) \| 0.523280 / 0.226044 (0.297236) \| 5.235969 / 2.268929 (2.967040) \| 2.897585 / 55.444624 (-52.547039) \| 2.541586 / 6.876477 (-4.334891) \| 2.564233 / 2.142072 (0.422160) \| 0.584714 / 4.805227 (-4.220513) \| 0.124611 / 6.500664 (-6.376053) \| 0.061774 / 0.075469 (-0.013695) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.349799 / 1.841788 (-0.491988) \| 18.225076 / 8.074308 (10.150768) \| 13.781518 / 10.191392 (3.590126) \| 0.130562 / 0.680424 (-0.549862) \| 0.016434 / 0.534201 (-0.517767) \| 0.331607 / 0.579283 (-0.247676) \| 0.343456 / 0.434364 (-0.090908) \| 0.380437 / 0.540337 (-0.159900) \| 0.522793 / 1.386936 (-0.864143) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f0a3dbbd2e7ace162346d95ec27db674e80c1e23 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.013721 / 0.011353 (0.002368) \| 0.005715 / 0.011008 (-0.005293) \| 0.090116 / 0.038508 (0.051608) \| 0.087185 / 0.023109 (0.064075) \| 0.427813 / 0.275898 (0.151915) \| 0.390614 / 0.323480 (0.067135) \| 0.006976 / 0.007986 (-0.001009) \| 0.004231 / 0.004328 (-0.000098) \| 0.078320 / 0.004250 (0.074070) \| 0.066235 / 0.037052 (0.029183) \| 0.439904 / 0.258489 (0.181415) \| 0.424119 / 0.293841 (0.130278) \| 0.050362 / 0.128546 (-0.078184) \| 0.014992 / 0.075646 (-0.060654) \| 0.293519 / 0.419271 (-0.125753) \| 0.066906 / 0.043533 (0.023373) \| 0.449657 / 0.255139 (0.194518) \| 0.393800 / 0.283200 (0.110600) \| 0.032258 / 0.141683 (-0.109425) \| 1.539534 / 1.452155 (0.087379) \| 1.675292 / 1.492716 (0.182576) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.210515 / 0.018006 (0.192508) \| 0.506817 / 0.000490 (0.506327) \| 0.001938 / 0.000200 (0.001738) \| 0.000118 / 0.000054 (0.000064) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.026019 / 0.037411 (-0.011393) \| 0.080635 / 0.014526 (0.066109) \| 0.103050 / 0.176557 (-0.073507) \| 0.160597 / 0.737135 (-0.576538) \| 0.095844 / 0.296338 (-0.200495) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.506359 / 0.215209 (0.291150) \| 5.041586 / 2.077655 (2.963931) \| 2.198288 / 1.504120 (0.694168) \| 1.987544 / 1.541195 (0.446349) \| 1.866790 / 1.468490 (0.398300) \| 0.681642 / 4.584777 (-3.903135) \| 4.719306 / 3.745712 (0.973593) \| 7.669869 / 5.269862 (2.400008) \| 4.466082 / 4.565676 (-0.099595) \| 0.092974 / 0.424275 (-0.331301) \| 0.008196 / 0.007607 (0.000589) \| 0.707656 / 0.226044 (0.481612) \| 6.974507 / 2.268929 (4.705579) \| 3.254206 / 55.444624 (-52.190418) \| 2.499019 / 6.876477 (-4.377457) \| 2.509089 / 2.142072 (0.367017) \| 0.915952 / 4.805227 (-3.889276) \| 0.192119 / 6.500664 (-6.308545) \| 0.065473 / 0.075469 (-0.009996) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.309078 / 1.841788 (-0.532710) \| 19.660348 / 8.074308 (11.586040) \| 16.659582 / 10.191392 (6.468190) \| 0.194315 / 0.680424 (-0.486109) \| 0.027773 / 0.534201 (-0.506428) \| 0.401241 / 0.579283 (-0.178042) \| 0.515799 / 0.434364 (0.081435) \| 0.488772 / 0.540337 (-0.051566) \| 0.604790 / 1.386936 (-0.782146) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006823 / 0.011353 (-0.004530) \| 0.003940 / 0.011008 (-0.007068) \| 0.061533 / 0.038508 (0.023025) \| 0.065241 / 0.023109 (0.042132) \| 0.411790 / 0.275898 (0.135892) \| 0.475720 / 0.323480 (0.152241) \| 0.005376 / 0.007986 (-0.002609) \| 0.003433 / 0.004328 (-0.000895) \| 0.065703 / 0.004250 (0.061452) \| 0.050736 / 0.037052 (0.013683) \| 0.435890 / 0.258489 (0.177401) \| 0.436698 / 0.293841 (0.142857) \| 0.040357 / 0.128546 (-0.088189) \| 0.011578 / 0.075646 (-0.064069) \| 0.072831 / 0.419271 (-0.346440) \| 0.055698 / 0.043533 (0.012165) \| 0.408225 / 0.255139 (0.153086) \| 0.439551 / 0.283200 (0.156352) \| 0.030469 / 0.141683 (-0.111214) \| 1.443866 / 1.452155 (-0.008289) \| 1.502022 / 1.492716 (0.009306) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.290338 / 0.018006 (0.272332) \| 0.540726 / 0.000490 (0.540236) \| 0.003244 / 0.000200 (0.003044) \| 0.000170 / 0.000054 (0.000116) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.030865 / 0.037411 (-0.006547) \| 0.090866 / 0.014526 (0.076340) \| 0.106224 / 0.176557 (-0.070332) \| 0.166583 / 0.737135 (-0.570553) \| 0.104448 / 0.296338 (-0.191891) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.518025 / 0.215209 (0.302816) \| 6.027065 / 2.077655 (3.949410) \| 2.671840 / 1.504120 (1.167720) \| 2.273949 / 1.541195 (0.732754) \| 2.414892 / 1.468490 (0.946402) \| 0.774318 / 4.584777 (-3.810459) \| 5.020364 / 3.745712 (1.274652) \| 4.146927 / 5.269862 (-1.122934) \| 2.584598 / 4.565676 (-1.981078) \| 0.089519 / 0.424275 (-0.334756) \| 0.009181 / 0.007607 (0.001574) \| 0.654467 / 0.226044 (0.428423) \| 6.421595 / 2.268929 (4.152666) \| 3.091589 / 55.444624 (-52.353036) \| 2.554798 / 6.876477 (-4.321679) \| 2.441354 / 2.142072 (0.299282) \| 0.943386 / 4.805227 (-3.861841) \| 0.173641 / 6.500664 (-6.327023) \| 0.072209 / 0.075469 (-0.003260) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.557147 / 1.841788 (-0.284641) \| 19.980747 / 8.074308 (11.906439) \| 17.816813 / 10.191392 (7.625421) \| 0.212078 / 0.680424 (-0.468346) \| 0.025435 / 0.534201 (-0.508766) \| 0.396200 / 0.579283 (-0.183084) \| 0.546249 / 0.434364 (0.111885) \| 0.459632 / 0.540337 (-0.080705) \| 0.616548 / 1.386936 (-0.770388) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#535e972a70a3d4f8490a7e1a77ac43d5a4ab2655 \"CML watermark\")\n" ]	2023-07-04T15:02:37Z	2023-07-06T15:32:41Z	2023-07-06T15:22:43Z	COLLABORATOR	null	null	null	`hfh` and `transformers` have dropped Python 3.7 support, so we should do the same :). (Based on the stats, it seems less than 10% of the users use `datasets` with Python 3.7)	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6005/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6005/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6005.diff", "html_url": "https://github.com/huggingface/datasets/pull/6005", "merged_at": "2023-07-06T15:22:43Z", "patch_url": "https://github.com/huggingface/datasets/pull/6005.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6005" }
https://api.github.com/repos/huggingface/datasets/issues/6442	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6442/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6442/comments	https://api.github.com/repos/huggingface/datasets/issues/6442/events	https://github.com/huggingface/datasets/issues/6442	2,006,086,907	I_kwDODunzps53knT7	6,442	Trouble loading image folder with additional features - metadata file ignored	{ "avatar_url": "https://avatars.githubusercontent.com/u/57615435?v=4", "events_url": "https://api.github.com/users/linoytsaban/events{/privacy}", "followers_url": "https://api.github.com/users/linoytsaban/followers", "following_url": "https://api.github.com/users/linoytsaban/following{/other_user}", "gists_url": "https://api.github.com/users/linoytsaban/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/linoytsaban", "id": 57615435, "login": "linoytsaban", "node_id": "MDQ6VXNlcjU3NjE1NDM1", "organizations_url": "https://api.github.com/users/linoytsaban/orgs", "received_events_url": "https://api.github.com/users/linoytsaban/received_events", "repos_url": "https://api.github.com/users/linoytsaban/repos", "site_admin": false, "starred_url": "https://api.github.com/users/linoytsaban/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/linoytsaban/subscriptions", "type": "User", "url": "https://api.github.com/users/linoytsaban", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "I reproduced too:\r\n- root: metadata file is ignored (https://huggingface.co/datasets/severo/doc-image-3)\r\n- data/ dir: metadata file is ignored (https://huggingface.co/datasets/severo/doc-image-4)\r\n- train/ dir: works (https://huggingface.co/datasets/severo/doc-image-5)" ]	2023-11-22T11:01:35Z	2023-11-24T17:13:03Z	2023-11-24T17:13:03Z	NONE	null	null	{ "completed": 0, "percent_completed": 0, "total": 0 }	### Describe the bug Loading image folder with a caption column using `load_dataset(<image_folder_path>)` doesn't load the captions. When loading a local image folder with captions using `datasets==2.13.0` ``` from datasets import load_dataset data = load_dataset(<image_folder_path>) data.column_names ``` yields `{'train': ['image', 'prompt']}` but when using `datasets==2.15.0` yeilds `{'train': ['image']}` Putting the images and `metadata.jsonl` file into a nested `train` folder or loading with `load_dataset("imagefolder", data_dir=<image_folder_path>)` solves the issue and yields `{'train': ['image', 'prompt']}` ### Steps to reproduce the bug 1. create a folder `<image_folder_path>` that contains images and a metadata file with additional features- e.g. "prompt" 2. run: ``` from datasets import load_dataset data = load_dataset("<image_folder_path>") data.column_names ``` ### Expected behavior `{'train': ['image', 'prompt']}` ### Environment info - `datasets` version: 2.15.0 - Platform: Linux-5.15.120+-x86_64-with-glibc2.35 - Python version: 3.10.12 - `huggingface_hub` version: 0.19.4 - PyArrow version: 9.0.0 - Pandas version: 1.5.3 - `fsspec` version: 2023.6.0	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6442/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6442/timeline	null	completed	null	null
https://api.github.com/repos/huggingface/datasets/issues/6167	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6167/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6167/comments	https://api.github.com/repos/huggingface/datasets/issues/6167/events	https://github.com/huggingface/datasets/pull/6167	1,861,474,327	PR_kwDODunzps5Yf9-t	6,167	Allow hyphen in split name	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	[]	closed	false	null	[]	null	[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007342 / 0.011353 (-0.004011) \| 0.004586 / 0.011008 (-0.006422) \| 0.100430 / 0.038508 (0.061922) \| 0.081053 / 0.023109 (0.057944) \| 0.368130 / 0.275898 (0.092232) \| 0.402852 / 0.323480 (0.079372) \| 0.004504 / 0.007986 (-0.003482) \| 0.003824 / 0.004328 (-0.000505) \| 0.075326 / 0.004250 (0.071076) \| 0.063329 / 0.037052 (0.026277) \| 0.372837 / 0.258489 (0.114348) \| 0.437857 / 0.293841 (0.144017) \| 0.035512 / 0.128546 (-0.093034) \| 0.009756 / 0.075646 (-0.065890) \| 0.341035 / 0.419271 (-0.078236) \| 0.060503 / 0.043533 (0.016970) \| 0.362555 / 0.255139 (0.107416) \| 0.409216 / 0.283200 (0.126017) \| 0.030093 / 0.141683 (-0.111590) \| 1.751550 / 1.452155 (0.299395) \| 1.848676 / 1.492716 (0.355959) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.229448 / 0.018006 (0.211442) \| 0.500300 / 0.000490 (0.499811) \| 0.005195 / 0.000200 (0.004995) \| 0.000092 / 0.000054 (0.000037) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.031753 / 0.037411 (-0.005658) \| 0.096075 / 0.014526 (0.081549) \| 0.111476 / 0.176557 (-0.065081) \| 0.179236 / 0.737135 (-0.557899) \| 0.113599 / 0.296338 (-0.182739) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.472817 / 0.215209 (0.257608) \| 4.715029 / 2.077655 (2.637374) \| 2.417934 / 1.504120 (0.913814) \| 2.235014 / 1.541195 (0.693819) \| 2.323588 / 1.468490 (0.855098) \| 0.553751 / 4.584777 (-4.031026) \| 4.153467 / 3.745712 (0.407755) \| 3.858836 / 5.269862 (-1.411025) \| 2.377499 / 4.565676 (-2.188178) \| 0.066528 / 0.424275 (-0.357747) \| 0.008979 / 0.007607 (0.001372) \| 0.561076 / 0.226044 (0.335032) \| 5.609817 / 2.268929 (3.340888) \| 3.011098 / 55.444624 (-52.433526) \| 2.594162 / 6.876477 (-4.282314) \| 2.863597 / 2.142072 (0.721525) \| 0.681135 / 4.805227 (-4.124092) \| 0.158863 / 6.500664 (-6.341801) \| 0.072551 / 0.075469 (-0.002918) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.492230 / 1.841788 (-0.349558) \| 23.028828 / 8.074308 (14.954519) \| 16.663265 / 10.191392 (6.471873) \| 0.173146 / 0.680424 (-0.507278) \| 0.021635 / 0.534201 (-0.512566) \| 0.478919 / 0.579283 (-0.100364) \| 0.472908 / 0.434364 (0.038544) \| 0.547248 / 0.540337 (0.006910) \| 0.770288 / 1.386936 (-0.616648) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.007728 / 0.011353 (-0.003625) \| 0.004477 / 0.011008 (-0.006531) \| 0.074858 / 0.038508 (0.036350) \| 0.084266 / 0.023109 (0.061157) \| 0.420280 / 0.275898 (0.144382) \| 0.466835 / 0.323480 (0.143356) \| 0.005980 / 0.007986 (-0.002006) \| 0.003600 / 0.004328 (-0.000729) \| 0.074941 / 0.004250 (0.070691) \| 0.066414 / 0.037052 (0.029361) \| 0.425949 / 0.258489 (0.167460) \| 0.473236 / 0.293841 (0.179395) \| 0.037213 / 0.128546 (-0.091333) \| 0.009743 / 0.075646 (-0.065903) \| 0.083758 / 0.419271 (-0.335513) \| 0.057916 / 0.043533 (0.014383) \| 0.423031 / 0.255139 (0.167892) \| 0.451107 / 0.283200 (0.167907) \| 0.028577 / 0.141683 (-0.113106) \| 1.810509 / 1.452155 (0.358354) \| 1.875579 / 1.492716 (0.382863) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.296052 / 0.018006 (0.278046) \| 0.496618 / 0.000490 (0.496128) \| 0.028667 / 0.000200 (0.028467) \| 0.000140 / 0.000054 (0.000086) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.036694 / 0.037411 (-0.000717) \| 0.110873 / 0.014526 (0.096347) \| 0.126550 / 0.176557 (-0.050007) \| 0.182924 / 0.737135 (-0.554212) \| 0.123793 / 0.296338 (-0.172545) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.509881 / 0.215209 (0.294672) \| 5.067402 / 2.077655 (2.989747) \| 2.696028 / 1.504120 (1.191908) \| 2.489861 / 1.541195 (0.948666) \| 2.563400 / 1.468490 (1.094910) \| 0.571184 / 4.584777 (-4.013593) \| 4.154231 / 3.745712 (0.408519) \| 3.891004 / 5.269862 (-1.378858) \| 2.435290 / 4.565676 (-2.130387) \| 0.065825 / 0.424275 (-0.358450) \| 0.008460 / 0.007607 (0.000853) \| 0.597579 / 0.226044 (0.371534) \| 5.914954 / 2.268929 (3.646025) \| 3.219305 / 55.444624 (-52.225319) \| 2.843548 / 6.876477 (-4.032929) \| 3.070300 / 2.142072 (0.928228) \| 0.686018 / 4.805227 (-4.119209) \| 0.160077 / 6.500664 (-6.340587) \| 0.074058 / 0.075469 (-0.001411) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.598748 / 1.841788 (-0.243039) \| 23.475685 / 8.074308 (15.401377) \| 17.257831 / 10.191392 (7.066439) \| 0.176539 / 0.680424 (-0.503885) \| 0.021969 / 0.534201 (-0.512232) \| 0.473565 / 0.579283 (-0.105718) \| 0.465471 / 0.434364 (0.031107) \| 0.567107 / 0.540337 (0.026769) \| 0.783757 / 1.386936 (-0.603179) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2f6bb450b4a3065a7d5fc50ea67711082749a337 \"CML watermark\")\n", "Note that the https://github.com/huggingface/datasets-server/ explicitly relies on the fact that a split cannot contain a hyphen. cc @lhoestq ", "We can't enable this that easily unfortunately because it could make arrow file names ambiguous in the cache.\r\n\r\ne.g. dataset_name-train-0000-of-0008.arrow", "Oh, this would indeed make the caching for the multi-proc case ambiguous. Implementing this is only worth it if we get more requests, so I'm closing this PR for now." ]	2023-08-22T13:30:59Z	2024-01-11T06:31:31Z	2023-08-22T15:38:53Z	COLLABORATOR	null	null	null	To fix https://discuss.huggingface.co/t/error-when-setting-up-the-dataset-viewer-streamingrowserror/51276.	{ "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "gravatar_id": "", "html_url": "https://github.com/mariosasko", "id": 47462742, "login": "mariosasko", "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "repos_url": "https://api.github.com/users/mariosasko/repos", "site_admin": false, "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "type": "User", "url": "https://api.github.com/users/mariosasko", "user_view_type": "public" }	{ "+1": 0, "-1": 0, "confused": 0, "eyes": 0, "heart": 0, "hooray": 0, "laugh": 0, "rocket": 0, "total_count": 0, "url": "https://api.github.com/repos/huggingface/datasets/issues/6167/reactions" }	https://api.github.com/repos/huggingface/datasets/issues/6167/timeline	null	null	0	{ "diff_url": "https://github.com/huggingface/datasets/pull/6167.diff", "html_url": "https://github.com/huggingface/datasets/pull/6167", "merged_at": null, "patch_url": "https://github.com/huggingface/datasets/pull/6167.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6167" }

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