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https://api.github.com/repos/huggingface/datasets/issues/6637	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6637/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6637/comments	https://api.github.com/repos/huggingface/datasets/issues/6637/events	https://github.com/huggingface/datasets/issues/6637	2,113,025,975	I_kwDODunzps598je3	6,637	'with_format' is extremely slow when used together with 'interleave_datasets' or 'shuffle' on IterableDatasets	{ "login": "tobycrisford", "id": 22883190, "node_id": "MDQ6VXNlcjIyODgzMTkw", "avatar_url": "https://avatars.githubusercontent.com/u/22883190?v=4", "gravatar_id": "", "url": "https://api.github.com/users/tobycrisford", "html_url": "https://github.com/tobycrisford", "followers_url": "https://api.github.com/users/tobycrisford/followers", "following_url": "https://api.github.com/users/tobycrisford/following{/other_user}", "gists_url": "https://api.github.com/users/tobycrisford/gists{/gist_id}", "starred_url": "https://api.github.com/users/tobycrisford/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/tobycrisford/subscriptions", "organizations_url": "https://api.github.com/users/tobycrisford/orgs", "repos_url": "https://api.github.com/users/tobycrisford/repos", "events_url": "https://api.github.com/users/tobycrisford/events{/privacy}", "received_events_url": "https://api.github.com/users/tobycrisford/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	null	[ "The \"torch\" formatting is usually fast because we do zero-copy conversion from the Arrow data on your disk to Torch tensors. However IterableDataset shuffling seems to do data copies that slow down the pipeline, and it shuffles python objects instead of Arrow data.\r\n\r\nTo fix this we need to implement `BufferShuffledExamplesIterable.iter_arrow()` (same as regular `BufferShuffledExamplesIterable.__iter__()` but yields Arrow tables)\r\n\r\nhttps://github.com/huggingface/datasets/blob/b7d854b7fd3e9a330e21b76ee8421d4a7ebb4a7a/src/datasets/iterable_dataset.py#L968-L974\r\n" ]	"2024-02-01T17:16:54"	"2024-02-05T10:43:47"	null	NONE	null	### Describe the bug If you: 1. Interleave two iterable datasets together with the interleave_datasets function, or shuffle an iterable dataset 2. Set the output format to torch tensors with .with_format('torch') Then iterating through the dataset becomes over 100x slower than it is if you don't apply the torch formatting. ### Steps to reproduce the bug ```python import datasets import torch from tqdm import tqdm rand_a = torch.randn(3,224,224) rand_b = torch.randn(3,224,224) a = torch.stack([rand_a] * 1000) b = torch.stack([rand_b] * 1000) features = datasets.Features({"tensor": datasets.Array3D(shape=(3,224,224), dtype="float32")}) ds_a = datasets.Dataset.from_dict({"tensor": a}, features=features).to_iterable_dataset() ds_b = datasets.Dataset.from_dict({"tensor": b}, features=features).to_iterable_dataset() # Iterating through either dataset with torch formatting is really fast (2000it/s on my machine) for example in tqdm(ds_a.with_format('torch')): pass # Iterating through either dataset shuffled is also pretty fast (100it/s on my machine) for example in tqdm(ds_a.shuffle()): pass # Iterating through this interleaved dataset is pretty fast (200it/s on my machine) ds_fast = datasets.interleave_datasets([ds_a, ds_b]) for example in tqdm(ds_fast): pass # Iterating through either dataset with torch formatting after shuffling is really slow... (<2it/s on my machine) for example in tqdm(ds_a.shuffle().with_format('torch')): pass # Iterating through this torch formatted interleaved dataset is also really slow (<2it/s on my machine)... ds_slow = datasets.interleave_datasets([ds_a, ds_b]).with_format('torch') for example in tqdm(ds_slow): pass # Even doing this is way faster!! (70it/s on my machine) for example in tqdm(ds_fast): test = torch.tensor(example['tensor']) ``` ### Expected behavior Applying torch formatting to the interleaved dataset shouldn't increase the time taken to iterate through the dataset by very much, since even explicitly converting every example is over 70x faster than calling .with_format('torch'). ### Environment info - `datasets` version: 2.16.1 - Platform: Linux-6.5.0-15-generic-x86_64-with-glibc2.38 - Python version: 3.11.6 - `huggingface_hub` version: 0.20.3 - PyArrow version: 15.0.0 - Pandas version: 2.2.0 - `fsspec` version: 2023.10.0	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6637/reactions", "total_count": 1, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 1 }	https://api.github.com/repos/huggingface/datasets/issues/6637/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6636	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6636/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6636/comments	https://api.github.com/repos/huggingface/datasets/issues/6636/events	https://github.com/huggingface/datasets/pull/6636	2,110,781,097	PR_kwDODunzps5lm4zI	6,636	Faster column validation and reordering	{ "login": "psmyth94", "id": 11325244, "node_id": "MDQ6VXNlcjExMzI1MjQ0", "avatar_url": "https://avatars.githubusercontent.com/u/11325244?v=4", "gravatar_id": "", "url": "https://api.github.com/users/psmyth94", "html_url": "https://github.com/psmyth94", "followers_url": "https://api.github.com/users/psmyth94/followers", "following_url": "https://api.github.com/users/psmyth94/following{/other_user}", "gists_url": "https://api.github.com/users/psmyth94/gists{/gist_id}", "starred_url": "https://api.github.com/users/psmyth94/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/psmyth94/subscriptions", "organizations_url": "https://api.github.com/users/psmyth94/orgs", "repos_url": "https://api.github.com/users/psmyth94/repos", "events_url": "https://api.github.com/users/psmyth94/events{/privacy}", "received_events_url": "https://api.github.com/users/psmyth94/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6636). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "Thanks @mariosasko, I made the changes. However, I did some tests with `map` and I still saw that it took ~3.5 minutes per batch on 6000 features when using `dataset.map(lambda x: x, batched=True)`. From the profile, the culprits were mainly with `ArrowWriter.write_batch` and `ArrowWriter._build_writer`. The slow down from `_build_writer` is due to updating existing features with the inferred ones. I don't think this can be optimized any further, but fortunately, I can avoid this by setting the `features` in `map`. On the other hand, `write_batch` selects cols based on intersection and difference between schema names and example keys using two for loops. The same exists in `ArrowWriter.write_examples_on_file`. Optimizing the column selection using set operations effectively brings it from 3.5 minutes per batch down to 6 seconds per batch. Can we add these changes along with this PR?\r\n\r\nEdit: Ah just realized you can avoid the issue with inferring features altogether when you set the format to arrow (or pandas).", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004990 / 0.011353 (-0.006363) \| 0.003138 / 0.011008 (-0.007870) \| 0.062368 / 0.038508 (0.023860) \| 0.028634 / 0.023109 (0.005524) \| 0.241297 / 0.275898 (-0.034601) \| 0.264433 / 0.323480 (-0.059047) \| 0.003133 / 0.007986 (-0.004852) \| 0.003444 / 0.004328 (-0.000885) \| 0.048522 / 0.004250 (0.044271) \| 0.043700 / 0.037052 (0.006648) \| 0.257054 / 0.258489 (-0.001435) \| 0.277551 / 0.293841 (-0.016290) \| 0.027132 / 0.128546 (-0.101414) \| 0.010395 / 0.075646 (-0.065251) \| 0.208003 / 0.419271 (-0.211269) \| 0.035814 / 0.043533 (-0.007719) \| 0.250098 / 0.255139 (-0.005041) \| 0.266726 / 0.283200 (-0.016474) \| 0.018424 / 0.141683 (-0.123259) \| 1.129242 / 1.452155 (-0.322912) \| 1.167674 / 1.492716 (-0.325042) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091808 / 0.018006 (0.073802) \| 0.298726 / 0.000490 (0.298236) \| 0.000219 / 0.000200 (0.000019) \| 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) \| 0.019119 / 0.037411 (-0.018292) \| 0.061969 / 0.014526 (0.047443) \| 0.073392 / 0.176557 (-0.103165) \| 0.119460 / 0.737135 (-0.617675) \| 0.074072 / 0.296338 (-0.222266) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.281435 / 0.215209 (0.066226) \| 2.702094 / 2.077655 (0.624439) \| 1.411541 / 1.504120 (-0.092579) \| 1.284084 / 1.541195 (-0.257111) \| 1.302638 / 1.468490 (-0.165852) \| 0.562420 / 4.584777 (-4.022357) \| 2.364890 / 3.745712 (-1.380822) \| 2.744033 / 5.269862 (-2.525828) \| 1.699000 / 4.565676 (-2.866677) \| 0.062315 / 0.424275 (-0.361961) \| 0.004982 / 0.007607 (-0.002625) \| 0.334385 / 0.226044 (0.108341) \| 3.203268 / 2.268929 (0.934339) \| 1.766998 / 55.444624 (-53.677627) \| 1.497164 / 6.876477 (-5.379313) \| 1.509996 / 2.142072 (-0.632077) \| 0.633014 / 4.805227 (-4.172213) \| 0.115317 / 6.500664 (-6.385347) \| 0.041120 / 0.075469 (-0.034349) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.965877 / 1.841788 (-0.875911) \| 11.219909 / 8.074308 (3.145601) \| 9.333822 / 10.191392 (-0.857570) \| 0.136482 / 0.680424 (-0.543941) \| 0.013632 / 0.534201 (-0.520569) \| 0.287251 / 0.579283 (-0.292032) \| 0.262786 / 0.434364 (-0.171578) \| 0.322893 / 0.540337 (-0.217444) \| 0.418180 / 1.386936 (-0.968756) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005444 / 0.011353 (-0.005909) \| 0.003147 / 0.011008 (-0.007862) \| 0.049242 / 0.038508 (0.010734) \| 0.030944 / 0.023109 (0.007834) \| 0.281901 / 0.275898 (0.006003) \| 0.303820 / 0.323480 (-0.019660) \| 0.004326 / 0.007986 (-0.003659) \| 0.002696 / 0.004328 (-0.001632) \| 0.048306 / 0.004250 (0.044055) \| 0.044145 / 0.037052 (0.007093) \| 0.297253 / 0.258489 (0.038764) \| 0.324062 / 0.293841 (0.030221) \| 0.046724 / 0.128546 (-0.081823) \| 0.010079 / 0.075646 (-0.065567) \| 0.057635 / 0.419271 (-0.361636) \| 0.033621 / 0.043533 (-0.009912) \| 0.282303 / 0.255139 (0.027164) \| 0.300761 / 0.283200 (0.017561) \| 0.017116 / 0.141683 (-0.124567) \| 1.156519 / 1.452155 (-0.295636) \| 1.216087 / 1.492716 (-0.276630) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093011 / 0.018006 (0.075005) \| 0.301310 / 0.000490 (0.300820) \| 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) \| 0.023112 / 0.037411 (-0.014299) \| 0.075192 / 0.014526 (0.060666) \| 0.086213 / 0.176557 (-0.090343) \| 0.125853 / 0.737135 (-0.611282) \| 0.087754 / 0.296338 (-0.208585) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.301095 / 0.215209 (0.085886) \| 2.911769 / 2.077655 (0.834114) \| 1.614708 / 1.504120 (0.110588) \| 1.494497 / 1.541195 (-0.046698) \| 1.506978 / 1.468490 (0.038488) \| 0.572743 / 4.584777 (-4.012034) \| 2.417142 / 3.745712 (-1.328570) \| 2.755338 / 5.269862 (-2.514523) \| 1.711026 / 4.565676 (-2.854650) \| 0.062732 / 0.424275 (-0.361543) \| 0.005031 / 0.007607 (-0.002576) \| 0.352343 / 0.226044 (0.126298) \| 3.465183 / 2.268929 (1.196255) \| 1.958795 / 55.444624 (-53.485829) \| 1.682239 / 6.876477 (-5.194238) \| 1.688897 / 2.142072 (-0.453176) \| 0.643311 / 4.805227 (-4.161916) \| 0.115426 / 6.500664 (-6.385238) \| 0.040338 / 0.075469 (-0.035131) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.005322 / 1.841788 (-0.836466) \| 11.779380 / 8.074308 (3.705072) \| 10.041574 / 10.191392 (-0.149818) \| 0.127617 / 0.680424 (-0.552807) \| 0.015840 / 0.534201 (-0.518361) \| 0.286905 / 0.579283 (-0.292378) \| 0.275180 / 0.434364 (-0.159183) \| 0.332498 / 0.540337 (-0.207840) \| 0.410719 / 1.386936 (-0.976217) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#32b206d47f582380f9c64578dcfa6c48252db3b8 \"CML watermark\")\n" ]	"2024-01-31T19:08:28"	"2024-02-07T19:39:00"	"2024-02-06T23:03:38"	CONTRIBUTOR	null	I work with bioinformatics data and often these tables have thousands and even tens of thousands of features. These tables are also accompanied by metadata that I do not want to pass in the model. When I perform `set_format('pt', columns=large_column_list)` , it can take several minutes before it finishes. The culprit is when the following check is performed: `any(col not in self._data.column_names for col in columns)`. Replacing this by `set(columns) - (self._data.column_names)` is more efficient.	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6636/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6636/timeline	null	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6636", "html_url": "https://github.com/huggingface/datasets/pull/6636", "diff_url": "https://github.com/huggingface/datasets/pull/6636.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6636.patch", "merged_at": "2024-02-06T23:03:38" }	true
https://api.github.com/repos/huggingface/datasets/issues/6635	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6635/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6635/comments	https://api.github.com/repos/huggingface/datasets/issues/6635/events	https://github.com/huggingface/datasets/pull/6635	2,110,659,519	PR_kwDODunzps5lmeNO	6,635	Fix missing info when loading some datasets from Parquet export	{ "login": "lhoestq", "id": 42851186, "node_id": "MDQ6VXNlcjQyODUxMTg2", "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "gravatar_id": "", "url": "https://api.github.com/users/lhoestq", "html_url": "https://github.com/lhoestq", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "repos_url": "https://api.github.com/users/lhoestq/repos", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6635). 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) \| 0.005577 / 0.011353 (-0.005776) \| 0.004452 / 0.011008 (-0.006556) \| 0.067849 / 0.038508 (0.029341) \| 0.032328 / 0.023109 (0.009219) \| 0.256924 / 0.275898 (-0.018974) \| 0.273410 / 0.323480 (-0.050070) \| 0.004359 / 0.007986 (-0.003626) \| 0.003484 / 0.004328 (-0.000845) \| 0.053880 / 0.004250 (0.049630) \| 0.058142 / 0.037052 (0.021089) \| 0.268863 / 0.258489 (0.010374) \| 0.307977 / 0.293841 (0.014136) \| 0.028840 / 0.128546 (-0.099707) \| 0.011808 / 0.075646 (-0.063839) \| 0.216277 / 0.419271 (-0.202995) \| 0.039245 / 0.043533 (-0.004288) \| 0.250420 / 0.255139 (-0.004719) \| 0.273642 / 0.283200 (-0.009557) \| 0.019340 / 0.141683 (-0.122342) \| 1.176734 / 1.452155 (-0.275421) \| 1.250643 / 1.492716 (-0.242074) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.181210 / 0.018006 (0.163204) \| 1.070750 / 0.000490 (1.070261) \| 0.000315 / 0.000200 (0.000115) \| 0.000045 / 0.000054 (-0.000009) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022905 / 0.037411 (-0.014507) \| 0.064549 / 0.014526 (0.050023) \| 0.077113 / 0.176557 (-0.099443) \| 0.131976 / 0.737135 (-0.605159) \| 0.081266 / 0.296338 (-0.215072) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.291363 / 0.215209 (0.076154) \| 2.851691 / 2.077655 (0.774036) \| 1.592815 / 1.504120 (0.088695) \| 1.494550 / 1.541195 (-0.046645) \| 1.516464 / 1.468490 (0.047974) \| 0.583244 / 4.584777 (-4.001532) \| 2.504907 / 3.745712 (-1.240805) \| 3.183490 / 5.269862 (-2.086371) \| 1.932854 / 4.565676 (-2.632823) \| 0.067564 / 0.424275 (-0.356711) \| 0.006587 / 0.007607 (-0.001020) \| 0.346368 / 0.226044 (0.120324) \| 3.428256 / 2.268929 (1.159327) \| 1.994176 / 55.444624 (-53.450448) \| 1.688116 / 6.876477 (-5.188360) \| 1.767653 / 2.142072 (-0.374420) \| 0.673867 / 4.805227 (-4.131360) \| 0.125582 / 6.500664 (-6.375082) \| 0.047198 / 0.075469 (-0.028271) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.002895 / 1.841788 (-0.838893) \| 16.332893 / 8.074308 (8.258585) \| 10.781993 / 10.191392 (0.590601) \| 0.153919 / 0.680424 (-0.526505) \| 0.015528 / 0.534201 (-0.518673) \| 0.306182 / 0.579283 (-0.273101) \| 0.296380 / 0.434364 (-0.137984) \| 0.341432 / 0.540337 (-0.198905) \| 0.455900 / 1.386936 (-0.931036) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006442 / 0.011353 (-0.004911) \| 0.004433 / 0.011008 (-0.006576) \| 0.053327 / 0.038508 (0.014819) \| 0.035966 / 0.023109 (0.012856) \| 0.280913 / 0.275898 (0.005015) \| 0.308419 / 0.323480 (-0.015061) \| 0.005842 / 0.007986 (-0.002144) \| 0.003789 / 0.004328 (-0.000539) \| 0.053983 / 0.004250 (0.049732) \| 0.069052 / 0.037052 (0.032000) \| 0.299225 / 0.258489 (0.040736) \| 0.336470 / 0.293841 (0.042629) \| 0.068170 / 0.128546 (-0.060377) \| 0.012259 / 0.075646 (-0.063388) \| 0.064166 / 0.419271 (-0.355106) \| 0.037291 / 0.043533 (-0.006241) \| 0.281318 / 0.255139 (0.026179) \| 0.297093 / 0.283200 (0.013893) \| 0.021358 / 0.141683 (-0.120324) \| 1.189584 / 1.452155 (-0.262571) \| 1.256985 / 1.492716 (-0.235731) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.216726 / 0.018006 (0.198720) \| 2.496957 / 0.000490 (2.496467) \| 0.000336 / 0.000200 (0.000136) \| 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) \| 0.026604 / 0.037411 (-0.010807) \| 0.080398 / 0.014526 (0.065873) \| 0.094475 / 0.176557 (-0.082082) \| 0.136263 / 0.737135 (-0.600873) \| 0.097898 / 0.296338 (-0.198440) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.295171 / 0.215209 (0.079962) \| 2.947530 / 2.077655 (0.869875) \| 1.607531 / 1.504120 (0.103411) \| 1.485045 / 1.541195 (-0.056150) \| 1.524899 / 1.468490 (0.056409) \| 0.572934 / 4.584777 (-4.011843) \| 2.544320 / 3.745712 (-1.201393) \| 3.292630 / 5.269862 (-1.977232) \| 1.927138 / 4.565676 (-2.638539) \| 0.068560 / 0.424275 (-0.355715) \| 0.005982 / 0.007607 (-0.001625) \| 0.345833 / 0.226044 (0.119789) \| 3.424253 / 2.268929 (1.155324) \| 2.195017 / 55.444624 (-53.249608) \| 1.712037 / 6.876477 (-5.164440) \| 1.763899 / 2.142072 (-0.378174) \| 0.653776 / 4.805227 (-4.151451) \| 0.123056 / 6.500664 (-6.377609) \| 0.044572 / 0.075469 (-0.030897) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.033400 / 1.841788 (-0.808388) \| 15.409887 / 8.074308 (7.335579) \| 11.220990 / 10.191392 (1.029597) \| 0.153603 / 0.680424 (-0.526821) \| 0.016866 / 0.534201 (-0.517335) \| 0.311945 / 0.579283 (-0.267338) \| 0.307048 / 0.434364 (-0.127316) \| 0.350422 / 0.540337 (-0.189915) \| 0.447308 / 1.386936 (-0.939628) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#14d9afbb7ae1b787c450261ca0ff374551993031 \"CML watermark\")\n" ]	"2024-01-31T17:55:21"	"2024-02-07T16:48:55"	"2024-02-07T16:41:04"	MEMBER	null	Fix getting the info for script-based datasets with Parquet export with a single config not named "default". E.g. ```python from datasets import load_dataset_builder b = load_dataset_builder("bookcorpus") print(b.info.features) # should print {'text': Value(dtype='string', id=None)} ``` I fixed this by setting the default config name when there is only one config.	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6635/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6635/timeline	null	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6635", "html_url": "https://github.com/huggingface/datasets/pull/6635", "diff_url": "https://github.com/huggingface/datasets/pull/6635.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6635.patch", "merged_at": "2024-02-07T16:41:04" }	true
https://api.github.com/repos/huggingface/datasets/issues/6634	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6634/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6634/comments	https://api.github.com/repos/huggingface/datasets/issues/6634/events	https://github.com/huggingface/datasets/pull/6634	2,110,242,376	PR_kwDODunzps5llB9a	6,634	Support data_dir parameter in push_to_hub	{ "login": "albertvillanova", "id": 8515462, "node_id": "MDQ6VXNlcjg1MTU0NjI=", "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "gravatar_id": "", "url": "https://api.github.com/users/albertvillanova", "html_url": "https://github.com/albertvillanova", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "repos_url": "https://api.github.com/users/albertvillanova/repos", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6634). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "@huggingface/datasets, feel free to review this PR so that it can be included in the next release.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005125 / 0.011353 (-0.006228) \| 0.003772 / 0.011008 (-0.007236) \| 0.063258 / 0.038508 (0.024750) \| 0.029479 / 0.023109 (0.006370) \| 0.245554 / 0.275898 (-0.030344) \| 0.266395 / 0.323480 (-0.057085) \| 0.003063 / 0.007986 (-0.004922) \| 0.003298 / 0.004328 (-0.001031) \| 0.049242 / 0.004250 (0.044991) \| 0.042390 / 0.037052 (0.005338) \| 0.258176 / 0.258489 (-0.000313) \| 0.279935 / 0.293841 (-0.013906) \| 0.027910 / 0.128546 (-0.100637) \| 0.011033 / 0.075646 (-0.064613) \| 0.207763 / 0.419271 (-0.211509) \| 0.036127 / 0.043533 (-0.007405) \| 0.247363 / 0.255139 (-0.007776) \| 0.261309 / 0.283200 (-0.021890) \| 0.020259 / 0.141683 (-0.121424) \| 1.152760 / 1.452155 (-0.299395) \| 1.194853 / 1.492716 (-0.297863) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.088936 / 0.018006 (0.070930) \| 0.298396 / 0.000490 (0.297906) \| 0.000211 / 0.000200 (0.000011) \| 0.000050 / 0.000054 (-0.000004) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018434 / 0.037411 (-0.018977) \| 0.061991 / 0.014526 (0.047466) \| 0.072786 / 0.176557 (-0.103771) \| 0.120437 / 0.737135 (-0.616698) \| 0.078375 / 0.296338 (-0.217964) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.275821 / 0.215209 (0.060612) \| 2.703358 / 2.077655 (0.625703) \| 1.446783 / 1.504120 (-0.057337) \| 1.333556 / 1.541195 (-0.207639) \| 1.325753 / 1.468490 (-0.142737) \| 0.565196 / 4.584777 (-4.019581) \| 2.411193 / 3.745712 (-1.334520) \| 2.702764 / 5.269862 (-2.567098) \| 1.727425 / 4.565676 (-2.838252) \| 0.062966 / 0.424275 (-0.361309) \| 0.004985 / 0.007607 (-0.002622) \| 0.333473 / 0.226044 (0.107428) \| 3.270615 / 2.268929 (1.001687) \| 1.822213 / 55.444624 (-53.622411) \| 1.546572 / 6.876477 (-5.329905) \| 1.568767 / 2.142072 (-0.573305) \| 0.655907 / 4.805227 (-4.149321) \| 0.117173 / 6.500664 (-6.383491) \| 0.042415 / 0.075469 (-0.033054) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.987966 / 1.841788 (-0.853822) \| 11.851206 / 8.074308 (3.776898) \| 10.327751 / 10.191392 (0.136359) \| 0.127929 / 0.680424 (-0.552494) \| 0.013781 / 0.534201 (-0.520420) \| 0.286910 / 0.579283 (-0.292373) \| 0.273615 / 0.434364 (-0.160749) \| 0.323373 / 0.540337 (-0.216965) \| 0.426407 / 1.386936 (-0.960529) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005412 / 0.011353 (-0.005941) \| 0.003619 / 0.011008 (-0.007389) \| 0.049603 / 0.038508 (0.011095) \| 0.031246 / 0.023109 (0.008136) \| 0.279723 / 0.275898 (0.003825) \| 0.298557 / 0.323480 (-0.024923) \| 0.004253 / 0.007986 (-0.003733) \| 0.002758 / 0.004328 (-0.001570) \| 0.048931 / 0.004250 (0.044680) \| 0.044245 / 0.037052 (0.007193) \| 0.295876 / 0.258489 (0.037387) \| 0.322720 / 0.293841 (0.028879) \| 0.046746 / 0.128546 (-0.081800) \| 0.010841 / 0.075646 (-0.064805) \| 0.058528 / 0.419271 (-0.360744) \| 0.034224 / 0.043533 (-0.009308) \| 0.279192 / 0.255139 (0.024053) \| 0.299775 / 0.283200 (0.016576) \| 0.017862 / 0.141683 (-0.123820) \| 1.154478 / 1.452155 (-0.297677) \| 1.190483 / 1.492716 (-0.302234) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.088717 / 0.018006 (0.070710) \| 0.297905 / 0.000490 (0.297415) \| 0.000209 / 0.000200 (0.000009) \| 0.000050 / 0.000054 (-0.000004) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021458 / 0.037411 (-0.015953) \| 0.075616 / 0.014526 (0.061090) \| 0.087080 / 0.176557 (-0.089476) \| 0.125315 / 0.737135 (-0.611821) \| 0.088958 / 0.296338 (-0.207381) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.287085 / 0.215209 (0.071876) \| 2.807798 / 2.077655 (0.730143) \| 1.552201 / 1.504120 (0.048081) \| 1.422374 / 1.541195 (-0.118820) \| 1.437908 / 1.468490 (-0.030582) \| 0.569738 / 4.584777 (-4.015039) \| 2.493921 / 3.745712 (-1.251791) \| 2.648376 / 5.269862 (-2.621486) \| 1.741721 / 4.565676 (-2.823955) \| 0.063023 / 0.424275 (-0.361253) \| 0.005166 / 0.007607 (-0.002441) \| 0.336927 / 0.226044 (0.110882) \| 3.384517 / 2.268929 (1.115588) \| 1.909888 / 55.444624 (-53.534736) \| 1.641879 / 6.876477 (-5.234597) \| 1.727734 / 2.142072 (-0.414338) \| 0.647127 / 4.805227 (-4.158100) \| 0.115831 / 6.500664 (-6.384833) \| 0.041161 / 0.075469 (-0.034309) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.016310 / 1.841788 (-0.825477) \| 12.088500 / 8.074308 (4.014192) \| 10.799730 / 10.191392 (0.608338) \| 0.129049 / 0.680424 (-0.551375) \| 0.015379 / 0.534201 (-0.518822) \| 0.291352 / 0.579283 (-0.287931) \| 0.284579 / 0.434364 (-0.149785) \| 0.331214 / 0.540337 (-0.209124) \| 0.422902 / 1.386936 (-0.964034) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#991169ed4901d129d0e0ab8d7fccd6a0728da4b8 \"CML watermark\")\n" ]	"2024-01-31T14:37:36"	"2024-02-05T10:32:49"	"2024-02-05T10:26:40"	MEMBER	null	Support `data_dir` parameter in `push_to_hub`. This allows users to organize the data files according to their specific needs. For example, "wikimedia/wikipedia" files could be organized by year and/or date, e.g. "2024/20240101/20240101.en".	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6634/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6634/timeline	null	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6634", "html_url": "https://github.com/huggingface/datasets/pull/6634", "diff_url": "https://github.com/huggingface/datasets/pull/6634.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6634.patch", "merged_at": "2024-02-05T10:26:40" }	true
https://api.github.com/repos/huggingface/datasets/issues/6633	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6633/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6633/comments	https://api.github.com/repos/huggingface/datasets/issues/6633/events	https://github.com/huggingface/datasets/pull/6633	2,110,124,475	PR_kwDODunzps5lknz9	6,633	dataset viewer requires no-script	{ "login": "severo", "id": 1676121, "node_id": "MDQ6VXNlcjE2NzYxMjE=", "avatar_url": "https://avatars.githubusercontent.com/u/1676121?v=4", "gravatar_id": "", "url": "https://api.github.com/users/severo", "html_url": "https://github.com/severo", "followers_url": "https://api.github.com/users/severo/followers", "following_url": "https://api.github.com/users/severo/following{/other_user}", "gists_url": "https://api.github.com/users/severo/gists{/gist_id}", "starred_url": "https://api.github.com/users/severo/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/severo/subscriptions", "organizations_url": "https://api.github.com/users/severo/orgs", "repos_url": "https://api.github.com/users/severo/repos", "events_url": "https://api.github.com/users/severo/events{/privacy}", "received_events_url": "https://api.github.com/users/severo/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6633). 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) \| 0.005172 / 0.011353 (-0.006181) \| 0.003694 / 0.011008 (-0.007314) \| 0.063098 / 0.038508 (0.024590) \| 0.028161 / 0.023109 (0.005052) \| 0.262288 / 0.275898 (-0.013610) \| 0.281867 / 0.323480 (-0.041613) \| 0.004088 / 0.007986 (-0.003898) \| 0.002745 / 0.004328 (-0.001583) \| 0.049071 / 0.004250 (0.044820) \| 0.040629 / 0.037052 (0.003577) \| 0.282766 / 0.258489 (0.024277) \| 0.297998 / 0.293841 (0.004157) \| 0.028057 / 0.128546 (-0.100489) \| 0.010878 / 0.075646 (-0.064768) \| 0.207410 / 0.419271 (-0.211861) \| 0.035600 / 0.043533 (-0.007933) \| 0.260157 / 0.255139 (0.005018) \| 0.273252 / 0.283200 (-0.009948) \| 0.017403 / 0.141683 (-0.124280) \| 1.150798 / 1.452155 (-0.301356) \| 1.200485 / 1.492716 (-0.292231) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093783 / 0.018006 (0.075777) \| 0.302112 / 0.000490 (0.301622) \| 0.000225 / 0.000200 (0.000025) \| 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) \| 0.018254 / 0.037411 (-0.019158) \| 0.061083 / 0.014526 (0.046557) \| 0.074899 / 0.176557 (-0.101657) \| 0.119616 / 0.737135 (-0.617520) \| 0.075269 / 0.296338 (-0.221069) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.275878 / 0.215209 (0.060669) \| 2.694778 / 2.077655 (0.617123) \| 1.423810 / 1.504120 (-0.080310) \| 1.309444 / 1.541195 (-0.231750) \| 1.327898 / 1.468490 (-0.140592) \| 0.568621 / 4.584777 (-4.016155) \| 2.345849 / 3.745712 (-1.399863) \| 2.901281 / 5.269862 (-2.368580) \| 1.777959 / 4.565676 (-2.787717) \| 0.063539 / 0.424275 (-0.360736) \| 0.005011 / 0.007607 (-0.002596) \| 0.331212 / 0.226044 (0.105168) \| 3.200379 / 2.268929 (0.931451) \| 1.780766 / 55.444624 (-53.663859) \| 1.517178 / 6.876477 (-5.359299) \| 1.587307 / 2.142072 (-0.554765) \| 0.651939 / 4.805227 (-4.153288) \| 0.116646 / 6.500664 (-6.384018) \| 0.043325 / 0.075469 (-0.032144) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.996894 / 1.841788 (-0.844894) \| 11.495397 / 8.074308 (3.421089) \| 10.255784 / 10.191392 (0.064392) \| 0.129006 / 0.680424 (-0.551418) \| 0.013967 / 0.534201 (-0.520234) \| 0.284847 / 0.579283 (-0.294436) \| 0.265610 / 0.434364 (-0.168754) \| 0.320176 / 0.540337 (-0.220162) \| 0.429526 / 1.386936 (-0.957410) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005582 / 0.011353 (-0.005771) \| 0.003867 / 0.011008 (-0.007142) \| 0.050416 / 0.038508 (0.011908) \| 0.030996 / 0.023109 (0.007887) \| 0.275987 / 0.275898 (0.000089) \| 0.289487 / 0.323480 (-0.033993) \| 0.005149 / 0.007986 (-0.002837) \| 0.002806 / 0.004328 (-0.001522) \| 0.049617 / 0.004250 (0.045366) \| 0.046949 / 0.037052 (0.009897) \| 0.281596 / 0.258489 (0.023107) \| 0.330948 / 0.293841 (0.037108) \| 0.049645 / 0.128546 (-0.078901) \| 0.010953 / 0.075646 (-0.064693) \| 0.058546 / 0.419271 (-0.360725) \| 0.034010 / 0.043533 (-0.009523) \| 0.270525 / 0.255139 (0.015386) \| 0.289749 / 0.283200 (0.006550) \| 0.018755 / 0.141683 (-0.122927) \| 1.163072 / 1.452155 (-0.289082) \| 1.213400 / 1.492716 (-0.279316) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092397 / 0.018006 (0.074390) \| 0.299376 / 0.000490 (0.298886) \| 0.000211 / 0.000200 (0.000011) \| 0.000044 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022496 / 0.037411 (-0.014916) \| 0.076886 / 0.014526 (0.062361) \| 0.087186 / 0.176557 (-0.089371) \| 0.126092 / 0.737135 (-0.611044) \| 0.088832 / 0.296338 (-0.207507) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.288885 / 0.215209 (0.073676) \| 2.839851 / 2.077655 (0.762196) \| 1.587556 / 1.504120 (0.083436) \| 1.470249 / 1.541195 (-0.070945) \| 1.518080 / 1.468490 (0.049590) \| 0.569646 / 4.584777 (-4.015131) \| 2.417574 / 3.745712 (-1.328138) \| 2.737368 / 5.269862 (-2.532494) \| 1.784419 / 4.565676 (-2.781257) \| 0.064104 / 0.424275 (-0.360171) \| 0.005138 / 0.007607 (-0.002469) \| 0.346214 / 0.226044 (0.120169) \| 3.439541 / 2.268929 (1.170612) \| 1.944792 / 55.444624 (-53.499832) \| 1.675762 / 6.876477 (-5.200714) \| 1.851871 / 2.142072 (-0.290201) \| 0.652932 / 4.805227 (-4.152295) \| 0.118953 / 6.500664 (-6.381711) \| 0.041011 / 0.075469 (-0.034459) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.017690 / 1.841788 (-0.824098) \| 12.610531 / 8.074308 (4.536223) \| 11.223165 / 10.191392 (1.031773) \| 0.131637 / 0.680424 (-0.548786) \| 0.016733 / 0.534201 (-0.517468) \| 0.288491 / 0.579283 (-0.290792) \| 0.275899 / 0.434364 (-0.158465) \| 0.331837 / 0.540337 (-0.208500) \| 0.421695 / 1.386936 (-0.965241) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5d9dfa9a8c077c783729a279623926faa9e2f3f1 \"CML watermark\")\n" ]	"2024-01-31T13:41:54"	"2024-01-31T14:05:04"	"2024-01-31T13:59:01"	CONTRIBUTOR	null	null	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6633/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6633/timeline	null	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6633", "html_url": "https://github.com/huggingface/datasets/pull/6633", "diff_url": "https://github.com/huggingface/datasets/pull/6633.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6633.patch", "merged_at": "2024-01-31T13:59:01" }	true
https://api.github.com/repos/huggingface/datasets/issues/6632	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6632/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6632/comments	https://api.github.com/repos/huggingface/datasets/issues/6632/events	https://github.com/huggingface/datasets/pull/6632	2,108,541,678	PR_kwDODunzps5lfPuk	6,632	Fix reload cache with data dir	{ "login": "lhoestq", "id": 42851186, "node_id": "MDQ6VXNlcjQyODUxMTg2", "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "gravatar_id": "", "url": "https://api.github.com/users/lhoestq", "html_url": "https://github.com/lhoestq", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "repos_url": "https://api.github.com/users/lhoestq/repos", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6632). 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) \| 0.004913 / 0.011353 (-0.006440) \| 0.003595 / 0.011008 (-0.007413) \| 0.068385 / 0.038508 (0.029876) \| 0.028612 / 0.023109 (0.005503) \| 0.236590 / 0.275898 (-0.039308) \| 0.261890 / 0.323480 (-0.061590) \| 0.003027 / 0.007986 (-0.004958) \| 0.002674 / 0.004328 (-0.001654) \| 0.049255 / 0.004250 (0.045004) \| 0.040500 / 0.037052 (0.003447) \| 0.248759 / 0.258489 (-0.009730) \| 0.280299 / 0.293841 (-0.013542) \| 0.027300 / 0.128546 (-0.101247) \| 0.010475 / 0.075646 (-0.065171) \| 0.208744 / 0.419271 (-0.210527) \| 0.035214 / 0.043533 (-0.008319) \| 0.251922 / 0.255139 (-0.003217) \| 0.263582 / 0.283200 (-0.019618) \| 0.018738 / 0.141683 (-0.122945) \| 1.150940 / 1.452155 (-0.301215) \| 1.187240 / 1.492716 (-0.305476) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093505 / 0.018006 (0.075499) \| 0.301101 / 0.000490 (0.300611) \| 0.000232 / 0.000200 (0.000032) \| 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) \| 0.017779 / 0.037411 (-0.019632) \| 0.061412 / 0.014526 (0.046886) \| 0.074353 / 0.176557 (-0.102203) \| 0.118717 / 0.737135 (-0.618418) \| 0.074214 / 0.296338 (-0.222125) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.281722 / 0.215209 (0.066513) \| 2.716867 / 2.077655 (0.639212) \| 1.423379 / 1.504120 (-0.080741) \| 1.315379 / 1.541195 (-0.225816) \| 1.294638 / 1.468490 (-0.173852) \| 0.549658 / 4.584777 (-4.035119) \| 2.349889 / 3.745712 (-1.395823) \| 2.722354 / 5.269862 (-2.547507) \| 1.700271 / 4.565676 (-2.865406) \| 0.061099 / 0.424275 (-0.363176) \| 0.004931 / 0.007607 (-0.002677) \| 0.339181 / 0.226044 (0.113136) \| 3.242467 / 2.268929 (0.973538) \| 1.777929 / 55.444624 (-53.666696) \| 1.498380 / 6.876477 (-5.378097) \| 1.511482 / 2.142072 (-0.630590) \| 0.627076 / 4.805227 (-4.178151) \| 0.115936 / 6.500664 (-6.384729) \| 0.041791 / 0.075469 (-0.033678) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.983132 / 1.841788 (-0.858656) \| 11.431810 / 8.074308 (3.357502) \| 10.298918 / 10.191392 (0.107526) \| 0.139754 / 0.680424 (-0.540670) \| 0.013984 / 0.534201 (-0.520217) \| 0.283627 / 0.579283 (-0.295656) \| 0.264970 / 0.434364 (-0.169393) \| 0.323896 / 0.540337 (-0.216441) \| 0.420132 / 1.386936 (-0.966804) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005323 / 0.011353 (-0.006030) \| 0.003725 / 0.011008 (-0.007283) \| 0.050191 / 0.038508 (0.011683) \| 0.032196 / 0.023109 (0.009087) \| 0.265037 / 0.275898 (-0.010861) \| 0.289573 / 0.323480 (-0.033907) \| 0.004345 / 0.007986 (-0.003640) \| 0.002794 / 0.004328 (-0.001534) \| 0.048955 / 0.004250 (0.044705) \| 0.045421 / 0.037052 (0.008369) \| 0.279792 / 0.258489 (0.021303) \| 0.307374 / 0.293841 (0.013533) \| 0.046997 / 0.128546 (-0.081549) \| 0.010531 / 0.075646 (-0.065115) \| 0.058921 / 0.419271 (-0.360351) \| 0.033620 / 0.043533 (-0.009912) \| 0.268138 / 0.255139 (0.012999) \| 0.285941 / 0.283200 (0.002742) \| 0.018396 / 0.141683 (-0.123287) \| 1.151089 / 1.452155 (-0.301066) \| 1.209351 / 1.492716 (-0.283365) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| 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 (0.074252) \| 0.300893 / 0.000490 (0.300403) \| 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) \| 0.022233 / 0.037411 (-0.015178) \| 0.075220 / 0.014526 (0.060694) \| 0.085901 / 0.176557 (-0.090656) \| 0.125080 / 0.737135 (-0.612056) \| 0.086978 / 0.296338 (-0.209361) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.292877 / 0.215209 (0.077667) \| 2.841005 / 2.077655 (0.763350) \| 1.555168 / 1.504120 (0.051048) \| 1.420801 / 1.541195 (-0.120394) \| 1.431475 / 1.468490 (-0.037015) \| 0.569803 / 4.584777 (-4.014974) \| 2.451731 / 3.745712 (-1.293981) \| 2.662825 / 5.269862 (-2.607036) \| 1.732260 / 4.565676 (-2.833416) \| 0.063030 / 0.424275 (-0.361245) \| 0.004971 / 0.007607 (-0.002637) \| 0.345250 / 0.226044 (0.119206) \| 3.390909 / 2.268929 (1.121980) \| 1.908666 / 55.444624 (-53.535959) \| 1.628976 / 6.876477 (-5.247501) \| 1.719270 / 2.142072 (-0.422803) \| 0.653712 / 4.805227 (-4.151515) \| 0.116423 / 6.500664 (-6.384241) \| 0.040835 / 0.075469 (-0.034634) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.005538 / 1.841788 (-0.836250) \| 12.105381 / 8.074308 (4.031073) \| 10.656295 / 10.191392 (0.464903) \| 0.131850 / 0.680424 (-0.548574) \| 0.016297 / 0.534201 (-0.517904) \| 0.285566 / 0.579283 (-0.293717) \| 0.276086 / 0.434364 (-0.158278) \| 0.326663 / 0.540337 (-0.213675) \| 0.410639 / 1.386936 (-0.976297) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1dc3f04586ee65c890b74649afc42316121af689 \"CML watermark\")\n" ]	"2024-01-30T18:52:23"	"2024-02-06T17:27:35"	"2024-02-06T17:21:24"	MEMBER	null	The cache used to only check for the latest cache directory with a given config_name, but it was wrong (e.g. `default-data_dir=data%2Ffortran-data_dir=data%2Ffortran` instead of `default-data_dir=data%2Ffortran`) I fixed this by not passing the `config_kwargs` to the parent Builder `__init__`, and passing the config_id forged from the `config_kwargs` directly close https://github.com/huggingface/datasets/issues/6609	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6632/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6632/timeline	null	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6632", "html_url": "https://github.com/huggingface/datasets/pull/6632", "diff_url": "https://github.com/huggingface/datasets/pull/6632.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6632.patch", "merged_at": "2024-02-06T17:21:24" }	true
https://api.github.com/repos/huggingface/datasets/issues/6631	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6631/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6631/comments	https://api.github.com/repos/huggingface/datasets/issues/6631/events	https://github.com/huggingface/datasets/pull/6631	2,107,802,473	PR_kwDODunzps5lcu9A	6,631	Fix filelock: use current umask for filelock >= 3.10	{ "login": "lhoestq", "id": 42851186, "node_id": "MDQ6VXNlcjQyODUxMTg2", "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "gravatar_id": "", "url": "https://api.github.com/users/lhoestq", "html_url": "https://github.com/lhoestq", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "repos_url": "https://api.github.com/users/lhoestq/repos", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6631). 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) \| 0.005076 / 0.011353 (-0.006277) \| 0.003665 / 0.011008 (-0.007343) \| 0.063602 / 0.038508 (0.025094) \| 0.029103 / 0.023109 (0.005993) \| 0.233133 / 0.275898 (-0.042765) \| 0.257000 / 0.323480 (-0.066480) \| 0.003059 / 0.007986 (-0.004926) \| 0.004007 / 0.004328 (-0.000321) \| 0.049804 / 0.004250 (0.045553) \| 0.039946 / 0.037052 (0.002893) \| 0.248003 / 0.258489 (-0.010486) \| 0.272729 / 0.293841 (-0.021112) \| 0.027542 / 0.128546 (-0.101004) \| 0.010745 / 0.075646 (-0.064901) \| 0.207686 / 0.419271 (-0.211586) \| 0.035438 / 0.043533 (-0.008095) \| 0.236864 / 0.255139 (-0.018275) \| 0.258610 / 0.283200 (-0.024590) \| 0.017225 / 0.141683 (-0.124458) \| 1.130894 / 1.452155 (-0.321261) \| 1.171266 / 1.492716 (-0.321450) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092532 / 0.018006 (0.074525) \| 0.301650 / 0.000490 (0.301161) \| 0.000216 / 0.000200 (0.000016) \| 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) \| 0.018175 / 0.037411 (-0.019237) \| 0.061538 / 0.014526 (0.047012) \| 0.073673 / 0.176557 (-0.102884) \| 0.120676 / 0.737135 (-0.616460) \| 0.074753 / 0.296338 (-0.221586) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.283625 / 0.215209 (0.068416) \| 2.794903 / 2.077655 (0.717248) \| 1.485149 / 1.504120 (-0.018970) \| 1.361154 / 1.541195 (-0.180041) \| 1.371436 / 1.468490 (-0.097054) \| 0.580401 / 4.584777 (-4.004376) \| 2.457068 / 3.745712 (-1.288644) \| 2.760878 / 5.269862 (-2.508984) \| 1.725507 / 4.565676 (-2.840169) \| 0.063632 / 0.424275 (-0.360644) \| 0.005036 / 0.007607 (-0.002572) \| 0.337167 / 0.226044 (0.111122) \| 3.314508 / 2.268929 (1.045579) \| 1.863412 / 55.444624 (-53.581213) \| 1.621966 / 6.876477 (-5.254511) \| 1.600422 / 2.142072 (-0.541651) \| 0.647753 / 4.805227 (-4.157475) \| 0.117169 / 6.500664 (-6.383495) \| 0.042338 / 0.075469 (-0.033131) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.981818 / 1.841788 (-0.859969) \| 12.044657 / 8.074308 (3.970349) \| 10.654091 / 10.191392 (0.462699) \| 0.130693 / 0.680424 (-0.549731) \| 0.014733 / 0.534201 (-0.519468) \| 0.317432 / 0.579283 (-0.261851) \| 0.267196 / 0.434364 (-0.167168) \| 0.329310 / 0.540337 (-0.211028) \| 0.433379 / 1.386936 (-0.953557) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005502 / 0.011353 (-0.005851) \| 0.003951 / 0.011008 (-0.007057) \| 0.050651 / 0.038508 (0.012143) \| 0.031802 / 0.023109 (0.008693) \| 0.281384 / 0.275898 (0.005485) \| 0.303900 / 0.323480 (-0.019580) \| 0.004451 / 0.007986 (-0.003534) \| 0.002801 / 0.004328 (-0.001527) \| 0.048688 / 0.004250 (0.044438) \| 0.044717 / 0.037052 (0.007664) \| 0.295017 / 0.258489 (0.036528) \| 0.328003 / 0.293841 (0.034162) \| 0.048421 / 0.128546 (-0.080125) \| 0.011254 / 0.075646 (-0.064392) \| 0.058223 / 0.419271 (-0.361048) \| 0.033915 / 0.043533 (-0.009618) \| 0.279893 / 0.255139 (0.024754) \| 0.297605 / 0.283200 (0.014405) \| 0.017115 / 0.141683 (-0.124568) \| 1.146966 / 1.452155 (-0.305189) \| 1.191650 / 1.492716 (-0.301066) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092524 / 0.018006 (0.074518) \| 0.309332 / 0.000490 (0.308842) \| 0.000212 / 0.000200 (0.000012) \| 0.000044 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022265 / 0.037411 (-0.015146) \| 0.075732 / 0.014526 (0.061206) \| 0.087340 / 0.176557 (-0.089217) \| 0.126079 / 0.737135 (-0.611056) \| 0.090349 / 0.296338 (-0.205990) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.288882 / 0.215209 (0.073673) \| 2.833046 / 2.077655 (0.755392) \| 1.602905 / 1.504120 (0.098785) \| 1.473110 / 1.541195 (-0.068085) \| 1.491300 / 1.468490 (0.022810) \| 0.557799 / 4.584777 (-4.026978) \| 2.439526 / 3.745712 (-1.306186) \| 2.669336 / 5.269862 (-2.600526) \| 1.719472 / 4.565676 (-2.846204) \| 0.062456 / 0.424275 (-0.361819) \| 0.005058 / 0.007607 (-0.002549) \| 0.343706 / 0.226044 (0.117662) \| 3.422397 / 2.268929 (1.153469) \| 1.983679 / 55.444624 (-53.460946) \| 1.673784 / 6.876477 (-5.202693) \| 1.785144 / 2.142072 (-0.356928) \| 0.643127 / 4.805227 (-4.162100) \| 0.115254 / 6.500664 (-6.385410) \| 0.041235 / 0.075469 (-0.034235) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.005448 / 1.841788 (-0.836340) \| 12.240100 / 8.074308 (4.165792) \| 11.051965 / 10.191392 (0.860573) \| 0.130438 / 0.680424 (-0.549986) \| 0.015918 / 0.534201 (-0.518283) \| 0.287468 / 0.579283 (-0.291815) \| 0.287699 / 0.434364 (-0.146665) \| 0.324561 / 0.540337 (-0.215777) \| 0.418820 / 1.386936 (-0.968116) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#237a2a688155e23cfbcdfadd2d491ce1667fa494 \"CML watermark\")\n" ]	"2024-01-30T12:56:01"	"2024-01-30T15:34:49"	"2024-01-30T15:28:37"	MEMBER	null	reported in https://github.com/huggingface/evaluate/issues/542 cc @stas00 @williamberrios close https://github.com/huggingface/datasets/issues/6589	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6631/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6631/timeline	null	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6631", "html_url": "https://github.com/huggingface/datasets/pull/6631", "diff_url": "https://github.com/huggingface/datasets/pull/6631.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6631.patch", "merged_at": "2024-01-30T15:28:37" }	true
https://api.github.com/repos/huggingface/datasets/issues/6630	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6630/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6630/comments	https://api.github.com/repos/huggingface/datasets/issues/6630/events	https://github.com/huggingface/datasets/pull/6630	2,106,478,275	PR_kwDODunzps5lYPi3	6,630	Bump max range of dill to 0.3.8	{ "login": "ringohoffman", "id": 27844407, "node_id": "MDQ6VXNlcjI3ODQ0NDA3", "avatar_url": "https://avatars.githubusercontent.com/u/27844407?v=4", "gravatar_id": "", "url": "https://api.github.com/users/ringohoffman", "html_url": "https://github.com/ringohoffman", "followers_url": "https://api.github.com/users/ringohoffman/followers", "following_url": "https://api.github.com/users/ringohoffman/following{/other_user}", "gists_url": "https://api.github.com/users/ringohoffman/gists{/gist_id}", "starred_url": "https://api.github.com/users/ringohoffman/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/ringohoffman/subscriptions", "organizations_url": "https://api.github.com/users/ringohoffman/orgs", "repos_url": "https://api.github.com/users/ringohoffman/repos", "events_url": "https://api.github.com/users/ringohoffman/events{/privacy}", "received_events_url": "https://api.github.com/users/ringohoffman/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6630). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "Hmm these errors look pretty weird... can they be retried?", "Hi, thanks for working on this! To fix the errors, you also need to update [this file](https://github.com/huggingface/datasets/blob/main/src/datasets/utils/_dill.py) (by adding `version.parse(\"0.3.8\").release` to the lists)", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005068 / 0.011353 (-0.006285) \| 0.003657 / 0.011008 (-0.007351) \| 0.062914 / 0.038508 (0.024406) \| 0.027965 / 0.023109 (0.004855) \| 0.241804 / 0.275898 (-0.034094) \| 0.268069 / 0.323480 (-0.055411) \| 0.004066 / 0.007986 (-0.003920) \| 0.002704 / 0.004328 (-0.001624) \| 0.048745 / 0.004250 (0.044495) \| 0.042158 / 0.037052 (0.005106) \| 0.257670 / 0.258489 (-0.000819) \| 0.279419 / 0.293841 (-0.014422) \| 0.027193 / 0.128546 (-0.101353) \| 0.010379 / 0.075646 (-0.065267) \| 0.207009 / 0.419271 (-0.212262) \| 0.035494 / 0.043533 (-0.008039) \| 0.246025 / 0.255139 (-0.009114) \| 0.265906 / 0.283200 (-0.017294) \| 0.017335 / 0.141683 (-0.124348) \| 1.134052 / 1.452155 (-0.318103) \| 1.184668 / 1.492716 (-0.308049) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093137 / 0.018006 (0.075130) \| 0.302279 / 0.000490 (0.301789) \| 0.000210 / 0.000200 (0.000010) \| 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) \| 0.018190 / 0.037411 (-0.019221) \| 0.061436 / 0.014526 (0.046910) \| 0.073102 / 0.176557 (-0.103454) \| 0.119782 / 0.737135 (-0.617354) \| 0.074292 / 0.296338 (-0.222046) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.285905 / 0.215209 (0.070696) \| 2.809051 / 2.077655 (0.731397) \| 1.470305 / 1.504120 (-0.033814) \| 1.350457 / 1.541195 (-0.190738) \| 1.349111 / 1.468490 (-0.119379) \| 0.568277 / 4.584777 (-4.016500) \| 2.353046 / 3.745712 (-1.392666) \| 2.805862 / 5.269862 (-2.463999) \| 1.750275 / 4.565676 (-2.815401) \| 0.062370 / 0.424275 (-0.361905) \| 0.004954 / 0.007607 (-0.002653) \| 0.335609 / 0.226044 (0.109564) \| 3.367200 / 2.268929 (1.098271) \| 1.829431 / 55.444624 (-53.615193) \| 1.545093 / 6.876477 (-5.331384) \| 1.571107 / 2.142072 (-0.570966) \| 0.640279 / 4.805227 (-4.164949) \| 0.116209 / 6.500664 (-6.384455) \| 0.042308 / 0.075469 (-0.033161) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.982972 / 1.841788 (-0.858816) \| 11.424370 / 8.074308 (3.350062) \| 10.427111 / 10.191392 (0.235719) \| 0.129477 / 0.680424 (-0.550946) \| 0.014166 / 0.534201 (-0.520035) \| 0.287597 / 0.579283 (-0.291686) \| 0.265588 / 0.434364 (-0.168776) \| 0.324007 / 0.540337 (-0.216330) \| 0.430766 / 1.386936 (-0.956170) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005347 / 0.011353 (-0.006005) \| 0.003733 / 0.011008 (-0.007275) \| 0.049520 / 0.038508 (0.011011) \| 0.031177 / 0.023109 (0.008068) \| 0.281854 / 0.275898 (0.005956) \| 0.300937 / 0.323480 (-0.022543) \| 0.004385 / 0.007986 (-0.003601) \| 0.002841 / 0.004328 (-0.001488) \| 0.048661 / 0.004250 (0.044411) \| 0.044258 / 0.037052 (0.007205) \| 0.295651 / 0.258489 (0.037162) \| 0.322872 / 0.293841 (0.029031) \| 0.048924 / 0.128546 (-0.079622) \| 0.010742 / 0.075646 (-0.064905) \| 0.059327 / 0.419271 (-0.359944) \| 0.033938 / 0.043533 (-0.009595) \| 0.282235 / 0.255139 (0.027096) \| 0.297432 / 0.283200 (0.014233) \| 0.018295 / 0.141683 (-0.123388) \| 1.164459 / 1.452155 (-0.287696) \| 1.214511 / 1.492716 (-0.278205) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091441 / 0.018006 (0.073435) \| 0.303023 / 0.000490 (0.302533) \| 0.000211 / 0.000200 (0.000011) \| 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) \| 0.022024 / 0.037411 (-0.015388) \| 0.075570 / 0.014526 (0.061044) \| 0.086761 / 0.176557 (-0.089796) \| 0.126437 / 0.737135 (-0.610698) \| 0.088354 / 0.296338 (-0.207984) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.289360 / 0.215209 (0.074151) \| 2.816433 / 2.077655 (0.738779) \| 1.561442 / 1.504120 (0.057322) \| 1.438168 / 1.541195 (-0.103027) \| 1.453398 / 1.468490 (-0.015092) \| 0.579474 / 4.584777 (-4.005303) \| 2.458640 / 3.745712 (-1.287072) \| 2.638572 / 5.269862 (-2.631290) \| 1.725218 / 4.565676 (-2.840458) \| 0.063550 / 0.424275 (-0.360725) \| 0.005220 / 0.007607 (-0.002387) \| 0.338883 / 0.226044 (0.112838) \| 3.353585 / 2.268929 (1.084656) \| 1.913186 / 55.444624 (-53.531438) \| 1.667445 / 6.876477 (-5.209032) \| 1.740085 / 2.142072 (-0.401987) \| 0.646369 / 4.805227 (-4.158859) \| 0.116737 / 6.500664 (-6.383927) \| 0.041052 / 0.075469 (-0.034417) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.023180 / 1.841788 (-0.818608) \| 12.078398 / 8.074308 (4.004090) \| 10.952012 / 10.191392 (0.760620) \| 0.131335 / 0.680424 (-0.549089) \| 0.015701 / 0.534201 (-0.518499) \| 0.289709 / 0.579283 (-0.289574) \| 0.270495 / 0.434364 (-0.163869) \| 0.331773 / 0.540337 (-0.208565) \| 0.417660 / 1.386936 (-0.969276) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3b21d74f5c0ab8a85838af04de8ad85e71b0ac4f \"CML watermark\")\n" ]	"2024-01-29T21:35:55"	"2024-01-30T16:19:45"	"2024-01-30T15:12:25"	CONTRIBUTOR	null	Release on Jan 27, 2024: https://pypi.org/project/dill/0.3.8/#history	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6630/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6630/timeline	null	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6630", "html_url": "https://github.com/huggingface/datasets/pull/6630", "diff_url": "https://github.com/huggingface/datasets/pull/6630.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6630.patch", "merged_at": "2024-01-30T15:12:25" }	true
https://api.github.com/repos/huggingface/datasets/issues/6629	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6629/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6629/comments	https://api.github.com/repos/huggingface/datasets/issues/6629/events	https://github.com/huggingface/datasets/pull/6629	2,105,774,482	PR_kwDODunzps5lV0aF	6,629	Support push_to_hub without org/user to default to logged-in user	{ "login": "albertvillanova", "id": 8515462, "node_id": "MDQ6VXNlcjg1MTU0NjI=", "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "gravatar_id": "", "url": "https://api.github.com/users/albertvillanova", "html_url": "https://github.com/albertvillanova", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "repos_url": "https://api.github.com/users/albertvillanova/repos", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6629). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "@huggingface/datasets, feel free to review this PR so that it can be included in the next release.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005222 / 0.011353 (-0.006131) \| 0.003621 / 0.011008 (-0.007387) \| 0.063091 / 0.038508 (0.024583) \| 0.029395 / 0.023109 (0.006285) \| 0.231445 / 0.275898 (-0.044453) \| 0.256716 / 0.323480 (-0.066764) \| 0.004905 / 0.007986 (-0.003081) \| 0.002703 / 0.004328 (-0.001625) \| 0.048526 / 0.004250 (0.044276) \| 0.041382 / 0.037052 (0.004330) \| 0.247468 / 0.258489 (-0.011021) \| 0.270670 / 0.293841 (-0.023171) \| 0.028088 / 0.128546 (-0.100458) \| 0.010661 / 0.075646 (-0.064985) \| 0.205812 / 0.419271 (-0.213459) \| 0.035880 / 0.043533 (-0.007653) \| 0.237310 / 0.255139 (-0.017829) \| 0.255440 / 0.283200 (-0.027760) \| 0.018334 / 0.141683 (-0.123349) \| 1.128815 / 1.452155 (-0.323340) \| 1.204771 / 1.492716 (-0.287945) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.089175 / 0.018006 (0.071169) \| 0.298584 / 0.000490 (0.298095) \| 0.000206 / 0.000200 (0.000006) \| 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) \| 0.018532 / 0.037411 (-0.018880) \| 0.061158 / 0.014526 (0.046632) \| 0.074177 / 0.176557 (-0.102380) \| 0.119408 / 0.737135 (-0.617728) \| 0.073821 / 0.296338 (-0.222518) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.277630 / 0.215209 (0.062420) \| 2.735038 / 2.077655 (0.657383) \| 1.437251 / 1.504120 (-0.066868) \| 1.304596 / 1.541195 (-0.236598) \| 1.316830 / 1.468490 (-0.151661) \| 0.551057 / 4.584777 (-4.033720) \| 2.337247 / 3.745712 (-1.408465) \| 2.761501 / 5.269862 (-2.508361) \| 1.729000 / 4.565676 (-2.836677) \| 0.069398 / 0.424275 (-0.354877) \| 0.005059 / 0.007607 (-0.002548) \| 0.359594 / 0.226044 (0.133550) \| 3.283325 / 2.268929 (1.014397) \| 1.777410 / 55.444624 (-53.667214) \| 1.518522 / 6.876477 (-5.357954) \| 1.546712 / 2.142072 (-0.595361) \| 0.627047 / 4.805227 (-4.178180) \| 0.117058 / 6.500664 (-6.383606) \| 0.043437 / 0.075469 (-0.032032) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.056303 / 1.841788 (-0.785484) \| 11.552295 / 8.074308 (3.477987) \| 10.184582 / 10.191392 (-0.006810) \| 0.129061 / 0.680424 (-0.551363) \| 0.014093 / 0.534201 (-0.520108) \| 0.292268 / 0.579283 (-0.287015) \| 0.264750 / 0.434364 (-0.169614) \| 0.334770 / 0.540337 (-0.205567) \| 0.436749 / 1.386936 (-0.950187) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005408 / 0.011353 (-0.005945) \| 0.003650 / 0.011008 (-0.007358) \| 0.054263 / 0.038508 (0.015755) \| 0.031112 / 0.023109 (0.008003) \| 0.270582 / 0.275898 (-0.005316) \| 0.303506 / 0.323480 (-0.019974) \| 0.004351 / 0.007986 (-0.003635) \| 0.002654 / 0.004328 (-0.001674) \| 0.049631 / 0.004250 (0.045381) \| 0.045209 / 0.037052 (0.008156) \| 0.284992 / 0.258489 (0.026503) \| 0.316653 / 0.293841 (0.022812) \| 0.049526 / 0.128546 (-0.079020) \| 0.010696 / 0.075646 (-0.064951) \| 0.057859 / 0.419271 (-0.361413) \| 0.034227 / 0.043533 (-0.009306) \| 0.269656 / 0.255139 (0.014517) \| 0.288766 / 0.283200 (0.005567) \| 0.017892 / 0.141683 (-0.123791) \| 1.167492 / 1.452155 (-0.284662) \| 1.217263 / 1.492716 (-0.275454) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.089306 / 0.018006 (0.071299) \| 0.300774 / 0.000490 (0.300284) \| 0.000198 / 0.000200 (-0.000002) \| 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) \| 0.022050 / 0.037411 (-0.015361) \| 0.076781 / 0.014526 (0.062255) \| 0.086597 / 0.176557 (-0.089959) \| 0.125094 / 0.737135 (-0.612042) \| 0.089412 / 0.296338 (-0.206927) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.287444 / 0.215209 (0.072235) \| 2.830047 / 2.077655 (0.752392) \| 1.567492 / 1.504120 (0.063372) \| 1.439875 / 1.541195 (-0.101320) \| 1.461699 / 1.468490 (-0.006791) \| 0.569595 / 4.584777 (-4.015182) \| 2.454391 / 3.745712 (-1.291322) \| 2.655829 / 5.269862 (-2.614032) \| 1.756122 / 4.565676 (-2.809554) \| 0.063333 / 0.424275 (-0.360942) \| 0.005086 / 0.007607 (-0.002521) \| 0.351210 / 0.226044 (0.125166) \| 3.375545 / 2.268929 (1.106617) \| 1.945367 / 55.444624 (-53.499258) \| 1.662635 / 6.876477 (-5.213841) \| 1.762859 / 2.142072 (-0.379213) \| 0.651889 / 4.805227 (-4.153339) \| 0.118341 / 6.500664 (-6.382323) \| 0.040897 / 0.075469 (-0.034572) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.005270 / 1.841788 (-0.836518) \| 12.247847 / 8.074308 (4.173539) \| 10.828131 / 10.191392 (0.636739) \| 0.129741 / 0.680424 (-0.550683) \| 0.015184 / 0.534201 (-0.519017) \| 0.295440 / 0.579283 (-0.283843) \| 0.276759 / 0.434364 (-0.157605) \| 0.329046 / 0.540337 (-0.211291) \| 0.421750 / 1.386936 (-0.965186) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ea261ddc295527d0c1cd9f90fb61668f14135608 \"CML watermark\")\n" ]	"2024-01-29T15:36:52"	"2024-02-05T12:35:43"	"2024-02-05T12:29:36"	MEMBER	null	This behavior is aligned with: - the behavior of `datasets` before merging #6519 - the behavior described in the corresponding docstring - the behavior of `huggingface_hub.create_repo` Revert "Support push_to_hub canonical datasets (#6519)" - This reverts commit a887ee78835573f5d80f9e414e8443b4caff3541. Fix #6597.	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6629/reactions", "total_count": 1, "+1": 1, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6629/timeline	null	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6629", "html_url": "https://github.com/huggingface/datasets/pull/6629", "diff_url": "https://github.com/huggingface/datasets/pull/6629.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6629.patch", "merged_at": "2024-02-05T12:29:36" }	true
https://api.github.com/repos/huggingface/datasets/issues/6628	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6628/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6628/comments	https://api.github.com/repos/huggingface/datasets/issues/6628/events	https://github.com/huggingface/datasets/pull/6628	2,105,760,502	PR_kwDODunzps5lVxXU	6,628	Make CLI test support multi-processing	{ "login": "albertvillanova", "id": 8515462, "node_id": "MDQ6VXNlcjg1MTU0NjI=", "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "gravatar_id": "", "url": "https://api.github.com/users/albertvillanova", "html_url": "https://github.com/albertvillanova", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "repos_url": "https://api.github.com/users/albertvillanova/repos", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6628). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "@huggingface/datasets, feel free to review this PR so that it can be included in the next release.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004907 / 0.011353 (-0.006446) \| 0.003200 / 0.011008 (-0.007808) \| 0.062601 / 0.038508 (0.024093) \| 0.028607 / 0.023109 (0.005498) \| 0.242688 / 0.275898 (-0.033210) \| 0.263754 / 0.323480 (-0.059726) \| 0.003084 / 0.007986 (-0.004901) \| 0.002744 / 0.004328 (-0.001585) \| 0.048686 / 0.004250 (0.044436) \| 0.040734 / 0.037052 (0.003682) \| 0.262585 / 0.258489 (0.004096) \| 0.282822 / 0.293841 (-0.011019) \| 0.027470 / 0.128546 (-0.101076) \| 0.010356 / 0.075646 (-0.065290) \| 0.206397 / 0.419271 (-0.212874) \| 0.035440 / 0.043533 (-0.008093) \| 0.248599 / 0.255139 (-0.006540) \| 0.268869 / 0.283200 (-0.014331) \| 0.018542 / 0.141683 (-0.123141) \| 1.128139 / 1.452155 (-0.324016) \| 1.172115 / 1.492716 (-0.320602) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.107939 / 0.018006 (0.089933) \| 0.301801 / 0.000490 (0.301311) \| 0.000207 / 0.000200 (0.000007) \| 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) \| 0.018505 / 0.037411 (-0.018906) \| 0.061350 / 0.014526 (0.046824) \| 0.072645 / 0.176557 (-0.103912) \| 0.119459 / 0.737135 (-0.617676) \| 0.074711 / 0.296338 (-0.221628) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.275132 / 0.215209 (0.059922) \| 2.714936 / 2.077655 (0.637281) \| 1.434204 / 1.504120 (-0.069916) \| 1.328358 / 1.541195 (-0.212837) \| 1.320706 / 1.468490 (-0.147784) \| 0.555723 / 4.584777 (-4.029054) \| 2.401335 / 3.745712 (-1.344378) \| 2.765609 / 5.269862 (-2.504253) \| 1.715207 / 4.565676 (-2.850470) \| 0.074990 / 0.424275 (-0.349285) \| 0.004999 / 0.007607 (-0.002608) \| 0.328435 / 0.226044 (0.102390) \| 3.254945 / 2.268929 (0.986017) \| 1.781105 / 55.444624 (-53.663519) \| 1.509491 / 6.876477 (-5.366985) \| 1.520670 / 2.142072 (-0.621402) \| 0.636411 / 4.805227 (-4.168817) \| 0.115616 / 6.500664 (-6.385048) \| 0.041633 / 0.075469 (-0.033836) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.975462 / 1.841788 (-0.866326) \| 11.480359 / 8.074308 (3.406051) \| 10.528665 / 10.191392 (0.337273) \| 0.141323 / 0.680424 (-0.539100) \| 0.013510 / 0.534201 (-0.520691) \| 0.293570 / 0.579283 (-0.285713) \| 0.259956 / 0.434364 (-0.174408) \| 0.331440 / 0.540337 (-0.208898) \| 0.453487 / 1.386936 (-0.933449) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005278 / 0.011353 (-0.006075) \| 0.003400 / 0.011008 (-0.007608) \| 0.049442 / 0.038508 (0.010934) \| 0.031738 / 0.023109 (0.008628) \| 0.292334 / 0.275898 (0.016436) \| 0.308931 / 0.323480 (-0.014549) \| 0.004290 / 0.007986 (-0.003696) \| 0.002738 / 0.004328 (-0.001591) \| 0.048944 / 0.004250 (0.044694) \| 0.044273 / 0.037052 (0.007221) \| 0.301434 / 0.258489 (0.042945) \| 0.333067 / 0.293841 (0.039226) \| 0.048741 / 0.128546 (-0.079805) \| 0.010357 / 0.075646 (-0.065289) \| 0.057777 / 0.419271 (-0.361495) \| 0.033892 / 0.043533 (-0.009641) \| 0.286921 / 0.255139 (0.031782) \| 0.306204 / 0.283200 (0.023005) \| 0.018764 / 0.141683 (-0.122919) \| 1.142000 / 1.452155 (-0.310155) \| 1.206728 / 1.492716 (-0.285988) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094233 / 0.018006 (0.076227) \| 0.302553 / 0.000490 (0.302063) \| 0.000213 / 0.000200 (0.000013) \| 0.000044 / 0.000054 (-0.000010) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021814 / 0.037411 (-0.015598) \| 0.075143 / 0.014526 (0.060617) \| 0.087717 / 0.176557 (-0.088840) \| 0.126079 / 0.737135 (-0.611056) \| 0.089083 / 0.296338 (-0.207255) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.293844 / 0.215209 (0.078635) \| 2.859481 / 2.077655 (0.781827) \| 1.580366 / 1.504120 (0.076246) \| 1.462633 / 1.541195 (-0.078562) \| 1.471052 / 1.468490 (0.002562) \| 0.574755 / 4.584777 (-4.010022) \| 2.408925 / 3.745712 (-1.336787) \| 2.673618 / 5.269862 (-2.596243) \| 1.746218 / 4.565676 (-2.819459) \| 0.063435 / 0.424275 (-0.360840) \| 0.005023 / 0.007607 (-0.002584) \| 0.341990 / 0.226044 (0.115946) \| 3.430862 / 2.268929 (1.161933) \| 1.953869 / 55.444624 (-53.490755) \| 1.661276 / 6.876477 (-5.215201) \| 1.761575 / 2.142072 (-0.380498) \| 0.656388 / 4.805227 (-4.148839) \| 0.117774 / 6.500664 (-6.382890) \| 0.040290 / 0.075469 (-0.035179) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.004315 / 1.841788 (-0.837473) \| 12.249719 / 8.074308 (4.175411) \| 10.942703 / 10.191392 (0.751311) \| 0.128552 / 0.680424 (-0.551872) \| 0.015958 / 0.534201 (-0.518242) \| 0.287330 / 0.579283 (-0.291953) \| 0.274336 / 0.434364 (-0.160028) \| 0.326233 / 0.540337 (-0.214104) \| 0.414548 / 1.386936 (-0.972388) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#db47d6d95c5346368710d3c852f20ffc1b0f1c1c \"CML watermark\")\n" ]	"2024-01-29T15:30:09"	"2024-02-05T10:29:20"	"2024-02-05T10:23:13"	MEMBER	null	Support passing `--num_proc` to CLI test. This was really useful recently to run the command on `pubmed`: https://huggingface.co/datasets/pubmed/discussions/11	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6628/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6628/timeline	null	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6628", "html_url": "https://github.com/huggingface/datasets/pull/6628", "diff_url": "https://github.com/huggingface/datasets/pull/6628.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6628.patch", "merged_at": "2024-02-05T10:23:13" }	true
https://api.github.com/repos/huggingface/datasets/issues/6627	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6627/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6627/comments	https://api.github.com/repos/huggingface/datasets/issues/6627/events	https://github.com/huggingface/datasets/pull/6627	2,105,735,816	PR_kwDODunzps5lVr-t	6,627	Disable `tqdm` bars in non-interactive environments	{ "login": "mariosasko", "id": 47462742, "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "gravatar_id": "", "url": "https://api.github.com/users/mariosasko", "html_url": "https://github.com/mariosasko", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "repos_url": "https://api.github.com/users/mariosasko/repos", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6627). 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) \| 0.004944 / 0.011353 (-0.006409) \| 0.003279 / 0.011008 (-0.007729) \| 0.063041 / 0.038508 (0.024533) \| 0.029888 / 0.023109 (0.006779) \| 0.259138 / 0.275898 (-0.016760) \| 0.276907 / 0.323480 (-0.046573) \| 0.004015 / 0.007986 (-0.003970) \| 0.002647 / 0.004328 (-0.001682) \| 0.048944 / 0.004250 (0.044693) \| 0.039412 / 0.037052 (0.002360) \| 0.278069 / 0.258489 (0.019580) \| 0.299139 / 0.293841 (0.005298) \| 0.027272 / 0.128546 (-0.101274) \| 0.010445 / 0.075646 (-0.065202) \| 0.206925 / 0.419271 (-0.212347) \| 0.035589 / 0.043533 (-0.007944) \| 0.256805 / 0.255139 (0.001666) \| 0.275128 / 0.283200 (-0.008072) \| 0.017888 / 0.141683 (-0.123795) \| 1.136983 / 1.452155 (-0.315172) \| 1.167495 / 1.492716 (-0.325222) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.088167 / 0.018006 (0.070161) \| 0.297360 / 0.000490 (0.296871) \| 0.000231 / 0.000200 (0.000031) \| 0.000049 / 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) \| 0.018114 / 0.037411 (-0.019297) \| 0.061217 / 0.014526 (0.046691) \| 0.072269 / 0.176557 (-0.104288) \| 0.120607 / 0.737135 (-0.616528) \| 0.073517 / 0.296338 (-0.222822) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.282580 / 0.215209 (0.067371) \| 2.758650 / 2.077655 (0.680995) \| 1.425125 / 1.504120 (-0.078995) \| 1.303182 / 1.541195 (-0.238013) \| 1.341035 / 1.468490 (-0.127455) \| 0.549485 / 4.584777 (-4.035292) \| 2.346297 / 3.745712 (-1.399415) \| 2.686457 / 5.269862 (-2.583405) \| 1.684789 / 4.565676 (-2.880888) \| 0.061279 / 0.424275 (-0.362996) \| 0.004902 / 0.007607 (-0.002705) \| 0.333089 / 0.226044 (0.107044) \| 3.297016 / 2.268929 (1.028087) \| 1.765614 / 55.444624 (-53.679010) \| 1.499314 / 6.876477 (-5.377162) \| 1.501275 / 2.142072 (-0.640797) \| 0.619039 / 4.805227 (-4.186189) \| 0.114284 / 6.500664 (-6.386380) \| 0.041481 / 0.075469 (-0.033988) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.973924 / 1.841788 (-0.867863) \| 11.268266 / 8.074308 (3.193958) \| 10.304738 / 10.191392 (0.113346) \| 0.129297 / 0.680424 (-0.551127) \| 0.014894 / 0.534201 (-0.519307) \| 0.287658 / 0.579283 (-0.291626) \| 0.266476 / 0.434364 (-0.167888) \| 0.322199 / 0.540337 (-0.218138) \| 0.419568 / 1.386936 (-0.967368) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005220 / 0.011353 (-0.006133) \| 0.003310 / 0.011008 (-0.007698) \| 0.049707 / 0.038508 (0.011199) \| 0.031148 / 0.023109 (0.008039) \| 0.284644 / 0.275898 (0.008746) \| 0.302767 / 0.323480 (-0.020712) \| 0.004245 / 0.007986 (-0.003740) \| 0.002677 / 0.004328 (-0.001651) \| 0.049870 / 0.004250 (0.045620) \| 0.043922 / 0.037052 (0.006870) \| 0.294955 / 0.258489 (0.036466) \| 0.322144 / 0.293841 (0.028303) \| 0.047211 / 0.128546 (-0.081336) \| 0.010492 / 0.075646 (-0.065155) \| 0.058152 / 0.419271 (-0.361120) \| 0.033508 / 0.043533 (-0.010025) \| 0.281266 / 0.255139 (0.026127) \| 0.300010 / 0.283200 (0.016810) \| 0.017616 / 0.141683 (-0.124067) \| 1.124658 / 1.452155 (-0.327496) \| 1.167222 / 1.492716 (-0.325495) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.089085 / 0.018006 (0.071079) \| 0.297912 / 0.000490 (0.297423) \| 0.000211 / 0.000200 (0.000011) \| 0.000056 / 0.000054 (0.000002) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021669 / 0.037411 (-0.015742) \| 0.075648 / 0.014526 (0.061123) \| 0.086054 / 0.176557 (-0.090503) \| 0.125236 / 0.737135 (-0.611899) \| 0.088146 / 0.296338 (-0.208192) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.295238 / 0.215209 (0.080029) \| 2.870002 / 2.077655 (0.792347) \| 1.582534 / 1.504120 (0.078414) \| 1.466710 / 1.541195 (-0.074485) \| 1.475352 / 1.468490 (0.006861) \| 0.554745 / 4.584777 (-4.030032) \| 2.412533 / 3.745712 (-1.333179) \| 2.583863 / 5.269862 (-2.685999) \| 1.689124 / 4.565676 (-2.876552) \| 0.061353 / 0.424275 (-0.362922) \| 0.005015 / 0.007607 (-0.002592) \| 0.338733 / 0.226044 (0.112688) \| 3.356710 / 2.268929 (1.087781) \| 1.932143 / 55.444624 (-53.512481) \| 1.660081 / 6.876477 (-5.216396) \| 1.764961 / 2.142072 (-0.377111) \| 0.640002 / 4.805227 (-4.165225) \| 0.115251 / 6.500664 (-6.385413) \| 0.040627 / 0.075469 (-0.034842) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.992296 / 1.841788 (-0.849492) \| 11.821259 / 8.074308 (3.746951) \| 10.715570 / 10.191392 (0.524178) \| 0.142934 / 0.680424 (-0.537489) \| 0.015680 / 0.534201 (-0.518521) \| 0.287435 / 0.579283 (-0.291848) \| 0.276817 / 0.434364 (-0.157547) \| 0.327823 / 0.540337 (-0.212515) \| 0.413404 / 1.386936 (-0.973532) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#82c78b614d34ee42180d35a882875a28d6281db0 \"CML watermark\")\n" ]	"2024-01-29T15:18:21"	"2024-01-29T15:47:34"	"2024-01-29T15:41:32"	CONTRIBUTOR	null	Replace `disable=False` with `disable=None` in the `tqdm` bars to disable them in non-interactive environments (by default). For more info, see a [similar PR](https://github.com/huggingface/huggingface_hub/pull/2000) in `huggingface_hub`.	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6627/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6627/timeline	null	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6627", "html_url": "https://github.com/huggingface/datasets/pull/6627", "diff_url": "https://github.com/huggingface/datasets/pull/6627.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6627.patch", "merged_at": "2024-01-29T15:41:32" }	true
https://api.github.com/repos/huggingface/datasets/issues/6626	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6626/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6626/comments	https://api.github.com/repos/huggingface/datasets/issues/6626/events	https://github.com/huggingface/datasets/pull/6626	2,105,482,522	PR_kwDODunzps5lU0I2	6,626	Raise error on bad split name	{ "login": "lhoestq", "id": 42851186, "node_id": "MDQ6VXNlcjQyODUxMTg2", "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "gravatar_id": "", "url": "https://api.github.com/users/lhoestq", "html_url": "https://github.com/lhoestq", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "repos_url": "https://api.github.com/users/lhoestq/repos", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6626). 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) \| 0.005085 / 0.011353 (-0.006268) \| 0.003592 / 0.011008 (-0.007417) \| 0.062591 / 0.038508 (0.024083) \| 0.031063 / 0.023109 (0.007954) \| 0.247029 / 0.275898 (-0.028869) \| 0.273706 / 0.323480 (-0.049774) \| 0.004034 / 0.007986 (-0.003951) \| 0.002672 / 0.004328 (-0.001657) \| 0.048407 / 0.004250 (0.044156) \| 0.049229 / 0.037052 (0.012177) \| 0.264316 / 0.258489 (0.005827) \| 0.284953 / 0.293841 (-0.008888) \| 0.027712 / 0.128546 (-0.100834) \| 0.010619 / 0.075646 (-0.065027) \| 0.210017 / 0.419271 (-0.209254) \| 0.035636 / 0.043533 (-0.007897) \| 0.252830 / 0.255139 (-0.002309) \| 0.278772 / 0.283200 (-0.004428) \| 0.017356 / 0.141683 (-0.124326) \| 1.140202 / 1.452155 (-0.311953) \| 1.204807 / 1.492716 (-0.287909) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.089130 / 0.018006 (0.071123) \| 0.300115 / 0.000490 (0.299626) \| 0.000213 / 0.000200 (0.000013) \| 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) \| 0.018352 / 0.037411 (-0.019059) \| 0.061431 / 0.014526 (0.046905) \| 0.073911 / 0.176557 (-0.102646) \| 0.121230 / 0.737135 (-0.615906) \| 0.074867 / 0.296338 (-0.221471) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.282272 / 0.215209 (0.067063) \| 2.737413 / 2.077655 (0.659759) \| 1.446651 / 1.504120 (-0.057469) \| 1.319686 / 1.541195 (-0.221508) \| 1.327479 / 1.468490 (-0.141011) \| 0.558003 / 4.584777 (-4.026774) \| 2.361623 / 3.745712 (-1.384089) \| 2.770436 / 5.269862 (-2.499425) \| 1.703450 / 4.565676 (-2.862227) \| 0.062034 / 0.424275 (-0.362241) \| 0.005070 / 0.007607 (-0.002537) \| 0.337265 / 0.226044 (0.111221) \| 3.299438 / 2.268929 (1.030509) \| 1.781273 / 55.444624 (-53.663351) \| 1.512743 / 6.876477 (-5.363734) \| 1.530995 / 2.142072 (-0.611077) \| 0.630210 / 4.805227 (-4.175017) \| 0.116219 / 6.500664 (-6.384445) \| 0.042220 / 0.075469 (-0.033249) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.946341 / 1.841788 (-0.895446) \| 11.462179 / 8.074308 (3.387871) \| 10.603314 / 10.191392 (0.411922) \| 0.128826 / 0.680424 (-0.551598) \| 0.013994 / 0.534201 (-0.520207) \| 0.288142 / 0.579283 (-0.291141) \| 0.266941 / 0.434364 (-0.167422) \| 0.329392 / 0.540337 (-0.210946) \| 0.431720 / 1.386936 (-0.955216) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005303 / 0.011353 (-0.006050) \| 0.003587 / 0.011008 (-0.007422) \| 0.049437 / 0.038508 (0.010929) \| 0.031940 / 0.023109 (0.008831) \| 0.276651 / 0.275898 (0.000752) \| 0.297240 / 0.323480 (-0.026240) \| 0.004202 / 0.007986 (-0.003784) \| 0.002709 / 0.004328 (-0.001619) \| 0.048647 / 0.004250 (0.044397) \| 0.044147 / 0.037052 (0.007095) \| 0.291171 / 0.258489 (0.032682) \| 0.319297 / 0.293841 (0.025456) \| 0.048167 / 0.128546 (-0.080379) \| 0.010630 / 0.075646 (-0.065016) \| 0.058402 / 0.419271 (-0.360869) \| 0.033817 / 0.043533 (-0.009716) \| 0.300546 / 0.255139 (0.045407) \| 0.319396 / 0.283200 (0.036197) \| 0.017736 / 0.141683 (-0.123946) \| 1.159590 / 1.452155 (-0.292565) \| 1.191778 / 1.492716 (-0.300939) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.088971 / 0.018006 (0.070965) \| 0.299721 / 0.000490 (0.299231) \| 0.000219 / 0.000200 (0.000019) \| 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) \| 0.021895 / 0.037411 (-0.015516) \| 0.075388 / 0.014526 (0.060862) \| 0.087446 / 0.176557 (-0.089111) \| 0.126339 / 0.737135 (-0.610796) \| 0.089329 / 0.296338 (-0.207010) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.296642 / 0.215209 (0.081433) \| 2.916023 / 2.077655 (0.838368) \| 1.593180 / 1.504120 (0.089060) \| 1.470491 / 1.541195 (-0.070704) \| 1.485713 / 1.468490 (0.017223) \| 0.577204 / 4.584777 (-4.007573) \| 2.436463 / 3.745712 (-1.309249) \| 2.651004 / 5.269862 (-2.618858) \| 1.754026 / 4.565676 (-2.811651) \| 0.064943 / 0.424275 (-0.359332) \| 0.005115 / 0.007607 (-0.002492) \| 0.362082 / 0.226044 (0.136038) \| 3.498198 / 2.268929 (1.229270) \| 1.951936 / 55.444624 (-53.492688) \| 1.682027 / 6.876477 (-5.194450) \| 1.751768 / 2.142072 (-0.390304) \| 0.668479 / 4.805227 (-4.136748) \| 0.119934 / 6.500664 (-6.380730) \| 0.041419 / 0.075469 (-0.034050) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.978145 / 1.841788 (-0.863643) \| 11.984984 / 8.074308 (3.910676) \| 10.732377 / 10.191392 (0.540985) \| 0.141868 / 0.680424 (-0.538555) \| 0.015256 / 0.534201 (-0.518945) \| 0.288488 / 0.579283 (-0.290795) \| 0.276091 / 0.434364 (-0.158273) \| 0.330429 / 0.540337 (-0.209908) \| 0.423964 / 1.386936 (-0.962972) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#bb8497b9dec2a3807c887b8184f902d1d8d7c25a \"CML watermark\")\n" ]	"2024-01-29T13:17:41"	"2024-01-29T15:18:25"	"2024-01-29T15:12:18"	MEMBER	null	e.g. dashes '-' are not allowed in split names This should add an error message on datasets with unsupported split names like https://huggingface.co/datasets/open-source-metrics/test cc @AndreaFrancis	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6626/reactions", "total_count": 1, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 1, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6626/timeline	null	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6626", "html_url": "https://github.com/huggingface/datasets/pull/6626", "diff_url": "https://github.com/huggingface/datasets/pull/6626.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6626.patch", "merged_at": "2024-01-29T15:12:18" }	true
https://api.github.com/repos/huggingface/datasets/issues/6624	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6624/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6624/comments	https://api.github.com/repos/huggingface/datasets/issues/6624/events	https://github.com/huggingface/datasets/issues/6624	2,103,950,718	I_kwDODunzps59Z71-	6,624	How to download the laion-coco dataset	{ "login": "vanpersie32", "id": 15981416, "node_id": "MDQ6VXNlcjE1OTgxNDE2", "avatar_url": "https://avatars.githubusercontent.com/u/15981416?v=4", "gravatar_id": "", "url": "https://api.github.com/users/vanpersie32", "html_url": "https://github.com/vanpersie32", "followers_url": "https://api.github.com/users/vanpersie32/followers", "following_url": "https://api.github.com/users/vanpersie32/following{/other_user}", "gists_url": "https://api.github.com/users/vanpersie32/gists{/gist_id}", "starred_url": "https://api.github.com/users/vanpersie32/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/vanpersie32/subscriptions", "organizations_url": "https://api.github.com/users/vanpersie32/orgs", "repos_url": "https://api.github.com/users/vanpersie32/repos", "events_url": "https://api.github.com/users/vanpersie32/events{/privacy}", "received_events_url": "https://api.github.com/users/vanpersie32/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "Hi, this dataset has been disabled by the authors, so unfortunately it's no longer possible to download it." ]	"2024-01-28T03:56:05"	"2024-02-06T09:43:31"	"2024-02-06T09:43:31"	NONE	null	The laion coco dataset is not available now. How to download it https://huggingface.co/datasets/laion/laion-coco	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6624/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6624/timeline	null	not_planned	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6623	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6623/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6623/comments	https://api.github.com/repos/huggingface/datasets/issues/6623/events	https://github.com/huggingface/datasets/issues/6623	2,103,870,123	I_kwDODunzps59ZoKr	6,623	streaming datasets doesn't work properly with multi-node	{ "login": "rohitgr7", "id": 30778939, "node_id": "MDQ6VXNlcjMwNzc4OTM5", "avatar_url": "https://avatars.githubusercontent.com/u/30778939?v=4", "gravatar_id": "", "url": "https://api.github.com/users/rohitgr7", "html_url": "https://github.com/rohitgr7", "followers_url": "https://api.github.com/users/rohitgr7/followers", "following_url": "https://api.github.com/users/rohitgr7/following{/other_user}", "gists_url": "https://api.github.com/users/rohitgr7/gists{/gist_id}", "starred_url": "https://api.github.com/users/rohitgr7/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/rohitgr7/subscriptions", "organizations_url": "https://api.github.com/users/rohitgr7/orgs", "repos_url": "https://api.github.com/users/rohitgr7/repos", "events_url": "https://api.github.com/users/rohitgr7/events{/privacy}", "received_events_url": "https://api.github.com/users/rohitgr7/received_events", "type": "User", "site_admin": false }	[ { "id": 1935892871, "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement", "name": "enhancement", "color": "a2eeef", "default": true, "description": "New feature or request" } ]	open	false	null	[]	null	[ "@mariosasko, @lhoestq, @albertvillanova\r\nhey guys! can anyone help? or can you guys suggest who can help with this?", "Hi ! \r\n\r\n1. When the dataset is running of of examples, the last batches received by the GPU can be incomplete or empty/missing. We haven't implemented yet a way to ignore the last batch. It might require the datasets to provide the number of examples per shard though, so that we can know when to stop.\r\n2. Samplers are not compatible with IterableDatasets in pytorch\r\n3. if `dataset.n_shards % world_size != 0` then all the nodes will read/stream the full dataset in order (possibly reading/streaming the same data multiple times), BUT will only yield one example out of `world_size` so that each example goes to one exactly one GPU.\r\n4. no, sharding should be down up-front and can take some time depending on the dataset size and format", "> if dataset.n_shards % world_size != 0 then all the nodes will read/stream the full dataset in order (possibly reading/streaming the same data multiple times), BUT will only yield one example out of world_size so that each example goes to one exactly one GPU.\r\n\r\nconsidering there's just 1 shard and 2 worker nodes, do you mean each worker node will load the whole dataset but still receive half of that shard while streaming?", "Yes both nodes will stream from the 1 shard, but each node will skip half of the examples. This way in total each example is seen once and exactly once during you distributed training.\r\n\r\nThough it terms of I/O, the dataset is effectively read/streamed twice.", "what if the number of samples in that shard % num_nodes != 0? it will break/get stuck? or is the data repeated in that case for gradient sync?", "In the case one at least one of the noes will get an empty/incomplete batch. The data is not repeated in that case. If the training loop doesn't take this into account it can lead to unexpected behaviors indeed.\r\n\r\nIn the future we'd like to add a feature that would allow the nodes to ignore the last batch, this way all the nodes would only have full batches.", "> In the case one at least one of the noes will get an empty/incomplete batch. The data is not repeated in that case. If the training loop doesn't take this into account it can lead to unexpected behaviors indeed.\r\n> \r\n> In the future we'd like to add a feature that would allow the nodes to ignore the last batch, this way all the nodes would only have full batches.\r\n\r\nIs there any method to modify one dataset's n_shard? modify the number of files is ok? one file == one shard?", "> modify the number of files is ok? one file == one shard?\r\n\r\nYep, one file == one shard :)" ]	"2024-01-27T23:46:13"	"2024-03-08T14:27:08"	null	NONE	null	### Feature request Let’s say I have a dataset with 5 samples with values [1, 2, 3, 4, 5], with 2 GPUs (for DDP) and batch size of 2. This dataset is an `IterableDataset` since I am streaming it. Now I split the dataset using `split_dataset_by_node` to ensure it doesn’t get repeated. And since it’s already splitted, I don’t have to use `DistributedSampler` (also they don't work with iterable datasets anyway)? But in this case I noticed that the: First iteraton: first GPU will get → [1, 2] first GPU will get → [3, 4] Second iteraton: first GPU will get → [5] first GPU will get → Nothing which actually creates an issue since in case of `DistributedSampler`, the samples are repeated internally to ensure non of the GPUs at any iteration is missing any data for gradient sync. So my questions are: 1. Here since splitting is happening before hand, how to make sure each GPU get’s a batch at each iteration to avoid gradient sync issues? 2. Do we need to use `DistributedSampler`? If yes, how? 3. in the docstrings of `split_dataset_by_node`, this is mentioned: "If the dataset has a number of shards that is a factor of `world_size` (i.e. if `dataset.n_shards % world_size == 0`), then the shards are evenly assigned across the nodes, which is the most optimized. Otherwise, each node keeps 1 example out of `world_size`, skipping the other examples." Can you explain the last part here? 4. If `dataset.n_shards % world_size != 0`, is it possible to shard the streaming dataset on the fly to avoid the case where data is missing? ### Motivation Somehow streaming datasets should work with DDP since for big LLMs a lot of data is required and DDP/multi-node is mostly used to train such models and streaming can actually help solve the data part of it. ### Your contribution Yes, I can help in submitting the PR once we get mutual understanding on how it should behave.	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6623/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6623/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6622	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6622/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6622/comments	https://api.github.com/repos/huggingface/datasets/issues/6622/events	https://github.com/huggingface/datasets/issues/6622	2,103,780,697	I_kwDODunzps59ZSVZ	6,622	multi-GPU map does not work	{ "login": "kopyl", "id": 17604849, "node_id": "MDQ6VXNlcjE3NjA0ODQ5", "avatar_url": "https://avatars.githubusercontent.com/u/17604849?v=4", "gravatar_id": "", "url": "https://api.github.com/users/kopyl", "html_url": "https://github.com/kopyl", "followers_url": "https://api.github.com/users/kopyl/followers", "following_url": "https://api.github.com/users/kopyl/following{/other_user}", "gists_url": "https://api.github.com/users/kopyl/gists{/gist_id}", "starred_url": "https://api.github.com/users/kopyl/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/kopyl/subscriptions", "organizations_url": "https://api.github.com/users/kopyl/orgs", "repos_url": "https://api.github.com/users/kopyl/repos", "events_url": "https://api.github.com/users/kopyl/events{/privacy}", "received_events_url": "https://api.github.com/users/kopyl/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "This should now be fixed by https://github.com/huggingface/datasets/pull/6550 and updated with https://github.com/huggingface/datasets/pull/6646\r\n\r\nFeel free to re-open if you're still having issues :)" ]	"2024-01-27T20:06:08"	"2024-02-08T11:18:21"	"2024-02-08T11:18:21"	NONE	null	### Describe the bug Here is the code for single-GPU processing: https://pastebin.com/bfmEeK2y Here is the code for multi-GPU processing: https://pastebin.com/gQ7i5AQy Here is the video showing that the multi-GPU mapping does not work as expected (there are so many things wrong here, it's better to watch the 3-minute video than explain here): https://youtu.be/RNbdPkSppc4 ### Steps to reproduce the bug - ### Expected behavior - ### Environment info x2 RTX A4000	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6622/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6622/timeline	null	completed	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6621	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6621/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6621/comments	https://api.github.com/repos/huggingface/datasets/issues/6621/events	https://github.com/huggingface/datasets/issues/6621	2,103,675,294	I_kwDODunzps59Y4me	6,621	deleted	{ "login": "kopyl", "id": 17604849, "node_id": "MDQ6VXNlcjE3NjA0ODQ5", "avatar_url": "https://avatars.githubusercontent.com/u/17604849?v=4", "gravatar_id": "", "url": "https://api.github.com/users/kopyl", "html_url": "https://github.com/kopyl", "followers_url": "https://api.github.com/users/kopyl/followers", "following_url": "https://api.github.com/users/kopyl/following{/other_user}", "gists_url": "https://api.github.com/users/kopyl/gists{/gist_id}", "starred_url": "https://api.github.com/users/kopyl/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/kopyl/subscriptions", "organizations_url": "https://api.github.com/users/kopyl/orgs", "repos_url": "https://api.github.com/users/kopyl/repos", "events_url": "https://api.github.com/users/kopyl/events{/privacy}", "received_events_url": "https://api.github.com/users/kopyl/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[]	"2024-01-27T16:59:58"	"2024-01-27T17:14:43"	"2024-01-27T17:14:43"	NONE	null	...	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6621/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6621/timeline	null	completed	null	null	false
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}	{ "login": "kiehls90", "id": 101498700, "node_id": "U_kgDOBgy_TA", "avatar_url": "https://avatars.githubusercontent.com/u/101498700?v=4", "gravatar_id": "", "url": "https://api.github.com/users/kiehls90", "html_url": "https://github.com/kiehls90", "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}", "starred_url": "https://api.github.com/users/kiehls90/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/kiehls90/subscriptions", "organizations_url": "https://api.github.com/users/kiehls90/orgs", "repos_url": "https://api.github.com/users/kiehls90/repos", "events_url": "https://api.github.com/users/kiehls90/events{/privacy}", "received_events_url": "https://api.github.com/users/kiehls90/received_events", "type": "User", "site_admin": false }	[]	closed	false	{ "login": "albertvillanova", "id": 8515462, "node_id": "MDQ6VXNlcjg1MTU0NjI=", "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "gravatar_id": "", "url": "https://api.github.com/users/albertvillanova", "html_url": "https://github.com/albertvillanova", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "repos_url": "https://api.github.com/users/albertvillanova/repos", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "type": "User", "site_admin": false }	[ { "login": "albertvillanova", "id": 8515462, "node_id": "MDQ6VXNlcjg1MTU0NjI=", "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "gravatar_id": "", "url": "https://api.github.com/users/albertvillanova", "html_url": "https://github.com/albertvillanova", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "repos_url": "https://api.github.com/users/albertvillanova/repos", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "type": "User", "site_admin": false } ]	null	[ "Thanks for reporting, @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" ]	"2024-01-27T01:00:09"	"2024-02-06T09:40:19"	"2024-02-06T09:40:19"	NONE	null	### 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	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6620/reactions", "total_count": 1, "+1": 1, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6620/timeline	null	not_planned	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6619	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6619/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6619/comments	https://api.github.com/repos/huggingface/datasets/issues/6619/events	https://github.com/huggingface/datasets/pull/6619	2,102,407,478	PR_kwDODunzps5lK2VY	6,619	Migrate from `setup.cfg` to `pyproject.toml`	{ "login": "mariosasko", "id": 47462742, "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "gravatar_id": "", "url": "https://api.github.com/users/mariosasko", "html_url": "https://github.com/mariosasko", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "repos_url": "https://api.github.com/users/mariosasko/repos", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6619). 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) \| 0.005066 / 0.011353 (-0.006287) \| 0.003678 / 0.011008 (-0.007330) \| 0.063057 / 0.038508 (0.024549) \| 0.031250 / 0.023109 (0.008140) \| 0.248856 / 0.275898 (-0.027042) \| 0.266932 / 0.323480 (-0.056548) \| 0.003814 / 0.007986 (-0.004172) \| 0.002843 / 0.004328 (-0.001485) \| 0.049210 / 0.004250 (0.044959) \| 0.041514 / 0.037052 (0.004462) \| 0.264874 / 0.258489 (0.006385) \| 0.288834 / 0.293841 (-0.005007) \| 0.027457 / 0.128546 (-0.101089) \| 0.011071 / 0.075646 (-0.064575) \| 0.206433 / 0.419271 (-0.212839) \| 0.035381 / 0.043533 (-0.008152) \| 0.246829 / 0.255139 (-0.008310) \| 0.271094 / 0.283200 (-0.012106) \| 0.017790 / 0.141683 (-0.123893) \| 1.134618 / 1.452155 (-0.317536) \| 1.182600 / 1.492716 (-0.310116) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094970 / 0.018006 (0.076964) \| 0.306438 / 0.000490 (0.305949) \| 0.000212 / 0.000200 (0.000012) \| 0.000044 / 0.000054 (-0.000010) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.017786 / 0.037411 (-0.019625) \| 0.060652 / 0.014526 (0.046127) \| 0.072619 / 0.176557 (-0.103937) \| 0.119460 / 0.737135 (-0.617676) \| 0.073580 / 0.296338 (-0.222759) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.279304 / 0.215209 (0.064095) \| 2.747179 / 2.077655 (0.669524) \| 1.438291 / 1.504120 (-0.065829) \| 1.313405 / 1.541195 (-0.227789) \| 1.354569 / 1.468490 (-0.113921) \| 0.578375 / 4.584777 (-4.006402) \| 2.424576 / 3.745712 (-1.321136) \| 2.831513 / 5.269862 (-2.438348) \| 1.756062 / 4.565676 (-2.809614) \| 0.064460 / 0.424275 (-0.359815) \| 0.005065 / 0.007607 (-0.002542) \| 0.335003 / 0.226044 (0.108958) \| 3.310500 / 2.268929 (1.041571) \| 1.778017 / 55.444624 (-53.666607) \| 1.504743 / 6.876477 (-5.371734) \| 1.532843 / 2.142072 (-0.609229) \| 0.662110 / 4.805227 (-4.143118) \| 0.118239 / 6.500664 (-6.382425) \| 0.042135 / 0.075469 (-0.033335) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.945650 / 1.841788 (-0.896137) \| 11.623179 / 8.074308 (3.548871) \| 10.927315 / 10.191392 (0.735923) \| 0.131050 / 0.680424 (-0.549374) \| 0.014725 / 0.534201 (-0.519476) \| 0.290716 / 0.579283 (-0.288567) \| 0.272357 / 0.434364 (-0.162007) \| 0.323274 / 0.540337 (-0.217064) \| 0.426692 / 1.386936 (-0.960244) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005478 / 0.011353 (-0.005875) \| 0.003618 / 0.011008 (-0.007390) \| 0.049599 / 0.038508 (0.011091) \| 0.030814 / 0.023109 (0.007705) \| 0.273663 / 0.275898 (-0.002235) \| 0.292099 / 0.323480 (-0.031381) \| 0.004196 / 0.007986 (-0.003790) \| 0.002779 / 0.004328 (-0.001550) \| 0.047812 / 0.004250 (0.043562) \| 0.045095 / 0.037052 (0.008043) \| 0.286288 / 0.258489 (0.027799) \| 0.314125 / 0.293841 (0.020284) \| 0.047940 / 0.128546 (-0.080606) \| 0.010714 / 0.075646 (-0.064932) \| 0.057453 / 0.419271 (-0.361819) \| 0.033482 / 0.043533 (-0.010051) \| 0.273391 / 0.255139 (0.018252) \| 0.284936 / 0.283200 (0.001736) \| 0.017805 / 0.141683 (-0.123878) \| 1.148303 / 1.452155 (-0.303852) \| 1.185268 / 1.492716 (-0.307448) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092442 / 0.018006 (0.074436) \| 0.309908 / 0.000490 (0.309418) \| 0.000213 / 0.000200 (0.000013) \| 0.000043 / 0.000054 (-0.000012) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022874 / 0.037411 (-0.014537) \| 0.078238 / 0.014526 (0.063712) \| 0.088844 / 0.176557 (-0.087713) \| 0.127054 / 0.737135 (-0.610081) \| 0.089809 / 0.296338 (-0.206530) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.292360 / 0.215209 (0.077151) \| 2.842700 / 2.077655 (0.765045) \| 1.571071 / 1.504120 (0.066951) \| 1.450773 / 1.541195 (-0.090422) \| 1.467090 / 1.468490 (-0.001400) \| 0.583529 / 4.584777 (-4.001248) \| 2.469284 / 3.745712 (-1.276428) \| 2.844426 / 5.269862 (-2.425435) \| 1.773336 / 4.565676 (-2.792341) \| 0.064585 / 0.424275 (-0.359690) \| 0.005098 / 0.007607 (-0.002509) \| 0.342816 / 0.226044 (0.116771) \| 3.363309 / 2.268929 (1.094381) \| 1.922834 / 55.444624 (-53.521790) \| 1.649702 / 6.876477 (-5.226774) \| 1.672727 / 2.142072 (-0.469345) \| 0.665015 / 4.805227 (-4.140212) \| 0.124764 / 6.500664 (-6.375900) \| 0.041564 / 0.075469 (-0.033905) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.988970 / 1.841788 (-0.852818) \| 12.148983 / 8.074308 (4.074675) \| 11.132697 / 10.191392 (0.941305) \| 0.131596 / 0.680424 (-0.548828) \| 0.015700 / 0.534201 (-0.518501) \| 0.288819 / 0.579283 (-0.290464) \| 0.276692 / 0.434364 (-0.157672) \| 0.330260 / 0.540337 (-0.210078) \| 0.421612 / 1.386936 (-0.965324) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d627fb8357f39d78d79e704712609c7b34bdeba4 \"CML watermark\")\n" ]	"2024-01-26T15:27:10"	"2024-01-26T15:53:40"	"2024-01-26T15:47:32"	CONTRIBUTOR	null	Based on https://github.com/huggingface/huggingface_hub/pull/1971 in `hfh`	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6619/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6619/timeline	null	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6619", "html_url": "https://github.com/huggingface/datasets/pull/6619", "diff_url": "https://github.com/huggingface/datasets/pull/6619.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6619.patch", "merged_at": "2024-01-26T15:47:32" }	true
https://api.github.com/repos/huggingface/datasets/issues/6618	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6618/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6618/comments	https://api.github.com/repos/huggingface/datasets/issues/6618/events	https://github.com/huggingface/datasets/issues/6618	2,101,868,198	I_kwDODunzps59R_am	6,618	While importing load_dataset from datasets	{ "login": "Era-cell", "id": 77973415, "node_id": "MDQ6VXNlcjc3OTczNDE1", "avatar_url": "https://avatars.githubusercontent.com/u/77973415?v=4", "gravatar_id": "", "url": "https://api.github.com/users/Era-cell", "html_url": "https://github.com/Era-cell", "followers_url": "https://api.github.com/users/Era-cell/followers", "following_url": "https://api.github.com/users/Era-cell/following{/other_user}", "gists_url": "https://api.github.com/users/Era-cell/gists{/gist_id}", "starred_url": "https://api.github.com/users/Era-cell/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/Era-cell/subscriptions", "organizations_url": "https://api.github.com/users/Era-cell/orgs", "repos_url": "https://api.github.com/users/Era-cell/repos", "events_url": "https://api.github.com/users/Era-cell/events{/privacy}", "received_events_url": "https://api.github.com/users/Era-cell/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "Hi! Can you please share the error's stack trace so we can see where it comes from?", "We cannot reproduce the issue and we do not have enough information: environment info (need to run `datasets-cli env`), stack trace,...\r\n\r\nI am closing the issue. Feel free to reopen it (with additional information) if the problem persists.", "Yeah 👍\r\n\r\nOn Tue, 6 Feb 2024 at 2:56 PM, Albert Villanova del Moral <\r\n*@.> wrote:\r\n\r\n> We cannot reproduce the issue and we do not have enough information:\r\n> environment info (need to run datasets-cli env), stack trace,...\r\n>\r\n> I am closing the issue. Feel free to reopen it (with additional\r\n> information) if the problem persists.\r\n>\r\n> —\r\n> Reply to this email directly, view it on GitHub\r\n> <https://github.com/huggingface/datasets/issues/6618#issuecomment-1929102334>,\r\n> or unsubscribe\r\n> <https://github.com/notifications/unsubscribe-auth/ASS4PJ3XOIIWISPY3VX3QRTYSHZK5AVCNFSM6AAAAABCL3BT4SVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMYTSMRZGEYDEMZTGQ>\r\n> .\r\n> You are receiving this because you authored the thread.Message ID:\r\n> @.*>\r\n>\r\n" ]	"2024-01-26T09:21:57"	"2024-02-06T10:57:01"	"2024-02-06T09:25:54"	NONE	null	### Describe the bug cannot import name 'DEFAULT_CIPHERS' from 'urllib3.util.ssl_' this is the error i received ### Steps to reproduce the bug from datasets import load_dataset ### Expected behavior No errors ### Environment info python 3.11.5	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6618/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6618/timeline	null	not_planned	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6617	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6617/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6617/comments	https://api.github.com/repos/huggingface/datasets/issues/6617/events	https://github.com/huggingface/datasets/pull/6617	2,100,459,449	PR_kwDODunzps5lEagV	6,617	Fix CI: pyarrow 15, pandas 2.2 and sqlachemy	{ "login": "lhoestq", "id": 42851186, "node_id": "MDQ6VXNlcjQyODUxMTg2", "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "gravatar_id": "", "url": "https://api.github.com/users/lhoestq", "html_url": "https://github.com/lhoestq", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "repos_url": "https://api.github.com/users/lhoestq/repos", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6617). 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) \| 0.004774 / 0.011353 (-0.006579) \| 0.003397 / 0.011008 (-0.007611) \| 0.063862 / 0.038508 (0.025354) \| 0.029353 / 0.023109 (0.006244) \| 0.245921 / 0.275898 (-0.029977) \| 0.268414 / 0.323480 (-0.055066) \| 0.002834 / 0.007986 (-0.005152) \| 0.002606 / 0.004328 (-0.001723) \| 0.049690 / 0.004250 (0.045439) \| 0.041637 / 0.037052 (0.004585) \| 0.262526 / 0.258489 (0.004037) \| 0.288200 / 0.293841 (-0.005641) \| 0.027233 / 0.128546 (-0.101313) \| 0.010322 / 0.075646 (-0.065324) \| 0.213860 / 0.419271 (-0.205411) \| 0.034930 / 0.043533 (-0.008602) \| 0.249256 / 0.255139 (-0.005883) \| 0.270016 / 0.283200 (-0.013184) \| 0.019413 / 0.141683 (-0.122270) \| 1.124801 / 1.452155 (-0.327354) \| 1.166224 / 1.492716 (-0.326492) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091641 / 0.018006 (0.073635) \| 0.299679 / 0.000490 (0.299189) \| 0.000209 / 0.000200 (0.000009) \| 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) \| 0.018084 / 0.037411 (-0.019327) \| 0.060143 / 0.014526 (0.045617) \| 0.072556 / 0.176557 (-0.104001) \| 0.118555 / 0.737135 (-0.618580) \| 0.073786 / 0.296338 (-0.222553) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.278193 / 0.215209 (0.062984) \| 2.707954 / 2.077655 (0.630300) \| 1.483575 / 1.504120 (-0.020545) \| 1.371939 / 1.541195 (-0.169256) \| 1.395009 / 1.468490 (-0.073481) \| 0.559949 / 4.584777 (-4.024828) \| 2.372529 / 3.745712 (-1.373183) \| 2.823641 / 5.269862 (-2.446221) \| 1.722999 / 4.565676 (-2.842678) \| 0.062535 / 0.424275 (-0.361741) \| 0.004970 / 0.007607 (-0.002637) \| 0.338625 / 0.226044 (0.112580) \| 3.317576 / 2.268929 (1.048648) \| 1.854552 / 55.444624 (-53.590073) \| 1.589323 / 6.876477 (-5.287154) \| 1.624630 / 2.142072 (-0.517442) \| 0.638388 / 4.805227 (-4.166839) \| 0.116675 / 6.500664 (-6.383989) \| 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) \| 0.938025 / 1.841788 (-0.903763) \| 11.450072 / 8.074308 (3.375764) \| 10.414943 / 10.191392 (0.223551) \| 0.128416 / 0.680424 (-0.552007) \| 0.013798 / 0.534201 (-0.520403) \| 0.287997 / 0.579283 (-0.291286) \| 0.259976 / 0.434364 (-0.174387) \| 0.320737 / 0.540337 (-0.219601) \| 0.424292 / 1.386936 (-0.962644) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005107 / 0.011353 (-0.006246) \| 0.003374 / 0.011008 (-0.007634) \| 0.050067 / 0.038508 (0.011559) \| 0.031419 / 0.023109 (0.008310) \| 0.275303 / 0.275898 (-0.000595) \| 0.286736 / 0.323480 (-0.036744) \| 0.004177 / 0.007986 (-0.003808) \| 0.002742 / 0.004328 (-0.001586) \| 0.049011 / 0.004250 (0.044761) \| 0.044373 / 0.037052 (0.007321) \| 0.289189 / 0.258489 (0.030700) \| 0.320117 / 0.293841 (0.026276) \| 0.050154 / 0.128546 (-0.078392) \| 0.010541 / 0.075646 (-0.065106) \| 0.058318 / 0.419271 (-0.360954) \| 0.033090 / 0.043533 (-0.010443) \| 0.276820 / 0.255139 (0.021681) \| 0.290854 / 0.283200 (0.007654) \| 0.017268 / 0.141683 (-0.124415) \| 1.159345 / 1.452155 (-0.292809) \| 1.224829 / 1.492716 (-0.267887) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092468 / 0.018006 (0.074462) \| 0.301176 / 0.000490 (0.300686) \| 0.000216 / 0.000200 (0.000017) \| 0.000043 / 0.000054 (-0.000012) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021858 / 0.037411 (-0.015553) \| 0.074873 / 0.014526 (0.060347) \| 0.086238 / 0.176557 (-0.090318) \| 0.125555 / 0.737135 (-0.611580) \| 0.087791 / 0.296338 (-0.208547) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.292283 / 0.215209 (0.077073) \| 2.847306 / 2.077655 (0.769651) \| 1.600833 / 1.504120 (0.096713) \| 1.474253 / 1.541195 (-0.066942) \| 1.474871 / 1.468490 (0.006381) \| 0.576427 / 4.584777 (-4.008350) \| 2.380116 / 3.745712 (-1.365596) \| 2.782059 / 5.269862 (-2.487803) \| 1.730642 / 4.565676 (-2.835035) \| 0.063860 / 0.424275 (-0.360415) \| 0.005019 / 0.007607 (-0.002588) \| 0.343247 / 0.226044 (0.117202) \| 3.393427 / 2.268929 (1.124498) \| 1.935346 / 55.444624 (-53.509278) \| 1.680124 / 6.876477 (-5.196353) \| 1.665788 / 2.142072 (-0.476285) \| 0.648767 / 4.805227 (-4.156460) \| 0.121962 / 6.500664 (-6.378702) \| 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) \| 0.996535 / 1.841788 (-0.845252) \| 12.074553 / 8.074308 (4.000245) \| 10.812740 / 10.191392 (0.621348) \| 0.142690 / 0.680424 (-0.537734) \| 0.014977 / 0.534201 (-0.519224) \| 0.285619 / 0.579283 (-0.293664) \| 0.269401 / 0.434364 (-0.164963) \| 0.329882 / 0.540337 (-0.210456) \| 0.416169 / 1.386936 (-0.970767) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#129b9e0565e7a2ceaca64b99dcbf39504661cfa9 \"CML watermark\")\n" ]	"2024-01-25T13:57:41"	"2024-01-26T14:56:46"	"2024-01-26T14:50:44"	MEMBER	null	this should fix the CI failures on `main` close https://github.com/huggingface/datasets/issues/5477	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6617/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6617/timeline	null	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6617", "html_url": "https://github.com/huggingface/datasets/pull/6617", "diff_url": "https://github.com/huggingface/datasets/pull/6617.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6617.patch", "merged_at": "2024-01-26T14:50:44" }	true
https://api.github.com/repos/huggingface/datasets/issues/6616	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6616/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6616/comments	https://api.github.com/repos/huggingface/datasets/issues/6616/events	https://github.com/huggingface/datasets/pull/6616	2,100,125,709	PR_kwDODunzps5lDSEL	6,616	Use schema metadata only if it matches features	{ "login": "lhoestq", "id": 42851186, "node_id": "MDQ6VXNlcjQyODUxMTg2", "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "gravatar_id": "", "url": "https://api.github.com/users/lhoestq", "html_url": "https://github.com/lhoestq", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "repos_url": "https://api.github.com/users/lhoestq/repos", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6616). 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) \| 0.005382 / 0.011353 (-0.005970) \| 0.003853 / 0.011008 (-0.007155) \| 0.062629 / 0.038508 (0.024121) \| 0.030344 / 0.023109 (0.007234) \| 0.245394 / 0.275898 (-0.030505) \| 0.266004 / 0.323480 (-0.057476) \| 0.003183 / 0.007986 (-0.004802) \| 0.002795 / 0.004328 (-0.001533) \| 0.048357 / 0.004250 (0.044107) \| 0.043834 / 0.037052 (0.006782) \| 0.255979 / 0.258489 (-0.002510) \| 0.280803 / 0.293841 (-0.013038) \| 0.028200 / 0.128546 (-0.100347) \| 0.010856 / 0.075646 (-0.064791) \| 0.207076 / 0.419271 (-0.212195) \| 0.036286 / 0.043533 (-0.007247) \| 0.246492 / 0.255139 (-0.008647) \| 0.265861 / 0.283200 (-0.017338) \| 0.018309 / 0.141683 (-0.123374) \| 1.155136 / 1.452155 (-0.297018) \| 1.214342 / 1.492716 (-0.278375) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092530 / 0.018006 (0.074524) \| 0.344951 / 0.000490 (0.344461) \| 0.000207 / 0.000200 (0.000007) \| 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) \| 0.018324 / 0.037411 (-0.019087) \| 0.063137 / 0.014526 (0.048611) \| 0.074683 / 0.176557 (-0.101874) \| 0.120224 / 0.737135 (-0.616912) \| 0.083107 / 0.296338 (-0.213232) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.288631 / 0.215209 (0.073422) \| 2.817992 / 2.077655 (0.740337) \| 1.473609 / 1.504120 (-0.030511) \| 1.336610 / 1.541195 (-0.204585) \| 1.354807 / 1.468490 (-0.113683) \| 0.568776 / 4.584777 (-4.016001) \| 2.412607 / 3.745712 (-1.333105) \| 2.832816 / 5.269862 (-2.437045) \| 1.789899 / 4.565676 (-2.775778) \| 0.063602 / 0.424275 (-0.360673) \| 0.004993 / 0.007607 (-0.002615) \| 0.338830 / 0.226044 (0.112786) \| 3.302550 / 2.268929 (1.033621) \| 1.827907 / 55.444624 (-53.616717) \| 1.589857 / 6.876477 (-5.286620) \| 1.647746 / 2.142072 (-0.494326) \| 0.658461 / 4.805227 (-4.146766) \| 0.120360 / 6.500664 (-6.380304) \| 0.042989 / 0.075469 (-0.032480) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.945487 / 1.841788 (-0.896301) \| 11.846335 / 8.074308 (3.772027) \| 10.483199 / 10.191392 (0.291807) \| 0.131853 / 0.680424 (-0.548570) \| 0.014230 / 0.534201 (-0.519971) \| 0.288700 / 0.579283 (-0.290584) \| 0.276086 / 0.434364 (-0.158278) \| 0.326225 / 0.540337 (-0.214112) \| 0.422874 / 1.386936 (-0.964062) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.006234 / 0.011353 (-0.005118) \| 0.004104 / 0.011008 (-0.006904) \| 0.049967 / 0.038508 (0.011459) \| 0.037157 / 0.023109 (0.014048) \| 0.261892 / 0.275898 (-0.014006) \| 0.284304 / 0.323480 (-0.039176) \| 0.004482 / 0.007986 (-0.003504) \| 0.002920 / 0.004328 (-0.001409) \| 0.048827 / 0.004250 (0.044577) \| 0.052258 / 0.037052 (0.015206) \| 0.277121 / 0.258489 (0.018632) \| 0.304177 / 0.293841 (0.010336) \| 0.053537 / 0.128546 (-0.075009) \| 0.011137 / 0.075646 (-0.064509) \| 0.058188 / 0.419271 (-0.361083) \| 0.034283 / 0.043533 (-0.009250) \| 0.261912 / 0.255139 (0.006773) \| 0.273851 / 0.283200 (-0.009348) \| 0.017824 / 0.141683 (-0.123859) \| 1.130454 / 1.452155 (-0.321701) \| 1.176834 / 1.492716 (-0.315882) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.102104 / 0.018006 (0.084098) \| 0.302873 / 0.000490 (0.302383) \| 0.000208 / 0.000200 (0.000008) \| 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) \| 0.022470 / 0.037411 (-0.014941) \| 0.076776 / 0.014526 (0.062250) \| 0.088220 / 0.176557 (-0.088337) \| 0.130030 / 0.737135 (-0.607105) \| 0.089955 / 0.296338 (-0.206383) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.284070 / 0.215209 (0.068861) \| 2.769130 / 2.077655 (0.691475) \| 1.546379 / 1.504120 (0.042259) \| 1.435849 / 1.541195 (-0.105346) \| 1.478616 / 1.468490 (0.010126) \| 0.569185 / 4.584777 (-4.015592) \| 2.504721 / 3.745712 (-1.240992) \| 2.778267 / 5.269862 (-2.491595) \| 1.860360 / 4.565676 (-2.705316) \| 0.073465 / 0.424275 (-0.350810) \| 0.005108 / 0.007607 (-0.002499) \| 0.335185 / 0.226044 (0.109140) \| 3.314799 / 2.268929 (1.045870) \| 1.934824 / 55.444624 (-53.509801) \| 1.656247 / 6.876477 (-5.220229) \| 1.785422 / 2.142072 (-0.356650) \| 0.673677 / 4.805227 (-4.131551) \| 0.117692 / 6.500664 (-6.382972) \| 0.041648 / 0.075469 (-0.033821) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.972143 / 1.841788 (-0.869645) \| 12.980353 / 8.074308 (4.906045) \| 11.056189 / 10.191392 (0.864797) \| 0.134592 / 0.680424 (-0.545832) \| 0.015972 / 0.534201 (-0.518229) \| 0.301691 / 0.579283 (-0.277593) \| 0.286332 / 0.434364 (-0.148032) \| 0.329025 / 0.540337 (-0.211312) \| 0.422585 / 1.386936 (-0.964351) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6eb492c7072f21cb417801957c087888f252d2d1 \"CML watermark\")\n" ]	"2024-01-25T11:01:14"	"2024-01-26T16:25:24"	"2024-01-26T16:19:12"	MEMBER	null	e.g. if we use `map` in arrow format and transform the table, the returned table might have new columns but the metadata might be wrong	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6616/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6616/timeline	null	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6616", "html_url": "https://github.com/huggingface/datasets/pull/6616", "diff_url": "https://github.com/huggingface/datasets/pull/6616.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6616.patch", "merged_at": "2024-01-26T16:19:12" }	true
https://api.github.com/repos/huggingface/datasets/issues/6615	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6615/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6615/comments	https://api.github.com/repos/huggingface/datasets/issues/6615/events	https://github.com/huggingface/datasets/issues/6615	2,098,951,409	I_kwDODunzps59G3Tx	6,615	...	{ "login": "ftkeys", "id": 22179777, "node_id": "MDQ6VXNlcjIyMTc5Nzc3", "avatar_url": "https://avatars.githubusercontent.com/u/22179777?v=4", "gravatar_id": "", "url": "https://api.github.com/users/ftkeys", "html_url": "https://github.com/ftkeys", "followers_url": "https://api.github.com/users/ftkeys/followers", "following_url": "https://api.github.com/users/ftkeys/following{/other_user}", "gists_url": "https://api.github.com/users/ftkeys/gists{/gist_id}", "starred_url": "https://api.github.com/users/ftkeys/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/ftkeys/subscriptions", "organizations_url": "https://api.github.com/users/ftkeys/orgs", "repos_url": "https://api.github.com/users/ftkeys/repos", "events_url": "https://api.github.com/users/ftkeys/events{/privacy}", "received_events_url": "https://api.github.com/users/ftkeys/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "Sorry I posted in the wrong repo, please delete.. thanks!" ]	"2024-01-24T19:37:03"	"2024-01-24T19:42:30"	"2024-01-24T19:40:11"	NONE	null	...	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6615/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6615/timeline	null	not_planned	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6614	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6614/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6614/comments	https://api.github.com/repos/huggingface/datasets/issues/6614/events	https://github.com/huggingface/datasets/issues/6614	2,098,884,520	I_kwDODunzps59Gm-o	6,614	`datasets/downloads` cleanup tool	{ "login": "stas00", "id": 10676103, "node_id": "MDQ6VXNlcjEwNjc2MTAz", "avatar_url": "https://avatars.githubusercontent.com/u/10676103?v=4", "gravatar_id": "", "url": "https://api.github.com/users/stas00", "html_url": "https://github.com/stas00", "followers_url": "https://api.github.com/users/stas00/followers", "following_url": "https://api.github.com/users/stas00/following{/other_user}", "gists_url": "https://api.github.com/users/stas00/gists{/gist_id}", "starred_url": "https://api.github.com/users/stas00/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/stas00/subscriptions", "organizations_url": "https://api.github.com/users/stas00/orgs", "repos_url": "https://api.github.com/users/stas00/repos", "events_url": "https://api.github.com/users/stas00/events{/privacy}", "received_events_url": "https://api.github.com/users/stas00/received_events", "type": "User", "site_admin": false }	[ { "id": 1935892871, "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement", "name": "enhancement", "color": "a2eeef", "default": true, "description": "New feature or request" } ]	open	false	null	[]	null	[]	"2024-01-24T18:52:10"	"2024-01-24T18:55:09"	null	CONTRIBUTOR	null	### Feature request Splitting off https://github.com/huggingface/huggingface_hub/issues/1997 - currently `huggingface-cli delete-cache` doesn't take care of cleaning `datasets` temp files e.g. I discovered having millions of files under `datasets/downloads` cache, I had to do: ``` sudo find /data/huggingface/datasets/downloads -type f -mtime +3 -exec rm {} \+ sudo find /data/huggingface/datasets/downloads -type d -empty -delete ``` could the cleanup be integrated into `huggingface-cli` or a different tool provided to keep the folders tidy and not consume inodes and space e.g. there were tens of thousands of `.lock` files - I don't know why they never get removed - lock files should be temporary for the duration of the operation requiring the lock and not remain after the operation finished, IMHO. Also I think one should be able to nuke `datasets/downloads` w/o hurting the cache, but I think there are some datasets that rely on files extracted under this dir - or at least they did in the past - which is very difficult to manage since one has no idea what is safe to delete and what not. Thank you @Wauplin (requested to be tagged)	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6614/reactions", "total_count": 2, "+1": 2, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6614/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6612	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6612/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6612/comments	https://api.github.com/repos/huggingface/datasets/issues/6612/events	https://github.com/huggingface/datasets/issues/6612	2,098,078,210	I_kwDODunzps59DiIC	6,612	cnn_dailymail repeats itself	{ "login": "KeremZaman", "id": 8274752, "node_id": "MDQ6VXNlcjgyNzQ3NTI=", "avatar_url": "https://avatars.githubusercontent.com/u/8274752?v=4", "gravatar_id": "", "url": "https://api.github.com/users/KeremZaman", "html_url": "https://github.com/KeremZaman", "followers_url": "https://api.github.com/users/KeremZaman/followers", "following_url": "https://api.github.com/users/KeremZaman/following{/other_user}", "gists_url": "https://api.github.com/users/KeremZaman/gists{/gist_id}", "starred_url": "https://api.github.com/users/KeremZaman/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/KeremZaman/subscriptions", "organizations_url": "https://api.github.com/users/KeremZaman/orgs", "repos_url": "https://api.github.com/users/KeremZaman/repos", "events_url": "https://api.github.com/users/KeremZaman/events{/privacy}", "received_events_url": "https://api.github.com/users/KeremZaman/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "Hi ! We recently updated `cnn_dailymail` and now `datasets>=2.14` is needed to load it.\r\n\r\nYou can update `datasets` with\r\n\r\n```\r\npip install -U datasets\r\n```" ]	"2024-01-24T11:38:25"	"2024-02-01T08:14:50"	"2024-02-01T08:14:50"	NONE	null	### Describe the bug When I try to load `cnn_dailymail` dataset, it takes longer than usual and when I checked the dataset it's 3x bigger than it's supposed to be. Check https://huggingface.co/datasets/cnn_dailymail: it says 287k rows for train. But when I check length of train split it says 861339. Also I checked data: ``` >>> ds['train']['highlights'][0] "Harry Potter star Daniel Radcliffe gets £20M fortune as he turns 18 Monday . Young actor says he has no plans to fritter his cash away . Radcliffe's earnings from first five Potter films have been held in trust fund ."```` >>> ds['train']['highlights'][0] "Harry Potter star Daniel Radcliffe gets £20M fortune as he turns 18 Monday . Young actor says he has no plans to fritter his cash away . Radcliffe's earnings from first five Potter films have been held in trust fund ."```` >>> ds['train']['highlights'][287113] "Harry Potter star Daniel Radcliffe gets £20M fortune as he turns 18 Monday .\nYoung actor says he has no plans to fritter his cash away .\nRadcliffe's earnings from first five Potter films have been held in trust fund ."```` >>> ds['train']['highlights'][574226] "Harry Potter star Daniel Radcliffe gets £20M fortune as he turns 18 Monday .\nYoung actor says he has no plans to fritter his cash away .\nRadcliffe's earnings from first five Potter films have been held in trust fund ." ``` The datasets seems to be updated 6 days ago to convert it to Parquet. Probably, there is some issue with backward compatability. ### Steps to reproduce the bug 1. ``` from datasets import load_dataset ds = load_dataset('cnn_dailymail', '3.0.0') len(ds['train']) ``` ### Expected behavior It should not repeat itself. ### Environment info datasets==2.13.2 Python==3.7.13	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6612/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6612/timeline	null	completed	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6611	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6611/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6611/comments	https://api.github.com/repos/huggingface/datasets/issues/6611/events	https://github.com/huggingface/datasets/issues/6611	2,096,004,858	I_kwDODunzps587n76	6,611	`load_from_disk` with large dataset from S3 runs into `botocore.exceptions.ClientError`	{ "login": "zotroneneis", "id": 15320635, "node_id": "MDQ6VXNlcjE1MzIwNjM1", "avatar_url": "https://avatars.githubusercontent.com/u/15320635?v=4", "gravatar_id": "", "url": "https://api.github.com/users/zotroneneis", "html_url": "https://github.com/zotroneneis", "followers_url": "https://api.github.com/users/zotroneneis/followers", "following_url": "https://api.github.com/users/zotroneneis/following{/other_user}", "gists_url": "https://api.github.com/users/zotroneneis/gists{/gist_id}", "starred_url": "https://api.github.com/users/zotroneneis/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/zotroneneis/subscriptions", "organizations_url": "https://api.github.com/users/zotroneneis/orgs", "repos_url": "https://api.github.com/users/zotroneneis/repos", "events_url": "https://api.github.com/users/zotroneneis/events{/privacy}", "received_events_url": "https://api.github.com/users/zotroneneis/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	null	[]	"2024-01-23T12:37:57"	"2024-01-23T12:37:57"	null	NONE	null	### Describe the bug When loading a large dataset (>1000GB) from S3 I run into the following error: ``` Traceback (most recent call last): File "/home/alp/.local/lib/python3.10/site-packages/s3fs/core.py", line 113, in _error_wrapper return await func(args, kwargs) File "/home/alp/.local/lib/python3.10/site-packages/aiobotocore/client.py", line 383, in _make_api_call raise error_class(parsed_response, operation_name) botocore.exceptions.ClientError: An error occurred (RequestTimeTooSkewed) when calling the GetObject operation: The difference between the request time and the current time is too large. The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/home/alp/phoneme-classification.monorepo/aws_sagemaker/data_processing/inspect_final_dataset.py", line 13, in <module> dataset = load_from_disk("s3://speech-recognition-processed-data/whisper/de/train_data/", storage_options=storage_options) File "/home/alp/.local/lib/python3.10/site-packages/datasets/load.py", line 1902, in load_from_disk return Dataset.load_from_disk(dataset_path, keep_in_memory=keep_in_memory, storage_options=storage_options) File "/home/alp/.local/lib/python3.10/site-packages/datasets/arrow_dataset.py", line 1686, in load_from_disk fs.download(src_dataset_path, [dest_dataset_path.as](http://dest_dataset_path.as/)_posix(), recursive=True) File "/home/alp/.local/lib/python3.10/site-packages/fsspec/spec.py", line 1480, in download return self.get(rpath, lpath, recursive=recursive, kwargs) File "/home/alp/.local/lib/python3.10/site-packages/fsspec/asyn.py", line 121, in wrapper return sync(self.loop, func, args, *kwargs) File "/home/alp/.local/lib/python3.10/site-packages/fsspec/asyn.py", line 106, in sync raise return_result File "/home/alp/.local/lib/python3.10/site-packages/fsspec/asyn.py", line 61, in _runner result[0] = await coro File "/home/alp/.local/lib/python3.10/site-packages/fsspec/asyn.py", line 604, in _get return await _run_coros_in_chunks( File "/home/alp/.local/lib/python3.10/site-packages/fsspec/asyn.py", line 257, in _run_coros_in_chunks await asyncio.gather(chunk, return_exceptions=return_exceptions), File "/usr/lib/python3.10/asyncio/tasks.py", line 408, in wait_for return await fut File "/home/alp/.local/lib/python3.10/site-packages/s3fs/core.py", line 1193, in _get_file body, content_length = await _open_file(range=0) File "/home/alp/.local/lib/python3.10/site-packages/s3fs/core.py", line 1184, in _open_file resp = await self._call_s3( File "/home/alp/.local/lib/python3.10/site-packages/s3fs/core.py", line 348, in _call_s3 return await _error_wrapper( File "/home/alp/.local/lib/python3.10/site-packages/s3fs/core.py", line 140, in _error_wrapper raise err PermissionError: The difference between the request time and the current time is too large. ``` The usual problem for this error is that the time on my local machine is out of sync with the current time. However, this is not the case here. I checked the time and even reset it with no success. See resources here: - https://stackoverflow.com/questions/4770635/s3-error-the-difference-between-the-request-time-and-the-current-time-is-too-la - https://stackoverflow.com/questions/25964491/aws-s3-upload-fails-requesttimetooskewed The error does not appear when loading a smaller dataset (e.g. our test set) from the same s3 path. ### Steps to reproduce the bug 1. Create large dataset 2. Try loading it from s3 using: ``` dataset = load_from_disk("s3://...", storage_options=storage_options) ``` ### Expected behavior Load dataset without running into this error. ### Environment info - `datasets` version: 2.13.1 - Platform: Linux-5.15.0-91-generic-x86_64-with-glibc2.35 - Python version: 3.10.12 - Huggingface_hub version: 0.19.3 - PyArrow version: 12.0.1 - Pandas version: 2.0.3	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6611/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6611/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6610	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6610/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6610/comments	https://api.github.com/repos/huggingface/datasets/issues/6610/events	https://github.com/huggingface/datasets/issues/6610	2,095,643,711	I_kwDODunzps586Pw_	6,610	cast_column to Sequence(subfeatures_dict) has err	{ "login": "neiblegy", "id": 16574677, "node_id": "MDQ6VXNlcjE2NTc0Njc3", "avatar_url": "https://avatars.githubusercontent.com/u/16574677?v=4", "gravatar_id": "", "url": "https://api.github.com/users/neiblegy", "html_url": "https://github.com/neiblegy", "followers_url": "https://api.github.com/users/neiblegy/followers", "following_url": "https://api.github.com/users/neiblegy/following{/other_user}", "gists_url": "https://api.github.com/users/neiblegy/gists{/gist_id}", "starred_url": "https://api.github.com/users/neiblegy/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/neiblegy/subscriptions", "organizations_url": "https://api.github.com/users/neiblegy/orgs", "repos_url": "https://api.github.com/users/neiblegy/repos", "events_url": "https://api.github.com/users/neiblegy/events{/privacy}", "received_events_url": "https://api.github.com/users/neiblegy/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "Hi! You are passing the wrong feature type to `cast_column`. This is the fixed call:\r\n```python\r\nais_dataset = ais_dataset.cast_column(\"my_labeled_bbox\", {\"bbox\": Sequence(Value(dtype=\"int64\")), \"label\": ClassLabel(names=[\"cat\", \"dog\"])})\r\n```", "> Hi! You are passing the wrong feature type to `cast_column`. This is the fixed call:\r\n> \r\n> ```python\r\n> ais_dataset = ais_dataset.cast_column(\"my_labeled_bbox\", {\"bbox\": Sequence(Value(dtype=\"int64\")), \"label\": ClassLabel(names=[\"cat\", \"dog\"])})\r\n> ```\r\n\r\nthanks" ]	"2024-01-23T09:32:32"	"2024-01-25T02:15:23"	"2024-01-25T02:15:23"	NONE	null	### Describe the bug I am working with the following demo code: ``` from datasets import load_dataset from datasets.features import Sequence, Value, ClassLabel, Features ais_dataset = load_dataset("/data/ryan.gao/ais_dataset_cache/raw/1978/") ais_dataset = ais_dataset["train"] def add_class(example): example["my_labeled_bbox"] = {"bbox": [100,100,200,200], "label": "cat"} return example ais_dataset = ais_dataset.map(add_class, batched=False, num_proc=32) ais_dataset = ais_dataset.cast_column("my_labeled_bbox", Sequence( { "bbox": Sequence(Value(dtype="int64")), "label": ClassLabel(names=["cat", "dog"]) })) print(ais_dataset[0]) ``` However, executing this code results in an error: ``` File "/home/protoss.gao/.local/lib/python3.9/site-packages/datasets/table.py", line 2111, in cast_array_to_feature raise TypeError(f"Couldn't cast array of type\n{array.type}\nto\n{feature}") TypeError: Couldn't cast array of type int64 to Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None) ``` Upon examining the source code in datasets/table.py at line 2035: ``` if isinstance(feature, Sequence) and isinstance(feature.feature, dict): feature = { name: Sequence(subfeature, length=feature.length) for name, subfeature in feature.feature.items() } ``` I noticed that if subfeature is of type Sequence, the code results in Sequence(Sequence(...), ...) and Sequence(ClassLabel(...), ...), which appears to be the source of the error. ### Steps to reproduce the bug run my demo code ### Expected behavior no exception ### Environment info python 3.9 datasets: 2.16.1	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6610/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6610/timeline	null	completed	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6609	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6609/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6609/comments	https://api.github.com/repos/huggingface/datasets/issues/6609/events	https://github.com/huggingface/datasets/issues/6609	2,095,085,650	I_kwDODunzps584HhS	6,609	Wrong path for cache directory in offline mode	{ "login": "je-santos", "id": 42117435, "node_id": "MDQ6VXNlcjQyMTE3NDM1", "avatar_url": "https://avatars.githubusercontent.com/u/42117435?v=4", "gravatar_id": "", "url": "https://api.github.com/users/je-santos", "html_url": "https://github.com/je-santos", "followers_url": "https://api.github.com/users/je-santos/followers", "following_url": "https://api.github.com/users/je-santos/following{/other_user}", "gists_url": "https://api.github.com/users/je-santos/gists{/gist_id}", "starred_url": "https://api.github.com/users/je-santos/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/je-santos/subscriptions", "organizations_url": "https://api.github.com/users/je-santos/orgs", "repos_url": "https://api.github.com/users/je-santos/repos", "events_url": "https://api.github.com/users/je-santos/events{/privacy}", "received_events_url": "https://api.github.com/users/je-santos/received_events", "type": "User", "site_admin": false }	[]	closed	false	{ "login": "lhoestq", "id": 42851186, "node_id": "MDQ6VXNlcjQyODUxMTg2", "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "gravatar_id": "", "url": "https://api.github.com/users/lhoestq", "html_url": "https://github.com/lhoestq", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "repos_url": "https://api.github.com/users/lhoestq/repos", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "type": "User", "site_admin": false }	[ { "login": "lhoestq", "id": 42851186, "node_id": "MDQ6VXNlcjQyODUxMTg2", "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "gravatar_id": "", "url": "https://api.github.com/users/lhoestq", "html_url": "https://github.com/lhoestq", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "repos_url": "https://api.github.com/users/lhoestq/repos", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "type": "User", "site_admin": false } ]	null	[ "+1", "same error in 2.16.1", "@kongjiellx any luck with the issue?", "I opened https://github.com/huggingface/datasets/pull/6632 to fix this issue. Once it's merged we'll do a new release of `datasets`", "Thanks @lhoestq !" ]	"2024-01-23T01:47:19"	"2024-02-06T17:21:25"	"2024-02-06T17:21:25"	NONE	null	### Describe the bug Dear huggingfacers, I'm trying to use a subset of the-stack dataset. When I run the command the first time ``` dataset = load_dataset( path='bigcode/the-stack', data_dir='data/fortran', split='train' ) ``` It downloads the files and caches them normally. Nevertheless, since my compute nodes are not online (`HF_DATASETS_OFFLINE=1`) . Whenever I try to run the command again, the library is passing the wrong cache path: `Cache directory for the-stack doesn't exist at /Users/user/.cache/huggingface/datasets/bigcode___the-stack/default-data_dir=data%2Ffortran-data_dir=data%2Ffortran` when the right path is: `'/Users/user/.cache/huggingface/datasets/bigcode___the-stack/default-data_dir=data\%2Ffortran` Not sure why those redundancies are included in the path. If I try adding the correct path through the the cache_dir argument it throws an error: ConnectionError: Couldn't reach the Hugging Face Hub for dataset 'bigcode/the-stack': Offline mode is enabled. Your help with this issue is greatly appreciated. Thanks a lot for the great work. ### Steps to reproduce the bug 1: `dataset = load_dataset( path='bigcode/the-stack', data_dir='data/fortran', split='train' )` 2: `HF_DATASETS_OFFLINE=1` 3: `dataset = load_dataset( path='bigcode/the-stack', data_dir='data/fortran', split='train' )` ### Expected behavior being able to use the cached data ### Environment info several different systems	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6609/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6609/timeline	null	completed	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6608	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6608/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6608/comments	https://api.github.com/repos/huggingface/datasets/issues/6608/events	https://github.com/huggingface/datasets/pull/6608	2,094,153,292	PR_kwDODunzps5ku_lN	6,608	Add `with_rank` param to `Dataset.filter`	{ "login": "mariosasko", "id": 47462742, "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "gravatar_id": "", "url": "https://api.github.com/users/mariosasko", "html_url": "https://github.com/mariosasko", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "repos_url": "https://api.github.com/users/mariosasko/repos", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6608). 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) \| 0.005376 / 0.011353 (-0.005977) \| 0.004691 / 0.011008 (-0.006317) \| 0.064061 / 0.038508 (0.025553) \| 0.030397 / 0.023109 (0.007288) \| 0.242656 / 0.275898 (-0.033242) \| 0.275586 / 0.323480 (-0.047894) \| 0.003460 / 0.007986 (-0.004526) \| 0.003125 / 0.004328 (-0.001203) \| 0.050496 / 0.004250 (0.046246) \| 0.045833 / 0.037052 (0.008781) \| 0.255222 / 0.258489 (-0.003267) \| 0.287303 / 0.293841 (-0.006538) \| 0.027755 / 0.128546 (-0.100791) \| 0.011251 / 0.075646 (-0.064396) \| 0.208456 / 0.419271 (-0.210816) \| 0.037219 / 0.043533 (-0.006314) \| 0.249592 / 0.255139 (-0.005547) \| 0.261243 / 0.283200 (-0.021957) \| 0.020735 / 0.141683 (-0.120948) \| 1.130017 / 1.452155 (-0.322137) \| 1.208558 / 1.492716 (-0.284158) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.098891 / 0.018006 (0.080885) \| 0.439042 / 0.000490 (0.438552) \| 0.000333 / 0.000200 (0.000133) \| 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) \| 0.018356 / 0.037411 (-0.019055) \| 0.062416 / 0.014526 (0.047891) \| 0.075613 / 0.176557 (-0.100944) \| 0.122009 / 0.737135 (-0.615126) \| 0.078195 / 0.296338 (-0.218144) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.273804 / 0.215209 (0.058595) \| 2.706480 / 2.077655 (0.628826) \| 1.456196 / 1.504120 (-0.047924) \| 1.353301 / 1.541195 (-0.187893) \| 1.378913 / 1.468490 (-0.089577) \| 0.556885 / 4.584777 (-4.027892) \| 2.358961 / 3.745712 (-1.386752) \| 2.871830 / 5.269862 (-2.398031) \| 1.765212 / 4.565676 (-2.800464) \| 0.062172 / 0.424275 (-0.362103) \| 0.004974 / 0.007607 (-0.002633) \| 0.330375 / 0.226044 (0.104331) \| 3.264550 / 2.268929 (0.995621) \| 1.824444 / 55.444624 (-53.620181) \| 1.561189 / 6.876477 (-5.315287) \| 1.671020 / 2.142072 (-0.471052) \| 0.633408 / 4.805227 (-4.171819) \| 0.116080 / 6.500664 (-6.384584) \| 0.044606 / 0.075469 (-0.030863) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.980757 / 1.841788 (-0.861031) \| 12.553534 / 8.074308 (4.479225) \| 10.517668 / 10.191392 (0.326276) \| 0.130528 / 0.680424 (-0.549896) \| 0.013960 / 0.534201 (-0.520241) \| 0.289615 / 0.579283 (-0.289668) \| 0.267277 / 0.434364 (-0.167087) \| 0.324139 / 0.540337 (-0.216198) \| 0.440325 / 1.386936 (-0.946611) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated 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) \| 0.004043 / 0.011008 (-0.006966) \| 0.050514 / 0.038508 (0.012005) \| 0.031413 / 0.023109 (0.008303) \| 0.275122 / 0.275898 (-0.000776) \| 0.307518 / 0.323480 (-0.015962) \| 0.004440 / 0.007986 (-0.003546) \| 0.003301 / 0.004328 (-0.001027) \| 0.049200 / 0.004250 (0.044949) \| 0.045704 / 0.037052 (0.008651) \| 0.285265 / 0.258489 (0.026776) \| 0.318942 / 0.293841 (0.025101) \| 0.053893 / 0.128546 (-0.074653) \| 0.011855 / 0.075646 (-0.063791) \| 0.060951 / 0.419271 (-0.358321) \| 0.034397 / 0.043533 (-0.009136) \| 0.276108 / 0.255139 (0.020969) \| 0.290981 / 0.283200 (0.007781) \| 0.019986 / 0.141683 (-0.121697) \| 1.205695 / 1.452155 (-0.246460) \| 1.255942 / 1.492716 (-0.236774) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.101910 / 0.018006 (0.083904) \| 0.320551 / 0.000490 (0.320061) \| 0.000299 / 0.000200 (0.000099) \| 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) \| 0.022387 / 0.037411 (-0.015024) \| 0.076380 / 0.014526 (0.061854) \| 0.090404 / 0.176557 (-0.086153) \| 0.127106 / 0.737135 (-0.610030) \| 0.089873 / 0.296338 (-0.206465) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.288433 / 0.215209 (0.073223) \| 2.827005 / 2.077655 (0.749350) \| 1.548760 / 1.504120 (0.044640) \| 1.419545 / 1.541195 (-0.121650) \| 1.456531 / 1.468490 (-0.011959) \| 0.570254 / 4.584777 (-4.014523) \| 2.441318 / 3.745712 (-1.304394) \| 2.778647 / 5.269862 (-2.491215) \| 1.755255 / 4.565676 (-2.810422) \| 0.062581 / 0.424275 (-0.361694) \| 0.005205 / 0.007607 (-0.002402) \| 0.342189 / 0.226044 (0.116145) \| 3.401208 / 2.268929 (1.132279) \| 1.941447 / 55.444624 (-53.503178) \| 1.652578 / 6.876477 (-5.223899) \| 1.768558 / 2.142072 (-0.373514) \| 0.656537 / 4.805227 (-4.148690) \| 0.116901 / 6.500664 (-6.383763) \| 0.041408 / 0.075469 (-0.034061) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.001715 / 1.841788 (-0.840073) \| 12.533073 / 8.074308 (4.458765) \| 11.086084 / 10.191392 (0.894692) \| 0.134368 / 0.680424 (-0.546055) \| 0.015255 / 0.534201 (-0.518946) \| 0.291769 / 0.579283 (-0.287514) \| 0.283311 / 0.434364 (-0.151053) \| 0.327857 / 0.540337 (-0.212481) \| 0.413854 / 1.386936 (-0.973083) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#46931085bd8a3fdbc63b68b5ee4b8f62029c7557 \"CML watermark\")\n" ]	"2024-01-22T15:19:16"	"2024-01-29T16:43:11"	"2024-01-29T16:36:53"	CONTRIBUTOR	null	Fix #6564	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6608/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6608/timeline	null	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6608", "html_url": "https://github.com/huggingface/datasets/pull/6608", "diff_url": "https://github.com/huggingface/datasets/pull/6608.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6608.patch", "merged_at": "2024-01-29T16:36:53" }	true
https://api.github.com/repos/huggingface/datasets/issues/6607	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6607/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6607/comments	https://api.github.com/repos/huggingface/datasets/issues/6607/events	https://github.com/huggingface/datasets/pull/6607	2,091,766,063	PR_kwDODunzps5knGse	6,607	Update features.py to avoid bfloat16 unsupported error	{ "login": "skaulintel", "id": 75697181, "node_id": "MDQ6VXNlcjc1Njk3MTgx", "avatar_url": "https://avatars.githubusercontent.com/u/75697181?v=4", "gravatar_id": "", "url": "https://api.github.com/users/skaulintel", "html_url": "https://github.com/skaulintel", "followers_url": "https://api.github.com/users/skaulintel/followers", "following_url": "https://api.github.com/users/skaulintel/following{/other_user}", "gists_url": "https://api.github.com/users/skaulintel/gists{/gist_id}", "starred_url": "https://api.github.com/users/skaulintel/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/skaulintel/subscriptions", "organizations_url": "https://api.github.com/users/skaulintel/orgs", "repos_url": "https://api.github.com/users/skaulintel/repos", "events_url": "https://api.github.com/users/skaulintel/events{/privacy}", "received_events_url": "https://api.github.com/users/skaulintel/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	null	[ "I think not all torch tensors should be converted to float, what if it's a tensor of integers for example ?\r\nMaybe you can check for the tensor dtype before converting" ]	"2024-01-20T00:39:44"	"2024-03-22T00:34:48"	null	NONE	null	Fixes https://github.com/huggingface/datasets/issues/6566 Let me know if there's any tests I need to clear.	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6607/reactions", "total_count": 1, "+1": 1, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6607/timeline	null	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6607", "html_url": "https://github.com/huggingface/datasets/pull/6607", "diff_url": "https://github.com/huggingface/datasets/pull/6607.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6607.patch", "merged_at": null }	true
https://api.github.com/repos/huggingface/datasets/issues/6606	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6606/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6606/comments	https://api.github.com/repos/huggingface/datasets/issues/6606/events	https://github.com/huggingface/datasets/pull/6606	2,091,088,785	PR_kwDODunzps5kk3KB	6,606	Dedicated RNG object for fingerprinting	{ "login": "mariosasko", "id": 47462742, "node_id": "MDQ6VXNlcjQ3NDYyNzQy", "avatar_url": "https://avatars.githubusercontent.com/u/47462742?v=4", "gravatar_id": "", "url": "https://api.github.com/users/mariosasko", "html_url": "https://github.com/mariosasko", "followers_url": "https://api.github.com/users/mariosasko/followers", "following_url": "https://api.github.com/users/mariosasko/following{/other_user}", "gists_url": "https://api.github.com/users/mariosasko/gists{/gist_id}", "starred_url": "https://api.github.com/users/mariosasko/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/mariosasko/subscriptions", "organizations_url": "https://api.github.com/users/mariosasko/orgs", "repos_url": "https://api.github.com/users/mariosasko/repos", "events_url": "https://api.github.com/users/mariosasko/events{/privacy}", "received_events_url": "https://api.github.com/users/mariosasko/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6606). 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) \| 0.005625 / 0.011353 (-0.005728) \| 0.003313 / 0.011008 (-0.007695) \| 0.063997 / 0.038508 (0.025489) \| 0.028949 / 0.023109 (0.005839) \| 0.250069 / 0.275898 (-0.025829) \| 0.271412 / 0.323480 (-0.052068) \| 0.003837 / 0.007986 (-0.004148) \| 0.002632 / 0.004328 (-0.001697) \| 0.048351 / 0.004250 (0.044100) \| 0.040664 / 0.037052 (0.003612) \| 0.267540 / 0.258489 (0.009051) \| 0.285237 / 0.293841 (-0.008604) \| 0.026962 / 0.128546 (-0.101584) \| 0.010417 / 0.075646 (-0.065229) \| 0.211430 / 0.419271 (-0.207842) \| 0.035411 / 0.043533 (-0.008122) \| 0.258867 / 0.255139 (0.003728) \| 0.278562 / 0.283200 (-0.004638) \| 0.017690 / 0.141683 (-0.123993) \| 1.128813 / 1.452155 (-0.323342) \| 1.169384 / 1.492716 (-0.323333) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091322 / 0.018006 (0.073316) \| 0.303272 / 0.000490 (0.302782) \| 0.000202 / 0.000200 (0.000002) \| 0.000044 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.017551 / 0.037411 (-0.019861) \| 0.060027 / 0.014526 (0.045502) \| 0.073431 / 0.176557 (-0.103125) \| 0.120550 / 0.737135 (-0.616585) \| 0.073107 / 0.296338 (-0.223231) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.283064 / 0.215209 (0.067855) \| 2.754593 / 2.077655 (0.676938) \| 1.477303 / 1.504120 (-0.026817) \| 1.341072 / 1.541195 (-0.200123) \| 1.366625 / 1.468490 (-0.101865) \| 0.573467 / 4.584777 (-4.011310) \| 2.395225 / 3.745712 (-1.350487) \| 2.777021 / 5.269862 (-2.492841) \| 1.720733 / 4.565676 (-2.844944) \| 0.063339 / 0.424275 (-0.360936) \| 0.004954 / 0.007607 (-0.002653) \| 0.350359 / 0.226044 (0.124315) \| 3.376221 / 2.268929 (1.107293) \| 1.835539 / 55.444624 (-53.609086) \| 1.558064 / 6.876477 (-5.318413) \| 1.582778 / 2.142072 (-0.559294) \| 0.649918 / 4.805227 (-4.155309) \| 0.117761 / 6.500664 (-6.382903) \| 0.041771 / 0.075469 (-0.033698) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.950202 / 1.841788 (-0.891586) \| 11.476160 / 8.074308 (3.401852) \| 10.290618 / 10.191392 (0.099226) \| 0.140659 / 0.680424 (-0.539765) \| 0.014525 / 0.534201 (-0.519676) \| 0.287253 / 0.579283 (-0.292030) \| 0.266204 / 0.434364 (-0.168160) \| 0.327818 / 0.540337 (-0.212519) \| 0.431680 / 1.386936 (-0.955256) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005096 / 0.011353 (-0.006257) \| 0.003460 / 0.011008 (-0.007548) \| 0.049474 / 0.038508 (0.010966) \| 0.031063 / 0.023109 (0.007954) \| 0.272899 / 0.275898 (-0.002999) \| 0.291859 / 0.323480 (-0.031621) \| 0.004858 / 0.007986 (-0.003128) \| 0.002598 / 0.004328 (-0.001731) \| 0.049074 / 0.004250 (0.044824) \| 0.044722 / 0.037052 (0.007669) \| 0.285262 / 0.258489 (0.026772) \| 0.314168 / 0.293841 (0.020327) \| 0.046346 / 0.128546 (-0.082200) \| 0.010384 / 0.075646 (-0.065262) \| 0.058331 / 0.419271 (-0.360940) \| 0.033728 / 0.043533 (-0.009805) \| 0.276217 / 0.255139 (0.021078) \| 0.295465 / 0.283200 (0.012265) \| 0.018215 / 0.141683 (-0.123467) \| 1.163847 / 1.452155 (-0.288308) \| 1.213901 / 1.492716 (-0.278816) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091953 / 0.018006 (0.073947) \| 0.299977 / 0.000490 (0.299487) \| 0.000212 / 0.000200 (0.000012) \| 0.000044 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022031 / 0.037411 (-0.015381) \| 0.075067 / 0.014526 (0.060541) \| 0.087305 / 0.176557 (-0.089251) \| 0.125530 / 0.737135 (-0.611605) \| 0.088761 / 0.296338 (-0.207578) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.302682 / 0.215209 (0.087473) \| 2.941509 / 2.077655 (0.863854) \| 1.643399 / 1.504120 (0.139280) \| 1.530148 / 1.541195 (-0.011046) \| 1.542067 / 1.468490 (0.073577) \| 0.575883 / 4.584777 (-4.008894) \| 2.434320 / 3.745712 (-1.311392) \| 2.761683 / 5.269862 (-2.508179) \| 1.732068 / 4.565676 (-2.833609) \| 0.063543 / 0.424275 (-0.360732) \| 0.005089 / 0.007607 (-0.002518) \| 0.351314 / 0.226044 (0.125269) \| 3.494572 / 2.268929 (1.225643) \| 2.032503 / 55.444624 (-53.412121) \| 1.697949 / 6.876477 (-5.178528) \| 1.700392 / 2.142072 (-0.441680) \| 0.650757 / 4.805227 (-4.154471) \| 0.116719 / 6.500664 (-6.383945) \| 0.040559 / 0.075469 (-0.034910) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.978218 / 1.841788 (-0.863570) \| 11.972379 / 8.074308 (3.898071) \| 10.725735 / 10.191392 (0.534343) \| 0.130564 / 0.680424 (-0.549860) \| 0.015396 / 0.534201 (-0.518805) \| 0.286900 / 0.579283 (-0.292383) \| 0.279633 / 0.434364 (-0.154730) \| 0.327483 / 0.540337 (-0.212854) \| 0.417848 / 1.386936 (-0.969088) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#adfe8f8fa37b9f220c152f5b8b2473ba2cef0307 \"CML watermark\")\n" ]	"2024-01-19T18:34:47"	"2024-01-26T15:11:38"	"2024-01-26T15:05:34"	CONTRIBUTOR	null	Closes https://github.com/huggingface/datasets/issues/6604, closes https://github.com/huggingface/datasets/issues/2775	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6606/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6606/timeline	null	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6606", "html_url": "https://github.com/huggingface/datasets/pull/6606", "diff_url": "https://github.com/huggingface/datasets/pull/6606.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6606.patch", "merged_at": "2024-01-26T15:05:34" }	true
https://api.github.com/repos/huggingface/datasets/issues/6605	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6605/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6605/comments	https://api.github.com/repos/huggingface/datasets/issues/6605/events	https://github.com/huggingface/datasets/issues/6605	2,090,188,376	I_kwDODunzps58lb5Y	6,605	ELI5 no longer available, but referenced in example code	{ "login": "drdsgvo", "id": 81480344, "node_id": "MDQ6VXNlcjgxNDgwMzQ0", "avatar_url": "https://avatars.githubusercontent.com/u/81480344?v=4", "gravatar_id": "", "url": "https://api.github.com/users/drdsgvo", "html_url": "https://github.com/drdsgvo", "followers_url": "https://api.github.com/users/drdsgvo/followers", "following_url": "https://api.github.com/users/drdsgvo/following{/other_user}", "gists_url": "https://api.github.com/users/drdsgvo/gists{/gist_id}", "starred_url": "https://api.github.com/users/drdsgvo/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/drdsgvo/subscriptions", "organizations_url": "https://api.github.com/users/drdsgvo/orgs", "repos_url": "https://api.github.com/users/drdsgvo/repos", "events_url": "https://api.github.com/users/drdsgvo/events{/privacy}", "received_events_url": "https://api.github.com/users/drdsgvo/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "Addressed in https://github.com/huggingface/transformers/pull/28715." ]	"2024-01-19T10:21:52"	"2024-02-01T17:58:23"	"2024-02-01T17:58:22"	NONE	null	Here, an example code is given: https://huggingface.co/docs/transformers/tasks/language_modeling This code + article references the ELI5 dataset. ELI5 is no longer available, as the ELI5 dataset page states: https://huggingface.co/datasets/eli5 "Defunct: Dataset "eli5" is defunct and no longer accessible due to unavailability of the source data. Reddit recently [changed the terms of access](https://www.reddit.com/r/reddit/comments/12qwagm/an_update_regarding_reddits_api/) to its API, making the source data for this dataset unavailable. " Please change the example code to use a different dataset.	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6605/reactions", "total_count": 3, "+1": 3, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6605/timeline	null	completed	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6604	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6604/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6604/comments	https://api.github.com/repos/huggingface/datasets/issues/6604/events	https://github.com/huggingface/datasets/issues/6604	2,089,713,945	I_kwDODunzps58joEZ	6,604	Transform fingerprint collisions due to setting fixed random seed	{ "login": "normster", "id": 6687910, "node_id": "MDQ6VXNlcjY2ODc5MTA=", "avatar_url": "https://avatars.githubusercontent.com/u/6687910?v=4", "gravatar_id": "", "url": "https://api.github.com/users/normster", "html_url": "https://github.com/normster", "followers_url": "https://api.github.com/users/normster/followers", "following_url": "https://api.github.com/users/normster/following{/other_user}", "gists_url": "https://api.github.com/users/normster/gists{/gist_id}", "starred_url": "https://api.github.com/users/normster/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/normster/subscriptions", "organizations_url": "https://api.github.com/users/normster/orgs", "repos_url": "https://api.github.com/users/normster/repos", "events_url": "https://api.github.com/users/normster/events{/privacy}", "received_events_url": "https://api.github.com/users/normster/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "I've opened a PR with a fix.", "I don't think the PR fixes the root cause, since it still relies on the `random` library which will often have its seed fixed. I think the builtin `uuid.uuid4()` is a better choice: https://docs.python.org/3/library/uuid.html" ]	"2024-01-19T06:32:25"	"2024-01-26T15:05:35"	"2024-01-26T15:05:35"	NONE	null	### Describe the bug The transform fingerprinting logic relies on the `random` library for random bits when the function is not hashable (e.g. bound methods as used in `trl`: https://github.com/huggingface/trl/blob/main/trl/trainer/dpo_trainer.py#L356). This causes collisions when the training code sets a fixed random seed, which is common practice: https://github.com/huggingface/alignment-handbook/blob/main/recipes/zephyr-7b-beta/sft/config_full.yaml#L45. This results in fingerprint collisions which leads to silently loading incorrect cache files corresponding to completely different datasets. ### Steps to reproduce the bug n/a ### Expected behavior Use `uuid` v4 instead of `random.getrandbits()` ### Environment info `datasets` main branch	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6604/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6604/timeline	null	completed	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6603	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6603/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6603/comments	https://api.github.com/repos/huggingface/datasets/issues/6603/events	https://github.com/huggingface/datasets/issues/6603	2,089,230,766	I_kwDODunzps58hyGu	6,603	datasets map `cache_file_name` does not work	{ "login": "ChenchaoZhao", "id": 35147961, "node_id": "MDQ6VXNlcjM1MTQ3OTYx", "avatar_url": "https://avatars.githubusercontent.com/u/35147961?v=4", "gravatar_id": "", "url": "https://api.github.com/users/ChenchaoZhao", "html_url": "https://github.com/ChenchaoZhao", "followers_url": "https://api.github.com/users/ChenchaoZhao/followers", "following_url": "https://api.github.com/users/ChenchaoZhao/following{/other_user}", "gists_url": "https://api.github.com/users/ChenchaoZhao/gists{/gist_id}", "starred_url": "https://api.github.com/users/ChenchaoZhao/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/ChenchaoZhao/subscriptions", "organizations_url": "https://api.github.com/users/ChenchaoZhao/orgs", "repos_url": "https://api.github.com/users/ChenchaoZhao/repos", "events_url": "https://api.github.com/users/ChenchaoZhao/events{/privacy}", "received_events_url": "https://api.github.com/users/ChenchaoZhao/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	null	[ "Unfortunately, I'm unable to reproduce this error. Can you share the reproducer?", "```\r\nds = datasets.Dataset.from_dict(dict(a=[i for i in range(100)]))\r\nds.map(lambda item: dict(b=item['a'] * 2), cache_file_name=\"/tmp/whatever-fn\") # this worked\r\nds.map(lambda item: dict(b=item['a'] * 2), cache_file_name=\"/tmp/whatever-folder/filename\") # this failed\r\nds.map(lambda item: dict(b=item['a'] * 2), cache_file_name=\"/tmp/whatever-folder/\") # this failed\r\n\r\n\r\nFileNotFoundError: [Errno 2] No such file or directory: '/tmp/whatever-folder/tmp1_izxvoo'\r\n```\r\n\r\nIt will fail if the filename parents do not exists. If we have `os.makedirs(\"/tmp/whatever-folder\")`, then it worked.\r\n\r\nMaybe add the `mkdir -p` into the map function?" ]	"2024-01-18T23:08:30"	"2024-01-28T04:01:15"	null	NONE	null	### Describe the bug In the documentation `datasets.Dataset.map` arg `cache_file_name` is said to be a string, but it doesn't work. ### Steps to reproduce the bug 1. pick a dataset 2. write a map function 3. do `ds.map(..., cache_file_name='some_filename')` 4. it crashes ### Expected behavior It will tell you the filename you specified does not exist or it will generate a new file and tell you the filename does not exist. ### Environment info - `datasets` version: 2.16.0 - Platform: Linux-5.10.201-168.748.amzn2int.x86_64-x86_64-with-glibc2.26 - Python version: 3.10.13 - `huggingface_hub` version: 0.20.2 - PyArrow version: 14.0.2 - Pandas version: 2.1.4 - `fsspec` version: 2023.12.2	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6603/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6603/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6602	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6602/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6602/comments	https://api.github.com/repos/huggingface/datasets/issues/6602/events	https://github.com/huggingface/datasets/issues/6602	2,089,217,483	I_kwDODunzps58hu3L	6,602	Index error when data is large	{ "login": "ChenchaoZhao", "id": 35147961, "node_id": "MDQ6VXNlcjM1MTQ3OTYx", "avatar_url": "https://avatars.githubusercontent.com/u/35147961?v=4", "gravatar_id": "", "url": "https://api.github.com/users/ChenchaoZhao", "html_url": "https://github.com/ChenchaoZhao", "followers_url": "https://api.github.com/users/ChenchaoZhao/followers", "following_url": "https://api.github.com/users/ChenchaoZhao/following{/other_user}", "gists_url": "https://api.github.com/users/ChenchaoZhao/gists{/gist_id}", "starred_url": "https://api.github.com/users/ChenchaoZhao/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/ChenchaoZhao/subscriptions", "organizations_url": "https://api.github.com/users/ChenchaoZhao/orgs", "repos_url": "https://api.github.com/users/ChenchaoZhao/repos", "events_url": "https://api.github.com/users/ChenchaoZhao/events{/privacy}", "received_events_url": "https://api.github.com/users/ChenchaoZhao/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	null	[]	"2024-01-18T23:00:47"	"2024-01-18T23:00:47"	null	NONE	null	### Describe the bug At `save_to_disk` step, the `max_shard_size` by default is `500MB`. However, one row of the dataset might be larger than `500MB` then the saving will throw an index error. Without looking at the source code, the bug is due to wrong calculation of number of shards which i think is `total_size / min(max_shard_size, row_size)` which should be `total_size / max(max_shard_size, row_size)` The fix is setting a larger `max_shard_size` ### Steps to reproduce the bug 1. create a dataset with large dense tensors per row 2. set a small `max_shard_size` say 1MB 3. `save_to_disk` ### Expected behavior ``` raise IndexError(f"Index {index} out of range for dataset of size {size}.") IndexError: Index 10 out of range for dataset of size 10. ``` ### Environment info - `datasets` version: 2.16.0 - Platform: Linux-5.10.201-168.748.amzn2int.x86_64-x86_64-with-glibc2.26 - Python version: 3.10.13 - `huggingface_hub` version: 0.20.2 - PyArrow version: 14.0.2 - Pandas version: 2.1.4 - `fsspec` version: 2023.12.2	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6602/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6602/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6601	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6601/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6601/comments	https://api.github.com/repos/huggingface/datasets/issues/6601/events	https://github.com/huggingface/datasets/pull/6601	2,088,624,054	PR_kwDODunzps5kcWN0	6,601	add safety checks when using only part of dataset	{ "login": "benseddikismail", "id": 63422923, "node_id": "MDQ6VXNlcjYzNDIyOTIz", "avatar_url": "https://avatars.githubusercontent.com/u/63422923?v=4", "gravatar_id": "", "url": "https://api.github.com/users/benseddikismail", "html_url": "https://github.com/benseddikismail", "followers_url": "https://api.github.com/users/benseddikismail/followers", "following_url": "https://api.github.com/users/benseddikismail/following{/other_user}", "gists_url": "https://api.github.com/users/benseddikismail/gists{/gist_id}", "starred_url": "https://api.github.com/users/benseddikismail/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/benseddikismail/subscriptions", "organizations_url": "https://api.github.com/users/benseddikismail/orgs", "repos_url": "https://api.github.com/users/benseddikismail/repos", "events_url": "https://api.github.com/users/benseddikismail/events{/privacy}", "received_events_url": "https://api.github.com/users/benseddikismail/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	null	[ "Hi ! The metrics in `datasets` are deprecated in favor of https://github.com/huggingface/evaluate\r\n\r\nYou can open a PR here instead: https://huggingface.co/spaces/evaluate-metric/squad_v2/tree/main" ]	"2024-01-18T16:16:59"	"2024-02-08T14:33:10"	null	NONE	null	Added some checks to prevent errors that arrise when using evaluate.py on only a portion of the squad 2.0 dataset.	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6601/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6601/timeline	null	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6601", "html_url": "https://github.com/huggingface/datasets/pull/6601", "diff_url": "https://github.com/huggingface/datasets/pull/6601.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6601.patch", "merged_at": null }	true
https://api.github.com/repos/huggingface/datasets/issues/6600	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6600/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6600/comments	https://api.github.com/repos/huggingface/datasets/issues/6600/events	https://github.com/huggingface/datasets/issues/6600	2,088,446,385	I_kwDODunzps58eymx	6,600	Loading CSV exported dataset has unexpected format	{ "login": "OrianeN", "id": 59572247, "node_id": "MDQ6VXNlcjU5NTcyMjQ3", "avatar_url": "https://avatars.githubusercontent.com/u/59572247?v=4", "gravatar_id": "", "url": "https://api.github.com/users/OrianeN", "html_url": "https://github.com/OrianeN", "followers_url": "https://api.github.com/users/OrianeN/followers", "following_url": "https://api.github.com/users/OrianeN/following{/other_user}", "gists_url": "https://api.github.com/users/OrianeN/gists{/gist_id}", "starred_url": "https://api.github.com/users/OrianeN/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/OrianeN/subscriptions", "organizations_url": "https://api.github.com/users/OrianeN/orgs", "repos_url": "https://api.github.com/users/OrianeN/repos", "events_url": "https://api.github.com/users/OrianeN/events{/privacy}", "received_events_url": "https://api.github.com/users/OrianeN/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	null	[ "Hi! Parquet is the only format that supports complex/nested features such as `Translation`. So, this should work:\r\n```python\r\ntest_dataset = load_dataset(\"opus100\", name=\"en-fr\", split=\"test\")\r\n\r\n# Save with .to_parquet()\r\ntest_parquet_path = \"try_testset_save.parquet\"\r\ntest_dataset.to_parquet(test_parquet_path)\r\n\r\n# Load dataset from the Parquet\r\nloaded_dataset = load_dataset(\"parquet\", data_files=test_parquet_path)\r\nprint(test_dataset_fromfile[0][\"translation\"])\r\nprint(test_dataset_fromfile[0][\"translation\"][\"en\"])\r\n```", "Indeed this works great, thank you !" ]	"2024-01-18T14:48:27"	"2024-01-23T14:42:32"	null	NONE	null	### Describe the bug I wanted to be able to save a HF dataset for translations and load it again in another script, but I'm a bit confused with the documentation and the result I've got so I'm opening this issue to ask if this behavior is as expected. ### Steps to reproduce the bug The documentation I've mainly consulted is https://huggingface.co/docs/datasets/v2.16.1/en/package_reference/loading_methods#datasets.load_dataset and https://huggingface.co/docs/datasets/package_reference/main_classes#datasets.Dataset (where I've found `.to_csv()`) ```python # Load a dataset of translations test_dataset = load_dataset("opus100", name="en-fr", split="test") # Save with .to_csv() test_csv_path = "try_testset_save.csv" test_dataset.to_csv(test_csv_path) # Load dataset from the CSV loaded_dataset = load_dataset("csv", data_files=test_csv_path) print(test_dataset_fromfile[0]["translation"]) print(test_dataset_fromfile[0]["translation"]["en"]) ``` ``` Creating CSV from Arrow format: 100% 2/2 [00:00<00:00, 47.99ba/s] Downloading data files: 100% 1/1 [00:00<00:00, 65.33it/s] Extracting data files: 100% 1/1 [00:00<00:00, 42.10it/s] Generating train split: 2000/0 [00:00<00:00, 47486.09 examples/s] {'en': "She wasn't going to vaccinate her kid against polio, no way.", 'fr': 'Elle ne vaccinerait pas son enfant contre la polio. Pas question.'} --------------------------------------------------------------------------- TypeError Traceback (most recent call last) Cell In[29], line 11 9 loaded_dataset = load_dataset("csv", data_files=test_csv_path) 10 print(test_dataset_fromfile[0]["translation"]) ---> 11 print(test_dataset_fromfile[0]["translation"]["en"]) TypeError: string indices must be integers, not 'str' ``` ### Expected behavior Each translation was saved as a stringified dict like `"{'en': ""She wasn't going to vaccinate her kid against polio, no way."", 'fr': 'Elle ne vaccinerait pas son enfant contre la polio. Pas question.'}"` where I would have expected 2 columns (1st with English segments, and 2nd with French segments), and I was expecting `load_dataset` to infer the type of feature automatically as I haven't seen anything about it in the documentation. Do you have an example of how to effectively save and load datasets of translations ? ### Environment info - `datasets` version: 2.15.0 - Platform: Linux-3.10.0-1160.36.2.el7.x86_64-x86_64-with-glibc2.17 - Python version: 3.11.5 - `huggingface_hub` version: 0.16.4 - PyArrow version: 14.0.2 - Pandas version: 2.1.4 - `fsspec` version: 2023.10.0	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6600/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6600/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6599	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6599/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6599/comments	https://api.github.com/repos/huggingface/datasets/issues/6599/events	https://github.com/huggingface/datasets/issues/6599	2,086,684,664	I_kwDODunzps58YEf4	6,599	Easy way to segment into 30s snippets given an m4a file and a vtt file	{ "login": "RonanKMcGovern", "id": 78278410, "node_id": "MDQ6VXNlcjc4Mjc4NDEw", "avatar_url": "https://avatars.githubusercontent.com/u/78278410?v=4", "gravatar_id": "", "url": "https://api.github.com/users/RonanKMcGovern", "html_url": "https://github.com/RonanKMcGovern", "followers_url": "https://api.github.com/users/RonanKMcGovern/followers", "following_url": "https://api.github.com/users/RonanKMcGovern/following{/other_user}", "gists_url": "https://api.github.com/users/RonanKMcGovern/gists{/gist_id}", "starred_url": "https://api.github.com/users/RonanKMcGovern/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/RonanKMcGovern/subscriptions", "organizations_url": "https://api.github.com/users/RonanKMcGovern/orgs", "repos_url": "https://api.github.com/users/RonanKMcGovern/repos", "events_url": "https://api.github.com/users/RonanKMcGovern/events{/privacy}", "received_events_url": "https://api.github.com/users/RonanKMcGovern/received_events", "type": "User", "site_admin": false }	[ { "id": 1935892871, "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement", "name": "enhancement", "color": "a2eeef", "default": true, "description": "New feature or request" } ]	closed	false	null	[]	null	[ "Hi! Non-generic data processing is out of this library's scope, so it's downstream libraries/users' responsibility to implement such logic.", "That's fair. Thanks" ]	"2024-01-17T17:51:40"	"2024-01-23T10:42:17"	"2024-01-22T15:35:49"	NONE	null	### Feature request Uploading datasets is straightforward thanks to the ability to push Audio to hub. However, it would be nice if the data (text and audio) could be segmented when being pushed (if not possible already). ### Motivation It's easy to create a vtt file from an audio file. If there could be auto-segmenting, this would make the creation of datasets much faster. ### Your contribution I have made a custom script to do this but it's not all that clean - uses librosa and pydub.	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6599/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6599/timeline	null	not_planned	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6598	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6598/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6598/comments	https://api.github.com/repos/huggingface/datasets/issues/6598/events	https://github.com/huggingface/datasets/issues/6598	2,084,236,605	I_kwDODunzps58Ou09	6,598	Unexpected keyword argument 'hf' when downloading CSV dataset from S3	{ "login": "dguenms", "id": 5592111, "node_id": "MDQ6VXNlcjU1OTIxMTE=", "avatar_url": "https://avatars.githubusercontent.com/u/5592111?v=4", "gravatar_id": "", "url": "https://api.github.com/users/dguenms", "html_url": "https://github.com/dguenms", "followers_url": "https://api.github.com/users/dguenms/followers", "following_url": "https://api.github.com/users/dguenms/following{/other_user}", "gists_url": "https://api.github.com/users/dguenms/gists{/gist_id}", "starred_url": "https://api.github.com/users/dguenms/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/dguenms/subscriptions", "organizations_url": "https://api.github.com/users/dguenms/orgs", "repos_url": "https://api.github.com/users/dguenms/repos", "events_url": "https://api.github.com/users/dguenms/events{/privacy}", "received_events_url": "https://api.github.com/users/dguenms/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	null	[ "I am facing similar issue while reading a csv file from s3. Wondering if somebody has found a workaround. ", "same thing happened to other formats like parquet", "I am facing similar issue while reading a parquet file from s3.\r\ni try with every version between 2.14 to 2.16.1 but it dosen't work ", "Re-define the DownloadConfig might work:\r\n\r\n```\r\nclass ReviseDownloadConfig(DownloadConfig):\r\n def __post_init__(self, use_auth_token):\r\n if use_auth_token != \"deprecated\":\r\n warnings.warn(\r\n \"'use_auth_token' was deprecated in favor of 'token' in version 2.14.0 and will be removed in 3.0.0.\\n\"\r\n f\"You can remove this warning by passing 'token={use_auth_token}' instead.\",\r\n FutureWarning,\r\n )\r\n self.token = use_auth_token\r\n\r\n def copy(self):\r\n return self.__class__({k: copy.deepcopy(v) for k, v in self.__dict__.items()})\r\n\r\ndownloadconfig = ReviseDownloadConfig()\r\n```\r\n", "> Re-define the DownloadConfig might work:\r\n> \r\n> ```\r\n> class ReviseDownloadConfig(DownloadConfig):\r\n> def __post_init__(self, use_auth_token):\r\n> if use_auth_token != \"deprecated\":\r\n> warnings.warn(\r\n> \"'use_auth_token' was deprecated in favor of 'token' in version 2.14.0 and will be removed in 3.0.0.\\n\"\r\n> f\"You can remove this warning by passing 'token={use_auth_token}' instead.\",\r\n> FutureWarning,\r\n> )\r\n> self.token = use_auth_token\r\n> ```\r\nThis seemed to work for me.\r\n", "use pandas and then convert to `Dataset`", "I am currently facing the same issue while using a custom loading script with files located in a remote S3 instance. I was using the `download_custom` functionality but now it is deprecated mentioning that I should use the native S3 loading, which is not working. \r\n\r\nAs stated before, the library forces the existence of a `hf` key in the `storage_options` variable, which is not accepted by `s3fs` : \r\n\r\n```python\r\n.../site-packages/s3fs/core.py\", line 516, in set_session\r\n self.session = aiobotocore.session.AioSession(self.kwargs)\r\nTypeError: __init__() got an unexpected keyword argument 'hf'.\r\n````\r\n\r\nMeanwhile, if my `storage_options` var stays like:\r\n```python\r\n{'key': '...',\r\n 'secret': '...',\r\n 'client_kwargs': {'endpoint_url': '...'}}\r\n```\r\nit works alright. " ]	"2024-01-16T15:16:01"	"2024-04-24T13:43:50"	null	NONE	null	### Describe the bug I receive this error message when using `load_dataset` with "csv" path and `dataset_files=s3://...`: ``` TypeError: Session.__init__() got an unexpected keyword argument 'hf' ``` I found a similar issue here: https://stackoverflow.com/questions/77596258/aws-issue-load-dataset-from-s3-fails-with-unexpected-keyword-argument-error-in Full stacktrace: ``` .../site-packages/datasets/load.py:2549: in load_dataset builder_instance.download_and_prepare( .../site-packages/datasets/builder.py:1005: in download_and_prepare self._download_and_prepare( .../site-packages/datasets/builder.py:1078: in _download_and_prepare split_generators = self._split_generators(dl_manager, split_generators_kwargs) .../site-packages/datasets/packaged_modules/csv/csv.py:147: in _split_generators data_files = dl_manager.download_and_extract(self.config.data_files) .../site-packages/datasets/download/download_manager.py:562: in download_and_extract return self.extract(self.download(url_or_urls)) .../site-packages/datasets/download/download_manager.py:426: in download downloaded_path_or_paths = map_nested( .../site-packages/datasets/utils/py_utils.py:466: in map_nested mapped = [ .../site-packages/datasets/utils/py_utils.py:467: in <listcomp> _single_map_nested((function, obj, types, None, True, None)) .../site-packages/datasets/utils/py_utils.py:387: in _single_map_nested mapped = [_single_map_nested((function, v, types, None, True, None)) for v in pbar] .../site-packages/datasets/utils/py_utils.py:387: in <listcomp> mapped = [_single_map_nested((function, v, types, None, True, None)) for v in pbar] .../site-packages/datasets/utils/py_utils.py:370: in _single_map_nested return function(data_struct) .../site-packages/datasets/download/download_manager.py:451: in _download out = cached_path(url_or_filename, download_config=download_config) .../site-packages/datasets/utils/file_utils.py:188: in cached_path output_path = get_from_cache( ...1/site-packages/datasets/utils/file_utils.py:511: in get_from_cache response = fsspec_head(url, storage_options=storage_options) .../site-packages/datasets/utils/file_utils.py:316: in fsspec_head fs, _, paths = fsspec.get_fs_token_paths(url, storage_options=storage_options) .../site-packages/fsspec/core.py:622: in get_fs_token_paths fs = filesystem(protocol, inkwargs) .../site-packages/fsspec/registry.py:290: in filesystem return cls(*storage_options) .../site-packages/fsspec/spec.py:79: in __call__ obj = super().__call__(args, kwargs) .../site-packages/s3fs/core.py:187: in __init__ self.s3 = self.connect() _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ self = <s3fs.core.S3FileSystem object at 0x1500a1310>, refresh = True def connect(self, refresh=True): """ Establish S3 connection object. Parameters ---------- refresh : bool Whether to create new session/client, even if a previous one with the same parameters already exists. If False (default), an existing one will be used if possible """ if refresh is False: # back compat: we store whole FS instance now return self.s3 anon, key, secret, kwargs, ckwargs, token, ssl = ( self.anon, self.key, self.secret, self.kwargs, self.client_kwargs, self.token, self.use_ssl) if not self.passed_in_session: > self.session = botocore.session.Session(self.kwargs) E TypeError: Session.__init__() got an unexpected keyword argument 'hf' ``` ### Steps to reproduce the bug 1. Assuming a valid CSV file located at `s3://bucket/data.csv` 2. Run the below code: ``` storage_options = { "key": "...", "secret": "...", "client_kwargs": { "endpoint_url": "...", } } load_dataset("csv", data_files="s3://bucket/data.csv", storage_options=storage_options) ``` Encountered in version `2.16.1` but also reproduced in `2.16.0` and `2.15.0`. Note: I encountered this in a unit test using a `moto` mock for S3, however since the error occurs before the session is instantiated, it should not be the issue. ### Expected behavior No exception is raised, the boto3 session is created successfully, and the CSV file is downloaded successfully and returned as a dataset. === After some research I found that `DownloadConfig` has a `__post_init__` method that always forces this value to be set in its `storage_options`, even though in case of an S3 location the storage options get passed on to the S3 Session which does not expect this parameter. I assume this parameter is needed when reading from the huggingface hub and should not be set in this context. Unfortunately there is nothing the user can do to work around it. Even if you manually do something like: ``` download_config = DownloadConfig() del download_config.storage_options["hf"] load_dataset("csv", data_files="s3://bucket/data.csv", download_config=download_config) ``` the library will still reinsert this parameter when `download_config = self.download_config.copy()` in line 418 of `download_manager.py` (`DownloadManager.download`). Therefore `load_dataset` currently cannot be used to read a dataset in CSV format from an S3 location. ### Environment info - `datasets` version: 2.16.1 - Platform: macOS-14.2.1-arm64-arm-64bit - Python version: 3.11.7 - `huggingface_hub` version: 0.20.2 - PyArrow version: 14.0.2 - Pandas version: 2.1.4 - `fsspec` version: 2023.10.0	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6598/reactions", "total_count": 4, "+1": 4, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6598/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6597	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6597/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6597/comments	https://api.github.com/repos/huggingface/datasets/issues/6597/events	https://github.com/huggingface/datasets/issues/6597	2,083,708,521	I_kwDODunzps58Mt5p	6,597	Dataset.push_to_hub of a canonical dataset creates an additional dataset under the user namespace	{ "login": "albertvillanova", "id": 8515462, "node_id": "MDQ6VXNlcjg1MTU0NjI=", "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "gravatar_id": "", "url": "https://api.github.com/users/albertvillanova", "html_url": "https://github.com/albertvillanova", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "repos_url": "https://api.github.com/users/albertvillanova/repos", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "type": "User", "site_admin": false }	[ { "id": 1935892857, "node_id": "MDU6TGFiZWwxOTM1ODkyODU3", "url": "https://api.github.com/repos/huggingface/datasets/labels/bug", "name": "bug", "color": "d73a4a", "default": true, "description": "Something isn't working" } ]	closed	false	{ "login": "albertvillanova", "id": 8515462, "node_id": "MDQ6VXNlcjg1MTU0NjI=", "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "gravatar_id": "", "url": "https://api.github.com/users/albertvillanova", "html_url": "https://github.com/albertvillanova", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "repos_url": "https://api.github.com/users/albertvillanova/repos", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "type": "User", "site_admin": false }	[ { "login": "albertvillanova", "id": 8515462, "node_id": "MDQ6VXNlcjg1MTU0NjI=", "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "gravatar_id": "", "url": "https://api.github.com/users/albertvillanova", "html_url": "https://github.com/albertvillanova", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "repos_url": "https://api.github.com/users/albertvillanova/repos", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "type": "User", "site_admin": false } ]	null	[ "It is caused by these code lines: https://github.com/huggingface/datasets/blob/9d6d16117a30ba345b0236407975f701c5b288d4/src/datasets/dataset_dict.py#L1688-L1694", "Also note the information in the docstring: https://github.com/huggingface/datasets/blob/9d6d16117a30ba345b0236407975f701c5b288d4/src/datasets/dataset_dict.py#L1582-L1585\r\n\r\n> Also accepts `<dataset_name>`, which will default to the namespace of the logged-in user.\r\n\r\nThis behavior was \"reverted\" by the PR: \r\n- #6519\r\n\r\nWe have therefore contradictory requirements. We should decide:\r\n- whether to support passing dataset_namespace without user/org that defaults to the logged-in user (and not support canonical datasets)\r\n- or vice-versa, to support canonical datasets and not support passing only dataset_name\r\n\r\nAs canonical datasets are \"deprecated\" (and will eventually disappear), I would choose the first option. However, if so, the Space to convert datasets to Parquet will not work for canonical datasets: https://huggingface.co/spaces/albertvillanova/convert-dataset-to-parquet", "IIUC, this could also be \"fixed\" by `create_repo(\"dataset_name\")` not defaulting to `create_repo(\"user/dataset_name\")` (when the user's token is available), which would be consistent with the rest of the `HfApi` ops used in the `push_to_hub` implementation. This is a (small) breaking change for `huggingface_hub`, but justified to make the API more consistent.", "I tag @Wauplin to have his opinion as well.", "Hmm, creating repo with implicit namespace (e.g. `create_repo(\"dataset_name\")`) is a convenient feature used in a lot of integrations. It is not consistent with other HfApi methods specifically because it is the method to create repos. Once the repo is created, the return value provides the explicit repo_id (`namespace/repo_name`) that has to be passed to every `HfApi` method. Otherwise, libraries/scripts would often need to do a `whoami` call to get the namespace before creating a repo.\r\n\r\n Another solution for https://github.com/huggingface/datasets/issues/6597#issuecomment-1893746690 could be that implicit namespace is allowed (same as today) except if the `repo_id` is in a hard-coded list of canonical datasets. This list can be maintained automatically and should be slowly decreasing. Caveat: as a normal user I wouldn't be able to implicitly push to `imagenet-1k` if I wanted to push to `Wauplin/imagenet-1k`. Shouldn't be too problematic, no? Worse case, would need to add a `whoami` call and allow implicit-canonical-name for non-HF users for instance (a bit too over-engineered IMO but doable). ", "As canonical datasets are going to disappear in the following couple of months, I would not make any effort on their support.\r\n\r\nI propose reverting #6519, so that the behavior of `push_to_hub` is aligned with the one described in its dosctring: \"Also accepts `<dataset_name>`, which will default to the namespace of the logged-in user.\"\r\n\r\nI'm opening a PR." ]	"2024-01-16T11:27:07"	"2024-02-05T12:29:37"	"2024-02-05T12:29:37"	MEMBER	null	While using `Dataset.push_to_hub` of a canonical dataset, an additional dataset was created under my user namespace. ## Steps to reproduce the bug The command: ```python commit_info = ds.push_to_hub( "caner", config_name="default", commit_message="Convert dataset to Parquet", commit_description="Convert dataset to Parquet.", create_pr=True, token=token, ) ``` creates the additional dataset `albertvillanova/caner`.	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6597/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6597/timeline	null	completed	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6596	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6596/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6596/comments	https://api.github.com/repos/huggingface/datasets/issues/6596/events	https://github.com/huggingface/datasets/pull/6596	2,083,108,156	PR_kwDODunzps5kJceH	6,596	Drop redundant None guard.	{ "login": "xkszltl", "id": 5203025, "node_id": "MDQ6VXNlcjUyMDMwMjU=", "avatar_url": "https://avatars.githubusercontent.com/u/5203025?v=4", "gravatar_id": "", "url": "https://api.github.com/users/xkszltl", "html_url": "https://github.com/xkszltl", "followers_url": "https://api.github.com/users/xkszltl/followers", "following_url": "https://api.github.com/users/xkszltl/following{/other_user}", "gists_url": "https://api.github.com/users/xkszltl/gists{/gist_id}", "starred_url": "https://api.github.com/users/xkszltl/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/xkszltl/subscriptions", "organizations_url": "https://api.github.com/users/xkszltl/orgs", "repos_url": "https://api.github.com/users/xkszltl/repos", "events_url": "https://api.github.com/users/xkszltl/events{/privacy}", "received_events_url": "https://api.github.com/users/xkszltl/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6596). 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) \| 0.004768 / 0.011353 (-0.006585) \| 0.003084 / 0.011008 (-0.007924) \| 0.062775 / 0.038508 (0.024267) \| 0.029909 / 0.023109 (0.006800) \| 0.242905 / 0.275898 (-0.032993) \| 0.265609 / 0.323480 (-0.057871) \| 0.003856 / 0.007986 (-0.004130) \| 0.002610 / 0.004328 (-0.001718) \| 0.048631 / 0.004250 (0.044381) \| 0.040464 / 0.037052 (0.003412) \| 0.256023 / 0.258489 (-0.002467) \| 0.285914 / 0.293841 (-0.007927) \| 0.027305 / 0.128546 (-0.101241) \| 0.010345 / 0.075646 (-0.065301) \| 0.206264 / 0.419271 (-0.213008) \| 0.035290 / 0.043533 (-0.008243) \| 0.247785 / 0.255139 (-0.007353) \| 0.267053 / 0.283200 (-0.016147) \| 0.017910 / 0.141683 (-0.123773) \| 1.166096 / 1.452155 (-0.286059) \| 1.210717 / 1.492716 (-0.281999) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.095759 / 0.018006 (0.077753) \| 0.311030 / 0.000490 (0.310540) \| 0.000234 / 0.000200 (0.000034) \| 0.000042 / 0.000054 (-0.000013) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.017828 / 0.037411 (-0.019583) \| 0.060123 / 0.014526 (0.045597) \| 0.071947 / 0.176557 (-0.104610) \| 0.119353 / 0.737135 (-0.617782) \| 0.073529 / 0.296338 (-0.222809) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.282737 / 0.215209 (0.067528) \| 2.761914 / 2.077655 (0.684260) \| 1.480310 / 1.504120 (-0.023810) \| 1.329977 / 1.541195 (-0.211218) \| 1.332686 / 1.468490 (-0.135804) \| 0.566309 / 4.584777 (-4.018468) \| 2.361838 / 3.745712 (-1.383874) \| 2.775613 / 5.269862 (-2.494249) \| 1.744985 / 4.565676 (-2.820692) \| 0.063038 / 0.424275 (-0.361237) \| 0.004969 / 0.007607 (-0.002638) \| 0.335543 / 0.226044 (0.109499) \| 3.293779 / 2.268929 (1.024851) \| 1.816093 / 55.444624 (-53.628532) \| 1.562658 / 6.876477 (-5.313819) \| 1.544888 / 2.142072 (-0.597185) \| 0.641762 / 4.805227 (-4.163465) \| 0.117904 / 6.500664 (-6.382760) \| 0.042534 / 0.075469 (-0.032935) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.935577 / 1.841788 (-0.906211) \| 11.565833 / 8.074308 (3.491525) \| 10.314723 / 10.191392 (0.123331) \| 0.138912 / 0.680424 (-0.541512) \| 0.013968 / 0.534201 (-0.520233) \| 0.296270 / 0.579283 (-0.283013) \| 0.266106 / 0.434364 (-0.168258) \| 0.334729 / 0.540337 (-0.205609) \| 0.443191 / 1.386936 (-0.943745) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004865 / 0.011353 (-0.006488) \| 0.003523 / 0.011008 (-0.007485) \| 0.049303 / 0.038508 (0.010795) \| 0.029252 / 0.023109 (0.006143) \| 0.271288 / 0.275898 (-0.004610) \| 0.290529 / 0.323480 (-0.032951) \| 0.003982 / 0.007986 (-0.004004) \| 0.002740 / 0.004328 (-0.001589) \| 0.048513 / 0.004250 (0.044262) \| 0.044473 / 0.037052 (0.007420) \| 0.282072 / 0.258489 (0.023583) \| 0.311321 / 0.293841 (0.017480) \| 0.028825 / 0.128546 (-0.099721) \| 0.010311 / 0.075646 (-0.065335) \| 0.057071 / 0.419271 (-0.362200) \| 0.052629 / 0.043533 (0.009097) \| 0.273134 / 0.255139 (0.017995) \| 0.290989 / 0.283200 (0.007789) \| 0.018074 / 0.141683 (-0.123609) \| 1.171724 / 1.452155 (-0.280431) \| 1.236178 / 1.492716 (-0.256538) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.097099 / 0.018006 (0.079093) \| 0.309788 / 0.000490 (0.309298) \| 0.000221 / 0.000200 (0.000021) \| 0.000053 / 0.000054 (-0.000001) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021703 / 0.037411 (-0.015708) \| 0.076104 / 0.014526 (0.061578) \| 0.088202 / 0.176557 (-0.088355) \| 0.127351 / 0.737135 (-0.609784) \| 0.089754 / 0.296338 (-0.206585) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.294574 / 0.215209 (0.079365) \| 2.851581 / 2.077655 (0.773926) \| 1.599117 / 1.504120 (0.094997) \| 1.476183 / 1.541195 (-0.065012) \| 1.512309 / 1.468490 (0.043819) \| 0.559785 / 4.584777 (-4.024992) \| 2.453287 / 3.745712 (-1.292425) \| 2.660101 / 5.269862 (-2.609760) \| 1.743043 / 4.565676 (-2.822633) \| 0.063450 / 0.424275 (-0.360825) \| 0.005019 / 0.007607 (-0.002589) \| 0.351507 / 0.226044 (0.125462) \| 3.431587 / 2.268929 (1.162658) \| 1.943349 / 55.444624 (-53.501275) \| 1.658706 / 6.876477 (-5.217771) \| 1.780042 / 2.142072 (-0.362030) \| 0.641364 / 4.805227 (-4.163863) \| 0.118052 / 6.500664 (-6.382612) \| 0.040961 / 0.075469 (-0.034508) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.974219 / 1.841788 (-0.867568) \| 12.257824 / 8.074308 (4.183516) \| 10.821225 / 10.191392 (0.629833) \| 0.139399 / 0.680424 (-0.541025) \| 0.015277 / 0.534201 (-0.518924) \| 0.286975 / 0.579283 (-0.292309) \| 0.283419 / 0.434364 (-0.150945) \| 0.324299 / 0.540337 (-0.216039) \| 0.424538 / 1.386936 (-0.962398) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f2ba3f30bae5ff75ac48f0e653240b924d7982d5 \"CML watermark\")\n" ]	"2024-01-16T06:31:54"	"2024-01-16T17:16:16"	"2024-01-16T17:05:52"	CONTRIBUTOR	null	`xxx if xxx is not None else None` is no-op.	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6596/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6596/timeline	null	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6596", "html_url": "https://github.com/huggingface/datasets/pull/6596", "diff_url": "https://github.com/huggingface/datasets/pull/6596.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6596.patch", "merged_at": "2024-01-16T17:05:52" }	true
https://api.github.com/repos/huggingface/datasets/issues/6595	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6595/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6595/comments	https://api.github.com/repos/huggingface/datasets/issues/6595/events	https://github.com/huggingface/datasets/issues/6595	2,082,896,148	I_kwDODunzps58JnkU	6,595	Loading big dataset raises pyarrow.lib.ArrowNotImplementedError 2	{ "login": "kopyl", "id": 17604849, "node_id": "MDQ6VXNlcjE3NjA0ODQ5", "avatar_url": "https://avatars.githubusercontent.com/u/17604849?v=4", "gravatar_id": "", "url": "https://api.github.com/users/kopyl", "html_url": "https://github.com/kopyl", "followers_url": "https://api.github.com/users/kopyl/followers", "following_url": "https://api.github.com/users/kopyl/following{/other_user}", "gists_url": "https://api.github.com/users/kopyl/gists{/gist_id}", "starred_url": "https://api.github.com/users/kopyl/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/kopyl/subscriptions", "organizations_url": "https://api.github.com/users/kopyl/orgs", "repos_url": "https://api.github.com/users/kopyl/repos", "events_url": "https://api.github.com/users/kopyl/events{/privacy}", "received_events_url": "https://api.github.com/users/kopyl/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "Hi ! I think the issue comes from the \"float16\" features that are not supported yet in Parquet\r\n\r\nFeel free to open an issue in `pyarrow` about this. In the meantime, I'd encourage you to use \"float32\" for your \"pooled_prompt_embeds\" and \"prompt_embeds\" features.\r\n\r\nYou can cast them to \"float32\" using\r\n\r\n```python\r\nfrom datasets import Value\r\n\r\nds = ds.cast_column(\"pooled_prompt_embeds\", Value(\"float32\"))\r\nds = ds.cast_column(\"prompt_embeds\", Value(\"float32\"))\r\n```", "@lhoestq hm. Thank you very much.\r\n\r\nDo you think it won't have any impact on the training? That it won't break it or the quality won't degrade because of this?\r\n\r\nI need to use it for [SDXL training](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image_sdxl.py)", "Increasing the precision should not degrade training (it only increases the precision), but make sure that it doesn't break your pytorch code (e.g. if it expects a float16 instead of a float32 somewhere)", "@lhoestq just fyi pyarrow 15.0.0 (just released) supports float16 as the underlying parquetcpp does as well now :)", "Oh that's amazing ! (and great timing ^^)\r\n\r\n@kopyl can you try to update `pyarrow` and try again ?\r\n\r\nBtw @assignUser there seems to be some casting implementations missing with float16 in 15.0.0, e.g.\r\n\r\n```\r\nArrowNotImplementedError: Unsupported cast from int64 to halffloat using function cast_half_float\r\n```\r\n\r\n```\r\nArrowNotImplementedError: Unsupported cast from double to halffloat using function cast_half_float\r\n```", "Ah you are right casting is not implemented yet, it's even mentioned in the docs. This pr references the relevant issues if you'd like to track them\nhttps://github.com/apache/arrow/pull/38494", "Cool thank you :)", "@lhoestq i just recently found out that it's supported in 15.0.0, but wanted to try it first before telling you...\r\n\r\nTrying this right now and it seemingly works (although i need to wait till the end to make sure there is nothing wrong). Will update you when it's finished.\r\n\r\n<img width=\"918\" alt=\"image\" src=\"https://github.com/huggingface/datasets/assets/17604849/4821e215-e782-4736-8c76-d06187078175\">\r\n\r\nA couple of questions though:\r\n\r\n1. What does that missing casting implementation mean for my specific case and what does it mean in general?\r\n2. Do you know how to `push_to_hub` with multiple processes?", "@lhoestq also it's strange that there was no error for a dataset with the same features, same data type, but smaller (much smaller).\r\n\r\nAltho i'm not sure about this, but chances are the dataset was loaded directly, not `load_from_disk`.... Maybe because of this.", "> What does that missing casting implementation mean for my specific case and what does it mean in general?\r\n\r\nNothing for you, just that casting to float16 using `.cast_column(\"my_column_name\", Value(\"float16\"))` raises an error\r\n\r\n> Do you know how to push_to_hub with multiple processes?\r\n\r\nIt's not possible (yet ?). Mostly because we haven't implemented yet how to do parallel uploads to the Hub from `datasets`.\r\nThough if you want faster uploads you can already enable `hf_transfer` \r\n\r\n```\r\npip install hf_transfer\r\n```\r\n\r\nand setting `HF_HUB_ENABLE_HF_TRANSFER=1` as an environment variable\r\n\r\nsee https://huggingface.co/docs/huggingface_hub/guides/upload#tips-and-tricks-for-large-uploads", "@lhoestq thank you very much.\r\n\r\nThat would be amazing, I need to create a feature request for this :)\r\n\r\nBy the way, in short, how does hf_transfer improves the upload speed under the hood?", "@lhoestq i was just able to successfully upload without the dataset with the new pyarrow update and without increasing the precision :)", "Awesome !\r\n\r\nRegarding hf_transfer: it's been optimized in rust ;)", "@lhoestq wow, cool :)" ]	"2024-01-16T02:03:09"	"2024-01-27T18:26:33"	"2024-01-26T02:28:32"	NONE	null	### Describe the bug I'm aware of the issue #5695 . I'm using a modified SDXL trainer: https://github.com/kopyl/diffusers/blob/5e70f604155aeecee254a5c63c5e4236ad4a0d3d/examples/text_to_image/train_text_to_image_sdxl.py#L1027C16-L1027C16 So i 1. Map dataset 2. Save to disk 3. Try to upload: ``` import datasets from datasets import load_from_disk dataset = load_from_disk("ds") datasets.config.DEFAULT_MAX_BATCH_SIZE = 1 dataset.push_to_hub("kopyl/ds", private=True, max_shard_size="500MB") ``` And i get this error: `pyarrow.lib.ArrowNotImplementedError: Unhandled type for Arrow to Parquet schema conversion: halffloat` Full traceback: ``` >>> dataset.push_to_hub("kopyl/3M_icons_monochrome_only_no_captioning_mapped-for-SDXL-2", private=True, max_shard_size="500MB") Map: 100%\|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\| 1451/1451 [00:00<00:00, 6827.40 examples/s] Uploading the dataset shards: 0%\| \| 0/2099 [00:00<?, ?it/s] Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/usr/local/lib/python3.10/dist-packages/datasets/dataset_dict.py", line 1705, in push_to_hub split_additions, uploaded_size, dataset_nbytes = self[split]._push_parquet_shards_to_hub( File "/usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py", line 5208, in _push_parquet_shards_to_hub shard.to_parquet(buffer) File "/usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py", line 4931, in to_parquet return ParquetDatasetWriter(self, path_or_buf, batch_size=batch_size, parquet_writer_kwargs).write() File "/usr/local/lib/python3.10/dist-packages/datasets/io/parquet.py", line 129, in write written = self._write(file_obj=self.path_or_buf, batch_size=batch_size, self.parquet_writer_kwargs) File "/usr/local/lib/python3.10/dist-packages/datasets/io/parquet.py", line 141, in _write writer = pq.ParquetWriter(file_obj, schema=schema, *parquet_writer_kwargs) File "/usr/local/lib/python3.10/dist-packages/pyarrow/parquet/core.py", line 1016, in __init__ self.writer = _parquet.ParquetWriter( File "pyarrow/_parquet.pyx", line 1869, in pyarrow._parquet.ParquetWriter.__cinit__ 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.ArrowNotImplementedError: Unhandled type for Arrow to Parquet schema conversion: halffloat ``` Smaller datasets with the same way of saving and pushing work wonders. Big ones are not. I'm currently trying to upload dataset like this: `HfApi().upload_folder...` But i'm not sure that in this case "load_dataset" would work well. This setting num_shards does not help too: ``` dataset.push_to_hub("kopyl/3M_icons_monochrome_only_no_captioning_mapped-for-SDXL-2", private=True, num_shards={'train': 500}) ``` Tried 3000, 500, 478, 100 Also do you know if it's possible to push a dataset with multiple processes? It would take an eternity pushing 1TB... ### Steps to reproduce the bug Described above ### Expected behavior Should be able to upload... ### Environment info Total dataset size: 978G Amount of `.arrow` files: 2101 Each `.arrow` file size: 477M (i know 477 megabytes 2101 does not equal 978G, but i just checked the size of a couple `.arrow` files, i don't know if some might have different size) Some files: - "ds/train/state.json": https://pastebin.com/tJ3ZLGAg - "ds/train/dataset_info.json": https://pastebin.com/JdXMQ5ih	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6595/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6595/timeline	null	completed	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6594	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6594/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6594/comments	https://api.github.com/repos/huggingface/datasets/issues/6594/events	https://github.com/huggingface/datasets/issues/6594	2,082,748,275	I_kwDODunzps58JDdz	6,594	IterableDataset sharding logic needs improvement	{ "login": "rwightman", "id": 5702664, "node_id": "MDQ6VXNlcjU3MDI2NjQ=", "avatar_url": "https://avatars.githubusercontent.com/u/5702664?v=4", "gravatar_id": "", "url": "https://api.github.com/users/rwightman", "html_url": "https://github.com/rwightman", "followers_url": "https://api.github.com/users/rwightman/followers", "following_url": "https://api.github.com/users/rwightman/following{/other_user}", "gists_url": "https://api.github.com/users/rwightman/gists{/gist_id}", "starred_url": "https://api.github.com/users/rwightman/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/rwightman/subscriptions", "organizations_url": "https://api.github.com/users/rwightman/orgs", "repos_url": "https://api.github.com/users/rwightman/repos", "events_url": "https://api.github.com/users/rwightman/events{/privacy}", "received_events_url": "https://api.github.com/users/rwightman/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	null	[]	"2024-01-15T22:22:36"	"2024-01-15T22:25:10"	null	NONE	null	### Describe the bug The sharding of IterableDatasets with respect to distributed and dataloader worker processes appears problematic with significant performance traps and inconsistencies wrt to distributed train processes vs worker processes. Splitting across num_workers (per train process loader processes) and world_size (distributed training processes) appears inconsistent. * worker split: https://github.com/huggingface/datasets/blob/9d6d16117a30ba345b0236407975f701c5b288d4/src/datasets/iterable_dataset.py#L1266-L1283 * distributed split: https://github.com/huggingface/datasets/blob/9d6d16117a30ba345b0236407975f701c5b288d4/src/datasets/iterable_dataset.py#L1335-L1356 In the case of the distributed split, there is a modulus check that flips between two very different behaviours, why is this different than splitting across the data loader workers? For IterableDatasets the DataLoaders worker processes are independent, so whether it's workers within one train process or across a distributed world the shards should be distributed the same, across `world_size * num_worker` independent workers in either case... Further, the fallback case when the `n_shards % world_size == 0` check fails is a rather extreme change. I argue it is not desirable to do that implicitly, it should be an explicit case for specific scenarios (ie reliable validation). A train scenario would likely be much better handled with improved wrapping / stopping behaviour to eg also fix #6437. Changing from stepping shards to stepping samples means that every single process reads ALL of the shards. This was never an intended default for sharded training, shards gain their performance advantage in large scale distributed training by explicitly avoiding the need to have every process overlapping in the data they read, by default, only the data allocated to each process via their assigned shards should be read in each pass of the dataset. Using a large scale CLIP example, some of the larger datasets have 10-20k shards across 100+TB of data. Training with 1000 GPUs we are switching between reading 100 terabytes per epoch to 100 petabytes if say change 20k % 1000 and drop one gpu-node to 20k % 992. The 'step over samples' case might be worth the overhead in specific validation scenarios where gaurantees of at least/most once samples seen are more important and do not make up a significant portion of train time or are done in smaller world sizes outside of train. ### Steps to reproduce the bug N/A ### Expected behavior We have an iterable dataset with N shards, to split across workers * shuffle shards (same seed across all train processes) * step shard iterator across distributed processes * step shard iterator across dataloader worker processes * shuffle samples in every worker via shuffle buffer (different seed in each worker, but ideally controllable (based on base seed + worker id + epoch). * end up with (possibly uneven) number of shards per worker but each shard only ever accessed by 1 worker per pass (epoch) ### Environment info N/A	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6594/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6594/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6592	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6592/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6592/comments	https://api.github.com/repos/huggingface/datasets/issues/6592/events	https://github.com/huggingface/datasets/issues/6592	2,082,410,257	I_kwDODunzps58Hw8R	6,592	Logs are delayed when doing .map when `docker logs`	{ "login": "kopyl", "id": 17604849, "node_id": "MDQ6VXNlcjE3NjA0ODQ5", "avatar_url": "https://avatars.githubusercontent.com/u/17604849?v=4", "gravatar_id": "", "url": "https://api.github.com/users/kopyl", "html_url": "https://github.com/kopyl", "followers_url": "https://api.github.com/users/kopyl/followers", "following_url": "https://api.github.com/users/kopyl/following{/other_user}", "gists_url": "https://api.github.com/users/kopyl/gists{/gist_id}", "starred_url": "https://api.github.com/users/kopyl/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/kopyl/subscriptions", "organizations_url": "https://api.github.com/users/kopyl/orgs", "repos_url": "https://api.github.com/users/kopyl/repos", "events_url": "https://api.github.com/users/kopyl/events{/privacy}", "received_events_url": "https://api.github.com/users/kopyl/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "Hi! `tqdm` doesn't work well in non-interactive environments, so there isn't much we can do about this. It's best to [disable it](https://huggingface.co/docs/datasets/v2.16.1/en/package_reference/utilities#datasets.disable_progress_bars) in such environments and instead use logging to track progress." ]	"2024-01-15T17:05:21"	"2024-02-12T17:35:21"	"2024-02-12T17:35:21"	NONE	null	### Describe the bug When I run my SD training in a Docker image and then listen to logs like `docker logs train -f`, the progress bar is delayed. It's updating every few percent. When you have a large dataset that has to be mapped (like 1+ million samples), it's crucial to see the updates in real-time, not every couple hours to make sure nothing got frozen or broken ### Steps to reproduce the bug 1. Run any huge dataset processing as a Docker image 2. `docker logs image_name` to it ### Expected behavior ... ### Environment info ...	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6592/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6592/timeline	null	not_planned	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6591	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6591/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6591/comments	https://api.github.com/repos/huggingface/datasets/issues/6591/events	https://github.com/huggingface/datasets/issues/6591	2,082,378,957	I_kwDODunzps58HpTN	6,591	The datasets models housed in Dropbox can't support a lot of users downloading them	{ "login": "RDaneelOlivav", "id": 4933774, "node_id": "MDQ6VXNlcjQ5MzM3NzQ=", "avatar_url": "https://avatars.githubusercontent.com/u/4933774?v=4", "gravatar_id": "", "url": "https://api.github.com/users/RDaneelOlivav", "html_url": "https://github.com/RDaneelOlivav", "followers_url": "https://api.github.com/users/RDaneelOlivav/followers", "following_url": "https://api.github.com/users/RDaneelOlivav/following{/other_user}", "gists_url": "https://api.github.com/users/RDaneelOlivav/gists{/gist_id}", "starred_url": "https://api.github.com/users/RDaneelOlivav/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/RDaneelOlivav/subscriptions", "organizations_url": "https://api.github.com/users/RDaneelOlivav/orgs", "repos_url": "https://api.github.com/users/RDaneelOlivav/repos", "events_url": "https://api.github.com/users/RDaneelOlivav/events{/privacy}", "received_events_url": "https://api.github.com/users/RDaneelOlivav/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "Hi! Indeed, Dropbox is not a reliable host. I've just merged https://huggingface.co/datasets/PolyAI/minds14/discussions/24 to fix this by hosting the data files inside the repo." ]	"2024-01-15T16:43:38"	"2024-01-22T23:18:09"	"2024-01-22T23:18:09"	NONE	null	### Describe the bug I'm using the datasets ``` from datasets import load_dataset, Audio dataset = load_dataset("PolyAI/minds14", name="en-US", split="train") ``` And it seems that sometimes when I imagine a lot of users are accessing the same resources, the Dropbox host fails: `raise ConnectionError(f"Couldn't reach {url} (error {response.status_code})") ConnectionError: Couldn't reach https://www.dropbox.com/s/e2us0hcs3ilr20e/MInDS-14.zip?dl=1 (error 429)` My question is if we can somehow host these files elsewhere or can you change the limit of simultaneous users accessing those resources or any other solution? Also, has anyone had this issue before? Thanks ### Steps to reproduce the bug 1: Create a python script like so: ``` from datasets import load_dataset, Audio dataset = load_dataset("PolyAI/minds14", name="en-US", split="train") ``` 2: Execute this by a certain number of users at the same time ### Expected behavior I woudl expect that this shouldnt happen unless its a huge amount of users, which it is not the case ### Environment info This was done in an Ubuntu 22 environment.	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6591/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6591/timeline	null	completed	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6590	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6590/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6590/comments	https://api.github.com/repos/huggingface/datasets/issues/6590/events	https://github.com/huggingface/datasets/issues/6590	2,082,000,084	I_kwDODunzps58GMzU	6,590	Feature request: Multi-GPU dataset mapping for SDXL training	{ "login": "kopyl", "id": 17604849, "node_id": "MDQ6VXNlcjE3NjA0ODQ5", "avatar_url": "https://avatars.githubusercontent.com/u/17604849?v=4", "gravatar_id": "", "url": "https://api.github.com/users/kopyl", "html_url": "https://github.com/kopyl", "followers_url": "https://api.github.com/users/kopyl/followers", "following_url": "https://api.github.com/users/kopyl/following{/other_user}", "gists_url": "https://api.github.com/users/kopyl/gists{/gist_id}", "starred_url": "https://api.github.com/users/kopyl/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/kopyl/subscriptions", "organizations_url": "https://api.github.com/users/kopyl/orgs", "repos_url": "https://api.github.com/users/kopyl/repos", "events_url": "https://api.github.com/users/kopyl/events{/privacy}", "received_events_url": "https://api.github.com/users/kopyl/received_events", "type": "User", "site_admin": false }	[ { "id": 1935892871, "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement", "name": "enhancement", "color": "a2eeef", "default": true, "description": "New feature or request" } ]	open	false	null	[]	null	[]	"2024-01-15T13:06:06"	"2024-01-15T13:07:07"	null	NONE	null	### Feature request We need to speed up SDXL dataset pre-process. Please make it possible to use multiple GPUs for the [official SDXL trainer](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image_sdxl.py) :) ### Motivation Pre-computing 3 million of images takes around 2 days. Would be nice to be able to be able to do multi-GPU (or even better – multi-GPU + multi-node) vae and embedding precompute... ### Your contribution I'm not sure i can wrap my head around the multi-GPU mapping... Plus it's too expensive for me to take x2 A100 and spend a day just figuring out the staff since I don't have a job right now.	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6590/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6590/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6589	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6589/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6589/comments	https://api.github.com/repos/huggingface/datasets/issues/6589/events	https://github.com/huggingface/datasets/issues/6589	2,081,358,619	I_kwDODunzps58DwMb	6,589	After `2.16.0` version, there are `PermissionError` when users use shared cache_dir	{ "login": "minhopark-neubla", "id": 106717516, "node_id": "U_kgDOBlxhTA", "avatar_url": "https://avatars.githubusercontent.com/u/106717516?v=4", "gravatar_id": "", "url": "https://api.github.com/users/minhopark-neubla", "html_url": "https://github.com/minhopark-neubla", "followers_url": "https://api.github.com/users/minhopark-neubla/followers", "following_url": "https://api.github.com/users/minhopark-neubla/following{/other_user}", "gists_url": "https://api.github.com/users/minhopark-neubla/gists{/gist_id}", "starred_url": "https://api.github.com/users/minhopark-neubla/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/minhopark-neubla/subscriptions", "organizations_url": "https://api.github.com/users/minhopark-neubla/orgs", "repos_url": "https://api.github.com/users/minhopark-neubla/repos", "events_url": "https://api.github.com/users/minhopark-neubla/events{/privacy}", "received_events_url": "https://api.github.com/users/minhopark-neubla/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "We'll do a new release of `datasets` in the coming days with a fix !", "@lhoestq Thank you very much!" ]	"2024-01-15T06:46:27"	"2024-02-02T07:55:38"	"2024-01-30T15:28:38"	NONE	null	### Describe the bug - We use shared `cache_dir` using `HF_HOME="{shared_directory}"` - After dataset version 2.16.0, datasets uses `filelock` package for file locking #6445 - But, `filelock` package make `.lock` file with `644` permission - Dataset is not available to other users except the user who created the lock file via `load_dataset`. ### Steps to reproduce the bug 1. `pip install datasets==2.16.0` 2. `export HF_HOME="{shared_directory}"` 3. download dataset with `load_dataset` 4. logout and login another user 5. `pip install datasets==2.16.0` 6. `export HF_HOME="{shared_directory}"` 7. download dataset with `load_dataset` 8. `PermissionError` occurs ### Expected behavior - Users can share `cache_dir` using environment variable `HF_HOME` ### Environment info - python == 3.9.10 - datasets == 2.16.0 - ubuntu 22.04 - shared_directory has ACL ![image (1)](https://github.com/huggingface/datasets/assets/106717516/5ca759db-ad0c-4883-9a97-9c8fccd00d8a) - users are same group (developers)	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6589/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6589/timeline	null	completed	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6588	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6588/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6588/comments	https://api.github.com/repos/huggingface/datasets/issues/6588/events	https://github.com/huggingface/datasets/issues/6588	2,081,284,253	I_kwDODunzps58DeCd	6,588	fix os.listdir return name is empty string	{ "login": "d710055071", "id": 12895488, "node_id": "MDQ6VXNlcjEyODk1NDg4", "avatar_url": "https://avatars.githubusercontent.com/u/12895488?v=4", "gravatar_id": "", "url": "https://api.github.com/users/d710055071", "html_url": "https://github.com/d710055071", "followers_url": "https://api.github.com/users/d710055071/followers", "following_url": "https://api.github.com/users/d710055071/following{/other_user}", "gists_url": "https://api.github.com/users/d710055071/gists{/gist_id}", "starred_url": "https://api.github.com/users/d710055071/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/d710055071/subscriptions", "organizations_url": "https://api.github.com/users/d710055071/orgs", "repos_url": "https://api.github.com/users/d710055071/repos", "events_url": "https://api.github.com/users/d710055071/events{/privacy}", "received_events_url": "https://api.github.com/users/d710055071/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[]	"2024-01-15T05:34:36"	"2024-01-24T10:08:29"	"2024-01-24T10:08:29"	CONTRIBUTOR	null	### Describe the bug xlistdir return name is empty string Overloaded os.listdir ### Steps to reproduce the bug ```python from datasets.download.streaming_download_manager import xjoin from datasets.download.streaming_download_manager import xlistdir config = DownloadConfig(storage_options=options) manger = StreamingDownloadManager("ILSVRC2012",download_config=config) input_path = "lakefs://datalab/main/imagenet/ILSVRC2012.zip" download_files = manger.download_and_extract(input_path) current_dir = xjoin(download_files,"ILSVRC2012/Images/ILSVRC2012_img_train") folder_list = xlistdir(current_dir) ``` in xlistdir function Obj ["name"] ends with "/" last return "" ### Expected behavior Obj ["name"] ends with "/" return folder name ### Environment info no	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6588/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6588/timeline	null	completed	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6587	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6587/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6587/comments	https://api.github.com/repos/huggingface/datasets/issues/6587/events	https://github.com/huggingface/datasets/pull/6587	2,080,348,016	PR_kwDODunzps5kAT_5	6,587	Allow concatenation of datasets with mixed structs	{ "login": "Dref360", "id": 8976546, "node_id": "MDQ6VXNlcjg5NzY1NDY=", "avatar_url": "https://avatars.githubusercontent.com/u/8976546?v=4", "gravatar_id": "", "url": "https://api.github.com/users/Dref360", "html_url": "https://github.com/Dref360", "followers_url": "https://api.github.com/users/Dref360/followers", "following_url": "https://api.github.com/users/Dref360/following{/other_user}", "gists_url": "https://api.github.com/users/Dref360/gists{/gist_id}", "starred_url": "https://api.github.com/users/Dref360/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/Dref360/subscriptions", "organizations_url": "https://api.github.com/users/Dref360/orgs", "repos_url": "https://api.github.com/users/Dref360/repos", "events_url": "https://api.github.com/users/Dref360/events{/privacy}", "received_events_url": "https://api.github.com/users/Dref360/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6587). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "friendly bump", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005403 / 0.011353 (-0.005950) \| 0.003807 / 0.011008 (-0.007201) \| 0.063850 / 0.038508 (0.025342) \| 0.028242 / 0.023109 (0.005132) \| 0.242866 / 0.275898 (-0.033032) \| 0.266015 / 0.323480 (-0.057464) \| 0.004111 / 0.007986 (-0.003875) \| 0.002816 / 0.004328 (-0.001513) \| 0.048862 / 0.004250 (0.044611) \| 0.043036 / 0.037052 (0.005984) \| 0.255149 / 0.258489 (-0.003340) \| 0.280105 / 0.293841 (-0.013736) \| 0.028182 / 0.128546 (-0.100365) \| 0.010997 / 0.075646 (-0.064649) \| 0.208131 / 0.419271 (-0.211141) \| 0.036030 / 0.043533 (-0.007502) \| 0.241551 / 0.255139 (-0.013588) \| 0.260741 / 0.283200 (-0.022459) \| 0.018045 / 0.141683 (-0.123638) \| 1.175308 / 1.452155 (-0.276847) \| 1.192160 / 1.492716 (-0.300556) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094579 / 0.018006 (0.076573) \| 0.309850 / 0.000490 (0.309360) \| 0.000232 / 0.000200 (0.000032) \| 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) \| 0.019519 / 0.037411 (-0.017892) \| 0.062201 / 0.014526 (0.047675) \| 0.074017 / 0.176557 (-0.102539) \| 0.121987 / 0.737135 (-0.615148) \| 0.078958 / 0.296338 (-0.217380) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.286306 / 0.215209 (0.071097) \| 2.777004 / 2.077655 (0.699350) \| 1.481445 / 1.504120 (-0.022675) \| 1.348643 / 1.541195 (-0.192552) \| 1.382257 / 1.468490 (-0.086234) \| 0.571436 / 4.584777 (-4.013341) \| 2.373279 / 3.745712 (-1.372433) \| 2.749366 / 5.269862 (-2.520496) \| 1.724937 / 4.565676 (-2.840739) \| 0.062233 / 0.424275 (-0.362042) \| 0.005013 / 0.007607 (-0.002594) \| 0.339623 / 0.226044 (0.113579) \| 3.385770 / 2.268929 (1.116842) \| 1.832023 / 55.444624 (-53.612601) \| 1.556172 / 6.876477 (-5.320305) \| 1.573301 / 2.142072 (-0.568772) \| 0.648866 / 4.805227 (-4.156361) \| 0.121228 / 6.500664 (-6.379436) \| 0.041684 / 0.075469 (-0.033786) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.974595 / 1.841788 (-0.867192) \| 11.519692 / 8.074308 (3.445383) \| 9.773075 / 10.191392 (-0.418317) \| 0.138149 / 0.680424 (-0.542274) \| 0.014068 / 0.534201 (-0.520133) \| 0.288161 / 0.579283 (-0.291122) \| 0.272832 / 0.434364 (-0.161532) \| 0.324476 / 0.540337 (-0.215862) \| 0.419962 / 1.386936 (-0.966974) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005668 / 0.011353 (-0.005685) \| 0.003637 / 0.011008 (-0.007371) \| 0.049582 / 0.038508 (0.011074) \| 0.030982 / 0.023109 (0.007872) \| 0.273036 / 0.275898 (-0.002862) \| 0.297562 / 0.323480 (-0.025918) \| 0.004382 / 0.007986 (-0.003603) \| 0.002763 / 0.004328 (-0.001566) \| 0.050807 / 0.004250 (0.046556) \| 0.046914 / 0.037052 (0.009862) \| 0.287443 / 0.258489 (0.028954) \| 0.319694 / 0.293841 (0.025853) \| 0.051110 / 0.128546 (-0.077436) \| 0.010650 / 0.075646 (-0.064997) \| 0.058254 / 0.419271 (-0.361018) \| 0.033419 / 0.043533 (-0.010114) \| 0.275634 / 0.255139 (0.020495) \| 0.288618 / 0.283200 (0.005419) \| 0.018004 / 0.141683 (-0.123678) \| 1.134166 / 1.452155 (-0.317989) \| 1.192533 / 1.492716 (-0.300183) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.098573 / 0.018006 (0.080566) \| 0.308152 / 0.000490 (0.307662) \| 0.000249 / 0.000200 (0.000049) \| 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) \| 0.022443 / 0.037411 (-0.014968) \| 0.075628 / 0.014526 (0.061103) \| 0.088807 / 0.176557 (-0.087750) \| 0.127519 / 0.737135 (-0.609617) \| 0.090156 / 0.296338 (-0.206182) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.294493 / 0.215209 (0.079284) \| 2.862084 / 2.077655 (0.784429) \| 1.585962 / 1.504120 (0.081842) \| 1.466366 / 1.541195 (-0.074829) \| 1.503306 / 1.468490 (0.034816) \| 0.581524 / 4.584777 (-4.003253) \| 2.475593 / 3.745712 (-1.270120) \| 2.852014 / 5.269862 (-2.417847) \| 1.834047 / 4.565676 (-2.731630) \| 0.064009 / 0.424275 (-0.360266) \| 0.005094 / 0.007607 (-0.002514) \| 0.355960 / 0.226044 (0.129916) \| 3.428849 / 2.268929 (1.159920) \| 1.958501 / 55.444624 (-53.486124) \| 1.675448 / 6.876477 (-5.201029) \| 1.719960 / 2.142072 (-0.422113) \| 0.659609 / 4.805227 (-4.145618) \| 0.119036 / 6.500664 (-6.381628) \| 0.041800 / 0.075469 (-0.033669) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.025955 / 1.841788 (-0.815833) \| 12.432417 / 8.074308 (4.358108) \| 10.444854 / 10.191392 (0.253462) \| 0.130106 / 0.680424 (-0.550318) \| 0.015655 / 0.534201 (-0.518546) \| 0.288184 / 0.579283 (-0.291099) \| 0.285023 / 0.434364 (-0.149340) \| 0.329244 / 0.540337 (-0.211093) \| 0.415484 / 1.386936 (-0.971452) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b262b060525efd973cac3f2073ba3944f3ddd7e3 \"CML watermark\")\n" ]	"2024-01-13T15:33:20"	"2024-02-15T15:20:06"	"2024-02-08T14:38:32"	CONTRIBUTOR	null	Fixes #6466 The idea is to do a recursive check for structs. PyArrow handles it well enough. For a demo you can do: ```python from datasets import Dataset, concatenate_datasets ds = Dataset.from_dict({'speaker': [{'name': 'Ben', 'email': None}]}) ds2 = Dataset.from_dict({'speaker': [{'name': 'Fred', 'email': 'abc@aol.com'}]}) print(concatenate_datasets([ds, ds2]).features) print(concatenate_datasets([ds, ds2]).to_dict()) ``` Now both the features and the rows are fixed. I note that Sequence suffers from the same problem, so I can fix that in a future PR once this one is merged.	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6587/reactions", "total_count": 1, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 1, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6587/timeline	null	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6587", "html_url": "https://github.com/huggingface/datasets/pull/6587", "diff_url": "https://github.com/huggingface/datasets/pull/6587.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6587.patch", "merged_at": "2024-02-08T14:38:32" }	true
https://api.github.com/repos/huggingface/datasets/issues/6586	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6586/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6586/comments	https://api.github.com/repos/huggingface/datasets/issues/6586/events	https://github.com/huggingface/datasets/pull/6586	2,079,192,651	PR_kwDODunzps5j8aJn	6,586	keep more info in DatasetInfo.from_merge #6585	{ "login": "JochenSiegWork", "id": 135010976, "node_id": "U_kgDOCAwaoA", "avatar_url": "https://avatars.githubusercontent.com/u/135010976?v=4", "gravatar_id": "", "url": "https://api.github.com/users/JochenSiegWork", "html_url": "https://github.com/JochenSiegWork", "followers_url": "https://api.github.com/users/JochenSiegWork/followers", "following_url": "https://api.github.com/users/JochenSiegWork/following{/other_user}", "gists_url": "https://api.github.com/users/JochenSiegWork/gists{/gist_id}", "starred_url": "https://api.github.com/users/JochenSiegWork/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/JochenSiegWork/subscriptions", "organizations_url": "https://api.github.com/users/JochenSiegWork/orgs", "repos_url": "https://api.github.com/users/JochenSiegWork/repos", "events_url": "https://api.github.com/users/JochenSiegWork/events{/privacy}", "received_events_url": "https://api.github.com/users/JochenSiegWork/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "@JochenSiegWork fyi, that seems to also affect the `trainer.push_to_hub()` method, which I guess also needs to parse that DatasetInfo from the `kwargs` used by `push_to_hub`.\r\nThere is short discussion about it [here](https://github.com/huggingface/blog/issues/1623).\r\nWould be great if you can check if your PR would also fix that!", "> @JochenSiegWork fyi, that seems to also affect the `trainer.push_to_hub()` method, which I guess also needs to parse that DatasetInfo from the `kwargs` used by `push_to_hub`. There is short discussion about it [here](https://github.com/huggingface/blog/issues/1623). Would be great if you can check if your PR would also fix that!\r\n\r\nHi @thiagobarbosa, it might be related but I didn't worked with `push_to_hub` yet. I don't see a minimal example reproducing the specific error in your link. However, if you have a running version producing the error locally you can test it by pulling this PR and run your specific example locally. ", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6586). 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) \| 0.004729 / 0.011353 (-0.006624) \| 0.002983 / 0.011008 (-0.008025) \| 0.062482 / 0.038508 (0.023974) \| 0.028406 / 0.023109 (0.005297) \| 0.255896 / 0.275898 (-0.020002) \| 0.276423 / 0.323480 (-0.047057) \| 0.003828 / 0.007986 (-0.004157) \| 0.002601 / 0.004328 (-0.001728) \| 0.048954 / 0.004250 (0.044704) \| 0.040661 / 0.037052 (0.003609) \| 0.277710 / 0.258489 (0.019221) \| 0.290360 / 0.293841 (-0.003481) \| 0.027105 / 0.128546 (-0.101441) \| 0.010168 / 0.075646 (-0.065478) \| 0.206835 / 0.419271 (-0.212436) \| 0.035226 / 0.043533 (-0.008306) \| 0.262567 / 0.255139 (0.007428) \| 0.273979 / 0.283200 (-0.009221) \| 0.017576 / 0.141683 (-0.124106) \| 1.125588 / 1.452155 (-0.326566) \| 1.185018 / 1.492716 (-0.307698) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092192 / 0.018006 (0.074186) \| 0.298350 / 0.000490 (0.297861) \| 0.000217 / 0.000200 (0.000017) \| 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) \| 0.017925 / 0.037411 (-0.019486) \| 0.060285 / 0.014526 (0.045759) \| 0.076579 / 0.176557 (-0.099978) \| 0.118830 / 0.737135 (-0.618305) \| 0.073017 / 0.296338 (-0.223322) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.288149 / 0.215209 (0.072940) \| 2.840004 / 2.077655 (0.762349) \| 1.495758 / 1.504120 (-0.008361) \| 1.362338 / 1.541195 (-0.178857) \| 1.389746 / 1.468490 (-0.078744) \| 0.576891 / 4.584777 (-4.007886) \| 2.375724 / 3.745712 (-1.369988) \| 2.707405 / 5.269862 (-2.562457) \| 1.719850 / 4.565676 (-2.845826) \| 0.067055 / 0.424275 (-0.357220) \| 0.005039 / 0.007607 (-0.002568) \| 0.346626 / 0.226044 (0.120581) \| 3.468346 / 2.268929 (1.199418) \| 1.860686 / 55.444624 (-53.583938) \| 1.582929 / 6.876477 (-5.293548) \| 1.613131 / 2.142072 (-0.528941) \| 0.659022 / 4.805227 (-4.146206) \| 0.118477 / 6.500664 (-6.382187) \| 0.041614 / 0.075469 (-0.033855) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.005062 / 1.841788 (-0.836726) \| 11.203210 / 8.074308 (3.128902) \| 10.320764 / 10.191392 (0.129372) \| 0.128541 / 0.680424 (-0.551883) \| 0.014646 / 0.534201 (-0.519555) \| 0.285280 / 0.579283 (-0.294003) \| 0.263613 / 0.434364 (-0.170751) \| 0.321161 / 0.540337 (-0.219177) \| 0.420565 / 1.386936 (-0.966371) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005288 / 0.011353 (-0.006065) \| 0.003048 / 0.011008 (-0.007960) \| 0.049196 / 0.038508 (0.010688) \| 0.032104 / 0.023109 (0.008994) \| 0.279345 / 0.275898 (0.003447) \| 0.300194 / 0.323480 (-0.023286) \| 0.004045 / 0.007986 (-0.003941) \| 0.002594 / 0.004328 (-0.001735) \| 0.047680 / 0.004250 (0.043430) \| 0.044294 / 0.037052 (0.007241) \| 0.292330 / 0.258489 (0.033841) \| 0.318610 / 0.293841 (0.024769) \| 0.050417 / 0.128546 (-0.078129) \| 0.010326 / 0.075646 (-0.065320) \| 0.057372 / 0.419271 (-0.361899) \| 0.032985 / 0.043533 (-0.010548) \| 0.277717 / 0.255139 (0.022579) \| 0.295692 / 0.283200 (0.012493) \| 0.017756 / 0.141683 (-0.123927) \| 1.166277 / 1.452155 (-0.285877) \| 1.213337 / 1.492716 (-0.279380) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091365 / 0.018006 (0.073359) \| 0.296261 / 0.000490 (0.295772) \| 0.000225 / 0.000200 (0.000025) \| 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) \| 0.021973 / 0.037411 (-0.015438) \| 0.074631 / 0.014526 (0.060106) \| 0.085645 / 0.176557 (-0.090911) \| 0.125181 / 0.737135 (-0.611955) \| 0.086893 / 0.296338 (-0.209445) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.294110 / 0.215209 (0.078901) \| 2.855531 / 2.077655 (0.777876) \| 1.583204 / 1.504120 (0.079084) \| 1.453911 / 1.541195 (-0.087284) \| 1.467031 / 1.468490 (-0.001460) \| 0.581214 / 4.584777 (-4.003562) \| 2.423626 / 3.745712 (-1.322086) \| 2.736665 / 5.269862 (-2.533197) \| 1.707000 / 4.565676 (-2.858676) \| 0.061171 / 0.424275 (-0.363104) \| 0.004789 / 0.007607 (-0.002818) \| 0.344546 / 0.226044 (0.118502) \| 3.530955 / 2.268929 (1.262027) \| 1.962532 / 55.444624 (-53.482092) \| 1.670207 / 6.876477 (-5.206270) \| 1.669041 / 2.142072 (-0.473031) \| 0.642298 / 4.805227 (-4.162929) \| 0.115503 / 6.500664 (-6.385161) \| 0.040729 / 0.075469 (-0.034740) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.973101 / 1.841788 (-0.868687) \| 11.823894 / 8.074308 (3.749586) \| 10.664592 / 10.191392 (0.473200) \| 0.139848 / 0.680424 (-0.540576) \| 0.015728 / 0.534201 (-0.518473) \| 0.289135 / 0.579283 (-0.290148) \| 0.271325 / 0.434364 (-0.163039) \| 0.332253 / 0.540337 (-0.208085) \| 0.416982 / 1.386936 (-0.969954) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ca76ca1152fce82bfeaab9f9a33849d4d7f9dd63 \"CML watermark\")\n" ]	"2024-01-12T16:08:16"	"2024-01-26T15:59:35"	"2024-01-26T15:53:28"	CONTRIBUTOR	null	* try not to merge DatasetInfos if they're equal * fixes losing DatasetInfo during parallel Dataset.map	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6586/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6586/timeline	null	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6586", "html_url": "https://github.com/huggingface/datasets/pull/6586", "diff_url": "https://github.com/huggingface/datasets/pull/6586.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6586.patch", "merged_at": "2024-01-26T15:53:28" }	true
https://api.github.com/repos/huggingface/datasets/issues/6585	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6585/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6585/comments	https://api.github.com/repos/huggingface/datasets/issues/6585/events	https://github.com/huggingface/datasets/issues/6585	2,078,874,005	I_kwDODunzps576RmV	6,585	losing DatasetInfo in Dataset.map when num_proc > 1	{ "login": "JochenSiegWork", "id": 135010976, "node_id": "U_kgDOCAwaoA", "avatar_url": "https://avatars.githubusercontent.com/u/135010976?v=4", "gravatar_id": "", "url": "https://api.github.com/users/JochenSiegWork", "html_url": "https://github.com/JochenSiegWork", "followers_url": "https://api.github.com/users/JochenSiegWork/followers", "following_url": "https://api.github.com/users/JochenSiegWork/following{/other_user}", "gists_url": "https://api.github.com/users/JochenSiegWork/gists{/gist_id}", "starred_url": "https://api.github.com/users/JochenSiegWork/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/JochenSiegWork/subscriptions", "organizations_url": "https://api.github.com/users/JochenSiegWork/orgs", "repos_url": "https://api.github.com/users/JochenSiegWork/repos", "events_url": "https://api.github.com/users/JochenSiegWork/events{/privacy}", "received_events_url": "https://api.github.com/users/JochenSiegWork/received_events", "type": "User", "site_admin": false }	[]	open	false	{ "login": "JochenSiegWork", "id": 135010976, "node_id": "U_kgDOCAwaoA", "avatar_url": "https://avatars.githubusercontent.com/u/135010976?v=4", "gravatar_id": "", "url": "https://api.github.com/users/JochenSiegWork", "html_url": "https://github.com/JochenSiegWork", "followers_url": "https://api.github.com/users/JochenSiegWork/followers", "following_url": "https://api.github.com/users/JochenSiegWork/following{/other_user}", "gists_url": "https://api.github.com/users/JochenSiegWork/gists{/gist_id}", "starred_url": "https://api.github.com/users/JochenSiegWork/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/JochenSiegWork/subscriptions", "organizations_url": "https://api.github.com/users/JochenSiegWork/orgs", "repos_url": "https://api.github.com/users/JochenSiegWork/repos", "events_url": "https://api.github.com/users/JochenSiegWork/events{/privacy}", "received_events_url": "https://api.github.com/users/JochenSiegWork/received_events", "type": "User", "site_admin": false }	[ { "login": "JochenSiegWork", "id": 135010976, "node_id": "U_kgDOCAwaoA", "avatar_url": "https://avatars.githubusercontent.com/u/135010976?v=4", "gravatar_id": "", "url": "https://api.github.com/users/JochenSiegWork", "html_url": "https://github.com/JochenSiegWork", "followers_url": "https://api.github.com/users/JochenSiegWork/followers", "following_url": "https://api.github.com/users/JochenSiegWork/following{/other_user}", "gists_url": "https://api.github.com/users/JochenSiegWork/gists{/gist_id}", "starred_url": "https://api.github.com/users/JochenSiegWork/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/JochenSiegWork/subscriptions", "organizations_url": "https://api.github.com/users/JochenSiegWork/orgs", "repos_url": "https://api.github.com/users/JochenSiegWork/repos", "events_url": "https://api.github.com/users/JochenSiegWork/events{/privacy}", "received_events_url": "https://api.github.com/users/JochenSiegWork/received_events", "type": "User", "site_admin": false } ]	null	[ "Hi ! This issue comes from the fact that `map()` with `num_proc>1` shards the dataset in multiple chunks to be processed (one per process) and merges them. The DatasetInfos of each chunk are then merged together, but for some fields like `dataset_name` it's not been implemented and default to None.\r\n\r\nThe DatasetInfo merge is defined here, in case you'd like to contribute an improvement: \r\n\r\nhttps://github.com/huggingface/datasets/blob/d2e0034122a788015c0834a72e6c6279e7ecbac5/src/datasets/info.py#L269-L270", "#self-assign" ]	"2024-01-12T13:39:19"	"2024-01-12T14:08:24"	null	CONTRIBUTOR	null	### Describe the bug Hello and thanks for developing this package! When I process a Dataset with the map function using multiple processors some set attributes of the DatasetInfo get lost and are None in the resulting Dataset. ### Steps to reproduce the bug ```python from datasets import Dataset, DatasetInfo def run_map(num_proc): dataset = Dataset.from_dict( {"col1": [0, 1], "col2": [3, 4]}, info=DatasetInfo( dataset_name="my_dataset", ), ) ds = dataset.map(lambda x: x, num_proc=num_proc) print(ds.info.dataset_name) run_map(1) run_map(2) ``` This puts out: ```bash Map: 100%\|██████████\| 2/2 [00:00<00:00, 724.66 examples/s] my_dataset Map (num_proc=2): 100%\|██████████\| 2/2 [00:00<00:00, 18.25 examples/s] None ``` ### Expected behavior I expect the DatasetInfo to be kept as it was and there should be no difference in the output of running map with num_proc=1 and num_proc=2. Expected output: ```bash Map: 100%\|██████████\| 2/2 [00:00<00:00, 724.66 examples/s] my_dataset Map (num_proc=2): 100%\|██████████\| 2/2 [00:00<00:00, 18.25 examples/s] my_dataset ``` ### Environment info - `datasets` version: 2.16.1 - Platform: Linux-5.10.102.1-microsoft-standard-WSL2-x86_64-with-glibc2.17 - Python version: 3.8.18 - `huggingface_hub` version: 0.20.2 - PyArrow version: 12.0.1 - Pandas version: 2.0.3 - `fsspec` version: 2023.9.2	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6585/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6585/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6584	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6584/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6584/comments	https://api.github.com/repos/huggingface/datasets/issues/6584/events	https://github.com/huggingface/datasets/issues/6584	2,078,454,878	I_kwDODunzps574rRe	6,584	np.fromfile not supported	{ "login": "d710055071", "id": 12895488, "node_id": "MDQ6VXNlcjEyODk1NDg4", "avatar_url": "https://avatars.githubusercontent.com/u/12895488?v=4", "gravatar_id": "", "url": "https://api.github.com/users/d710055071", "html_url": "https://github.com/d710055071", "followers_url": "https://api.github.com/users/d710055071/followers", "following_url": "https://api.github.com/users/d710055071/following{/other_user}", "gists_url": "https://api.github.com/users/d710055071/gists{/gist_id}", "starred_url": "https://api.github.com/users/d710055071/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/d710055071/subscriptions", "organizations_url": "https://api.github.com/users/d710055071/orgs", "repos_url": "https://api.github.com/users/d710055071/repos", "events_url": "https://api.github.com/users/d710055071/events{/privacy}", "received_events_url": "https://api.github.com/users/d710055071/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	null	[ "@lhoestq\r\nCan you provide me with some ideas？", "Hi ! What's the error ?", "@lhoestq \r\n```\r\nTraceback (most recent call last):\r\n File \"/home/dongzf/miniconda3/envs/dataset_ai/lib/python3.11/runpy.py\", line 198, in _run_module_as_main\r\n return _run_code(code, main_globals, None,\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/home/dongzf/miniconda3/envs/dataset_ai/lib/python3.11/runpy.py\", line 88, in _run_code\r\n exec(code, run_globals)\r\n File \"/home/dongzf/.vscode/extensions/ms-python.python-2023.22.1/pythonFiles/lib/python/debugpy/adapter/../../debugpy/launcher/../../debugpy/__main__.py\", line 39, in <module>\r\n cli.main()\r\n File \"/home/dongzf/.vscode/extensions/ms-python.python-2023.22.1/pythonFiles/lib/python/debugpy/adapter/../../debugpy/launcher/../../debugpy/../debugpy/server/cli.py\", line 430, in main\r\n run()\r\n File \"/home/dongzf/.vscode/extensions/ms-python.python-2023.22.1/pythonFiles/lib/python/debugpy/adapter/../../debugpy/launcher/../../debugpy/../debugpy/server/cli.py\", line 284, in run_file\r\n runpy.run_path(target, run_name=\"__main__\")\r\n File \"/home/dongzf/.vscode/extensions/ms-python.python-2023.22.1/pythonFiles/lib/python/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_runpy.py\", line 321, in run_path\r\n return _run_module_code(code, init_globals, run_name,\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/home/dongzf/.vscode/extensions/ms-python.python-2023.22.1/pythonFiles/lib/python/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_runpy.py\", line 135, in _run_module_code\r\n _run_code(code, mod_globals, init_globals,\r\n File \"/home/dongzf/.vscode/extensions/ms-python.python-2023.22.1/pythonFiles/lib/python/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_runpy.py\", line 124, in _run_code\r\n exec(code, run_globals)\r\n File \"/mnt/sda/code/dataset_ai/dataset_ai/example/test.py\", line 83, in <module>\r\n data = xnumpy_fromfile(current_dir, download_config=config,dtype=numpy.float32,)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/mnt/sda/code/dataset_ai/dataset_ai/src/datasets/download/streaming_download_manager.py\", line 765, in xnumpy_fromfile\r\n return np.fromfile(xopen(filepath_or_buffer, \"rb\", download_config=download_config).read(), args, kwargs)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\nValueError: embedded null byte\r\n```", " not add read() \r\nthe error is \r\n\r\nreturn np.fromfile(xopen(filepath_or_buffer, \"rb\", download_config=download_config), args, **kwargs)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\nio.UnsupportedOperation: fileno", "xopen return obj do not have fileno function\r\nI don't know why?", "I used this method to read point cloud data in the script\r\n\r\n\r\n```python\r\nwith open(velodyne_filepath,\"rb\") as obj:\r\n velodyne_data = numpy.frombuffer(obj.read(), dtype=numpy.float32).reshape([-1, 4])\r\n```" ]	"2024-01-12T09:46:17"	"2024-01-15T05:20:50"	null	CONTRIBUTOR	null	How to do np.fromfile to use it like np.load ```python def xnumpy_fromfile(filepath_or_buffer, args, download_config: Optional[DownloadConfig] = None, kwargs): import numpy as np if hasattr(filepath_or_buffer, "read"): return np.fromfile(filepath_or_buffer, args, *kwargs) else: filepath_or_buffer = str(filepath_or_buffer) return np.fromfile(xopen(filepath_or_buffer, "rb", download_config=download_config).read(), args, **kwargs) ``` this is not work	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6584/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6584/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6583	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6583/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6583/comments	https://api.github.com/repos/huggingface/datasets/issues/6583/events	https://github.com/huggingface/datasets/pull/6583	2,077,049,491	PR_kwDODunzps5j1DzY	6,583	remove eli5 test	{ "login": "lhoestq", "id": 42851186, "node_id": "MDQ6VXNlcjQyODUxMTg2", "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "gravatar_id": "", "url": "https://api.github.com/users/lhoestq", "html_url": "https://github.com/lhoestq", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "repos_url": "https://api.github.com/users/lhoestq/repos", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6583). 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) \| 0.005024 / 0.011353 (-0.006329) \| 0.003172 / 0.011008 (-0.007836) \| 0.062934 / 0.038508 (0.024426) \| 0.031737 / 0.023109 (0.008628) \| 0.249251 / 0.275898 (-0.026647) \| 0.273084 / 0.323480 (-0.050396) \| 0.002958 / 0.007986 (-0.005027) \| 0.002726 / 0.004328 (-0.001603) \| 0.048519 / 0.004250 (0.044269) \| 0.043608 / 0.037052 (0.006556) \| 0.253648 / 0.258489 (-0.004841) \| 0.280095 / 0.293841 (-0.013746) \| 0.027500 / 0.128546 (-0.101046) \| 0.010545 / 0.075646 (-0.065101) \| 0.206781 / 0.419271 (-0.212490) \| 0.035515 / 0.043533 (-0.008018) \| 0.259449 / 0.255139 (0.004310) \| 0.271488 / 0.283200 (-0.011712) \| 0.019352 / 0.141683 (-0.122331) \| 1.152002 / 1.452155 (-0.300153) \| 1.190325 / 1.492716 (-0.302391) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093253 / 0.018006 (0.075247) \| 0.302182 / 0.000490 (0.301692) \| 0.000216 / 0.000200 (0.000016) \| 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) \| 0.017889 / 0.037411 (-0.019523) \| 0.060292 / 0.014526 (0.045766) \| 0.072640 / 0.176557 (-0.103917) \| 0.121320 / 0.737135 (-0.615815) \| 0.073866 / 0.296338 (-0.222472) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.282910 / 0.215209 (0.067701) \| 2.779815 / 2.077655 (0.702160) \| 1.537929 / 1.504120 (0.033809) \| 1.405990 / 1.541195 (-0.135205) \| 1.407911 / 1.468490 (-0.060579) \| 0.561551 / 4.584777 (-4.023226) \| 2.368053 / 3.745712 (-1.377659) \| 2.732608 / 5.269862 (-2.537254) \| 1.710274 / 4.565676 (-2.855402) \| 0.061925 / 0.424275 (-0.362350) \| 0.004975 / 0.007607 (-0.002632) \| 0.338843 / 0.226044 (0.112799) \| 3.328579 / 2.268929 (1.059650) \| 1.865994 / 55.444624 (-53.578631) \| 1.603145 / 6.876477 (-5.273332) \| 1.615440 / 2.142072 (-0.526633) \| 0.635646 / 4.805227 (-4.169581) \| 0.116185 / 6.500664 (-6.384479) \| 0.041964 / 0.075469 (-0.033505) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.956977 / 1.841788 (-0.884811) \| 11.539802 / 8.074308 (3.465494) \| 10.048855 / 10.191392 (-0.142537) \| 0.128758 / 0.680424 (-0.551666) \| 0.013491 / 0.534201 (-0.520710) \| 0.287330 / 0.579283 (-0.291953) \| 0.262416 / 0.434364 (-0.171947) \| 0.327327 / 0.540337 (-0.213011) \| 0.418423 / 1.386936 (-0.968513) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004963 / 0.011353 (-0.006390) \| 0.003335 / 0.011008 (-0.007673) \| 0.052082 / 0.038508 (0.013574) \| 0.029302 / 0.023109 (0.006192) \| 0.284986 / 0.275898 (0.009088) \| 0.304082 / 0.323480 (-0.019398) \| 0.004065 / 0.007986 (-0.003921) \| 0.002643 / 0.004328 (-0.001685) \| 0.049504 / 0.004250 (0.045253) \| 0.044514 / 0.037052 (0.007461) \| 0.287064 / 0.258489 (0.028575) \| 0.312921 / 0.293841 (0.019080) \| 0.029195 / 0.128546 (-0.099351) \| 0.010471 / 0.075646 (-0.065175) \| 0.057620 / 0.419271 (-0.361651) \| 0.050221 / 0.043533 (0.006689) \| 0.285392 / 0.255139 (0.030253) \| 0.302111 / 0.283200 (0.018912) \| 0.018690 / 0.141683 (-0.122993) \| 1.165637 / 1.452155 (-0.286518) \| 1.203757 / 1.492716 (-0.288959) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.095035 / 0.018006 (0.077028) \| 0.304447 / 0.000490 (0.303957) \| 0.000231 / 0.000200 (0.000031) \| 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) \| 0.022345 / 0.037411 (-0.015066) \| 0.077195 / 0.014526 (0.062669) \| 0.089564 / 0.176557 (-0.086992) \| 0.129248 / 0.737135 (-0.607887) \| 0.091974 / 0.296338 (-0.204365) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.300641 / 0.215209 (0.085432) \| 2.936669 / 2.077655 (0.859014) \| 1.649100 / 1.504120 (0.144980) \| 1.510693 / 1.541195 (-0.030502) \| 1.517011 / 1.468490 (0.048521) \| 0.572511 / 4.584777 (-4.012266) \| 2.442704 / 3.745712 (-1.303009) \| 2.833089 / 5.269862 (-2.436772) \| 1.762668 / 4.565676 (-2.803008) \| 0.063754 / 0.424275 (-0.360521) \| 0.005034 / 0.007607 (-0.002573) \| 0.401631 / 0.226044 (0.175586) \| 3.418986 / 2.268929 (1.150057) \| 1.989639 / 55.444624 (-53.454986) \| 1.695776 / 6.876477 (-5.180701) \| 1.712822 / 2.142072 (-0.429250) \| 0.654029 / 4.805227 (-4.151198) \| 0.117624 / 6.500664 (-6.383040) \| 0.041058 / 0.075469 (-0.034411) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.986008 / 1.841788 (-0.855779) \| 12.146838 / 8.074308 (4.072530) \| 11.105900 / 10.191392 (0.914508) \| 0.139938 / 0.680424 (-0.540486) \| 0.015117 / 0.534201 (-0.519084) \| 0.286151 / 0.579283 (-0.293132) \| 0.272960 / 0.434364 (-0.161404) \| 0.323370 / 0.540337 (-0.216967) \| 0.427379 / 1.386936 (-0.959557) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#91888ea888fec1f2c96d8316a569439e64eb508e \"CML watermark\")\n" ]	"2024-01-11T16:05:20"	"2024-01-11T16:15:34"	"2024-01-11T16:09:24"	MEMBER	null	since the dataset is defunct	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6583/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6583/timeline	null	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6583", "html_url": "https://github.com/huggingface/datasets/pull/6583", "diff_url": "https://github.com/huggingface/datasets/pull/6583.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6583.patch", "merged_at": "2024-01-11T16:09:24" }	true
https://api.github.com/repos/huggingface/datasets/issues/6582	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6582/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6582/comments	https://api.github.com/repos/huggingface/datasets/issues/6582/events	https://github.com/huggingface/datasets/pull/6582	2,076,072,101	PR_kwDODunzps5jxpry	6,582	Fix for Incorrect ex_iterable used with multi num_worker	{ "login": "kq-chen", "id": 136600500, "node_id": "U_kgDOCCRbtA", "avatar_url": "https://avatars.githubusercontent.com/u/136600500?v=4", "gravatar_id": "", "url": "https://api.github.com/users/kq-chen", "html_url": "https://github.com/kq-chen", "followers_url": "https://api.github.com/users/kq-chen/followers", "following_url": "https://api.github.com/users/kq-chen/following{/other_user}", "gists_url": "https://api.github.com/users/kq-chen/gists{/gist_id}", "starred_url": "https://api.github.com/users/kq-chen/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/kq-chen/subscriptions", "organizations_url": "https://api.github.com/users/kq-chen/orgs", "repos_url": "https://api.github.com/users/kq-chen/repos", "events_url": "https://api.github.com/users/kq-chen/events{/privacy}", "received_events_url": "https://api.github.com/users/kq-chen/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "A toy example to reveal the bug.\r\n\r\n```python\r\n\"\"\"\r\nDATASETS_VERBOSITY=debug torchrun --nproc-per-node 2 main.py \r\n\"\"\"\r\nimport torch.utils.data\r\nimport torch.distributed\r\nimport datasets.distributed\r\nimport datasets\r\n\r\n# num shards = 4\r\nshards = [(0, 100), (100, 200), (200, 300), (300, 400)]\r\n\r\n\r\ndef gen(shards):\r\n for st, ed in shards:\r\n yield from range(st, ed)\r\n\r\ntorch.distributed.init_process_group()\r\n\r\n# want to create total worker = world_size * 8\r\nds = datasets.IterableDataset.from_generator(gen, gen_kwargs={'shards': shards})\r\nds = datasets.distributed.split_dataset_by_node(\r\n ds,\r\n rank=torch.distributed.get_rank(),\r\n world_size=torch.distributed.get_world_size(),\r\n)\r\ndl = torch.utils.data.DataLoader(ds, batch_size=10, num_workers=8)\r\n\r\nfor x in dl:\r\n print(f\"RANK={torch.distributed.get_rank()} {x}\")\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) \| 0.005401 / 0.011353 (-0.005952) \| 0.004023 / 0.011008 (-0.006985) \| 0.064601 / 0.038508 (0.026093) \| 0.028567 / 0.023109 (0.005457) \| 0.245476 / 0.275898 (-0.030422) \| 0.292727 / 0.323480 (-0.030752) \| 0.003080 / 0.007986 (-0.004905) \| 0.002779 / 0.004328 (-0.001549) \| 0.050046 / 0.004250 (0.045796) \| 0.043906 / 0.037052 (0.006854) \| 0.273896 / 0.258489 (0.015407) \| 0.308430 / 0.293841 (0.014589) \| 0.028442 / 0.128546 (-0.100104) \| 0.010694 / 0.075646 (-0.064953) \| 0.209048 / 0.419271 (-0.210223) \| 0.036062 / 0.043533 (-0.007471) \| 0.242689 / 0.255139 (-0.012450) \| 0.261695 / 0.283200 (-0.021504) \| 0.018519 / 0.141683 (-0.123163) \| 1.122735 / 1.452155 (-0.329420) \| 1.172680 / 1.492716 (-0.320036) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093827 / 0.018006 (0.075820) \| 0.302650 / 0.000490 (0.302161) \| 0.000218 / 0.000200 (0.000018) \| 0.000045 / 0.000054 (-0.000009) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018778 / 0.037411 (-0.018633) \| 0.067516 / 0.014526 (0.052990) \| 0.079693 / 0.176557 (-0.096864) \| 0.125907 / 0.737135 (-0.611228) \| 0.081771 / 0.296338 (-0.214568) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.281809 / 0.215209 (0.066600) \| 2.773937 / 2.077655 (0.696283) \| 1.443622 / 1.504120 (-0.060497) \| 1.334359 / 1.541195 (-0.206836) \| 1.364813 / 1.468490 (-0.103677) \| 0.561670 / 4.584777 (-4.023107) \| 2.338292 / 3.745712 (-1.407420) \| 2.807595 / 5.269862 (-2.462267) \| 1.734162 / 4.565676 (-2.831514) \| 0.063681 / 0.424275 (-0.360594) \| 0.004934 / 0.007607 (-0.002673) \| 0.336781 / 0.226044 (0.110737) \| 3.311744 / 2.268929 (1.042815) \| 1.826802 / 55.444624 (-53.617822) \| 1.579604 / 6.876477 (-5.296872) \| 1.620526 / 2.142072 (-0.521546) \| 0.647061 / 4.805227 (-4.158166) \| 0.117729 / 6.500664 (-6.382935) \| 0.042216 / 0.075469 (-0.033253) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.994289 / 1.841788 (-0.847499) \| 12.266185 / 8.074308 (4.191877) \| 9.634035 / 10.191392 (-0.557357) \| 0.144521 / 0.680424 (-0.535902) \| 0.013787 / 0.534201 (-0.520414) \| 0.288353 / 0.579283 (-0.290930) \| 0.262183 / 0.434364 (-0.172181) \| 0.336960 / 0.540337 (-0.203378) \| 0.441142 / 1.386936 (-0.945794) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005678 / 0.011353 (-0.005675) \| 0.004011 / 0.011008 (-0.006998) \| 0.049319 / 0.038508 (0.010811) \| 0.032543 / 0.023109 (0.009434) \| 0.276389 / 0.275898 (0.000491) \| 0.298495 / 0.323480 (-0.024985) \| 0.004192 / 0.007986 (-0.003794) \| 0.002765 / 0.004328 (-0.001563) \| 0.048739 / 0.004250 (0.044489) \| 0.046212 / 0.037052 (0.009160) \| 0.286614 / 0.258489 (0.028125) \| 0.315949 / 0.293841 (0.022108) \| 0.029833 / 0.128546 (-0.098714) \| 0.010762 / 0.075646 (-0.064884) \| 0.058489 / 0.419271 (-0.360783) \| 0.052258 / 0.043533 (0.008725) \| 0.275873 / 0.255139 (0.020734) \| 0.288668 / 0.283200 (0.005468) \| 0.018828 / 0.141683 (-0.122855) \| 1.140196 / 1.452155 (-0.311959) \| 1.229500 / 1.492716 (-0.263217) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.094161 / 0.018006 (0.076155) \| 0.303519 / 0.000490 (0.303030) \| 0.000219 / 0.000200 (0.000019) \| 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) \| 0.022088 / 0.037411 (-0.015324) \| 0.076376 / 0.014526 (0.061850) \| 0.088705 / 0.176557 (-0.087851) \| 0.127602 / 0.737135 (-0.609533) \| 0.088689 / 0.296338 (-0.207649) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.292363 / 0.215209 (0.077154) \| 2.859215 / 2.077655 (0.781561) \| 1.566389 / 1.504120 (0.062270) \| 1.439195 / 1.541195 (-0.102000) \| 1.463805 / 1.468490 (-0.004685) \| 0.551660 / 4.584777 (-4.033116) \| 2.427462 / 3.745712 (-1.318250) \| 2.712372 / 5.269862 (-2.557490) \| 1.811331 / 4.565676 (-2.754346) \| 0.061539 / 0.424275 (-0.362736) \| 0.005062 / 0.007607 (-0.002545) \| 0.341984 / 0.226044 (0.115940) \| 3.352171 / 2.268929 (1.083242) \| 1.917550 / 55.444624 (-53.527074) \| 1.642668 / 6.876477 (-5.233809) \| 1.817204 / 2.142072 (-0.324868) \| 0.630849 / 4.805227 (-4.174379) \| 0.115788 / 6.500664 (-6.384876) \| 0.041041 / 0.075469 (-0.034428) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.017725 / 1.841788 (-0.824062) \| 12.976994 / 8.074308 (4.902686) \| 10.307414 / 10.191392 (0.116022) \| 0.141090 / 0.680424 (-0.539334) \| 0.015548 / 0.534201 (-0.518653) \| 0.288184 / 0.579283 (-0.291099) \| 0.276409 / 0.434364 (-0.157955) \| 0.328289 / 0.540337 (-0.212048) \| 0.429138 / 1.386936 (-0.957798) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#31ae21ff806c3d1fc19a48ce41178c82d2f69368 \"CML watermark\")\n" ]	"2024-01-11T08:49:43"	"2024-03-01T19:09:14"	"2024-03-01T19:02:33"	CONTRIBUTOR	null	Corrects an issue where `self._ex_iterable` was erroneously used instead of `ex_iterable`, when both Distributed Data Parallel (DDP) and multi num_worker are used concurrently. This improper usage led to the generation of incorrect `shards_indices`, subsequently causing issues with the control flow responsible for worker creation. The fix ensures the appropriate iterable is used, thus providing a more accurate determination of whether a new worker should be instantiated or not.	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6582/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6582/timeline	null	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6582", "html_url": "https://github.com/huggingface/datasets/pull/6582", "diff_url": "https://github.com/huggingface/datasets/pull/6582.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6582.patch", "merged_at": "2024-03-01T19:02:33" }	true
https://api.github.com/repos/huggingface/datasets/issues/6581	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6581/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6581/comments	https://api.github.com/repos/huggingface/datasets/issues/6581/events	https://github.com/huggingface/datasets/pull/6581	2,075,919,265	PR_kwDODunzps5jxIbt	6,581	fix os.listdir return name is empty string	{ "login": "d710055071", "id": 12895488, "node_id": "MDQ6VXNlcjEyODk1NDg4", "avatar_url": "https://avatars.githubusercontent.com/u/12895488?v=4", "gravatar_id": "", "url": "https://api.github.com/users/d710055071", "html_url": "https://github.com/d710055071", "followers_url": "https://api.github.com/users/d710055071/followers", "following_url": "https://api.github.com/users/d710055071/following{/other_user}", "gists_url": "https://api.github.com/users/d710055071/gists{/gist_id}", "starred_url": "https://api.github.com/users/d710055071/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/d710055071/subscriptions", "organizations_url": "https://api.github.com/users/d710055071/orgs", "repos_url": "https://api.github.com/users/d710055071/repos", "events_url": "https://api.github.com/users/d710055071/events{/privacy}", "received_events_url": "https://api.github.com/users/d710055071/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6581). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "\r\nObj [\"name\"] ends with \"/\"", "@lhoestq \r\n\r\nhello,\r\nCan you help me check if there are any issues with this PR? Why hasn't anyone merged?\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) \| 0.004968 / 0.011353 (-0.006385) \| 0.003516 / 0.011008 (-0.007492) \| 0.063787 / 0.038508 (0.025279) \| 0.031695 / 0.023109 (0.008586) \| 0.240081 / 0.275898 (-0.035817) \| 0.260984 / 0.323480 (-0.062496) \| 0.003832 / 0.007986 (-0.004153) \| 0.002680 / 0.004328 (-0.001648) \| 0.049199 / 0.004250 (0.044948) \| 0.044720 / 0.037052 (0.007668) \| 0.255812 / 0.258489 (-0.002677) \| 0.275923 / 0.293841 (-0.017918) \| 0.026849 / 0.128546 (-0.101697) \| 0.010473 / 0.075646 (-0.065174) \| 0.209069 / 0.419271 (-0.210202) \| 0.035731 / 0.043533 (-0.007802) \| 0.246596 / 0.255139 (-0.008543) \| 0.265889 / 0.283200 (-0.017311) \| 0.017607 / 0.141683 (-0.124075) \| 1.128648 / 1.452155 (-0.323507) \| 1.174379 / 1.492716 (-0.318338) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.098214 / 0.018006 (0.080207) \| 0.311969 / 0.000490 (0.311480) \| 0.000266 / 0.000200 (0.000066) \| 0.000056 / 0.000054 (0.000002) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018401 / 0.037411 (-0.019010) \| 0.061347 / 0.014526 (0.046821) \| 0.073628 / 0.176557 (-0.102928) \| 0.121359 / 0.737135 (-0.615776) \| 0.075148 / 0.296338 (-0.221190) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.274098 / 0.215209 (0.058889) \| 2.707633 / 2.077655 (0.629978) \| 1.453615 / 1.504120 (-0.050504) \| 1.311942 / 1.541195 (-0.229253) \| 1.332394 / 1.468490 (-0.136096) \| 0.566947 / 4.584777 (-4.017830) \| 2.383291 / 3.745712 (-1.362421) \| 2.754779 / 5.269862 (-2.515083) \| 1.725164 / 4.565676 (-2.840512) \| 0.062124 / 0.424275 (-0.362152) \| 0.005111 / 0.007607 (-0.002496) \| 0.334217 / 0.226044 (0.108173) \| 3.271619 / 2.268929 (1.002690) \| 1.776906 / 55.444624 (-53.667718) \| 1.519238 / 6.876477 (-5.357239) \| 1.534722 / 2.142072 (-0.607351) \| 0.646143 / 4.805227 (-4.159084) \| 0.117015 / 6.500664 (-6.383649) \| 0.042578 / 0.075469 (-0.032891) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.948488 / 1.841788 (-0.893299) \| 11.598027 / 8.074308 (3.523719) \| 10.269199 / 10.191392 (0.077807) \| 0.144887 / 0.680424 (-0.535537) \| 0.014745 / 0.534201 (-0.519456) \| 0.289185 / 0.579283 (-0.290099) \| 0.275243 / 0.434364 (-0.159120) \| 0.328088 / 0.540337 (-0.212250) \| 0.430161 / 1.386936 (-0.956775) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005020 / 0.011353 (-0.006333) \| 0.003246 / 0.011008 (-0.007762) \| 0.049810 / 0.038508 (0.011302) \| 0.032215 / 0.023109 (0.009105) \| 0.271033 / 0.275898 (-0.004866) \| 0.294957 / 0.323480 (-0.028523) \| 0.004192 / 0.007986 (-0.003793) \| 0.002652 / 0.004328 (-0.001677) \| 0.049132 / 0.004250 (0.044881) \| 0.047818 / 0.037052 (0.010766) \| 0.292370 / 0.258489 (0.033881) \| 0.316142 / 0.293841 (0.022301) \| 0.049539 / 0.128546 (-0.079007) \| 0.010533 / 0.075646 (-0.065113) \| 0.058131 / 0.419271 (-0.361141) \| 0.033807 / 0.043533 (-0.009725) \| 0.277623 / 0.255139 (0.022484) \| 0.292294 / 0.283200 (0.009094) \| 0.021110 / 0.141683 (-0.120573) \| 1.160997 / 1.452155 (-0.291157) \| 1.213553 / 1.492716 (-0.279163) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.098220 / 0.018006 (0.080214) \| 0.312342 / 0.000490 (0.311852) \| 0.000231 / 0.000200 (0.000031) \| 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) \| 0.022893 / 0.037411 (-0.014519) \| 0.075572 / 0.014526 (0.061046) \| 0.088357 / 0.176557 (-0.088199) \| 0.126354 / 0.737135 (-0.610782) \| 0.089763 / 0.296338 (-0.206575) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.284368 / 0.215209 (0.069159) \| 2.785497 / 2.077655 (0.707842) \| 1.499364 / 1.504120 (-0.004756) \| 1.376020 / 1.541195 (-0.165175) \| 1.394270 / 1.468490 (-0.074220) \| 0.571945 / 4.584777 (-4.012832) \| 2.419148 / 3.745712 (-1.326564) \| 2.796974 / 5.269862 (-2.472887) \| 1.749531 / 4.565676 (-2.816145) \| 0.064088 / 0.424275 (-0.360187) \| 0.005294 / 0.007607 (-0.002313) \| 0.336250 / 0.226044 (0.110206) \| 3.315933 / 2.268929 (1.047004) \| 1.877165 / 55.444624 (-53.567459) \| 1.592336 / 6.876477 (-5.284140) \| 1.599979 / 2.142072 (-0.542093) \| 0.655617 / 4.805227 (-4.149610) \| 0.117636 / 6.500664 (-6.383028) \| 0.040813 / 0.075469 (-0.034656) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.976887 / 1.841788 (-0.864901) \| 12.668753 / 8.074308 (4.594445) \| 11.081253 / 10.191392 (0.889861) \| 0.134494 / 0.680424 (-0.545930) \| 0.016053 / 0.534201 (-0.518148) \| 0.291607 / 0.579283 (-0.287676) \| 0.287726 / 0.434364 (-0.146638) \| 0.328108 / 0.540337 (-0.212229) \| 0.425194 / 1.386936 (-0.961742) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#32672349e3e5abe21505fdbda122dd3426f8920f \"CML watermark\")\n" ]	"2024-01-11T07:10:55"	"2024-01-24T10:14:43"	"2024-01-24T10:08:28"	CONTRIBUTOR	null	fix #6588 xlistdir return name is empty string for example： ` from datasets.download.streaming_download_manager import xjoin from datasets.download.streaming_download_manager import xlistdir config = DownloadConfig(storage_options=options) manger = StreamingDownloadManager("ILSVRC2012",download_config=config) input_path = "lakefs://datalab/main/imagenet/ILSVRC2012.zip" download_files = manger.download_and_extract(input_path) current_dir = xjoin(download_files,"ILSVRC2012/Images/ILSVRC2012_img_train") folder_list = xlistdir(current_dir) `	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6581/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6581/timeline	null	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6581", "html_url": "https://github.com/huggingface/datasets/pull/6581", "diff_url": "https://github.com/huggingface/datasets/pull/6581.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6581.patch", "merged_at": "2024-01-24T10:08:28" }	true
https://api.github.com/repos/huggingface/datasets/issues/6580	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6580/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6580/comments	https://api.github.com/repos/huggingface/datasets/issues/6580/events	https://github.com/huggingface/datasets/issues/6580	2,075,645,042	I_kwDODunzps57t9Ry	6,580	dataset cache only stores one config of the dataset in parquet dir, and uses that for all other configs resulting in showing same data in all configs.	{ "login": "kartikgupta321", "id": 78641018, "node_id": "MDQ6VXNlcjc4NjQxMDE4", "avatar_url": "https://avatars.githubusercontent.com/u/78641018?v=4", "gravatar_id": "", "url": "https://api.github.com/users/kartikgupta321", "html_url": "https://github.com/kartikgupta321", "followers_url": "https://api.github.com/users/kartikgupta321/followers", "following_url": "https://api.github.com/users/kartikgupta321/following{/other_user}", "gists_url": "https://api.github.com/users/kartikgupta321/gists{/gist_id}", "starred_url": "https://api.github.com/users/kartikgupta321/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/kartikgupta321/subscriptions", "organizations_url": "https://api.github.com/users/kartikgupta321/orgs", "repos_url": "https://api.github.com/users/kartikgupta321/repos", "events_url": "https://api.github.com/users/kartikgupta321/events{/privacy}", "received_events_url": "https://api.github.com/users/kartikgupta321/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[]	"2024-01-11T03:14:18"	"2024-01-20T12:46:16"	"2024-01-20T12:46:16"	NONE	null	### Describe the bug ds = load_dataset("ai2_arc", "ARC-Easy"), i have tried to force redownload, delete cache and changing the cache dir. ### Steps to reproduce the bug dataset = [] dataset_name = "ai2_arc" possible_configs = [ 'ARC-Challenge', 'ARC-Easy' ] for config in possible_configs: dataset_slice = load_dataset(dataset_name, config,ignore_verifications=True,cache_dir='ai2_arc_files') dataset.append(dataset_slice) ### Expected behavior all configs should get saved in cache with their respective names. ### Environment info ai2_arc	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6580/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6580/timeline	null	completed	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6579	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6579/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6579/comments	https://api.github.com/repos/huggingface/datasets/issues/6579/events	https://github.com/huggingface/datasets/issues/6579	2,075,407,473	I_kwDODunzps57tDRx	6,579	Unable to load `eli5` dataset with streaming	{ "login": "haok1402", "id": 89672451, "node_id": "MDQ6VXNlcjg5NjcyNDUx", "avatar_url": "https://avatars.githubusercontent.com/u/89672451?v=4", "gravatar_id": "", "url": "https://api.github.com/users/haok1402", "html_url": "https://github.com/haok1402", "followers_url": "https://api.github.com/users/haok1402/followers", "following_url": "https://api.github.com/users/haok1402/following{/other_user}", "gists_url": "https://api.github.com/users/haok1402/gists{/gist_id}", "starred_url": "https://api.github.com/users/haok1402/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/haok1402/subscriptions", "organizations_url": "https://api.github.com/users/haok1402/orgs", "repos_url": "https://api.github.com/users/haok1402/repos", "events_url": "https://api.github.com/users/haok1402/events{/privacy}", "received_events_url": "https://api.github.com/users/haok1402/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "Hi @haok1402, I have created an issue in the Discussion tab of the corresponding dataset: https://huggingface.co/datasets/eli5/discussions/7\r\nLet's continue the discussion there!" ]	"2024-01-10T23:44:20"	"2024-01-11T09:19:18"	"2024-01-11T09:19:17"	NONE	null	### Describe the bug Unable to load `eli5` dataset with streaming. ### Steps to reproduce the bug This fails with FileNotFoundError: https://files.pushshift.io/reddit/submissions ``` from datasets import load_dataset load_dataset("eli5", streaming=True) ``` This works correctly. ``` from datasets import load_dataset load_dataset("eli5") ``` ### Expected behavior - Loading `eli5` dataset should not raise an error under the streaming mode. - Or at the very least, show a warning that streaming mode is not supported with `eli5` dataset. ### Environment info - `datasets` version: 2.16.1 - Platform: Linux-6.2.0-39-generic-x86_64-with-glibc2.35 - Python version: 3.10.12 - `huggingface_hub` version: 0.19.4 - PyArrow version: 12.0.1 - Pandas version: 2.0.3 - `fsspec` version: 2023.6.0	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6579/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6579/timeline	null	not_planned	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6578	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6578/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6578/comments	https://api.github.com/repos/huggingface/datasets/issues/6578/events	https://github.com/huggingface/datasets/pull/6578	2,074,923,321	PR_kwDODunzps5jtthB	6,578	Faster webdataset streaming	{ "login": "lhoestq", "id": 42851186, "node_id": "MDQ6VXNlcjQyODUxMTg2", "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "gravatar_id": "", "url": "https://api.github.com/users/lhoestq", "html_url": "https://github.com/lhoestq", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "repos_url": "https://api.github.com/users/lhoestq/repos", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6578). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "I added faster streaming support using streaming Requests instances in `huggingface_hub` and will be available in 0.21.\r\n\r\nThis PR can be used with https://github.com/huggingface/huggingface_hub/pull/1967 to get fast WebDataset streaming", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004941 / 0.011353 (-0.006412) \| 0.003431 / 0.011008 (-0.007577) \| 0.062768 / 0.038508 (0.024260) \| 0.029212 / 0.023109 (0.006103) \| 0.253053 / 0.275898 (-0.022845) \| 0.273061 / 0.323480 (-0.050419) \| 0.004114 / 0.007986 (-0.003871) \| 0.002713 / 0.004328 (-0.001616) \| 0.048481 / 0.004250 (0.044231) \| 0.040001 / 0.037052 (0.002949) \| 0.268461 / 0.258489 (0.009971) \| 0.287767 / 0.293841 (-0.006074) \| 0.027885 / 0.128546 (-0.100661) \| 0.010474 / 0.075646 (-0.065172) \| 0.207989 / 0.419271 (-0.211282) \| 0.035893 / 0.043533 (-0.007640) \| 0.256833 / 0.255139 (0.001694) \| 0.274197 / 0.283200 (-0.009003) \| 0.017283 / 0.141683 (-0.124400) \| 1.133597 / 1.452155 (-0.318558) \| 1.206661 / 1.492716 (-0.286055) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.089610 / 0.018006 (0.071604) \| 0.306051 / 0.000490 (0.305562) \| 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) \| 0.018686 / 0.037411 (-0.018725) \| 0.061253 / 0.014526 (0.046727) \| 0.073654 / 0.176557 (-0.102903) \| 0.120499 / 0.737135 (-0.616637) \| 0.074827 / 0.296338 (-0.221511) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.293756 / 0.215209 (0.078547) \| 2.897755 / 2.077655 (0.820100) \| 1.558146 / 1.504120 (0.054026) \| 1.458020 / 1.541195 (-0.083174) \| 1.453489 / 1.468490 (-0.015001) \| 0.576666 / 4.584777 (-4.008111) \| 2.423441 / 3.745712 (-1.322271) \| 2.727760 / 5.269862 (-2.542102) \| 1.750287 / 4.565676 (-2.815390) \| 0.062094 / 0.424275 (-0.362181) \| 0.004940 / 0.007607 (-0.002667) \| 0.338815 / 0.226044 (0.112770) \| 3.342677 / 2.268929 (1.073748) \| 1.928335 / 55.444624 (-53.516290) \| 1.629965 / 6.876477 (-5.246511) \| 1.651836 / 2.142072 (-0.490236) \| 0.644354 / 4.805227 (-4.160874) \| 0.117890 / 6.500664 (-6.382774) \| 0.041907 / 0.075469 (-0.033562) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.984399 / 1.841788 (-0.857389) \| 11.516572 / 8.074308 (3.442264) \| 10.326922 / 10.191392 (0.135530) \| 0.130821 / 0.680424 (-0.549603) \| 0.014084 / 0.534201 (-0.520117) \| 0.287078 / 0.579283 (-0.292205) \| 0.263466 / 0.434364 (-0.170898) \| 0.326867 / 0.540337 (-0.213470) \| 0.425313 / 1.386936 (-0.961623) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005305 / 0.011353 (-0.006048) \| 0.003646 / 0.011008 (-0.007362) \| 0.049402 / 0.038508 (0.010894) \| 0.031719 / 0.023109 (0.008610) \| 0.272579 / 0.275898 (-0.003319) \| 0.295241 / 0.323480 (-0.028239) \| 0.004309 / 0.007986 (-0.003677) \| 0.002781 / 0.004328 (-0.001548) \| 0.048134 / 0.004250 (0.043883) \| 0.044702 / 0.037052 (0.007650) \| 0.288201 / 0.258489 (0.029712) \| 0.320351 / 0.293841 (0.026510) \| 0.051327 / 0.128546 (-0.077219) \| 0.011019 / 0.075646 (-0.064628) \| 0.057983 / 0.419271 (-0.361288) \| 0.034211 / 0.043533 (-0.009322) \| 0.272856 / 0.255139 (0.017717) \| 0.290007 / 0.283200 (0.006807) \| 0.018656 / 0.141683 (-0.123027) \| 1.135017 / 1.452155 (-0.317138) \| 1.183904 / 1.492716 (-0.308813) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.090854 / 0.018006 (0.072847) \| 0.299654 / 0.000490 (0.299165) \| 0.000224 / 0.000200 (0.000024) \| 0.000063 / 0.000054 (0.000009) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021882 / 0.037411 (-0.015529) \| 0.075297 / 0.014526 (0.060771) \| 0.086620 / 0.176557 (-0.089937) \| 0.127125 / 0.737135 (-0.610011) \| 0.088622 / 0.296338 (-0.207717) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.287104 / 0.215209 (0.071895) \| 2.802723 / 2.077655 (0.725068) \| 1.570137 / 1.504120 (0.066017) \| 1.452234 / 1.541195 (-0.088961) \| 1.465457 / 1.468490 (-0.003033) \| 0.564965 / 4.584777 (-4.019812) \| 2.416724 / 3.745712 (-1.328988) \| 2.645057 / 5.269862 (-2.624805) \| 1.727599 / 4.565676 (-2.838078) \| 0.063338 / 0.424275 (-0.360937) \| 0.005018 / 0.007607 (-0.002589) \| 0.345280 / 0.226044 (0.119235) \| 3.384323 / 2.268929 (1.115395) \| 1.957227 / 55.444624 (-53.487397) \| 1.667620 / 6.876477 (-5.208856) \| 1.795339 / 2.142072 (-0.346733) \| 0.642049 / 4.805227 (-4.163178) \| 0.114853 / 6.500664 (-6.385811) \| 0.040459 / 0.075469 (-0.035010) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.023640 / 1.841788 (-0.818147) \| 11.998130 / 8.074308 (3.923822) \| 10.858137 / 10.191392 (0.666744) \| 0.130235 / 0.680424 (-0.550189) \| 0.016201 / 0.534201 (-0.518000) \| 0.289743 / 0.579283 (-0.289540) \| 0.275100 / 0.434364 (-0.159264) \| 0.329299 / 0.540337 (-0.211039) \| 0.418632 / 1.386936 (-0.968304) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#98495237883c5ed5a36fac125e68cad97598916f \"CML watermark\")\n" ]	"2024-01-10T18:18:09"	"2024-01-30T18:46:02"	"2024-01-30T18:39:51"	MEMBER	null	requests.get(..., streaming=True) is faster than using HTTP range requests when streaming large TAR files it can be enabled using block_size=0 in fsspec cc @rwightman	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6578/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6578/timeline	null	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6578", "html_url": "https://github.com/huggingface/datasets/pull/6578", "diff_url": "https://github.com/huggingface/datasets/pull/6578.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6578.patch", "merged_at": "2024-01-30T18:39:51" }	true
https://api.github.com/repos/huggingface/datasets/issues/6577	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6577/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6577/comments	https://api.github.com/repos/huggingface/datasets/issues/6577/events	https://github.com/huggingface/datasets/issues/6577	2,074,790,848	I_kwDODunzps57qsvA	6,577	502 Server Errors when streaming large dataset	{ "login": "sanchit-gandhi", "id": 93869735, "node_id": "U_kgDOBZhWpw", "avatar_url": "https://avatars.githubusercontent.com/u/93869735?v=4", "gravatar_id": "", "url": "https://api.github.com/users/sanchit-gandhi", "html_url": "https://github.com/sanchit-gandhi", "followers_url": "https://api.github.com/users/sanchit-gandhi/followers", "following_url": "https://api.github.com/users/sanchit-gandhi/following{/other_user}", "gists_url": "https://api.github.com/users/sanchit-gandhi/gists{/gist_id}", "starred_url": "https://api.github.com/users/sanchit-gandhi/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/sanchit-gandhi/subscriptions", "organizations_url": "https://api.github.com/users/sanchit-gandhi/orgs", "repos_url": "https://api.github.com/users/sanchit-gandhi/repos", "events_url": "https://api.github.com/users/sanchit-gandhi/events{/privacy}", "received_events_url": "https://api.github.com/users/sanchit-gandhi/received_events", "type": "User", "site_admin": false }	[ { "id": 3287858981, "node_id": "MDU6TGFiZWwzMjg3ODU4OTgx", "url": "https://api.github.com/repos/huggingface/datasets/labels/streaming", "name": "streaming", "color": "fef2c0", "default": false, "description": "" } ]	closed	false	null	[]	null	[ "cc @mariosasko @lhoestq ", "Hi! We should be able to avoid this error by retrying to read the data when it happens. I'll open a PR in `huggingface_hub` to address this.", "Thanks for the fix @mariosasko! Just wondering whether \"500 error\" should also be excluded? I got these errors overnight:\r\n\r\n```\r\nhuggingface_hub.utils._errors.HfHubHTTPError: 500 Server Error: Internal Server Error for url: https://huggingface.co/da\r\ntasets/sanchit-gandhi/concatenated-train-set-label-length-256/resolve/91e6a0cd0356605b021384ded813cfcf356a221c/train/tra\r\nin-02618-of-04012.parquet (Request ID: Root=1-65b18b81-627f2c2943bbb8ab68d19ee2;129537bd-1934-4257-a4d8-1cb774f8e1f8) \r\n \r\nInternal Error - We're working hard to fix this as soon as possible! \r\n```", "Gently pining @mariosasko and @Wauplin - when trying to stream this large dataset from the HF Hub, I'm running into `500 Internal Server Errors` as described above. I'd love to be able to use the Hub exclusively to stream data when training, but this error pops up a few times a week, terminating training runs and causing me to have to rewind to the last saved checkpoint. Do we reckon there's a way we can protect Datasets' streaming against these errors? The same reproducer as the [original comment](https://github.com/huggingface/datasets/issues/6577#issue-2074790848) can be used, but it's somewhat random whether we hit a 500 error. Leaving the full traceback below: \r\n\r\n```\r\nTraceback (most recent call last): \r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/torch/utils/data/_utils/worker.py\", line 308, in _worker_loo\r\np \r\n data = fetcher.fetch(index) \r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py\", line 32, in fetch \r\n data.append(next(self.dataset_iter)) \r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 1367, in __iter__ \r\n yield from self._iter_pytorch() \r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 1302, in _iter_pytorch \r\n for key, example in ex_iterable: \r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 987, in __iter__ \r\n for x in self.ex_iterable: \r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 867, in __iter__ \r\n yield from self._iter() \r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 904, in _iter \r\n for key, example in iterator: \r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 679, in __iter__ \r\n yield from self._iter() \r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 741, in _iter [235/1892]\r\n for key, example in iterator: \r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 1119, in __iter__ \r\n for key, example in self.ex_iterable: \r\n File \"/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py\", line 282, in __iter__ \r\n for key, pa_table in self.generate_tables_fn(*self.kwargs): \r\n File \"/home/sanchitgandhi/datasets/src/datasets/packaged_modules/parquet/parquet.py\", line 87, in _generate_tables \r\n for batch_idx, record_batch in enumerate( \r\n File \"pyarrow/_parquet.pyx\", line 1587, in iter_batches \r\n File \"pyarrow/types.pxi\", line 88, in pyarrow.lib._datatype_to_pep3118 \r\n File \"/home/sanchitgandhi/datasets/src/datasets/download/streaming_download_manager.py\", line 342, in read_with_retrie\r\ns \r\n out = read(args, **kwargs) \r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/fsspec/spec.py\", line 1856, in read \r\n out = self.cache._fetch(self.loc, self.loc + length) \r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/fsspec/caching.py\", line 189, in _fetch \r\n self.cache = self.fetcher(start, end) # new block replaces old \r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/huggingface_hub/hf_file_system.py\", line 629, in _fetch_rang\r\ne \r\n hf_raise_for_status(r) \r\n File \"/home/sanchitgandhi/hf/lib/python3.10/site-packages/huggingface_hub/utils/_errors.py\", line 362, in hf_raise_for\r\n_status \r\n raise HfHubHTTPError(str(e), response=response) from e \r\nhuggingface_hub.utils._errors.HfHubHTTPError: 500 Server Error: Internal Server Error for url: https://huggingface.co/da\r\ntasets/sanchit-gandhi/concatenated-train-set-label-length-256-conditioned/resolve/3c3c0cce51df9f9d2e75968bb2a1851894f504\r\n0d/train/train-03515-of-04010.parquet (Request ID: Root=1-65c7c4c4-153fe71401558c8c2d272c8a;fec3ec68-4a0a-4bfd-95ba-b0a0\r\n5684d612) \r\n \r\nInternal Error - We're working hard to fix this as soon as possible! ", "@sanchit-gandhi thanks for the feedback. I've opened https://github.com/huggingface/huggingface_hub/pull/2026 to make the download process more robust. I believe that you've witness this problem on Saturday due to the Hub outage. Hope the PR will make your life easier though :)", "Awesome, thanks @Wauplin! Makes sense re the Hub outage" ]	"2024-01-10T16:59:36"	"2024-02-12T11:46:03"	"2024-01-15T16:05:44"	CONTRIBUTOR	null	### Describe the bug When streaming a [large ASR dataset](https://huggingface.co/datasets/sanchit-gandhi/concatenated-train-set) from the Hug (~3TB) I often encounter 502 Server Errors seemingly randomly during streaming: ``` huggingface_hub.utils._errors.HfHubHTTPError: 502 Server Error: Bad Gateway for url: https://huggingface.co/datasets/sanchit-gandhi/concatenated-train-set/resolve/7d2acc5c59de848e456e951a76e805304d6fb350/train/train-00288-of-07135.parquet ``` This is despite the parquet file definitely existing on the Hub: https://huggingface.co/datasets/sanchit-gandhi/concatenated-train-set/blob/main/train/train-00228-of-07135.parquet And having the correct commit id: [7d2acc5c59de848e456e951a76e805304d6fb350](https://huggingface.co/datasets/sanchit-gandhi/concatenated-train-set/commits/main/train) I’m wondering whether this is coming from datasets? Or from the Hub side? ### Steps to reproduce the bug Reproducer: ```python from datasets import load_dataset from torch.utils.data import DataLoader from tqdm import tqdm NUM_EPOCHS = 20 dataset = load_dataset("sanchit-gandhi/concatenated-train-set", "train", streaming=True) dataset = dataset.with_format("torch") dataloader = DataLoader(dataset["train"], batch_size=256, drop_last=True, pin_memory=True, num_workers=16) for epoch in tqdm(range(NUM_EPOCHS), desc="Epoch", position=0): for batch in tqdm(dataloader, desc="Batch", position=1): continue ``` Running the above script tends to fail within about 2 hours with a traceback like the following: <details> <summary> Traceback: </summary> ```python 1029 for batch in train_loader: 1030 File "/home/sanchitgandhi/hf/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 630, in __next__ 1031 data = self._next_data() 1032 File "/home/sanchitgandhi/hf/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1325, in _next_data 1033 return self._process_data(data) 1034 File "/home/sanchitgandhi/hf/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 1371, in _process_data 1035 data.reraise() 1036 File "/home/sanchitgandhi/hf/lib/python3.8/site-packages/torch/_utils.py", line 694, in reraise 1037 raise exception 1038 huggingface_hub.utils._errors.HfHubHTTPError: Caught HfHubHTTPError in DataLoader worker process 10. 1039 Original Traceback (most recent call last): 1040 File "/home/sanchitgandhi/hf/lib/python3.8/site-packages/huggingface_hub/utils/_errors.py", line 286, in hf_raise_for_status 1041 response.raise_for_status() 1042 File "/home/sanchitgandhi/hf/lib/python3.8/site-packages/requests/models.py", line 1021, in raise_for_status 1043 raise HTTPError(http_error_msg, response=self) 1044 requests.exceptions.HTTPError: 502 Server Error: Bad Gateway for url: https://huggingface.co/datasets/sanchit-gandhi/concatenated-train-set/resolve/7d2acc5c59de848e456e951a76e805304d6fb350/train/train-00288-of-07135.parquet 1045 The above exception was the direct cause of the following exception: 1046 Traceback (most recent call last): 1047 File "/home/sanchitgandhi/hf/lib/python3.8/site-packages/torch/utils/data/_utils/worker.py", line 308, in _worker_loop 1048 data = fetcher.fetch(index) 1049 File "/home/sanchitgandhi/hf/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 32, in fetch 1050 data.append(next(self.dataset_iter)) 1051 File "/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py", line 1363, in __iter__ 1052 yield from self._iter_pytorch() 1053 File "/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py", line 1298, in _iter_pytorch 1054 for key, example in ex_iterable: 1055 File "/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py", line 983, in __iter__ 1056 for x in self.ex_iterable: 1057 File "/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py", line 863, in __iter__ 1058 yield from self._iter() 1059 File "/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py", line 900, in _iter 1060 for key, example in iterator: 1061 File "/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py", line 679, in __iter__ 1062 yield from self._iter() 1063 File "/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py", line 741, in _iter 1064 for key, example in iterator: 1065 File "/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py", line 863, in __iter__ 1066 yield from self._iter() 1067 File "/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py", line 900, in _iter 1068 for key, example in iterator: 1069 File "/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py", line 1115, in __iter__ 1070 for key, example in self.ex_iterable: 1071 File "/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py", line 679, in __iter__ 1072 yield from self._iter() 1073 File "/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py", line 741, in _iter 1074 for key, example in iterator: 1075 File "/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py", line 1115, in __iter__ 1076 for key, example in self.ex_iterable: 1077 File "/home/sanchitgandhi/datasets/src/datasets/iterable_dataset.py", line 282, in __iter__ 1078 for key, pa_table in self.generate_tables_fn(*self.kwargs): 1079 File "/home/sanchitgandhi/datasets/src/datasets/packaged_modules/parquet/parquet.py", line 87, in _generate_tables 1080 for batch_idx, record_batch in enumerate( 1081 File "pyarrow/_parquet.pyx", line 1367, in iter_batches 1082 File "pyarrow/types.pxi", line 88, in pyarrow.lib._datatype_to_pep3118 1083 File "/home/sanchitgandhi/datasets/src/datasets/download/streaming_download_manager.py", line 341, in read_with_retries 1084 out = read(args, **kwargs) 1085 File "/home/sanchitgandhi/hf/lib/python3.8/site-packages/fsspec/spec.py", line 1856, in read 1086 out = self.cache._fetch(self.loc, self.loc + length) 1087 File "/home/sanchitgandhi/hf/lib/python3.8/site-packages/fsspec/caching.py", line 189, in _fetch 1088 self.cache = self.fetcher(start, end) # new block replaces old 1089 File "/home/sanchitgandhi/hf/lib/python3.8/site-packages/huggingface_hub/hf_file_system.py", line 626, in _fetch_range 1090 hf_raise_for_status(r) 1091 File "/home/sanchitgandhi/hf/lib/python3.8/site-packages/huggingface_hub/utils/_errors.py", line 333, in hf_raise_for_status 1092 raise HfHubHTTPError(str(e), response=response) from e 1093 huggingface_hub.utils._errors.HfHubHTTPError: 502 Server Error: Bad Gateway for url: https://huggingface.co/datasets/sanchit-gandhi/concatenated-train-set/resolve/7d2acc5c59de848e456e951a76e805304d6fb350/train/train-00288-of-07135.parquet ``` </details> ### Expected behavior Should be able to stream the dataset without any 502 error. ### Environment info - `datasets` version: 2.16.2.dev0 - Platform: Linux-5.13.0-1023-gcp-x86_64-with-glibc2.29 - Python version: 3.8.10 - `huggingface_hub` version: 0.20.1 - PyArrow version: 14.0.2 - Pandas version: 2.0.3 - `fsspec` version: 2023.10.0	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6577/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6577/timeline	null	completed	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6576	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6576/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6576/comments	https://api.github.com/repos/huggingface/datasets/issues/6576/events	https://github.com/huggingface/datasets/issues/6576	2,073,710,124	I_kwDODunzps57mk4s	6,576	document page 404 not found after redirection	{ "login": "annahung31", "id": 39179888, "node_id": "MDQ6VXNlcjM5MTc5ODg4", "avatar_url": "https://avatars.githubusercontent.com/u/39179888?v=4", "gravatar_id": "", "url": "https://api.github.com/users/annahung31", "html_url": "https://github.com/annahung31", "followers_url": "https://api.github.com/users/annahung31/followers", "following_url": "https://api.github.com/users/annahung31/following{/other_user}", "gists_url": "https://api.github.com/users/annahung31/gists{/gist_id}", "starred_url": "https://api.github.com/users/annahung31/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/annahung31/subscriptions", "organizations_url": "https://api.github.com/users/annahung31/orgs", "repos_url": "https://api.github.com/users/annahung31/repos", "events_url": "https://api.github.com/users/annahung31/events{/privacy}", "received_events_url": "https://api.github.com/users/annahung31/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "Thanks for reporting! I've opened a PR with a fix." ]	"2024-01-10T06:48:14"	"2024-01-17T14:01:31"	"2024-01-17T14:01:31"	NONE	null	### Describe the bug The redirected page encountered 404 not found. ### Steps to reproduce the bug 1. In this tutorial: https://huggingface.co/learn/nlp-course/chapter5/4?fw=pt original md: https://github.com/huggingface/course/blob/2c733c2246b8b7e0e6f19a9e5d15bb12df43b2a3/chapters/en/chapter5/4.mdx#L49 ``` By default, 🤗 Datasets will decompress the files needed to load a dataset. If you want to preserve hard drive space, you can pass `DownloadConfig(delete_extracted=True)` to the `download_config` argument of `load_dataset()`. See the [documentation](https://huggingface.co/docs/datasets/package_reference/builder_classes.html?#datasets.utils.DownloadConfig) for more details. ``` The documentation points to `https://huggingface.co/docs/datasets/package_reference/builder_classes.html?#datasets.utils.DownloadConfig` it shows `The documentation page PACKAGE_REFERENCE/BUILDER_CLASSES.HTML doesn’t exist in v2.16.1, but exists on the main version. Click [here](https://huggingface.co/docs/datasets/main/en/package_reference/builder_classes.html) to redirect to the main version of the documentation.` But the redirected website `https://huggingface.co/docs/datasets/main/en/package_reference/builder_classes.html` is 404 not found. ### Expected behavior I Guess the redirected webisite should be `https://huggingface.co/docs/datasets/main/en/package_reference/builder_classes` (without `.html`) or `https://huggingface.co/docs/datasets/main/en/package_reference/builder_classes#datasets.DownloadConfig`. ### Environment info Datasets main	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6576/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6576/timeline	null	completed	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6575	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6575/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6575/comments	https://api.github.com/repos/huggingface/datasets/issues/6575/events	https://github.com/huggingface/datasets/pull/6575	2,072,617,406	PR_kwDODunzps5jl1V6	6,575	[IterableDataset] Fix `drop_last_batch`in map after shuffling or sharding	{ "login": "lhoestq", "id": 42851186, "node_id": "MDQ6VXNlcjQyODUxMTg2", "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "gravatar_id": "", "url": "https://api.github.com/users/lhoestq", "html_url": "https://github.com/lhoestq", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "repos_url": "https://api.github.com/users/lhoestq/repos", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6575). 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) \| 0.005095 / 0.011353 (-0.006257) \| 0.003531 / 0.011008 (-0.007478) \| 0.063634 / 0.038508 (0.025126) \| 0.031187 / 0.023109 (0.008078) \| 0.246375 / 0.275898 (-0.029523) \| 0.261204 / 0.323480 (-0.062276) \| 0.002898 / 0.007986 (-0.005088) \| 0.003280 / 0.004328 (-0.001049) \| 0.050739 / 0.004250 (0.046488) \| 0.042905 / 0.037052 (0.005852) \| 0.244506 / 0.258489 (-0.013983) \| 0.269403 / 0.293841 (-0.024438) \| 0.027588 / 0.128546 (-0.100959) \| 0.010860 / 0.075646 (-0.064787) \| 0.208332 / 0.419271 (-0.210939) \| 0.035762 / 0.043533 (-0.007771) \| 0.244448 / 0.255139 (-0.010691) \| 0.278464 / 0.283200 (-0.004735) \| 0.019839 / 0.141683 (-0.121844) \| 1.145340 / 1.452155 (-0.306815) \| 1.173240 / 1.492716 (-0.319476) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.090472 / 0.018006 (0.072466) \| 0.300883 / 0.000490 (0.300394) \| 0.000202 / 0.000200 (0.000003) \| 0.000049 / 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) \| 0.017884 / 0.037411 (-0.019527) \| 0.060629 / 0.014526 (0.046103) \| 0.073157 / 0.176557 (-0.103400) \| 0.120065 / 0.737135 (-0.617070) \| 0.074519 / 0.296338 (-0.221820) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.289586 / 0.215209 (0.074377) \| 2.821042 / 2.077655 (0.743387) \| 1.515515 / 1.504120 (0.011395) \| 1.390569 / 1.541195 (-0.150625) \| 1.433238 / 1.468490 (-0.035252) \| 0.567357 / 4.584777 (-4.017420) \| 2.345483 / 3.745712 (-1.400229) \| 2.803964 / 5.269862 (-2.465898) \| 1.775343 / 4.565676 (-2.790334) \| 0.063186 / 0.424275 (-0.361089) \| 0.005013 / 0.007607 (-0.002594) \| 0.335607 / 0.226044 (0.109562) \| 3.307071 / 2.268929 (1.038143) \| 1.875228 / 55.444624 (-53.569396) \| 1.618286 / 6.876477 (-5.258191) \| 1.615963 / 2.142072 (-0.526109) \| 0.642633 / 4.805227 (-4.162594) \| 0.117222 / 6.500664 (-6.383443) \| 0.042590 / 0.075469 (-0.032879) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.960724 / 1.841788 (-0.881064) \| 11.652978 / 8.074308 (3.578670) \| 10.069318 / 10.191392 (-0.122074) \| 0.128161 / 0.680424 (-0.552263) \| 0.014095 / 0.534201 (-0.520106) \| 0.288386 / 0.579283 (-0.290897) \| 0.260373 / 0.434364 (-0.173991) \| 0.327443 / 0.540337 (-0.212894) \| 0.419020 / 1.386936 (-0.967916) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005018 / 0.011353 (-0.006335) \| 0.003503 / 0.011008 (-0.007505) \| 0.049718 / 0.038508 (0.011210) \| 0.029311 / 0.023109 (0.006202) \| 0.271097 / 0.275898 (-0.004801) \| 0.297370 / 0.323480 (-0.026110) \| 0.004230 / 0.007986 (-0.003755) \| 0.002741 / 0.004328 (-0.001587) \| 0.049686 / 0.004250 (0.045435) \| 0.044171 / 0.037052 (0.007119) \| 0.274851 / 0.258489 (0.016362) \| 0.309554 / 0.293841 (0.015714) \| 0.029488 / 0.128546 (-0.099058) \| 0.010767 / 0.075646 (-0.064880) \| 0.057739 / 0.419271 (-0.361532) \| 0.053319 / 0.043533 (0.009786) \| 0.277739 / 0.255139 (0.022600) \| 0.291341 / 0.283200 (0.008142) \| 0.019587 / 0.141683 (-0.122096) \| 1.113823 / 1.452155 (-0.338332) \| 1.169409 / 1.492716 (-0.323307) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091889 / 0.018006 (0.073883) \| 0.309162 / 0.000490 (0.308672) \| 0.000222 / 0.000200 (0.000022) \| 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) \| 0.022202 / 0.037411 (-0.015209) \| 0.076113 / 0.014526 (0.061587) \| 0.088416 / 0.176557 (-0.088141) \| 0.126822 / 0.737135 (-0.610314) \| 0.089540 / 0.296338 (-0.206798) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.293697 / 0.215209 (0.078487) \| 2.880680 / 2.077655 (0.803026) \| 1.580122 / 1.504120 (0.076002) \| 1.449492 / 1.541195 (-0.091703) \| 1.478900 / 1.468490 (0.010410) \| 0.563402 / 4.584777 (-4.021375) \| 2.408692 / 3.745712 (-1.337020) \| 2.794108 / 5.269862 (-2.475754) \| 1.728549 / 4.565676 (-2.837128) \| 0.063152 / 0.424275 (-0.361123) \| 0.004985 / 0.007607 (-0.002622) \| 0.343340 / 0.226044 (0.117295) \| 3.426454 / 2.268929 (1.157525) \| 1.932918 / 55.444624 (-53.511706) \| 1.649533 / 6.876477 (-5.226944) \| 1.673416 / 2.142072 (-0.468656) \| 0.640000 / 4.805227 (-4.165227) \| 0.115501 / 6.500664 (-6.385163) \| 0.040756 / 0.075469 (-0.034713) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.992468 / 1.841788 (-0.849319) \| 12.392072 / 8.074308 (4.317764) \| 11.025362 / 10.191392 (0.833970) \| 0.130788 / 0.680424 (-0.549635) \| 0.015647 / 0.534201 (-0.518554) \| 0.285914 / 0.579283 (-0.293369) \| 0.277208 / 0.434364 (-0.157156) \| 0.322917 / 0.540337 (-0.217420) \| 0.427308 / 1.386936 (-0.959628) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#999790bcf52f883de1b5233c5632ae73395021cf \"CML watermark\")\n" ]	"2024-01-09T15:35:31"	"2024-01-11T16:16:54"	"2024-01-11T16:10:30"	MEMBER	null	It was not taken into account e.g. when passing to a DataLoader with num_workers>0 Fix https://github.com/huggingface/datasets/issues/6565	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6575/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6575/timeline	null	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6575", "html_url": "https://github.com/huggingface/datasets/pull/6575", "diff_url": "https://github.com/huggingface/datasets/pull/6575.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6575.patch", "merged_at": "2024-01-11T16:10:30" }	true
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	{ "login": "lhoestq", "id": 42851186, "node_id": "MDQ6VXNlcjQyODUxMTg2", "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "gravatar_id": "", "url": "https://api.github.com/users/lhoestq", "html_url": "https://github.com/lhoestq", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "repos_url": "https://api.github.com/users/lhoestq/repos", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_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" ]	"2024-01-09T15:16:16"	"2024-01-09T16:11:33"	"2024-01-09T16:05:13"	MEMBER	null	...now that `squad` and `paws` don't have a script anymore	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6574/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6574/timeline	null	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6574", "html_url": "https://github.com/huggingface/datasets/pull/6574", "diff_url": "https://github.com/huggingface/datasets/pull/6574.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6574.patch", "merged_at": "2024-01-09T16:05:13" }	true
https://api.github.com/repos/huggingface/datasets/issues/6573	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6573/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6573/comments	https://api.github.com/repos/huggingface/datasets/issues/6573/events	https://github.com/huggingface/datasets/pull/6573	2,072,553,951	PR_kwDODunzps5jlnaj	6,573	[WebDataset] Audio support and bug fixes	{ "login": "lhoestq", "id": 42851186, "node_id": "MDQ6VXNlcjQyODUxMTg2", "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "gravatar_id": "", "url": "https://api.github.com/users/lhoestq", "html_url": "https://github.com/lhoestq", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "repos_url": "https://api.github.com/users/lhoestq/repos", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6573). 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) \| 0.005421 / 0.011353 (-0.005932) \| 0.003915 / 0.011008 (-0.007094) \| 0.065109 / 0.038508 (0.026601) \| 0.031274 / 0.023109 (0.008165) \| 0.248702 / 0.275898 (-0.027196) \| 0.275688 / 0.323480 (-0.047792) \| 0.003007 / 0.007986 (-0.004978) \| 0.002942 / 0.004328 (-0.001387) \| 0.050928 / 0.004250 (0.046678) \| 0.043751 / 0.037052 (0.006699) \| 0.263860 / 0.258489 (0.005371) \| 0.291499 / 0.293841 (-0.002342) \| 0.028268 / 0.128546 (-0.100278) \| 0.011467 / 0.075646 (-0.064180) \| 0.210531 / 0.419271 (-0.208740) \| 0.036302 / 0.043533 (-0.007231) \| 0.251565 / 0.255139 (-0.003574) \| 0.272001 / 0.283200 (-0.011199) \| 0.020370 / 0.141683 (-0.121313) \| 1.175493 / 1.452155 (-0.276662) \| 1.229167 / 1.492716 (-0.263550) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.095713 / 0.018006 (0.077707) \| 0.308912 / 0.000490 (0.308422) \| 0.000231 / 0.000200 (0.000031) \| 0.000057 / 0.000054 (0.000002) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.019080 / 0.037411 (-0.018332) \| 0.062043 / 0.014526 (0.047517) \| 0.075642 / 0.176557 (-0.100915) \| 0.122789 / 0.737135 (-0.614347) \| 0.077507 / 0.296338 (-0.218831) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.279929 / 0.215209 (0.064720) \| 2.773336 / 2.077655 (0.695682) \| 1.481740 / 1.504120 (-0.022379) \| 1.357207 / 1.541195 (-0.183987) \| 1.414314 / 1.468490 (-0.054176) \| 0.573776 / 4.584777 (-4.011000) \| 2.399273 / 3.745712 (-1.346439) \| 2.918885 / 5.269862 (-2.350977) \| 1.798867 / 4.565676 (-2.766809) \| 0.064352 / 0.424275 (-0.359923) \| 0.005164 / 0.007607 (-0.002443) \| 0.337141 / 0.226044 (0.111097) \| 3.402291 / 2.268929 (1.133362) \| 1.854308 / 55.444624 (-53.590317) \| 1.555789 / 6.876477 (-5.320687) \| 1.625873 / 2.142072 (-0.516199) \| 0.658589 / 4.805227 (-4.146638) \| 0.122273 / 6.500664 (-6.378391) \| 0.043910 / 0.075469 (-0.031560) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.933127 / 1.841788 (-0.908660) \| 12.436657 / 8.074308 (4.362348) \| 10.891750 / 10.191392 (0.700358) \| 0.143236 / 0.680424 (-0.537187) \| 0.014636 / 0.534201 (-0.519565) \| 0.290375 / 0.579283 (-0.288908) \| 0.275473 / 0.434364 (-0.158891) \| 0.327007 / 0.540337 (-0.213331) \| 0.425888 / 1.386936 (-0.961048) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005820 / 0.011353 (-0.005533) \| 0.003684 / 0.011008 (-0.007324) \| 0.050234 / 0.038508 (0.011726) \| 0.030744 / 0.023109 (0.007634) \| 0.279560 / 0.275898 (0.003662) \| 0.305829 / 0.323480 (-0.017651) \| 0.004053 / 0.007986 (-0.003933) \| 0.002743 / 0.004328 (-0.001585) \| 0.051087 / 0.004250 (0.046836) \| 0.047601 / 0.037052 (0.010549) \| 0.290441 / 0.258489 (0.031951) \| 0.326719 / 0.293841 (0.032878) \| 0.030245 / 0.128546 (-0.098301) \| 0.011508 / 0.075646 (-0.064139) \| 0.058436 / 0.419271 (-0.360835) \| 0.059235 / 0.043533 (0.015702) \| 0.278978 / 0.255139 (0.023839) \| 0.298146 / 0.283200 (0.014946) \| 0.020926 / 0.141683 (-0.120757) \| 1.205608 / 1.452155 (-0.246547) \| 1.224920 / 1.492716 (-0.267796) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.098384 / 0.018006 (0.080378) \| 0.307975 / 0.000490 (0.307485) \| 0.000233 / 0.000200 (0.000033) \| 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) \| 0.023012 / 0.037411 (-0.014399) \| 0.077450 / 0.014526 (0.062924) \| 0.089314 / 0.176557 (-0.087242) \| 0.128610 / 0.737135 (-0.608526) \| 0.091521 / 0.296338 (-0.204818) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.310620 / 0.215209 (0.095411) \| 3.030374 / 2.077655 (0.952720) \| 1.653468 / 1.504120 (0.149348) \| 1.526860 / 1.541195 (-0.014334) \| 1.605328 / 1.468490 (0.136838) \| 0.585444 / 4.584777 (-3.999333) \| 2.471700 / 3.745712 (-1.274012) \| 2.791268 / 5.269862 (-2.478594) \| 1.815965 / 4.565676 (-2.749712) \| 0.064713 / 0.424275 (-0.359562) \| 0.005095 / 0.007607 (-0.002512) \| 0.364843 / 0.226044 (0.138799) \| 3.601633 / 2.268929 (1.332705) \| 2.022642 / 55.444624 (-53.421982) \| 1.737164 / 6.876477 (-5.139312) \| 1.923636 / 2.142072 (-0.218437) \| 0.670673 / 4.805227 (-4.134554) \| 0.121547 / 6.500664 (-6.379117) \| 0.042880 / 0.075469 (-0.032589) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.987873 / 1.841788 (-0.853914) \| 13.279323 / 8.074308 (5.205015) \| 11.480806 / 10.191392 (1.289414) \| 0.142118 / 0.680424 (-0.538306) \| 0.016486 / 0.534201 (-0.517715) \| 0.291617 / 0.579283 (-0.287667) \| 0.284639 / 0.434364 (-0.149725) \| 0.329596 / 0.540337 (-0.210742) \| 0.430168 / 1.386936 (-0.956768) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4a5b7d9562231c9fbb36e30c1cf0ac54133d1e77 \"CML watermark\")\n" ]	"2024-01-09T15:03:04"	"2024-01-11T16:17:28"	"2024-01-11T16:11:04"	MEMBER	null	- Add audio support - Fix an issue where user-provided features with additional fields are not taken into account Close https://github.com/huggingface/datasets/issues/6569	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6573/reactions", "total_count": 1, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 1, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6573/timeline	null	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6573", "html_url": "https://github.com/huggingface/datasets/pull/6573", "diff_url": "https://github.com/huggingface/datasets/pull/6573.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6573.patch", "merged_at": "2024-01-11T16:11:04" }	true
https://api.github.com/repos/huggingface/datasets/issues/6572	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6572/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6572/comments	https://api.github.com/repos/huggingface/datasets/issues/6572/events	https://github.com/huggingface/datasets/pull/6572	2,072,384,281	PR_kwDODunzps5jlCO5	6,572	Adding option for multipart achive download	{ "login": "jpodivin", "id": 66251151, "node_id": "MDQ6VXNlcjY2MjUxMTUx", "avatar_url": "https://avatars.githubusercontent.com/u/66251151?v=4", "gravatar_id": "", "url": "https://api.github.com/users/jpodivin", "html_url": "https://github.com/jpodivin", "followers_url": "https://api.github.com/users/jpodivin/followers", "following_url": "https://api.github.com/users/jpodivin/following{/other_user}", "gists_url": "https://api.github.com/users/jpodivin/gists{/gist_id}", "starred_url": "https://api.github.com/users/jpodivin/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/jpodivin/subscriptions", "organizations_url": "https://api.github.com/users/jpodivin/orgs", "repos_url": "https://api.github.com/users/jpodivin/repos", "events_url": "https://api.github.com/users/jpodivin/events{/privacy}", "received_events_url": "https://api.github.com/users/jpodivin/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "On closer examination, this appears to be unnecessary. " ]	"2024-01-09T13:35:44"	"2024-02-25T08:13:01"	"2024-02-25T08:13:01"	NONE	null	Right now we can only download multiple separate archives or a single file archive, but not multipart archives, such as those produced by `tar --multi-volume`. This PR allows for downloading and extraction of archives split into multiple parts. With the new `multi_part` field of the `DownloadConfig` set, the downloader will first retrieve all the files and attempt to concatenate them before starting extraction. This will obviously fail if files retrieved are actually multiple separate archives, so the option is set to `False` by default. Tests and docs incoming.	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6572/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6572/timeline	null	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6572", "html_url": "https://github.com/huggingface/datasets/pull/6572", "diff_url": "https://github.com/huggingface/datasets/pull/6572.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6572.patch", "merged_at": null }	true
https://api.github.com/repos/huggingface/datasets/issues/6571	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6571/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6571/comments	https://api.github.com/repos/huggingface/datasets/issues/6571/events	https://github.com/huggingface/datasets/issues/6571	2,072,111,000	I_kwDODunzps57geeY	6,571	Make DatasetDict.column_names return a list instead of dict	{ "login": "albertvillanova", "id": 8515462, "node_id": "MDQ6VXNlcjg1MTU0NjI=", "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "gravatar_id": "", "url": "https://api.github.com/users/albertvillanova", "html_url": "https://github.com/albertvillanova", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "repos_url": "https://api.github.com/users/albertvillanova/repos", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "type": "User", "site_admin": false }	[ { "id": 1935892871, "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement", "name": "enhancement", "color": "a2eeef", "default": true, "description": "New feature or request" } ]	open	false	null	[]	null	[]	"2024-01-09T10:45:17"	"2024-01-09T10:45:17"	null	MEMBER	null	Currently, `DatasetDict.column_names` returns a dict, with each split name as keys and the corresponding list of column names as values. However, by construction, all splits have the same column names. I think it makes more sense to return a single list with the column names, which is the same for all the split keys.	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6571/reactions", "total_count": 1, "+1": 1, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6571/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6570	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6570/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6570/comments	https://api.github.com/repos/huggingface/datasets/issues/6570/events	https://github.com/huggingface/datasets/issues/6570	2,071,805,265	I_kwDODunzps57fT1R	6,570	No online docs for 2.16 release	{ "login": "albertvillanova", "id": 8515462, "node_id": "MDQ6VXNlcjg1MTU0NjI=", "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "gravatar_id": "", "url": "https://api.github.com/users/albertvillanova", "html_url": "https://github.com/albertvillanova", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "repos_url": "https://api.github.com/users/albertvillanova/repos", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "type": "User", "site_admin": false }	[ { "id": 1935892857, "node_id": "MDU6TGFiZWwxOTM1ODkyODU3", "url": "https://api.github.com/repos/huggingface/datasets/labels/bug", "name": "bug", "color": "d73a4a", "default": true, "description": "Something isn't working" }, { "id": 1935892861, "node_id": "MDU6TGFiZWwxOTM1ODkyODYx", "url": "https://api.github.com/repos/huggingface/datasets/labels/documentation", "name": "documentation", "color": "0075ca", "default": true, "description": "Improvements or additions to documentation" } ]	closed	false	null	[]	null	[ "Though the `build / build_main_documentation` CI job ran for 2.16.0: https://github.com/huggingface/datasets/actions/runs/7300836845/job/19896275099 🤔 ", "Yes, I saw it. Maybe @mishig25 can give us some hint...", "fixed https://huggingface.co/docs/datasets/v2.16.0/en/index", "Still missing 2.16.1.", "> Still missing 2.16.1.\r\n\r\nre-running the doc-buld job for the missing ones should fix\r\n\r\n", "Re-running the job for the 2.16.1 release: https://github.com/huggingface/datasets/actions/runs/7365231552/job/20310278583", "Fixed for 2.16.1: https://huggingface.co/docs/datasets/v2.16.1/en/index" ]	"2024-01-09T07:43:30"	"2024-01-09T16:45:50"	"2024-01-09T16:45:50"	MEMBER	null	We do not have the online docs for the latest minor release 2.16 (2.16.0 nor 2.16.1). In the online docs, the latest version appearing is 2.15.0: https://huggingface.co/docs/datasets/index ![Screenshot from 2024-01-09 08-43-08](https://github.com/huggingface/datasets/assets/8515462/83613222-867f-41f4-8833-7a4a76582f44)	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6570/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6570/timeline	null	completed	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6569	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6569/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6569/comments	https://api.github.com/repos/huggingface/datasets/issues/6569/events	https://github.com/huggingface/datasets/issues/6569	2,070,251,122	I_kwDODunzps57ZYZy	6,569	WebDataset ignores features defined in YAML or passed to load_dataset	{ "login": "lhoestq", "id": 42851186, "node_id": "MDQ6VXNlcjQyODUxMTg2", "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "gravatar_id": "", "url": "https://api.github.com/users/lhoestq", "html_url": "https://github.com/lhoestq", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "repos_url": "https://api.github.com/users/lhoestq/repos", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "type": "User", "site_admin": false }	[]	closed	false	{ "login": "lhoestq", "id": 42851186, "node_id": "MDQ6VXNlcjQyODUxMTg2", "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "gravatar_id": "", "url": "https://api.github.com/users/lhoestq", "html_url": "https://github.com/lhoestq", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "repos_url": "https://api.github.com/users/lhoestq/repos", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "type": "User", "site_admin": false }	[ { "login": "lhoestq", "id": 42851186, "node_id": "MDQ6VXNlcjQyODUxMTg2", "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "gravatar_id": "", "url": "https://api.github.com/users/lhoestq", "html_url": "https://github.com/lhoestq", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "repos_url": "https://api.github.com/users/lhoestq/repos", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "type": "User", "site_admin": false } ]	null	[]	"2024-01-08T11:24:21"	"2024-01-11T16:11:06"	"2024-01-11T16:11:05"	MEMBER	null	we should not override if the features exist already https://github.com/huggingface/datasets/blob/d26abadce0b884db32382b92422d8a6aa997d40a/src/datasets/packaged_modules/webdataset/webdataset.py#L78-L85	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6569/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6569/timeline	null	completed	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6568	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6568/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6568/comments	https://api.github.com/repos/huggingface/datasets/issues/6568/events	https://github.com/huggingface/datasets/issues/6568	2,069,922,151	I_kwDODunzps57YIFn	6,568	keep_in_memory=True does not seem to work	{ "login": "kopyl", "id": 17604849, "node_id": "MDQ6VXNlcjE3NjA0ODQ5", "avatar_url": "https://avatars.githubusercontent.com/u/17604849?v=4", "gravatar_id": "", "url": "https://api.github.com/users/kopyl", "html_url": "https://github.com/kopyl", "followers_url": "https://api.github.com/users/kopyl/followers", "following_url": "https://api.github.com/users/kopyl/following{/other_user}", "gists_url": "https://api.github.com/users/kopyl/gists{/gist_id}", "starred_url": "https://api.github.com/users/kopyl/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/kopyl/subscriptions", "organizations_url": "https://api.github.com/users/kopyl/orgs", "repos_url": "https://api.github.com/users/kopyl/repos", "events_url": "https://api.github.com/users/kopyl/events{/privacy}", "received_events_url": "https://api.github.com/users/kopyl/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	null	[ "Seems like I just used the old code which did not have `keep_in_memory=True` argument, sorry.\r\n\r\nAlthough i encountered a different problem – at 97% my python process just hung for around 11 minutes with no logs (when running dataset.map without `keep_in_memory=True` over around 3 million of dataset samples)...", "Can you open a new issue and provide a bit more details ? What kind of map operations did you run ?", "Hey. I will try to find some free time to describe it.\r\n\r\n(can't do it now, cause i need to reproduce it myself to be sure about everything, which requires spinning a new Azuree VM, copying a huge dataset to drive from network disk for a long time etc...)", "@lhoestq loading dataset like this does not spawn 50 python processes:\r\n\r\n```\r\ndatasets.load_dataset(\"/preprocessed_2256k/train\", num_proc=50)\r\n```\r\n\r\nI have 64 vCPU so i hoped it could speed up the dataset loading...\r\n\r\nMy dataset onlly has images and metadata.csv with text column alongside image file path column", "now noticed\r\n```\r\n'Setting num_proc from 50 back to 1 for the train split to disable multiprocessing as it only contains one shard\r\n```\r\n\r\nAny way to work around this?", "@lhoestq thanks, [this helped](https://github.com/huggingface/datasets/blob/9d6d16117a30ba345b0236407975f701c5b288d4/src/datasets/arrow_dataset.py#L1053)\r\n\r\n" ]	"2024-01-08T08:03:58"	"2024-01-13T04:53:04"	null	NONE	null	UPD: [Fixed](https://github.com/huggingface/datasets/issues/6568#issuecomment-1880817794) . But a new issue came up :(	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6568/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6568/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6567	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6567/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6567/comments	https://api.github.com/repos/huggingface/datasets/issues/6567/events	https://github.com/huggingface/datasets/issues/6567	2,069,808,842	I_kwDODunzps57XsbK	6,567	AttributeError: 'str' object has no attribute 'to'	{ "login": "andysingal", "id": 20493493, "node_id": "MDQ6VXNlcjIwNDkzNDkz", "avatar_url": "https://avatars.githubusercontent.com/u/20493493?v=4", "gravatar_id": "", "url": "https://api.github.com/users/andysingal", "html_url": "https://github.com/andysingal", "followers_url": "https://api.github.com/users/andysingal/followers", "following_url": "https://api.github.com/users/andysingal/following{/other_user}", "gists_url": "https://api.github.com/users/andysingal/gists{/gist_id}", "starred_url": "https://api.github.com/users/andysingal/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/andysingal/subscriptions", "organizations_url": "https://api.github.com/users/andysingal/orgs", "repos_url": "https://api.github.com/users/andysingal/repos", "events_url": "https://api.github.com/users/andysingal/events{/privacy}", "received_events_url": "https://api.github.com/users/andysingal/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "I think you are reporting an issue with the `transformers` library. Note this is the repository of the `datasets` library. I recommend that you open an issue in their repository: https://github.com/huggingface/transformers/issues\r\n\r\nEDIT: I have not the rights to transfer the issue\r\n~~I am transferring your issue to their repository.~~", "Thanks, I hope someone from transformers library addresses this issue.\r\n\r\nOn Mon, Jan 8, 2024 at 15:29 Albert Villanova del Moral <\r\n*@.> wrote:\r\n\r\n> I think you are reporting an issue with the transformers library. Note\r\n> this is the repository of the datasets library. I am transferring your\r\n> issue to their repository.\r\n>\r\n> —\r\n> Reply to this email directly, view it on GitHub\r\n> <https://github.com/huggingface/datasets/issues/6567#issuecomment-1880688586>,\r\n> or unsubscribe\r\n> <https://github.com/notifications/unsubscribe-auth/AE4LJNOYMD6WJMXFKPMH6DLYNO7PJAVCNFSM6AAAAABBQ63HWOVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMYTQOBQGY4DQNJYGY>\r\n> .\r\n> You are receiving this because you authored the thread.Message ID:\r\n> @.*>\r\n>\r\n", "@andysingal, I recommend that you open an issue in their repository: https://github.com/huggingface/transformers/issues\r\nI don't have the rights to transfer this issue to their repo." ]	"2024-01-08T06:40:21"	"2024-01-08T11:56:19"	"2024-01-08T10:03:17"	NONE	null	### Describe the bug ``` -------------------------------------------------------------------------- AttributeError Traceback (most recent call last) [<ipython-input-6-80c6086794e8>](https://localhost:8080/#) in <cell line: 10>() 8 report_to="wandb") 9 ---> 10 trainer = Trainer( 11 model=model, 12 args=training_args, 1 frames [/usr/local/lib/python3.10/dist-packages/transformers/trainer.py](https://localhost:8080/#) in _move_model_to_device(self, model, device) 688 689 def _move_model_to_device(self, model, device): --> 690 model = model.to(device) 691 # Moving a model to an XLA device disconnects the tied weights, so we have to retie them. 692 if self.args.parallel_mode == ParallelMode.TPU and hasattr(model, "tie_weights"): AttributeError: 'str' object has no attribute 'to' ``` ### Steps to reproduce the bug here is the notebook: ``` https://colab.research.google.com/drive/10JDBNsLlYrQdnI2FWfDK3F5M8wvVUDXG?usp=sharing ``` ### Expected behavior run the Training ### Environment info Colab Notebook , T4	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6567/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6567/timeline	null	completed	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6566	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6566/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6566/comments	https://api.github.com/repos/huggingface/datasets/issues/6566/events	https://github.com/huggingface/datasets/issues/6566	2,069,495,429	I_kwDODunzps57Wf6F	6,566	I train controlnet_sdxl in bf16 datatype, got unsupported ERROR in datasets	{ "login": "HelloWorldBeginner", "id": 25008090, "node_id": "MDQ6VXNlcjI1MDA4MDkw", "avatar_url": "https://avatars.githubusercontent.com/u/25008090?v=4", "gravatar_id": "", "url": "https://api.github.com/users/HelloWorldBeginner", "html_url": "https://github.com/HelloWorldBeginner", "followers_url": "https://api.github.com/users/HelloWorldBeginner/followers", "following_url": "https://api.github.com/users/HelloWorldBeginner/following{/other_user}", "gists_url": "https://api.github.com/users/HelloWorldBeginner/gists{/gist_id}", "starred_url": "https://api.github.com/users/HelloWorldBeginner/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/HelloWorldBeginner/subscriptions", "organizations_url": "https://api.github.com/users/HelloWorldBeginner/orgs", "repos_url": "https://api.github.com/users/HelloWorldBeginner/repos", "events_url": "https://api.github.com/users/HelloWorldBeginner/events{/privacy}", "received_events_url": "https://api.github.com/users/HelloWorldBeginner/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	null	[ "I also see the same error and get passed it by casting that line to float. \r\n\r\nso `for x in obj.detach().cpu().numpy()` becomes `for x in obj.detach().to(torch.float).cpu().numpy()`\r\n\r\nI got the idea from [this ](https://github.com/kohya-ss/sd-webui-additional-networks/pull/128/files) PR where someone was facing a similar issue (in a different repository). I guess numpy doesn't support bfloat16.\r\n\r\n" ]	"2024-01-08T02:37:03"	"2024-01-20T00:29:18"	null	NONE	null	### Describe the bug ``` Traceback (most recent call last): File "train_controlnet_sdxl.py", line 1252, in <module> main(args) File "train_controlnet_sdxl.py", line 1013, in main train_dataset = train_dataset.map(compute_embeddings_fn, batched=True, new_fingerprint=new_fingerprint) File "/home/miniconda3/envs/mhh_df/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 592, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, args, kwargs) File "/home/miniconda3/envs/mhh_df/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 557, in wrapper out: Union["Dataset", "DatasetDict"] = func(self, args, kwargs) File "/home/miniconda3/envs/mhh_df/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 3093, in map for rank, done, content in Dataset._map_single(dataset_kwargs): File "/home/miniconda3/envs/mhh_df/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 3489, in _map_single writer.write_batch(batch) File "/home/miniconda3/envs/mhh_df/lib/python3.8/site-packages/datasets/arrow_writer.py", line 557, in write_batch arrays.append(pa.array(typed_sequence)) File "pyarrow/array.pxi", line 248, in pyarrow.lib.array File "pyarrow/array.pxi", line 113, in pyarrow.lib._handle_arrow_array_protocol File "/home/miniconda3/envs/mhh_df/lib/python3.8/site-packages/datasets/arrow_writer.py", line 191, in __arrow_array__ out = pa.array(cast_to_python_objects(data, only_1d_for_numpy=True)) File "/home/miniconda3/envs/mhh_df/lib/python3.8/site-packages/datasets/features/features.py", line 447, in cast_to_python_objects return _cast_to_python_objects( File "/home/miniconda3/envs/mhh_df/lib/python3.8/site-packages/datasets/features/features.py", line 324, in _cast_to_python_objects for x in obj.detach().cpu().numpy() TypeError: Got unsupported ScalarType BFloat16 ``` ### Steps to reproduce the bug Here is my train script I use BF16 type，I use diffusers train my model ``` export MODEL_DIR="/home/mhh/sd_models/stable-diffusion-xl-base-1.0" export OUTPUT_DIR="./control_net" export VAE_NAME="/home/mhh/sd_models/sdxl-vae-fp16-fix" accelerate launch train_controlnet_sdxl.py \ --pretrained_model_name_or_path=$MODEL_DIR \ --output_dir=$OUTPUT_DIR \ --pretrained_vae_model_name_or_path=$VAE_NAME \ --dataset_name=/home/mhh/sd_datasets/fusing/fill50k \ --mixed_precision="bf16" \ --resolution=1024 \ --learning_rate=1e-5 \ --max_train_steps=200 \ --validation_image "/home/mhh/sd_datasets/controlnet_image/conditioning_image_1.png" "/home/mhh/sd_datasets/controlnet_image/conditioning_image_2.png" \ --validation_prompt "red circle with blue background" "cyan circle with brown floral background" \ --validation_steps=50 \ --train_batch_size=1 \ --gradient_accumulation_steps=4 \ --report_to="wandb" \ --seed=42 \ ``` ### Expected behavior When I changed the data type to fp16, it worked. ### Environment info datasets 2.16.1 numpy 1.24.4	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6566/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6566/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6565	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6565/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6565/comments	https://api.github.com/repos/huggingface/datasets/issues/6565/events	https://github.com/huggingface/datasets/issues/6565	2,068,939,670	I_kwDODunzps57UYOW	6,565	`drop_last_batch=True` for IterableDataset map function is ignored with multiprocessing DataLoader	{ "login": "naba89", "id": 12119806, "node_id": "MDQ6VXNlcjEyMTE5ODA2", "avatar_url": "https://avatars.githubusercontent.com/u/12119806?v=4", "gravatar_id": "", "url": "https://api.github.com/users/naba89", "html_url": "https://github.com/naba89", "followers_url": "https://api.github.com/users/naba89/followers", "following_url": "https://api.github.com/users/naba89/following{/other_user}", "gists_url": "https://api.github.com/users/naba89/gists{/gist_id}", "starred_url": "https://api.github.com/users/naba89/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/naba89/subscriptions", "organizations_url": "https://api.github.com/users/naba89/orgs", "repos_url": "https://api.github.com/users/naba89/repos", "events_url": "https://api.github.com/users/naba89/events{/privacy}", "received_events_url": "https://api.github.com/users/naba89/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "My current workaround this issue is to return `None` in the second element and then filter out samples which have `None` in them.\r\n\r\n```python\r\ndef merge_samples(batch):\r\n if len(batch['a']) == 1:\r\n batch['c'] = [batch['a'][0]]\r\n batch['d'] = [None]\r\n else:\r\n batch['c'] = [batch['a'][0]]\r\n batch['d'] = [batch['a'][1]]\r\n return batch\r\n \r\ndef filter_fn(x):\r\n return x['d'] is not None\r\n\r\n# other code...\r\nmapped = mapped.filter(filter_fn)\r\n```" ]	"2024-01-07T02:46:50"	"2024-01-11T16:10:31"	"2024-01-11T16:10:31"	NONE	null	### Describe the bug Scenario: - Interleaving two iterable datasets of unequal lengths (`all_exhausted`), followed by a batch mapping with batch size 2 to effectively merge the two datasets and get a sample from each dataset in a single batch, with `drop_last_batch=True` to skip the last batch in case it doesn't have two samples. What works: - Using DataLoader with `num_workers=0` What does not work: - Using DataLoader with `num_workers=1`, errors in the last batch. Basically, `drop_last_batch=True` is ignored when using multiple dataloading workers. Please take a look at the minimal repro script below. ### Steps to reproduce the bug ```python from datasets import Dataset, interleave_datasets from torch.utils.data import DataLoader def merge_samples(batch): assert len(batch['a']) == 2, "Batch size must be 2" batch['c'] = [batch['a'][0]] batch['d'] = [batch['a'][1]] return batch def gen1(): for ii in range(1, 8385): yield {"a": ii} def gen2(): for ii in range(1, 5302): yield {"a": ii} if __name__ == '__main__': dataset1 = Dataset.from_generator(gen1).to_iterable_dataset(num_shards=1024) dataset2 = Dataset.from_generator(gen2).to_iterable_dataset(num_shards=1024) interleaved = interleave_datasets([dataset1, dataset2], stopping_strategy="all_exhausted") mapped = interleaved.map(merge_samples, batched=True, batch_size=2, remove_columns=interleaved.column_names, drop_last_batch=True) # Works loader = DataLoader(mapped, batch_size=32, num_workers=0) i = 0 for b in loader: print(i, b['c'].shape, b['d'].shape) i += 1 print("DataLoader with num_workers=0 works") # Doesn't work loader = DataLoader(mapped, batch_size=32, num_workers=1) i = 0 for b in loader: print(i, b['c'].shape, b['d'].shape) i += 1 ``` ### Expected behavior `drop_last_batch=True` should have same behaviour for `num_workers=0` and `num_workers>=1` ### Environment info - `datasets` version: 2.16.1 - Platform: macOS-10.16-x86_64-i386-64bit - Python version: 3.10.12 - `huggingface_hub` version: 0.20.2 - PyArrow version: 12.0.1 - Pandas version: 2.0.3 - `fsspec` version: 2023.6.0 I have also tested on Linux and got the same behavior.	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6565/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6565/timeline	null	completed	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6564	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6564/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6564/comments	https://api.github.com/repos/huggingface/datasets/issues/6564/events	https://github.com/huggingface/datasets/issues/6564	2,068,893,194	I_kwDODunzps57UM4K	6,564	`Dataset.filter` missing `with_rank` parameter	{ "login": "kopyl", "id": 17604849, "node_id": "MDQ6VXNlcjE3NjA0ODQ5", "avatar_url": "https://avatars.githubusercontent.com/u/17604849?v=4", "gravatar_id": "", "url": "https://api.github.com/users/kopyl", "html_url": "https://github.com/kopyl", "followers_url": "https://api.github.com/users/kopyl/followers", "following_url": "https://api.github.com/users/kopyl/following{/other_user}", "gists_url": "https://api.github.com/users/kopyl/gists{/gist_id}", "starred_url": "https://api.github.com/users/kopyl/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/kopyl/subscriptions", "organizations_url": "https://api.github.com/users/kopyl/orgs", "repos_url": "https://api.github.com/users/kopyl/repos", "events_url": "https://api.github.com/users/kopyl/events{/privacy}", "received_events_url": "https://api.github.com/users/kopyl/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "Thanks for reporting! I've opened a PR with a fix", "@mariosasko thank you very much :)" ]	"2024-01-06T23:48:13"	"2024-01-29T16:36:55"	"2024-01-29T16:36:54"	NONE	null	### Describe the bug The issue shall be open: https://github.com/huggingface/datasets/issues/6435 When i try to pass `with_rank` to `Dataset.filter()`, i get this: `Dataset.filter() got an unexpected keyword argument 'with_rank'` ### Steps to reproduce the bug Run notebook: https://colab.research.google.com/drive/1WUNKph8BdP0on5ve3gQnh_PE0cFLQqTn?usp=sharing ### Expected behavior Should work? ### Environment info NVIDIA RTX 4090	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6564/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6564/timeline	null	completed	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6563	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6563/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6563/comments	https://api.github.com/repos/huggingface/datasets/issues/6563/events	https://github.com/huggingface/datasets/issues/6563	2,068,302,402	I_kwDODunzps57R8pC	6,563	`ImportError`: cannot import name 'insecure_hashlib' from 'huggingface_hub.utils' (.../huggingface_hub/utils/__init__.py)	{ "login": "wasertech", "id": 79070834, "node_id": "MDQ6VXNlcjc5MDcwODM0", "avatar_url": "https://avatars.githubusercontent.com/u/79070834?v=4", "gravatar_id": "", "url": "https://api.github.com/users/wasertech", "html_url": "https://github.com/wasertech", "followers_url": "https://api.github.com/users/wasertech/followers", "following_url": "https://api.github.com/users/wasertech/following{/other_user}", "gists_url": "https://api.github.com/users/wasertech/gists{/gist_id}", "starred_url": "https://api.github.com/users/wasertech/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/wasertech/subscriptions", "organizations_url": "https://api.github.com/users/wasertech/orgs", "repos_url": "https://api.github.com/users/wasertech/repos", "events_url": "https://api.github.com/users/wasertech/events{/privacy}", "received_events_url": "https://api.github.com/users/wasertech/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "@Wauplin Do you happen to know what's up?", "<del>Installing `datasets` from `main` did the trick so I guess it will be fixed in the next release.\r\n\r\nNVM https://github.com/huggingface/datasets/blob/d26abadce0b884db32382b92422d8a6aa997d40a/src/datasets/utils/info_utils.py#L5", "@wasertech upgrading `huggingface_hub` to a newer version should fix your issue. Latest version is 0.20.2. ", "Ha yes I had pinned `tokenizers` to an old version so it downgraded `huggingface_hub`. Note to myself keep HuggingFace modules relatively close together chronologically release wise.", "Glad to know your problem's solved! ", "@Wauplin Thanks for your insight 👍", "pip install --upgrade huggingface-hub" ]	"2024-01-06T02:28:54"	"2024-03-14T02:59:42"	"2024-01-06T16:13:27"	NONE	null	### Describe the bug Yep its not [there](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/utils/__init__.py) anymore. ```text + python /home/trainer/sft_train.py --model_name cognitivecomputations/dolphin-2.2.1-mistral-7b --dataset_name wasertech/OneOS --load_in_4bit --use_peft --batch_size 4 --num_train_epochs 1 --learning_rate 1.41e-5 --gradient_accumulation_steps 8 --seq_length 4096 --output_dir output --log_with wandb Traceback (most recent call last): File "/home/trainer/sft_train.py", line 22, in <module> from datasets import load_dataset File "/home/trainer/llm-train/lib/python3.8/site-packages/datasets/__init__.py", line 22, in <module> from .arrow_dataset import Dataset File "/home/trainer/llm-train/lib/python3.8/site-packages/datasets/arrow_dataset.py", line 66, in <module> from .arrow_reader import ArrowReader File "/home/trainer/llm-train/lib/python3.8/site-packages/datasets/arrow_reader.py", line 30, in <module> from .download.download_config import DownloadConfig File "/home/trainer/llm-train/lib/python3.8/site-packages/datasets/download/__init__.py", line 9, in <module> from .download_manager import DownloadManager, DownloadMode File "/home/trainer/llm-train/lib/python3.8/site-packages/datasets/download/download_manager.py", line 31, in <module> from ..utils import tqdm as hf_tqdm File "/home/trainer/llm-train/lib/python3.8/site-packages/datasets/utils/__init__.py", line 19, in <module> from .info_utils import VerificationMode File "/home/trainer/llm-train/lib/python3.8/site-packages/datasets/utils/info_utils.py", line 5, in <module> from huggingface_hub.utils import insecure_hashlib ImportError: cannot import name 'insecure_hashlib' from 'huggingface_hub.utils' (/home/trainer/llm-train/lib/python3.8/site-packages/huggingface_hub/utils/__init__.py) ``` ### Steps to reproduce the bug Using `datasets==2.16.1` and `huggingface_hub== 0.17.3`, load a dataset with `load_dataset`. ### Expected behavior The dataset should be (downloaded - if needed - and) returned. ### Environment info ```text trainer@a311ae86939e:/mnt$ pip show datasets Name: datasets Version: 2.16.1 Summary: HuggingFace community-driven open-source library of datasets Home-page: https://github.com/huggingface/datasets Author: HuggingFace Inc. Author-email: thomas@huggingface.co License: Apache 2.0 Location: /home/trainer/llm-train/lib/python3.8/site-packages Requires: packaging, pyyaml, multiprocess, pyarrow-hotfix, pandas, pyarrow, xxhash, dill, numpy, aiohttp, tqdm, fsspec, requests, filelock, huggingface-hub Required-by: trl, lm-eval, evaluate trainer@a311ae86939e:/mnt$ pip show huggingface_hub Name: huggingface-hub Version: 0.17.3 Summary: Client library to download and publish models, datasets and other repos on the huggingface.co hub Home-page: https://github.com/huggingface/huggingface_hub Author: Hugging Face, Inc. Author-email: julien@huggingface.co License: Apache Location: /home/trainer/llm-train/lib/python3.8/site-packages Requires: requests, pyyaml, packaging, typing-extensions, tqdm, filelock, fsspec Required-by: transformers, tokenizers, peft, evaluate, datasets, accelerate trainer@a311ae86939e:/mnt$ huggingface-cli env Copy-and-paste the text below in your GitHub issue. - huggingface_hub version: 0.17.3 - Platform: Linux-6.5.13-7-MANJARO-x86_64-with-glibc2.29 - Python version: 3.8.10 - Running in iPython ?: No - Running in notebook ?: No - Running in Google Colab ?: No - Token path ?: /home/trainer/.cache/huggingface/token - Has saved token ?: True - Who am I ?: wasertech - Configured git credential helpers: - FastAI: N/A - Tensorflow: N/A - Torch: 2.1.2 - Jinja2: 3.1.2 - Graphviz: N/A - Pydot: N/A - Pillow: 10.2.0 - hf_transfer: N/A - gradio: N/A - tensorboard: N/A - numpy: 1.24.4 - pydantic: N/A - aiohttp: 3.9.1 - ENDPOINT: https://huggingface.co - HUGGINGFACE_HUB_CACHE: /home/trainer/.cache/huggingface/hub - HUGGINGFACE_ASSETS_CACHE: /home/trainer/.cache/huggingface/assets - HF_TOKEN_PATH: /home/trainer/.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 ```	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6563/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6563/timeline	null	completed	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6562	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6562/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6562/comments	https://api.github.com/repos/huggingface/datasets/issues/6562/events	https://github.com/huggingface/datasets/issues/6562	2,067,904,504	I_kwDODunzps57Qbf4	6,562	datasets.DownloadMode.FORCE_REDOWNLOAD use cache to download dataset features with load_dataset function	{ "login": "LsTam91", "id": 73234162, "node_id": "MDQ6VXNlcjczMjM0MTYy", "avatar_url": "https://avatars.githubusercontent.com/u/73234162?v=4", "gravatar_id": "", "url": "https://api.github.com/users/LsTam91", "html_url": "https://github.com/LsTam91", "followers_url": "https://api.github.com/users/LsTam91/followers", "following_url": "https://api.github.com/users/LsTam91/following{/other_user}", "gists_url": "https://api.github.com/users/LsTam91/gists{/gist_id}", "starred_url": "https://api.github.com/users/LsTam91/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/LsTam91/subscriptions", "organizations_url": "https://api.github.com/users/LsTam91/orgs", "repos_url": "https://api.github.com/users/LsTam91/repos", "events_url": "https://api.github.com/users/LsTam91/events{/privacy}", "received_events_url": "https://api.github.com/users/LsTam91/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	null	[]	"2024-01-05T19:10:25"	"2024-01-05T19:10:25"	null	NONE	null	### Describe the bug I have updated my dataset by adding a new feature, and push it to the hub. When I want to download it on my machine which contain the old version by using `datasets.load_dataset("your_dataset_name", download_mode=datasets.DownloadMode.FORCE_REDOWNLOAD)` I get an error (paste bellow). Seems that the load_dataset function still use the old features schema instead of downloading everything new from the HUB. I find a way to go around this issue by manually deleting the old dataset cache. But from my understanding of `datasets.DownloadMode.FORCE_REDOWNLOAD` option, the dataset cache should be ignored. ### Steps to reproduce the bug 1. Download your dataset in your machine using `datasets.load_dataset` 2. Create a new feature in your dataset and push it to the hub 3. On the same machine redownload your dataset using `datasets.load_dataset("your_dataset_name", download_mode=datasets.DownloadMode.FORCE_REDOWNLOAD)` ### Expected behavior ` ValueError: Couldn't cast id: string level: string context: list<element: string> child 0, element: string type: string answer: string question: string supporting_facts: list<element: string> child 0, element: string fra_answer: string fra_question: string -- schema metadata -- huggingface: '{"info": {"features": {"id": {"dtype": "string", "_type": "' + 490 to {'id': Value(dtype='string', id=None), 'level': Value(dtype='string', id=None), 'context': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None), 'type': Value(dtype='string', id=None), 'answer': Value(dtype='string', id=None), 'question': Value(dtype='string', id=None), 'supporting_facts': Sequence(feature=Value(dtype='string', id=None), length=-1, id=None)} because column names don't match The above exception was the direct cause of the following exception: DatasetGenerationError ... DatasetGenerationError: An error occurred while generating the dataset` ### Environment info datasets-2.16.1 huggingface-hub-0.20.2	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6562/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6562/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6561	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6561/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6561/comments	https://api.github.com/repos/huggingface/datasets/issues/6561/events	https://github.com/huggingface/datasets/issues/6561	2,067,404,951	I_kwDODunzps57OhiX	6,561	Document YAML configuration with "data_dir"	{ "login": "severo", "id": 1676121, "node_id": "MDQ6VXNlcjE2NzYxMjE=", "avatar_url": "https://avatars.githubusercontent.com/u/1676121?v=4", "gravatar_id": "", "url": "https://api.github.com/users/severo", "html_url": "https://github.com/severo", "followers_url": "https://api.github.com/users/severo/followers", "following_url": "https://api.github.com/users/severo/following{/other_user}", "gists_url": "https://api.github.com/users/severo/gists{/gist_id}", "starred_url": "https://api.github.com/users/severo/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/severo/subscriptions", "organizations_url": "https://api.github.com/users/severo/orgs", "repos_url": "https://api.github.com/users/severo/repos", "events_url": "https://api.github.com/users/severo/events{/privacy}", "received_events_url": "https://api.github.com/users/severo/received_events", "type": "User", "site_admin": false }	[ { "id": 1935892861, "node_id": "MDU6TGFiZWwxOTM1ODkyODYx", "url": "https://api.github.com/repos/huggingface/datasets/labels/documentation", "name": "documentation", "color": "0075ca", "default": true, "description": "Improvements or additions to documentation" } ]	open	false	null	[]	null	[ "In particular, I would like to have an example of how to replace the following configuration (from https://huggingface.co/docs/hub/datasets-manual-configuration#splits)\r\n\r\n```\r\n---\r\nconfigs:\r\n- config_name: default\r\n data_files:\r\n - split: train\r\n path: \"data/.csv\"\r\n - split: test\r\n path: \"holdout/.csv\"\r\n---\r\n```\r\n\r\nwith the `data_dir` field." ]	"2024-01-05T14:03:33"	"2024-01-05T14:06:18"	null	CONTRIBUTOR	null	See https://huggingface.co/datasets/uonlp/CulturaX/discussions/15#6597e83f185db94370d6bf50 for reference	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6561/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6561/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6560	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6560/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6560/comments	https://api.github.com/repos/huggingface/datasets/issues/6560/events	https://github.com/huggingface/datasets/issues/6560	2,065,637,625	I_kwDODunzps57HyD5	6,560	Support Video	{ "login": "yuvalkirstain", "id": 57996478, "node_id": "MDQ6VXNlcjU3OTk2NDc4", "avatar_url": "https://avatars.githubusercontent.com/u/57996478?v=4", "gravatar_id": "", "url": "https://api.github.com/users/yuvalkirstain", "html_url": "https://github.com/yuvalkirstain", "followers_url": "https://api.github.com/users/yuvalkirstain/followers", "following_url": "https://api.github.com/users/yuvalkirstain/following{/other_user}", "gists_url": "https://api.github.com/users/yuvalkirstain/gists{/gist_id}", "starred_url": "https://api.github.com/users/yuvalkirstain/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/yuvalkirstain/subscriptions", "organizations_url": "https://api.github.com/users/yuvalkirstain/orgs", "repos_url": "https://api.github.com/users/yuvalkirstain/repos", "events_url": "https://api.github.com/users/yuvalkirstain/events{/privacy}", "received_events_url": "https://api.github.com/users/yuvalkirstain/received_events", "type": "User", "site_admin": false }	[ { "id": 1935892871, "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement", "name": "enhancement", "color": "a2eeef", "default": true, "description": "New feature or request" } ]	open	false	null	[]	null	[]	"2024-01-04T13:10:58"	"2024-01-04T13:10:58"	null	NONE	null	### Feature request HF datasets are awesome in supporting text and images. Will be great to see such a support in videos :) ### Motivation Video generation :) ### Your contribution Will probably be limited to raising this feature request ;)	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6560/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6560/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6559	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6559/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6559/comments	https://api.github.com/repos/huggingface/datasets/issues/6559/events	https://github.com/huggingface/datasets/issues/6559	2,065,118,332	I_kwDODunzps57FzR8	6,559	Latest version 2.16.1, when load dataset error occurs. ValueError: BuilderConfig 'allenai--c4' not found. Available: ['default']	{ "login": "zhulinJulia24", "id": 145004780, "node_id": "U_kgDOCKSY7A", "avatar_url": "https://avatars.githubusercontent.com/u/145004780?v=4", "gravatar_id": "", "url": "https://api.github.com/users/zhulinJulia24", "html_url": "https://github.com/zhulinJulia24", "followers_url": "https://api.github.com/users/zhulinJulia24/followers", "following_url": "https://api.github.com/users/zhulinJulia24/following{/other_user}", "gists_url": "https://api.github.com/users/zhulinJulia24/gists{/gist_id}", "starred_url": "https://api.github.com/users/zhulinJulia24/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/zhulinJulia24/subscriptions", "organizations_url": "https://api.github.com/users/zhulinJulia24/orgs", "repos_url": "https://api.github.com/users/zhulinJulia24/repos", "events_url": "https://api.github.com/users/zhulinJulia24/events{/privacy}", "received_events_url": "https://api.github.com/users/zhulinJulia24/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "Hi ! The \"allenai--c4\" config doesn't exist (this naming schema comes from old versions of `datasets`)\r\n\r\nYou can load it this way instead:\r\n\r\n```python\r\nfrom datasets import load_dataset\r\ncache_dir = 'path/to/your/cache/directory'\r\ndataset = load_dataset('allenai/c4', data_files={'train': 'en/c4-train.00000-of-01024.json.gz'}, split='train', cache_dir=cache_dir)\r\n```", "> Hi ! The \"allenai--c4\" config doesn't exist (this naming schema comes from old versions of `datasets`)\r\n> \r\n> You can load it this way instead:\r\n> \r\n> ```python\r\n> from datasets import load_dataset\r\n> cache_dir = 'path/to/your/cache/directory'\r\n> dataset = load_dataset('allenai/c4', data_files={'train': 'en/c4-train.00000-of-01024.json.gz'}, split='train', cache_dir=cache_dir)\r\n> ```\r\n\r\nthanks, the command run successfully in the latest version\r\n", "> Hi ! The \"allenai--c4\" config doesn't exist (this naming schema comes from old versions of `datasets`)\r\n> \r\n> You can load it this way instead:\r\n> \r\n> ```python\r\n> from datasets import load_dataset\r\n> cache_dir = 'path/to/your/cache/directory'\r\n> dataset = load_dataset('allenai/c4', data_files={'train': 'en/c4-train.00000-of-01024.json.gz'}, split='train', cache_dir=cache_dir)\r\n> ```\r\n\r\n@lhoestq \r\nIn this case, should we traverse through al 1024 json files to load the whole dataset?\r\nThanks!", "It will only load the first file (`data_files={'train': 'en/c4-train.00000-of-01024.json.gz'}` only mentions one file)", "> It will only load the first file (`data_files={'train': 'en/c4-train.00000-of-01024.json.gz'}` only mentions one file)\r\n\r\nThen what if we want to load the whole dataset?", "There is a \"en\" subset that you can load (see the list in the \"subset\" dropdown at https://huggingface.co/datasets/allenai/c4)\r\n\r\n```python\r\ndataset = load_dataset('allenai/c4', 'en', split=\"train\")\r\n```\r\n\r\nalternatively you can specify all the the files yourself using a glob pattern (or a list):\r\n\r\n```python\r\ndataset = load_dataset('allenai/c4', data_files='en/c4-train.00000-of-.json.gz', split=\"train\")\r\n```", "> There is a \"en\" subset that you can load (see the list in the \"subset\" dropdown at https://huggingface.co/datasets/allenai/c4)\r\n> \r\n> ```python\r\n> dataset = load_dataset('allenai/c4', 'en', split=\"train\")\r\n> ```\r\n> \r\n> alternatively you can specify all the the files yourself using a glob pattern (or a list):\r\n> \r\n> ```python\r\n> dataset = load_dataset('allenai/c4', data_files='en/c4-train.00000-of-.json.gz', split=\"train\")\r\n> ```\r\n\r\nThanks, the second solution works. The first line simply fails due to missing schema specific to this dataset.", "The latest version of `datasets` seems to have broken my dataset for my users (see this Hugging Face issue: https://huggingface.co/datasets/umarbutler/open-australian-legal-corpus/discussions/3). I changed it by renaming my dataset's config to `default` instead of `train` and then updating my dataset card accordingly." ]	"2024-01-04T07:04:48"	"2024-04-03T10:40:53"	"2024-01-05T01:26:25"	NONE	null	### Describe the bug python script is: ``` from datasets import load_dataset cache_dir = 'path/to/your/cache/directory' dataset = load_dataset('allenai/c4','allenai--c4', data_files={'train': 'en/c4-train.00000-of-01024.json.gz'}, split='train', use_auth_token=False, cache_dir=cache_dir) ``` the script success when datasets version is 2.14.7. when using 2.16.1, error occurs ` ValueError: BuilderConfig 'allenai--c4' not found. Available: ['default']` ### Steps to reproduce the bug 1. pip install datasets==2.16.1 2. run python script: ``` from datasets import load_dataset cache_dir = 'path/to/your/cache/directory' dataset = load_dataset('allenai/c4','allenai--c4', data_files={'train': 'en/c4-train.00000-of-01024.json.gz'}, split='train', use_auth_token=False, cache_dir=cache_dir) ``` ### Expected behavior the dataset should be loaded successful in the latest version. ### Environment info datasets 2.16.1	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6559/reactions", "total_count": 1, "+1": 1, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6559/timeline	null	completed	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6558	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6558/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6558/comments	https://api.github.com/repos/huggingface/datasets/issues/6558/events	https://github.com/huggingface/datasets/issues/6558	2,064,885,984	I_kwDODunzps57E6jg	6,558	OSError: image file is truncated (1 bytes not processed) #28323	{ "login": "andysingal", "id": 20493493, "node_id": "MDQ6VXNlcjIwNDkzNDkz", "avatar_url": "https://avatars.githubusercontent.com/u/20493493?v=4", "gravatar_id": "", "url": "https://api.github.com/users/andysingal", "html_url": "https://github.com/andysingal", "followers_url": "https://api.github.com/users/andysingal/followers", "following_url": "https://api.github.com/users/andysingal/following{/other_user}", "gists_url": "https://api.github.com/users/andysingal/gists{/gist_id}", "starred_url": "https://api.github.com/users/andysingal/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/andysingal/subscriptions", "organizations_url": "https://api.github.com/users/andysingal/orgs", "repos_url": "https://api.github.com/users/andysingal/repos", "events_url": "https://api.github.com/users/andysingal/events{/privacy}", "received_events_url": "https://api.github.com/users/andysingal/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "You can add \r\n\r\n```python\r\nfrom PIL import ImageFile\r\nImageFile.LOAD_TRUNCATED_IMAGES = True\r\n```\r\n\r\nafter the imports to be able to read truncated images." ]	"2024-01-04T02:15:13"	"2024-02-21T00:38:12"	"2024-02-21T00:38:12"	NONE	null	### Describe the bug ``` --------------------------------------------------------------------------- OSError Traceback (most recent call last) Cell In[24], line 28 23 return example 25 # Filter the dataset 26 # filtered_dataset = dataset.filter(contains_number) 27 # Add the 'label' field in the dataset ---> 28 labeled_dataset = dataset.filter(contains_number).map(add_label) 29 # View the structure of the updated dataset 30 print(labeled_dataset) File /usr/local/lib/python3.10/dist-packages/datasets/dataset_dict.py:975, in DatasetDict.filter(self, function, with_indices, input_columns, batched, batch_size, keep_in_memory, load_from_cache_file, cache_file_names, writer_batch_size, fn_kwargs, num_proc, desc) 972 if cache_file_names is None: 973 cache_file_names = {k: None for k in self} 974 return DatasetDict( --> 975 { 976 k: dataset.filter( 977 function=function, 978 with_indices=with_indices, 979 input_columns=input_columns, 980 batched=batched, 981 batch_size=batch_size, 982 keep_in_memory=keep_in_memory, 983 load_from_cache_file=load_from_cache_file, 984 cache_file_name=cache_file_names[k], 985 writer_batch_size=writer_batch_size, 986 fn_kwargs=fn_kwargs, 987 num_proc=num_proc, 988 desc=desc, 989 ) 990 for k, dataset in self.items() 991 } 992 ) File /usr/local/lib/python3.10/dist-packages/datasets/dataset_dict.py:976, in <dictcomp>(.0) 972 if cache_file_names is None: 973 cache_file_names = {k: None for k in self} 974 return DatasetDict( 975 { --> 976 k: dataset.filter( 977 function=function, 978 with_indices=with_indices, 979 input_columns=input_columns, 980 batched=batched, 981 batch_size=batch_size, 982 keep_in_memory=keep_in_memory, 983 load_from_cache_file=load_from_cache_file, 984 cache_file_name=cache_file_names[k], 985 writer_batch_size=writer_batch_size, 986 fn_kwargs=fn_kwargs, 987 num_proc=num_proc, 988 desc=desc, 989 ) 990 for k, dataset in self.items() 991 } 992 ) File /usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py:557, in transmit_format.<locals>.wrapper(args, kwargs) 550 self_format = { 551 "type": self._format_type, 552 "format_kwargs": self._format_kwargs, 553 "columns": self._format_columns, 554 "output_all_columns": self._output_all_columns, 555 } 556 # apply actual function --> 557 out: Union["Dataset", "DatasetDict"] = func(self, args, *kwargs) 558 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out] 559 # re-apply format to the output File /usr/local/lib/python3.10/dist-packages/datasets/fingerprint.py:481, in fingerprint_transform.<locals>._fingerprint.<locals>.wrapper(args, *kwargs) 477 validate_fingerprint(kwargs[fingerprint_name]) 479 # Call actual function --> 481 out = func(dataset, args, *kwargs) 483 # Update fingerprint of in-place transforms + update in-place history of transforms 485 if inplace: # update after calling func so that the fingerprint doesn't change if the function fails File /usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py:3623, in Dataset.filter(self, function, with_indices, input_columns, batched, batch_size, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, fn_kwargs, num_proc, suffix_template, new_fingerprint, desc) 3620 if len(self) == 0: 3621 return self -> 3623 indices = self.map( 3624 function=partial( 3625 get_indices_from_mask_function, function, batched, with_indices, input_columns, self._indices 3626 ), 3627 with_indices=True, 3628 features=Features({"indices": Value("uint64")}), 3629 batched=True, 3630 batch_size=batch_size, 3631 remove_columns=self.column_names, 3632 keep_in_memory=keep_in_memory, 3633 load_from_cache_file=load_from_cache_file, 3634 cache_file_name=cache_file_name, 3635 writer_batch_size=writer_batch_size, 3636 fn_kwargs=fn_kwargs, 3637 num_proc=num_proc, 3638 suffix_template=suffix_template, 3639 new_fingerprint=new_fingerprint, 3640 input_columns=input_columns, 3641 desc=desc or "Filter", 3642 ) 3643 new_dataset = copy.deepcopy(self) 3644 new_dataset._indices = indices.data File /usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py:592, in transmit_tasks.<locals>.wrapper(args, *kwargs) 590 self: "Dataset" = kwargs.pop("self") 591 # apply actual function --> 592 out: Union["Dataset", "DatasetDict"] = func(self, args, *kwargs) 593 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out] 594 for dataset in datasets: 595 # Remove task templates if a column mapping of the template is no longer valid File /usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py:557, in transmit_format.<locals>.wrapper(args, *kwargs) 550 self_format = { 551 "type": self._format_type, 552 "format_kwargs": self._format_kwargs, 553 "columns": self._format_columns, 554 "output_all_columns": self._output_all_columns, 555 } 556 # apply actual function --> 557 out: Union["Dataset", "DatasetDict"] = func(self, args, kwargs) 558 datasets: List["Dataset"] = list(out.values()) if isinstance(out, dict) else [out] 559 # re-apply format to the output File /usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py:3093, in Dataset.map(self, function, with_indices, with_rank, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint, desc) 3087 if transformed_dataset is None: 3088 with hf_tqdm( 3089 unit=" examples", 3090 total=pbar_total, 3091 desc=desc or "Map", 3092 ) as pbar: -> 3093 for rank, done, content in Dataset._map_single(dataset_kwargs): 3094 if done: 3095 shards_done += 1 File /usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py:3470, in Dataset._map_single(shard, function, with_indices, with_rank, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset) 3466 indices = list( 3467 range((slice(i, i + batch_size).indices(shard.num_rows))) 3468 ) # Something simpler? 3469 try: -> 3470 batch = apply_function_on_filtered_inputs( 3471 batch, 3472 indices, 3473 check_same_num_examples=len(shard.list_indexes()) > 0, 3474 offset=offset, 3475 ) 3476 except NumExamplesMismatchError: 3477 raise DatasetTransformationNotAllowedError( 3478 "Using `.map` in batched mode on a dataset with attached indexes is allowed only if it doesn't create or remove existing examples. You can first run `.drop_index() to remove your index and then re-add it." 3479 ) from None File /usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py:3349, in Dataset._map_single.<locals>.apply_function_on_filtered_inputs(pa_inputs, indices, check_same_num_examples, offset) 3347 if with_rank: 3348 additional_args += (rank,) -> 3349 processed_inputs = function(fn_args, additional_args, fn_kwargs) 3350 if isinstance(processed_inputs, LazyDict): 3351 processed_inputs = { 3352 k: v for k, v in processed_inputs.data.items() if k not in processed_inputs.keys_to_format 3353 } File /usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py:6212, in get_indices_from_mask_function(function, batched, with_indices, input_columns, indices_mapping, args, **fn_kwargs) 6209 if input_columns is None: 6210 # inputs only contains a batch of examples 6211 batch: dict = inputs[0] -> 6212 num_examples = len(batch[next(iter(batch.keys()))]) 6213 for i in range(num_examples): 6214 example = {key: batch[key][i] for key in batch} File /usr/local/lib/python3.10/dist-packages/datasets/formatting/formatting.py:272, in LazyDict.__getitem__(self, key) 270 value = self.data[key] 271 if key in self.keys_to_format: --> 272 value = self.format(key) 273 self.data[key] = value 274 self.keys_to_format.remove(key) File /usr/local/lib/python3.10/dist-packages/datasets/formatting/formatting.py:375, in LazyBatch.format(self, key) 374 def format(self, key): --> 375 return self.formatter.format_column(self.pa_table.select([key])) File /usr/local/lib/python3.10/dist-packages/datasets/formatting/formatting.py:442, in PythonFormatter.format_column(self, pa_table) 440 def format_column(self, pa_table: pa.Table) -> list: 441 column = self.python_arrow_extractor().extract_column(pa_table) --> 442 column = self.python_features_decoder.decode_column(column, pa_table.column_names[0]) 443 return column File /usr/local/lib/python3.10/dist-packages/datasets/formatting/formatting.py:218, in PythonFeaturesDecoder.decode_column(self, column, column_name) 217 def decode_column(self, column: list, column_name: str) -> list: --> 218 return self.features.decode_column(column, column_name) if self.features else column File /usr/local/lib/python3.10/dist-packages/datasets/features/features.py:1951, in Features.decode_column(self, column, column_name) 1938 def decode_column(self, column: list, column_name: str): 1939 """Decode column with custom feature decoding. 1940 1941 Args: (...) 1948 `list[Any]` 1949 """ 1950 return ( -> 1951 [decode_nested_example(self[column_name], value) if value is not None else None for value in column] 1952 if self._column_requires_decoding[column_name] 1953 else column 1954 ) File /usr/local/lib/python3.10/dist-packages/datasets/features/features.py:1951, in <listcomp>(.0) 1938 def decode_column(self, column: list, column_name: str): 1939 """Decode column with custom feature decoding. 1940 1941 Args: (...) 1948 `list[Any]` 1949 """ 1950 return ( -> 1951 [decode_nested_example(self[column_name], value) if value is not None else None for value in column] 1952 if self._column_requires_decoding[column_name] 1953 else column 1954 ) File /usr/local/lib/python3.10/dist-packages/datasets/features/features.py:1339, in decode_nested_example(schema, obj, token_per_repo_id) 1336 elif isinstance(schema, (Audio, Image)): 1337 # we pass the token to read and decode files from private repositories in streaming mode 1338 if obj is not None and schema.decode: -> 1339 return schema.decode_example(obj, token_per_repo_id=token_per_repo_id) 1340 return obj File /usr/local/lib/python3.10/dist-packages/datasets/features/image.py:185, in Image.decode_example(self, value, token_per_repo_id) 183 else: 184 image = PIL.Image.open(BytesIO(bytes_)) --> 185 image.load() # to avoid "Too many open files" errors 186 return image File /usr/local/lib/python3.10/dist-packages/PIL/ImageFile.py:254, in ImageFile.load(self) 252 break 253 else: --> 254 raise OSError( 255 "image file is truncated " 256 f"({len(b)} bytes not processed)" 257 ) 259 b = b + s 260 n, err_code = decoder.decode(b) OSError: image file is truncated (1 bytes not processed) ``` ### Steps to reproduce the bug ``` from datasets import load_dataset dataset = load_dataset("mehul7/captioned_military_aircraft") from transformers import AutoImageProcessor checkpoint = "microsoft/resnet-50" image_processor = AutoImageProcessor.from_pretrained(checkpoint) import re from PIL import Image import io def contains_number(example): try: image = Image.open(io.BytesIO(example["image"]['bytes'])) t = image_processor(images=image, return_tensors="pt")['pixel_values'] except Exception as e: print(f"Error processing image：{example['text']}") return False return bool(re.search(r'\d', example['text'])) # Define a function to add the 'label' field def add_label(example): lab = example['text'].split() temp = 'NOT' for item in lab: if str(item[-1]).isdigit(): temp = item break example['label'] = temp return example # Filter the dataset # filtered_dataset = dataset.filter(contains_number) # Add the 'label' field in the dataset labeled_dataset = dataset.filter(contains_number).map(add_label) # View the structure of the updated dataset print(labeled_dataset) ``` ### Expected behavior needs to form labels same as : https://www.kaggle.com/code/jiabaowangts/dataset-air/notebook ### Environment info Kaggle notebook P100	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6558/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6558/timeline	null	completed	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6557	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6557/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6557/comments	https://api.github.com/repos/huggingface/datasets/issues/6557/events	https://github.com/huggingface/datasets/pull/6557	2,064,341,965	PR_kwDODunzps5jJ63z	6,557	Support standalone yaml	{ "login": "lhoestq", "id": 42851186, "node_id": "MDQ6VXNlcjQyODUxMTg2", "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "gravatar_id": "", "url": "https://api.github.com/users/lhoestq", "html_url": "https://github.com/lhoestq", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "repos_url": "https://api.github.com/users/lhoestq/repos", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6557). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "@lhoestq \r\nhello\r\nI think it should be defined in config.py\r\nDATASET_ README_ FILENAME=\"README. md\"\r\nThis can replace all \"README. md\"\r\n", "Thanks for the feedback :) merging now", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004890 / 0.011353 (-0.006463) \| 0.003535 / 0.011008 (-0.007473) \| 0.062894 / 0.038508 (0.024386) \| 0.029133 / 0.023109 (0.006024) \| 0.242387 / 0.275898 (-0.033511) \| 0.262720 / 0.323480 (-0.060760) \| 0.002880 / 0.007986 (-0.005106) \| 0.002674 / 0.004328 (-0.001655) \| 0.048932 / 0.004250 (0.044682) \| 0.041669 / 0.037052 (0.004617) \| 0.255922 / 0.258489 (-0.002567) \| 0.282106 / 0.293841 (-0.011734) \| 0.028137 / 0.128546 (-0.100409) \| 0.010620 / 0.075646 (-0.065026) \| 0.207799 / 0.419271 (-0.211473) \| 0.035499 / 0.043533 (-0.008034) \| 0.246158 / 0.255139 (-0.008981) \| 0.262671 / 0.283200 (-0.020528) \| 0.017297 / 0.141683 (-0.124386) \| 1.118681 / 1.452155 (-0.333474) \| 1.156732 / 1.492716 (-0.335985) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091670 / 0.018006 (0.073664) \| 0.300327 / 0.000490 (0.299837) \| 0.000212 / 0.000200 (0.000012) \| 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) \| 0.018080 / 0.037411 (-0.019332) \| 0.060357 / 0.014526 (0.045831) \| 0.072221 / 0.176557 (-0.104336) \| 0.119281 / 0.737135 (-0.617855) \| 0.073861 / 0.296338 (-0.222477) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.289848 / 0.215209 (0.074639) \| 2.845203 / 2.077655 (0.767549) \| 1.531271 / 1.504120 (0.027152) \| 1.366110 / 1.541195 (-0.175085) \| 1.395041 / 1.468490 (-0.073449) \| 0.563353 / 4.584777 (-4.021424) \| 2.389074 / 3.745712 (-1.356638) \| 2.752960 / 5.269862 (-2.516901) \| 1.715508 / 4.565676 (-2.850168) \| 0.063063 / 0.424275 (-0.361212) \| 0.004967 / 0.007607 (-0.002640) \| 0.340757 / 0.226044 (0.114713) \| 3.387667 / 2.268929 (1.118739) \| 1.845182 / 55.444624 (-53.599442) \| 1.569616 / 6.876477 (-5.306861) \| 1.571393 / 2.142072 (-0.570679) \| 0.643455 / 4.805227 (-4.161772) \| 0.116919 / 6.500664 (-6.383745) \| 0.042551 / 0.075469 (-0.032918) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.943761 / 1.841788 (-0.898027) \| 11.481068 / 8.074308 (3.406760) \| 10.422180 / 10.191392 (0.230788) \| 0.132015 / 0.680424 (-0.548408) \| 0.013932 / 0.534201 (-0.520268) \| 0.288340 / 0.579283 (-0.290943) \| 0.263695 / 0.434364 (-0.170669) \| 0.324459 / 0.540337 (-0.215878) \| 0.415204 / 1.386936 (-0.971732) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005042 / 0.011353 (-0.006310) \| 0.003465 / 0.011008 (-0.007543) \| 0.050107 / 0.038508 (0.011599) \| 0.029542 / 0.023109 (0.006433) \| 0.273645 / 0.275898 (-0.002253) \| 0.293661 / 0.323480 (-0.029818) \| 0.004099 / 0.007986 (-0.003887) \| 0.002667 / 0.004328 (-0.001661) \| 0.048281 / 0.004250 (0.044030) \| 0.044406 / 0.037052 (0.007353) \| 0.284245 / 0.258489 (0.025756) \| 0.312303 / 0.293841 (0.018462) \| 0.030057 / 0.128546 (-0.098489) \| 0.010675 / 0.075646 (-0.064971) \| 0.058404 / 0.419271 (-0.360868) \| 0.051874 / 0.043533 (0.008342) \| 0.273308 / 0.255139 (0.018169) \| 0.289356 / 0.283200 (0.006157) \| 0.018628 / 0.141683 (-0.123055) \| 1.148764 / 1.452155 (-0.303391) \| 1.194181 / 1.492716 (-0.298535) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091383 / 0.018006 (0.073376) \| 0.300221 / 0.000490 (0.299731) \| 0.000232 / 0.000200 (0.000032) \| 0.000044 / 0.000054 (-0.000011) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.021814 / 0.037411 (-0.015597) \| 0.076420 / 0.014526 (0.061894) \| 0.087404 / 0.176557 (-0.089152) \| 0.126184 / 0.737135 (-0.610951) \| 0.089738 / 0.296338 (-0.206600) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.299839 / 0.215209 (0.084630) \| 2.929260 / 2.077655 (0.851605) \| 1.608327 / 1.504120 (0.104207) \| 1.479757 / 1.541195 (-0.061437) \| 1.494768 / 1.468490 (0.026278) \| 0.563873 / 4.584777 (-4.020904) \| 2.434442 / 3.745712 (-1.311270) \| 2.641384 / 5.269862 (-2.628478) \| 1.724222 / 4.565676 (-2.841454) \| 0.062125 / 0.424275 (-0.362150) \| 0.004994 / 0.007607 (-0.002613) \| 0.350895 / 0.226044 (0.124851) \| 3.448550 / 2.268929 (1.179621) \| 1.928910 / 55.444624 (-53.515714) \| 1.669887 / 6.876477 (-5.206590) \| 1.781304 / 2.142072 (-0.360768) \| 0.649301 / 4.805227 (-4.155926) \| 0.116255 / 6.500664 (-6.384409) \| 0.040947 / 0.075469 (-0.034522) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.977537 / 1.841788 (-0.864251) \| 12.119913 / 8.074308 (4.045605) \| 10.874078 / 10.191392 (0.682686) \| 0.130174 / 0.680424 (-0.550250) \| 0.016176 / 0.534201 (-0.518025) \| 0.287967 / 0.579283 (-0.291316) \| 0.280591 / 0.434364 (-0.153773) \| 0.324332 / 0.540337 (-0.216005) \| 0.419479 / 1.386936 (-0.967457) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9d6d16117a30ba345b0236407975f701c5b288d4 \"CML watermark\")\n" ]	"2024-01-03T16:47:35"	"2024-01-11T17:59:51"	"2024-01-11T17:53:42"	MEMBER	null	see (internal) https://huggingface.slack.com/archives/C02V51Q3800/p1703885853581679	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6557/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6557/timeline	null	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6557", "html_url": "https://github.com/huggingface/datasets/pull/6557", "diff_url": "https://github.com/huggingface/datasets/pull/6557.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6557.patch", "merged_at": "2024-01-11T17:53:42" }	true
https://api.github.com/repos/huggingface/datasets/issues/6556	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6556/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6556/comments	https://api.github.com/repos/huggingface/datasets/issues/6556/events	https://github.com/huggingface/datasets/pull/6556	2,064,018,208	PR_kwDODunzps5jI0nN	6,556	Fix imagefolder with one image	{ "login": "lhoestq", "id": 42851186, "node_id": "MDQ6VXNlcjQyODUxMTg2", "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "gravatar_id": "", "url": "https://api.github.com/users/lhoestq", "html_url": "https://github.com/lhoestq", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "repos_url": "https://api.github.com/users/lhoestq/repos", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6556). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "Fixed in dataset viewer: https://huggingface.co/datasets/multimodalart/repro_1_image\r\n\r\n<img width=\"682\" alt=\"Capture d’écran 2024-02-12 à 22 57 08\" src=\"https://github.com/huggingface/datasets/assets/1676121/be9a8dbc-2d78-4ffc-aed4-293a7c57bc0d\">\r\n" ]	"2024-01-03T13:13:02"	"2024-02-12T21:57:34"	"2024-01-09T13:06:30"	MEMBER	null	A dataset repository with one image and one metadata file was considered a JSON dataset instead of an ImageFolder dataset. This is because we pick the dataset type with the most compatible data file extensions present in the repository and it results in a tie in this case. e.g. for https://huggingface.co/datasets/multimodalart/repro_1_image I fixed this by deprioritizing metadata files in the count. fix https://github.com/huggingface/datasets/issues/6545	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6556/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6556/timeline	null	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6556", "html_url": "https://github.com/huggingface/datasets/pull/6556", "diff_url": "https://github.com/huggingface/datasets/pull/6556.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6556.patch", "merged_at": "2024-01-09T13:06:30" }	true
https://api.github.com/repos/huggingface/datasets/issues/6555	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6555/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6555/comments	https://api.github.com/repos/huggingface/datasets/issues/6555/events	https://github.com/huggingface/datasets/pull/6555	2,063,841,286	PR_kwDODunzps5jIM79	6,555	Do not use Parquet exports if revision is passed	{ "login": "albertvillanova", "id": 8515462, "node_id": "MDQ6VXNlcjg1MTU0NjI=", "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "gravatar_id": "", "url": "https://api.github.com/users/albertvillanova", "html_url": "https://github.com/albertvillanova", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "repos_url": "https://api.github.com/users/albertvillanova/repos", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6555). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "As shared on slack, `HubDatasetModuleFactoryWithParquetExport` raises a `DatasetsServerError` already if the user tries to load another revision that the one from the parquet export. And therefore it fall backs on using `HubDatasetModuleFactoryWithScript`", "@lhoestq I would say that although current implementation finally returns `HubDatasetModuleFactoryWithScript` as expected, with this PR we avoid the useless call to `HubDatasetModuleFactoryWithParquetExport.get_module`, so this is more optimal.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005596 / 0.011353 (-0.005757) \| 0.004022 / 0.011008 (-0.006986) \| 0.064041 / 0.038508 (0.025533) \| 0.030683 / 0.023109 (0.007574) \| 0.245236 / 0.275898 (-0.030662) \| 0.269657 / 0.323480 (-0.053823) \| 0.003142 / 0.007986 (-0.004844) \| 0.002821 / 0.004328 (-0.001507) \| 0.048774 / 0.004250 (0.044523) \| 0.043771 / 0.037052 (0.006719) \| 0.258202 / 0.258489 (-0.000287) \| 0.288381 / 0.293841 (-0.005460) \| 0.028154 / 0.128546 (-0.100392) \| 0.011071 / 0.075646 (-0.064576) \| 0.209836 / 0.419271 (-0.209436) \| 0.035923 / 0.043533 (-0.007609) \| 0.248361 / 0.255139 (-0.006777) \| 0.268728 / 0.283200 (-0.014472) \| 0.019982 / 0.141683 (-0.121701) \| 1.172330 / 1.452155 (-0.279824) \| 1.192262 / 1.492716 (-0.300455) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.089231 / 0.018006 (0.071225) \| 0.299192 / 0.000490 (0.298702) \| 0.000214 / 0.000200 (0.000014) \| 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) \| 0.018358 / 0.037411 (-0.019053) \| 0.062633 / 0.014526 (0.048107) \| 0.076276 / 0.176557 (-0.100280) \| 0.120862 / 0.737135 (-0.616274) \| 0.075958 / 0.296338 (-0.220380) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.291575 / 0.215209 (0.076366) \| 2.855908 / 2.077655 (0.778253) \| 1.459891 / 1.504120 (-0.044229) \| 1.374945 / 1.541195 (-0.166250) \| 1.333759 / 1.468490 (-0.134731) \| 0.575428 / 4.584777 (-4.009348) \| 2.414253 / 3.745712 (-1.331459) \| 2.768222 / 5.269862 (-2.501639) \| 1.705005 / 4.565676 (-2.860672) \| 0.063406 / 0.424275 (-0.360869) \| 0.004981 / 0.007607 (-0.002626) \| 0.343826 / 0.226044 (0.117781) \| 3.418143 / 2.268929 (1.149215) \| 1.856571 / 55.444624 (-53.588053) \| 1.571318 / 6.876477 (-5.305159) \| 1.609897 / 2.142072 (-0.532175) \| 0.646779 / 4.805227 (-4.158448) \| 0.118143 / 6.500664 (-6.382521) \| 0.042408 / 0.075469 (-0.033061) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.965091 / 1.841788 (-0.876697) \| 11.569655 / 8.074308 (3.495347) \| 10.587818 / 10.191392 (0.396426) \| 0.128518 / 0.680424 (-0.551905) \| 0.013954 / 0.534201 (-0.520247) \| 0.287244 / 0.579283 (-0.292039) \| 0.263755 / 0.434364 (-0.170609) \| 0.321661 / 0.540337 (-0.218676) \| 0.428753 / 1.386936 (-0.958183) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005568 / 0.011353 (-0.005785) \| 0.003755 / 0.011008 (-0.007253) \| 0.049134 / 0.038508 (0.010626) \| 0.032113 / 0.023109 (0.009004) \| 0.276645 / 0.275898 (0.000747) \| 0.299240 / 0.323480 (-0.024240) \| 0.004297 / 0.007986 (-0.003689) \| 0.002727 / 0.004328 (-0.001602) \| 0.048420 / 0.004250 (0.044170) \| 0.045070 / 0.037052 (0.008017) \| 0.288597 / 0.258489 (0.030108) \| 0.320824 / 0.293841 (0.026983) \| 0.053293 / 0.128546 (-0.075253) \| 0.011002 / 0.075646 (-0.064644) \| 0.057747 / 0.419271 (-0.361524) \| 0.034389 / 0.043533 (-0.009143) \| 0.277914 / 0.255139 (0.022775) \| 0.292919 / 0.283200 (0.009719) \| 0.018252 / 0.141683 (-0.123431) \| 1.187245 / 1.452155 (-0.264910) \| 1.199823 / 1.492716 (-0.292893) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.088338 / 0.018006 (0.070332) \| 0.297498 / 0.000490 (0.297008) \| 0.000206 / 0.000200 (0.000006) \| 0.000048 / 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) \| 0.021445 / 0.037411 (-0.015966) \| 0.075522 / 0.014526 (0.060996) \| 0.086010 / 0.176557 (-0.090546) \| 0.124938 / 0.737135 (-0.612197) \| 0.087542 / 0.296338 (-0.208796) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.292460 / 0.215209 (0.077251) \| 2.841290 / 2.077655 (0.763635) \| 1.537941 / 1.504120 (0.033821) \| 1.409903 / 1.541195 (-0.131291) \| 1.435339 / 1.468490 (-0.033151) \| 0.578967 / 4.584777 (-4.005810) \| 2.398588 / 3.745712 (-1.347125) \| 2.662342 / 5.269862 (-2.607520) \| 1.743055 / 4.565676 (-2.822622) \| 0.064043 / 0.424275 (-0.360232) \| 0.005030 / 0.007607 (-0.002577) \| 0.348542 / 0.226044 (0.122498) \| 3.395854 / 2.268929 (1.126926) \| 1.918935 / 55.444624 (-53.525689) \| 1.639320 / 6.876477 (-5.237157) \| 1.740406 / 2.142072 (-0.401666) \| 0.653346 / 4.805227 (-4.151881) \| 0.117298 / 6.500664 (-6.383366) \| 0.040635 / 0.075469 (-0.034834) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.008277 / 1.841788 (-0.833510) \| 12.069369 / 8.074308 (3.995061) \| 10.967322 / 10.191392 (0.775930) \| 0.131938 / 0.680424 (-0.548486) \| 0.015418 / 0.534201 (-0.518783) \| 0.297257 / 0.579283 (-0.282026) \| 0.270742 / 0.434364 (-0.163622) \| 0.332296 / 0.540337 (-0.208042) \| 0.421606 / 1.386936 (-0.965330) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8f22ec79a1ce4fbf0a1728d53f0338d5fdf664d8 \"CML watermark\")\n" ]	"2024-01-03T11:33:10"	"2024-02-02T10:41:33"	"2024-02-02T10:35:28"	MEMBER	null	Fix #6554.	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6555/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6555/timeline	null	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6555", "html_url": "https://github.com/huggingface/datasets/pull/6555", "diff_url": "https://github.com/huggingface/datasets/pull/6555.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6555.patch", "merged_at": "2024-02-02T10:35:28" }	true
https://api.github.com/repos/huggingface/datasets/issues/6554	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6554/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6554/comments	https://api.github.com/repos/huggingface/datasets/issues/6554/events	https://github.com/huggingface/datasets/issues/6554	2,063,839,916	I_kwDODunzps57A7Ks	6,554	Parquet exports are used even if revision is passed	{ "login": "albertvillanova", "id": 8515462, "node_id": "MDQ6VXNlcjg1MTU0NjI=", "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "gravatar_id": "", "url": "https://api.github.com/users/albertvillanova", "html_url": "https://github.com/albertvillanova", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "repos_url": "https://api.github.com/users/albertvillanova/repos", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "type": "User", "site_admin": false }	[ { "id": 1935892857, "node_id": "MDU6TGFiZWwxOTM1ODkyODU3", "url": "https://api.github.com/repos/huggingface/datasets/labels/bug", "name": "bug", "color": "d73a4a", "default": true, "description": "Something isn't working" } ]	closed	false	{ "login": "albertvillanova", "id": 8515462, "node_id": "MDQ6VXNlcjg1MTU0NjI=", "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "gravatar_id": "", "url": "https://api.github.com/users/albertvillanova", "html_url": "https://github.com/albertvillanova", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "repos_url": "https://api.github.com/users/albertvillanova/repos", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "type": "User", "site_admin": false }	[ { "login": "albertvillanova", "id": 8515462, "node_id": "MDQ6VXNlcjg1MTU0NjI=", "avatar_url": "https://avatars.githubusercontent.com/u/8515462?v=4", "gravatar_id": "", "url": "https://api.github.com/users/albertvillanova", "html_url": "https://github.com/albertvillanova", "followers_url": "https://api.github.com/users/albertvillanova/followers", "following_url": "https://api.github.com/users/albertvillanova/following{/other_user}", "gists_url": "https://api.github.com/users/albertvillanova/gists{/gist_id}", "starred_url": "https://api.github.com/users/albertvillanova/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/albertvillanova/subscriptions", "organizations_url": "https://api.github.com/users/albertvillanova/orgs", "repos_url": "https://api.github.com/users/albertvillanova/repos", "events_url": "https://api.github.com/users/albertvillanova/events{/privacy}", "received_events_url": "https://api.github.com/users/albertvillanova/received_events", "type": "User", "site_admin": false } ]	null	[ "I don't think this bug is a thing ? Do you have some code that leads to this issue ?" ]	"2024-01-03T11:32:26"	"2024-02-02T10:35:29"	"2024-02-02T10:35:29"	MEMBER	null	We should not used Parquet exports if `revision` is passed. I think this is a regression.	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6554/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6554/timeline	null	completed	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6553	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6553/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6553/comments	https://api.github.com/repos/huggingface/datasets/issues/6553/events	https://github.com/huggingface/datasets/issues/6553	2,063,474,183	I_kwDODunzps56_h4H	6,553	Cannot import name 'load_dataset' from .... module ‘datasets’	{ "login": "ciaoyizhen", "id": 83450192, "node_id": "MDQ6VXNlcjgzNDUwMTky", "avatar_url": "https://avatars.githubusercontent.com/u/83450192?v=4", "gravatar_id": "", "url": "https://api.github.com/users/ciaoyizhen", "html_url": "https://github.com/ciaoyizhen", "followers_url": "https://api.github.com/users/ciaoyizhen/followers", "following_url": "https://api.github.com/users/ciaoyizhen/following{/other_user}", "gists_url": "https://api.github.com/users/ciaoyizhen/gists{/gist_id}", "starred_url": "https://api.github.com/users/ciaoyizhen/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/ciaoyizhen/subscriptions", "organizations_url": "https://api.github.com/users/ciaoyizhen/orgs", "repos_url": "https://api.github.com/users/ciaoyizhen/repos", "events_url": "https://api.github.com/users/ciaoyizhen/events{/privacy}", "received_events_url": "https://api.github.com/users/ciaoyizhen/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "I don't know My conpany conputer cannot work. but in my computer, it work?", "Do you have a folder in your working directory called datasets?" ]	"2024-01-03T08:18:21"	"2024-02-21T00:38:24"	"2024-02-21T00:38:24"	NONE	null	### Describe the bug use python -m pip install datasets to install ### Steps to reproduce the bug from datasets import load_dataset ### Expected behavior it doesn't work ### Environment info datasets version==2.15.0 python == 3.10.12 linux version I don't know??	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6553/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6553/timeline	null	completed	null	null	false
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".	{ "login": "KelSolaar", "id": 99779, "node_id": "MDQ6VXNlcjk5Nzc5", "avatar_url": "https://avatars.githubusercontent.com/u/99779?v=4", "gravatar_id": "", "url": "https://api.github.com/users/KelSolaar", "html_url": "https://github.com/KelSolaar", "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}", "starred_url": "https://api.github.com/users/KelSolaar/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/KelSolaar/subscriptions", "organizations_url": "https://api.github.com/users/KelSolaar/orgs", "repos_url": "https://api.github.com/users/KelSolaar/repos", "events_url": "https://api.github.com/users/KelSolaar/events{/privacy}", "received_events_url": "https://api.github.com/users/KelSolaar/received_events", "type": "User", "site_admin": false }	[]	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!" ]	"2024-01-03T02:18:17"	"2024-01-08T10:09:04"	"2024-01-08T10:09:04"	NONE	null	### 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	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6552/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6552/timeline	null	completed	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6551	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6551/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6551/comments	https://api.github.com/repos/huggingface/datasets/issues/6551/events	https://github.com/huggingface/datasets/pull/6551	2,062,768,400	PR_kwDODunzps5jEi1C	6,551	Fix parallel downloads for datasets without scripts	{ "login": "lhoestq", "id": 42851186, "node_id": "MDQ6VXNlcjQyODUxMTg2", "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "gravatar_id": "", "url": "https://api.github.com/users/lhoestq", "html_url": "https://github.com/lhoestq", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "repos_url": "https://api.github.com/users/lhoestq/repos", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6551). 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) \| 0.005002 / 0.011353 (-0.006350) \| 0.003300 / 0.011008 (-0.007708) \| 0.062509 / 0.038508 (0.024001) \| 0.029807 / 0.023109 (0.006698) \| 0.249935 / 0.275898 (-0.025963) \| 0.264320 / 0.323480 (-0.059160) \| 0.003790 / 0.007986 (-0.004195) \| 0.002554 / 0.004328 (-0.001774) \| 0.048207 / 0.004250 (0.043956) \| 0.042033 / 0.037052 (0.004981) \| 0.245725 / 0.258489 (-0.012764) \| 0.276695 / 0.293841 (-0.017146) \| 0.026502 / 0.128546 (-0.102044) \| 0.010379 / 0.075646 (-0.065268) \| 0.207002 / 0.419271 (-0.212269) \| 0.034648 / 0.043533 (-0.008885) \| 0.247957 / 0.255139 (-0.007182) \| 0.263921 / 0.283200 (-0.019278) \| 0.017710 / 0.141683 (-0.123973) \| 1.105851 / 1.452155 (-0.346304) \| 1.163315 / 1.492716 (-0.329401) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.089842 / 0.018006 (0.071836) \| 0.352499 / 0.000490 (0.352009) \| 0.000201 / 0.000200 (0.000001) \| 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) \| 0.018094 / 0.037411 (-0.019317) \| 0.060463 / 0.014526 (0.045937) \| 0.073257 / 0.176557 (-0.103300) \| 0.119771 / 0.737135 (-0.617364) \| 0.075210 / 0.296338 (-0.221128) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.288365 / 0.215209 (0.073156) \| 2.825377 / 2.077655 (0.747722) \| 1.532436 / 1.504120 (0.028316) \| 1.393475 / 1.541195 (-0.147719) \| 1.381859 / 1.468490 (-0.086632) \| 0.564155 / 4.584777 (-4.020622) \| 2.398177 / 3.745712 (-1.347535) \| 2.730271 / 5.269862 (-2.539590) \| 1.713779 / 4.565676 (-2.851898) \| 0.062789 / 0.424275 (-0.361486) \| 0.004991 / 0.007607 (-0.002616) \| 0.340789 / 0.226044 (0.114744) \| 3.323543 / 2.268929 (1.054615) \| 1.861925 / 55.444624 (-53.582700) \| 1.555181 / 6.876477 (-5.321296) \| 1.559512 / 2.142072 (-0.582560) \| 0.634565 / 4.805227 (-4.170663) \| 0.116529 / 6.500664 (-6.384135) \| 0.041312 / 0.075469 (-0.034157) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.945739 / 1.841788 (-0.896049) \| 11.376130 / 8.074308 (3.301822) \| 10.007752 / 10.191392 (-0.183640) \| 0.126815 / 0.680424 (-0.553609) \| 0.013898 / 0.534201 (-0.520303) \| 0.287438 / 0.579283 (-0.291845) \| 0.261532 / 0.434364 (-0.172832) \| 0.320197 / 0.540337 (-0.220140) \| 0.414444 / 1.386936 (-0.972492) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004994 / 0.011353 (-0.006359) \| 0.003407 / 0.011008 (-0.007601) \| 0.049281 / 0.038508 (0.010773) \| 0.042815 / 0.023109 (0.019706) \| 0.268291 / 0.275898 (-0.007607) \| 0.285877 / 0.323480 (-0.037603) \| 0.004006 / 0.007986 (-0.003980) \| 0.002607 / 0.004328 (-0.001721) \| 0.047682 / 0.004250 (0.043431) \| 0.044281 / 0.037052 (0.007228) \| 0.268287 / 0.258489 (0.009798) \| 0.298649 / 0.293841 (0.004808) \| 0.028607 / 0.128546 (-0.099939) \| 0.010367 / 0.075646 (-0.065279) \| 0.057114 / 0.419271 (-0.362158) \| 0.053753 / 0.043533 (0.010220) \| 0.269010 / 0.255139 (0.013871) \| 0.285057 / 0.283200 (0.001858) \| 0.017693 / 0.141683 (-0.123990) \| 1.134718 / 1.452155 (-0.317436) \| 1.186609 / 1.492716 (-0.306107) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.091109 / 0.018006 (0.073103) \| 0.298603 / 0.000490 (0.298113) \| 0.000216 / 0.000200 (0.000016) \| 0.000050 / 0.000054 (-0.000004) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022125 / 0.037411 (-0.015286) \| 0.076570 / 0.014526 (0.062044) \| 0.088903 / 0.176557 (-0.087654) \| 0.126427 / 0.737135 (-0.610708) \| 0.091001 / 0.296338 (-0.205338) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.300332 / 0.215209 (0.085123) \| 2.971106 / 2.077655 (0.893452) \| 1.617886 / 1.504120 (0.113766) \| 1.476679 / 1.541195 (-0.064516) \| 1.483750 / 1.468490 (0.015260) \| 0.582569 / 4.584777 (-4.002208) \| 2.441804 / 3.745712 (-1.303908) \| 2.753927 / 5.269862 (-2.515935) \| 1.733546 / 4.565676 (-2.832130) \| 0.062653 / 0.424275 (-0.361622) \| 0.005019 / 0.007607 (-0.002588) \| 0.355556 / 0.226044 (0.129512) \| 3.497431 / 2.268929 (1.228503) \| 1.951711 / 55.444624 (-53.492913) \| 1.663874 / 6.876477 (-5.212602) \| 1.657363 / 2.142072 (-0.484709) \| 0.653488 / 4.805227 (-4.151739) \| 0.117055 / 6.500664 (-6.383609) \| 0.040687 / 0.075469 (-0.034782) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.969485 / 1.841788 (-0.872303) \| 12.064793 / 8.074308 (3.990485) \| 10.851531 / 10.191392 (0.660139) \| 0.129060 / 0.680424 (-0.551364) \| 0.015339 / 0.534201 (-0.518862) \| 0.287215 / 0.579283 (-0.292069) \| 0.276545 / 0.434364 (-0.157819) \| 0.322748 / 0.540337 (-0.217589) \| 0.421363 / 1.386936 (-0.965573) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d26abadce0b884db32382b92422d8a6aa997d40a \"CML watermark\")\n", "@lhoestq \r\n<img width=\"1015\" alt=\"image\" src=\"https://github.com/huggingface/datasets/assets/17604849/b19b9d92-c6f7-4e3a-8c9d-1178e56c67ea\">\r\nit's still not fixed =(", "@lhoestq i was thinking uninstalling `datasets` and then `pip install git+https://github.com/huggingface/datasets.git` has to fix it. Buuuuut. I'm not sure what's going on actually...\r\n\r\nNow instead of showing progress bars one after another it seems to be downloading the dataset way way way faster (like 4 mins instead of 58, thank you very much) but does not show any progress bars related to downloading at all.\r\n\r\n<img width=\"1170\" alt=\"image\" src=\"https://github.com/huggingface/datasets/assets/17604849/21a84908-c44d-41b4-bb0d-8061cab3bc64\">\r\n\r\n<img width=\"1159\" alt=\"image\" src=\"https://github.com/huggingface/datasets/assets/17604849/26684a8a-c10a-4fa2-bd84-cab4f938ffcc\">\r\n" ]	"2024-01-02T18:06:18"	"2024-01-06T20:14:57"	"2024-01-03T13:19:48"	MEMBER	null	Enable parallel downloads using multiprocessing when `num_proc` is passed to `load_dataset`. It was enabled for datasets with scripts already (if they passed lists to `dl_manager.download`) but not for no-script datasets (we pass dicts {split: [list of files]} to `dl_manager.download` for those ones). I fixed this by parallelising on the lists contained in the data files dicts when possible. I also added a context manager `stack_multiprocessing_download_progress_bars` in `DownloadManager` to stack the progress bard of the downloads (from `cached_path(...)` calls). Otherwise the progress bars overlap each other with an annoying flickering effect.	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6551/reactions", "total_count": 2, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 2, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6551/timeline	null	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6551", "html_url": "https://github.com/huggingface/datasets/pull/6551", "diff_url": "https://github.com/huggingface/datasets/pull/6551.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6551.patch", "merged_at": "2024-01-03T13:19:47" }	true
https://api.github.com/repos/huggingface/datasets/issues/6550	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6550/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6550/comments	https://api.github.com/repos/huggingface/datasets/issues/6550/events	https://github.com/huggingface/datasets/pull/6550	2,062,556,493	PR_kwDODunzps5jD1OL	6,550	Multi gpu docs	{ "login": "lhoestq", "id": 42851186, "node_id": "MDQ6VXNlcjQyODUxMTg2", "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "gravatar_id": "", "url": "https://api.github.com/users/lhoestq", "html_url": "https://github.com/lhoestq", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "repos_url": "https://api.github.com/users/lhoestq/repos", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6550). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "Thanks @lhoestq . This is a very important fix for code to run on multiple GPUs. Otherwise, only one GPU is working. I wish it can be merged soon. \r\nI also wrote a [blog post](https://forrestbao.github.io/2024/01/30/datasets_map_with_rank_multiple_GPUs.html) with a complete example in case it can be helpful to someone. Please feel free to use complete example in any documentation. \r\n", "Thanks a lot @forrestbao ! I reused parts of your code for the documentation, I'm sure it will be useful to many people !", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005662 / 0.011353 (-0.005691) \| 0.003930 / 0.011008 (-0.007078) \| 0.063807 / 0.038508 (0.025299) \| 0.030227 / 0.023109 (0.007118) \| 0.235338 / 0.275898 (-0.040560) \| 0.264433 / 0.323480 (-0.059047) \| 0.004226 / 0.007986 (-0.003759) \| 0.002847 / 0.004328 (-0.001481) \| 0.048998 / 0.004250 (0.044747) \| 0.042713 / 0.037052 (0.005660) \| 0.250504 / 0.258489 (-0.007985) \| 0.281101 / 0.293841 (-0.012740) \| 0.029123 / 0.128546 (-0.099423) \| 0.011388 / 0.075646 (-0.064258) \| 0.211342 / 0.419271 (-0.207930) \| 0.036437 / 0.043533 (-0.007096) \| 0.238909 / 0.255139 (-0.016230) \| 0.255853 / 0.283200 (-0.027347) \| 0.018852 / 0.141683 (-0.122831) \| 1.131870 / 1.452155 (-0.320284) \| 1.209007 / 1.492716 (-0.283710) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092433 / 0.018006 (0.074427) \| 0.303045 / 0.000490 (0.302556) \| 0.000291 / 0.000200 (0.000091) \| 0.000079 / 0.000054 (0.000025) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018349 / 0.037411 (-0.019062) \| 0.062527 / 0.014526 (0.048002) \| 0.075347 / 0.176557 (-0.101210) \| 0.120587 / 0.737135 (-0.616549) \| 0.075171 / 0.296338 (-0.221167) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.288364 / 0.215209 (0.073155) \| 2.775779 / 2.077655 (0.698124) \| 1.490875 / 1.504120 (-0.013245) \| 1.375451 / 1.541195 (-0.165744) \| 1.398923 / 1.468490 (-0.069567) \| 0.588659 / 4.584777 (-3.996117) \| 2.458114 / 3.745712 (-1.287598) \| 2.928910 / 5.269862 (-2.340951) \| 1.834221 / 4.565676 (-2.731456) \| 0.064503 / 0.424275 (-0.359772) \| 0.005028 / 0.007607 (-0.002580) \| 0.340386 / 0.226044 (0.114341) \| 3.408697 / 2.268929 (1.139769) \| 1.843613 / 55.444624 (-53.601012) \| 1.569300 / 6.876477 (-5.307177) \| 1.636761 / 2.142072 (-0.505312) \| 0.687854 / 4.805227 (-4.117374) \| 0.123462 / 6.500664 (-6.377202) \| 0.042877 / 0.075469 (-0.032593) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.984054 / 1.841788 (-0.857734) \| 12.243934 / 8.074308 (4.169626) \| 10.835244 / 10.191392 (0.643852) \| 0.131609 / 0.680424 (-0.548815) \| 0.014000 / 0.534201 (-0.520201) \| 0.292070 / 0.579283 (-0.287213) \| 0.271958 / 0.434364 (-0.162406) \| 0.326866 / 0.540337 (-0.213471) \| 0.440880 / 1.386936 (-0.946056) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005954 / 0.011353 (-0.005399) \| 0.004123 / 0.011008 (-0.006885) \| 0.050371 / 0.038508 (0.011863) \| 0.034387 / 0.023109 (0.011277) \| 0.273254 / 0.275898 (-0.002644) \| 0.297785 / 0.323480 (-0.025695) \| 0.004619 / 0.007986 (-0.003367) \| 0.002884 / 0.004328 (-0.001444) \| 0.050236 / 0.004250 (0.045986) \| 0.048586 / 0.037052 (0.011533) \| 0.283878 / 0.258489 (0.025389) \| 0.315218 / 0.293841 (0.021377) \| 0.060688 / 0.128546 (-0.067859) \| 0.011991 / 0.075646 (-0.063655) \| 0.059518 / 0.419271 (-0.359753) \| 0.036113 / 0.043533 (-0.007420) \| 0.274767 / 0.255139 (0.019628) \| 0.290620 / 0.283200 (0.007420) \| 0.020070 / 0.141683 (-0.121613) \| 1.164635 / 1.452155 (-0.287519) \| 1.189482 / 1.492716 (-0.303234) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.095171 / 0.018006 (0.077165) \| 0.307129 / 0.000490 (0.306639) \| 0.000227 / 0.000200 (0.000027) \| 0.000045 / 0.000054 (-0.000009) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022777 / 0.037411 (-0.014634) \| 0.076761 / 0.014526 (0.062235) \| 0.087654 / 0.176557 (-0.088902) \| 0.126729 / 0.737135 (-0.610406) \| 0.089491 / 0.296338 (-0.206847) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.292208 / 0.215209 (0.076999) \| 2.890491 / 2.077655 (0.812836) \| 1.625696 / 1.504120 (0.121576) \| 1.463484 / 1.541195 (-0.077710) \| 1.490889 / 1.468490 (0.022399) \| 0.582155 / 4.584777 (-4.002622) \| 2.492209 / 3.745712 (-1.253503) \| 2.817020 / 5.269862 (-2.452842) \| 1.806812 / 4.565676 (-2.758864) \| 0.065830 / 0.424275 (-0.358445) \| 0.005089 / 0.007607 (-0.002518) \| 0.356067 / 0.226044 (0.130022) \| 3.489652 / 2.268929 (1.220723) \| 1.959276 / 55.444624 (-53.485348) \| 1.678819 / 6.876477 (-5.197657) \| 1.853581 / 2.142072 (-0.288491) \| 0.660515 / 4.805227 (-4.144712) \| 0.119884 / 6.500664 (-6.380780) \| 0.041713 / 0.075469 (-0.033757) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.021701 / 1.841788 (-0.820087) \| 12.918290 / 8.074308 (4.843982) \| 11.469371 / 10.191392 (1.277979) \| 0.144830 / 0.680424 (-0.535594) \| 0.015858 / 0.534201 (-0.518343) \| 0.290136 / 0.579283 (-0.289148) \| 0.277894 / 0.434364 (-0.156470) \| 0.330091 / 0.540337 (-0.210247) \| 0.422697 / 1.386936 (-0.964240) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#13b36ee5c6d77f7eacbb4dd545a21e785db7fd3e \"CML watermark\")\n" ]	"2024-01-02T15:11:58"	"2024-01-31T13:45:15"	"2024-01-31T13:38:59"	MEMBER	null	after discussions in https://github.com/huggingface/datasets/pull/6415	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6550/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6550/timeline	null	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6550", "html_url": "https://github.com/huggingface/datasets/pull/6550", "diff_url": "https://github.com/huggingface/datasets/pull/6550.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6550.patch", "merged_at": "2024-01-31T13:38:59" }	true
https://api.github.com/repos/huggingface/datasets/issues/6549	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6549/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6549/comments	https://api.github.com/repos/huggingface/datasets/issues/6549/events	https://github.com/huggingface/datasets/issues/6549	2,062,420,259	I_kwDODunzps567gkj	6,549	Loading from hf hub with clearer error message	{ "login": "thomwolf", "id": 7353373, "node_id": "MDQ6VXNlcjczNTMzNzM=", "avatar_url": "https://avatars.githubusercontent.com/u/7353373?v=4", "gravatar_id": "", "url": "https://api.github.com/users/thomwolf", "html_url": "https://github.com/thomwolf", "followers_url": "https://api.github.com/users/thomwolf/followers", "following_url": "https://api.github.com/users/thomwolf/following{/other_user}", "gists_url": "https://api.github.com/users/thomwolf/gists{/gist_id}", "starred_url": "https://api.github.com/users/thomwolf/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/thomwolf/subscriptions", "organizations_url": "https://api.github.com/users/thomwolf/orgs", "repos_url": "https://api.github.com/users/thomwolf/repos", "events_url": "https://api.github.com/users/thomwolf/events{/privacy}", "received_events_url": "https://api.github.com/users/thomwolf/received_events", "type": "User", "site_admin": false }	[ { "id": 1935892871, "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement", "name": "enhancement", "color": "a2eeef", "default": true, "description": "New feature or request" } ]	open	false	null	[]	null	[ "Maybe we can add a helper message like `Maybe try again using \"hf://path/without/resolve\"` if the path contains `/resolve/` ?\r\n\r\ne.g.\r\n\r\n```\r\nFileNotFoundError: Unable to find 'hf://datasets/HuggingFaceTB/eval_data/resolve/main/eval_data_context_and_answers.json'\r\nIt looks like you used parts of the URL of the file from the Hugging Face website, but you should remove the \"/resolve/<revision>\" part to have a valid `hf://` path.\r\nPlease try again using this path instead:\r\n hf://datasets/HuggingFaceTB/eval_data/eval_data_context_and_answers.json\r\n```\r\n\r\nand suggest `f\"hf://datasets/HuggingFaceTB/eval_data@{revision}/eval_data_context_and_answers.json\"` if revision != \"main\"\r\n\r\nEDIT: I think this message should also be raised from the `huggingface_hub`'s `HfFileSystem` implementation" ]	"2024-01-02T13:26:34"	"2024-01-02T14:06:49"	null	MEMBER	null	### Feature request Shouldn't this kinda work ? ``` Dataset.from_json("hf://datasets/HuggingFaceTB/eval_data/resolve/main/eval_data_context_and_answers.json") ``` I got an error ``` File ~/miniconda3/envs/datatrove/lib/python3.10/site-packages/datasets/data_files.py:380, in resolve_pattern(pattern, base_path, allowed_extensions, download_config) 378 if allowed_extensions is not None: 379 error_msg += f" with any supported extension {list(allowed_extensions)}" --> 380 raise FileNotFoundError(error_msg) 381 return out FileNotFoundError: Unable to find 'hf://datasets/HuggingFaceTB/eval_data/resolve/main/eval_data_context_and_answers.json' (I'm logged in) ``` Fix: the correct path is ``` hf://datasets/HuggingFaceTB/eval_data/eval_data_context_and_answers.json ``` Proposal: raise a clearer error ### Motivation Clearer error message ### Your contribution Can open a PR	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6549/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6549/timeline	null	null	null	null	false
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	{ "login": "hadianasliwa", "id": 143214684, "node_id": "U_kgDOCIlIXA", "avatar_url": "https://avatars.githubusercontent.com/u/143214684?v=4", "gravatar_id": "", "url": "https://api.github.com/users/hadianasliwa", "html_url": "https://github.com/hadianasliwa", "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}", "starred_url": "https://api.github.com/users/hadianasliwa/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/hadianasliwa/subscriptions", "organizations_url": "https://api.github.com/users/hadianasliwa/orgs", "repos_url": "https://api.github.com/users/hadianasliwa/repos", "events_url": "https://api.github.com/users/hadianasliwa/events{/privacy}", "received_events_url": "https://api.github.com/users/hadianasliwa/received_events", "type": "User", "site_admin": false }	[]	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." ]	"2023-12-31T12:41:26"	"2024-01-02T10:33:17"	null	NONE	null	### 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	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6548/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6548/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6547	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6547/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6547/comments	https://api.github.com/repos/huggingface/datasets/issues/6547/events	https://github.com/huggingface/datasets/pull/6547	2,060,796,927	PR_kwDODunzps5i-Jni	6,547	set dev version	{ "login": "lhoestq", "id": 42851186, "node_id": "MDQ6VXNlcjQyODUxMTg2", "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "gravatar_id": "", "url": "https://api.github.com/users/lhoestq", "html_url": "https://github.com/lhoestq", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "repos_url": "https://api.github.com/users/lhoestq/repos", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6547). 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) \| 0.004855 / 0.011353 (-0.006498) \| 0.003552 / 0.011008 (-0.007456) \| 0.062328 / 0.038508 (0.023820) \| 0.031142 / 0.023109 (0.008032) \| 0.247726 / 0.275898 (-0.028172) \| 0.270951 / 0.323480 (-0.052528) \| 0.002887 / 0.007986 (-0.005099) \| 0.002663 / 0.004328 (-0.001665) \| 0.047888 / 0.004250 (0.043638) \| 0.042932 / 0.037052 (0.005880) \| 0.253660 / 0.258489 (-0.004829) \| 0.274997 / 0.293841 (-0.018844) \| 0.027200 / 0.128546 (-0.101347) \| 0.010851 / 0.075646 (-0.064796) \| 0.206566 / 0.419271 (-0.212706) \| 0.035311 / 0.043533 (-0.008222) \| 0.254146 / 0.255139 (-0.000993) \| 0.269074 / 0.283200 (-0.014126) \| 0.019221 / 0.141683 (-0.122462) \| 1.101986 / 1.452155 (-0.350169) \| 1.155541 / 1.492716 (-0.337175) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004749 / 0.018006 (-0.013257) \| 0.301627 / 0.000490 (0.301138) \| 0.000208 / 0.000200 (0.000008) \| 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) \| 0.018205 / 0.037411 (-0.019206) \| 0.060420 / 0.014526 (0.045894) \| 0.072533 / 0.176557 (-0.104023) \| 0.119807 / 0.737135 (-0.617328) \| 0.073249 / 0.296338 (-0.223089) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.284947 / 0.215209 (0.069738) \| 2.796939 / 2.077655 (0.719285) \| 1.486076 / 1.504120 (-0.018043) \| 1.358247 / 1.541195 (-0.182948) \| 1.383680 / 1.468490 (-0.084811) \| 0.550253 / 4.584777 (-4.034524) \| 2.364783 / 3.745712 (-1.380929) \| 2.765631 / 5.269862 (-2.504230) \| 1.695694 / 4.565676 (-2.869983) \| 0.061519 / 0.424275 (-0.362756) \| 0.004914 / 0.007607 (-0.002693) \| 0.340370 / 0.226044 (0.114325) \| 3.313175 / 2.268929 (1.044247) \| 1.805421 / 55.444624 (-53.639203) \| 1.532151 / 6.876477 (-5.344325) \| 1.541195 / 2.142072 (-0.600878) \| 0.625266 / 4.805227 (-4.179961) \| 0.119980 / 6.500664 (-6.380684) \| 0.042334 / 0.075469 (-0.033135) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.952893 / 1.841788 (-0.888895) \| 11.322232 / 8.074308 (3.247924) \| 9.982108 / 10.191392 (-0.209284) \| 0.130034 / 0.680424 (-0.550389) \| 0.013192 / 0.534201 (-0.521009) \| 0.286041 / 0.579283 (-0.293243) \| 0.269802 / 0.434364 (-0.164562) \| 0.323582 / 0.540337 (-0.216755) \| 0.428641 / 1.386936 (-0.958295) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005071 / 0.011353 (-0.006282) \| 0.003368 / 0.011008 (-0.007640) \| 0.049003 / 0.038508 (0.010495) \| 0.029507 / 0.023109 (0.006398) \| 0.271859 / 0.275898 (-0.004039) \| 0.294660 / 0.323480 (-0.028820) \| 0.004218 / 0.007986 (-0.003767) \| 0.002686 / 0.004328 (-0.001642) \| 0.047947 / 0.004250 (0.043696) \| 0.044499 / 0.037052 (0.007447) \| 0.273982 / 0.258489 (0.015493) \| 0.303393 / 0.293841 (0.009552) \| 0.029649 / 0.128546 (-0.098898) \| 0.010555 / 0.075646 (-0.065091) \| 0.057553 / 0.419271 (-0.361718) \| 0.051686 / 0.043533 (0.008153) \| 0.274079 / 0.255139 (0.018940) \| 0.292535 / 0.283200 (0.009335) \| 0.019211 / 0.141683 (-0.122472) \| 1.130629 / 1.452155 (-0.321526) \| 1.196791 / 1.492716 (-0.295925) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.093617 / 0.018006 (0.075611) \| 0.302698 / 0.000490 (0.302209) \| 0.000222 / 0.000200 (0.000022) \| 0.000046 / 0.000054 (-0.000009) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022830 / 0.037411 (-0.014581) \| 0.077061 / 0.014526 (0.062535) \| 0.089464 / 0.176557 (-0.087092) \| 0.127487 / 0.737135 (-0.609649) \| 0.092133 / 0.296338 (-0.204205) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.295362 / 0.215209 (0.080153) \| 2.902251 / 2.077655 (0.824596) \| 1.600508 / 1.504120 (0.096388) \| 1.477763 / 1.541195 (-0.063431) \| 1.492242 / 1.468490 (0.023752) \| 0.569347 / 4.584777 (-4.015430) \| 2.449873 / 3.745712 (-1.295839) \| 2.787207 / 5.269862 (-2.482655) \| 1.723852 / 4.565676 (-2.841825) \| 0.063076 / 0.424275 (-0.361199) \| 0.005060 / 0.007607 (-0.002547) \| 0.349614 / 0.226044 (0.123569) \| 3.429735 / 2.268929 (1.160806) \| 1.953883 / 55.444624 (-53.490741) \| 1.664232 / 6.876477 (-5.212245) \| 1.648864 / 2.142072 (-0.493209) \| 0.640295 / 4.805227 (-4.164932) \| 0.117053 / 6.500664 (-6.383611) \| 0.041314 / 0.075469 (-0.034156) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.970663 / 1.841788 (-0.871125) \| 12.144810 / 8.074308 (4.070502) \| 10.938985 / 10.191392 (0.747593) \| 0.140502 / 0.680424 (-0.539922) \| 0.015522 / 0.534201 (-0.518679) \| 0.286629 / 0.579283 (-0.292654) \| 0.283695 / 0.434364 (-0.150669) \| 0.327298 / 0.540337 (-0.213039) \| 0.424635 / 1.386936 (-0.962301) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e23a59ef7ba2b50d4e5588825c41212a3cfd1331 \"CML watermark\")\n" ]	"2023-12-30T16:47:17"	"2023-12-30T16:53:38"	"2023-12-30T16:47:27"	MEMBER	null	null	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6547/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6547/timeline	null	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6547", "html_url": "https://github.com/huggingface/datasets/pull/6547", "diff_url": "https://github.com/huggingface/datasets/pull/6547.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6547.patch", "merged_at": "2023-12-30T16:47:27" }	true
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	{ "login": "lhoestq", "id": 42851186, "node_id": "MDQ6VXNlcjQyODUxMTg2", "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "gravatar_id": "", "url": "https://api.github.com/users/lhoestq", "html_url": "https://github.com/lhoestq", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "repos_url": "https://api.github.com/users/lhoestq/repos", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_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" ]	"2023-12-30T16:44:51"	"2023-12-30T16:52:07"	"2023-12-30T16:45:52"	MEMBER	null	null	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6546/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6546/timeline	null	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6546", "html_url": "https://github.com/huggingface/datasets/pull/6546", "diff_url": "https://github.com/huggingface/datasets/pull/6546.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6546.patch", "merged_at": "2023-12-30T16:45:52" }	true
https://api.github.com/repos/huggingface/datasets/issues/6545	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6545/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6545/comments	https://api.github.com/repos/huggingface/datasets/issues/6545/events	https://github.com/huggingface/datasets/issues/6545	2,060,789,507	I_kwDODunzps561ScD	6,545	`image` column not automatically inferred if image dataset only contains 1 image	{ "login": "apolinario", "id": 788417, "node_id": "MDQ6VXNlcjc4ODQxNw==", "avatar_url": "https://avatars.githubusercontent.com/u/788417?v=4", "gravatar_id": "", "url": "https://api.github.com/users/apolinario", "html_url": "https://github.com/apolinario", "followers_url": "https://api.github.com/users/apolinario/followers", "following_url": "https://api.github.com/users/apolinario/following{/other_user}", "gists_url": "https://api.github.com/users/apolinario/gists{/gist_id}", "starred_url": "https://api.github.com/users/apolinario/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/apolinario/subscriptions", "organizations_url": "https://api.github.com/users/apolinario/orgs", "repos_url": "https://api.github.com/users/apolinario/repos", "events_url": "https://api.github.com/users/apolinario/events{/privacy}", "received_events_url": "https://api.github.com/users/apolinario/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[]	"2023-12-30T16:17:29"	"2024-01-09T13:06:31"	"2024-01-09T13:06:31"	NONE	null	### Describe the bug By default, the standard Image Dataset maps out `file_name` to `image` when loading an Image Dataset. However, if the dataset contains only 1 image, this does not take place ### Steps to reproduce the bug Input (dataset with one image `multimodalart/repro_1_image`) ```py from datasets import load_dataset dataset = load_dataset("multimodalart/repro_1_image") dataset ``` Output: ```py DatasetDict({ train: Dataset({ features: ['file_name', 'prompt'], num_rows: 1 }) }) ``` Input (dataset with 2+ images `multimodalart/repro_2_image`) ```py from datasets import load_dataset dataset = load_dataset("multimodalart/repro_2_image") dataset ``` Output: ```py DatasetDict({ train: Dataset({ features: ['image', 'prompt'], num_rows: 2 }) }) ``` ### Expected behavior Expected to map `file_name` → `image` for all dataset sizes, including 1. ### Environment info Both latest main and 2.16.0	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6545/reactions", "total_count": 1, "+1": 1, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6545/timeline	null	completed	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6544	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6544/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6544/comments	https://api.github.com/repos/huggingface/datasets/issues/6544/events	https://github.com/huggingface/datasets/pull/6544	2,060,782,594	PR_kwDODunzps5i-G4_	6,544	Fix custom configs from script	{ "login": "lhoestq", "id": 42851186, "node_id": "MDQ6VXNlcjQyODUxMTg2", "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "gravatar_id": "", "url": "https://api.github.com/users/lhoestq", "html_url": "https://github.com/lhoestq", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "repos_url": "https://api.github.com/users/lhoestq/repos", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6544). 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) \| 0.005462 / 0.011353 (-0.005891) \| 0.003918 / 0.011008 (-0.007090) \| 0.065021 / 0.038508 (0.026513) \| 0.032620 / 0.023109 (0.009511) \| 0.249794 / 0.275898 (-0.026104) \| 0.277330 / 0.323480 (-0.046150) \| 0.002962 / 0.007986 (-0.005023) \| 0.003435 / 0.004328 (-0.000894) \| 0.048992 / 0.004250 (0.044742) \| 0.046841 / 0.037052 (0.009788) \| 0.252459 / 0.258489 (-0.006030) \| 0.287889 / 0.293841 (-0.005952) \| 0.028322 / 0.128546 (-0.100224) \| 0.011214 / 0.075646 (-0.064432) \| 0.208555 / 0.419271 (-0.210717) \| 0.037004 / 0.043533 (-0.006529) \| 0.262537 / 0.255139 (0.007398) \| 0.307418 / 0.283200 (0.024218) \| 0.021552 / 0.141683 (-0.120131) \| 1.144252 / 1.452155 (-0.307903) \| 1.195687 / 1.492716 (-0.297029) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004766 / 0.018006 (-0.013240) \| 0.301926 / 0.000490 (0.301436) \| 0.000218 / 0.000200 (0.000018) \| 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) \| 0.017891 / 0.037411 (-0.019521) \| 0.066848 / 0.014526 (0.052322) \| 0.075522 / 0.176557 (-0.101035) \| 0.120762 / 0.737135 (-0.616374) \| 0.075980 / 0.296338 (-0.220359) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.284843 / 0.215209 (0.069634) \| 2.816260 / 2.077655 (0.738605) \| 1.484370 / 1.504120 (-0.019750) \| 1.362090 / 1.541195 (-0.179104) \| 1.421729 / 1.468490 (-0.046762) \| 0.561673 / 4.584777 (-4.023104) \| 2.370793 / 3.745712 (-1.374919) \| 2.982639 / 5.269862 (-2.287223) \| 1.834614 / 4.565676 (-2.731063) \| 0.063158 / 0.424275 (-0.361117) \| 0.005044 / 0.007607 (-0.002563) \| 0.339834 / 0.226044 (0.113790) \| 3.369051 / 2.268929 (1.100122) \| 1.821040 / 55.444624 (-53.623584) \| 1.544009 / 6.876477 (-5.332468) \| 1.603902 / 2.142072 (-0.538171) \| 0.638151 / 4.805227 (-4.167076) \| 0.117012 / 6.500664 (-6.383652) \| 0.042999 / 0.075469 (-0.032470) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.941809 / 1.841788 (-0.899978) \| 12.279635 / 8.074308 (4.205326) \| 10.212876 / 10.191392 (0.021484) \| 0.129904 / 0.680424 (-0.550519) \| 0.014210 / 0.534201 (-0.519991) \| 0.286140 / 0.579283 (-0.293143) \| 0.267453 / 0.434364 (-0.166911) \| 0.324417 / 0.540337 (-0.215921) \| 0.428262 / 1.386936 (-0.958674) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005351 / 0.011353 (-0.006002) \| 0.003591 / 0.011008 (-0.007417) \| 0.048755 / 0.038508 (0.010247) \| 0.030857 / 0.023109 (0.007748) \| 0.270301 / 0.275898 (-0.005597) \| 0.294459 / 0.323480 (-0.029021) \| 0.004265 / 0.007986 (-0.003720) \| 0.002712 / 0.004328 (-0.001616) \| 0.047725 / 0.004250 (0.043475) \| 0.048392 / 0.037052 (0.011339) \| 0.274226 / 0.258489 (0.015737) \| 0.304010 / 0.293841 (0.010169) \| 0.029283 / 0.128546 (-0.099263) \| 0.011196 / 0.075646 (-0.064450) \| 0.057213 / 0.419271 (-0.362058) \| 0.057504 / 0.043533 (0.013971) \| 0.266091 / 0.255139 (0.010952) \| 0.285991 / 0.283200 (0.002791) \| 0.020030 / 0.141683 (-0.121653) \| 1.121514 / 1.452155 (-0.330641) \| 1.192608 / 1.492716 (-0.300108) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.095041 / 0.018006 (0.077035) \| 0.301255 / 0.000490 (0.300765) \| 0.000218 / 0.000200 (0.000018) \| 0.000044 / 0.000054 (-0.000010) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.022265 / 0.037411 (-0.015146) \| 0.078416 / 0.014526 (0.063890) \| 0.091097 / 0.176557 (-0.085460) \| 0.129864 / 0.737135 (-0.607272) \| 0.091683 / 0.296338 (-0.204655) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.294104 / 0.215209 (0.078895) \| 2.886809 / 2.077655 (0.809154) \| 1.601931 / 1.504120 (0.097811) \| 1.469353 / 1.541195 (-0.071842) \| 1.525132 / 1.468490 (0.056642) \| 0.565164 / 4.584777 (-4.019613) \| 2.432873 / 3.745712 (-1.312839) \| 2.885849 / 5.269862 (-2.384013) \| 1.780474 / 4.565676 (-2.785203) \| 0.064358 / 0.424275 (-0.359917) \| 0.005186 / 0.007607 (-0.002421) \| 0.349374 / 0.226044 (0.123329) \| 3.424751 / 2.268929 (1.155823) \| 1.956874 / 55.444624 (-53.487750) \| 1.679002 / 6.876477 (-5.197475) \| 1.718821 / 2.142072 (-0.423252) \| 0.656974 / 4.805227 (-4.148254) \| 0.120645 / 6.500664 (-6.380019) \| 0.042355 / 0.075469 (-0.033114) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 1.001923 / 1.841788 (-0.839864) \| 13.208127 / 8.074308 (5.133819) \| 11.164863 / 10.191392 (0.973471) \| 0.131964 / 0.680424 (-0.548460) \| 0.015344 / 0.534201 (-0.518857) \| 0.287961 / 0.579283 (-0.291322) \| 0.273986 / 0.434364 (-0.160378) \| 0.327280 / 0.540337 (-0.213058) \| 0.426761 / 1.386936 (-0.960175) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ebb913eca80807521239efece1ff305625cb89b4 \"CML watermark\")\n", "Thanks for the fix and the patch release. This confirms that, as I suggested in the Summer, maybe we should avoid making a release right before leaving on holidays." ]	"2023-12-30T15:51:25"	"2024-01-02T11:02:39"	"2023-12-30T16:09:49"	MEMBER	null	We should not use the parquet export when the user is passing config_kwargs I also fixed a regression that would disallow creating a custom config when a dataset has multiple predefined configs fix https://github.com/huggingface/datasets/issues/6533	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6544/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6544/timeline	null	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6544", "html_url": "https://github.com/huggingface/datasets/pull/6544", "diff_url": "https://github.com/huggingface/datasets/pull/6544.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6544.patch", "merged_at": "2023-12-30T16:09:49" }	true
https://api.github.com/repos/huggingface/datasets/issues/6543	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6543/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6543/comments	https://api.github.com/repos/huggingface/datasets/issues/6543/events	https://github.com/huggingface/datasets/pull/6543	2,060,776,174	PR_kwDODunzps5i-Frx	6,543	Fix dl_manager.extract returning FileNotFoundError	{ "login": "lhoestq", "id": 42851186, "node_id": "MDQ6VXNlcjQyODUxMTg2", "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "gravatar_id": "", "url": "https://api.github.com/users/lhoestq", "html_url": "https://github.com/lhoestq", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "repos_url": "https://api.github.com/users/lhoestq/repos", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6543). 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) \| 0.004950 / 0.011353 (-0.006403) \| 0.003502 / 0.011008 (-0.007506) \| 0.062517 / 0.038508 (0.024009) \| 0.030965 / 0.023109 (0.007856) \| 0.250661 / 0.275898 (-0.025237) \| 0.279165 / 0.323480 (-0.044314) \| 0.002960 / 0.007986 (-0.005026) \| 0.003382 / 0.004328 (-0.000946) \| 0.048174 / 0.004250 (0.043923) \| 0.042975 / 0.037052 (0.005922) \| 0.248079 / 0.258489 (-0.010410) \| 0.283770 / 0.293841 (-0.010070) \| 0.027935 / 0.128546 (-0.100611) \| 0.010634 / 0.075646 (-0.065012) \| 0.207039 / 0.419271 (-0.212233) \| 0.035863 / 0.043533 (-0.007670) \| 0.257426 / 0.255139 (0.002287) \| 0.274222 / 0.283200 (-0.008978) \| 0.017590 / 0.141683 (-0.124093) \| 1.126889 / 1.452155 (-0.325266) \| 1.160795 / 1.492716 (-0.331921) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.004298 / 0.018006 (-0.013708) \| 0.301366 / 0.000490 (0.300876) \| 0.000202 / 0.000200 (0.000002) \| 0.000050 / 0.000054 (-0.000005) \|\n\n### Benchmark: benchmark_indices_mapping.json\n\n\| metric \| select \| shard \| shuffle \| sort \| train_test_split \|\n\|--------\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.018159 / 0.037411 (-0.019252) \| 0.060566 / 0.014526 (0.046041) \| 0.072500 / 0.176557 (-0.104057) \| 0.119612 / 0.737135 (-0.617523) \| 0.074467 / 0.296338 (-0.221871) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.281859 / 0.215209 (0.066650) \| 2.760157 / 2.077655 (0.682502) \| 1.450632 / 1.504120 (-0.053487) \| 1.326636 / 1.541195 (-0.214559) \| 1.363381 / 1.468490 (-0.105109) \| 0.576199 / 4.584777 (-4.008578) \| 2.355776 / 3.745712 (-1.389936) \| 2.807308 / 5.269862 (-2.462553) \| 1.745449 / 4.565676 (-2.820228) \| 0.063413 / 0.424275 (-0.360862) \| 0.004978 / 0.007607 (-0.002630) \| 0.332738 / 0.226044 (0.106693) \| 3.267677 / 2.268929 (0.998748) \| 1.766074 / 55.444624 (-53.678551) \| 1.500853 / 6.876477 (-5.375624) \| 1.532434 / 2.142072 (-0.609639) \| 0.648238 / 4.805227 (-4.156989) \| 0.116030 / 6.500664 (-6.384634) \| 0.042018 / 0.075469 (-0.033451) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.934325 / 1.841788 (-0.907463) \| 11.439765 / 8.074308 (3.365457) \| 9.958624 / 10.191392 (-0.232768) \| 0.130295 / 0.680424 (-0.550129) \| 0.014437 / 0.534201 (-0.519764) \| 0.286073 / 0.579283 (-0.293210) \| 0.262430 / 0.434364 (-0.171934) \| 0.323905 / 0.540337 (-0.216432) \| 0.416615 / 1.386936 (-0.970321) \|\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n\| metric \| read_batch_formatted_as_numpy after write_array2d \| read_batch_formatted_as_numpy after write_flattened_sequence \| read_batch_formatted_as_numpy after write_nested_sequence \| read_batch_unformated after write_array2d \| read_batch_unformated after write_flattened_sequence \| read_batch_unformated after write_nested_sequence \| read_col_formatted_as_numpy after write_array2d \| read_col_formatted_as_numpy after write_flattened_sequence \| read_col_formatted_as_numpy after write_nested_sequence \| read_col_unformated after write_array2d \| read_col_unformated after write_flattened_sequence \| read_col_unformated after write_nested_sequence \| read_formatted_as_numpy after write_array2d \| read_formatted_as_numpy after write_flattened_sequence \| read_formatted_as_numpy after write_nested_sequence \| read_unformated after write_array2d \| read_unformated after write_flattened_sequence \| read_unformated after write_nested_sequence \| write_array2d \| write_flattened_sequence \| write_nested_sequence \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.005149 / 0.011353 (-0.006204) \| 0.003517 / 0.011008 (-0.007491) \| 0.048658 / 0.038508 (0.010150) \| 0.029638 / 0.023109 (0.006529) \| 0.271002 / 0.275898 (-0.004896) \| 0.324910 / 0.323480 (0.001430) \| 0.004086 / 0.007986 (-0.003900) \| 0.002609 / 0.004328 (-0.001719) \| 0.047806 / 0.004250 (0.043556) \| 0.045422 / 0.037052 (0.008369) \| 0.274317 / 0.258489 (0.015828) \| 0.304544 / 0.293841 (0.010703) \| 0.029318 / 0.128546 (-0.099229) \| 0.010626 / 0.075646 (-0.065020) \| 0.057838 / 0.419271 (-0.361434) \| 0.052408 / 0.043533 (0.008875) \| 0.267736 / 0.255139 (0.012597) \| 0.292024 / 0.283200 (0.008824) \| 0.019244 / 0.141683 (-0.122439) \| 1.167728 / 1.452155 (-0.284427) \| 1.226364 / 1.492716 (-0.266352) \|\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n\| metric \| get_batch_of\\_1024\\_random_rows \| get_batch_of\\_1024\\_rows \| get_first_row \| get_last_row \|\n\|--------\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.092441 / 0.018006 (0.074435) \| 0.310316 / 0.000490 (0.309827) \| 0.000218 / 0.000200 (0.000018) \| 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) \| 0.021818 / 0.037411 (-0.015594) \| 0.076515 / 0.014526 (0.061989) \| 0.089179 / 0.176557 (-0.087377) \| 0.127034 / 0.737135 (-0.610102) \| 0.089646 / 0.296338 (-0.206692) \|\n\n### Benchmark: benchmark_iterating.json\n\n\| metric \| read 5000 \| read 50000 \| read_batch 50000 10 \| read_batch 50000 100 \| read_batch 50000 1000 \| read_formatted numpy 5000 \| read_formatted pandas 5000 \| read_formatted tensorflow 5000 \| read_formatted torch 5000 \| read_formatted_batch numpy 5000 10 \| read_formatted_batch numpy 5000 1000 \| shuffled read 5000 \| shuffled read 50000 \| shuffled read_batch 50000 10 \| shuffled read_batch 50000 100 \| shuffled read_batch 50000 1000 \| shuffled read_formatted numpy 5000 \| shuffled read_formatted_batch numpy 5000 10 \| shuffled read_formatted_batch numpy 5000 1000 \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.292056 / 0.215209 (0.076847) \| 2.841410 / 2.077655 (0.763756) \| 1.550626 / 1.504120 (0.046506) \| 1.426204 / 1.541195 (-0.114990) \| 1.445838 / 1.468490 (-0.022652) \| 0.555777 / 4.584777 (-4.029000) \| 2.441077 / 3.745712 (-1.304635) \| 2.773445 / 5.269862 (-2.496416) \| 1.728951 / 4.565676 (-2.836726) \| 0.062579 / 0.424275 (-0.361697) \| 0.005063 / 0.007607 (-0.002544) \| 0.350749 / 0.226044 (0.124705) \| 3.461702 / 2.268929 (1.192773) \| 1.892506 / 55.444624 (-53.552118) \| 1.625958 / 6.876477 (-5.250519) \| 1.649175 / 2.142072 (-0.492898) \| 0.636123 / 4.805227 (-4.169105) \| 0.116548 / 6.500664 (-6.384116) \| 0.041174 / 0.075469 (-0.034295) \|\n\n### Benchmark: benchmark_map_filter.json\n\n\| metric \| filter \| map fast-tokenizer batched \| map identity \| map identity batched \| map no-op batched \| map no-op batched numpy \| map no-op batched pandas \| map no-op batched pytorch \| map no-op batched tensorflow \|\n\|--------\|---\|---\|---\|---\|---\|---\|---\|---\|---\|\n\| new / old (diff) \| 0.973468 / 1.841788 (-0.868320) \| 12.104761 / 8.074308 (4.030453) \| 11.131691 / 10.191392 (0.940299) \| 0.132309 / 0.680424 (-0.548115) \| 0.016191 / 0.534201 (-0.518010) \| 0.284748 / 0.579283 (-0.294535) \| 0.282661 / 0.434364 (-0.151703) \| 0.323797 / 0.540337 (-0.216540) \| 0.417767 / 1.386936 (-0.969169) \|\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#72b440325f6a84d341ea57539d8c368a001e2e75 \"CML watermark\")\n" ]	"2023-12-30T15:24:50"	"2023-12-30T16:00:06"	"2023-12-30T15:53:59"	MEMBER	null	The dl_manager base path is remote (e.g. a hf:// path), so local cached paths should be passed as absolute paths. This could happen if users provide a relative path as `cache_dir` fix https://github.com/huggingface/datasets/issues/6536	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6543/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6543/timeline	null	null	false	{ "url": "https://api.github.com/repos/huggingface/datasets/pulls/6543", "html_url": "https://github.com/huggingface/datasets/pull/6543", "diff_url": "https://github.com/huggingface/datasets/pull/6543.diff", "patch_url": "https://github.com/huggingface/datasets/pull/6543.patch", "merged_at": "2023-12-30T15:53:59" }	true
https://api.github.com/repos/huggingface/datasets/issues/6542	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6542/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6542/comments	https://api.github.com/repos/huggingface/datasets/issues/6542/events	https://github.com/huggingface/datasets/issues/6542	2,059,198,575	I_kwDODunzps56vOBv	6,542	Datasets : wikipedia 20220301.en error	{ "login": "ppx666", "id": 53203620, "node_id": "MDQ6VXNlcjUzMjAzNjIw", "avatar_url": "https://avatars.githubusercontent.com/u/53203620?v=4", "gravatar_id": "", "url": "https://api.github.com/users/ppx666", "html_url": "https://github.com/ppx666", "followers_url": "https://api.github.com/users/ppx666/followers", "following_url": "https://api.github.com/users/ppx666/following{/other_user}", "gists_url": "https://api.github.com/users/ppx666/gists{/gist_id}", "starred_url": "https://api.github.com/users/ppx666/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/ppx666/subscriptions", "organizations_url": "https://api.github.com/users/ppx666/orgs", "repos_url": "https://api.github.com/users/ppx666/repos", "events_url": "https://api.github.com/users/ppx666/events{/privacy}", "received_events_url": "https://api.github.com/users/ppx666/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "Hi ! We now recommend using the `wikimedia/wikipedia` dataset, can you try loading this one instead ?\r\n\r\n```python\r\nwiki_dataset = load_dataset(\"wikimedia/wikipedia\", \"20231101.en\")\r\n```", "This bug has been fixed in `2.16.1` thanks to https://github.com/huggingface/datasets/pull/6544, feel free to update `datasets` and re-run your code :)\r\n\r\n```\r\npip install -U datasets\r\n```" ]	"2023-12-29T08:34:51"	"2024-01-02T13:21:06"	"2024-01-02T13:20:30"	NONE	null	### Describe the bug When I used load_dataset to download this data set, the following error occurred. The main problem was that the target data did not exist. ### Steps to reproduce the bug 1.I tried downloading directly. ```python wiki_dataset = load_dataset("wikipedia", "20220301.en") ``` An exception occurred ``` MissingBeamOptions: Trying to generate a dataset using Apache Beam, yet no Beam Runner or PipelineOptions() has been provided in `load_dataset` or in the builder arguments. For big datasets it has to run on large-scale data processing tools like Dataflow, Spark, etc. More information about Apache Beam runners at https://beam.apache.org/documentation/runners/capability-matrix/ If you really want to run it locally because you feel like the Dataset is small enough, you can use the local beam runner called `DirectRunner` (you may run out of memory). Example of usage: `load_dataset('wikipedia', '20220301.en', beam_runner='DirectRunner')` ``` 2.I modified the code as prompted. ```python wiki_dataset = load_dataset('wikipedia', '20220301.en', beam_runner='DirectRunner') ``` An exception occurred: ``` FileNotFoundError: Couldn't find file at https://dumps.wikimedia.org/enwiki/20220301/dumpstatus.json ``` ### Expected behavior I searched in the parent directory of the corresponding URL, but there was no corresponding "20220301" directory. I really need this data set and hope to provide a download method. ### Environment info python 3.8 datasets 2.16.0 apache-beam 2.52.0 dill 0.3.7	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6542/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6542/timeline	null	completed	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6541	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6541/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6541/comments	https://api.github.com/repos/huggingface/datasets/issues/6541/events	https://github.com/huggingface/datasets/issues/6541	2,058,983,826	I_kwDODunzps56uZmS	6,541	Dataset not loading successfully.	{ "login": "hi-sushanta", "id": 93595990, "node_id": "U_kgDOBZQpVg", "avatar_url": "https://avatars.githubusercontent.com/u/93595990?v=4", "gravatar_id": "", "url": "https://api.github.com/users/hi-sushanta", "html_url": "https://github.com/hi-sushanta", "followers_url": "https://api.github.com/users/hi-sushanta/followers", "following_url": "https://api.github.com/users/hi-sushanta/following{/other_user}", "gists_url": "https://api.github.com/users/hi-sushanta/gists{/gist_id}", "starred_url": "https://api.github.com/users/hi-sushanta/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/hi-sushanta/subscriptions", "organizations_url": "https://api.github.com/users/hi-sushanta/orgs", "repos_url": "https://api.github.com/users/hi-sushanta/repos", "events_url": "https://api.github.com/users/hi-sushanta/events{/privacy}", "received_events_url": "https://api.github.com/users/hi-sushanta/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "This is a problem with your environment. You should be able to fix it by upgrading `numpy` based on [this](https://github.com/numpy/numpy/issues/23570) issue.", "Bro I already update numpy package.", "Then, this shouldn't throw an error on your machine:\r\n```python\r\nimport numpy\r\nnumpy._no_nep50_warning\r\n```\r\n\r\nIf it does, run `python -m pip install numpy` to ensure the correct `pip` is used for the package installation.", "Your suggestion to run `python -m pip install numpy` proved to be successful, and my issue has been resolved. I am grateful for your assistance, @mariosasko" ]	"2023-12-29T01:35:47"	"2024-01-17T00:40:46"	"2024-01-17T00:40:45"	NONE	null	### Describe the bug When I run down the below code shows this error: AttributeError: module 'numpy' has no attribute '_no_nep50_warning' I also added this issue in transformers library please check out: [link](https://github.com/huggingface/transformers/issues/28099) ### Steps to reproduce the bug ## Reproduction Hi, please check this line of code, when I run Show attribute error. ``` from datasets import load_dataset from transformers import WhisperProcessor, WhisperForConditionalGeneration # Select an audio file and read it: ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") audio_sample = ds[0]["audio"] waveform = audio_sample["array"] sampling_rate = audio_sample["sampling_rate"] # Load the Whisper model in Hugging Face format: processor = WhisperProcessor.from_pretrained("openai/whisper-tiny.en") model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny.en") # Use the model and processor to transcribe the audio: input_features = processor( waveform, sampling_rate=sampling_rate, return_tensors="pt" ).input_features # Generate token ids predicted_ids = model.generate(input_features) # Decode token ids to text transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True) transcription[0] ``` Attribute Error ``` AttributeError Traceback (most recent call last) Cell In[9], line 6 4 # Select an audio file and read it: 5 ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation") ----> 6 audio_sample = ds[0]["audio"] 7 waveform = audio_sample["array"] 8 sampling_rate = audio_sample["sampling_rate"] File /opt/pytorch/lib/python3.8/site-packages/datasets/arrow_dataset.py:2795, in Dataset.__getitem__(self, key) 2793 def __getitem__(self, key): # noqa: F811 2794 """Can be used to index columns (by string names) or rows (by integer index or iterable of indices or bools).""" -> 2795 return self._getitem(key) File /opt/pytorch/lib/python3.8/site-packages/datasets/arrow_dataset.py:2780, in Dataset._getitem(self, key, kwargs) 2778 formatter = get_formatter(format_type, features=self._info.features, format_kwargs) 2779 pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None) -> 2780 formatted_output = format_table( 2781 pa_subtable, key, formatter=formatter, format_columns=format_columns, output_all_columns=output_all_columns 2782 ) 2783 return formatted_output File /opt/pytorch/lib/python3.8/site-packages/datasets/formatting/formatting.py:629, in format_table(table, key, formatter, format_columns, output_all_columns) 627 python_formatter = PythonFormatter(features=formatter.features) 628 if format_columns is None: --> 629 return formatter(pa_table, query_type=query_type) 630 elif query_type == "column": 631 if key in format_columns: File /opt/pytorch/lib/python3.8/site-packages/datasets/formatting/formatting.py:396, in Formatter.__call__(self, pa_table, query_type) 394 def __call__(self, pa_table: pa.Table, query_type: str) -> Union[RowFormat, ColumnFormat, BatchFormat]: 395 if query_type == "row": --> 396 return self.format_row(pa_table) 397 elif query_type == "column": 398 return self.format_column(pa_table) File /opt/pytorch/lib/python3.8/site-packages/datasets/formatting/formatting.py:437, in PythonFormatter.format_row(self, pa_table) 435 return LazyRow(pa_table, self) 436 row = self.python_arrow_extractor().extract_row(pa_table) --> 437 row = self.python_features_decoder.decode_row(row) 438 return row File /opt/pytorch/lib/python3.8/site-packages/datasets/formatting/formatting.py:215, in PythonFeaturesDecoder.decode_row(self, row) 214 def decode_row(self, row: dict) -> dict: --> 215 return self.features.decode_example(row) if self.features else row File /opt/pytorch/lib/python3.8/site-packages/datasets/features/features.py:1917, in Features.decode_example(self, example, token_per_repo_id) 1903 def decode_example(self, example: dict, token_per_repo_id: Optional[Dict[str, Union[str, bool, None]]] = None): 1904 """Decode example with custom feature decoding. 1905 1906 Args: (...) 1914 `dict[str, Any]` 1915 """ -> 1917 return { 1918 column_name: decode_nested_example(feature, value, token_per_repo_id=token_per_repo_id) 1919 if self._column_requires_decoding[column_name] 1920 else value 1921 for column_name, (feature, value) in zip_dict( 1922 {key: value for key, value in self.items() if key in example}, example 1923 ) 1924 } File /opt/pytorch/lib/python3.8/site-packages/datasets/features/features.py:1918, in <dictcomp>(.0) 1903 def decode_example(self, example: dict, token_per_repo_id: Optional[Dict[str, Union[str, bool, None]]] = None): 1904 """Decode example with custom feature decoding. 1905 1906 Args: (...) 1914 `dict[str, Any]` 1915 """ 1917 return { -> 1918 column_name: decode_nested_example(feature, value, token_per_repo_id=token_per_repo_id) 1919 if self._column_requires_decoding[column_name] 1920 else value 1921 for column_name, (feature, value) in zip_dict( 1922 {key: value for key, value in self.items() if key in example}, example 1923 ) 1924 } File /opt/pytorch/lib/python3.8/site-packages/datasets/features/features.py:1339, in decode_nested_example(schema, obj, token_per_repo_id) 1336 elif isinstance(schema, (Audio, Image)): 1337 # we pass the token to read and decode files from private repositories in streaming mode 1338 if obj is not None and schema.decode: -> 1339 return schema.decode_example(obj, token_per_repo_id=token_per_repo_id) 1340 return obj File /opt/pytorch/lib/python3.8/site-packages/datasets/features/audio.py:191, in Audio.decode_example(self, value, token_per_repo_id) 189 array = array.T 190 if self.mono: --> 191 array = librosa.to_mono(array) 192 if self.sampling_rate and self.sampling_rate != sampling_rate: 193 array = librosa.resample(array, orig_sr=sampling_rate, target_sr=self.sampling_rate) File /opt/pytorch/lib/python3.8/site-packages/lazy_loader/__init__.py:78, in attach.<locals>.__getattr__(name) 76 submod_path = f"{package_name}.{attr_to_modules[name]}" 77 submod = importlib.import_module(submod_path) ---> 78 attr = getattr(submod, name) 80 # If the attribute lives in a file (module) with the same 81 # name as the attribute, ensure that the attribute and not 82 # the module is accessible on the package. 83 if name == attr_to_modules[name]: File /opt/pytorch/lib/python3.8/site-packages/lazy_loader/__init__.py:77, in attach.<locals>.__getattr__(name) 75 elif name in attr_to_modules: 76 submod_path = f"{package_name}.{attr_to_modules[name]}" ---> 77 submod = importlib.import_module(submod_path) 78 attr = getattr(submod, name) 80 # If the attribute lives in a file (module) with the same 81 # name as the attribute, ensure that the attribute and not 82 # the module is accessible on the package. File /usr/lib/python3.8/importlib/__init__.py:127, in import_module(name, package) 125 break 126 level += 1 --> 127 return _bootstrap._gcd_import(name[level:], package, level) File <frozen importlib._bootstrap>:1014, in _gcd_import(name, package, level) File <frozen importlib._bootstrap>:991, in _find_and_load(name, import_) File <frozen importlib._bootstrap>:975, in _find_and_load_unlocked(name, import_) File <frozen importlib._bootstrap>:671, in _load_unlocked(spec) File <frozen importlib._bootstrap_external>:848, in exec_module(self, module) File <frozen importlib._bootstrap>:219, in _call_with_frames_removed(f, args, kwds) File /opt/pytorch/lib/python3.8/site-packages/librosa/core/audio.py:13 11 import audioread 12 import numpy as np ---> 13 import scipy.signal 14 import soxr 15 import lazy_loader as lazy File /opt/pytorch/lib/python3.8/site-packages/scipy/signal/__init__.py:323 314 from ._spline import ( # noqa: F401 315 cspline2d, 316 qspline2d, (...) 319 symiirorder2, 320 ) 322 from ._bsplines import --> 323 from ._filter_design import * 324 from ._fir_filter_design import * 325 from ._ltisys import * File /opt/pytorch/lib/python3.8/site-packages/scipy/signal/_filter_design.py:16 13 from numpy.polynomial.polynomial import polyval as npp_polyval 14 from numpy.polynomial.polynomial import polyvalfromroots ---> 16 from scipy import special, optimize, fft as sp_fft 17 from scipy.special import comb 18 from scipy._lib._util import float_factorial File /opt/pytorch/lib/python3.8/site-packages/scipy/optimize/__init__.py:405 1 """ 2 ===================================================== 3 Optimization and root finding (:mod:`scipy.optimize`) (...) 401 402 """ 404 from ._optimize import * --> 405 from ._minimize import * 406 from ._root import * 407 from ._root_scalar import * File /opt/pytorch/lib/python3.8/site-packages/scipy/optimize/_minimize.py:26 24 from ._trustregion_krylov import _minimize_trust_krylov 25 from ._trustregion_exact import _minimize_trustregion_exact ---> 26 from ._trustregion_constr import _minimize_trustregion_constr 28 # constrained minimization 29 from ._lbfgsb_py import _minimize_lbfgsb File /opt/pytorch/lib/python3.8/site-packages/scipy/optimize/_trustregion_constr/__init__.py:4 1 """This module contains the equality constrained SQP solver.""" ----> 4 from .minimize_trustregion_constr import _minimize_trustregion_constr 6 __all__ = ['_minimize_trustregion_constr'] File /opt/pytorch/lib/python3.8/site-packages/scipy/optimize/_trustregion_constr/minimize_trustregion_constr.py:5 3 from scipy.sparse.linalg import LinearOperator 4 from .._differentiable_functions import VectorFunction ----> 5 from .._constraints import ( 6 NonlinearConstraint, LinearConstraint, PreparedConstraint, strict_bounds) 7 from .._hessian_update_strategy import BFGS 8 from .._optimize import OptimizeResult File /opt/pytorch/lib/python3.8/site-packages/scipy/optimize/_constraints.py:8 6 from ._optimize import OptimizeWarning 7 from warnings import warn, catch_warnings, simplefilter ----> 8 from numpy.testing import suppress_warnings 9 from scipy.sparse import issparse 12 def _arr_to_scalar(x): 13 # If x is a numpy array, return x.item(). This will 14 # fail if the array has more than one element. File /opt/pytorch/lib/python3.8/site-packages/numpy/testing/__init__.py:11 8 from unittest import TestCase 10 from . import _private ---> 11 from ._private.utils import * 12 from ._private.utils import (_assert_valid_refcount, _gen_alignment_data) 13 from ._private import extbuild, decorators as dec File /opt/pytorch/lib/python3.8/site-packages/numpy/testing/_private/utils.py:480 476 pprint.pprint(desired, msg) 477 raise AssertionError(msg.getvalue()) --> 480 @np._no_nep50_warning() 481 def assert_almost_equal(actual,desired,decimal=7,err_msg='',verbose=True): 482 """ 483 Raises an AssertionError if two items are not equal up to desired 484 precision. (...) 548 549 """ 550 __tracebackhide__ = True # Hide traceback for py.test File /opt/pytorch/lib/python3.8/site-packages/numpy/__init__.py:313, in __getattr__(attr) 305 raise AttributeError(__former_attrs__[attr]) 307 # Importing Tester requires importing all of UnitTest which is not a 308 # cheap import Since it is mainly used in test suits, we lazy import it 309 # here to save on the order of 10 ms of import time for most users 310 # 311 # The previous way Tester was imported also had a side effect of adding 312 # the full `numpy.testing` namespace --> 313 if attr == 'testing': 314 import numpy.testing as testing 315 return testing AttributeError: module 'numpy' has no attribute '_no_nep50_warning' ``` ### Expected behavior ``` ' Mr. Quilter is the apostle of the middle classes, and we are glad to welcome his gospel.' ``` Also, make sure this script is provided for your official website so please update: [script](https://huggingface.co/docs/transformers/model_doc/whisper) ### Environment info System Info * transformers -> 4.36.1 * datasets -> 2.15.0 * huggingface_hub -> 0.19.4 * python -> 3.8.10 * accelerate -> 0.25.0 * pytorch -> 2.0.1+cpu * Using GPU in Script -> No	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6541/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6541/timeline	null	completed	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6540	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6540/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6540/comments	https://api.github.com/repos/huggingface/datasets/issues/6540/events	https://github.com/huggingface/datasets/issues/6540	2,058,965,157	I_kwDODunzps56uVCl	6,540	Extreme inefficiency for `save_to_disk` when merging datasets	{ "login": "KatarinaYuan", "id": 43512683, "node_id": "MDQ6VXNlcjQzNTEyNjgz", "avatar_url": "https://avatars.githubusercontent.com/u/43512683?v=4", "gravatar_id": "", "url": "https://api.github.com/users/KatarinaYuan", "html_url": "https://github.com/KatarinaYuan", "followers_url": "https://api.github.com/users/KatarinaYuan/followers", "following_url": "https://api.github.com/users/KatarinaYuan/following{/other_user}", "gists_url": "https://api.github.com/users/KatarinaYuan/gists{/gist_id}", "starred_url": "https://api.github.com/users/KatarinaYuan/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/KatarinaYuan/subscriptions", "organizations_url": "https://api.github.com/users/KatarinaYuan/orgs", "repos_url": "https://api.github.com/users/KatarinaYuan/repos", "events_url": "https://api.github.com/users/KatarinaYuan/events{/privacy}", "received_events_url": "https://api.github.com/users/KatarinaYuan/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	null	[ "Concatenating datasets doesn't create any indices mapping - so flattening indices is not needed (unless you shuffle the dataset).\r\nCan you share the snippet of code you are using to merge your datasets and save them to disk ?" ]	"2023-12-29T00:44:35"	"2023-12-30T15:05:48"	null	NONE	null	### Describe the bug Hi, I tried to merge in total 22M sequences of data, where each sequence is of maximum length 2000. I found that merging these datasets and then `save_to_disk` is extremely slow because of flattening the indices. Wondering if you have any suggestions or guidance on this. Thank you very much! ### Steps to reproduce the bug The source data is too big to demonstrate ### Expected behavior The source data is too big to demonstrate ### Environment info python 3.9.0 datasets 2.7.0 pytorch 2.0.0 tokenizers 0.13.1 transformers 4.31.0	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6540/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6540/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6539	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6539/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6539/comments	https://api.github.com/repos/huggingface/datasets/issues/6539/events	https://github.com/huggingface/datasets/issues/6539	2,058,493,960	I_kwDODunzps56siAI	6,539	'Repo card metadata block was not found' when loading a pragmeval dataset	{ "login": "lambdaofgod", "id": 3647577, "node_id": "MDQ6VXNlcjM2NDc1Nzc=", "avatar_url": "https://avatars.githubusercontent.com/u/3647577?v=4", "gravatar_id": "", "url": "https://api.github.com/users/lambdaofgod", "html_url": "https://github.com/lambdaofgod", "followers_url": "https://api.github.com/users/lambdaofgod/followers", "following_url": "https://api.github.com/users/lambdaofgod/following{/other_user}", "gists_url": "https://api.github.com/users/lambdaofgod/gists{/gist_id}", "starred_url": "https://api.github.com/users/lambdaofgod/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lambdaofgod/subscriptions", "organizations_url": "https://api.github.com/users/lambdaofgod/orgs", "repos_url": "https://api.github.com/users/lambdaofgod/repos", "events_url": "https://api.github.com/users/lambdaofgod/events{/privacy}", "received_events_url": "https://api.github.com/users/lambdaofgod/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	null	[]	"2023-12-28T14:18:25"	"2023-12-28T14:18:37"	null	NONE	null	### Describe the bug I can't load dataset subsets of 'pragmeval'. The funny thing is I ran the dataset author's [colab notebook](https://colab.research.google.com/drive/1sg--LF4z7XR1wxAOfp0-3d4J6kQ9nj_A?usp=sharing) and it works just fine. I tried to install exactly the same packages that are installed on colab using poetry, so my environment info only differs from the one from colab in linux version - I still get the same bug outside colab. ### Steps to reproduce the bug Install dependencies with poetry pyproject.toml ``` [tool.poetry] name = "project" version = "0.1.0" description = "" authors = [] [tool.poetry.dependencies] python = "^3.10" datasets = "2.16.0" pandas = "1.5.3" pyarrow = "10.0.1" huggingface-hub = "0.19.4" fsspec = "2023.6.0" [build-system] requires = ["poetry-core"] build-backend = "poetry.core.masonry.api" ``` `poetry run python -c "import datasets; print(datasets.get_dataset_config_names('pragmeval'))` prints ['default'] ### Expected behavior The command should print ``` ['emergent', 'emobank-arousal', 'emobank-dominance', 'emobank-valence', 'gum', 'mrda', 'pdtb', 'persuasiveness-claimtype', 'persuasiveness-eloquence', 'persuasiveness-premisetype', 'persuasiveness-relevance', 'persuasiveness-specificity', 'persuasiveness-strength', 'sarcasm', 'squinky-formality', 'squinky-implicature', 'squinky-informativeness', 'stac', 'switchboard', 'verifiability'] ``` ### Environment info - `datasets` version: 2.16.0 - Platform: Linux-6.2.0-37-generic-x86_64-with-glibc2.35 - Python version: 3.10.12 - `huggingface_hub` version: 0.19.4 - PyArrow version: 10.0.1 - Pandas version: 1.5.3 - `fsspec` version: 2023.6.0	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6539/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6539/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6538	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6538/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6538/comments	https://api.github.com/repos/huggingface/datasets/issues/6538/events	https://github.com/huggingface/datasets/issues/6538	2,057,377,630	I_kwDODunzps56oRde	6,538	ImportError: cannot import name 'SchemaInferenceError' from 'datasets.arrow_writer' (/opt/conda/lib/python3.10/site-packages/datasets/arrow_writer.py)	{ "login": "Sonali-Behera-TRT", "id": 131662185, "node_id": "U_kgDOB9kBaQ", "avatar_url": "https://avatars.githubusercontent.com/u/131662185?v=4", "gravatar_id": "", "url": "https://api.github.com/users/Sonali-Behera-TRT", "html_url": "https://github.com/Sonali-Behera-TRT", "followers_url": "https://api.github.com/users/Sonali-Behera-TRT/followers", "following_url": "https://api.github.com/users/Sonali-Behera-TRT/following{/other_user}", "gists_url": "https://api.github.com/users/Sonali-Behera-TRT/gists{/gist_id}", "starred_url": "https://api.github.com/users/Sonali-Behera-TRT/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/Sonali-Behera-TRT/subscriptions", "organizations_url": "https://api.github.com/users/Sonali-Behera-TRT/orgs", "repos_url": "https://api.github.com/users/Sonali-Behera-TRT/repos", "events_url": "https://api.github.com/users/Sonali-Behera-TRT/events{/privacy}", "received_events_url": "https://api.github.com/users/Sonali-Behera-TRT/received_events", "type": "User", "site_admin": false }	[]	closed	false	null	[]	null	[ "Hi ! Are you sure you have `datasets` 2.16 ? I just checked and on 2.16 I can run `from datasets.arrow_writer import SchemaInferenceError` without error", "I have the same issue - using with datasets version 2.16.1. Also this is on a kaggle notebook - other people with the same issue also seem to be having it on kaggle?", "I have the same issue now and didn't have this problem around 2 weeks ago.", "> Hi ! Are you sure you have `datasets` 2.16 ? I just checked and on 2.16 I can run `from datasets.arrow_writer import SchemaInferenceError` without error\r\n\r\nYes, I am sure\r\n\r\n```\r\n!pip show datasets\r\nName: datasets\r\nVersion: 2.16.1\r\nSummary: HuggingFace community-driven open-source library of datasets\r\nHome-page: https://github.com/huggingface/datasets\r\nAuthor: HuggingFace Inc.\r\nAuthor-email: thomas@huggingface.co\r\nLicense: Apache 2.0\r\nLocation: /opt/conda/lib/python3.10/site-packages\r\nRequires: aiohttp, dill, filelock, fsspec, huggingface-hub, multiprocess, numpy, packaging, pandas, pyarrow, pyarrow-hotfix, pyyaml, requests, tqdm, xxhash\r\nRequired-by: trl\r\n```", "> I have the same issue - using with datasets version 2.16.1. Also this is on a kaggle notebook - other people with the same issue also seem to be having it on kaggle?\r\n\r\nDon't know about other people. But I am having this issue whose solution I can't find anywhere. And this issue still persists. ", "> I have the same issue now and didn't have this problem around 2 weeks ago.\r\n\r\nSame here", "I was having the same issue but the datasets version was 2.6.1, after I updated it to latest(2.16), error is gone while importing.\r\n", "> I was having the same issue but the datasets version was 2.6.1, after I updated it to latest(2.16), error is gone while importing.\r\n\r\nI also have datasets version 2.16, but the error is still there.", "Can you try re-installing `datasets` ?", "> Can you try re-installing `datasets` ?\r\n\r\nI tried re-installing. Still getting the same error. \r\n", "> > Can you try re-installing `datasets` ?\r\n> \r\n> I tried re-installing. Still getting the same error.\r\n\r\nIn kaggle I used:\r\n- `%pip install -U datasets`\r\nand then restarted runtime and then everything works fine.", "> > > Can you try re-installing `datasets` ?\r\n> > \r\n> > \r\n> > I tried re-installing. Still getting the same error.\r\n> \r\n> In kaggle I used:\r\n> \r\n> * `%pip install -U datasets`\r\n> and then restarted runtime and then everything works fine.\r\n\r\nYes, this is working. When I restart the runtime after installing packages, it's working perfectly. Thank you so much. But why do we need to restart runtime every time after installing packages?", "> > > > Can you try re-installing `datasets` ?\r\n> > > \r\n> > > \r\n> > > I tried re-installing. Still getting the same error.\r\n> > \r\n> > \r\n> > In kaggle I used:\r\n> > \r\n> > * `%pip install -U datasets`\r\n> > and then restarted runtime and then everything works fine.\r\n> \r\n> Yes, this is working. When I restart the runtime after installing packages, it's working perfectly. Thank you so much. But why do we need to restart runtime every time after installing packages?\r\nFor some packages it is required.\r\nhttps://stackoverflow.com/questions/57831187/need-to-restart-runtime-before-import-an-installed-package-in-colab\r\n", "> > > > > Can you try re-installing `datasets` ?\r\n> > > > \r\n> > > > \r\n> > > > I tried re-installing. Still getting the same error.\r\n> > > \r\n> > > \r\n> > > In kaggle I used:\r\n> > > \r\n> > > * `%pip install -U datasets`\r\n> > > and then restarted runtime and then everything works fine.\r\n> > \r\n> > \r\n> > Yes, this is working. When I restart the runtime after installing packages, it's working perfectly. Thank you so much. But why do we need to restart runtime every time after installing packages?\r\n> > For some packages it is required.\r\n> > https://stackoverflow.com/questions/57831187/need-to-restart-runtime-before-import-an-installed-package-in-colab\r\n\r\nThank you for your assistance. I dedicated the past 2-3 weeks to resolving this issue. Interestingly, it runs flawlessly in Colab without requiring a runtime restart. However, the problem persisted exclusively in Kaggle. I appreciate your help once again. Thank you.", "Closing this issue as it is not related to the datasets library; rather, it's linked to platform-related issues." ]	"2023-12-27T13:31:16"	"2024-01-03T10:06:47"	"2024-01-03T10:04:58"	NONE	null	### Describe the bug While importing from packages getting the error Code: ``` import os import torch from datasets import load_dataset, Dataset from transformers import ( AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, HfArgumentParser, TrainingArguments, pipeline, logging ) from peft import LoraConfig, PeftModel from trl import SFTTrainer from huggingface_hub import login import pandas as pd ``` Error: ```` --------------------------------------------------------------------------- ImportError Traceback (most recent call last) Cell In[5], line 14 4 from transformers import ( 5 AutoModelForCausalLM, 6 AutoTokenizer, (...) 11 logging 12 ) 13 from peft import LoraConfig, PeftModel ---> 14 from trl import SFTTrainer 15 from huggingface_hub import login 16 import pandas as pd File /opt/conda/lib/python3.10/site-packages/trl/__init__.py:21 8 from .import_utils import ( 9 is_diffusers_available, 10 is_npu_available, (...) 13 is_xpu_available, 14 ) 15 from .models import ( 16 AutoModelForCausalLMWithValueHead, 17 AutoModelForSeq2SeqLMWithValueHead, 18 PreTrainedModelWrapper, 19 create_reference_model, 20 ) ---> 21 from .trainer import ( 22 DataCollatorForCompletionOnlyLM, 23 DPOTrainer, 24 IterativeSFTTrainer, 25 PPOConfig, 26 PPOTrainer, 27 RewardConfig, 28 RewardTrainer, 29 SFTTrainer, 30 ) 33 if is_diffusers_available(): 34 from .models import ( 35 DDPOPipelineOutput, 36 DDPOSchedulerOutput, 37 DDPOStableDiffusionPipeline, 38 DefaultDDPOStableDiffusionPipeline, 39 ) File /opt/conda/lib/python3.10/site-packages/trl/trainer/__init__.py:44 42 from .ppo_trainer import PPOTrainer 43 from .reward_trainer import RewardTrainer, compute_accuracy ---> 44 from .sft_trainer import SFTTrainer 45 from .training_configs import RewardConfig File /opt/conda/lib/python3.10/site-packages/trl/trainer/sft_trainer.py:23 21 import torch.nn as nn 22 from datasets import Dataset ---> 23 from datasets.arrow_writer import SchemaInferenceError 24 from datasets.builder import DatasetGenerationError 25 from transformers import ( 26 AutoModelForCausalLM, 27 AutoTokenizer, (...) 33 TrainingArguments, 34 ) ImportError: cannot import name 'SchemaInferenceError' from 'datasets.arrow_writer' (/opt/conda/lib/python3.10/site-packages/datasets/arrow_writer.py ```` transformers version: 4.36.2 python version: 3.10.12 datasets version: 2.16.1 ### Steps to reproduce the bug 1. Install packages ``` !pip install -U datasets trl accelerate peft bitsandbytes transformers trl huggingface_hub ``` 2. import packages ``` import os import torch from datasets import load_dataset, Dataset from transformers import ( AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, HfArgumentParser, TrainingArguments, pipeline, logging ) from peft import LoraConfig, PeftModel from trl import SFTTrainer from huggingface_hub import login import pandas as pd ``` ### Expected behavior No error while importing ### Environment info - `datasets` version: 2.16.0 - Platform: Linux-5.15.133+-x86_64-with-glibc2.35 - Python version: 3.10.12 - `huggingface_hub` version: 0.20.1 - PyArrow version: 11.0.0 - Pandas version: 2.1.4 - `fsspec` version: 2023.10.0	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6538/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6538/timeline	null	completed	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6537	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6537/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6537/comments	https://api.github.com/repos/huggingface/datasets/issues/6537/events	https://github.com/huggingface/datasets/issues/6537	2,057,132,173	I_kwDODunzps56nViN	6,537	Adding support for netCDF (*.nc) files	{ "login": "shermansiu", "id": 12627125, "node_id": "MDQ6VXNlcjEyNjI3MTI1", "avatar_url": "https://avatars.githubusercontent.com/u/12627125?v=4", "gravatar_id": "", "url": "https://api.github.com/users/shermansiu", "html_url": "https://github.com/shermansiu", "followers_url": "https://api.github.com/users/shermansiu/followers", "following_url": "https://api.github.com/users/shermansiu/following{/other_user}", "gists_url": "https://api.github.com/users/shermansiu/gists{/gist_id}", "starred_url": "https://api.github.com/users/shermansiu/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/shermansiu/subscriptions", "organizations_url": "https://api.github.com/users/shermansiu/orgs", "repos_url": "https://api.github.com/users/shermansiu/repos", "events_url": "https://api.github.com/users/shermansiu/events{/privacy}", "received_events_url": "https://api.github.com/users/shermansiu/received_events", "type": "User", "site_admin": false }	[ { "id": 1935892871, "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement", "name": "enhancement", "color": "a2eeef", "default": true, "description": "New feature or request" } ]	open	false	null	[]	null	[ "Related to #3113 ", "Conceptually, we can use xarray to load the netCDF file, then xarray -> pandas -> pyarrow.", "I'd still need to verify that such a conversion would be lossless, especially for multi-dimensional data." ]	"2023-12-27T09:27:29"	"2023-12-27T20:46:53"	null	NONE	null	### Feature request netCDF (.nc) is a file format for storing multidimensional scientific data, which is used by packages like `xarray` (labelled multi-dimensional arrays in Python). It would be nice to have native support for netCDF in `datasets`. ### Motivation When uploading .nc files onto Huggingface Hub through the `datasets` API, I would like to be able to preview the dataset without converting it to another format. ### Your contribution I can submit a PR, provided I have the time.	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6537/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6537/timeline	null	null	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6536	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6536/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6536/comments	https://api.github.com/repos/huggingface/datasets/issues/6536/events	https://github.com/huggingface/datasets/issues/6536	2,056,863,239	I_kwDODunzps56mT4H	6,536	datasets.load_dataset raises FileNotFoundError for datasets==2.16.0	{ "login": "ArvinZhuang", "id": 46237844, "node_id": "MDQ6VXNlcjQ2MjM3ODQ0", "avatar_url": "https://avatars.githubusercontent.com/u/46237844?v=4", "gravatar_id": "", "url": "https://api.github.com/users/ArvinZhuang", "html_url": "https://github.com/ArvinZhuang", "followers_url": "https://api.github.com/users/ArvinZhuang/followers", "following_url": "https://api.github.com/users/ArvinZhuang/following{/other_user}", "gists_url": "https://api.github.com/users/ArvinZhuang/gists{/gist_id}", "starred_url": "https://api.github.com/users/ArvinZhuang/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/ArvinZhuang/subscriptions", "organizations_url": "https://api.github.com/users/ArvinZhuang/orgs", "repos_url": "https://api.github.com/users/ArvinZhuang/repos", "events_url": "https://api.github.com/users/ArvinZhuang/events{/privacy}", "received_events_url": "https://api.github.com/users/ArvinZhuang/received_events", "type": "User", "site_admin": false }	[]	closed	false	{ "login": "lhoestq", "id": 42851186, "node_id": "MDQ6VXNlcjQyODUxMTg2", "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "gravatar_id": "", "url": "https://api.github.com/users/lhoestq", "html_url": "https://github.com/lhoestq", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "repos_url": "https://api.github.com/users/lhoestq/repos", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "type": "User", "site_admin": false }	[ { "login": "lhoestq", "id": 42851186, "node_id": "MDQ6VXNlcjQyODUxMTg2", "avatar_url": "https://avatars.githubusercontent.com/u/42851186?v=4", "gravatar_id": "", "url": "https://api.github.com/users/lhoestq", "html_url": "https://github.com/lhoestq", "followers_url": "https://api.github.com/users/lhoestq/followers", "following_url": "https://api.github.com/users/lhoestq/following{/other_user}", "gists_url": "https://api.github.com/users/lhoestq/gists{/gist_id}", "starred_url": "https://api.github.com/users/lhoestq/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/lhoestq/subscriptions", "organizations_url": "https://api.github.com/users/lhoestq/orgs", "repos_url": "https://api.github.com/users/lhoestq/repos", "events_url": "https://api.github.com/users/lhoestq/events{/privacy}", "received_events_url": "https://api.github.com/users/lhoestq/received_events", "type": "User", "site_admin": false } ]	null	[ "Hi ! Thanks for reporting\r\n\r\nThis is a bug in 2.16.0 for some datasets when `cache_dir` is a relative path. I opened https://github.com/huggingface/datasets/pull/6543 to fix this", "We just released 2.16.1 with a fix:\r\n\r\n```\r\npip install -U datasets\r\n```" ]	"2023-12-27T03:15:48"	"2023-12-30T18:58:04"	"2023-12-30T15:54:00"	NONE	null	### Describe the bug Seems `datasets.load_dataset` raises FileNotFoundError for some hub datasets with the latest `datasets==2.16.0` ### Steps to reproduce the bug For example `pip install datasets==2.16.0` then ```python import datasets datasets.load_dataset("wentingzhao/anthropic-hh-first-prompt", cache_dir='cache1')["train"] ``` This will raise: ```bash Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/Users/xxx/miniconda3/envs/env/lib/python3.9/site-packages/datasets/load.py", line 2545, in load_dataset builder_instance.download_and_prepare( File "/Users/xxx/miniconda3/envs/env/lib/python3.9/site-packages/datasets/builder.py", line 1003, in download_and_prepare self._download_and_prepare( File "/Users/xxx/miniconda3/envs/env/lib/python3.9/site-packages/datasets/builder.py", line 1076, in _download_and_prepare split_generators = self._split_generators(dl_manager, **split_generators_kwargs) File "/Users/xxx/miniconda3/envs/env/lib/python3.9/site-packages/datasets/packaged_modules/parquet/parquet.py", line 43, in _split_generators data_files = dl_manager.download_and_extract(self.config.data_files) File "/Users/xxx/miniconda3/envs/env/lib/python3.9/site-packages/datasets/download/download_manager.py", line 566, in download_and_extract return self.extract(self.download(url_or_urls)) File "/Users/xxx/miniconda3/envs/env/lib/python3.9/site-packages/datasets/download/download_manager.py", line 539, in extract extracted_paths = map_nested( File "/Users/xxx/miniconda3/envs/env/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 466, in map_nested mapped = [ File "/Users/xxx/miniconda3/envs/env/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 467, in <listcomp> _single_map_nested((function, obj, types, None, True, None)) File "/Users/xxx/miniconda3/envs/env/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 387, in _single_map_nested mapped = [_single_map_nested((function, v, types, None, True, None)) for v in pbar] File "/Users/xxx/miniconda3/envs/env/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 387, in <listcomp> mapped = [_single_map_nested((function, v, types, None, True, None)) for v in pbar] File "/Users/xxx/miniconda3/envs/env/lib/python3.9/site-packages/datasets/utils/py_utils.py", line 370, in _single_map_nested return function(data_struct) File "/Users/xxx/miniconda3/envs/env/lib/python3.9/site-packages/datasets/download/download_manager.py", line 451, in _download out = cached_path(url_or_filename, download_config=download_config) File "/Users/xxx/miniconda3/envs/env/lib/python3.9/site-packages/datasets/utils/file_utils.py", line 188, in cached_path output_path = get_from_cache( File "/Users/xxx/miniconda3/envs/env/lib/python3.9/site-packages/datasets/utils/file_utils.py", line 570, in get_from_cache raise FileNotFoundError(f"Couldn't find file at {url}") FileNotFoundError: Couldn't find file at https://huggingface.co/datasets/wentingzhao/anthropic-hh-first-prompt/resolve/11b393a5545f706a357ebcd4a5285d93db176715/cache1/downloads/87d66c365626feca116cba323c4856c9aae056e4503f09f23e34aa085eb9de15 ``` However, seems it works fine for some datasets, for example, if works fine for `datasets.load_dataset("ag_news", cache_dir='cache2')["test"]` But the dataset works fine for datasets==2.15.0, for example `pip install datasets==2.15.0`, then ```python import datasets datasets.load_dataset("wentingzhao/anthropic-hh-first-prompt", cache_dir='cache3')["train"] Dataset({ features: ['user', 'system', 'source'], num_rows: 8552 }) ``` ### Expected behavior 2.16.0 should work as same as 2.15.0 for all datasets ### Environment info python3.9 conda env tested on MacOS and Linux	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6536/reactions", "total_count": 1, "+1": 1, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6536/timeline	null	completed	null	null	false
https://api.github.com/repos/huggingface/datasets/issues/6535	https://api.github.com/repos/huggingface/datasets	https://api.github.com/repos/huggingface/datasets/issues/6535/labels{/name}	https://api.github.com/repos/huggingface/datasets/issues/6535/comments	https://api.github.com/repos/huggingface/datasets/issues/6535/events	https://github.com/huggingface/datasets/issues/6535	2,056,264,339	I_kwDODunzps56kBqT	6,535	IndexError: Invalid key: 47682 is out of bounds for size 0 while using PEFT	{ "login": "MahavirDabas18", "id": 57484266, "node_id": "MDQ6VXNlcjU3NDg0MjY2", "avatar_url": "https://avatars.githubusercontent.com/u/57484266?v=4", "gravatar_id": "", "url": "https://api.github.com/users/MahavirDabas18", "html_url": "https://github.com/MahavirDabas18", "followers_url": "https://api.github.com/users/MahavirDabas18/followers", "following_url": "https://api.github.com/users/MahavirDabas18/following{/other_user}", "gists_url": "https://api.github.com/users/MahavirDabas18/gists{/gist_id}", "starred_url": "https://api.github.com/users/MahavirDabas18/starred{/owner}{/repo}", "subscriptions_url": "https://api.github.com/users/MahavirDabas18/subscriptions", "organizations_url": "https://api.github.com/users/MahavirDabas18/orgs", "repos_url": "https://api.github.com/users/MahavirDabas18/repos", "events_url": "https://api.github.com/users/MahavirDabas18/events{/privacy}", "received_events_url": "https://api.github.com/users/MahavirDabas18/received_events", "type": "User", "site_admin": false }	[]	open	false	null	[]	null	[ "@sabman @pvl @kashif @vigsterkr ", "This is surely the same issue as https://discuss.huggingface.co/t/indexerror-invalid-key-16-is-out-of-bounds-for-size-0/14298/25 that comes from the `transformers` `Trainer`. You should add `remove_unused_columns=False` to `TrainingArguments`\r\n\r\nAlso check your logs: the `Trainer` should log the length of your dataset before training starts and it surely showed length=0.", "the same error \r\nIndexError: Invalid key: 22330 is out of bounds for size 0" ]	"2023-12-26T10:14:33"	"2024-02-05T08:42:31"	null	NONE	null	### Describe the bug I am trying to fine-tune the t5 model on the paraphrasing task. While running the same code without- model = get_peft_model(model, config) the model trains without any issues. However, using the model returned from get_peft_model raises the following error due to datasets- IndexError: Invalid key: 47682 is out of bounds for size 0. I had raised this in https://github.com/huggingface/peft/issues/1299#issue-2056173386 and they suggested that I raise it here. Here is the complete error- IndexError Traceback (most recent call last) in <cell line: 1>() ----> 1 trainer.train() 11 frames [/usr/local/lib/python3.10/dist-packages/transformers/trainer.py](https://localhost:8080/#) in train(self, resume_from_checkpoint, trial, ignore_keys_for_eval, kwargs) 1553 hf_hub_utils.enable_progress_bars() 1554 else: -> 1555 return inner_training_loop( 1556 args=args, 1557 resume_from_checkpoint=resume_from_checkpoint, [/usr/local/lib/python3.10/dist-packages/transformers/trainer.py](https://localhost:8080/#) in _inner_training_loop(self, batch_size, args, resume_from_checkpoint, trial, ignore_keys_for_eval) 1836 1837 step = -1 -> 1838 for step, inputs in enumerate(epoch_iterator): 1839 total_batched_samples += 1 1840 if rng_to_sync: [/usr/local/lib/python3.10/dist-packages/accelerate/data_loader.py](https://localhost:8080/#) in iter(self) 446 # We iterate one batch ahead to check when we are at the end 447 try: --> 448 current_batch = next(dataloader_iter) 449 except StopIteration: 450 yield [/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py](https://localhost:8080/#) in next(self) 628 # TODO(https://github.com/pytorch/pytorch/issues/76750) 629 self._reset() # type: ignore[call-arg] --> 630 data = self._next_data() 631 self._num_yielded += 1 632 if self._dataset_kind == _DatasetKind.Iterable and \ [/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py](https://localhost:8080/#) in _next_data(self) 672 def _next_data(self): 673 index = self._next_index() # may raise StopIteration --> 674 data = self._dataset_fetcher.fetch(index) # may raise StopIteration 675 if self._pin_memory: 676 data = _utils.pin_memory.pin_memory(data, self._pin_memory_device) [/usr/local/lib/python3.10/dist-packages/torch/utils/data/_utils/fetch.py](https://localhost:8080/#) in fetch(self, possibly_batched_index) 47 if self.auto_collation: 48 if hasattr(self.dataset, "getitems") and self.dataset.getitems: ---> 49 data = self.dataset.getitems(possibly_batched_index) 50 else: 51 data = [self.dataset[idx] for idx in possibly_batched_index] [/usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py](https://localhost:8080/#) in getitems(self, keys) 2802 def getitems(self, keys: List) -> List: 2803 """Can be used to get a batch using a list of integers indices.""" -> 2804 batch = self.getitem(keys) 2805 n_examples = len(batch[next(iter(batch))]) 2806 return [{col: array[i] for col, array in batch.items()} for i in range(n_examples)] [/usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py](https://localhost:8080/#) in getitem(self, key) 2798 def getitem(self, key): # noqa: F811 2799 """Can be used to index columns (by string names) or rows (by integer index or iterable of indices or bools).""" -> 2800 return self._getitem(key) 2801 2802 def getitems(self, keys: List) -> List: [/usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py](https://localhost:8080/#) in _getitem(self, key, kwargs) 2782 format_kwargs = format_kwargs if format_kwargs is not None else {} 2783 formatter = get_formatter(format_type, features=self._info.features, *format_kwargs) -> 2784 pa_subtable = query_table(self._data, key, indices=self._indices if self._indices is not None else None) 2785 formatted_output = format_table( 2786 pa_subtable, key, formatter=formatter, format_columns=format_columns, output_all_columns=output_all_columns [/usr/local/lib/python3.10/dist-packages/datasets/formatting/formatting.py](https://localhost:8080/#) in query_table(table, key, indices) 581 else: 582 size = indices.num_rows if indices is not None else table.num_rows --> 583 _check_valid_index_key(key, size) 584 # Query the main table 585 if indices is None: [/usr/local/lib/python3.10/dist-packages/datasets/formatting/formatting.py](https://localhost:8080/#) in _check_valid_index_key(key, size) 534 elif isinstance(key, Iterable): 535 if len(key) > 0: --> 536 _check_valid_index_key(int(max(key)), size=size) 537 _check_valid_index_key(int(min(key)), size=size) 538 else: [/usr/local/lib/python3.10/dist-packages/datasets/formatting/formatting.py](https://localhost:8080/#) in _check_valid_index_key(key, size) 524 if isinstance(key, int): 525 if (key < 0 and key + size < 0) or (key >= size): --> 526 raise IndexError(f"Invalid key: {key} is out of bounds for size {size}") 527 return 528 elif isinstance(key, slice): IndexError: Invalid key: 47682 is out of bounds for size 0 ### Steps to reproduce the bug device = "cuda:0" if torch.cuda.is_available() else "cpu" #defining model name for tokenizer and model loading model_name= "t5-small" #loading the tokenizer tokenizer = AutoTokenizer.from_pretrained(model_name) def preprocess_function(data, tokenizer): inputs = [f"Paraphrase this sentence: {doc}" for doc in data["text"]] model_inputs = tokenizer(inputs, max_length=150, truncation=True) labels = [ast.literal_eval(i)[0] for i in data['paraphrases']] labels = tokenizer(labels, max_length=150, truncation=True) model_inputs["labels"] = labels["input_ids"] return model_inputs train_dataset = load_dataset("humarin/chatgpt-paraphrases", split="train").shuffle(seed=42).select(range(50000)) val_dataset = load_dataset("humarin/chatgpt-paraphrases", split="train").shuffle(seed=42).select(range(50000,55000)) tokenized_train = train_dataset.map(lambda batch: preprocess_function(batch, tokenizer), batched=True) tokenized_val = val_dataset.map(lambda batch: preprocess_function(batch, tokenizer), batched=True) def print_trainable_parameters(model): """ Prints the number of trainable parameters in the model. """ trainable_params = 0 all_param = 0 for _, param in model.named_parameters(): all_param += param.numel() if param.requires_grad: trainable_params += param.numel() print( f"trainable params: {trainable_params} \|\| all params: {all_param} \|\| trainable%: {100 trainable_params / all_param}" ) config = LoraConfig( r=16, #attention heads lora_alpha=32, #alpha scaling lora_dropout=0.05, bias="none", task_type="Seq2Seq" ) #loading the model model = AutoModelForSeq2SeqLM.from_pretrained(model_name).to(device) model = get_peft_model(model, config) print_trainable_parameters(model) #loading the data collator data_collator = DataCollatorForSeq2Seq( tokenizer=tokenizer, model=model, label_pad_token_id=-100, padding="longest" ) #defining the training arguments training_args = Seq2SeqTrainingArguments( output_dir=os.getcwd(), evaluation_strategy="epoch", save_strategy="epoch", learning_rate=2e-5, per_device_train_batch_size=16, per_device_eval_batch_size=16, weight_decay=1e-3, save_total_limit=3, load_best_model_at_end=True, num_train_epochs=1, predict_with_generate=True ) def compute_metric_with_extra(tokenizer): def compute_metrics(eval_preds): metric = evaluate.load('rouge') preds, labels = eval_preds # decode preds and labels labels = np.where(labels != -100, labels, tokenizer.pad_token_id) decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True) decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True) # rougeLSum expects newline after each sentence decoded_preds = ["\n".join(nltk.sent_tokenize(pred.strip())) for pred in decoded_preds] decoded_labels = ["\n".join(nltk.sent_tokenize(label.strip())) for label in decoded_labels] result = metric.compute(predictions=decoded_preds, references=decoded_labels, use_stemmer=True) return result return compute_metrics trainer = Seq2SeqTrainer( model=model, args=training_args, train_dataset=tokenized_train, eval_dataset=tokenized_val, tokenizer=tokenizer, data_collator=data_collator, compute_metrics= compute_metric_with_extra(tokenizer) ) trainer.train() ### Expected behavior I would want the trainer to train normally as it was before I used- model = get_peft_model(model, config) ### Environment info datasets version- 2.16.0 peft version- 0.7.1 transformers version- 4.35.2 accelerate version- 0.25.0 python- 3.10.12 enviroment- google colab	{ "url": "https://api.github.com/repos/huggingface/datasets/issues/6535/reactions", "total_count": 0, "+1": 0, "-1": 0, "laugh": 0, "hooray": 0, "confused": 0, "heart": 0, "rocket": 0, "eyes": 0 }	https://api.github.com/repos/huggingface/datasets/issues/6535/timeline	null	null	null	null	false