Datasets:

marasaki

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like 0

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[ "@lhoestq - if you could take a final review here this would be great (if you have 5min :-) ) ", "Don't think the failures are related but not 100% sure", "Yes the CI fail is unrelated - you can ignore it" ]

"2022-03-01T13:42:28Z"

"2022-03-16T14:40:29Z"

"2022-03-16T14:40:26Z"

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"2021-11-30T17:38:50Z"

"2021-12-01T11:27:59Z"

"2021-12-01T11:27:58Z"

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[ "Hi @sanderland ! Thanks for reporting :) This is a bug, I opened a PR to fix it. We'll do a new release soon\r\n\r\nIn the meantime you can fix it by specifying in advance that the \"label\" are integers:\r\n```python\r\nimport numpy as np\r\n\r\nds = Dataset.from_dict(\r\n {\r\n \"text\": [\"the lazy dog jumps over the quick fox\", \"another sentence\"],\r\n \"label\": [[], []],\r\n }\r\n)\r\n# explicitly say that the \"label\" type is int64, even though it contains only null values\r\nds = ds.cast_column(\"label\", Sequence(Value(\"int64\")))\r\n\r\ndef mapper(features):\r\n features['label'] = [\r\n [0,0,0] for l in features['label']\r\n ]\r\n return features\r\n\r\nds_mapped = ds.map(mapper,batched=True)\r\n```" ]

"2022-06-13T10:47:52Z"

"2022-06-14T13:34:14Z"

"2022-06-14T13:34:14Z"

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[ "Hi @DrMatters, thanks for reporting.\r\n\r\nThis issue was fixed 14 days ago: #2950.\r\n\r\nCurrently, the fix is only in the master branch and will be made available in our next library release.\r\n\r\nIn the meantime, you can incorporate the fix by installing datasets from the master branch:\r\n```shell\r\npip install -U git+ssh://git@github.com/huggingface/datasets.git@master#egg=datasest\r\n```\r\nor\r\n```shell\r\npip install -U git+https://github.com/huggingface/datasets.git@master#egg=datasets\r\n```", "Thanks, sorry for bothering" ]

"2021-10-04T00:49:29Z"

"2021-10-11T10:18:01Z"

"2021-10-04T08:46:13Z"

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[ "_The documentation is not available anymore as the PR was closed or merged._", "on the hub this dataset is referenced as `sst-2` not `sst2` – is there a canonical orthography? If not, could we name it `sst-2`?", "@julien-c, we normally do not use hyphens for dataset names: whenever the original dataset name contains a hyphen, we usually:\r\n- either suppress it: CoNLL-2000 (`conll2000`), CORD-19 (`cord19`)\r\n- or replace it with underscore: CC-News (`cc_news`), SQuAD-es (`squad_es`)\r\n\r\nThere are some exceptions though... (I wonder why)\r\n\r\nI think, the reason is there was a 1-to-1 relation with the corresponding Python module name.\r\n\r\nI personally find confusing not having a rule and using both hyphens and underscores indistinctly: you never know which is the right orthography.\r\n\r\nWhichever the decision we make, I would prefer to be applied consistently.\r\n\r\nAlso note that we already implemented this dataset as part of GLUE: https://github.com/huggingface/datasets/blob/master/datasets/glue/glue.py#L163\r\n- dataset name: `glue`\r\n- config name: `sst2`\r\n\r\nOn the other hand, let's see how other libraries name it:\r\n- torchtext: `SST2` https://pytorch.org/text/stable/datasets.html#sst2\r\n- OpenAI CLIP: `rendered-sst2` https://github.com/openai/CLIP/blob/main/data/rendered-sst2.md\r\n- Kaggle: `SST2` https://www.kaggle.com/datasets/atulanandjha/stanford-sentiment-treebank-v2-sst2/version/22\r\n- TensorFlow Datasets: `glue/sst2` https://www.tensorflow.org/datasets/catalog/glue#gluesst2", "Ok, another option is to open PRs against the models in https://huggingface.co/models?datasets=sst-2 to change their dataset reference to `sst2`\r\n\r\n(BTW some models refer to `sst2` already – but they're less popular: https://huggingface.co/models?datasets=sst2)", "OK, I'm taking care of the subsequent PRs on models to align with this dataset name." ]

"2022-06-10T13:37:26Z"

"2022-06-13T14:11:34Z"

"2022-06-13T14:01:09Z"

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"2022-02-15T15:47:49Z"

"2022-02-15T16:55:45Z"

"2022-02-15T16:55:44Z"

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"2023-09-05T11:02:58Z"

"2023-09-26T15:32:52Z"

"2023-09-26T15:32:52Z"

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[ "@lhoestq ready for a second review" ]

"2020-12-08T10:07:50Z"

"2020-12-09T17:05:28Z"

"2020-12-09T17:05:28Z"

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[]

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[ "how is your dataset structured? (file types, how many commits and files are you trying to push, etc)", "I succeeded in uploading it after several attempts with an hour gap between each attempt (inconvenient but worked). The final dataset is [here](https://huggingface.co/datasets/yuvalkirstain/pickapic_v2), code and context to the dataset can be found [here](https://github.com/yuvalkirstain/PickScore/).\r\nI can close the issue if this behavior is intended, as most users probably do not need to upload large-scale datasets.", "We could fix this by creating a single commit for all the (Parquet) shards in `push_to_hub` instead of one commit per shard, as we currently do. \r\n\r\n@Wauplin Any updates on the 2-step commit process suggested by you that we need to implement this?", "> Any updates on the 2-step commit process suggested by you that we need to implement this?\r\n\r\nRe-prioritizing this, sorry. Will let you know but probably can be done this week." ]

"2023-09-25T06:11:43Z"

"2023-10-16T13:30:49Z"

"2023-10-16T13:30:48Z"

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"2023-02-10T19:13:03Z"

"2023-02-10T19:14:50Z"

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008882 / 0.011353 (-0.002471) | 0.004562 / 0.011008 (-0.006446) | 0.100035 / 0.038508 (0.061527) | 0.030654 / 0.023109 (0.007545) | 0.298745 / 0.275898 (0.022847) | 0.356869 / 0.323480 (0.033389) | 0.007170 / 0.007986 (-0.000815) | 0.003471 / 0.004328 (-0.000858) | 0.077975 / 0.004250 (0.073725) | 0.037861 / 0.037052 (0.000809) | 0.311643 / 0.258489 (0.053154) | 0.343504 / 0.293841 (0.049663) | 0.033768 / 0.128546 (-0.094778) | 0.011342 / 0.075646 (-0.064304) | 0.323953 / 0.419271 (-0.095319) | 0.040818 / 0.043533 (-0.002715) | 0.298492 / 0.255139 (0.043353) | 0.327292 / 0.283200 (0.044092) | 0.088423 / 0.141683 (-0.053260) | 1.489520 / 1.452155 (0.037366) | 1.532962 / 1.492716 (0.040245) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.223654 / 0.018006 (0.205647) | 0.415134 / 0.000490 (0.414644) | 0.007394 / 0.000200 (0.007194) | 0.000080 / 0.000054 (0.000026) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023616 / 0.037411 (-0.013795) | 0.096652 / 0.014526 (0.082126) | 0.105239 / 0.176557 (-0.071318) | 0.148637 / 0.737135 (-0.588498) | 0.107937 / 0.296338 (-0.188402) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.426816 / 0.215209 (0.211607) | 4.241533 / 2.077655 (2.163878) | 1.946493 / 1.504120 (0.442373) | 1.735765 / 1.541195 (0.194570) | 1.781424 / 1.468490 (0.312934) | 0.688082 / 4.584777 (-3.896694) | 3.396444 / 3.745712 (-0.349268) | 1.920333 / 5.269862 (-3.349528) | 1.293833 / 4.565676 (-3.271843) | 0.081967 / 0.424275 (-0.342308) | 0.012911 / 0.007607 (0.005304) | 0.536928 / 0.226044 (0.310884) | 5.452327 / 2.268929 (3.183399) | 2.505785 / 55.444624 (-52.938840) | 2.173627 / 6.876477 (-4.702850) | 2.119978 / 2.142072 (-0.022095) | 0.809012 / 4.805227 (-3.996215) | 0.149124 / 6.500664 (-6.351540) | 0.066008 / 0.075469 (-0.009461) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.215702 / 1.841788 (-0.626085) | 13.757525 / 8.074308 (5.683217) | 13.999208 / 10.191392 (3.807816) | 0.164875 / 0.680424 (-0.515549) | 0.028517 / 0.534201 (-0.505684) | 0.394829 / 0.579283 (-0.184454) | 0.404962 / 0.434364 (-0.029401) | 0.484455 / 0.540337 (-0.055882) | 0.575008 / 1.386936 (-0.811928) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006754 / 0.011353 (-0.004598) | 0.004579 / 0.011008 (-0.006430) | 0.076617 / 0.038508 (0.038109) | 0.027902 / 0.023109 (0.004793) | 0.346278 / 0.275898 (0.070380) | 0.398060 / 0.323480 (0.074580) | 0.004938 / 0.007986 (-0.003047) | 0.004681 / 0.004328 (0.000353) | 0.076336 / 0.004250 (0.072086) | 0.038018 / 0.037052 (0.000966) | 0.358701 / 0.258489 (0.100212) | 0.408413 / 0.293841 (0.114572) | 0.031772 / 0.128546 (-0.096774) | 0.011604 / 0.075646 (-0.064042) | 0.085964 / 0.419271 (-0.333308) | 0.042030 / 0.043533 (-0.001502) | 0.343568 / 0.255139 (0.088429) | 0.381805 / 0.283200 (0.098605) | 0.090759 / 0.141683 (-0.050924) | 1.504553 / 1.452155 (0.052398) | 1.594006 / 1.492716 (0.101289) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.227395 / 0.018006 (0.209389) | 0.403097 / 0.000490 (0.402608) | 0.000413 / 0.000200 (0.000213) | 0.000060 / 0.000054 (0.000006) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024693 / 0.037411 (-0.012718) | 0.100470 / 0.014526 (0.085944) | 0.108481 / 0.176557 (-0.068076) | 0.142791 / 0.737135 (-0.594345) | 0.109949 / 0.296338 (-0.186389) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.443674 / 0.215209 (0.228465) | 4.412207 / 2.077655 (2.334553) | 2.073752 / 1.504120 (0.569632) | 1.863153 / 1.541195 (0.321958) | 1.940063 / 1.468490 (0.471573) | 0.696456 / 4.584777 (-3.888321) | 3.422120 / 3.745712 (-0.323592) | 1.902579 / 5.269862 (-3.367282) | 1.184948 / 4.565676 (-3.380729) | 0.083079 / 0.424275 (-0.341196) | 0.012649 / 0.007607 (0.005042) | 0.542035 / 0.226044 (0.315991) | 5.421826 / 2.268929 (3.152897) | 2.525092 / 55.444624 (-52.919532) | 2.177144 / 6.876477 (-4.699332) | 2.225224 / 2.142072 (0.083151) | 0.804739 / 4.805227 (-4.000488) | 0.151000 / 6.500664 (-6.349664) | 0.066987 / 0.075469 (-0.008482) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.277199 / 1.841788 (-0.564589) | 14.184146 / 8.074308 (6.109838) | 13.413348 / 10.191392 (3.221956) | 0.128551 / 0.680424 (-0.551872) | 0.016461 / 0.534201 (-0.517740) | 0.379963 / 0.579283 (-0.199320) | 0.381350 / 0.434364 (-0.053014) | 0.439044 / 0.540337 (-0.101293) | 0.521559 / 1.386936 (-0.865377) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4f3c152c1c35df250d2fbeb25d5823a65714f2d8 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008876 / 0.011353 (-0.002477) | 0.004629 / 0.011008 (-0.006379) | 0.101697 / 0.038508 (0.063189) | 0.030373 / 0.023109 (0.007264) | 0.302206 / 0.275898 (0.026308) | 0.365835 / 0.323480 (0.042355) | 0.007877 / 0.007986 (-0.000109) | 0.004473 / 0.004328 (0.000144) | 0.077334 / 0.004250 (0.073084) | 0.038066 / 0.037052 (0.001014) | 0.308064 / 0.258489 (0.049575) | 0.347329 / 0.293841 (0.053488) | 0.034478 / 0.128546 (-0.094068) | 0.011651 / 0.075646 (-0.063995) | 0.323481 / 0.419271 (-0.095791) | 0.043515 / 0.043533 (-0.000018) | 0.299885 / 0.255139 (0.044746) | 0.328959 / 0.283200 (0.045760) | 0.095308 / 0.141683 (-0.046375) | 1.474058 / 1.452155 (0.021903) | 1.535335 / 1.492716 (0.042619) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.197416 / 0.018006 (0.179410) | 0.421935 / 0.000490 (0.421446) | 0.003490 / 0.000200 (0.003290) | 0.000074 / 0.000054 (0.000020) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024519 / 0.037411 (-0.012892) | 0.100710 / 0.014526 (0.086185) | 0.104520 / 0.176557 (-0.072036) | 0.142048 / 0.737135 (-0.595087) | 0.109274 / 0.296338 (-0.187064) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.408766 / 0.215209 (0.193557) | 4.101720 / 2.077655 (2.024065) | 1.812375 / 1.504120 (0.308256) | 1.605819 / 1.541195 (0.064624) | 1.688923 / 1.468490 (0.220433) | 0.691198 / 4.584777 (-3.893579) | 3.422137 / 3.745712 (-0.323575) | 1.921318 / 5.269862 (-3.348544) | 1.168770 / 4.565676 (-3.396906) | 0.082840 / 0.424275 (-0.341435) | 0.012740 / 0.007607 (0.005133) | 0.524333 / 0.226044 (0.298289) | 5.258077 / 2.268929 (2.989149) | 2.273177 / 55.444624 (-53.171447) | 1.931919 / 6.876477 (-4.944558) | 1.988415 / 2.142072 (-0.153658) | 0.812227 / 4.805227 (-3.993000) | 0.150043 / 6.500664 (-6.350622) | 0.066422 / 0.075469 (-0.009047) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.188069 / 1.841788 (-0.653718) | 13.942681 / 8.074308 (5.868373) | 14.104658 / 10.191392 (3.913266) | 0.151966 / 0.680424 (-0.528458) | 0.028833 / 0.534201 (-0.505368) | 0.395125 / 0.579283 (-0.184158) | 0.408512 / 0.434364 (-0.025852) | 0.487587 / 0.540337 (-0.052751) | 0.570023 / 1.386936 (-0.816913) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006860 / 0.011353 (-0.004493) | 0.004582 / 0.011008 (-0.006426) | 0.079902 / 0.038508 (0.041394) | 0.027565 / 0.023109 (0.004456) | 0.341393 / 0.275898 (0.065495) | 0.378911 / 0.323480 (0.055431) | 0.005847 / 0.007986 (-0.002138) | 0.004681 / 0.004328 (0.000353) | 0.079422 / 0.004250 (0.075171) | 0.039135 / 0.037052 (0.002083) | 0.342026 / 0.258489 (0.083537) | 0.387510 / 0.293841 (0.093669) | 0.031999 / 0.128546 (-0.096547) | 0.011782 / 0.075646 (-0.063865) | 0.088563 / 0.419271 (-0.330709) | 0.042435 / 0.043533 (-0.001098) | 0.343055 / 0.255139 (0.087916) | 0.367437 / 0.283200 (0.084237) | 0.091578 / 0.141683 (-0.050104) | 1.506828 / 1.452155 (0.054673) | 1.599590 / 1.492716 (0.106874) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.217939 / 0.018006 (0.199932) | 0.408352 / 0.000490 (0.407863) | 0.000394 / 0.000200 (0.000194) | 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) | 0.026344 / 0.037411 (-0.011067) | 0.102968 / 0.014526 (0.088442) | 0.110340 / 0.176557 (-0.066217) | 0.145696 / 0.737135 (-0.591439) | 0.111632 / 0.296338 (-0.184707) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.440764 / 0.215209 (0.225555) | 4.423179 / 2.077655 (2.345524) | 2.057016 / 1.504120 (0.552896) | 1.848741 / 1.541195 (0.307546) | 1.939827 / 1.468490 (0.471337) | 0.699370 / 4.584777 (-3.885407) | 3.472521 / 3.745712 (-0.273191) | 3.232557 / 5.269862 (-2.037305) | 1.755534 / 4.565676 (-2.810143) | 0.083469 / 0.424275 (-0.340807) | 0.012980 / 0.007607 (0.005373) | 0.557662 / 0.226044 (0.331618) | 5.435657 / 2.268929 (3.166729) | 2.545106 / 55.444624 (-52.899519) | 2.168047 / 6.876477 (-4.708430) | 2.234070 / 2.142072 (0.091997) | 0.804662 / 4.805227 (-4.000565) | 0.152832 / 6.500664 (-6.347833) | 0.069372 / 0.075469 (-0.006097) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.299189 / 1.841788 (-0.542598) | 14.752880 / 8.074308 (6.678572) | 13.607676 / 10.191392 (3.416284) | 0.150773 / 0.680424 (-0.529650) | 0.016701 / 0.534201 (-0.517500) | 0.379507 / 0.579283 (-0.199776) | 0.389401 / 0.434364 (-0.044963) | 0.444199 / 0.540337 (-0.096139) | 0.524264 / 1.386936 (-0.862672) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#12be850b36c0b9d4841af86c75e08c0a726ffb5c \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008694 / 0.011353 (-0.002659) | 0.004549 / 0.011008 (-0.006459) | 0.101164 / 0.038508 (0.062656) | 0.029644 / 0.023109 (0.006535) | 0.294849 / 0.275898 (0.018950) | 0.366755 / 0.323480 (0.043275) | 0.007205 / 0.007986 (-0.000780) | 0.004255 / 0.004328 (-0.000074) | 0.077433 / 0.004250 (0.073183) | 0.038024 / 0.037052 (0.000972) | 0.310380 / 0.258489 (0.051891) | 0.347093 / 0.293841 (0.053252) | 0.033232 / 0.128546 (-0.095314) | 0.011404 / 0.075646 (-0.064242) | 0.323341 / 0.419271 (-0.095930) | 0.040586 / 0.043533 (-0.002946) | 0.296083 / 0.255139 (0.040944) | 0.321870 / 0.283200 (0.038671) | 0.087377 / 0.141683 (-0.054306) | 1.466869 / 1.452155 (0.014715) | 1.514763 / 1.492716 (0.022046) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.010272 / 0.018006 (-0.007734) | 0.414645 / 0.000490 (0.414155) | 0.003730 / 0.000200 (0.003530) | 0.000076 / 0.000054 (0.000021) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024093 / 0.037411 (-0.013318) | 0.098718 / 0.014526 (0.084192) | 0.105526 / 0.176557 (-0.071030) | 0.141578 / 0.737135 (-0.595557) | 0.109679 / 0.296338 (-0.186660) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.412907 / 0.215209 (0.197698) | 4.134934 / 2.077655 (2.057280) | 1.881180 / 1.504120 (0.377060) | 1.693207 / 1.541195 (0.152012) | 1.753725 / 1.468490 (0.285235) | 0.693077 / 4.584777 (-3.891700) | 3.367409 / 3.745712 (-0.378303) | 2.749035 / 5.269862 (-2.520827) | 1.565015 / 4.565676 (-3.000662) | 0.082609 / 0.424275 (-0.341666) | 0.012500 / 0.007607 (0.004892) | 0.523619 / 0.226044 (0.297575) | 5.250188 / 2.268929 (2.981259) | 2.314255 / 55.444624 (-53.130369) | 1.962357 / 6.876477 (-4.914120) | 2.020632 / 2.142072 (-0.121441) | 0.812504 / 4.805227 (-3.992724) | 0.149921 / 6.500664 (-6.350743) | 0.065816 / 0.075469 (-0.009653) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.230811 / 1.841788 (-0.610977) | 14.008566 / 8.074308 (5.934258) | 14.371285 / 10.191392 (4.179893) | 0.166323 / 0.680424 (-0.514101) | 0.029702 / 0.534201 (-0.504499) | 0.408629 / 0.579283 (-0.170654) | 0.410529 / 0.434364 (-0.023835) | 0.484482 / 0.540337 (-0.055855) | 0.572360 / 1.386936 (-0.814576) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006873 / 0.011353 (-0.004480) | 0.004609 / 0.011008 (-0.006400) | 0.075492 / 0.038508 (0.036984) | 0.028560 / 0.023109 (0.005450) | 0.340321 / 0.275898 (0.064423) | 0.376758 / 0.323480 (0.053278) | 0.005271 / 0.007986 (-0.002715) | 0.004786 / 0.004328 (0.000457) | 0.074843 / 0.004250 (0.070592) | 0.041072 / 0.037052 (0.004019) | 0.339952 / 0.258489 (0.081463) | 0.384375 / 0.293841 (0.090534) | 0.031771 / 0.128546 (-0.096775) | 0.011607 / 0.075646 (-0.064039) | 0.084338 / 0.419271 (-0.334933) | 0.042251 / 0.043533 (-0.001282) | 0.338904 / 0.255139 (0.083765) | 0.365360 / 0.283200 (0.082160) | 0.093151 / 0.141683 (-0.048532) | 1.449833 / 1.452155 (-0.002322) | 1.601946 / 1.492716 (0.109229) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.225149 / 0.018006 (0.207142) | 0.409855 / 0.000490 (0.409365) | 0.000384 / 0.000200 (0.000184) | 0.000060 / 0.000054 (0.000006) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025914 / 0.037411 (-0.011497) | 0.100443 / 0.014526 (0.085917) | 0.108557 / 0.176557 (-0.067999) | 0.150338 / 0.737135 (-0.586798) | 0.111472 / 0.296338 (-0.184866) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.440221 / 0.215209 (0.225012) | 4.409268 / 2.077655 (2.331613) | 2.096008 / 1.504120 (0.591888) | 1.849443 / 1.541195 (0.308248) | 1.934901 / 1.468490 (0.466410) | 0.704072 / 4.584777 (-3.880705) | 3.371370 / 3.745712 (-0.374343) | 3.185478 / 5.269862 (-2.084384) | 1.514541 / 4.565676 (-3.051135) | 0.083724 / 0.424275 (-0.340551) | 0.012674 / 0.007607 (0.005067) | 0.542155 / 0.226044 (0.316111) | 5.413456 / 2.268929 (3.144528) | 2.508567 / 55.444624 (-52.936057) | 2.163235 / 6.876477 (-4.713242) | 2.193914 / 2.142072 (0.051842) | 0.810955 / 4.805227 (-3.994272) | 0.152769 / 6.500664 (-6.347895) | 0.068009 / 0.075469 (-0.007460) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.272511 / 1.841788 (-0.569276) | 14.334861 / 8.074308 (6.260553) | 13.555445 / 10.191392 (3.364053) | 0.160520 / 0.680424 (-0.519904) | 0.018363 / 0.534201 (-0.515838) | 0.384937 / 0.579283 (-0.194346) | 0.409138 / 0.434364 (-0.025225) | 0.484037 / 0.540337 (-0.056300) | 0.565595 / 1.386936 (-0.821341) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#23f076ef0187a4009d3c62b14a02e146baf0e35f \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.010077 / 0.011353 (-0.001276) | 0.005650 / 0.011008 (-0.005359) | 0.101285 / 0.038508 (0.062777) | 0.039571 / 0.023109 (0.016462) | 0.291855 / 0.275898 (0.015957) | 0.363582 / 0.323480 (0.040102) | 0.008513 / 0.007986 (0.000527) | 0.004472 / 0.004328 (0.000144) | 0.077314 / 0.004250 (0.073064) | 0.050707 / 0.037052 (0.013654) | 0.317282 / 0.258489 (0.058792) | 0.342348 / 0.293841 (0.048507) | 0.042951 / 0.128546 (-0.085595) | 0.012295 / 0.075646 (-0.063351) | 0.337269 / 0.419271 (-0.082003) | 0.048953 / 0.043533 (0.005420) | 0.292547 / 0.255139 (0.037408) | 0.325436 / 0.283200 (0.042236) | 0.111859 / 0.141683 (-0.029824) | 1.501958 / 1.452155 (0.049804) | 1.522281 / 1.492716 (0.029565) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.011775 / 0.018006 (-0.006231) | 0.513283 / 0.000490 (0.512793) | 0.002941 / 0.000200 (0.002741) | 0.000099 / 0.000054 (0.000044) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028702 / 0.037411 (-0.008710) | 0.108465 / 0.014526 (0.093940) | 0.121806 / 0.176557 (-0.054750) | 0.158424 / 0.737135 (-0.578712) | 0.128077 / 0.296338 (-0.168262) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.395392 / 0.215209 (0.180183) | 3.944138 / 2.077655 (1.866483) | 1.773698 / 1.504120 (0.269578) | 1.588907 / 1.541195 (0.047712) | 1.697794 / 1.468490 (0.229304) | 0.690281 / 4.584777 (-3.894496) | 3.819661 / 3.745712 (0.073948) | 3.228006 / 5.269862 (-2.041856) | 1.755625 / 4.565676 (-2.810052) | 0.083169 / 0.424275 (-0.341106) | 0.012337 / 0.007607 (0.004730) | 0.504730 / 0.226044 (0.278686) | 5.016916 / 2.268929 (2.747988) | 2.245484 / 55.444624 (-53.199141) | 1.911682 / 6.876477 (-4.964795) | 1.957659 / 2.142072 (-0.184413) | 0.818361 / 4.805227 (-3.986866) | 0.162386 / 6.500664 (-6.338279) | 0.062461 / 0.075469 (-0.013008) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.197654 / 1.841788 (-0.644134) | 15.465611 / 8.074308 (7.391303) | 14.409126 / 10.191392 (4.217734) | 0.171776 / 0.680424 (-0.508647) | 0.028749 / 0.534201 (-0.505452) | 0.439666 / 0.579283 (-0.139618) | 0.445159 / 0.434364 (0.010795) | 0.543992 / 0.540337 (0.003655) | 0.643911 / 1.386936 (-0.743025) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007036 / 0.011353 (-0.004317) | 0.005273 / 0.011008 (-0.005735) | 0.075314 / 0.038508 (0.036806) | 0.033075 / 0.023109 (0.009966) | 0.350133 / 0.275898 (0.074235) | 0.399366 / 0.323480 (0.075886) | 0.005945 / 0.007986 (-0.002041) | 0.004276 / 0.004328 (-0.000052) | 0.074975 / 0.004250 (0.070725) | 0.051758 / 0.037052 (0.014706) | 0.355077 / 0.258489 (0.096588) | 0.430296 / 0.293841 (0.136455) | 0.036257 / 0.128546 (-0.092290) | 0.012376 / 0.075646 (-0.063270) | 0.087441 / 0.419271 (-0.331830) | 0.049066 / 0.043533 (0.005534) | 0.339867 / 0.255139 (0.084728) | 0.384379 / 0.283200 (0.101179) | 0.104843 / 0.141683 (-0.036840) | 1.498897 / 1.452155 (0.046742) | 1.551400 / 1.492716 (0.058684) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.334504 / 0.018006 (0.316498) | 0.516551 / 0.000490 (0.516061) | 0.000450 / 0.000200 (0.000250) | 0.000057 / 0.000054 (0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029313 / 0.037411 (-0.008099) | 0.110667 / 0.014526 (0.096141) | 0.124001 / 0.176557 (-0.052556) | 0.159154 / 0.737135 (-0.577981) | 0.129503 / 0.296338 (-0.166836) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.416749 / 0.215209 (0.201540) | 4.171163 / 2.077655 (2.093508) | 1.981071 / 1.504120 (0.476951) | 1.788303 / 1.541195 (0.247108) | 1.912118 / 1.468490 (0.443628) | 0.708764 / 4.584777 (-3.876013) | 3.815222 / 3.745712 (0.069510) | 2.121633 / 5.269862 (-3.148229) | 1.347866 / 4.565676 (-3.217811) | 0.086340 / 0.424275 (-0.337935) | 0.012646 / 0.007607 (0.005039) | 0.525286 / 0.226044 (0.299241) | 5.254922 / 2.268929 (2.985994) | 2.488743 / 55.444624 (-52.955881) | 2.128069 / 6.876477 (-4.748408) | 2.180358 / 2.142072 (0.038286) | 0.841011 / 4.805227 (-3.964216) | 0.168732 / 6.500664 (-6.331932) | 0.065559 / 0.075469 (-0.009910) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.270518 / 1.841788 (-0.571270) | 15.557563 / 8.074308 (7.483255) | 13.660757 / 10.191392 (3.469365) | 0.185636 / 0.680424 (-0.494788) | 0.018152 / 0.534201 (-0.516049) | 0.423553 / 0.579283 (-0.155730) | 0.412718 / 0.434364 (-0.021646) | 0.528455 / 0.540337 (-0.011882) | 0.635274 / 1.386936 (-0.751662) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d40f05ef827c52344a2c6e83f7c8d13bb6b660d3 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.011194 / 0.011353 (-0.000159) | 0.006344 / 0.011008 (-0.004664) | 0.122013 / 0.038508 (0.083505) | 0.044323 / 0.023109 (0.021214) | 0.356665 / 0.275898 (0.080767) | 0.439871 / 0.323480 (0.116391) | 0.010694 / 0.007986 (0.002709) | 0.004648 / 0.004328 (0.000320) | 0.091140 / 0.004250 (0.086890) | 0.052457 / 0.037052 (0.015404) | 0.369282 / 0.258489 (0.110793) | 0.403279 / 0.293841 (0.109438) | 0.054075 / 0.128546 (-0.074472) | 0.014484 / 0.075646 (-0.061162) | 0.407932 / 0.419271 (-0.011340) | 0.060681 / 0.043533 (0.017148) | 0.350889 / 0.255139 (0.095750) | 0.392041 / 0.283200 (0.108841) | 0.121252 / 0.141683 (-0.020431) | 1.809527 / 1.452155 (0.357373) | 1.835141 / 1.492716 (0.342425) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.227372 / 0.018006 (0.209366) | 0.481908 / 0.000490 (0.481418) | 0.007262 / 0.000200 (0.007062) | 0.000148 / 0.000054 (0.000093) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031039 / 0.037411 (-0.006372) | 0.133947 / 0.014526 (0.119421) | 0.141935 / 0.176557 (-0.034622) | 0.197854 / 0.737135 (-0.539281) | 0.152393 / 0.296338 (-0.143945) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.517400 / 0.215209 (0.302191) | 4.899972 / 2.077655 (2.822317) | 2.171023 / 1.504120 (0.666903) | 2.008706 / 1.541195 (0.467511) | 1.988777 / 1.468490 (0.520287) | 0.859872 / 4.584777 (-3.724905) | 4.673923 / 3.745712 (0.928211) | 2.703189 / 5.269862 (-2.566672) | 1.891680 / 4.565676 (-2.673997) | 0.109601 / 0.424275 (-0.314674) | 0.014622 / 0.007607 (0.007015) | 0.618990 / 0.226044 (0.392946) | 6.255608 / 2.268929 (3.986679) | 2.822199 / 55.444624 (-52.622425) | 2.457684 / 6.876477 (-4.418793) | 2.500041 / 2.142072 (0.357968) | 1.054529 / 4.805227 (-3.750698) | 0.209501 / 6.500664 (-6.291163) | 0.074929 / 0.075469 (-0.000540) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.532780 / 1.841788 (-0.309008) | 19.159455 / 8.074308 (11.085147) | 17.817063 / 10.191392 (7.625671) | 0.194078 / 0.680424 (-0.486346) | 0.038211 / 0.534201 (-0.495990) | 0.537366 / 0.579283 (-0.041917) | 0.538995 / 0.434364 (0.104631) | 0.679431 / 0.540337 (0.139094) | 0.801960 / 1.386936 (-0.584976) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008729 / 0.011353 (-0.002624) | 0.005711 / 0.011008 (-0.005297) | 0.091570 / 0.038508 (0.053062) | 0.039805 / 0.023109 (0.016696) | 0.413507 / 0.275898 (0.137609) | 0.456342 / 0.323480 (0.132862) | 0.006201 / 0.007986 (-0.001785) | 0.009700 / 0.004328 (0.005372) | 0.089146 / 0.004250 (0.084896) | 0.057543 / 0.037052 (0.020490) | 0.420806 / 0.258489 (0.162317) | 0.471962 / 0.293841 (0.178121) | 0.043940 / 0.128546 (-0.084606) | 0.014457 / 0.075646 (-0.061190) | 0.106674 / 0.419271 (-0.312598) | 0.058930 / 0.043533 (0.015397) | 0.419111 / 0.255139 (0.163972) | 0.452974 / 0.283200 (0.169774) | 0.124573 / 0.141683 (-0.017110) | 1.864753 / 1.452155 (0.412599) | 1.935387 / 1.492716 (0.442670) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.275657 / 0.018006 (0.257651) | 0.498096 / 0.000490 (0.497606) | 0.000480 / 0.000200 (0.000280) | 0.000066 / 0.000054 (0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034377 / 0.037411 (-0.003035) | 0.138050 / 0.014526 (0.123524) | 0.153718 / 0.176557 (-0.022838) | 0.201445 / 0.737135 (-0.535690) | 0.160346 / 0.296338 (-0.135992) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.540670 / 0.215209 (0.325461) | 5.376291 / 2.077655 (3.298636) | 2.581799 / 1.504120 (1.077679) | 2.328858 / 1.541195 (0.787663) | 2.446458 / 1.468490 (0.977968) | 0.923005 / 4.584777 (-3.661772) | 4.815977 / 3.745712 (1.070265) | 4.205725 / 5.269862 (-1.064137) | 2.400466 / 4.565676 (-2.165211) | 0.107207 / 0.424275 (-0.317068) | 0.015427 / 0.007607 (0.007819) | 0.657267 / 0.226044 (0.431222) | 6.491256 / 2.268929 (4.222327) | 3.179099 / 55.444624 (-52.265525) | 2.722434 / 6.876477 (-4.154042) | 2.788202 / 2.142072 (0.646129) | 1.060016 / 4.805227 (-3.745211) | 0.206899 / 6.500664 (-6.293766) | 0.077868 / 0.075469 (0.002399) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.567894 / 1.841788 (-0.273893) | 19.314330 / 8.074308 (11.240022) | 17.597614 / 10.191392 (7.406222) | 0.195777 / 0.680424 (-0.484647) | 0.022160 / 0.534201 (-0.512041) | 0.530592 / 0.579283 (-0.048691) | 0.508591 / 0.434364 (0.074227) | 0.619794 / 0.540337 (0.079457) | 0.749773 / 1.386936 (-0.637163) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8637141a67639c510294620306c9bb25d31d34ef \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.012431 / 0.011353 (0.001078) | 0.006526 / 0.011008 (-0.004482) | 0.132266 / 0.038508 (0.093757) | 0.043199 / 0.023109 (0.020089) | 0.405230 / 0.275898 (0.129332) | 0.494643 / 0.323480 (0.171163) | 0.009927 / 0.007986 (0.001941) | 0.005227 / 0.004328 (0.000899) | 0.110914 / 0.004250 (0.106664) | 0.047815 / 0.037052 (0.010763) | 0.419099 / 0.258489 (0.160610) | 0.463405 / 0.293841 (0.169564) | 0.057858 / 0.128546 (-0.070688) | 0.018918 / 0.075646 (-0.056728) | 0.450584 / 0.419271 (0.031313) | 0.060457 / 0.043533 (0.016924) | 0.408234 / 0.255139 (0.153095) | 0.433722 / 0.283200 (0.150523) | 0.119403 / 0.141683 (-0.022280) | 1.966742 / 1.452155 (0.514587) | 1.980685 / 1.492716 (0.487969) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.292853 / 0.018006 (0.274847) | 0.619697 / 0.000490 (0.619207) | 0.002135 / 0.000200 (0.001935) | 0.000117 / 0.000054 (0.000062) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031283 / 0.037411 (-0.006129) | 0.128649 / 0.014526 (0.114123) | 0.150116 / 0.176557 (-0.026441) | 0.187605 / 0.737135 (-0.549530) | 0.153334 / 0.296338 (-0.143005) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.659660 / 0.215209 (0.444451) | 6.459749 / 2.077655 (4.382094) | 2.764566 / 1.504120 (1.260446) | 2.362630 / 1.541195 (0.821435) | 2.426421 / 1.468490 (0.957931) | 1.282407 / 4.584777 (-3.302370) | 5.668865 / 3.745712 (1.923153) | 3.236255 / 5.269862 (-2.033606) | 2.248836 / 4.565676 (-2.316841) | 0.145861 / 0.424275 (-0.278414) | 0.015707 / 0.007607 (0.008100) | 0.805218 / 0.226044 (0.579174) | 8.146831 / 2.268929 (5.877903) | 3.506283 / 55.444624 (-51.938341) | 2.736682 / 6.876477 (-4.139795) | 2.959039 / 2.142072 (0.816967) | 1.528428 / 4.805227 (-3.276799) | 0.270980 / 6.500664 (-6.229684) | 0.086824 / 0.075469 (0.011355) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.682506 / 1.841788 (-0.159282) | 18.844103 / 8.074308 (10.769795) | 21.008471 / 10.191392 (10.817079) | 0.258372 / 0.680424 (-0.422052) | 0.046505 / 0.534201 (-0.487696) | 0.574760 / 0.579283 (-0.004523) | 0.663745 / 0.434364 (0.229381) | 0.702411 / 0.540337 (0.162074) | 0.824024 / 1.386936 (-0.562912) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.010016 / 0.011353 (-0.001337) | 0.007459 / 0.011008 (-0.003549) | 0.103954 / 0.038508 (0.065446) | 0.036363 / 0.023109 (0.013254) | 0.464079 / 0.275898 (0.188181) | 0.504730 / 0.323480 (0.181250) | 0.007865 / 0.007986 (-0.000121) | 0.005210 / 0.004328 (0.000882) | 0.105018 / 0.004250 (0.100767) | 0.062191 / 0.037052 (0.025139) | 0.483304 / 0.258489 (0.224815) | 0.547030 / 0.293841 (0.253189) | 0.055436 / 0.128546 (-0.073110) | 0.021073 / 0.075646 (-0.054573) | 0.120952 / 0.419271 (-0.298319) | 0.075593 / 0.043533 (0.032060) | 0.459930 / 0.255139 (0.204791) | 0.486924 / 0.283200 (0.203724) | 0.129465 / 0.141683 (-0.012218) | 1.902322 / 1.452155 (0.450167) | 1.980809 / 1.492716 (0.488092) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.259263 / 0.018006 (0.241257) | 0.596703 / 0.000490 (0.596213) | 0.004520 / 0.000200 (0.004320) | 0.000124 / 0.000054 (0.000070) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032802 / 0.037411 (-0.004609) | 0.138751 / 0.014526 (0.124225) | 0.147106 / 0.176557 (-0.029451) | 0.194791 / 0.737135 (-0.542345) | 0.152643 / 0.296338 (-0.143696) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.678455 / 0.215209 (0.463246) | 6.673643 / 2.077655 (4.595989) | 2.943368 / 1.504120 (1.439248) | 2.591223 / 1.541195 (1.050029) | 2.741097 / 1.468490 (1.272607) | 1.261178 / 4.584777 (-3.323599) | 5.773853 / 3.745712 (2.028141) | 3.171559 / 5.269862 (-2.098303) | 2.124898 / 4.565676 (-2.440779) | 0.161849 / 0.424275 (-0.262426) | 0.015498 / 0.007607 (0.007891) | 0.857984 / 0.226044 (0.631940) | 8.456946 / 2.268929 (6.188018) | 3.818787 / 55.444624 (-51.625837) | 3.009953 / 6.876477 (-3.866523) | 3.113006 / 2.142072 (0.970934) | 1.477299 / 4.805227 (-3.327929) | 0.267207 / 6.500664 (-6.233457) | 0.087590 / 0.075469 (0.012121) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.757389 / 1.841788 (-0.084398) | 19.287690 / 8.074308 (11.213381) | 21.601991 / 10.191392 (11.410599) | 0.260464 / 0.680424 (-0.419960) | 0.028552 / 0.534201 (-0.505649) | 0.558934 / 0.579283 (-0.020349) | 0.673651 / 0.434364 (0.239287) | 0.714448 / 0.540337 (0.174111) | 0.857608 / 1.386936 (-0.529328) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2d3bd0134de444ffd10c4a39873dbf9aa3732c08 \"CML watermark\")\n", "Ready for review @mariosasko, LMKWYT :)\r\n\r\nSorry it tooks me a few tries to fix the CI - I ended up not trying to use the latest `torch` version in the CI.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009474 / 0.011353 (-0.001878) | 0.005507 / 0.011008 (-0.005501) | 0.101219 / 0.038508 (0.062711) | 0.035591 / 0.023109 (0.012481) | 0.305841 / 0.275898 (0.029943) | 0.339135 / 0.323480 (0.015656) | 0.007920 / 0.007986 (-0.000066) | 0.004252 / 0.004328 (-0.000077) | 0.076912 / 0.004250 (0.072662) | 0.041923 / 0.037052 (0.004871) | 0.301405 / 0.258489 (0.042916) | 0.356488 / 0.293841 (0.062647) | 0.039342 / 0.128546 (-0.089204) | 0.012711 / 0.075646 (-0.062935) | 0.334193 / 0.419271 (-0.085079) | 0.049112 / 0.043533 (0.005579) | 0.301484 / 0.255139 (0.046345) | 0.315306 / 0.283200 (0.032106) | 0.102959 / 0.141683 (-0.038724) | 1.420677 / 1.452155 (-0.031478) | 1.549493 / 1.492716 (0.056777) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.284639 / 0.018006 (0.266633) | 0.501226 / 0.000490 (0.500736) | 0.004328 / 0.000200 (0.004128) | 0.000091 / 0.000054 (0.000036) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027034 / 0.037411 (-0.010377) | 0.108066 / 0.014526 (0.093540) | 0.122106 / 0.176557 (-0.054451) | 0.162908 / 0.737135 (-0.574227) | 0.127233 / 0.296338 (-0.169105) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.394023 / 0.215209 (0.178813) | 3.932729 / 2.077655 (1.855075) | 1.771195 / 1.504120 (0.267075) | 1.582788 / 1.541195 (0.041594) | 1.703219 / 1.468490 (0.234728) | 0.702629 / 4.584777 (-3.882148) | 3.780187 / 3.745712 (0.034475) | 2.180433 / 5.269862 (-3.089428) | 1.504806 / 4.565676 (-3.060871) | 0.085289 / 0.424275 (-0.338986) | 0.012580 / 0.007607 (0.004973) | 0.515408 / 0.226044 (0.289363) | 5.010613 / 2.268929 (2.741685) | 2.256648 / 55.444624 (-53.187976) | 1.914971 / 6.876477 (-4.961505) | 2.038436 / 2.142072 (-0.103636) | 0.846240 / 4.805227 (-3.958987) | 0.164920 / 6.500664 (-6.335744) | 0.063899 / 0.075469 (-0.011570) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.224160 / 1.841788 (-0.617627) | 15.089995 / 8.074308 (7.015687) | 14.777003 / 10.191392 (4.585611) | 0.169873 / 0.680424 (-0.510551) | 0.029233 / 0.534201 (-0.504968) | 0.445424 / 0.579283 (-0.133859) | 0.439194 / 0.434364 (0.004830) | 0.536370 / 0.540337 (-0.003968) | 0.636694 / 1.386936 (-0.750242) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008230 / 0.011353 (-0.003122) | 0.005499 / 0.011008 (-0.005509) | 0.076108 / 0.038508 (0.037600) | 0.037444 / 0.023109 (0.014335) | 0.364420 / 0.275898 (0.088522) | 0.412308 / 0.323480 (0.088828) | 0.006704 / 0.007986 (-0.001282) | 0.004359 / 0.004328 (0.000031) | 0.075080 / 0.004250 (0.070830) | 0.057698 / 0.037052 (0.020646) | 0.366088 / 0.258489 (0.107599) | 0.409583 / 0.293841 (0.115742) | 0.037882 / 0.128546 (-0.090664) | 0.012421 / 0.075646 (-0.063225) | 0.087701 / 0.419271 (-0.331571) | 0.050669 / 0.043533 (0.007136) | 0.351139 / 0.255139 (0.096000) | 0.384340 / 0.283200 (0.101140) | 0.108097 / 0.141683 (-0.033586) | 1.445010 / 1.452155 (-0.007145) | 1.559570 / 1.492716 (0.066853) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.324114 / 0.018006 (0.306108) | 0.549134 / 0.000490 (0.548644) | 0.003544 / 0.000200 (0.003344) | 0.000097 / 0.000054 (0.000042) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030646 / 0.037411 (-0.006765) | 0.108573 / 0.014526 (0.094047) | 0.125291 / 0.176557 (-0.051266) | 0.174798 / 0.737135 (-0.562338) | 0.128000 / 0.296338 (-0.168338) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.428881 / 0.215209 (0.213672) | 4.282320 / 2.077655 (2.204665) | 2.061462 / 1.504120 (0.557342) | 1.858477 / 1.541195 (0.317283) | 1.971646 / 1.468490 (0.503156) | 0.723631 / 4.584777 (-3.861146) | 3.822376 / 3.745712 (0.076664) | 2.174427 / 5.269862 (-3.095434) | 1.386066 / 4.565676 (-3.179611) | 0.088391 / 0.424275 (-0.335884) | 0.012948 / 0.007607 (0.005341) | 0.524423 / 0.226044 (0.298378) | 5.249389 / 2.268929 (2.980460) | 2.528662 / 55.444624 (-52.915962) | 2.245329 / 6.876477 (-4.631147) | 2.402733 / 2.142072 (0.260660) | 0.868864 / 4.805227 (-3.936364) | 0.174066 / 6.500664 (-6.326598) | 0.066165 / 0.075469 (-0.009304) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.296922 / 1.841788 (-0.544865) | 15.814109 / 8.074308 (7.739801) | 14.086059 / 10.191392 (3.894667) | 0.190952 / 0.680424 (-0.489472) | 0.017679 / 0.534201 (-0.516522) | 0.428872 / 0.579283 (-0.150411) | 0.435399 / 0.434364 (0.001035) | 0.540856 / 0.540337 (0.000519) | 0.648904 / 1.386936 (-0.738032) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f401758c5019ede4404994d5d59220125984874d \"CML watermark\")\n" ]

"2023-02-08T13:38:59Z"

"2023-02-19T18:35:09Z"

"2023-02-19T18:27:29Z"

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[ "To give more context: after our discussion, if I understood properly, you are trying to fix a call to `datasets` that takes 15 minutes: https://github.com/huggingface/datasets-preview-backend/issues/17 Is this right?\r\n\r\n", "Yes, that's it. And to do that I'm trying to use https://pypi.org/project/stopit/, which will raise a stopit.TimeoutException exception. But currently, if this exception is raised, it's caught and considered as a \"FileNotFoundError\" while it should not be caught. ", "And what about passing the `timeout` parameter instead?", "It might be a good idea, but I would have to add a timeout argument to several methods, I'm not sure we want that (I want to ensure all my queries in https://github.com/huggingface/datasets-preview-backend/tree/master/src/datasets_preview_backend/queries resolve in a given time, be it with an error in case of timeout, or with the successful response). The methods are `prepare_module`, `import_main_class`, *builder_cls.*`get_all_exported_dataset_infos`, `load_dataset_builder`, and `load_dataset`", "I understand, you are trying to find a fix for your use case. OK.\r\n\r\nJust note that it is also an issue for `datasets` users. Once #2859 fixed in `datasets`, you will no longer have this issue...", "Closing, since 1. my problem is more #2859, and I was asking for that change in order to make a hack work on my side, 2. if we want to change how exceptions are handled, we surely want to do it on all the codebase, not only in this particular case." ]

"2021-09-01T12:18:12Z"

"2021-09-02T09:53:36Z"

"2021-09-02T09:52:03Z"

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008340 / 0.011353 (-0.003013) | 0.005053 / 0.011008 (-0.005955) | 0.103294 / 0.038508 (0.064786) | 0.069417 / 0.023109 (0.046308) | 0.436922 / 0.275898 (0.161024) | 0.461348 / 0.323480 (0.137868) | 0.006030 / 0.007986 (-0.001955) | 0.003727 / 0.004328 (-0.000601) | 0.076384 / 0.004250 (0.072134) | 0.056742 / 0.037052 (0.019689) | 0.439996 / 0.258489 (0.181507) | 0.469417 / 0.293841 (0.175577) | 0.044343 / 0.128546 (-0.084203) | 0.012634 / 0.075646 (-0.063013) | 0.359746 / 0.419271 (-0.059525) | 0.064842 / 0.043533 (0.021309) | 0.425960 / 0.255139 (0.170821) | 0.458568 / 0.283200 (0.175368) | 0.039802 / 0.141683 (-0.101881) | 1.687320 / 1.452155 (0.235165) | 1.806212 / 1.492716 (0.313496) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.255484 / 0.018006 (0.237478) | 0.563039 / 0.000490 (0.562549) | 0.000445 / 0.000200 (0.000245) | 0.000076 / 0.000054 (0.000022) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027511 / 0.037411 (-0.009900) | 0.089185 / 0.014526 (0.074659) | 0.098397 / 0.176557 (-0.078160) | 0.163897 / 0.737135 (-0.573238) | 0.099905 / 0.296338 (-0.196434) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.612737 / 0.215209 (0.397528) | 6.209948 / 2.077655 (4.132294) | 2.756060 / 1.504120 (1.251940) | 2.402115 / 1.541195 (0.860920) | 2.422665 / 1.468490 (0.954175) | 0.834799 / 4.584777 (-3.749977) | 5.251699 / 3.745712 (1.505986) | 5.554141 / 5.269862 (0.284280) | 3.254699 / 4.565676 (-1.310977) | 0.095697 / 0.424275 (-0.328578) | 0.009406 / 0.007607 (0.001799) | 0.729025 / 0.226044 (0.502980) | 7.195521 / 2.268929 (4.926593) | 3.360264 / 55.444624 (-52.084361) | 2.696764 / 6.876477 (-4.179713) | 2.702796 / 2.142072 (0.560724) | 0.974420 / 4.805227 (-3.830808) | 0.195215 / 6.500664 (-6.305450) | 0.069754 / 0.075469 (-0.005715) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.553458 / 1.841788 (-0.288330) | 21.972436 / 8.074308 (13.898128) | 20.027392 / 10.191392 (9.836000) | 0.216950 / 0.680424 (-0.463474) | 0.032196 / 0.534201 (-0.502005) | 0.449884 / 0.579283 (-0.129399) | 0.586213 / 0.434364 (0.151849) | 0.537227 / 0.540337 (-0.003111) | 0.751022 / 1.386936 (-0.635914) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007859 / 0.011353 (-0.003493) | 0.004762 / 0.011008 (-0.006246) | 0.086023 / 0.038508 (0.047515) | 0.069218 / 0.023109 (0.046109) | 0.449312 / 0.275898 (0.173414) | 0.481687 / 0.323480 (0.158207) | 0.006318 / 0.007986 (-0.001668) | 0.004063 / 0.004328 (-0.000266) | 0.076917 / 0.004250 (0.072667) | 0.058034 / 0.037052 (0.020981) | 0.474265 / 0.258489 (0.215775) | 0.497736 / 0.293841 (0.203895) | 0.044587 / 0.128546 (-0.083959) | 0.013880 / 0.075646 (-0.061766) | 0.089233 / 0.419271 (-0.330038) | 0.058760 / 0.043533 (0.015227) | 0.439515 / 0.255139 (0.184376) | 0.473246 / 0.283200 (0.190047) | 0.042968 / 0.141683 (-0.098715) | 1.802647 / 1.452155 (0.350493) | 1.778563 / 1.492716 (0.285847) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.343741 / 0.018006 (0.325735) | 0.567409 / 0.000490 (0.566919) | 0.029727 / 0.000200 (0.029527) | 0.000147 / 0.000054 (0.000092) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031021 / 0.037411 (-0.006390) | 0.096659 / 0.014526 (0.082133) | 0.103341 / 0.176557 (-0.073215) | 0.169893 / 0.737135 (-0.567242) | 0.103280 / 0.296338 (-0.193058) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.584724 / 0.215209 (0.369515) | 5.792596 / 2.077655 (3.714941) | 2.683133 / 1.504120 (1.179013) | 2.367837 / 1.541195 (0.826643) | 2.378567 / 1.468490 (0.910076) | 0.803427 / 4.584777 (-3.781350) | 5.179017 / 3.745712 (1.433305) | 4.446323 / 5.269862 (-0.823538) | 2.771731 / 4.565676 (-1.793945) | 0.100943 / 0.424275 (-0.323332) | 0.009875 / 0.007607 (0.002268) | 0.725260 / 0.226044 (0.499216) | 7.149728 / 2.268929 (4.880800) | 3.646438 / 55.444624 (-51.798187) | 2.793858 / 6.876477 (-4.082618) | 2.971966 / 2.142072 (0.829894) | 0.998147 / 4.805227 (-3.807080) | 0.198004 / 6.500664 (-6.302660) | 0.072581 / 0.075469 (-0.002888) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.696737 / 1.841788 (-0.145051) | 22.615193 / 8.074308 (14.540884) | 20.272421 / 10.191392 (10.081029) | 0.237459 / 0.680424 (-0.442965) | 0.034774 / 0.534201 (-0.499427) | 0.484649 / 0.579283 (-0.094634) | 0.590263 / 0.434364 (0.155899) | 0.547833 / 0.540337 (0.007495) | 0.762109 / 1.386936 (-0.624827) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4bc3628b5a8f71ad7cfc014d8ba5e798f26becb7 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.011183 / 0.011353 (-0.000170) | 0.005267 / 0.011008 (-0.005741) | 0.108506 / 0.038508 (0.069997) | 0.083541 / 0.023109 (0.060431) | 0.452189 / 0.275898 (0.176291) | 0.496229 / 0.323480 (0.172749) | 0.004951 / 0.007986 (-0.003035) | 0.004452 / 0.004328 (0.000124) | 0.085133 / 0.004250 (0.080883) | 0.061291 / 0.037052 (0.024239) | 0.450453 / 0.258489 (0.191964) | 0.506456 / 0.293841 (0.212616) | 0.049784 / 0.128546 (-0.078762) | 0.014738 / 0.075646 (-0.060908) | 0.372603 / 0.419271 (-0.046669) | 0.065223 / 0.043533 (0.021690) | 0.467872 / 0.255139 (0.212733) | 0.500062 / 0.283200 (0.216862) | 0.040911 / 0.141683 (-0.100772) | 1.852970 / 1.452155 (0.400816) | 2.016996 / 1.492716 (0.524280) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.262620 / 0.018006 (0.244614) | 0.593925 / 0.000490 (0.593435) | 0.000413 / 0.000200 (0.000213) | 0.000085 / 0.000054 (0.000030) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035713 / 0.037411 (-0.001698) | 0.111403 / 0.014526 (0.096878) | 0.117259 / 0.176557 (-0.059298) | 0.201545 / 0.737135 (-0.535590) | 0.133111 / 0.296338 (-0.163228) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.597318 / 0.215209 (0.382109) | 5.882691 / 2.077655 (3.805036) | 2.572203 / 1.504120 (1.068083) | 2.248016 / 1.541195 (0.706821) | 2.359103 / 1.468490 (0.890613) | 0.852023 / 4.584777 (-3.732754) | 5.270831 / 3.745712 (1.525119) | 4.712915 / 5.269862 (-0.556947) | 3.124295 / 4.565676 (-1.441381) | 0.092045 / 0.424275 (-0.332230) | 0.007834 / 0.007607 (0.000227) | 0.695711 / 0.226044 (0.469666) | 7.011760 / 2.268929 (4.742831) | 3.333300 / 55.444624 (-52.111325) | 2.745889 / 6.876477 (-4.130587) | 3.153458 / 2.142072 (1.011385) | 1.011089 / 4.805227 (-3.794139) | 0.207467 / 6.500664 (-6.293197) | 0.079802 / 0.075469 (0.004333) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.703784 / 1.841788 (-0.138003) | 24.414340 / 8.074308 (16.340032) | 22.534528 / 10.191392 (12.343136) | 0.276129 / 0.680424 (-0.404295) | 0.027954 / 0.534201 (-0.506247) | 0.484261 / 0.579283 (-0.095022) | 0.605316 / 0.434364 (0.170952) | 0.557219 / 0.540337 (0.016882) | 0.802209 / 1.386936 (-0.584727) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009109 / 0.011353 (-0.002244) | 0.005376 / 0.011008 (-0.005632) | 0.085141 / 0.038508 (0.046633) | 0.100560 / 0.023109 (0.077450) | 0.482673 / 0.275898 (0.206775) | 0.551582 / 0.323480 (0.228103) | 0.006756 / 0.007986 (-0.001229) | 0.004171 / 0.004328 (-0.000158) | 0.084184 / 0.004250 (0.079933) | 0.069283 / 0.037052 (0.032230) | 0.517722 / 0.258489 (0.259233) | 0.542641 / 0.293841 (0.248801) | 0.047790 / 0.128546 (-0.080756) | 0.014063 / 0.075646 (-0.061583) | 0.110591 / 0.419271 (-0.308680) | 0.064373 / 0.043533 (0.020840) | 0.496636 / 0.255139 (0.241497) | 0.551906 / 0.283200 (0.268707) | 0.046187 / 0.141683 (-0.095496) | 1.864836 / 1.452155 (0.412681) | 1.923765 / 1.492716 (0.431049) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.286558 / 0.018006 (0.268552) | 0.610353 / 0.000490 (0.609863) | 0.012647 / 0.000200 (0.012447) | 0.000162 / 0.000054 (0.000107) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.037099 / 0.037411 (-0.000313) | 0.108608 / 0.014526 (0.094082) | 0.120386 / 0.176557 (-0.056170) | 0.183450 / 0.737135 (-0.553686) | 0.124860 / 0.296338 (-0.171479) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.629006 / 0.215209 (0.413797) | 6.309206 / 2.077655 (4.231551) | 2.878558 / 1.504120 (1.374438) | 2.616093 / 1.541195 (1.074898) | 2.668096 / 1.468490 (1.199606) | 0.865732 / 4.584777 (-3.719045) | 5.312433 / 3.745712 (1.566721) | 4.799352 / 5.269862 (-0.470509) | 3.142207 / 4.565676 (-1.423469) | 0.099591 / 0.424275 (-0.324684) | 0.009159 / 0.007607 (0.001552) | 0.730999 / 0.226044 (0.504954) | 7.486442 / 2.268929 (5.217513) | 3.657699 / 55.444624 (-51.786925) | 3.080094 / 6.876477 (-3.796383) | 3.320976 / 2.142072 (1.178904) | 1.089324 / 4.805227 (-3.715904) | 0.222831 / 6.500664 (-6.277833) | 0.083976 / 0.075469 (0.008507) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.793181 / 1.841788 (-0.048607) | 25.307444 / 8.074308 (17.233136) | 21.321713 / 10.191392 (11.130321) | 0.216326 / 0.680424 (-0.464098) | 0.034298 / 0.534201 (-0.499903) | 0.497173 / 0.579283 (-0.082110) | 0.643550 / 0.434364 (0.209186) | 0.581213 / 0.540337 (0.040876) | 0.830973 / 1.386936 (-0.555963) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#24875bb8494c3a7803182b08c70747b1b1a6bf4d \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006886 / 0.011353 (-0.004467) | 0.004267 / 0.011008 (-0.006741) | 0.086182 / 0.038508 (0.047674) | 0.083405 / 0.023109 (0.060296) | 0.313717 / 0.275898 (0.037819) | 0.351476 / 0.323480 (0.027996) | 0.005702 / 0.007986 (-0.002284) | 0.003802 / 0.004328 (-0.000526) | 0.065759 / 0.004250 (0.061508) | 0.060056 / 0.037052 (0.023003) | 0.315871 / 0.258489 (0.057382) | 0.364520 / 0.293841 (0.070679) | 0.032067 / 0.128546 (-0.096479) | 0.008679 / 0.075646 (-0.066967) | 0.294968 / 0.419271 (-0.124303) | 0.054684 / 0.043533 (0.011152) | 0.314124 / 0.255139 (0.058985) | 0.337312 / 0.283200 (0.054113) | 0.025051 / 0.141683 (-0.116632) | 1.505242 / 1.452155 (0.053087) | 1.608263 / 1.492716 (0.115547) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.266562 / 0.018006 (0.248556) | 0.579887 / 0.000490 (0.579397) | 0.004161 / 0.000200 (0.003961) | 0.000090 / 0.000054 (0.000035) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031153 / 0.037411 (-0.006258) | 0.087703 / 0.014526 (0.073177) | 0.103864 / 0.176557 (-0.072693) | 0.159032 / 0.737135 (-0.578104) | 0.102482 / 0.296338 (-0.193857) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.405805 / 0.215209 (0.190596) | 4.050669 / 2.077655 (1.973014) | 2.064384 / 1.504120 (0.560264) | 1.892825 / 1.541195 (0.351630) | 2.001083 / 1.468490 (0.532593) | 0.478174 / 4.584777 (-4.106603) | 3.542580 / 3.745712 (-0.203132) | 3.319205 / 5.269862 (-1.950656) | 2.075868 / 4.565676 (-2.489808) | 0.057345 / 0.424275 (-0.366930) | 0.007459 / 0.007607 (-0.000148) | 0.483564 / 0.226044 (0.257520) | 4.827746 / 2.268929 (2.558818) | 2.579541 / 55.444624 (-52.865083) | 2.205125 / 6.876477 (-4.671352) | 2.489206 / 2.142072 (0.347133) | 0.575843 / 4.805227 (-4.229384) | 0.133010 / 6.500664 (-6.367654) | 0.061082 / 0.075469 (-0.014387) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.286059 / 1.841788 (-0.555729) | 20.575173 / 8.074308 (12.500865) | 14.351692 / 10.191392 (4.160300) | 0.150401 / 0.680424 (-0.530022) | 0.018678 / 0.534201 (-0.515523) | 0.397860 / 0.579283 (-0.181423) | 0.419474 / 0.434364 (-0.014890) | 0.474492 / 0.540337 (-0.065846) | 0.659510 / 1.386936 (-0.727426) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006948 / 0.011353 (-0.004405) | 0.004305 / 0.011008 (-0.006703) | 0.064220 / 0.038508 (0.025712) | 0.083251 / 0.023109 (0.060142) | 0.388148 / 0.275898 (0.112250) | 0.417834 / 0.323480 (0.094354) | 0.005762 / 0.007986 (-0.002224) | 0.003803 / 0.004328 (-0.000525) | 0.066365 / 0.004250 (0.062114) | 0.061808 / 0.037052 (0.024756) | 0.390889 / 0.258489 (0.132400) | 0.430619 / 0.293841 (0.136778) | 0.031777 / 0.128546 (-0.096770) | 0.008781 / 0.075646 (-0.066865) | 0.070844 / 0.419271 (-0.348427) | 0.050552 / 0.043533 (0.007019) | 0.378420 / 0.255139 (0.123281) | 0.403273 / 0.283200 (0.120074) | 0.024578 / 0.141683 (-0.117105) | 1.494790 / 1.452155 (0.042636) | 1.549408 / 1.492716 (0.056692) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.302668 / 0.018006 (0.284662) | 0.542235 / 0.000490 (0.541746) | 0.001847 / 0.000200 (0.001647) | 0.000092 / 0.000054 (0.000037) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031947 / 0.037411 (-0.005465) | 0.092220 / 0.014526 (0.077694) | 0.104525 / 0.176557 (-0.072031) | 0.162000 / 0.737135 (-0.575135) | 0.106795 / 0.296338 (-0.189543) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.412035 / 0.215209 (0.196826) | 4.106527 / 2.077655 (2.028872) | 2.111529 / 1.504120 (0.607409) | 1.953201 / 1.541195 (0.412006) | 2.079258 / 1.468490 (0.610768) | 0.479562 / 4.584777 (-4.105215) | 3.606256 / 3.745712 (-0.139456) | 5.175250 / 5.269862 (-0.094612) | 3.292465 / 4.565676 (-1.273212) | 0.057726 / 0.424275 (-0.366549) | 0.008247 / 0.007607 (0.000640) | 0.486143 / 0.226044 (0.260098) | 4.859051 / 2.268929 (2.590123) | 2.675629 / 55.444624 (-52.768995) | 2.267448 / 6.876477 (-4.609029) | 2.567639 / 2.142072 (0.425567) | 0.580822 / 4.805227 (-4.224406) | 0.134942 / 6.500664 (-6.365722) | 0.063825 / 0.075469 (-0.011644) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.334421 / 1.841788 (-0.507367) | 20.481428 / 8.074308 (12.407120) | 14.227943 / 10.191392 (4.036551) | 0.170711 / 0.680424 (-0.509713) | 0.018212 / 0.534201 (-0.515989) | 0.397212 / 0.579283 (-0.182071) | 0.411934 / 0.434364 (-0.022430) | 0.478019 / 0.540337 (-0.062319) | 0.645434 / 1.386936 (-0.741502) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ef3d3f10886e23a65cce3bfd939b8ec0d5a5c2c1 \"CML watermark\")\n" ]

"2023-07-24T12:36:38Z"

"2023-07-24T14:43:48Z"

"2023-07-24T14:34:43Z"

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[ "Metrics are deprecated in `datasets` and `evaluate` should be used instead: https://github.com/huggingface/evaluate" ]

"2021-08-06T09:12:36Z"

"2023-07-11T09:35:23Z"

"2023-07-11T09:35:23Z"

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[ "> Really good thanks !\r\n> \r\n> I left a few comments\r\n\r\nThanks, resolved them :) ", "looks like this PR includes changes about many other files than the ones for Code Search\r\n\r\ncan you create another branch and another PR please ?", "> looks like this PR includes changes about many other files than the ones for Code Search\r\n> \r\n> can you create another branch and another PR please ?\r\n\r\nOkay sure" ]

"2020-12-06T14:12:39Z"

"2020-12-09T16:40:15Z"

"2020-12-09T16:40:15Z"

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"2021-02-17T15:53:56Z"

"2021-02-17T17:20:49Z"

"2021-02-17T17:20:49Z"

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[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006784 / 0.011353 (-0.004569) | 0.004051 / 0.011008 (-0.006957) | 0.083790 / 0.038508 (0.045282) | 0.081219 / 0.023109 (0.058110) | 0.313195 / 0.275898 (0.037297) | 0.336954 / 0.323480 (0.013475) | 0.004324 / 0.007986 (-0.003662) | 0.004516 / 0.004328 (0.000188) | 0.065051 / 0.004250 (0.060801) | 0.057647 / 0.037052 (0.020595) | 0.316675 / 0.258489 (0.058186) | 0.357936 / 0.293841 (0.064095) | 0.030980 / 0.128546 (-0.097566) | 0.008844 / 0.075646 (-0.066802) | 0.287027 / 0.419271 (-0.132245) | 0.052130 / 0.043533 (0.008597) | 0.308125 / 0.255139 (0.052986) | 0.337345 / 0.283200 (0.054145) | 0.025781 / 0.141683 (-0.115902) | 1.466161 / 1.452155 (0.014006) | 1.565824 / 1.492716 (0.073108) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.299112 / 0.018006 (0.281106) | 0.640520 / 0.000490 (0.640030) | 0.008846 / 0.000200 (0.008647) | 0.000273 / 0.000054 (0.000219) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029853 / 0.037411 (-0.007559) | 0.081697 / 0.014526 (0.067172) | 0.099110 / 0.176557 (-0.077447) | 0.155864 / 0.737135 (-0.581271) | 0.098749 / 0.296338 (-0.197590) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.385722 / 0.215209 (0.170512) | 3.851490 / 2.077655 (1.773835) | 1.851995 / 1.504120 (0.347875) | 1.660398 / 1.541195 (0.119204) | 1.769370 / 1.468490 (0.300879) | 0.481523 / 4.584777 (-4.103254) | 3.550449 / 3.745712 (-0.195263) | 3.424782 / 5.269862 (-1.845079) | 2.106470 / 4.565676 (-2.459206) | 0.056500 / 0.424275 (-0.367775) | 0.007891 / 0.007607 (0.000284) | 0.465564 / 0.226044 (0.239520) | 4.662892 / 2.268929 (2.393964) | 2.305424 / 55.444624 (-53.139201) | 1.980524 / 6.876477 (-4.895953) | 2.218423 / 2.142072 (0.076350) | 0.584662 / 4.805227 (-4.220565) | 0.132325 / 6.500664 (-6.368340) | 0.060773 / 0.075469 (-0.014696) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.254261 / 1.841788 (-0.587527) | 19.479805 / 8.074308 (11.405497) | 14.222687 / 10.191392 (4.031295) | 0.149829 / 0.680424 (-0.530595) | 0.018630 / 0.534201 (-0.515571) | 0.395284 / 0.579283 (-0.183999) | 0.413385 / 0.434364 (-0.020978) | 0.462931 / 0.540337 (-0.077406) | 0.645359 / 1.386936 (-0.741577) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006991 / 0.011353 (-0.004362) | 0.004306 / 0.011008 (-0.006702) | 0.065213 / 0.038508 (0.026705) | 0.082442 / 0.023109 (0.059332) | 0.411294 / 0.275898 (0.135396) | 0.452176 / 0.323480 (0.128696) | 0.005802 / 0.007986 (-0.002183) | 0.003556 / 0.004328 (-0.000772) | 0.066163 / 0.004250 (0.061913) | 0.060680 / 0.037052 (0.023628) | 0.416975 / 0.258489 (0.158486) | 0.456353 / 0.293841 (0.162512) | 0.033584 / 0.128546 (-0.094963) | 0.008687 / 0.075646 (-0.066959) | 0.071300 / 0.419271 (-0.347972) | 0.049382 / 0.043533 (0.005849) | 0.409329 / 0.255139 (0.154190) | 0.434829 / 0.283200 (0.151629) | 0.022966 / 0.141683 (-0.118716) | 1.493847 / 1.452155 (0.041692) | 1.582372 / 1.492716 (0.089656) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.280578 / 0.018006 (0.262572) | 0.538122 / 0.000490 (0.537632) | 0.004515 / 0.000200 (0.004315) | 0.000098 / 0.000054 (0.000043) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033383 / 0.037411 (-0.004028) | 0.093426 / 0.014526 (0.078901) | 0.109314 / 0.176557 (-0.067242) | 0.162349 / 0.737135 (-0.574786) | 0.109849 / 0.296338 (-0.186490) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.431073 / 0.215209 (0.215864) | 4.311942 / 2.077655 (2.234287) | 2.291170 / 1.504120 (0.787051) | 2.132266 / 1.541195 (0.591072) | 2.236526 / 1.468490 (0.768036) | 0.492001 / 4.584777 (-4.092776) | 3.523013 / 3.745712 (-0.222699) | 3.413481 / 5.269862 (-1.856381) | 2.112979 / 4.565676 (-2.452698) | 0.058654 / 0.424275 (-0.365621) | 0.007729 / 0.007607 (0.000121) | 0.512027 / 0.226044 (0.285982) | 5.125264 / 2.268929 (2.856336) | 2.836281 / 55.444624 (-52.608344) | 2.447253 / 6.876477 (-4.429224) | 2.711908 / 2.142072 (0.569835) | 0.592598 / 4.805227 (-4.212629) | 0.134837 / 6.500664 (-6.365827) | 0.059813 / 0.075469 (-0.015656) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.373464 / 1.841788 (-0.468323) | 20.548983 / 8.074308 (12.474675) | 14.799833 / 10.191392 (4.608441) | 0.168601 / 0.680424 (-0.511823) | 0.020358 / 0.534201 (-0.513843) | 0.398790 / 0.579283 (-0.180494) | 0.416921 / 0.434364 (-0.017443) | 0.480542 / 0.540337 (-0.059795) | 0.645062 / 1.386936 (-0.741874) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#afd6fc193a91cb0461c8bf3b64db6943c23de846 \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008616 / 0.011353 (-0.002737) | 0.004957 / 0.011008 (-0.006051) | 0.102629 / 0.038508 (0.064121) | 0.080492 / 0.023109 (0.057383) | 0.461817 / 0.275898 (0.185919) | 0.487964 / 0.323480 (0.164484) | 0.006336 / 0.007986 (-0.001649) | 0.004607 / 0.004328 (0.000278) | 0.074311 / 0.004250 (0.070061) | 0.060368 / 0.037052 (0.023315) | 0.458076 / 0.258489 (0.199587) | 0.493028 / 0.293841 (0.199187) | 0.044153 / 0.128546 (-0.084394) | 0.014066 / 0.075646 (-0.061581) | 0.369848 / 0.419271 (-0.049424) | 0.061690 / 0.043533 (0.018157) | 0.439728 / 0.255139 (0.184590) | 0.484706 / 0.283200 (0.201506) | 0.034657 / 0.141683 (-0.107026) | 1.710591 / 1.452155 (0.258437) | 1.900225 / 1.492716 (0.407509) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.308837 / 0.018006 (0.290831) | 0.579561 / 0.000490 (0.579072) | 0.010163 / 0.000200 (0.009963) | 0.000613 / 0.000054 (0.000558) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028108 / 0.037411 (-0.009303) | 0.085072 / 0.014526 (0.070546) | 0.103375 / 0.176557 (-0.073182) | 0.173765 / 0.737135 (-0.563371) | 0.102460 / 0.296338 (-0.193879) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.602642 / 0.215209 (0.387433) | 5.582537 / 2.077655 (3.504882) | 2.405553 / 1.504120 (0.901434) | 2.057298 / 1.541195 (0.516103) | 2.223787 / 1.468490 (0.755297) | 0.846138 / 4.584777 (-3.738638) | 5.290306 / 3.745712 (1.544594) | 4.836066 / 5.269862 (-0.433795) | 2.951901 / 4.565676 (-1.613775) | 0.099432 / 0.424275 (-0.324843) | 0.009198 / 0.007607 (0.001591) | 0.731370 / 0.226044 (0.505325) | 6.663026 / 2.268929 (4.394098) | 3.200932 / 55.444624 (-52.243692) | 2.486654 / 6.876477 (-4.389823) | 2.833195 / 2.142072 (0.691123) | 0.989481 / 4.805227 (-3.815746) | 0.205176 / 6.500664 (-6.295488) | 0.073760 / 0.075469 (-0.001709) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.745494 / 1.841788 (-0.096294) | 24.649294 / 8.074308 (16.574986) | 22.312182 / 10.191392 (12.120790) | 0.245207 / 0.680424 (-0.435217) | 0.031971 / 0.534201 (-0.502230) | 0.495179 / 0.579283 (-0.084104) | 0.603233 / 0.434364 (0.168869) | 0.560906 / 0.540337 (0.020569) | 0.788292 / 1.386936 (-0.598644) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008922 / 0.011353 (-0.002431) | 0.005203 / 0.011008 (-0.005805) | 0.074414 / 0.038508 (0.035906) | 0.077552 / 0.023109 (0.054443) | 0.547217 / 0.275898 (0.271319) | 0.625298 / 0.323480 (0.301818) | 0.006135 / 0.007986 (-0.001851) | 0.004163 / 0.004328 (-0.000165) | 0.078014 / 0.004250 (0.073764) | 0.064484 / 0.037052 (0.027431) | 0.562356 / 0.258489 (0.303867) | 0.643613 / 0.293841 (0.349772) | 0.050155 / 0.128546 (-0.078391) | 0.013665 / 0.075646 (-0.061981) | 0.090224 / 0.419271 (-0.329048) | 0.063852 / 0.043533 (0.020319) | 0.560914 / 0.255139 (0.305775) | 0.591531 / 0.283200 (0.308331) | 0.036491 / 0.141683 (-0.105192) | 1.670898 / 1.452155 (0.218743) | 1.783924 / 1.492716 (0.291208) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.312764 / 0.018006 (0.294758) | 0.611116 / 0.000490 (0.610626) | 0.006367 / 0.000200 (0.006167) | 0.000130 / 0.000054 (0.000075) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033967 / 0.037411 (-0.003445) | 0.101550 / 0.014526 (0.087025) | 0.116953 / 0.176557 (-0.059604) | 0.180061 / 0.737135 (-0.557075) | 0.115220 / 0.296338 (-0.181118) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.642110 / 0.215209 (0.426901) | 6.361381 / 2.077655 (4.283727) | 2.948175 / 1.504120 (1.444055) | 2.633935 / 1.541195 (1.092740) | 2.822150 / 1.468490 (1.353660) | 0.931412 / 4.584777 (-3.653365) | 5.428540 / 3.745712 (1.682828) | 4.672920 / 5.269862 (-0.596941) | 3.102046 / 4.565676 (-1.463630) | 0.100825 / 0.424275 (-0.323450) | 0.009464 / 0.007607 (0.001857) | 0.774102 / 0.226044 (0.548058) | 7.715003 / 2.268929 (5.446074) | 3.987807 / 55.444624 (-51.456817) | 3.089129 / 6.876477 (-3.787347) | 3.333247 / 2.142072 (1.191174) | 1.012427 / 4.805227 (-3.792800) | 0.200662 / 6.500664 (-6.300002) | 0.072422 / 0.075469 (-0.003047) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.680364 / 1.841788 (-0.161424) | 24.484576 / 8.074308 (16.410268) | 21.920990 / 10.191392 (11.729598) | 0.218604 / 0.680424 (-0.461820) | 0.035818 / 0.534201 (-0.498383) | 0.470648 / 0.579283 (-0.108635) | 0.585108 / 0.434364 (0.150744) | 0.539152 / 0.540337 (-0.001185) | 0.763999 / 1.386936 (-0.622937) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#cfed1d09ed6c680085624d96eb99bfb2b0b27599 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006304 / 0.011353 (-0.005049) | 0.003884 / 0.011008 (-0.007125) | 0.084847 / 0.038508 (0.046339) | 0.069372 / 0.023109 (0.046263) | 0.318876 / 0.275898 (0.042978) | 0.344733 / 0.323480 (0.021253) | 0.005139 / 0.007986 (-0.002847) | 0.003203 / 0.004328 (-0.001125) | 0.065758 / 0.004250 (0.061507) | 0.054189 / 0.037052 (0.017137) | 0.317475 / 0.258489 (0.058986) | 0.359310 / 0.293841 (0.065469) | 0.030639 / 0.128546 (-0.097908) | 0.008657 / 0.075646 (-0.066989) | 0.289127 / 0.419271 (-0.130144) | 0.052344 / 0.043533 (0.008811) | 0.316122 / 0.255139 (0.060983) | 0.338339 / 0.283200 (0.055140) | 0.022677 / 0.141683 (-0.119006) | 1.551629 / 1.452155 (0.099474) | 1.617917 / 1.492716 (0.125201) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.231067 / 0.018006 (0.213061) | 0.450559 / 0.000490 (0.450070) | 0.008484 / 0.000200 (0.008284) | 0.000234 / 0.000054 (0.000179) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027054 / 0.037411 (-0.010357) | 0.081560 / 0.014526 (0.067034) | 0.094162 / 0.176557 (-0.082395) | 0.148583 / 0.737135 (-0.588552) | 0.093596 / 0.296338 (-0.202742) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.388616 / 0.215209 (0.173407) | 3.874905 / 2.077655 (1.797251) | 1.915845 / 1.504120 (0.411725) | 1.746410 / 1.541195 (0.205215) | 1.828789 / 1.468490 (0.360299) | 0.483270 / 4.584777 (-4.101506) | 3.489157 / 3.745712 (-0.256555) | 3.190086 / 5.269862 (-2.079776) | 1.978023 / 4.565676 (-2.587653) | 0.056290 / 0.424275 (-0.367985) | 0.007585 / 0.007607 (-0.000022) | 0.467051 / 0.226044 (0.241007) | 4.665971 / 2.268929 (2.397043) | 2.418550 / 55.444624 (-53.026075) | 2.048338 / 6.876477 (-4.828139) | 2.225275 / 2.142072 (0.083203) | 0.576601 / 4.805227 (-4.228626) | 0.131960 / 6.500664 (-6.368704) | 0.060177 / 0.075469 (-0.015292) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.249797 / 1.841788 (-0.591991) | 18.552939 / 8.074308 (10.478631) | 14.016616 / 10.191392 (3.825224) | 0.162869 / 0.680424 (-0.517555) | 0.018105 / 0.534201 (-0.516096) | 0.394838 / 0.579283 (-0.184445) | 0.403378 / 0.434364 (-0.030986) | 0.460931 / 0.540337 (-0.079407) | 0.637365 / 1.386936 (-0.749571) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006497 / 0.011353 (-0.004856) | 0.003928 / 0.011008 (-0.007080) | 0.063958 / 0.038508 (0.025450) | 0.069609 / 0.023109 (0.046500) | 0.401599 / 0.275898 (0.125701) | 0.428128 / 0.323480 (0.104648) | 0.005296 / 0.007986 (-0.002689) | 0.003332 / 0.004328 (-0.000996) | 0.063903 / 0.004250 (0.059652) | 0.056303 / 0.037052 (0.019250) | 0.400704 / 0.258489 (0.142214) | 0.435982 / 0.293841 (0.142141) | 0.032434 / 0.128546 (-0.096112) | 0.008570 / 0.075646 (-0.067077) | 0.070788 / 0.419271 (-0.348483) | 0.048252 / 0.043533 (0.004719) | 0.403269 / 0.255139 (0.148130) | 0.419796 / 0.283200 (0.136596) | 0.022598 / 0.141683 (-0.119085) | 1.481627 / 1.452155 (0.029472) | 1.578388 / 1.492716 (0.085672) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.224552 / 0.018006 (0.206546) | 0.444059 / 0.000490 (0.443570) | 0.003757 / 0.000200 (0.003557) | 0.000225 / 0.000054 (0.000171) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032173 / 0.037411 (-0.005239) | 0.092562 / 0.014526 (0.078036) | 0.104972 / 0.176557 (-0.071584) | 0.156467 / 0.737135 (-0.580669) | 0.104274 / 0.296338 (-0.192065) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.441693 / 0.215209 (0.226484) | 4.400217 / 2.077655 (2.322562) | 2.393862 / 1.504120 (0.889742) | 2.281178 / 1.541195 (0.739983) | 2.339895 / 1.468490 (0.871405) | 0.488734 / 4.584777 (-4.096043) | 3.523352 / 3.745712 (-0.222360) | 3.216761 / 5.269862 (-2.053101) | 2.007553 / 4.565676 (-2.558123) | 0.058050 / 0.424275 (-0.366225) | 0.007566 / 0.007607 (-0.000041) | 0.515439 / 0.226044 (0.289394) | 5.155086 / 2.268929 (2.886157) | 2.864958 / 55.444624 (-52.579666) | 2.592460 / 6.876477 (-4.284016) | 2.800449 / 2.142072 (0.658376) | 0.588441 / 4.805227 (-4.216786) | 0.131589 / 6.500664 (-6.369075) | 0.059075 / 0.075469 (-0.016394) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.353889 / 1.841788 (-0.487898) | 18.938285 / 8.074308 (10.863977) | 14.937141 / 10.191392 (4.745749) | 0.168811 / 0.680424 (-0.511613) | 0.020118 / 0.534201 (-0.514083) | 0.394791 / 0.579283 (-0.184492) | 0.414434 / 0.434364 (-0.019930) | 0.466821 / 0.540337 (-0.073517) | 0.629894 / 1.386936 (-0.757042) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#23921b08390db7dbb3186a8de40dc49a4066da76 \"CML watermark\")\n", "CI failures are unrelated", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005959 / 0.011353 (-0.005394) | 0.004164 / 0.011008 (-0.006844) | 0.082336 / 0.038508 (0.043828) | 0.070344 / 0.023109 (0.047234) | 0.348032 / 0.275898 (0.072134) | 0.366328 / 0.323480 (0.042848) | 0.003882 / 0.007986 (-0.004104) | 0.003619 / 0.004328 (-0.000709) | 0.063343 / 0.004250 (0.059093) | 0.056617 / 0.037052 (0.019564) | 0.351625 / 0.258489 (0.093136) | 0.395839 / 0.293841 (0.101998) | 0.030842 / 0.128546 (-0.097704) | 0.008363 / 0.075646 (-0.067284) | 0.300535 / 0.419271 (-0.118737) | 0.053303 / 0.043533 (0.009770) | 0.354782 / 0.255139 (0.099643) | 0.364918 / 0.283200 (0.081719) | 0.025365 / 0.141683 (-0.116318) | 1.555009 / 1.452155 (0.102854) | 1.597443 / 1.492716 (0.104727) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.239808 / 0.018006 (0.221801) | 0.488164 / 0.000490 (0.487675) | 0.013183 / 0.000200 (0.012983) | 0.000483 / 0.000054 (0.000429) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027938 / 0.037411 (-0.009473) | 0.078521 / 0.014526 (0.063995) | 0.095498 / 0.176557 (-0.081059) | 0.150884 / 0.737135 (-0.586251) | 0.097577 / 0.296338 (-0.198762) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.384546 / 0.215209 (0.169337) | 4.037707 / 2.077655 (1.960053) | 1.940321 / 1.504120 (0.436201) | 1.716741 / 1.541195 (0.175546) | 1.837200 / 1.468490 (0.368710) | 0.502112 / 4.584777 (-4.082665) | 3.770452 / 3.745712 (0.024740) | 3.325691 / 5.269862 (-1.944171) | 2.015622 / 4.565676 (-2.550055) | 0.056246 / 0.424275 (-0.368029) | 0.007320 / 0.007607 (-0.000287) | 0.445553 / 0.226044 (0.219509) | 4.567233 / 2.268929 (2.298304) | 2.319531 / 55.444624 (-53.125093) | 1.968664 / 6.876477 (-4.907813) | 2.122349 / 2.142072 (-0.019724) | 0.573688 / 4.805227 (-4.231540) | 0.131410 / 6.500664 (-6.369254) | 0.062767 / 0.075469 (-0.012702) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.255244 / 1.841788 (-0.586543) | 19.042480 / 8.074308 (10.968172) | 13.935342 / 10.191392 (3.743950) | 0.161259 / 0.680424 (-0.519165) | 0.020582 / 0.534201 (-0.513619) | 0.391365 / 0.579283 (-0.187918) | 0.417462 / 0.434364 (-0.016902) | 0.473121 / 0.540337 (-0.067216) | 0.674768 / 1.386936 (-0.712168) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006299 / 0.011353 (-0.005054) | 0.003969 / 0.011008 (-0.007040) | 0.063558 / 0.038508 (0.025050) | 0.073847 / 0.023109 (0.050738) | 0.407064 / 0.275898 (0.131166) | 0.440695 / 0.323480 (0.117215) | 0.005783 / 0.007986 (-0.002203) | 0.003517 / 0.004328 (-0.000812) | 0.065721 / 0.004250 (0.061470) | 0.056390 / 0.037052 (0.019338) | 0.419019 / 0.258489 (0.160530) | 0.450721 / 0.293841 (0.156880) | 0.034094 / 0.128546 (-0.094452) | 0.008594 / 0.075646 (-0.067052) | 0.069254 / 0.419271 (-0.350017) | 0.049218 / 0.043533 (0.005685) | 0.413312 / 0.255139 (0.158173) | 0.439454 / 0.283200 (0.156255) | 0.021481 / 0.141683 (-0.120202) | 1.517536 / 1.452155 (0.065382) | 1.530532 / 1.492716 (0.037815) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.235392 / 0.018006 (0.217386) | 0.477371 / 0.000490 (0.476881) | 0.007070 / 0.000200 (0.006870) | 0.000132 / 0.000054 (0.000077) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031909 / 0.037411 (-0.005502) | 0.092459 / 0.014526 (0.077933) | 0.105795 / 0.176557 (-0.070761) | 0.157745 / 0.737135 (-0.579390) | 0.104187 / 0.296338 (-0.192152) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.424385 / 0.215209 (0.209176) | 4.445371 / 2.077655 (2.367716) | 2.423639 / 1.504120 (0.919519) | 2.188167 / 1.541195 (0.646972) | 2.171023 / 1.468490 (0.702532) | 0.483566 / 4.584777 (-4.101211) | 3.825702 / 3.745712 (0.079990) | 3.276350 / 5.269862 (-1.993512) | 2.063075 / 4.565676 (-2.502602) | 0.061628 / 0.424275 (-0.362647) | 0.008176 / 0.007607 (0.000569) | 0.506697 / 0.226044 (0.280653) | 5.067924 / 2.268929 (2.798995) | 2.785567 / 55.444624 (-52.659057) | 2.457340 / 6.876477 (-4.419137) | 2.599646 / 2.142072 (0.457574) | 0.581550 / 4.805227 (-4.223677) | 0.131712 / 6.500664 (-6.368952) | 0.058776 / 0.075469 (-0.016693) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.356639 / 1.841788 (-0.485148) | 20.103463 / 8.074308 (12.029155) | 14.481010 / 10.191392 (4.289618) | 0.162870 / 0.680424 (-0.517554) | 0.023197 / 0.534201 (-0.511004) | 0.413042 / 0.579283 (-0.166241) | 0.427494 / 0.434364 (-0.006870) | 0.508457 / 0.540337 (-0.031880) | 0.662412 / 1.386936 (-0.724524) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#05fe5c06d42f84408b933c2809acb9b7449cbbb3 \"CML watermark\")\n" ]

"2023-09-15T14:23:33Z"

"2023-09-19T18:02:21Z"

"2023-09-19T17:53:17Z"

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"2022-06-30T10:59:58Z"

"2022-06-30T13:22:25Z"

"2022-06-30T13:22:25Z"

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[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008499 / 0.011353 (-0.002854) | 0.006155 / 0.011008 (-0.004853) | 0.124032 / 0.038508 (0.085524) | 0.037337 / 0.023109 (0.014228) | 0.389274 / 0.275898 (0.113376) | 0.427736 / 0.323480 (0.104257) | 0.006929 / 0.007986 (-0.001057) | 0.005017 / 0.004328 (0.000689) | 0.096356 / 0.004250 (0.092105) | 0.055694 / 0.037052 (0.018642) | 0.391417 / 0.258489 (0.132928) | 0.448098 / 0.293841 (0.154257) | 0.042442 / 0.128546 (-0.086105) | 0.013456 / 0.075646 (-0.062190) | 0.423502 / 0.419271 (0.004230) | 0.062919 / 0.043533 (0.019386) | 0.384317 / 0.255139 (0.129178) | 0.410851 / 0.283200 (0.127652) | 0.112807 / 0.141683 (-0.028875) | 1.746050 / 1.452155 (0.293895) | 1.977974 / 1.492716 (0.485257) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.306382 / 0.018006 (0.288375) | 0.620310 / 0.000490 (0.619820) | 0.009309 / 0.000200 (0.009109) | 0.000106 / 0.000054 (0.000052) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026900 / 0.037411 (-0.010511) | 0.140125 / 0.014526 (0.125599) | 0.136295 / 0.176557 (-0.040261) | 0.207721 / 0.737135 (-0.529414) | 0.146328 / 0.296338 (-0.150011) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.616712 / 0.215209 (0.401503) | 6.237820 / 2.077655 (4.160166) | 2.503809 / 1.504120 (0.999689) | 2.129739 / 1.541195 (0.588544) | 2.160768 / 1.468490 (0.692277) | 0.971273 / 4.584777 (-3.613504) | 5.687161 / 3.745712 (1.941449) | 2.738148 / 5.269862 (-2.531713) | 1.692695 / 4.565676 (-2.872981) | 0.113701 / 0.424275 (-0.310574) | 0.014809 / 0.007607 (0.007202) | 0.774795 / 0.226044 (0.548750) | 7.660012 / 2.268929 (5.391083) | 3.253036 / 55.444624 (-52.191588) | 2.607498 / 6.876477 (-4.268979) | 2.681678 / 2.142072 (0.539606) | 1.095275 / 4.805227 (-3.709952) | 0.239078 / 6.500664 (-6.261586) | 0.081034 / 0.075469 (0.005565) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.574547 / 1.841788 (-0.267240) | 18.323566 / 8.074308 (10.249258) | 19.274482 / 10.191392 (9.083090) | 0.210275 / 0.680424 (-0.470149) | 0.031843 / 0.534201 (-0.502358) | 0.514843 / 0.579283 (-0.064440) | 0.633782 / 0.434364 (0.199418) | 0.588569 / 0.540337 (0.048232) | 0.721401 / 1.386936 (-0.665535) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008866 / 0.011353 (-0.002487) | 0.006460 / 0.011008 (-0.004548) | 0.121337 / 0.038508 (0.082829) | 0.033896 / 0.023109 (0.010786) | 0.455702 / 0.275898 (0.179804) | 0.509685 / 0.323480 (0.186205) | 0.007650 / 0.007986 (-0.000336) | 0.005578 / 0.004328 (0.001250) | 0.098505 / 0.004250 (0.094255) | 0.056122 / 0.037052 (0.019069) | 0.478483 / 0.258489 (0.219994) | 0.560008 / 0.293841 (0.266167) | 0.044926 / 0.128546 (-0.083620) | 0.014562 / 0.075646 (-0.061085) | 0.115027 / 0.419271 (-0.304244) | 0.066494 / 0.043533 (0.022961) | 0.463434 / 0.255139 (0.208296) | 0.513856 / 0.283200 (0.230656) | 0.126436 / 0.141683 (-0.015247) | 1.874729 / 1.452155 (0.422575) | 1.925080 / 1.492716 (0.432364) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.012672 / 0.018006 (-0.005334) | 0.615797 / 0.000490 (0.615307) | 0.001606 / 0.000200 (0.001406) | 0.000118 / 0.000054 (0.000064) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031104 / 0.037411 (-0.006307) | 0.130107 / 0.014526 (0.115581) | 0.140587 / 0.176557 (-0.035970) | 0.205081 / 0.737135 (-0.532054) | 0.144068 / 0.296338 (-0.152270) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.646549 / 0.215209 (0.431340) | 6.403962 / 2.077655 (4.326307) | 2.812594 / 1.504120 (1.308474) | 2.478480 / 1.541195 (0.937285) | 2.552385 / 1.468490 (1.083895) | 0.991987 / 4.584777 (-3.592790) | 5.777917 / 3.745712 (2.032205) | 5.697830 / 5.269862 (0.427969) | 2.370583 / 4.565676 (-2.195094) | 0.109905 / 0.424275 (-0.314370) | 0.013801 / 0.007607 (0.006193) | 0.799932 / 0.226044 (0.573888) | 8.155672 / 2.268929 (5.886743) | 3.711662 / 55.444624 (-51.732963) | 3.042164 / 6.876477 (-3.834312) | 3.073549 / 2.142072 (0.931477) | 1.137515 / 4.805227 (-3.667712) | 0.231266 / 6.500664 (-6.269398) | 0.080893 / 0.075469 (0.005424) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.669210 / 1.841788 (-0.172577) | 18.747144 / 8.074308 (10.672836) | 21.084589 / 10.191392 (10.893197) | 0.241379 / 0.680424 (-0.439045) | 0.029473 / 0.534201 (-0.504728) | 0.524605 / 0.579283 (-0.054678) | 0.622852 / 0.434364 (0.188488) | 0.604941 / 0.540337 (0.064604) | 0.715978 / 1.386936 (-0.670958) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#142484a60b1330359d7713e906fc9e5e30aa9f64 \"CML watermark\")\n", "Cool ! what about `.github/workflows/build_pr_documentation.yml` and `.github/workflows/delete_doc_comment.yml` ?", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005973 / 0.011353 (-0.005380) | 0.004389 / 0.011008 (-0.006620) | 0.096076 / 0.038508 (0.057568) | 0.031569 / 0.023109 (0.008460) | 0.328300 / 0.275898 (0.052402) | 0.359356 / 0.323480 (0.035876) | 0.005378 / 0.007986 (-0.002607) | 0.003703 / 0.004328 (-0.000625) | 0.075251 / 0.004250 (0.071000) | 0.042340 / 0.037052 (0.005287) | 0.346103 / 0.258489 (0.087614) | 0.379896 / 0.293841 (0.086055) | 0.027493 / 0.128546 (-0.101053) | 0.009033 / 0.075646 (-0.066613) | 0.327829 / 0.419271 (-0.091442) | 0.064074 / 0.043533 (0.020541) | 0.337703 / 0.255139 (0.082564) | 0.355335 / 0.283200 (0.072136) | 0.101179 / 0.141683 (-0.040504) | 1.471738 / 1.452155 (0.019584) | 1.539031 / 1.492716 (0.046315) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.194097 / 0.018006 (0.176091) | 0.434190 / 0.000490 (0.433701) | 0.005730 / 0.000200 (0.005530) | 0.000088 / 0.000054 (0.000033) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025634 / 0.037411 (-0.011778) | 0.105080 / 0.014526 (0.090555) | 0.116508 / 0.176557 (-0.060049) | 0.173867 / 0.737135 (-0.563269) | 0.117749 / 0.296338 (-0.178590) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.401566 / 0.215209 (0.186357) | 4.003558 / 2.077655 (1.925903) | 1.802756 / 1.504120 (0.298636) | 1.604222 / 1.541195 (0.063027) | 1.656617 / 1.468490 (0.188127) | 0.523385 / 4.584777 (-4.061392) | 3.744292 / 3.745712 (-0.001420) | 1.794295 / 5.269862 (-3.475567) | 1.044690 / 4.565676 (-3.520987) | 0.064992 / 0.424275 (-0.359284) | 0.011542 / 0.007607 (0.003935) | 0.507830 / 0.226044 (0.281785) | 5.061574 / 2.268929 (2.792645) | 2.252896 / 55.444624 (-53.191729) | 1.912551 / 6.876477 (-4.963926) | 2.073510 / 2.142072 (-0.068562) | 0.642148 / 4.805227 (-4.163079) | 0.140151 / 6.500664 (-6.360513) | 0.062623 / 0.075469 (-0.012846) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.180367 / 1.841788 (-0.661421) | 14.263475 / 8.074308 (6.189167) | 12.917251 / 10.191392 (2.725859) | 0.143815 / 0.680424 (-0.536608) | 0.017286 / 0.534201 (-0.516915) | 0.388411 / 0.579283 (-0.190872) | 0.430512 / 0.434364 (-0.003851) | 0.466595 / 0.540337 (-0.073742) | 0.564545 / 1.386936 (-0.822391) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006059 / 0.011353 (-0.005294) | 0.004419 / 0.011008 (-0.006590) | 0.074206 / 0.038508 (0.035697) | 0.031180 / 0.023109 (0.008071) | 0.380031 / 0.275898 (0.104133) | 0.410373 / 0.323480 (0.086893) | 0.005397 / 0.007986 (-0.002589) | 0.003952 / 0.004328 (-0.000376) | 0.074426 / 0.004250 (0.070176) | 0.046256 / 0.037052 (0.009203) | 0.385543 / 0.258489 (0.127054) | 0.430724 / 0.293841 (0.136883) | 0.028052 / 0.128546 (-0.100494) | 0.008810 / 0.075646 (-0.066836) | 0.080749 / 0.419271 (-0.338522) | 0.046746 / 0.043533 (0.003214) | 0.380325 / 0.255139 (0.125186) | 0.398901 / 0.283200 (0.115701) | 0.099607 / 0.141683 (-0.042076) | 1.433343 / 1.452155 (-0.018812) | 1.520447 / 1.492716 (0.027730) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.202232 / 0.018006 (0.184225) | 0.431342 / 0.000490 (0.430852) | 0.001020 / 0.000200 (0.000820) | 0.000089 / 0.000054 (0.000035) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028762 / 0.037411 (-0.008649) | 0.111777 / 0.014526 (0.097251) | 0.119283 / 0.176557 (-0.057273) | 0.168151 / 0.737135 (-0.568985) | 0.126093 / 0.296338 (-0.170245) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.442689 / 0.215209 (0.227480) | 4.369202 / 2.077655 (2.291547) | 2.167703 / 1.504120 (0.663583) | 1.960580 / 1.541195 (0.419385) | 2.001459 / 1.468490 (0.532969) | 0.527169 / 4.584777 (-4.057608) | 3.738987 / 3.745712 (-0.006726) | 1.819002 / 5.269862 (-3.450860) | 1.082786 / 4.565676 (-3.482891) | 0.066209 / 0.424275 (-0.358066) | 0.011549 / 0.007607 (0.003942) | 0.545959 / 0.226044 (0.319915) | 5.466655 / 2.268929 (3.197727) | 2.671448 / 55.444624 (-52.773176) | 2.340968 / 6.876477 (-4.535509) | 2.358805 / 2.142072 (0.216733) | 0.649456 / 4.805227 (-4.155771) | 0.142009 / 6.500664 (-6.358655) | 0.064199 / 0.075469 (-0.011270) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.259819 / 1.841788 (-0.581969) | 14.456988 / 8.074308 (6.382680) | 14.478982 / 10.191392 (4.287590) | 0.163156 / 0.680424 (-0.517268) | 0.017090 / 0.534201 (-0.517111) | 0.391339 / 0.579283 (-0.187944) | 0.422021 / 0.434364 (-0.012343) | 0.465340 / 0.540337 (-0.074997) | 0.564517 / 1.386936 (-0.822419) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#97358c88f996a65f49923ec215358044e4146a95 \"CML watermark\")\n", "> .github/workflows/delete_doc_comment.yml \r\n\r\nis already updated https://github.com/huggingface/datasets/pull/5932/files\r\n\r\n> .github/workflows/build_pr_documentation.yml\r\n\r\nindeed no changes are needed" ]

"2023-06-07T16:09:39Z"

"2023-06-09T10:16:58Z"

"2023-06-09T09:53:16Z"

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5056). All of your documentation changes will be reflected on that endpoint.", "Thanks, @manandey. We have removed all dataset scripts from this repo. Subsequent PRs should be opened directly on the Hugging Face Hub." ]

"2022-10-03T13:13:22Z"

"2022-10-04T13:49:00Z"

"2022-10-04T13:48:59Z"

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[ "Also, I had a doubt while checking the code related to the indices... \r\n\r\n@lhoestq, there's a value in `config.py` named `DATASET_INDICES_FILENAME` which has the arrow extension (which I assume it should be `indices.faiss`, as the Elastic Search indices are not stored in a file, but not sure), and it's just used before actually saving an `ArrowDataset` in disk, but since those indices are never stored AFAIK, is that actually required?\r\n\r\nhttps://github.com/huggingface/datasets/blob/aec86ea4b790ccccc9b2e0376a496728b1c914cc/src/datasets/config.py#L183\r\n\r\nhttps://github.com/huggingface/datasets/blob/aec86ea4b790ccccc9b2e0376a496728b1c914cc/src/datasets/arrow_dataset.py#L1079-L1092\r\n\r\nSo should I also remove that?\r\n\r\nP.S. I also edited the following code comment which I found misleading as it's not actually storing the indices.\r\n\r\nhttps://github.com/huggingface/datasets/blob/8ddc4bbeb1e2bd307b21f5d21f884649aa2bf640/src/datasets/arrow_dataset.py#L1122", "_The documentation is not available anymore as the PR was closed or merged._", "> @lhoestq, there's a value in config.py named DATASET_INDICES_FILENAME which has the arrow extension (which I assume it should be indices.faiss, as the Elastic Search indices are not stored in a file, but not sure), and it's just used before actually saving an ArrowDataset in disk, but since those indices are never stored AFAIK, is that actually required?\r\n\r\nThe arrow file is used to store an indices mapping (when you shuffle the dataset for example) - not for a faiss index ;)", "Ok cool thanks a lot for the explanation @lhoestq I was not sure about that :+1: I'll also add it there as you suggested!", "CI failures are unrelated to this PR and fixed on master, merging" ]

"2022-06-21T12:18:49Z"

"2022-06-27T16:25:09Z"

"2022-06-27T16:14:36Z"

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"2022-01-03T16:43:57Z"

"2022-01-03T17:39:02Z"

"2022-01-03T17:39:01Z"

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[ "I think the main purpose of #4179 was how to be able to load both configs into one, so should we maybe add this part of the code: https://github.com/huggingface/datasets/issues/4179#issuecomment-1102383717 \r\n\r\nto the doc? \r\n\r\nActually @lhoestq would this work given that they have different split names: https://huggingface.co/datasets/librispeech_asr#data-splits ? ", "This doc extension does not explain why I can't simply load the whole dataset. Or what workaround I need to get the whole dataset, which is what people usually want for Librispeech.", "_The documentation is not available anymore as the PR was closed or merged._", "@lhoestq, I can add a `\"all\"` config to Librispeech have the datasets already cached somewhere ", "I'm closing this PR then, feel free to continue the discussion in https://github.com/huggingface/datasets/issues/4179\r\n" ]

"2022-04-19T14:26:59Z"

"2023-09-24T10:02:24Z"

"2022-04-19T15:15:20Z"

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[ "Hi, thanks for the suggestion. It's not possible at the moment. The viewer is part of the Hub codebase and only works on public datasets. Also, it relies on [Datasets Server](https://github.com/huggingface/datasets-server/), which prepares the data and provides an API to access the rows, size, etc.\r\n\r\nIf you're interested in hosting your data as a private dataset on the Hub, you might want to look at https://github.com/huggingface/datasets-server/issues/39.", "Hi, we are building an offline dataset viewer: https://github.com/Renumics/spotlight\r\nIt supports many HF datasets, but currently you have to use it via Pandas:\r\ndf=ds.to_pandas()\r\nspotlight.show(df)\r\n\r\nWould love to hear from you if that works for your use case. If not, feel free to open an issue on the repo: https://github.com/Renumics/spotlight/issues", "@ssuwelack thank you! I will definitely try it out.", "Related issues:\r\n- https://github.com/huggingface/datasets-server/issues/213\r\n- https://github.com/huggingface/datasets-server/issues/441\r\n- https://github.com/huggingface/datasets/issues/6014", "Closing for now, as developing and maintaining an offline viewer is not planned." ]

"2023-08-10T11:30:00Z"

"2023-09-29T13:10:23Z"

"2023-09-29T13:10:22Z"

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[ "Hi ! :)\r\n\r\nThis looks like the same issue as https://github.com/huggingface/datasets/issues/1856 \r\nBasically google drive has quota issues that makes it inconvenient for downloading files.\r\n\r\nIf the quota of a file is exceeded, you have to wait 24h for the quota to reset (which is painful).\r\n\r\nThe error says that the checksum of the downloaded file doesn't match because google drive returns a text file with the \"Quota Exceeded\" error instead of the actual data file.", "Thanks @lhoestq! Yes, it seems back to normal after a couple of days." ]

"2021-02-18T22:25:48Z"

"2021-02-22T23:22:05Z"

"2021-02-22T23:22:04Z"

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"2021-03-31T03:21:19Z"

"2021-04-01T10:20:37Z"

"2021-04-01T10:20:36Z"

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[ "I refreshed the dataset viewer manually, it's fixed now. Sorry for the inconvenience.\r\n<img width=\"1557\" alt=\"Capture d’écran 2022-07-28 à 09 17 39\" src=\"https://user-images.githubusercontent.com/1676121/181514666-92d7f8e1-ddc1-4769-84f3-f1edfdb902e8.png\">\r\n\r\n" ]

"2022-07-28T11:21:43Z"

"2022-07-28T13:17:56Z"

"2022-07-28T13:17:48Z"

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_3856). All of your documentation changes will be reflected on that endpoint." ]

"2022-03-08T11:07:09Z"

"2022-03-08T15:22:17Z"

"2022-03-08T15:22:16Z"

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[ "I have tried to extract ```0015896-b1054262f7da52a0518521e29c8e352c.txt``` from ```17ecf461bfccd469a1fbc264ccb03731f8606eea7b3e2e8b86e13d18040bf5b3/urlsf_subset00-16_data.xz``` with 7-zip\r\nand put the file into cache_path ```F://huggingface/datasets/downloads/extracted/0901d27f43b7e9ac0577da0d0061c8c632ba0b70ecd1b4bfb21562d9b7486faa```\r\nthere is still raise the same error and I find the file was removed from cache_path after I run the run_mlm.py with ```python run_mlm.py --model_type roberta --tokenizer_name roberta-base --dataset_name openwebtext --per_device_train_batch_size 8 --per_device_eval_batch_size 8 --do_train --do_eval --output_dir F:/model/roberta-base```." ]

"2022-08-16T04:04:22Z"

"2023-01-04T03:39:12Z"

"2023-01-04T03:39:12Z"

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"2023-10-11T08:59:36Z"

"2023-10-11T16:21:38Z"

"2023-10-11T16:21:38Z"

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

"2022-06-01T09:45:30Z"

"2022-06-02T17:43:47Z"

"2022-06-02T17:35:15Z"

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[ "This wasn’t working for me a bit earlier, but it looks to be back up now", "We had a minor issue updating the docs after the latest release. It should work now :)." ]

"2023-06-22T20:14:57Z"

"2023-06-26T15:45:03Z"

"2023-06-26T15:45:03Z"

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6117). All of your documentation changes will be reflected on that endpoint.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.012516 / 0.011353 (0.001163) | 0.004725 / 0.011008 (-0.006283) | 0.112245 / 0.038508 (0.073736) | 0.079146 / 0.023109 (0.056037) | 0.386415 / 0.275898 (0.110517) | 0.420441 / 0.323480 (0.096961) | 0.005682 / 0.007986 (-0.002304) | 0.004169 / 0.004328 (-0.000160) | 0.077847 / 0.004250 (0.073597) | 0.055763 / 0.037052 (0.018711) | 0.385529 / 0.258489 (0.127040) | 0.422711 / 0.293841 (0.128870) | 0.047212 / 0.128546 (-0.081334) | 0.013711 / 0.075646 (-0.061935) | 0.342856 / 0.419271 (-0.076416) | 0.066788 / 0.043533 (0.023255) | 0.380728 / 0.255139 (0.125589) | 0.416241 / 0.283200 (0.133041) | 0.034676 / 0.141683 (-0.107007) | 1.679661 / 1.452155 (0.227506) | 1.838014 / 1.492716 (0.345297) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.219556 / 0.018006 (0.201550) | 0.524728 / 0.000490 (0.524238) | 0.005045 / 0.000200 (0.004845) | 0.000124 / 0.000054 (0.000069) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025475 / 0.037411 (-0.011936) | 0.085937 / 0.014526 (0.071412) | 0.099245 / 0.176557 (-0.077311) | 0.158995 / 0.737135 (-0.578141) | 0.101504 / 0.296338 (-0.194835) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.582200 / 0.215209 (0.366991) | 5.794340 / 2.077655 (3.716685) | 2.473635 / 1.504120 (0.969515) | 2.168135 / 1.541195 (0.626941) | 2.215886 / 1.468490 (0.747396) | 0.855599 / 4.584777 (-3.729178) | 5.003067 / 3.745712 (1.257354) | 4.503566 / 5.269862 (-0.766295) | 2.912248 / 4.565676 (-1.653428) | 0.103267 / 0.424275 (-0.321008) | 0.012114 / 0.007607 (0.004507) | 0.712240 / 0.226044 (0.486196) | 7.131946 / 2.268929 (4.863017) | 3.280052 / 55.444624 (-52.164573) | 2.583472 / 6.876477 (-4.293004) | 2.820758 / 2.142072 (0.678686) | 1.132097 / 4.805227 (-3.673131) | 0.232191 / 6.500664 (-6.268473) | 0.082966 / 0.075469 (0.007497) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.581125 / 1.841788 (-0.260662) | 22.723878 / 8.074308 (14.649570) | 19.969347 / 10.191392 (9.777955) | 0.234365 / 0.680424 (-0.446059) | 0.030245 / 0.534201 (-0.503956) | 0.470843 / 0.579283 (-0.108440) | 0.558069 / 0.434364 (0.123705) | 0.534878 / 0.540337 (-0.005460) | 0.801025 / 1.386936 (-0.585911) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008524 / 0.011353 (-0.002829) | 0.005083 / 0.011008 (-0.005925) | 0.078054 / 0.038508 (0.039546) | 0.082025 / 0.023109 (0.058915) | 0.458027 / 0.275898 (0.182129) | 0.498232 / 0.323480 (0.174752) | 0.005938 / 0.007986 (-0.002048) | 0.003776 / 0.004328 (-0.000553) | 0.080413 / 0.004250 (0.076163) | 0.060485 / 0.037052 (0.023433) | 0.462816 / 0.258489 (0.204327) | 0.513970 / 0.293841 (0.220129) | 0.047574 / 0.128546 (-0.080973) | 0.013424 / 0.075646 (-0.062222) | 0.087707 / 0.419271 (-0.331565) | 0.065007 / 0.043533 (0.021474) | 0.465844 / 0.255139 (0.210705) | 0.498474 / 0.283200 (0.215274) | 0.033518 / 0.141683 (-0.108164) | 1.737507 / 1.452155 (0.285352) | 1.848291 / 1.492716 (0.355574) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.316710 / 0.018006 (0.298703) | 0.504415 / 0.000490 (0.503925) | 0.042128 / 0.000200 (0.041928) | 0.000171 / 0.000054 (0.000117) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032097 / 0.037411 (-0.005314) | 0.099371 / 0.014526 (0.084845) | 0.109311 / 0.176557 (-0.067246) | 0.177373 / 0.737135 (-0.559762) | 0.110753 / 0.296338 (-0.185585) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.688060 / 0.215209 (0.472851) | 6.255219 / 2.077655 (4.177564) | 2.696845 / 1.504120 (1.192725) | 2.395424 / 1.541195 (0.854230) | 2.414870 / 1.468490 (0.946380) | 0.865704 / 4.584777 (-3.719073) | 5.086828 / 3.745712 (1.341116) | 4.648107 / 5.269862 (-0.621754) | 3.091119 / 4.565676 (-1.474558) | 0.101787 / 0.424275 (-0.322489) | 0.008829 / 0.007607 (0.001222) | 0.772398 / 0.226044 (0.546354) | 7.700366 / 2.268929 (5.431438) | 3.608632 / 55.444624 (-51.835992) | 2.923309 / 6.876477 (-3.953168) | 2.952141 / 2.142072 (0.810069) | 1.093006 / 4.805227 (-3.712221) | 0.224363 / 6.500664 (-6.276301) | 0.074927 / 0.075469 (-0.000542) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.638414 / 1.841788 (-0.203374) | 23.486781 / 8.074308 (15.412473) | 21.129104 / 10.191392 (10.937712) | 0.259955 / 0.680424 (-0.420469) | 0.027305 / 0.534201 (-0.506895) | 0.464448 / 0.579283 (-0.114835) | 0.553737 / 0.434364 (0.119373) | 0.571318 / 0.540337 (0.030981) | 0.772917 / 1.386936 (-0.614019) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3ec5ee9e78b464364796651d995823c7ecb0f951 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009093 / 0.011353 (-0.002260) | 0.005283 / 0.011008 (-0.005725) | 0.112299 / 0.038508 (0.073791) | 0.081341 / 0.023109 (0.058232) | 0.363799 / 0.275898 (0.087901) | 0.409261 / 0.323480 (0.085781) | 0.006400 / 0.007986 (-0.001586) | 0.003965 / 0.004328 (-0.000363) | 0.074389 / 0.004250 (0.070139) | 0.060654 / 0.037052 (0.023602) | 0.391046 / 0.258489 (0.132557) | 0.430514 / 0.293841 (0.136673) | 0.054900 / 0.128546 (-0.073646) | 0.017972 / 0.075646 (-0.057675) | 0.410875 / 0.419271 (-0.008396) | 0.067405 / 0.043533 (0.023873) | 0.371468 / 0.255139 (0.116329) | 0.435061 / 0.283200 (0.151861) | 0.038063 / 0.141683 (-0.103620) | 1.733509 / 1.452155 (0.281354) | 1.833899 / 1.492716 (0.341182) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.243230 / 0.018006 (0.225224) | 0.605636 / 0.000490 (0.605146) | 0.004890 / 0.000200 (0.004690) | 0.000098 / 0.000054 (0.000043) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027624 / 0.037411 (-0.009787) | 0.084799 / 0.014526 (0.070273) | 0.104405 / 0.176557 (-0.072152) | 0.165383 / 0.737135 (-0.571752) | 0.102083 / 0.296338 (-0.194255) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.578334 / 0.215209 (0.363125) | 5.369520 / 2.077655 (3.291866) | 2.294174 / 1.504120 (0.790055) | 2.054195 / 1.541195 (0.513000) | 2.007304 / 1.468490 (0.538814) | 0.839283 / 4.584777 (-3.745494) | 5.262288 / 3.745712 (1.516576) | 4.363346 / 5.269862 (-0.906516) | 2.854903 / 4.565676 (-1.710773) | 0.096975 / 0.424275 (-0.327300) | 0.008237 / 0.007607 (0.000630) | 0.646746 / 0.226044 (0.420702) | 6.250621 / 2.268929 (3.981693) | 2.900377 / 55.444624 (-52.544247) | 2.283238 / 6.876477 (-4.593239) | 2.443785 / 2.142072 (0.301713) | 0.991719 / 4.805227 (-3.813508) | 0.189755 / 6.500664 (-6.310909) | 0.067906 / 0.075469 (-0.007563) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.515563 / 1.841788 (-0.326225) | 21.956499 / 8.074308 (13.882191) | 19.161750 / 10.191392 (8.970358) | 0.238199 / 0.680424 (-0.442225) | 0.026771 / 0.534201 (-0.507430) | 0.450195 / 0.579283 (-0.129088) | 0.585168 / 0.434364 (0.150804) | 0.522945 / 0.540337 (-0.017393) | 0.776244 / 1.386936 (-0.610693) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007997 / 0.011353 (-0.003356) | 0.005021 / 0.011008 (-0.005988) | 0.087308 / 0.038508 (0.048800) | 0.077760 / 0.023109 (0.054650) | 0.425313 / 0.275898 (0.149415) | 0.451470 / 0.323480 (0.127990) | 0.006848 / 0.007986 (-0.001137) | 0.004812 / 0.004328 (0.000484) | 0.071198 / 0.004250 (0.066947) | 0.058325 / 0.037052 (0.021273) | 0.427411 / 0.258489 (0.168922) | 0.466069 / 0.293841 (0.172228) | 0.048686 / 0.128546 (-0.079861) | 0.011841 / 0.075646 (-0.063806) | 0.086225 / 0.419271 (-0.333047) | 0.060500 / 0.043533 (0.016967) | 0.435580 / 0.255139 (0.180441) | 0.456919 / 0.283200 (0.173719) | 0.035094 / 0.141683 (-0.106588) | 1.582805 / 1.452155 (0.130650) | 1.717838 / 1.492716 (0.225122) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.283967 / 0.018006 (0.265960) | 0.517496 / 0.000490 (0.517006) | 0.014747 / 0.000200 (0.014547) | 0.000099 / 0.000054 (0.000045) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027870 / 0.037411 (-0.009541) | 0.083835 / 0.014526 (0.069309) | 0.099157 / 0.176557 (-0.077400) | 0.173210 / 0.737135 (-0.563925) | 0.094212 / 0.296338 (-0.202127) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.535720 / 0.215209 (0.320511) | 5.273730 / 2.077655 (3.196075) | 2.422560 / 1.504120 (0.918440) | 2.131416 / 1.541195 (0.590222) | 2.192000 / 1.468490 (0.723510) | 0.708469 / 4.584777 (-3.876308) | 4.758092 / 3.745712 (1.012380) | 3.940729 / 5.269862 (-1.329133) | 2.553093 / 4.565676 (-2.012583) | 0.084895 / 0.424275 (-0.339380) | 0.008730 / 0.007607 (0.001123) | 0.646975 / 0.226044 (0.420930) | 6.294811 / 2.268929 (4.025883) | 3.293964 / 55.444624 (-52.150660) | 2.568985 / 6.876477 (-4.307492) | 2.743786 / 2.142072 (0.601713) | 0.899733 / 4.805227 (-3.905494) | 0.193484 / 6.500664 (-6.307181) | 0.070012 / 0.075469 (-0.005457) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.502255 / 1.841788 (-0.339532) | 20.690234 / 8.074308 (12.615926) | 18.375791 / 10.191392 (8.184399) | 0.200135 / 0.680424 (-0.480289) | 0.029434 / 0.534201 (-0.504767) | 0.477267 / 0.579283 (-0.102016) | 0.566869 / 0.434364 (0.132505) | 0.543756 / 0.540337 (0.003418) | 0.700476 / 1.386936 (-0.686460) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ef17d9fd6c648bb41d43ba301c3de4d7b6f833d8 \"CML watermark\")\n" ]

"2023-08-03T14:46:04Z"

"2023-08-03T14:56:59Z"

"2023-08-03T14:46:18Z"

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[]

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[ "Hi, thanks!\r\n\r\n> I was using Hugging Face datasets to process some very large datasets and found that it would be quite handy to have a feature that will allow to \"split\" these large datasets into chunks with equal size\r\n\r\n`map`, the method we use for processing in `datasets`, already does that if `batched=True`. And you can control the batch size with `batch_size`.\r\n\r\n> Even better - be able to run through these chunks one by one in simple and convenient way\r\n\r\nIt's not hard to do this \"manually\" with the existing API:\r\n```python\r\nbatch_size = <BATCH_SIZE>\r\nfor i in range(len(dset) // batch_size)\r\n shard = dset[i * batch_size:(i+1) * batch_size] # a dict of lists\r\n shard = Dataset.from_dict(shard)\r\n```\r\n(should be of similar performance to your implementation)\r\n\r\nStill, I think an API like that could be useful if implemented efficiently (see [this](https://discuss.huggingface.co/t/why-is-it-so-slow-to-access-data-through-iteration-with-hugginface-dataset/20385) discussion to understand what's the issue with `select`/`__getitem__` on which your implementation relies on), which can be done with `pa.Table.to_reader` in PyArrow 8.0.0+, .\r\n\r\n@lhoestq @albertvillanova wdyt? We could use such API to efficiently iterate over the batches in `map` before processing them.", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_4798). All of your documentation changes will be reflected on that endpoint.", "This is more efficient since it doesn't bring the data in memory:\r\n```python\r\nfor i in range(len(dset) // batch_size)\r\n start = i * batch_size\r\n end = min((i+1) * batch_size, len(dset))\r\n shard = dset.select(range(start, end))\r\n```\r\n\r\n@marianna13 can you give more details on when it would be handy to have this shard generator ?", "> This is more efficient since it doesn't bring the data in memory:\r\n> \r\n> ```python\r\n> for i in range(len(dset) // batch_size)\r\n> start = i * batch_size\r\n> end = min((i+1) * batch_size, len(dset))\r\n> shard = dset.select(range(start, end))\r\n> ```\r\n> \r\n> @marianna13 can you give more details on when it would be handy to have this shard generator ?\r\n\r\nSure! I used such generator when I needed to process a very large dataset (>1TB) in parallel, I've found out empirically that it's much more efficient to do that by processing only one part of the dataset with the shard generator. I tried to use a map with batching but it causesd oom errors, I tried to use the normal shard and here's what I came up with. So I thought it might be helpful to someone else!", "I see thanks ! `map` should work just fine even at this scale, feel free to open an issue if you'd like to discuss your OOM issue.\r\n\r\nRegarding `shard_generator`, since it is pretty straightforward to get shards I'm not sure we need that extra Dataset method", "Hi again! We've just added `_iter_batches(batch_size)` to the `Dataset` API for fast iteration over batches/chunks, so I think we can close this PR. Compared to this implementation, `_iter_batches` leverages `pa.Table.to_reader` for chunking, which makes it significantly faster." ]

"2022-08-06T09:14:06Z"

"2022-10-03T15:35:10Z"

"2022-10-03T15:35:10Z"

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"2021-09-30T12:08:54Z"

"2021-10-01T13:57:46Z"

"2021-10-01T13:57:46Z"

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[ "This must be due to DatasetInfo.from_merge which drops them and is used in `concatenate_datasets`.\r\n\r\nAnd you're experiencing this issue because multiprocessing does concatenate the resulting datasets from each process.\r\n\r\nMaybe they should be kept if all the subdatasets share the same values for config_name and split", "That sounds like a clean workaround!" ]

"2023-06-19T17:27:36Z"

"2023-06-28T08:55:25Z"

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[ "@albertvillanova here's a separate issue for a bug similar to #3792", "Hi @afcruzs-ms, thanks for opening this separate issue for your problem.\r\n\r\nThe root problem in the other issue (#3792) was a change in the service of Google Drive.\r\n\r\nBut in your case, the `xcopa` dataset is not hosted on Google Drive. Therefore, the root cause should be a different one.\r\n\r\nLet me look at it... ", "@afcruzs-ms, I'm not able to reproduce the issue you reported:\r\n```python\r\nIn [1]: from datasets import load_dataset\r\n ...: dataset = load_dataset(\"xcopa\", \"it\")\r\nDownloading builder script: 5.21kB [00:00, 2.75MB/s] \r\nDownloading metadata: 28.6kB [00:00, 14.5MB/s] \r\nDownloading and preparing dataset xcopa/it (download: 627.09 KiB, generated: 76.43 KiB, post-processed: Unknown size, total: 703.52 KiB) to .../.cache/huggingface/datasets/xcopa/it/1.0.0/e1fab65f984b24c8b66bcf7ac27a26a1182f84adfb2e74035861be65e214b9e6...\r\nDownloading data: 642kB [00:00, 5.42MB/s]\r\nDataset xcopa downloaded and prepared to .../.cache/huggingface/datasets/xcopa/it/1.0.0/e1fab65f984b24c8b66bcf7ac27a26a1182f84adfb2e74035861be65e214b9e6. Subsequent calls will reuse this data. \r\n100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [00:00<00:00, 733.27it/s]\r\n\r\nIn [2]: dataset\r\nOut[2]: \r\nDatasetDict({\r\n test: Dataset({\r\n features: ['premise', 'choice1', 'choice2', 'question', 'label', 'idx', 'changed'],\r\n num_rows: 500\r\n })\r\n validation: Dataset({\r\n features: ['premise', 'choice1', 'choice2', 'question', 'label', 'idx', 'changed'],\r\n num_rows: 100\r\n })\r\n})\r\n```\r\n\r\nMaybe you have some issue with your cached data... Could you please try to force the redownload of the data?\r\n```python\r\ndataset = load_dataset(\"xcopa\", \"it\", download_mode=\"force_redownload\")\r\n```", "It works indeed, thanks! ", "unfortunately, i am having a similar problem with the irc_disentaglement dataset :/\r\nmy code:\r\n```\r\nfrom datasets import load_dataset\r\n\r\ndataset = load_dataset(\"irc_disentangle\", download_mode=\"force_redownload\")\r\n```\r\n\r\nhowever, it produces the same error as @afcruzs-ms \r\n```\r\n[38](file:///Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/datasets/utils/info_utils.py?line=37) if len(bad_urls) > 0:\r\n [39](file:///Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/datasets/utils/info_utils.py?line=38) error_msg = \"Checksums didn't match\" + for_verification_name + \":\\n\"\r\n---> [40](file:///Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/datasets/utils/info_utils.py?line=39) raise NonMatchingChecksumError(error_msg + str(bad_urls))\r\n [41](file:///Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages/datasets/utils/info_utils.py?line=40) logger.info(\"All the checksums matched successfully\" + for_verification_name)\r\n\r\nNonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https://github.com/jkkummerfeld/irc-disentanglement/tarball/master']\r\n```\r\n\r\nI attempted to use the `ignore_verifications' as such:\r\n```\r\nds = datasets.load_dataset('irc_disentangle', download_mode=\"force_redownload\", ignore_verifications=True)\r\n\r\n```\r\n```\r\nDownloading builder script: 12.0kB [00:00, 5.92MB/s] \r\nDownloading metadata: 7.58kB [00:00, 3.48MB/s] \r\nNo config specified, defaulting to: irc_disentangle/ubuntu\r\nDownloading and preparing dataset irc_disentangle/ubuntu (download: 112.98 MiB, generated: 60.05 MiB, post-processed: Unknown size, total: 173.03 MiB) to /Users/laylabouzoubaa/.cache/huggingface/datasets/irc_disentangle/ubuntu/1.0.0/0f24ab262a21d8c1d989fa53ed20caa928f5880be26c162bfbc02445dbade7e5...\r\nDownloading data: 118MB [00:09, 12.1MB/s] \r\n \r\nDataset irc_disentangle downloaded and prepared to /Users/laylabouzoubaa/.cache/huggingface/datasets/irc_disentangle/ubuntu/1.0.0/0f24ab262a21d8c1d989fa53ed20caa928f5880be26c162bfbc02445dbade7e5. Subsequent calls will reuse this data.\r\n100%|██████████| 3/3 [00:00<00:00, 675.38it/s]\r\n```\r\nbut, this returns an empty set?\r\n\r\n```\r\nDatasetDict({\r\n train: Dataset({\r\n features: ['id', 'raw', 'ascii', 'tokenized', 'date', 'connections'],\r\n num_rows: 0\r\n })\r\n test: Dataset({\r\n features: ['id', 'raw', 'ascii', 'tokenized', 'date', 'connections'],\r\n num_rows: 0\r\n })\r\n validation: Dataset({\r\n features: ['id', 'raw', 'ascii', 'tokenized', 'date', 'connections'],\r\n num_rows: 0\r\n })\r\n})\r\n```\r\n\r\nnot sure what else to try at this point?\r\nThanks in advanced🤗", "Thanks @labouz for reporting: yes, better opening a new GitHub issue as you did. I'm addressing it:\r\n- #4376" ]

"2022-03-02T18:10:19Z"

"2022-05-20T06:00:42Z"

"2022-03-03T17:40:31Z"

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

"2022-09-12T05:42:24Z"

"2022-09-12T08:00:06Z"

"2022-09-12T07:58:06Z"

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[ "Hi! Yes, this is expected behavior - we do this as a security measure to not leak local paths (this info would be useless on other users' machines anyways) and only push audio bytes. \r\n" ]

"2022-11-02T11:51:25Z"

"2022-11-02T12:55:02Z"

"2022-11-02T12:55:02Z"

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[ "_The documentation is not available anymore as the PR was closed or merged._", "To make it warn only once, feel free to use a global counter in python - and if the warning has already been done, you don't do it again", "> Added the same formatting for the error message :)\r\n\r\nnice!! thank you! \r\n\r\n> Oh and regarding the warning counter, you can do it in another PR maybe ?\r\n\r\nYes, more warnings is better then no warnings.... I'll merge when the CI passes" ]

"2022-10-26T14:21:14Z"

"2022-10-27T09:01:12Z"

"2022-10-27T08:58:58Z"

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[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006998 / 0.011353 (-0.004355) | 0.005093 / 0.011008 (-0.005916) | 0.100490 / 0.038508 (0.061982) | 0.032736 / 0.023109 (0.009627) | 0.297738 / 0.275898 (0.021840) | 0.322255 / 0.323480 (-0.001225) | 0.005583 / 0.007986 (-0.002402) | 0.004007 / 0.004328 (-0.000321) | 0.075863 / 0.004250 (0.071613) | 0.044212 / 0.037052 (0.007159) | 0.300033 / 0.258489 (0.041544) | 0.341997 / 0.293841 (0.048156) | 0.036172 / 0.128546 (-0.092374) | 0.012176 / 0.075646 (-0.063471) | 0.356052 / 0.419271 (-0.063220) | 0.050438 / 0.043533 (0.006905) | 0.294677 / 0.255139 (0.039538) | 0.318050 / 0.283200 (0.034850) | 0.104733 / 0.141683 (-0.036950) | 1.435681 / 1.452155 (-0.016474) | 1.534793 / 1.492716 (0.042076) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.242815 / 0.018006 (0.224809) | 0.565983 / 0.000490 (0.565494) | 0.006800 / 0.000200 (0.006600) | 0.000124 / 0.000054 (0.000070) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026548 / 0.037411 (-0.010863) | 0.104816 / 0.014526 (0.090290) | 0.116222 / 0.176557 (-0.060335) | 0.172143 / 0.737135 (-0.564992) | 0.121631 / 0.296338 (-0.174707) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.400126 / 0.215209 (0.184917) | 4.004538 / 2.077655 (1.926883) | 1.798822 / 1.504120 (0.294702) | 1.595191 / 1.541195 (0.053996) | 1.645777 / 1.468490 (0.177287) | 0.705643 / 4.584777 (-3.879134) | 3.750887 / 3.745712 (0.005175) | 2.136547 / 5.269862 (-3.133315) | 1.475881 / 4.565676 (-3.089795) | 0.086921 / 0.424275 (-0.337354) | 0.012379 / 0.007607 (0.004771) | 0.505824 / 0.226044 (0.279779) | 5.052364 / 2.268929 (2.783435) | 2.279983 / 55.444624 (-53.164641) | 1.932253 / 6.876477 (-4.944224) | 2.051359 / 2.142072 (-0.090714) | 0.851906 / 4.805227 (-3.953321) | 0.169566 / 6.500664 (-6.331098) | 0.064600 / 0.075469 (-0.010869) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.165859 / 1.841788 (-0.675929) | 15.049950 / 8.074308 (6.975642) | 14.095981 / 10.191392 (3.904589) | 0.151779 / 0.680424 (-0.528645) | 0.017537 / 0.534201 (-0.516664) | 0.420164 / 0.579283 (-0.159119) | 0.418932 / 0.434364 (-0.015432) | 0.488749 / 0.540337 (-0.051588) | 0.582359 / 1.386936 (-0.804577) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007426 / 0.011353 (-0.003927) | 0.005248 / 0.011008 (-0.005761) | 0.074118 / 0.038508 (0.035610) | 0.034223 / 0.023109 (0.011114) | 0.337780 / 0.275898 (0.061882) | 0.376300 / 0.323480 (0.052820) | 0.006142 / 0.007986 (-0.001843) | 0.004246 / 0.004328 (-0.000083) | 0.074177 / 0.004250 (0.069926) | 0.052698 / 0.037052 (0.015646) | 0.340229 / 0.258489 (0.081740) | 0.396172 / 0.293841 (0.102331) | 0.037293 / 0.128546 (-0.091253) | 0.012514 / 0.075646 (-0.063132) | 0.087144 / 0.419271 (-0.332128) | 0.051922 / 0.043533 (0.008390) | 0.333188 / 0.255139 (0.078049) | 0.355420 / 0.283200 (0.072220) | 0.110273 / 0.141683 (-0.031410) | 1.447826 / 1.452155 (-0.004329) | 1.561135 / 1.492716 (0.068419) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.269203 / 0.018006 (0.251197) | 0.551997 / 0.000490 (0.551508) | 0.001558 / 0.000200 (0.001359) | 0.000090 / 0.000054 (0.000035) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029511 / 0.037411 (-0.007900) | 0.108614 / 0.014526 (0.094089) | 0.123438 / 0.176557 (-0.053118) | 0.171596 / 0.737135 (-0.565539) | 0.126828 / 0.296338 (-0.169511) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.420520 / 0.215209 (0.205310) | 4.175672 / 2.077655 (2.098017) | 1.982220 / 1.504120 (0.478101) | 1.788575 / 1.541195 (0.247381) | 1.860840 / 1.468490 (0.392349) | 0.706730 / 4.584777 (-3.878047) | 3.858718 / 3.745712 (0.113005) | 3.069389 / 5.269862 (-2.200472) | 1.827603 / 4.565676 (-2.738073) | 0.087893 / 0.424275 (-0.336382) | 0.012613 / 0.007607 (0.005006) | 0.524177 / 0.226044 (0.298132) | 5.177077 / 2.268929 (2.908148) | 2.494397 / 55.444624 (-52.950227) | 2.189484 / 6.876477 (-4.686992) | 2.217626 / 2.142072 (0.075554) | 0.846326 / 4.805227 (-3.958901) | 0.176558 / 6.500664 (-6.324106) | 0.065018 / 0.075469 (-0.010451) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.268618 / 1.841788 (-0.573170) | 15.132711 / 8.074308 (7.058403) | 14.585530 / 10.191392 (4.394138) | 0.163454 / 0.680424 (-0.516970) | 0.017442 / 0.534201 (-0.516759) | 0.421746 / 0.579283 (-0.157537) | 0.425412 / 0.434364 (-0.008952) | 0.499178 / 0.540337 (-0.041159) | 0.595458 / 1.386936 (-0.791478) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ab77e58cd32413f4ef4828134a2470ebd53bb542 \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007980 / 0.011353 (-0.003373) | 0.005414 / 0.011008 (-0.005594) | 0.099226 / 0.038508 (0.060718) | 0.035442 / 0.023109 (0.012332) | 0.304851 / 0.275898 (0.028952) | 0.337144 / 0.323480 (0.013664) | 0.006162 / 0.007986 (-0.001823) | 0.004151 / 0.004328 (-0.000177) | 0.074708 / 0.004250 (0.070458) | 0.049690 / 0.037052 (0.012638) | 0.307658 / 0.258489 (0.049168) | 0.358472 / 0.293841 (0.064631) | 0.037181 / 0.128546 (-0.091365) | 0.012259 / 0.075646 (-0.063387) | 0.335426 / 0.419271 (-0.083846) | 0.050790 / 0.043533 (0.007257) | 0.301715 / 0.255139 (0.046576) | 0.320834 / 0.283200 (0.037634) | 0.102357 / 0.141683 (-0.039326) | 1.454750 / 1.452155 (0.002596) | 1.571994 / 1.492716 (0.079278) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.218708 / 0.018006 (0.200702) | 0.444391 / 0.000490 (0.443901) | 0.005717 / 0.000200 (0.005517) | 0.000089 / 0.000054 (0.000035) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028017 / 0.037411 (-0.009395) | 0.112753 / 0.014526 (0.098227) | 0.121003 / 0.176557 (-0.055554) | 0.181085 / 0.737135 (-0.556050) | 0.127211 / 0.296338 (-0.169127) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.400803 / 0.215209 (0.185594) | 4.007315 / 2.077655 (1.929660) | 1.826911 / 1.504120 (0.322791) | 1.637799 / 1.541195 (0.096605) | 1.699754 / 1.468490 (0.231264) | 0.709413 / 4.584777 (-3.875364) | 4.008904 / 3.745712 (0.263192) | 3.916540 / 5.269862 (-1.353322) | 1.902102 / 4.565676 (-2.663575) | 0.089048 / 0.424275 (-0.335227) | 0.012763 / 0.007607 (0.005155) | 0.498957 / 0.226044 (0.272913) | 4.979865 / 2.268929 (2.710937) | 2.301987 / 55.444624 (-53.142637) | 1.929404 / 6.876477 (-4.947073) | 2.107839 / 2.142072 (-0.034233) | 0.857253 / 4.805227 (-3.947974) | 0.171935 / 6.500664 (-6.328729) | 0.066753 / 0.075469 (-0.008716) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.186811 / 1.841788 (-0.654977) | 15.866319 / 8.074308 (7.792011) | 14.738555 / 10.191392 (4.547163) | 0.142879 / 0.680424 (-0.537544) | 0.017679 / 0.534201 (-0.516522) | 0.422840 / 0.579283 (-0.156443) | 0.450307 / 0.434364 (0.015943) | 0.491802 / 0.540337 (-0.048536) | 0.588837 / 1.386936 (-0.798099) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007659 / 0.011353 (-0.003694) | 0.005331 / 0.011008 (-0.005678) | 0.075360 / 0.038508 (0.036852) | 0.034011 / 0.023109 (0.010902) | 0.354488 / 0.275898 (0.078590) | 0.401781 / 0.323480 (0.078301) | 0.005806 / 0.007986 (-0.002179) | 0.004029 / 0.004328 (-0.000300) | 0.073822 / 0.004250 (0.069572) | 0.049067 / 0.037052 (0.012015) | 0.364483 / 0.258489 (0.105994) | 0.405637 / 0.293841 (0.111796) | 0.037166 / 0.128546 (-0.091380) | 0.012397 / 0.075646 (-0.063249) | 0.087346 / 0.419271 (-0.331926) | 0.050888 / 0.043533 (0.007355) | 0.334796 / 0.255139 (0.079657) | 0.387681 / 0.283200 (0.104481) | 0.105056 / 0.141683 (-0.036627) | 1.471630 / 1.452155 (0.019475) | 1.554764 / 1.492716 (0.062047) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.231825 / 0.018006 (0.213819) | 0.449746 / 0.000490 (0.449256) | 0.000888 / 0.000200 (0.000688) | 0.000078 / 0.000054 (0.000023) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030363 / 0.037411 (-0.007049) | 0.115234 / 0.014526 (0.100708) | 0.123005 / 0.176557 (-0.053551) | 0.172772 / 0.737135 (-0.564363) | 0.127818 / 0.296338 (-0.168520) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.425761 / 0.215209 (0.210552) | 4.237950 / 2.077655 (2.160295) | 1.992045 / 1.504120 (0.487925) | 1.801622 / 1.541195 (0.260427) | 1.918477 / 1.468490 (0.449987) | 0.722730 / 4.584777 (-3.862047) | 4.015968 / 3.745712 (0.270256) | 3.720412 / 5.269862 (-1.549450) | 1.763111 / 4.565676 (-2.802566) | 0.089041 / 0.424275 (-0.335234) | 0.012608 / 0.007607 (0.005001) | 0.522645 / 0.226044 (0.296601) | 5.227108 / 2.268929 (2.958180) | 2.444714 / 55.444624 (-52.999910) | 2.109745 / 6.876477 (-4.766732) | 2.194042 / 2.142072 (0.051969) | 0.871781 / 4.805227 (-3.933447) | 0.173149 / 6.500664 (-6.327515) | 0.066192 / 0.075469 (-0.009277) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.312051 / 1.841788 (-0.529737) | 16.024315 / 8.074308 (7.950007) | 15.123823 / 10.191392 (4.932431) | 0.163997 / 0.680424 (-0.516427) | 0.017595 / 0.534201 (-0.516606) | 0.426379 / 0.579283 (-0.152904) | 0.467709 / 0.434364 (0.033345) | 0.498308 / 0.540337 (-0.042030) | 0.591426 / 1.386936 (-0.795510) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#13488cc110b67090289794f48d5c84a4fd0c063a \"CML watermark\")\n", "CI is failing due to unrelated issues, hopefully https://github.com/huggingface/datasets/pull/5642 fixes it", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006478 / 0.011353 (-0.004875) | 0.004347 / 0.011008 (-0.006661) | 0.097103 / 0.038508 (0.058595) | 0.027650 / 0.023109 (0.004541) | 0.372355 / 0.275898 (0.096457) | 0.408794 / 0.323480 (0.085314) | 0.005034 / 0.007986 (-0.002952) | 0.003252 / 0.004328 (-0.001076) | 0.074068 / 0.004250 (0.069818) | 0.035542 / 0.037052 (-0.001510) | 0.367392 / 0.258489 (0.108903) | 0.409644 / 0.293841 (0.115803) | 0.031745 / 0.128546 (-0.096801) | 0.011501 / 0.075646 (-0.064145) | 0.323355 / 0.419271 (-0.095917) | 0.043065 / 0.043533 (-0.000467) | 0.377313 / 0.255139 (0.122174) | 0.395326 / 0.283200 (0.112127) | 0.087101 / 0.141683 (-0.054582) | 1.461228 / 1.452155 (0.009073) | 1.529413 / 1.492716 (0.036696) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.199245 / 0.018006 (0.181239) | 0.409978 / 0.000490 (0.409488) | 0.002655 / 0.000200 (0.002455) | 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) | 0.023903 / 0.037411 (-0.013508) | 0.097855 / 0.014526 (0.083330) | 0.106405 / 0.176557 (-0.070152) | 0.166889 / 0.737135 (-0.570247) | 0.110256 / 0.296338 (-0.186082) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.440351 / 0.215209 (0.225142) | 4.382848 / 2.077655 (2.305194) | 2.049602 / 1.504120 (0.545482) | 1.824638 / 1.541195 (0.283443) | 1.850519 / 1.468490 (0.382029) | 0.702652 / 4.584777 (-3.882125) | 3.394571 / 3.745712 (-0.351141) | 1.940608 / 5.269862 (-3.329254) | 1.263961 / 4.565676 (-3.301716) | 0.083985 / 0.424275 (-0.340290) | 0.013046 / 0.007607 (0.005439) | 0.538272 / 0.226044 (0.312228) | 5.407563 / 2.268929 (3.138634) | 2.519207 / 55.444624 (-52.925418) | 2.153379 / 6.876477 (-4.723098) | 2.394512 / 2.142072 (0.252439) | 0.812840 / 4.805227 (-3.992387) | 0.152868 / 6.500664 (-6.347796) | 0.067823 / 0.075469 (-0.007646) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.220031 / 1.841788 (-0.621757) | 13.781237 / 8.074308 (5.706929) | 14.203975 / 10.191392 (4.012583) | 0.141077 / 0.680424 (-0.539347) | 0.016518 / 0.534201 (-0.517682) | 0.379079 / 0.579283 (-0.200204) | 0.378916 / 0.434364 (-0.055448) | 0.434589 / 0.540337 (-0.105749) | 0.521129 / 1.386936 (-0.865807) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006997 / 0.011353 (-0.004356) | 0.004599 / 0.011008 (-0.006410) | 0.078700 / 0.038508 (0.040192) | 0.027902 / 0.023109 (0.004793) | 0.344406 / 0.275898 (0.068508) | 0.392918 / 0.323480 (0.069438) | 0.005175 / 0.007986 (-0.002811) | 0.004755 / 0.004328 (0.000427) | 0.077707 / 0.004250 (0.073457) | 0.039409 / 0.037052 (0.002357) | 0.343250 / 0.258489 (0.084761) | 0.405544 / 0.293841 (0.111703) | 0.032286 / 0.128546 (-0.096260) | 0.011674 / 0.075646 (-0.063972) | 0.087633 / 0.419271 (-0.331639) | 0.043346 / 0.043533 (-0.000186) | 0.355076 / 0.255139 (0.099937) | 0.382155 / 0.283200 (0.098955) | 0.090914 / 0.141683 (-0.050769) | 1.518369 / 1.452155 (0.066215) | 1.583530 / 1.492716 (0.090813) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.160369 / 0.018006 (0.142362) | 0.406844 / 0.000490 (0.406354) | 0.002651 / 0.000200 (0.002451) | 0.000080 / 0.000054 (0.000025) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025295 / 0.037411 (-0.012116) | 0.101490 / 0.014526 (0.086964) | 0.108825 / 0.176557 (-0.067732) | 0.161673 / 0.737135 (-0.575462) | 0.113610 / 0.296338 (-0.182729) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.443514 / 0.215209 (0.228305) | 4.436722 / 2.077655 (2.359067) | 2.144008 / 1.504120 (0.639888) | 2.005324 / 1.541195 (0.464129) | 2.123356 / 1.468490 (0.654866) | 0.697217 / 4.584777 (-3.887560) | 3.401105 / 3.745712 (-0.344607) | 1.874621 / 5.269862 (-3.395240) | 1.165069 / 4.565676 (-3.400608) | 0.082799 / 0.424275 (-0.341476) | 0.012806 / 0.007607 (0.005199) | 0.542688 / 0.226044 (0.316644) | 5.420963 / 2.268929 (3.152034) | 2.579034 / 55.444624 (-52.865590) | 2.240201 / 6.876477 (-4.636276) | 2.261309 / 2.142072 (0.119237) | 0.800246 / 4.805227 (-4.004981) | 0.150380 / 6.500664 (-6.350285) | 0.066880 / 0.075469 (-0.008589) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.281721 / 1.841788 (-0.560067) | 13.906361 / 8.074308 (5.832053) | 14.135336 / 10.191392 (3.943944) | 0.128865 / 0.680424 (-0.551559) | 0.016452 / 0.534201 (-0.517749) | 0.373563 / 0.579283 (-0.205720) | 0.385321 / 0.434364 (-0.049043) | 0.437198 / 0.540337 (-0.103139) | 0.530720 / 1.386936 (-0.856216) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e2f8e17f3c8f8d0cb77a4c566a78e31fab47108c \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008099 / 0.011353 (-0.003254) | 0.005093 / 0.011008 (-0.005916) | 0.106258 / 0.038508 (0.067750) | 0.037051 / 0.023109 (0.013942) | 0.347960 / 0.275898 (0.072062) | 0.370849 / 0.323480 (0.047369) | 0.006122 / 0.007986 (-0.001863) | 0.004094 / 0.004328 (-0.000235) | 0.079549 / 0.004250 (0.075299) | 0.046563 / 0.037052 (0.009510) | 0.332735 / 0.258489 (0.074246) | 0.417061 / 0.293841 (0.123220) | 0.038105 / 0.128546 (-0.090441) | 0.011886 / 0.075646 (-0.063760) | 0.342103 / 0.419271 (-0.077169) | 0.053233 / 0.043533 (0.009700) | 0.344754 / 0.255139 (0.089615) | 0.355354 / 0.283200 (0.072155) | 0.101059 / 0.141683 (-0.040624) | 1.518561 / 1.452155 (0.066406) | 1.558652 / 1.492716 (0.065935) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.225919 / 0.018006 (0.207913) | 0.518539 / 0.000490 (0.518049) | 0.006230 / 0.000200 (0.006030) | 0.000124 / 0.000054 (0.000070) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026782 / 0.037411 (-0.010629) | 0.108457 / 0.014526 (0.093931) | 0.125203 / 0.176557 (-0.051353) | 0.175726 / 0.737135 (-0.561409) | 0.127051 / 0.296338 (-0.169287) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.416427 / 0.215209 (0.201217) | 4.168851 / 2.077655 (2.091196) | 1.962238 / 1.504120 (0.458118) | 1.825224 / 1.541195 (0.284029) | 1.831200 / 1.468490 (0.362710) | 0.765526 / 4.584777 (-3.819250) | 4.303957 / 3.745712 (0.558245) | 2.193467 / 5.269862 (-3.076395) | 1.654605 / 4.565676 (-2.911071) | 0.096709 / 0.424275 (-0.327566) | 0.013792 / 0.007607 (0.006185) | 0.537862 / 0.226044 (0.311818) | 5.152230 / 2.268929 (2.883302) | 2.520938 / 55.444624 (-52.923686) | 2.108422 / 6.876477 (-4.768054) | 2.214220 / 2.142072 (0.072147) | 0.834320 / 4.805227 (-3.970907) | 0.170635 / 6.500664 (-6.330029) | 0.063131 / 0.075469 (-0.012338) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.215767 / 1.841788 (-0.626020) | 15.254781 / 8.074308 (7.180473) | 14.360764 / 10.191392 (4.169372) | 0.172511 / 0.680424 (-0.507913) | 0.020161 / 0.534201 (-0.514040) | 0.426936 / 0.579283 (-0.152347) | 0.438771 / 0.434364 (0.004407) | 0.486973 / 0.540337 (-0.053364) | 0.584238 / 1.386936 (-0.802698) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006777 / 0.011353 (-0.004576) | 0.005304 / 0.011008 (-0.005704) | 0.073717 / 0.038508 (0.035209) | 0.033604 / 0.023109 (0.010494) | 0.340448 / 0.275898 (0.064550) | 0.351861 / 0.323480 (0.028381) | 0.005786 / 0.007986 (-0.002199) | 0.005013 / 0.004328 (0.000685) | 0.071263 / 0.004250 (0.067012) | 0.048189 / 0.037052 (0.011137) | 0.339457 / 0.258489 (0.080968) | 0.384383 / 0.293841 (0.090542) | 0.035563 / 0.128546 (-0.092983) | 0.011509 / 0.075646 (-0.064137) | 0.083722 / 0.419271 (-0.335550) | 0.048886 / 0.043533 (0.005353) | 0.350184 / 0.255139 (0.095045) | 0.361037 / 0.283200 (0.077837) | 0.105191 / 0.141683 (-0.036492) | 1.503247 / 1.452155 (0.051093) | 1.582298 / 1.492716 (0.089581) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.221687 / 0.018006 (0.203681) | 0.466489 / 0.000490 (0.465999) | 0.000484 / 0.000200 (0.000284) | 0.000069 / 0.000054 (0.000015) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027978 / 0.037411 (-0.009434) | 0.119572 / 0.014526 (0.105047) | 0.133530 / 0.176557 (-0.043026) | 0.177892 / 0.737135 (-0.559243) | 0.127045 / 0.296338 (-0.169294) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.430198 / 0.215209 (0.214989) | 4.435512 / 2.077655 (2.357858) | 2.007183 / 1.504120 (0.503063) | 1.799230 / 1.541195 (0.258036) | 1.884750 / 1.468490 (0.416260) | 0.745232 / 4.584777 (-3.839545) | 4.088069 / 3.745712 (0.342357) | 4.114669 / 5.269862 (-1.155193) | 2.374086 / 4.565676 (-2.191590) | 0.089154 / 0.424275 (-0.335121) | 0.012938 / 0.007607 (0.005331) | 0.505954 / 0.226044 (0.279909) | 5.194226 / 2.268929 (2.925298) | 2.487230 / 55.444624 (-52.957394) | 2.163353 / 6.876477 (-4.713124) | 2.177879 / 2.142072 (0.035807) | 0.828728 / 4.805227 (-3.976499) | 0.171157 / 6.500664 (-6.329507) | 0.062883 / 0.075469 (-0.012586) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.275906 / 1.841788 (-0.565882) | 15.235484 / 8.074308 (7.161176) | 14.467396 / 10.191392 (4.276004) | 0.198994 / 0.680424 (-0.481430) | 0.020203 / 0.534201 (-0.513998) | 0.447904 / 0.579283 (-0.131380) | 0.454210 / 0.434364 (0.019846) | 0.528062 / 0.540337 (-0.012275) | 0.619311 / 1.386936 (-0.767625) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#11cd0f73acbce1d16174f2555e56fda511d5a08b \"CML watermark\")\n" ]

"2023-03-15T16:48:59Z"

"2023-03-16T13:47:37Z"

"2023-03-16T13:40:12Z"

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[ "@lhoestq thanks for the review! I've modified the labels to follow other NLI datasets.\r\nPlease review my change and let me know if I miss anything." ]

"2021-11-20T20:46:03Z"

"2021-11-25T14:51:48Z"

"2021-11-25T14:51:48Z"

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[ "Hi! `load_dataset` is intended to be used to load a canonical dataset (`wikipedia`), a packaged dataset (`csv`, `json`, ...) or a dataset hosted on the Hub. For local datasets saved with `save_to_disk(\"path/to/dataset\")`, use `load_from_disk(\"path/to/dataset\")`.", "Maybe we should raise an informative error message in this case..." ]

"2022-02-10T15:05:53Z"

"2022-02-11T22:56:39Z"

"2022-02-11T22:56:39Z"

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"2021-11-19T20:51:08Z"

"2021-11-25T22:14:43Z"

"2021-11-22T17:04:19Z"

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[ "Hi @dconathan, thanks for reporting.\r\n\r\nWe rely on Arrow as a backend, and as far as I know currently support for `float16` in Arrow is not fully implemented in Python (C++), hence the `ArrowNotImplementedError` you get.\r\n\r\nSee, e.g.: https://arrow.apache.org/docs/status.html?highlight=float16#data-types", "Thanks for the link…. didn’t realize arrow didn’t support it yet. Should it be removed from https://huggingface.co/docs/datasets/v2.4.0/en/package_reference/main_classes#datasets.Value until Arrow supports it?", "Yes, you are right: maybe we should either remove it from our docs or add a comment explaining the issue.\r\n\r\nThe thing is that in Arrow it is partially supported: you can create `float16` values, but you can't cast them from/to other types. And current implementation of `Value` always tries to perform a cast from `float64` to `float16`.", "Maybe we can just add a note in the `Value` documentation ?", "Would you accept a PR to fix this? @lhoestq Do you have an idea of how hard it would be to fix?", "I think the issue comes mostly from pyarrow not supporting `float16` completely.\r\n\r\nFor example you stil can't cast from/to `float16`\r\n```python\r\nimport numpy as np\r\nimport pyarrow as pa\r\n\r\npa.array(range(5)).cast(pa.float16())\r\n# ArrowNotImplementedError: Unsupported cast from int64 to halffloat using function cast_half_float\r\npa.array(range(5), pa.float32()).cast(pa.float16())\r\n# ArrowNotImplementedError: Unsupported cast from float to halffloat using function cast_half_float\r\npa.array(range(5), pa.float16())\r\n# ArrowTypeError: Expected np.float16 instance\r\npa.array(np.arange(5, dtype=np.float16())).cast(pa.float32())\r\n# ArrowNotImplementedError: Unsupported cast from halffloat to float using function cast_float\r\n```", "Hmm it seems like we can either:\r\n1. try to fix pyarrow upstream\r\n2. half-support float16 with some workaround to make sure we don't ever do casting internally\r\n" ]

"2022-09-15T21:03:24Z"

"2023-03-22T21:40:09Z"

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"2020-12-09T15:37:38Z"

"2020-12-10T18:17:05Z"

"2020-12-10T18:17:05Z"

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[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006797 / 0.011353 (-0.004556) | 0.003966 / 0.011008 (-0.007042) | 0.085296 / 0.038508 (0.046788) | 0.076873 / 0.023109 (0.053764) | 0.355795 / 0.275898 (0.079897) | 0.397132 / 0.323480 (0.073652) | 0.005325 / 0.007986 (-0.002660) | 0.003343 / 0.004328 (-0.000986) | 0.064966 / 0.004250 (0.060716) | 0.054519 / 0.037052 (0.017467) | 0.357864 / 0.258489 (0.099374) | 0.409238 / 0.293841 (0.115397) | 0.031620 / 0.128546 (-0.096926) | 0.008529 / 0.075646 (-0.067117) | 0.288502 / 0.419271 (-0.130769) | 0.053260 / 0.043533 (0.009728) | 0.355245 / 0.255139 (0.100106) | 0.384139 / 0.283200 (0.100939) | 0.024507 / 0.141683 (-0.117176) | 1.494696 / 1.452155 (0.042541) | 1.579847 / 1.492716 (0.087130) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.204011 / 0.018006 (0.186005) | 0.451729 / 0.000490 (0.451239) | 0.004628 / 0.000200 (0.004428) | 0.000081 / 0.000054 (0.000026) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028342 / 0.037411 (-0.009069) | 0.084647 / 0.014526 (0.070121) | 0.096174 / 0.176557 (-0.080383) | 0.151753 / 0.737135 (-0.585382) | 0.096347 / 0.296338 (-0.199991) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.387179 / 0.215209 (0.171970) | 3.861552 / 2.077655 (1.783898) | 1.844033 / 1.504120 (0.339913) | 1.678811 / 1.541195 (0.137616) | 1.793207 / 1.468490 (0.324717) | 0.485836 / 4.584777 (-4.098941) | 3.566274 / 3.745712 (-0.179438) | 3.269888 / 5.269862 (-1.999974) | 2.042850 / 4.565676 (-2.522827) | 0.057088 / 0.424275 (-0.367187) | 0.007627 / 0.007607 (0.000019) | 0.460510 / 0.226044 (0.234465) | 4.602019 / 2.268929 (2.333090) | 2.390984 / 55.444624 (-53.053641) | 1.976150 / 6.876477 (-4.900327) | 2.193394 / 2.142072 (0.051322) | 0.582775 / 4.805227 (-4.222453) | 0.133408 / 6.500664 (-6.367256) | 0.060577 / 0.075469 (-0.014893) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.248505 / 1.841788 (-0.593283) | 19.771301 / 8.074308 (11.696993) | 14.327871 / 10.191392 (4.136479) | 0.155288 / 0.680424 (-0.525136) | 0.018310 / 0.534201 (-0.515891) | 0.393664 / 0.579283 (-0.185619) | 0.410578 / 0.434364 (-0.023786) | 0.459301 / 0.540337 (-0.081037) | 0.631921 / 1.386936 (-0.755015) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006827 / 0.011353 (-0.004526) | 0.004094 / 0.011008 (-0.006915) | 0.065299 / 0.038508 (0.026791) | 0.079496 / 0.023109 (0.056387) | 0.403661 / 0.275898 (0.127763) | 0.434449 / 0.323480 (0.110969) | 0.005398 / 0.007986 (-0.002588) | 0.003410 / 0.004328 (-0.000919) | 0.064832 / 0.004250 (0.060582) | 0.056303 / 0.037052 (0.019250) | 0.397848 / 0.258489 (0.139359) | 0.438244 / 0.293841 (0.144403) | 0.032637 / 0.128546 (-0.095909) | 0.008584 / 0.075646 (-0.067063) | 0.071406 / 0.419271 (-0.347866) | 0.048265 / 0.043533 (0.004732) | 0.397814 / 0.255139 (0.142675) | 0.421601 / 0.283200 (0.138402) | 0.023815 / 0.141683 (-0.117868) | 1.504814 / 1.452155 (0.052659) | 1.577185 / 1.492716 (0.084469) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.231775 / 0.018006 (0.213769) | 0.445437 / 0.000490 (0.444948) | 0.005252 / 0.000200 (0.005052) | 0.000093 / 0.000054 (0.000039) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032777 / 0.037411 (-0.004634) | 0.095054 / 0.014526 (0.080528) | 0.106429 / 0.176557 (-0.070127) | 0.160111 / 0.737135 (-0.577024) | 0.108075 / 0.296338 (-0.188263) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.426034 / 0.215209 (0.210825) | 4.244668 / 2.077655 (2.167013) | 2.257938 / 1.504120 (0.753818) | 2.087993 / 1.541195 (0.546798) | 2.170878 / 1.468490 (0.702387) | 0.485228 / 4.584777 (-4.099549) | 3.725912 / 3.745712 (-0.019800) | 3.286925 / 5.269862 (-1.982937) | 2.059929 / 4.565676 (-2.505748) | 0.057813 / 0.424275 (-0.366462) | 0.007518 / 0.007607 (-0.000089) | 0.506632 / 0.226044 (0.280588) | 5.048340 / 2.268929 (2.779411) | 2.744756 / 55.444624 (-52.699869) | 2.406636 / 6.876477 (-4.469841) | 2.617552 / 2.142072 (0.475480) | 0.588476 / 4.805227 (-4.216751) | 0.133518 / 6.500664 (-6.367146) | 0.060778 / 0.075469 (-0.014691) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.356416 / 1.841788 (-0.485372) | 20.467516 / 8.074308 (12.393208) | 15.265443 / 10.191392 (5.074051) | 0.169201 / 0.680424 (-0.511223) | 0.020087 / 0.534201 (-0.514114) | 0.402332 / 0.579283 (-0.176951) | 0.414848 / 0.434364 (-0.019516) | 0.470422 / 0.540337 (-0.069916) | 0.647266 / 1.386936 (-0.739670) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#eb001b4cee7f1d71e393c3ad489a8a5cd8119df5 \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005804 / 0.011353 (-0.005549) | 0.003519 / 0.011008 (-0.007489) | 0.080003 / 0.038508 (0.041495) | 0.055419 / 0.023109 (0.032309) | 0.395254 / 0.275898 (0.119356) | 0.432714 / 0.323480 (0.109234) | 0.004438 / 0.007986 (-0.003548) | 0.002832 / 0.004328 (-0.001496) | 0.062026 / 0.004250 (0.057775) | 0.044334 / 0.037052 (0.007282) | 0.401278 / 0.258489 (0.142789) | 0.451516 / 0.293841 (0.157675) | 0.026791 / 0.128546 (-0.101755) | 0.007946 / 0.075646 (-0.067700) | 0.265166 / 0.419271 (-0.154106) | 0.044119 / 0.043533 (0.000586) | 0.399621 / 0.255139 (0.144482) | 0.422808 / 0.283200 (0.139609) | 0.019998 / 0.141683 (-0.121685) | 1.433559 / 1.452155 (-0.018596) | 1.596902 / 1.492716 (0.104186) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.195662 / 0.018006 (0.177656) | 0.423167 / 0.000490 (0.422677) | 0.003426 / 0.000200 (0.003227) | 0.000066 / 0.000054 (0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023318 / 0.037411 (-0.014094) | 0.072532 / 0.014526 (0.058006) | 0.082181 / 0.176557 (-0.094375) | 0.142214 / 0.737135 (-0.594921) | 0.083423 / 0.296338 (-0.212915) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.402270 / 0.215209 (0.187061) | 4.027607 / 2.077655 (1.949953) | 2.059803 / 1.504120 (0.555684) | 1.865115 / 1.541195 (0.323920) | 1.934976 / 1.468490 (0.466485) | 0.502145 / 4.584777 (-4.082632) | 2.970865 / 3.745712 (-0.774847) | 2.784155 / 5.269862 (-2.485707) | 1.822003 / 4.565676 (-2.743673) | 0.057699 / 0.424275 (-0.366576) | 0.006668 / 0.007607 (-0.000939) | 0.471164 / 0.226044 (0.245120) | 4.733079 / 2.268929 (2.464150) | 2.445119 / 55.444624 (-52.999505) | 2.132956 / 6.876477 (-4.743521) | 2.335998 / 2.142072 (0.193926) | 0.594881 / 4.805227 (-4.210347) | 0.125801 / 6.500664 (-6.374863) | 0.060780 / 0.075469 (-0.014689) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.233170 / 1.841788 (-0.608618) | 17.942205 / 8.074308 (9.867897) | 13.587020 / 10.191392 (3.395628) | 0.142110 / 0.680424 (-0.538314) | 0.016600 / 0.534201 (-0.517601) | 0.328659 / 0.579283 (-0.250624) | 0.347759 / 0.434364 (-0.086605) | 0.378651 / 0.540337 (-0.161687) | 0.523474 / 1.386936 (-0.863462) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006028 / 0.011353 (-0.005325) | 0.003552 / 0.011008 (-0.007456) | 0.062175 / 0.038508 (0.023667) | 0.057602 / 0.023109 (0.034493) | 0.444585 / 0.275898 (0.168687) | 0.471238 / 0.323480 (0.147758) | 0.004562 / 0.007986 (-0.003423) | 0.002871 / 0.004328 (-0.001457) | 0.063101 / 0.004250 (0.058851) | 0.046072 / 0.037052 (0.009020) | 0.448253 / 0.258489 (0.189764) | 0.478734 / 0.293841 (0.184893) | 0.028463 / 0.128546 (-0.100084) | 0.008090 / 0.075646 (-0.067557) | 0.068142 / 0.419271 (-0.351130) | 0.040517 / 0.043533 (-0.003016) | 0.447145 / 0.255139 (0.192006) | 0.469472 / 0.283200 (0.186273) | 0.019391 / 0.141683 (-0.122291) | 1.471195 / 1.452155 (0.019040) | 1.532966 / 1.492716 (0.040249) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.259894 / 0.018006 (0.241888) | 0.412987 / 0.000490 (0.412497) | 0.020780 / 0.000200 (0.020580) | 0.000084 / 0.000054 (0.000030) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026352 / 0.037411 (-0.011060) | 0.080024 / 0.014526 (0.065498) | 0.088041 / 0.176557 (-0.088516) | 0.142987 / 0.737135 (-0.594148) | 0.090108 / 0.296338 (-0.206231) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.458874 / 0.215209 (0.243665) | 4.573005 / 2.077655 (2.495351) | 2.507885 / 1.504120 (1.003765) | 2.335432 / 1.541195 (0.794238) | 2.379617 / 1.468490 (0.911126) | 0.503331 / 4.584777 (-4.081446) | 3.078284 / 3.745712 (-0.667428) | 2.750580 / 5.269862 (-2.519282) | 1.828100 / 4.565676 (-2.737577) | 0.057572 / 0.424275 (-0.366703) | 0.006553 / 0.007607 (-0.001054) | 0.532283 / 0.226044 (0.306239) | 5.310584 / 2.268929 (3.041656) | 2.943559 / 55.444624 (-52.501065) | 2.587544 / 6.876477 (-4.288932) | 2.718261 / 2.142072 (0.576188) | 0.590267 / 4.805227 (-4.214961) | 0.123229 / 6.500664 (-6.377435) | 0.060219 / 0.075469 (-0.015250) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.340773 / 1.841788 (-0.501014) | 18.420766 / 8.074308 (10.346458) | 14.630550 / 10.191392 (4.439158) | 0.146666 / 0.680424 (-0.533758) | 0.017905 / 0.534201 (-0.516296) | 0.332483 / 0.579283 (-0.246801) | 0.355490 / 0.434364 (-0.078874) | 0.382618 / 0.540337 (-0.157720) | 0.531336 / 1.386936 (-0.855600) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d438617fc577bc0222527714edafea0c52ebf239 \"CML watermark\")\n", "There were CI errors unrelated to this PR.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008702 / 0.011353 (-0.002651) | 0.005060 / 0.011008 (-0.005948) | 0.097017 / 0.038508 (0.058509) | 0.073740 / 0.023109 (0.050631) | 0.435138 / 0.275898 (0.159240) | 0.512776 / 0.323480 (0.189296) | 0.006186 / 0.007986 (-0.001800) | 0.003970 / 0.004328 (-0.000358) | 0.089523 / 0.004250 (0.085273) | 0.054441 / 0.037052 (0.017389) | 0.447415 / 0.258489 (0.188926) | 0.464851 / 0.293841 (0.171010) | 0.050264 / 0.128546 (-0.078283) | 0.016643 / 0.075646 (-0.059004) | 0.350565 / 0.419271 (-0.068707) | 0.071220 / 0.043533 (0.027687) | 0.432531 / 0.255139 (0.177392) | 0.472994 / 0.283200 (0.189795) | 0.040229 / 0.141683 (-0.101454) | 1.743431 / 1.452155 (0.291276) | 1.778653 / 1.492716 (0.285936) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.261992 / 0.018006 (0.243986) | 0.571979 / 0.000490 (0.571489) | 0.006270 / 0.000200 (0.006071) | 0.000109 / 0.000054 (0.000054) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027821 / 0.037411 (-0.009590) | 0.081874 / 0.014526 (0.067348) | 0.103725 / 0.176557 (-0.072831) | 0.170593 / 0.737135 (-0.566542) | 0.108749 / 0.296338 (-0.187590) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.690774 / 0.215209 (0.475565) | 6.770902 / 2.077655 (4.693247) | 2.887218 / 1.504120 (1.383098) | 2.456226 / 1.541195 (0.915032) | 2.509422 / 1.468490 (1.040932) | 0.768451 / 4.584777 (-3.816326) | 4.988933 / 3.745712 (1.243221) | 4.151460 / 5.269862 (-1.118402) | 2.640472 / 4.565676 (-1.925205) | 0.093522 / 0.424275 (-0.330753) | 0.008614 / 0.007607 (0.001007) | 0.696281 / 0.226044 (0.470237) | 6.721077 / 2.268929 (4.452149) | 3.229760 / 55.444624 (-52.214864) | 2.668521 / 6.876477 (-4.207956) | 2.866420 / 2.142072 (0.724347) | 0.945328 / 4.805227 (-3.859899) | 0.197645 / 6.500664 (-6.303019) | 0.074442 / 0.075469 (-0.001027) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.630468 / 1.841788 (-0.211320) | 22.991661 / 8.074308 (14.917353) | 19.816919 / 10.191392 (9.625527) | 0.257410 / 0.680424 (-0.423014) | 0.027228 / 0.534201 (-0.506973) | 0.444515 / 0.579283 (-0.134768) | 0.597067 / 0.434364 (0.162703) | 0.528151 / 0.540337 (-0.012186) | 0.771276 / 1.386936 (-0.615660) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009154 / 0.011353 (-0.002199) | 0.004648 / 0.011008 (-0.006360) | 0.073054 / 0.038508 (0.034546) | 0.077146 / 0.023109 (0.054037) | 0.481659 / 0.275898 (0.205761) | 0.516985 / 0.323480 (0.193505) | 0.007447 / 0.007986 (-0.000538) | 0.003890 / 0.004328 (-0.000438) | 0.078701 / 0.004250 (0.074450) | 0.059183 / 0.037052 (0.022131) | 0.475350 / 0.258489 (0.216861) | 0.547834 / 0.293841 (0.253993) | 0.058440 / 0.128546 (-0.070106) | 0.013563 / 0.075646 (-0.062083) | 0.084320 / 0.419271 (-0.334951) | 0.065965 / 0.043533 (0.022433) | 0.483541 / 0.255139 (0.228402) | 0.513940 / 0.283200 (0.230740) | 0.042889 / 0.141683 (-0.098794) | 1.676050 / 1.452155 (0.223895) | 1.759206 / 1.492716 (0.266489) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.274848 / 0.018006 (0.256841) | 0.588965 / 0.000490 (0.588475) | 0.006312 / 0.000200 (0.006112) | 0.000120 / 0.000054 (0.000065) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033871 / 0.037411 (-0.003540) | 0.104013 / 0.014526 (0.089487) | 0.118457 / 0.176557 (-0.058099) | 0.178268 / 0.737135 (-0.558868) | 0.116972 / 0.296338 (-0.179366) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.609952 / 0.215209 (0.394743) | 5.788754 / 2.077655 (3.711100) | 2.812166 / 1.504120 (1.308046) | 2.362861 / 1.541195 (0.821666) | 2.641295 / 1.468490 (1.172804) | 0.767601 / 4.584777 (-3.817176) | 5.027439 / 3.745712 (1.281727) | 4.612511 / 5.269862 (-0.657351) | 2.654364 / 4.565676 (-1.911312) | 0.103100 / 0.424275 (-0.321175) | 0.012233 / 0.007607 (0.004626) | 0.749283 / 0.226044 (0.523238) | 7.511093 / 2.268929 (5.242165) | 3.585867 / 55.444624 (-51.858757) | 3.255110 / 6.876477 (-3.621366) | 3.260174 / 2.142072 (1.118102) | 0.958422 / 4.805227 (-3.846806) | 0.209096 / 6.500664 (-6.291568) | 0.075014 / 0.075469 (-0.000455) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.728283 / 1.841788 (-0.113504) | 25.411147 / 8.074308 (17.336839) | 21.335202 / 10.191392 (11.143810) | 0.199090 / 0.680424 (-0.481334) | 0.031288 / 0.534201 (-0.502913) | 0.449226 / 0.579283 (-0.130057) | 0.555570 / 0.434364 (0.121206) | 0.570297 / 0.540337 (0.029960) | 0.758673 / 1.386936 (-0.628263) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#fa696b4b4f0d11c5b8592eb31cb1d54a707e3d33 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006862 / 0.011353 (-0.004491) | 0.003959 / 0.011008 (-0.007049) | 0.087219 / 0.038508 (0.048711) | 0.078335 / 0.023109 (0.055226) | 0.319019 / 0.275898 (0.043121) | 0.342871 / 0.323480 (0.019391) | 0.004065 / 0.007986 (-0.003921) | 0.004346 / 0.004328 (0.000017) | 0.065243 / 0.004250 (0.060993) | 0.056698 / 0.037052 (0.019646) | 0.326906 / 0.258489 (0.068417) | 0.354323 / 0.293841 (0.060482) | 0.031252 / 0.128546 (-0.097295) | 0.008587 / 0.075646 (-0.067060) | 0.300323 / 0.419271 (-0.118948) | 0.052810 / 0.043533 (0.009277) | 0.323866 / 0.255139 (0.068727) | 0.346011 / 0.283200 (0.062811) | 0.025584 / 0.141683 (-0.116099) | 1.464475 / 1.452155 (0.012320) | 1.530868 / 1.492716 (0.038152) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.208927 / 0.018006 (0.190921) | 0.454147 / 0.000490 (0.453657) | 0.003945 / 0.000200 (0.003746) | 0.000081 / 0.000054 (0.000026) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029901 / 0.037411 (-0.007511) | 0.088889 / 0.014526 (0.074363) | 0.098181 / 0.176557 (-0.078375) | 0.156787 / 0.737135 (-0.580349) | 0.099015 / 0.296338 (-0.197324) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.384981 / 0.215209 (0.169772) | 3.831040 / 2.077655 (1.753386) | 1.858312 / 1.504120 (0.354192) | 1.686846 / 1.541195 (0.145651) | 1.771509 / 1.468490 (0.303019) | 0.485618 / 4.584777 (-4.099159) | 3.430961 / 3.745712 (-0.314751) | 3.264489 / 5.269862 (-2.005372) | 2.040125 / 4.565676 (-2.525551) | 0.057218 / 0.424275 (-0.367057) | 0.007640 / 0.007607 (0.000033) | 0.468072 / 0.226044 (0.242027) | 4.677214 / 2.268929 (2.408286) | 2.348425 / 55.444624 (-53.096199) | 1.994352 / 6.876477 (-4.882125) | 2.217020 / 2.142072 (0.074948) | 0.587467 / 4.805227 (-4.217760) | 0.133550 / 6.500664 (-6.367114) | 0.060571 / 0.075469 (-0.014898) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.271003 / 1.841788 (-0.570785) | 19.986365 / 8.074308 (11.912057) | 14.574046 / 10.191392 (4.382654) | 0.146212 / 0.680424 (-0.534212) | 0.018320 / 0.534201 (-0.515881) | 0.394524 / 0.579283 (-0.184759) | 0.399707 / 0.434364 (-0.034657) | 0.458965 / 0.540337 (-0.081372) | 0.619940 / 1.386936 (-0.766996) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006982 / 0.011353 (-0.004371) | 0.004061 / 0.011008 (-0.006947) | 0.064520 / 0.038508 (0.026012) | 0.076828 / 0.023109 (0.053719) | 0.402989 / 0.275898 (0.127090) | 0.439697 / 0.323480 (0.116217) | 0.005511 / 0.007986 (-0.002475) | 0.003378 / 0.004328 (-0.000950) | 0.064727 / 0.004250 (0.060477) | 0.058114 / 0.037052 (0.021062) | 0.402054 / 0.258489 (0.143565) | 0.442377 / 0.293841 (0.148536) | 0.032808 / 0.128546 (-0.095738) | 0.008604 / 0.075646 (-0.067043) | 0.070994 / 0.419271 (-0.348278) | 0.048738 / 0.043533 (0.005205) | 0.399786 / 0.255139 (0.144647) | 0.423537 / 0.283200 (0.140338) | 0.022397 / 0.141683 (-0.119286) | 1.504613 / 1.452155 (0.052458) | 1.571064 / 1.492716 (0.078348) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.226876 / 0.018006 (0.208870) | 0.451477 / 0.000490 (0.450987) | 0.004511 / 0.000200 (0.004311) | 0.000095 / 0.000054 (0.000041) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032998 / 0.037411 (-0.004413) | 0.095843 / 0.014526 (0.081317) | 0.105684 / 0.176557 (-0.070873) | 0.158175 / 0.737135 (-0.578960) | 0.107297 / 0.296338 (-0.189041) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.434912 / 0.215209 (0.219703) | 4.326394 / 2.077655 (2.248740) | 2.287310 / 1.504120 (0.783190) | 2.127987 / 1.541195 (0.586793) | 2.202485 / 1.468490 (0.733995) | 0.494305 / 4.584777 (-4.090472) | 3.575176 / 3.745712 (-0.170536) | 3.354358 / 5.269862 (-1.915504) | 2.074293 / 4.565676 (-2.491383) | 0.058967 / 0.424275 (-0.365308) | 0.007712 / 0.007607 (0.000105) | 0.513734 / 0.226044 (0.287690) | 5.107538 / 2.268929 (2.838610) | 2.776190 / 55.444624 (-52.668434) | 2.425051 / 6.876477 (-4.451426) | 2.666715 / 2.142072 (0.524643) | 0.598844 / 4.805227 (-4.206383) | 0.134186 / 6.500664 (-6.366478) | 0.062403 / 0.075469 (-0.013066) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.346730 / 1.841788 (-0.495058) | 20.533190 / 8.074308 (12.458882) | 15.174443 / 10.191392 (4.983051) | 0.167204 / 0.680424 (-0.513219) | 0.020619 / 0.534201 (-0.513582) | 0.399033 / 0.579283 (-0.180250) | 0.394428 / 0.434364 (-0.039936) | 0.468792 / 0.540337 (-0.071545) | 0.640122 / 1.386936 (-0.746814) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2c4c2b529e2a262a5006e4caa55fbc003378006a \"CML watermark\")\n" ]

"2023-09-04T06:07:12Z"

"2023-09-04T09:22:19Z"

"2023-09-04T09:13:32Z"

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[ "_The documentation is not available anymore as the PR was closed or merged._", "I still don't know if the way we implemented data files resolution could support the metadata.jsonl file without bad side effects for the other packaged builders. In particular here if you have a folder of csv/parquet/whatever files and a metadata.jsonl file, it would return \r\n```\r\nsplit: patterns_dict[split] + [METADATA_PATTERN]\r\n```\r\nwhich is a bit unexpected and can lead to errors.\r\n\r\nMaybe this logic can be specific to imagefolder somehow ? This could be an additional pattern `[\"metadata.jsonl\", \"**/metadata.jsonl\"]` just for imagefolder, that is only used when `data_files=` is not specified by the user.\r\n\r\nI guess it's ok to have patterns that lead to duplicate metadata.jsonl files for imagefolder, since the imagefolder logic only considers the closest metadata file for each image.\r\n\r\nWhat do you think ?", "Yes, that's indeed the problem. My solution in https://github.com/huggingface/datasets/commit/4d20618ea7a19bc143ddc5fdff9d79e671fcbb95 that accounts for that (include metadata files only if image files are present; not ideal): https://github.com/huggingface/datasets/blob/4d20618ea7a19bc143ddc5fdff9d79e671fcbb95/src/datasets/data_files.py#L119-L125.\r\nPerhaps a cleaner approach would be to check for metadata files after the packaged module type is inferred as `imagefolder` and append metadata files to already resolved data files (if there are any). WDYT?", "@lhoestq \r\n\r\n> Perhaps a cleaner approach would be to check for metadata files after the packaged module type is inferred as imagefolder and append metadata files to already resolved data files (if there are any). WDYT?\r\n\r\nI decided to go with this approach.\r\n\r\n Not sure if you meant the same thing with this comment:\r\n\r\n> Maybe this logic can be specific to imagefolder somehow ? This could be an additional pattern [\"metadata.jsonl\", \"**/metadata.jsonl\"] just for imagefolder, that is only used when data_files= is not specified by the user.\r\n\r\n\r\nIt adds more code but is easy to follow IMO.\r\n", "The CI still struggles but you can merge since at least one of the two WIN CI succeeded" ]

"2022-06-27T12:01:29Z"

"2022-07-01T12:44:55Z"

"2022-06-30T10:15:32Z"

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[ "Works smoothly with the `transformers.Trainer` class now, thank you!" ]

"2022-01-27T17:21:02Z"

"2022-01-28T16:34:29Z"

"2022-01-28T16:34:28Z"

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.011875 / 0.011353 (0.000522) | 0.008188 / 0.011008 (-0.002821) | 0.131137 / 0.038508 (0.092629) | 0.038127 / 0.023109 (0.015018) | 0.383864 / 0.275898 (0.107966) | 0.458617 / 0.323480 (0.135137) | 0.010989 / 0.007986 (0.003003) | 0.004892 / 0.004328 (0.000563) | 0.101955 / 0.004250 (0.097704) | 0.045081 / 0.037052 (0.008029) | 0.409768 / 0.258489 (0.151279) | 0.446597 / 0.293841 (0.152756) | 0.058588 / 0.128546 (-0.069958) | 0.020872 / 0.075646 (-0.054774) | 0.432982 / 0.419271 (0.013711) | 0.075875 / 0.043533 (0.032342) | 0.380923 / 0.255139 (0.125784) | 0.432994 / 0.283200 (0.149795) | 0.122678 / 0.141683 (-0.019005) | 1.857865 / 1.452155 (0.405710) | 1.927801 / 1.492716 (0.435085) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.212941 / 0.018006 (0.194935) | 0.527977 / 0.000490 (0.527488) | 0.002996 / 0.000200 (0.002797) | 0.000105 / 0.000054 (0.000051) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030046 / 0.037411 (-0.007366) | 0.126384 / 0.014526 (0.111858) | 0.138307 / 0.176557 (-0.038250) | 0.185338 / 0.737135 (-0.551797) | 0.144733 / 0.296338 (-0.151606) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.627096 / 0.215209 (0.411887) | 6.418014 / 2.077655 (4.340360) | 2.547675 / 1.504120 (1.043555) | 2.195552 / 1.541195 (0.654357) | 2.200377 / 1.468490 (0.731887) | 1.289935 / 4.584777 (-3.294842) | 5.670839 / 3.745712 (1.925127) | 5.252597 / 5.269862 (-0.017265) | 2.878470 / 4.565676 (-1.687207) | 0.143754 / 0.424275 (-0.280521) | 0.014814 / 0.007607 (0.007207) | 0.810073 / 0.226044 (0.584028) | 8.183757 / 2.268929 (5.914829) | 3.375525 / 55.444624 (-52.069099) | 2.594048 / 6.876477 (-4.282428) | 2.598095 / 2.142072 (0.456023) | 1.554493 / 4.805227 (-3.250734) | 0.263159 / 6.500664 (-6.237505) | 0.089822 / 0.075469 (0.014353) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.660847 / 1.841788 (-0.180941) | 18.434283 / 8.074308 (10.359975) | 21.764887 / 10.191392 (11.573495) | 0.264524 / 0.680424 (-0.415900) | 0.048519 / 0.534201 (-0.485682) | 0.587468 / 0.579283 (0.008185) | 0.634142 / 0.434364 (0.199778) | 0.675374 / 0.540337 (0.135037) | 0.777510 / 1.386936 (-0.609426) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.010021 / 0.011353 (-0.001332) | 0.006207 / 0.011008 (-0.004801) | 0.130490 / 0.038508 (0.091982) | 0.037957 / 0.023109 (0.014848) | 0.489381 / 0.275898 (0.213483) | 0.536522 / 0.323480 (0.213042) | 0.008611 / 0.007986 (0.000626) | 0.004894 / 0.004328 (0.000565) | 0.101617 / 0.004250 (0.097367) | 0.052629 / 0.037052 (0.015577) | 0.509211 / 0.258489 (0.250721) | 0.545023 / 0.293841 (0.251182) | 0.057468 / 0.128546 (-0.071078) | 0.023393 / 0.075646 (-0.052253) | 0.431408 / 0.419271 (0.012137) | 0.064967 / 0.043533 (0.021434) | 0.495261 / 0.255139 (0.240122) | 0.527098 / 0.283200 (0.243898) | 0.113172 / 0.141683 (-0.028511) | 1.937072 / 1.452155 (0.484918) | 2.048413 / 1.492716 (0.555697) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.245406 / 0.018006 (0.227399) | 0.526772 / 0.000490 (0.526283) | 0.004379 / 0.000200 (0.004179) | 0.000114 / 0.000054 (0.000060) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031785 / 0.037411 (-0.005626) | 0.130949 / 0.014526 (0.116424) | 0.145660 / 0.176557 (-0.030896) | 0.186991 / 0.737135 (-0.550144) | 0.151000 / 0.296338 (-0.145338) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.708643 / 0.215209 (0.493434) | 7.179252 / 2.077655 (5.101597) | 3.143375 / 1.504120 (1.639255) | 2.714298 / 1.541195 (1.173103) | 2.773441 / 1.468490 (1.304951) | 1.312821 / 4.584777 (-3.271956) | 5.798396 / 3.745712 (2.052684) | 3.253215 / 5.269862 (-2.016646) | 2.147260 / 4.565676 (-2.418416) | 0.154673 / 0.424275 (-0.269602) | 0.014918 / 0.007607 (0.007311) | 0.860618 / 0.226044 (0.634573) | 8.774455 / 2.268929 (6.505527) | 3.925020 / 55.444624 (-51.519604) | 3.139361 / 6.876477 (-3.737115) | 3.208883 / 2.142072 (1.066810) | 1.547305 / 4.805227 (-3.257922) | 0.268814 / 6.500664 (-6.231850) | 0.084578 / 0.075469 (0.009109) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.694990 / 1.841788 (-0.146798) | 18.619183 / 8.074308 (10.544875) | 21.929886 / 10.191392 (11.738494) | 0.265763 / 0.680424 (-0.414661) | 0.028325 / 0.534201 (-0.505876) | 0.552910 / 0.579283 (-0.026373) | 0.616864 / 0.434364 (0.182500) | 0.637858 / 0.540337 (0.097521) | 0.744508 / 1.386936 (-0.642428) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5f819ba3d0306748aaf9fd8ea040b981dd08e5e5 \"CML watermark\")\n" ]

"2023-01-20T10:11:02Z"

"2023-01-20T10:38:13Z"

"2023-01-20T10:28:43Z"

{ "diff_url": "https://github.com/huggingface/datasets/pull/5447.diff", "html_url": "https://github.com/huggingface/datasets/pull/5447", "merged_at": "2023-01-20T10:28:43Z", "patch_url": "https://github.com/huggingface/datasets/pull/5447.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5447" }

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[ { "color": "a2eeef", "default": true, "description": "New feature or request", "id": 1935892871, "name": "enhancement", "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement" } ]

[]

[ "Hi @mariosasko!\r\n\r\nHas this been patched into any of the releases?", "Hi! Not yet, would you be interested in contributing a PR? I can give you some pointers if needed. ", "@mariosasko did this ever get implemented? Willing to help if you are still up for it.", "@dcruiz01 No, but here is an example of how to do this with the existing API:\r\n\r\n\r\n```python\r\nds = Dataset.from_dict({\"tokens\": [[\"a\", \"b\"], [\"c\", \"a\"], [\"c\", \"e\"]]})\r\n\r\ndef flatten_tokens(pa_table):\r\n return pa.table([pc.list_flatten(pa_table[\"tokens\"])], [\"flat_tokens\"])\r\n\r\nds = ds.with_format(\"arrow\")\r\nds = ds.map(flatten_tokens, batched=True)\r\nds = ds.with_format(None)\r\n\r\nunique_tokens = ds.unique(\"flat_tokens\")\r\n```\r\n\r\nWhen I think about it, `.unique` on `Sequence(Value(...))` should return unique sequences/arrays, not unique elements of these sequences..." ]

"2021-11-19T13:16:06Z"

"2023-05-19T14:45:40Z"

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[ "Conll has `multilingual` but is only tagged as `en`", "good catch, that was a bad copy paste x)" ]

"2021-01-21T13:50:10Z"

"2021-01-22T09:40:01Z"

"2021-01-22T09:40:00Z"

{ "diff_url": "https://github.com/huggingface/datasets/pull/1760.diff", "html_url": "https://github.com/huggingface/datasets/pull/1760", "merged_at": "2021-01-22T09:40:00Z", "patch_url": "https://github.com/huggingface/datasets/pull/1760.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/1760" }

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007220 / 0.011353 (-0.004133) | 0.004558 / 0.011008 (-0.006451) | 0.116647 / 0.038508 (0.078139) | 0.046845 / 0.023109 (0.023736) | 0.352429 / 0.275898 (0.076531) | 0.429739 / 0.323480 (0.106259) | 0.006620 / 0.007986 (-0.001366) | 0.003731 / 0.004328 (-0.000597) | 0.088683 / 0.004250 (0.084433) | 0.070583 / 0.037052 (0.033530) | 0.366699 / 0.258489 (0.108210) | 0.420730 / 0.293841 (0.126889) | 0.037342 / 0.128546 (-0.091204) | 0.010041 / 0.075646 (-0.065605) | 0.383477 / 0.419271 (-0.035795) | 0.060279 / 0.043533 (0.016746) | 0.349988 / 0.255139 (0.094849) | 0.371423 / 0.283200 (0.088224) | 0.026725 / 0.141683 (-0.114958) | 1.736886 / 1.452155 (0.284731) | 1.812874 / 1.492716 (0.320157) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.253256 / 0.018006 (0.235250) | 0.563470 / 0.000490 (0.562980) | 0.010475 / 0.000200 (0.010275) | 0.000164 / 0.000054 (0.000110) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030518 / 0.037411 (-0.006893) | 0.133324 / 0.014526 (0.118798) | 0.137095 / 0.176557 (-0.039461) | 0.202227 / 0.737135 (-0.534909) | 0.144195 / 0.296338 (-0.152143) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.480870 / 0.215209 (0.265661) | 4.822713 / 2.077655 (2.745058) | 2.124183 / 1.504120 (0.620064) | 1.910733 / 1.541195 (0.369538) | 1.970266 / 1.468490 (0.501776) | 0.624695 / 4.584777 (-3.960082) | 4.459659 / 3.745712 (0.713947) | 2.210123 / 5.269862 (-3.059739) | 1.300520 / 4.565676 (-3.265157) | 0.077096 / 0.424275 (-0.347180) | 0.013333 / 0.007607 (0.005726) | 0.596841 / 0.226044 (0.370797) | 5.917397 / 2.268929 (3.648469) | 2.699397 / 55.444624 (-52.745228) | 2.274833 / 6.876477 (-4.601644) | 2.525376 / 2.142072 (0.383304) | 0.755718 / 4.805227 (-4.049510) | 0.163587 / 6.500664 (-6.337077) | 0.072817 / 0.075469 (-0.002653) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.524306 / 1.841788 (-0.317481) | 18.843312 / 8.074308 (10.769004) | 15.694644 / 10.191392 (5.503252) | 0.177400 / 0.680424 (-0.503024) | 0.020104 / 0.534201 (-0.514097) | 0.466421 / 0.579283 (-0.112862) | 0.537274 / 0.434364 (0.102910) | 0.576920 / 0.540337 (0.036583) | 0.718889 / 1.386936 (-0.668047) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007671 / 0.011353 (-0.003682) | 0.004850 / 0.011008 (-0.006158) | 0.090085 / 0.038508 (0.051576) | 0.052023 / 0.023109 (0.028914) | 0.508575 / 0.275898 (0.232677) | 0.590024 / 0.323480 (0.266544) | 0.004564 / 0.007986 (-0.003422) | 0.005345 / 0.004328 (0.001017) | 0.087904 / 0.004250 (0.083653) | 0.064446 / 0.037052 (0.027394) | 0.525625 / 0.258489 (0.267136) | 0.584307 / 0.293841 (0.290466) | 0.037221 / 0.128546 (-0.091325) | 0.010588 / 0.075646 (-0.065059) | 0.098612 / 0.419271 (-0.320659) | 0.059597 / 0.043533 (0.016064) | 0.488064 / 0.255139 (0.232925) | 0.522330 / 0.283200 (0.239131) | 0.030004 / 0.141683 (-0.111679) | 1.732512 / 1.452155 (0.280357) | 1.809027 / 1.492716 (0.316310) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.218741 / 0.018006 (0.200735) | 0.494946 / 0.000490 (0.494456) | 0.004580 / 0.000200 (0.004380) | 0.000104 / 0.000054 (0.000049) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034916 / 0.037411 (-0.002495) | 0.133695 / 0.014526 (0.119169) | 0.147964 / 0.176557 (-0.028592) | 0.213210 / 0.737135 (-0.523926) | 0.148850 / 0.296338 (-0.147488) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.508855 / 0.215209 (0.293646) | 5.065088 / 2.077655 (2.987433) | 2.473110 / 1.504120 (0.968990) | 2.259765 / 1.541195 (0.718570) | 2.359189 / 1.468490 (0.890699) | 0.639082 / 4.584777 (-3.945695) | 4.768195 / 3.745712 (1.022482) | 2.253803 / 5.269862 (-3.016059) | 1.442996 / 4.565676 (-3.122680) | 0.078761 / 0.424275 (-0.345514) | 0.013936 / 0.007607 (0.006329) | 0.625977 / 0.226044 (0.399933) | 6.260817 / 2.268929 (3.991888) | 3.149640 / 55.444624 (-52.294985) | 2.753555 / 6.876477 (-4.122921) | 2.831872 / 2.142072 (0.689799) | 0.781294 / 4.805227 (-4.023933) | 0.169109 / 6.500664 (-6.331555) | 0.075810 / 0.075469 (0.000341) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.533282 / 1.841788 (-0.308506) | 19.460579 / 8.074308 (11.386271) | 17.250424 / 10.191392 (7.059032) | 0.193485 / 0.680424 (-0.486939) | 0.020650 / 0.534201 (-0.513551) | 0.472110 / 0.579283 (-0.107173) | 0.532276 / 0.434364 (0.097912) | 0.613152 / 0.540337 (0.072814) | 0.684684 / 1.386936 (-0.702252) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#650a86ee122209d4a8c8e8068c01ebfd3ba553f5 \"CML watermark\")\n" ]

"2023-06-13T12:38:59Z"

"2023-06-14T15:11:55Z"

"2023-06-14T15:03:33Z"

{ "diff_url": "https://github.com/huggingface/datasets/pull/5948.diff", "html_url": "https://github.com/huggingface/datasets/pull/5948", "merged_at": "2023-06-14T15:03:33Z", "patch_url": "https://github.com/huggingface/datasets/pull/5948.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/5948" }

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[ { "color": "a2eeef", "default": true, "description": "New feature or request", "id": 1935892871, "name": "enhancement", "node_id": "MDU6TGFiZWwxOTM1ODkyODcx", "url": "https://api.github.com/repos/huggingface/datasets/labels/enhancement" } ]

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[ "A duplicate of https://github.com/huggingface/datasets/issues/4796.\r\n\r\nWe could get this for free by implementing the `Image` feature as an extension type, as shown in [this](https://colab.research.google.com/drive/1Uzm_tXVpGTwbzleDConWcNjacwO1yxE4?usp=sharing) Colab (example with UUIDs).\r\n", "+1 to this\r\nCalling this line with a df that contains a PIL image (as they are returned from load_dataset)\r\n`ds = Dataset.from_pandas(df)`\r\nResults in this error:\r\n`ArrowInvalid: ('Could not convert <PIL.PngImagePlugin.PngImageFile image mode=RGB size=1024x1024 at 0x2B41F2D70> with type PngImageFile: did not recognize Python value type when inferring an Arrow data type', 'Conversion failed for column image with type object')`" ]

"2023-10-10T10:29:16Z"

"2023-10-12T17:36:27Z"

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[]

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"2021-11-04T17:38:06Z"

"2021-11-05T10:57:38Z"

"2021-11-05T10:57:37Z"

{ "diff_url": "https://github.com/huggingface/datasets/pull/3216.diff", "html_url": "https://github.com/huggingface/datasets/pull/3216", "merged_at": "2021-11-05T10:57:37Z", "patch_url": "https://github.com/huggingface/datasets/pull/3216.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/3216" }

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.010844 / 0.011353 (-0.000509) | 0.007329 / 0.011008 (-0.003680) | 0.133764 / 0.038508 (0.095256) | 0.040213 / 0.023109 (0.017103) | 0.413466 / 0.275898 (0.137568) | 0.452860 / 0.323480 (0.129380) | 0.008109 / 0.007986 (0.000123) | 0.005773 / 0.004328 (0.001444) | 0.109969 / 0.004250 (0.105718) | 0.053001 / 0.037052 (0.015949) | 0.416377 / 0.258489 (0.157888) | 0.477486 / 0.293841 (0.183645) | 0.056556 / 0.128546 (-0.071990) | 0.024322 / 0.075646 (-0.051324) | 0.437750 / 0.419271 (0.018479) | 0.087732 / 0.043533 (0.044199) | 0.421540 / 0.255139 (0.166401) | 0.429143 / 0.283200 (0.145944) | 0.144864 / 0.141683 (0.003181) | 1.882785 / 1.452155 (0.430631) | 1.980721 / 1.492716 (0.488005) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.285497 / 0.018006 (0.267491) | 0.601820 / 0.000490 (0.601331) | 0.005003 / 0.000200 (0.004804) | 0.000122 / 0.000054 (0.000067) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030673 / 0.037411 (-0.006739) | 0.126883 / 0.014526 (0.112357) | 0.137677 / 0.176557 (-0.038880) | 0.211504 / 0.737135 (-0.525632) | 0.144752 / 0.296338 (-0.151587) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.665845 / 0.215209 (0.450636) | 6.369040 / 2.077655 (4.291385) | 2.708979 / 1.504120 (1.204859) | 2.370842 / 1.541195 (0.829647) | 2.445987 / 1.468490 (0.977497) | 1.260806 / 4.584777 (-3.323971) | 5.979216 / 3.745712 (2.233504) | 3.334350 / 5.269862 (-1.935512) | 2.187298 / 4.565676 (-2.378379) | 0.155494 / 0.424275 (-0.268781) | 0.017351 / 0.007607 (0.009744) | 0.853626 / 0.226044 (0.627581) | 8.375001 / 2.268929 (6.106072) | 3.528312 / 55.444624 (-51.916313) | 2.890509 / 6.876477 (-3.985968) | 3.051016 / 2.142072 (0.908944) | 1.529811 / 4.805227 (-3.275416) | 0.273883 / 6.500664 (-6.226781) | 0.086617 / 0.075469 (0.011148) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.648231 / 1.841788 (-0.193557) | 19.487109 / 8.074308 (11.412801) | 23.474621 / 10.191392 (13.283229) | 0.221392 / 0.680424 (-0.459032) | 0.028878 / 0.534201 (-0.505323) | 0.582302 / 0.579283 (0.003019) | 0.615059 / 0.434364 (0.180695) | 0.656082 / 0.540337 (0.115745) | 0.740544 / 1.386936 (-0.646392) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.010687 / 0.011353 (-0.000665) | 0.007114 / 0.011008 (-0.003894) | 0.135426 / 0.038508 (0.096918) | 0.041027 / 0.023109 (0.017918) | 0.466441 / 0.275898 (0.190543) | 0.503545 / 0.323480 (0.180065) | 0.009418 / 0.007986 (0.001432) | 0.004976 / 0.004328 (0.000647) | 0.101342 / 0.004250 (0.097092) | 0.058289 / 0.037052 (0.021237) | 0.473715 / 0.258489 (0.215226) | 0.539556 / 0.293841 (0.245715) | 0.063138 / 0.128546 (-0.065408) | 0.020429 / 0.075646 (-0.055217) | 0.124179 / 0.419271 (-0.295093) | 0.066400 / 0.043533 (0.022867) | 0.450793 / 0.255139 (0.195654) | 0.494163 / 0.283200 (0.210964) | 0.131179 / 0.141683 (-0.010504) | 1.876396 / 1.452155 (0.424241) | 1.974148 / 1.492716 (0.481432) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.313362 / 0.018006 (0.295356) | 0.602618 / 0.000490 (0.602129) | 0.008279 / 0.000200 (0.008079) | 0.000155 / 0.000054 (0.000101) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.037250 / 0.037411 (-0.000161) | 0.144151 / 0.014526 (0.129625) | 0.155733 / 0.176557 (-0.020824) | 0.214334 / 0.737135 (-0.522801) | 0.167124 / 0.296338 (-0.129214) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.686471 / 0.215209 (0.471262) | 6.749174 / 2.077655 (4.671520) | 3.024941 / 1.504120 (1.520821) | 2.553363 / 1.541195 (1.012168) | 2.679107 / 1.468490 (1.210617) | 1.317212 / 4.584777 (-3.267565) | 5.917575 / 3.745712 (2.171862) | 3.412715 / 5.269862 (-1.857146) | 2.203478 / 4.565676 (-2.362198) | 0.150387 / 0.424275 (-0.273888) | 0.015977 / 0.007607 (0.008370) | 0.862999 / 0.226044 (0.636954) | 8.706459 / 2.268929 (6.437530) | 3.762648 / 55.444624 (-51.681977) | 2.992544 / 6.876477 (-3.883933) | 3.135796 / 2.142072 (0.993724) | 1.504140 / 4.805227 (-3.301088) | 0.268265 / 6.500664 (-6.232399) | 0.083297 / 0.075469 (0.007828) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.690193 / 1.841788 (-0.151594) | 19.912854 / 8.074308 (11.838546) | 23.568217 / 10.191392 (13.376825) | 0.285125 / 0.680424 (-0.395299) | 0.030593 / 0.534201 (-0.503608) | 0.565305 / 0.579283 (-0.013978) | 0.659283 / 0.434364 (0.224919) | 0.678864 / 0.540337 (0.138527) | 0.793634 / 1.386936 (-0.593302) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9d0edbe3f3258b7e580d1b58c0eea6637b5e22b2 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.011615 / 0.011353 (0.000262) | 0.006716 / 0.011008 (-0.004292) | 0.146868 / 0.038508 (0.108360) | 0.037621 / 0.023109 (0.014512) | 0.425563 / 0.275898 (0.149664) | 0.483217 / 0.323480 (0.159737) | 0.007830 / 0.007986 (-0.000156) | 0.005940 / 0.004328 (0.001612) | 0.100771 / 0.004250 (0.096521) | 0.063907 / 0.037052 (0.026854) | 0.422993 / 0.258489 (0.164503) | 0.496514 / 0.293841 (0.202673) | 0.056004 / 0.128546 (-0.072542) | 0.021441 / 0.075646 (-0.054206) | 0.453589 / 0.419271 (0.034317) | 0.067555 / 0.043533 (0.024022) | 0.442490 / 0.255139 (0.187351) | 0.503941 / 0.283200 (0.220742) | 0.134023 / 0.141683 (-0.007660) | 1.886329 / 1.452155 (0.434175) | 2.030867 / 1.492716 (0.538150) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.288063 / 0.018006 (0.270057) | 0.627177 / 0.000490 (0.626687) | 0.006335 / 0.000200 (0.006135) | 0.000171 / 0.000054 (0.000116) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032424 / 0.037411 (-0.004987) | 0.132749 / 0.014526 (0.118223) | 0.144727 / 0.176557 (-0.031829) | 0.232577 / 0.737135 (-0.504558) | 0.157315 / 0.296338 (-0.139024) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.623058 / 0.215209 (0.407849) | 6.272447 / 2.077655 (4.194792) | 2.506778 / 1.504120 (1.002658) | 2.203094 / 1.541195 (0.661899) | 2.346972 / 1.468490 (0.878482) | 1.358498 / 4.584777 (-3.226279) | 5.879670 / 3.745712 (2.133958) | 5.818406 / 5.269862 (0.548545) | 3.231936 / 4.565676 (-1.333741) | 0.154013 / 0.424275 (-0.270263) | 0.021541 / 0.007607 (0.013934) | 0.823746 / 0.226044 (0.597702) | 8.140304 / 2.268929 (5.871375) | 3.366911 / 55.444624 (-52.077714) | 2.696856 / 6.876477 (-4.179621) | 2.845743 / 2.142072 (0.703671) | 1.522363 / 4.805227 (-3.282864) | 0.278938 / 6.500664 (-6.221726) | 0.085044 / 0.075469 (0.009575) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.681348 / 1.841788 (-0.160440) | 19.686703 / 8.074308 (11.612395) | 22.995655 / 10.191392 (12.804263) | 0.218876 / 0.680424 (-0.461548) | 0.029334 / 0.534201 (-0.504867) | 0.560846 / 0.579283 (-0.018438) | 0.645210 / 0.434364 (0.210846) | 0.697842 / 0.540337 (0.157505) | 0.832875 / 1.386936 (-0.554061) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009509 / 0.011353 (-0.001844) | 0.006471 / 0.011008 (-0.004537) | 0.101477 / 0.038508 (0.062969) | 0.035281 / 0.023109 (0.012171) | 0.470032 / 0.275898 (0.194134) | 0.501475 / 0.323480 (0.177995) | 0.007641 / 0.007986 (-0.000344) | 0.006784 / 0.004328 (0.002455) | 0.096111 / 0.004250 (0.091861) | 0.055199 / 0.037052 (0.018146) | 0.470095 / 0.258489 (0.211606) | 0.530955 / 0.293841 (0.237114) | 0.056161 / 0.128546 (-0.072385) | 0.022055 / 0.075646 (-0.053591) | 0.121585 / 0.419271 (-0.297686) | 0.063736 / 0.043533 (0.020203) | 0.470771 / 0.255139 (0.215632) | 0.490546 / 0.283200 (0.207346) | 0.128825 / 0.141683 (-0.012858) | 1.898639 / 1.452155 (0.446484) | 2.052305 / 1.492716 (0.559589) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.322526 / 0.018006 (0.304520) | 0.628096 / 0.000490 (0.627607) | 0.006837 / 0.000200 (0.006637) | 0.000199 / 0.000054 (0.000145) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033830 / 0.037411 (-0.003581) | 0.136217 / 0.014526 (0.121691) | 0.147006 / 0.176557 (-0.029551) | 0.203950 / 0.737135 (-0.533185) | 0.150327 / 0.296338 (-0.146011) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.654287 / 0.215209 (0.439078) | 6.430306 / 2.077655 (4.352651) | 2.881750 / 1.504120 (1.377630) | 2.489505 / 1.541195 (0.948310) | 2.543037 / 1.468490 (1.074547) | 1.226682 / 4.584777 (-3.358094) | 5.902076 / 3.745712 (2.156364) | 3.335344 / 5.269862 (-1.934518) | 2.156738 / 4.565676 (-2.408939) | 0.151804 / 0.424275 (-0.272472) | 0.015238 / 0.007607 (0.007631) | 0.816364 / 0.226044 (0.590319) | 8.126367 / 2.268929 (5.857438) | 3.653222 / 55.444624 (-51.791402) | 2.886667 / 6.876477 (-3.989809) | 3.120852 / 2.142072 (0.978779) | 1.421423 / 4.805227 (-3.383804) | 0.264590 / 6.500664 (-6.236074) | 0.085716 / 0.075469 (0.010247) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.745258 / 1.841788 (-0.096530) | 19.379253 / 8.074308 (11.304945) | 23.827046 / 10.191392 (13.635654) | 0.267702 / 0.680424 (-0.412722) | 0.030253 / 0.534201 (-0.503948) | 0.542037 / 0.579283 (-0.037246) | 0.655946 / 0.434364 (0.221582) | 0.683525 / 0.540337 (0.143188) | 0.831333 / 1.386936 (-0.555603) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5b011a258329375aa4dc7b414bd4e7b6363c5357 \"CML watermark\")\n" ]

"2023-04-26T17:09:32Z"

"2023-04-26T17:49:03Z"

"2023-04-26T17:39:12Z"

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[ "_The documentation is not available anymore as the PR was closed or merged._" ]

"2022-12-01T15:19:28Z"

"2022-12-14T16:37:16Z"

"2022-12-14T16:33:30Z"

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[ "@lhoestq @albertvillanova Iwas trying to fix the failing tests in circleCI but is there a test elasticsearch instance somewhere? If not, can I launch a docker container to have one?" ]

"2021-10-07T13:28:35Z"

"2022-07-06T15:19:48Z"

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[ { "color": "d73a4a", "default": true, "description": "Something isn't working", "id": 1935892857, "name": "bug", "node_id": "MDU6TGFiZWwxOTM1ODkyODU3", "url": "https://api.github.com/repos/huggingface/datasets/labels/bug" } ]

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[ "I did some investigation and, as it seems, the bug stems from [this line](https://github.com/huggingface/datasets/blob/8004d7c3e1d74b29c3e5b0d1660331cd26758363/src/datasets/arrow_dataset.py#L325). The lifecycle of the dataset from the linked line is bound to one of the returned `tf.data.Dataset`. So my (hacky) solution involves wrapping the linked dataset with `weakref.proxy` and adding a custom `__del__` to `tf.python.data.ops.dataset_ops.TensorSliceDataset` (this is the type of a dataset that is returned by `tf.data.Dataset.from_tensor_slices`; this works for TF 2.x, but I'm not sure `tf.python.data.ops.dataset_ops` is a valid path for TF 1.x) that deletes the linked dataset, which is assigned to the dataset object as a property. Will open a draft PR soon!", "Thanks a lot for investigating !" ]

"2021-09-17T15:26:53Z"

"2021-10-13T09:03:23Z"

"2021-10-13T09:03:23Z"

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[ "I believe is due to the fact that doesn't work with .tgz files.", "`JourneyDB/JourneyDB` is a gated dataset, so this error means you are not authenticated to access it, either by using an invalid token or by not agreeing to the terms in the dialog on the dataset page.\r\n\r\n> I believe is due to the fact that doesn't work with .tgz files.\r\n\r\nIndeed, the dataset's data files structure is not supported natively by `datasets`. To load it, one option is to clone the repo (or download it with `huggingface_hub.snapshot_download`) and use `Dataset.from_generator` to process the files.", "> JourneyDB/JourneyDB is a gated dataset, so this error means you are not authenticated to access it, either by using an invalid token or by not agreeing to the terms in the dialog on the dataset page.´\r\n\r\nI did authentication with:\r\n\r\n```\r\nfrom huggingface_hub import notebook_login\r\nnotebook_login()\r\n```\r\n\r\nIsn't that the correct way to do it?\r\n\r\n> Indeed, the dataset's data files structure is not supported natively by datasets. To load it, one option is to clone the repo (or download it with huggingface_hub.snapshot_download) and use Dataset.from_generator to process the files.\r\n\r\nGreat suggestion I will give it a try.", "Have you accepted the terms in the dialog [here](https://huggingface.co/datasets/JourneyDB/JourneyDB)?\r\n\r\nIIRC Kaggle preinstalls an outdated `datasets` version, so it's also a good idea to update it before importing `datasets` (and do the same for `huggingface_hub`)", "Sorry for the late reply. Yes, I did. Thanks for the tip!" ]

"2023-09-26T15:46:25Z"

"2023-10-05T10:23:23Z"

"2023-10-05T10:23:22Z"

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[ "Same problem", "This was fixed in https://github.com/huggingface/datasets/pull/6155, which will be included in the next release (or you can install `datasets` from source to use it immediately)." ]

"2023-08-25T22:21:22Z"

"2023-08-29T13:26:22Z"

"2023-08-29T13:26:22Z"

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"2023-04-24T10:38:03Z"

"2023-04-27T12:57:30Z"

"2023-04-27T12:57:30Z"

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[ "_The documentation is not available anymore as the PR was closed or merged._", "Thanks for the reviews @apsdehal and @lhoestq! As suggested by @lhoestq, I'll separate the train/val/test splits, apply the validation split fixes and shuffle the images files to simplify the script and make streaming faster.", "@apsdehal I dismissed your review as it's no longer relevant after the data files changes suggested by @lhoestq. " ]

"2022-05-09T20:49:18Z"

"2022-05-25T14:54:59Z"

"2022-05-25T14:46:16Z"

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"2021-11-18T06:56:01Z"

"2021-11-18T10:28:05Z"

"2021-11-18T10:28:04Z"

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007633 / 0.011353 (-0.003720) | 0.004666 / 0.011008 (-0.006343) | 0.097768 / 0.038508 (0.059260) | 0.085153 / 0.023109 (0.062044) | 0.400315 / 0.275898 (0.124417) | 0.452903 / 0.323480 (0.129423) | 0.006227 / 0.007986 (-0.001759) | 0.003814 / 0.004328 (-0.000515) | 0.074586 / 0.004250 (0.070336) | 0.064295 / 0.037052 (0.027242) | 0.408082 / 0.258489 (0.149593) | 0.446921 / 0.293841 (0.153080) | 0.034593 / 0.128546 (-0.093953) | 0.009191 / 0.075646 (-0.066456) | 0.337099 / 0.419271 (-0.082173) | 0.075320 / 0.043533 (0.031787) | 0.403488 / 0.255139 (0.148349) | 0.435309 / 0.283200 (0.152109) | 0.035675 / 0.141683 (-0.106008) | 1.732642 / 1.452155 (0.280487) | 1.770238 / 1.492716 (0.277522) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.235879 / 0.018006 (0.217873) | 0.500330 / 0.000490 (0.499841) | 0.005221 / 0.000200 (0.005021) | 0.000150 / 0.000054 (0.000096) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032479 / 0.037411 (-0.004933) | 0.095873 / 0.014526 (0.081348) | 0.107118 / 0.176557 (-0.069438) | 0.173809 / 0.737135 (-0.563326) | 0.109832 / 0.296338 (-0.186507) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.444342 / 0.215209 (0.229133) | 4.459010 / 2.077655 (2.381355) | 2.209687 / 1.504120 (0.705567) | 2.007556 / 1.541195 (0.466362) | 2.113683 / 1.468490 (0.645193) | 0.544281 / 4.584777 (-4.040496) | 4.037151 / 3.745712 (0.291439) | 4.852644 / 5.269862 (-0.417217) | 3.134126 / 4.565676 (-1.431550) | 0.066815 / 0.424275 (-0.357460) | 0.008836 / 0.007607 (0.001229) | 0.560904 / 0.226044 (0.334859) | 5.302760 / 2.268929 (3.033832) | 2.750182 / 55.444624 (-52.694442) | 2.322595 / 6.876477 (-4.553882) | 2.547486 / 2.142072 (0.405414) | 0.665766 / 4.805227 (-4.139461) | 0.151613 / 6.500664 (-6.349051) | 0.071155 / 0.075469 (-0.004314) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.473717 / 1.841788 (-0.368071) | 22.584179 / 8.074308 (14.509871) | 15.888001 / 10.191392 (5.696609) | 0.181073 / 0.680424 (-0.499351) | 0.021395 / 0.534201 (-0.512806) | 0.452693 / 0.579283 (-0.126590) | 0.447709 / 0.434364 (0.013345) | 0.529599 / 0.540337 (-0.010738) | 0.699241 / 1.386936 (-0.687695) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007917 / 0.011353 (-0.003436) | 0.004544 / 0.011008 (-0.006464) | 0.074566 / 0.038508 (0.036058) | 0.087530 / 0.023109 (0.064421) | 0.419753 / 0.275898 (0.143854) | 0.452352 / 0.323480 (0.128872) | 0.005882 / 0.007986 (-0.002104) | 0.003904 / 0.004328 (-0.000425) | 0.073539 / 0.004250 (0.069289) | 0.071320 / 0.037052 (0.034267) | 0.432899 / 0.258489 (0.174409) | 0.470365 / 0.293841 (0.176524) | 0.036198 / 0.128546 (-0.092348) | 0.009342 / 0.075646 (-0.066304) | 0.080970 / 0.419271 (-0.338301) | 0.058769 / 0.043533 (0.015236) | 0.413397 / 0.255139 (0.158258) | 0.448362 / 0.283200 (0.165162) | 0.034177 / 0.141683 (-0.107506) | 1.706217 / 1.452155 (0.254063) | 1.776743 / 1.492716 (0.284026) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.198779 / 0.018006 (0.180773) | 0.499862 / 0.000490 (0.499372) | 0.003891 / 0.000200 (0.003692) | 0.000108 / 0.000054 (0.000053) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034671 / 0.037411 (-0.002740) | 0.103165 / 0.014526 (0.088639) | 0.115813 / 0.176557 (-0.060744) | 0.177407 / 0.737135 (-0.559728) | 0.117733 / 0.296338 (-0.178606) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.476859 / 0.215209 (0.261650) | 4.823063 / 2.077655 (2.745409) | 2.524133 / 1.504120 (1.020013) | 2.374482 / 1.541195 (0.833288) | 2.518047 / 1.468490 (1.049557) | 0.559131 / 4.584777 (-4.025646) | 4.126213 / 3.745712 (0.380501) | 6.488570 / 5.269862 (1.218708) | 3.816540 / 4.565676 (-0.749137) | 0.064742 / 0.424275 (-0.359533) | 0.008476 / 0.007607 (0.000869) | 0.576432 / 0.226044 (0.350387) | 5.835133 / 2.268929 (3.566205) | 3.237833 / 55.444624 (-52.206791) | 2.726596 / 6.876477 (-4.149880) | 2.799212 / 2.142072 (0.657139) | 0.661628 / 4.805227 (-4.143599) | 0.153997 / 6.500664 (-6.346667) | 0.070621 / 0.075469 (-0.004848) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.648505 / 1.841788 (-0.193282) | 22.454019 / 8.074308 (14.379711) | 16.077098 / 10.191392 (5.885706) | 0.217875 / 0.680424 (-0.462549) | 0.021285 / 0.534201 (-0.512916) | 0.459837 / 0.579283 (-0.119446) | 0.476211 / 0.434364 (0.041847) | 0.525903 / 0.540337 (-0.014435) | 0.717224 / 1.386936 (-0.669712) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b767e9c3ef30f9da30d47cfcaccf9a7ac2500c43 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008929 / 0.011353 (-0.002424) | 0.004188 / 0.011008 (-0.006820) | 0.097030 / 0.038508 (0.058522) | 0.071363 / 0.023109 (0.048254) | 0.333116 / 0.275898 (0.057218) | 0.371272 / 0.323480 (0.047792) | 0.006430 / 0.007986 (-0.001555) | 0.003689 / 0.004328 (-0.000639) | 0.068666 / 0.004250 (0.064416) | 0.057562 / 0.037052 (0.020510) | 0.347208 / 0.258489 (0.088719) | 0.390514 / 0.293841 (0.096673) | 0.050560 / 0.128546 (-0.077987) | 0.013372 / 0.075646 (-0.062275) | 0.311345 / 0.419271 (-0.107927) | 0.068990 / 0.043533 (0.025457) | 0.363026 / 0.255139 (0.107887) | 0.379793 / 0.283200 (0.096593) | 0.036891 / 0.141683 (-0.104792) | 1.583481 / 1.452155 (0.131327) | 1.688727 / 1.492716 (0.196011) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.209777 / 0.018006 (0.191771) | 0.507267 / 0.000490 (0.506777) | 0.003637 / 0.000200 (0.003438) | 0.000105 / 0.000054 (0.000051) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029309 / 0.037411 (-0.008102) | 0.088386 / 0.014526 (0.073861) | 0.104974 / 0.176557 (-0.071582) | 0.171999 / 0.737135 (-0.565137) | 0.110797 / 0.296338 (-0.185542) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.543465 / 0.215209 (0.328256) | 5.361491 / 2.077655 (3.283836) | 2.348712 / 1.504120 (0.844592) | 2.012527 / 1.541195 (0.471332) | 2.069776 / 1.468490 (0.601286) | 0.874262 / 4.584777 (-3.710515) | 4.877317 / 3.745712 (1.131605) | 5.327459 / 5.269862 (0.057597) | 3.336823 / 4.565676 (-1.228854) | 0.100456 / 0.424275 (-0.323819) | 0.008503 / 0.007607 (0.000895) | 0.692009 / 0.226044 (0.465965) | 6.912731 / 2.268929 (4.643802) | 3.110548 / 55.444624 (-52.334076) | 2.443665 / 6.876477 (-4.432811) | 2.528713 / 2.142072 (0.386641) | 1.076358 / 4.805227 (-3.728869) | 0.220352 / 6.500664 (-6.280312) | 0.080293 / 0.075469 (0.004824) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.538444 / 1.841788 (-0.303344) | 21.121221 / 8.074308 (13.046913) | 19.810609 / 10.191392 (9.619216) | 0.225406 / 0.680424 (-0.455018) | 0.026652 / 0.534201 (-0.507549) | 0.430372 / 0.579283 (-0.148911) | 0.510722 / 0.434364 (0.076358) | 0.514347 / 0.540337 (-0.025991) | 0.686050 / 1.386936 (-0.700886) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007675 / 0.011353 (-0.003678) | 0.004542 / 0.011008 (-0.006466) | 0.069655 / 0.038508 (0.031147) | 0.069338 / 0.023109 (0.046229) | 0.436505 / 0.275898 (0.160607) | 0.481806 / 0.323480 (0.158326) | 0.005315 / 0.007986 (-0.002670) | 0.004455 / 0.004328 (0.000127) | 0.072674 / 0.004250 (0.068424) | 0.058088 / 0.037052 (0.021035) | 0.445825 / 0.258489 (0.187336) | 0.501706 / 0.293841 (0.207865) | 0.047123 / 0.128546 (-0.081424) | 0.012943 / 0.075646 (-0.062703) | 0.093491 / 0.419271 (-0.325780) | 0.060169 / 0.043533 (0.016637) | 0.436530 / 0.255139 (0.181391) | 0.466873 / 0.283200 (0.183674) | 0.040453 / 0.141683 (-0.101230) | 1.586438 / 1.452155 (0.134283) | 1.671081 / 1.492716 (0.178365) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.180607 / 0.018006 (0.162601) | 0.520145 / 0.000490 (0.519655) | 0.004824 / 0.000200 (0.004624) | 0.000116 / 0.000054 (0.000061) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029308 / 0.037411 (-0.008103) | 0.093652 / 0.014526 (0.079126) | 0.102332 / 0.176557 (-0.074224) | 0.162414 / 0.737135 (-0.574721) | 0.098017 / 0.296338 (-0.198321) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.583949 / 0.215209 (0.368740) | 6.035191 / 2.077655 (3.957536) | 2.801274 / 1.504120 (1.297155) | 2.566150 / 1.541195 (1.024955) | 2.437122 / 1.468490 (0.968632) | 0.865038 / 4.584777 (-3.719739) | 4.841727 / 3.745712 (1.096015) | 4.683919 / 5.269862 (-0.585943) | 2.941240 / 4.565676 (-1.624437) | 0.104888 / 0.424275 (-0.319387) | 0.007747 / 0.007607 (0.000140) | 0.780041 / 0.226044 (0.553997) | 7.771314 / 2.268929 (5.502385) | 3.680814 / 55.444624 (-51.763811) | 2.938472 / 6.876477 (-3.938004) | 2.981740 / 2.142072 (0.839668) | 1.065411 / 4.805227 (-3.739816) | 0.222265 / 6.500664 (-6.278399) | 0.082428 / 0.075469 (0.006959) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.626774 / 1.841788 (-0.215014) | 21.618284 / 8.074308 (13.543976) | 20.596743 / 10.191392 (10.405351) | 0.240969 / 0.680424 (-0.439454) | 0.025630 / 0.534201 (-0.508570) | 0.481981 / 0.579283 (-0.097302) | 0.547914 / 0.434364 (0.113550) | 0.522296 / 0.540337 (-0.018041) | 0.729174 / 1.386936 (-0.657762) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b8067c0262073891180869f700ebef5ac3dc5cce \"CML watermark\")\n" ]

"2023-07-11T12:16:29Z"

"2023-07-11T16:07:28Z"

"2023-07-11T15:56:46Z"

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[ "Possibly related:\r\n- https://github.com/pytorch/pytorch/issues/22462" ]

"2023-07-31T11:12:19Z"

"2023-08-01T11:22:43Z"

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"2022-06-29T01:40:01Z"

"2022-06-29T04:03:44Z"

"2022-06-29T04:03:44Z"

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[ "Hi @nsaphra, thanks for reporting.\r\n\r\nThis issue was fixed in `datasets` version 1.3.0. Could you please update `datasets` and tell me if the problem persists?\r\n```shell\r\npip install -U datasets\r\n```", "There might be a bug in the conda version of `datasets` 1.2.1 where the datasets/metric scripts are downloaded from `master` instead of the `1.2.1` repo.\r\n\r\nYou can try setting the env var `HF_SCRIPTS_VERSION=\"1.2.1\"` as a workaround. Let me know if that helps.", "I just faced the same issue. I was using 1.2.1 from conda and received the same AttributeError complaining about 'add_start_docstrings'. Uninstalling the conda installed datasets and then installing the latest datasets (version 1.5.0) using pip install solved the issue for me. I don't like mixing up conda and pip installs in the same environments but this will have to do for now, until 1.5.0 is made available through conda.", "Yep, seems to have fixed things! The conda package could really do with an update. Thanks!" ]

"2021-04-12T20:26:01Z"

"2021-04-23T15:20:02Z"

"2021-04-23T15:20:02Z"

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[ "@severo, maybe somehow related to this PR ?\r\n- #3129", "@severo I'll keep that in mind.\r\n\r\nYou can track progress on the Image feature in #3163 (still in the early stage). ", "Hi ! As discussed with @severo offline it looks like the dataset viewer already supports reading PIL images, so maybe the dataset viewer doesn't need to disable decoding after all", "Fixed with https://github.com/huggingface/datasets/pull/3163" ]

"2021-10-22T13:23:49Z"

"2021-12-22T11:05:37Z"

"2021-12-22T11:05:36Z"

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008581 / 0.011353 (-0.002772) | 0.004519 / 0.011008 (-0.006490) | 0.099721 / 0.038508 (0.061213) | 0.029217 / 0.023109 (0.006107) | 0.298229 / 0.275898 (0.022331) | 0.332605 / 0.323480 (0.009125) | 0.006880 / 0.007986 (-0.001106) | 0.003324 / 0.004328 (-0.001005) | 0.078143 / 0.004250 (0.073892) | 0.034262 / 0.037052 (-0.002790) | 0.304162 / 0.258489 (0.045673) | 0.342351 / 0.293841 (0.048510) | 0.033387 / 0.128546 (-0.095159) | 0.011397 / 0.075646 (-0.064249) | 0.321527 / 0.419271 (-0.097744) | 0.040886 / 0.043533 (-0.002647) | 0.299968 / 0.255139 (0.044829) | 0.322484 / 0.283200 (0.039285) | 0.083832 / 0.141683 (-0.057851) | 1.482241 / 1.452155 (0.030086) | 1.548438 / 1.492716 (0.055721) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.191002 / 0.018006 (0.172996) | 0.403423 / 0.000490 (0.402933) | 0.002493 / 0.000200 (0.002293) | 0.000074 / 0.000054 (0.000020) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023720 / 0.037411 (-0.013691) | 0.100806 / 0.014526 (0.086281) | 0.105314 / 0.176557 (-0.071242) | 0.141490 / 0.737135 (-0.595645) | 0.108695 / 0.296338 (-0.187644) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.412250 / 0.215209 (0.197041) | 4.124830 / 2.077655 (2.047175) | 1.851948 / 1.504120 (0.347828) | 1.651597 / 1.541195 (0.110403) | 1.712486 / 1.468490 (0.243996) | 0.696634 / 4.584777 (-3.888143) | 3.304220 / 3.745712 (-0.441492) | 1.862776 / 5.269862 (-3.407086) | 1.159452 / 4.565676 (-3.406224) | 0.082930 / 0.424275 (-0.341345) | 0.012586 / 0.007607 (0.004979) | 0.524499 / 0.226044 (0.298455) | 5.249235 / 2.268929 (2.980307) | 2.293187 / 55.444624 (-53.151437) | 1.950101 / 6.876477 (-4.926376) | 2.008274 / 2.142072 (-0.133799) | 0.811641 / 4.805227 (-3.993586) | 0.148785 / 6.500664 (-6.351879) | 0.064461 / 0.075469 (-0.011008) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.232227 / 1.841788 (-0.609561) | 13.235896 / 8.074308 (5.161588) | 13.837420 / 10.191392 (3.646028) | 0.135586 / 0.680424 (-0.544838) | 0.028935 / 0.534201 (-0.505266) | 0.397064 / 0.579283 (-0.182220) | 0.393814 / 0.434364 (-0.040549) | 0.480450 / 0.540337 (-0.059887) | 0.561159 / 1.386936 (-0.825777) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006696 / 0.011353 (-0.004657) | 0.004528 / 0.011008 (-0.006480) | 0.077335 / 0.038508 (0.038827) | 0.027181 / 0.023109 (0.004072) | 0.345379 / 0.275898 (0.069481) | 0.372544 / 0.323480 (0.049064) | 0.006808 / 0.007986 (-0.001178) | 0.003284 / 0.004328 (-0.001045) | 0.077379 / 0.004250 (0.073129) | 0.039954 / 0.037052 (0.002901) | 0.348094 / 0.258489 (0.089605) | 0.382315 / 0.293841 (0.088474) | 0.031694 / 0.128546 (-0.096852) | 0.011714 / 0.075646 (-0.063933) | 0.086425 / 0.419271 (-0.332846) | 0.041778 / 0.043533 (-0.001754) | 0.342161 / 0.255139 (0.087022) | 0.363798 / 0.283200 (0.080599) | 0.091315 / 0.141683 (-0.050368) | 1.462066 / 1.452155 (0.009912) | 1.541417 / 1.492716 (0.048700) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.235840 / 0.018006 (0.217834) | 0.397096 / 0.000490 (0.396606) | 0.004597 / 0.000200 (0.004397) | 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) | 0.024296 / 0.037411 (-0.013115) | 0.099167 / 0.014526 (0.084641) | 0.108257 / 0.176557 (-0.068299) | 0.143434 / 0.737135 (-0.593701) | 0.111933 / 0.296338 (-0.184406) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.440306 / 0.215209 (0.225096) | 4.374065 / 2.077655 (2.296410) | 2.072653 / 1.504120 (0.568533) | 1.864829 / 1.541195 (0.323635) | 1.927970 / 1.468490 (0.459479) | 0.710118 / 4.584777 (-3.874659) | 3.391216 / 3.745712 (-0.354496) | 1.888847 / 5.269862 (-3.381015) | 1.178740 / 4.565676 (-3.386936) | 0.083950 / 0.424275 (-0.340325) | 0.012567 / 0.007607 (0.004960) | 0.540557 / 0.226044 (0.314513) | 5.437621 / 2.268929 (3.168692) | 2.531165 / 55.444624 (-52.913460) | 2.181450 / 6.876477 (-4.695027) | 2.209108 / 2.142072 (0.067035) | 0.814236 / 4.805227 (-3.990991) | 0.153000 / 6.500664 (-6.347664) | 0.066769 / 0.075469 (-0.008700) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.301057 / 1.841788 (-0.540731) | 14.066786 / 8.074308 (5.992478) | 13.641455 / 10.191392 (3.450063) | 0.138838 / 0.680424 (-0.541586) | 0.016733 / 0.534201 (-0.517468) | 0.391823 / 0.579283 (-0.187460) | 0.390817 / 0.434364 (-0.043547) | 0.487682 / 0.540337 (-0.052656) | 0.581134 / 1.386936 (-0.805802) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b065547654efa0ec633cf373ac1512884c68b2e1 \"CML watermark\")\n" ]

"2023-02-01T10:20:55Z"

"2023-02-09T09:29:08Z"

"2023-02-09T09:22:23Z"

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