Datasets:

DanielPFlorian

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Hugging-Face-Datasets-Github-Issues

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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) | 0.006941 / 0.011353 (-0.004412) | 0.004255 / 0.011008 (-0.006753) | 0.085237 / 0.038508 (0.046729) | 0.080962 / 0.023109 (0.057853) | 0.312016 / 0.275898 (0.036118) | 0.353161 / 0.323480 (0.029681) | 0.005756 / 0.007986 (-0.002230) | 0.003591 / 0.004328 (-0.000738) | 0.065416 / 0.004250 (0.061166) | 0.057837 / 0.037052 (0.020785) | 0.316169 / 0.258489 (0.057680) | 0.372345 / 0.293841 (0.078504) | 0.031958 / 0.128546 (-0.096588) | 0.008798 / 0.075646 (-0.066848) | 0.294764 / 0.419271 (-0.124507) | 0.053954 / 0.043533 (0.010421) | 0.310961 / 0.255139 (0.055822) | 0.330063 / 0.283200 (0.046864) | 0.025298 / 0.141683 (-0.116385) | 1.454715 / 1.452155 (0.002560) | 1.557915 / 1.492716 (0.065198) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.274830 / 0.018006 (0.256824) | 0.565890 / 0.000490 (0.565400) | 0.009242 / 0.000200 (0.009042) | 0.000321 / 0.000054 (0.000266) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031092 / 0.037411 (-0.006320) | 0.087558 / 0.014526 (0.073033) | 0.103395 / 0.176557 (-0.073162) | 0.160078 / 0.737135 (-0.577057) | 0.102356 / 0.296338 (-0.193983) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.402912 / 0.215209 (0.187703) | 4.029374 / 2.077655 (1.951719) | 2.048237 / 1.504120 (0.544117) | 1.887470 / 1.541195 (0.346276) | 1.994807 / 1.468490 (0.526316) | 0.491109 / 4.584777 (-4.093668) | 3.645059 / 3.745712 (-0.100653) | 3.516376 / 5.269862 (-1.753486) | 2.103267 / 4.565676 (-2.462409) | 0.058072 / 0.424275 (-0.366203) | 0.007796 / 0.007607 (0.000189) | 0.480544 / 0.226044 (0.254499) | 4.795422 / 2.268929 (2.526494) | 2.507770 / 55.444624 (-52.936854) | 2.187106 / 6.876477 (-4.689371) | 2.271005 / 2.142072 (0.128933) | 0.585376 / 4.805227 (-4.219851) | 0.134741 / 6.500664 (-6.365923) | 0.060684 / 0.075469 (-0.014785) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.264349 / 1.841788 (-0.577439) | 19.448735 / 8.074308 (11.374427) | 14.521197 / 10.191392 (4.329805) | 0.167295 / 0.680424 (-0.513129) | 0.018352 / 0.534201 (-0.515849) | 0.396345 / 0.579283 (-0.182938) | 0.418690 / 0.434364 (-0.015674) | 0.469703 / 0.540337 (-0.070635) | 0.637852 / 1.386936 (-0.749084) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006939 / 0.011353 (-0.004414) | 0.004196 / 0.011008 (-0.006812) | 0.064719 / 0.038508 (0.026211) | 0.077517 / 0.023109 (0.054407) | 0.401977 / 0.275898 (0.126079) | 0.431089 / 0.323480 (0.107609) | 0.005624 / 0.007986 (-0.002362) | 0.003680 / 0.004328 (-0.000649) | 0.065817 / 0.004250 (0.061567) | 0.058297 / 0.037052 (0.021245) | 0.399614 / 0.258489 (0.141125) | 0.440089 / 0.293841 (0.146248) | 0.032492 / 0.128546 (-0.096054) | 0.008974 / 0.075646 (-0.066672) | 0.071311 / 0.419271 (-0.347961) | 0.048001 / 0.043533 (0.004468) | 0.394763 / 0.255139 (0.139624) | 0.416754 / 0.283200 (0.133554) | 0.023730 / 0.141683 (-0.117953) | 1.509677 / 1.452155 (0.057522) | 1.605711 / 1.492716 (0.112994) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.265490 / 0.018006 (0.247483) | 0.561745 / 0.000490 (0.561255) | 0.004616 / 0.000200 (0.004417) | 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) | 0.033371 / 0.037411 (-0.004040) | 0.092763 / 0.014526 (0.078238) | 0.108905 / 0.176557 (-0.067652) | 0.160380 / 0.737135 (-0.576756) | 0.106968 / 0.296338 (-0.189370) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.430268 / 0.215209 (0.215059) | 4.299313 / 2.077655 (2.221658) | 2.308971 / 1.504120 (0.804851) | 2.155855 / 1.541195 (0.614661) | 2.392698 / 1.468490 (0.924208) | 0.498464 / 4.584777 (-4.086313) | 3.694473 / 3.745712 (-0.051239) | 3.409625 / 5.269862 (-1.860236) | 2.106144 / 4.565676 (-2.459532) | 0.058992 / 0.424275 (-0.365283) | 0.007395 / 0.007607 (-0.000212) | 0.511291 / 0.226044 (0.285247) | 5.101806 / 2.268929 (2.832877) | 2.853100 / 55.444624 (-52.591524) | 2.527216 / 6.876477 (-4.349260) | 2.819380 / 2.142072 (0.677308) | 0.635155 / 4.805227 (-4.170072) | 0.135816 / 6.500664 (-6.364848) | 0.062056 / 0.075469 (-0.013413) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.353479 / 1.841788 (-0.488308) | 20.318513 / 8.074308 (12.244205) | 15.105336 / 10.191392 (4.913944) | 0.166186 / 0.680424 (-0.514238) | 0.020742 / 0.534201 (-0.513459) | 0.399286 / 0.579283 (-0.179997) | 0.431785 / 0.434364 (-0.002579) | 0.478667 / 0.540337 (-0.061671) | 0.654683 / 1.386936 (-0.732253) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b39d1ce0b8f231649752f28cb724971f4df1c7ae \"CML watermark\")\n", "Yea I think some of it should be in the Hub docs indeed, let me open a new PR there.\r\n\r\nThen I'll update the `datasets` docs anyway to avoid redundant stuff and add redirects instead" ]

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[ "I am having issues as well with this. \r\n\r\nHowever, the error I am getting is :\r\n`RuntimeError: It appears that you are attempting to reference SparkContext from a broadcast variable, action, or transformation. SparkContext can only be used on the driver, not in code that it run on workers. For more information, see SPARK-5063.`\r\n\r\nAlso did not work with pyspark==3.3.0 and py4j==0.10.9.5" ]

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[ "solved.\r\nfsspec version problem", "I'm using the latest datasets and fsspec , but still got this error!\r\n\r\ndatasets : Version: 2.13.0\r\n\r\nfsspec Version: 2023.10.0\r\n\r\n```\r\nFile \"/home/guoby/app/Anaconda3-2021.05/envs/news/lib/python3.8/site-packages/datasets/load.py\", line 1892, in load_from_disk\r\n return DatasetDict.load_from_disk(dataset_path, keep_in_memory=keep_in_memory, storage_options=storage_options)\r\n File \"/home/guoby/app/Anaconda3-2021.05/envs/news/lib/python3.8/site-packages/datasets/dataset_dict.py\", line 1371, in load_from_disk\r\n dataset_dict[k] = Dataset.load_from_disk(\r\n File \"/home/guoby/app/Anaconda3-2021.05/envs/news/lib/python3.8/site-packages/datasets/arrow_dataset.py\", line 1639, in load_from_disk\r\n fs_token_paths = fsspec.get_fs_token_paths(dataset_path, storage_options=storage_options)\r\n File \"/home/guoby/app/Anaconda3-2021.05/envs/news/lib/python3.8/site-packages/fsspec/core.py\", line 610, in get_fs_token_paths\r\n chain = _un_chain(urlpath0, storage_options or {})\r\n File \"/home/guoby/app/Anaconda3-2021.05/envs/news/lib/python3.8/site-packages/fsspec/core.py\", line 325, in _un_chain\r\n cls = get_filesystem_class(protocol)\r\n File \"/home/guoby/app/Anaconda3-2021.05/envs/news/lib/python3.8/site-packages/fsspec/registry.py\", line 232, in get_filesystem_class\r\n raise ValueError(f\"Protocol not known: {protocol}\")\r\n```", "These two versions work.\r\n<img width=\"807\" alt=\"截圖 2023-11-22 下午5 55 28\" src=\"https://github.com/huggingface/datasets/assets/77866896/faa8333f-0519-4d69-b243-a8880cd7fc1f\">\r\n", "datasets==2.10.1 and fsspec==2023.6.0 also works for me." ]

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[ "+1 \r\n```\r\nFound cached dataset csv (file:///home/ubuntu/.cache/huggingface/datasets/theSquarePond___csv/theSquarePond--XXXXX-bbf0a8365d693d2c/0.0.0/eea64c71ca8b46dd3f537ed218fc9bf495d5707789152eb2764f5c78fa66d59d)\r\n---------------------------------------------------------------------------\r\nNotImplementedError Traceback (most recent call last)\r\nCell In[14], line 4\r\n 1 get_ipython().system('pip install -U datasets')\r\n 3 # Load dataset from the hub\r\n----> 4 dataset = load_dataset(dataset_name)\r\n\r\nFile ~/anaconda3/envs/python38-env/lib/python3.8/site-packages/datasets/load.py:1810, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, num_proc, storage_options, **config_kwargs)\r\n 1806 # Build dataset for splits\r\n 1807 keep_in_memory = (\r\n 1808 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size)\r\n 1809 )\r\n-> 1810 ds = builder_instance.as_dataset(split=split, verification_mode=verification_mode, in_memory=keep_in_memory)\r\n 1811 # Rename and cast features to match task schema\r\n 1812 if task is not None:\r\n\r\nFile ~/anaconda3/envs/python38-env/lib/python3.8/site-packages/datasets/builder.py:1128, in DatasetBuilder.as_dataset(self, split, run_post_process, verification_mode, ignore_verifications, in_memory)\r\n 1126 is_local = not is_remote_filesystem(self._fs)\r\n 1127 if not is_local:\r\n-> 1128 raise NotImplementedError(f\"Loading a dataset cached in a {type(self._fs).__name__} is not supported.\")\r\n 1129 if not os.path.exists(self._output_dir):\r\n 1130 raise FileNotFoundError(\r\n 1131 f\"Dataset {self.name}: could not find data in {self._output_dir}. Please make sure to call \"\r\n 1132 \"builder.download_and_prepare(), or use \"\r\n 1133 \"datasets.load_dataset() before trying to access the Dataset object.\"\r\n 1134 )\r\n\r\nNotImplementedError: Loading a dataset cached in a LocalFileSystem is not supported.\r\n```", "+1\r\n\r\n```\r\nFound cached dataset csv ([file://C:/Users/Shady/.cache/huggingface/datasets/knkarthick___csv/knkarthick--dialogsum-cd36827d3490488d/0.0.0/6954658bab30a358235fa864b05cf819af0e179325c740e4bc853bcc7ec513e1](file:///C:/Users/Shady/.cache/huggingface/datasets/knkarthick___csv/knkarthick--dialogsum-cd36827d3490488d/0.0.0/6954658bab30a358235fa864b05cf819af0e179325c740e4bc853bcc7ec513e1))\r\n---------------------------------------------------------------------------\r\nNotImplementedError Traceback (most recent call last)\r\nCell In[38], line 3\r\n 1 huggingface_dataset_name = \"knkarthick/dialogsum\"\r\n----> 3 dataset = load_dataset(huggingface_dataset_name)\r\n\r\nFile D:\\Desktop\\Workspace\\GenAI\\genai\\lib\\site-packages\\datasets\\load.py:1804, in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, num_proc, storage_options, **config_kwargs)\r\n 1800 # Build dataset for splits\r\n 1801 keep_in_memory = (\r\n 1802 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size)\r\n 1803 )\r\n-> 1804 ds = builder_instance.as_dataset(split=split, verification_mode=verification_mode, in_memory=keep_in_memory)\r\n 1805 # Rename and cast features to match task schema\r\n 1806 if task is not None:\r\n\r\nFile D:\\Desktop\\Workspace\\GenAI\\genai\\lib\\site-packages\\datasets\\builder.py:1108, in DatasetBuilder.as_dataset(self, split, run_post_process, verification_mode, ignore_verifications, in_memory)\r\n 1106 is_local = not is_remote_filesystem(self._fs)\r\n 1107 if not is_local:\r\n-> 1108 raise NotImplementedError(f\"Loading a dataset cached in a {type(self._fs).__name__} is not supported.\")\r\n 1109 if not os.path.exists(self._output_dir):\r\n 1110 raise FileNotFoundError(\r\n 1111 f\"Dataset {self.name}: could not find data in {self._output_dir}. Please make sure to call \"\r\n 1112 \"builder.download_and_prepare(), or use \"\r\n 1113 \"datasets.load_dataset() before trying to access the Dataset object.\"\r\n 1114 )\r\n\r\nNotImplementedError: Loading a dataset cached in a LocalFileSystem is not supported.\r\n```", "This error stems from a breaking change in `fsspec`. It has been fixed in the latest `datasets` release (`2.14.6`). Updating the installation with `pip install -U datasets` should fix the issue.\r\n", "> 此错误源于 中的重大更改。此问题已在最新版本 （） 中修复。更新安装应该可以解决此问题。`fsspec``datasets``2.14.6``pip install -U datasets`\r\n\r\nthanks , 太好啦，刚好解决了我的问题，GPT都没解决了，终于被你搞定了", "https://stackoverflow.com/questions/77433096/notimplementederror-loading-a-dataset-cached-in-a-localfilesystem-is-not-suppor/77433141#77433141", "Fixed by:\r\n- https://github.com/huggingface/datasets/pull/6334\r\n\r\nThe fix was released in `datasets-2.14.6`." ]

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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) | 0.007718 / 0.011353 (-0.003635) | 0.004730 / 0.011008 (-0.006278) | 0.097262 / 0.038508 (0.058754) | 0.077880 / 0.023109 (0.054771) | 0.363855 / 0.275898 (0.087957) | 0.394470 / 0.323480 (0.070990) | 0.006416 / 0.007986 (-0.001570) | 0.003596 / 0.004328 (-0.000732) | 0.076494 / 0.004250 (0.072243) | 0.062656 / 0.037052 (0.025603) | 0.366160 / 0.258489 (0.107671) | 0.421383 / 0.293841 (0.127542) | 0.035756 / 0.128546 (-0.092791) | 0.009430 / 0.075646 (-0.066217) | 0.327722 / 0.419271 (-0.091550) | 0.061252 / 0.043533 (0.017719) | 0.352167 / 0.255139 (0.097028) | 0.385166 / 0.283200 (0.101966) | 0.026656 / 0.141683 (-0.115027) | 1.718533 / 1.452155 (0.266378) | 1.886646 / 1.492716 (0.393930) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.254564 / 0.018006 (0.236558) | 0.490942 / 0.000490 (0.490452) | 0.011656 / 0.000200 (0.011456) | 0.000313 / 0.000054 (0.000259) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028753 / 0.037411 (-0.008659) | 0.093076 / 0.014526 (0.078550) | 0.096441 / 0.176557 (-0.080116) | 0.154848 / 0.737135 (-0.582287) | 0.092903 / 0.296338 (-0.203435) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.395611 / 0.215209 (0.180402) | 3.860736 / 2.077655 (1.783082) | 1.908808 / 1.504120 (0.404688) | 1.708975 / 1.541195 (0.167781) | 1.848173 / 1.468490 (0.379683) | 0.527022 / 4.584777 (-4.057755) | 3.815171 / 3.745712 (0.069459) | 3.621132 / 5.269862 (-1.648730) | 2.220238 / 4.565676 (-2.345439) | 0.063169 / 0.424275 (-0.361106) | 0.008906 / 0.007607 (0.001299) | 0.510478 / 0.226044 (0.284433) | 4.828116 / 2.268929 (2.559187) | 2.340801 / 55.444624 (-53.103824) | 2.040834 / 6.876477 (-4.835642) | 2.092316 / 2.142072 (-0.049757) | 0.579194 / 4.805227 (-4.226033) | 0.135525 / 6.500664 (-6.365139) | 0.062720 / 0.075469 (-0.012749) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.393091 / 1.841788 (-0.448697) | 19.751526 / 8.074308 (11.677218) | 14.161795 / 10.191392 (3.970403) | 0.163340 / 0.680424 (-0.517084) | 0.021504 / 0.534201 (-0.512697) | 0.393183 / 0.579283 (-0.186100) | 0.448407 / 0.434364 (0.014043) | 0.504169 / 0.540337 (-0.036169) | 0.663698 / 1.386936 (-0.723238) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007390 / 0.011353 (-0.003962) | 0.004381 / 0.011008 (-0.006628) | 0.074501 / 0.038508 (0.035993) | 0.078242 / 0.023109 (0.055133) | 0.481108 / 0.275898 (0.205210) | 0.512111 / 0.323480 (0.188631) | 0.006280 / 0.007986 (-0.001705) | 0.003820 / 0.004328 (-0.000509) | 0.071602 / 0.004250 (0.067351) | 0.068359 / 0.037052 (0.031307) | 0.478484 / 0.258489 (0.219995) | 0.519543 / 0.293841 (0.225702) | 0.036211 / 0.128546 (-0.092335) | 0.009433 / 0.075646 (-0.066213) | 0.086140 / 0.419271 (-0.333132) | 0.054177 / 0.043533 (0.010644) | 0.466726 / 0.255139 (0.211587) | 0.514085 / 0.283200 (0.230885) | 0.026729 / 0.141683 (-0.114954) | 1.743770 / 1.452155 (0.291615) | 1.833469 / 1.492716 (0.340753) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.251339 / 0.018006 (0.233333) | 0.472294 / 0.000490 (0.471804) | 0.013381 / 0.000200 (0.013181) | 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) | 0.037845 / 0.037411 (0.000433) | 0.105977 / 0.014526 (0.091451) | 0.124446 / 0.176557 (-0.052111) | 0.180432 / 0.737135 (-0.556703) | 0.120844 / 0.296338 (-0.175495) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.470928 / 0.215209 (0.255719) | 4.738154 / 2.077655 (2.660499) | 2.558618 / 1.504120 (1.054498) | 2.359745 / 1.541195 (0.818550) | 2.458438 / 1.468490 (0.989948) | 0.548580 / 4.584777 (-4.036197) | 3.912145 / 3.745712 (0.166433) | 3.764174 / 5.269862 (-1.505687) | 2.325265 / 4.565676 (-2.240411) | 0.078022 / 0.424275 (-0.346254) | 0.008279 / 0.007607 (0.000672) | 0.571635 / 0.226044 (0.345590) | 5.672445 / 2.268929 (3.403517) | 2.760577 / 55.444624 (-52.684047) | 2.544229 / 6.876477 (-4.332248) | 2.537509 / 2.142072 (0.395436) | 0.609858 / 4.805227 (-4.195369) | 0.131053 / 6.500664 (-6.369611) | 0.056433 / 0.075469 (-0.019036) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.567231 / 1.841788 (-0.274556) | 21.415586 / 8.074308 (13.341278) | 15.982328 / 10.191392 (5.790936) | 0.167648 / 0.680424 (-0.512776) | 0.023562 / 0.534201 (-0.510639) | 0.477307 / 0.579283 (-0.101976) | 0.471929 / 0.434364 (0.037566) | 0.549996 / 0.540337 (0.009659) | 0.753927 / 1.386936 (-0.633009) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1fb2785be9198997e8b9006225b0e231f4d8ed31 \"CML watermark\")\n" ]

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[ "`load_dataset` returns a `DatasetDict` object unless `split` is defined, in which case it returns a `Dataset` (or a list of datasets if `split` is a list). We've discussed dropping `DatasetDict` from the API in https://github.com/huggingface/datasets/issues/5189 to always return the same type in `load_dataset` and support datasets without (explicit) splits. IIRC the main discussion point is deciding what to return when loading a dataset with multiple splits, but `split` is not specified. What would you expect as a return value in that scenario?", "> `load_dataset` returns a `DatasetDict` object unless `split` is defined, in which case it returns a `Dataset` (or a list of datasets if `split` is a list). We've discussed dropping `DatasetDict` from the API in #5189 to always return the same type in `load_dataset` and support datasets without (explicit) splits. IIRC the main discussion point is deciding what to return when loading a dataset with multiple splits, but `split` is not specified. What would you expect as a return value in that scenario?\r\n\r\nWouldn't a dataset with multiple splits already have keys and their related data arrays?\r\n\r\nLets say the dataset has \"train\" : trainset, \"valid\": validset and \"test\": testset\r\n\r\nSo a dictionary can be returned,, i.e.\r\n\r\n{ \r\n\"train\": trainset,\r\n\"valid\": validset,\r\n\"test\": testset\r\n}\r\n\r\nif a split is provided split=['train[:80%]', 'valid[80%:90%]', 'test[90%:100%]']\r\n\r\nwould also return the same dictionary as above.\r\n\r\nsplit='train[:10%]' should return the same value as split=['train[:10%]']\r\n\r\n{\r\n\"train\": trainset\r\n}\r\n " ]

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[ "I'm unable to reproduce this error. The server hosting the files may have been down temporarily, so try again.", "getting the same error" ]

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[ "This was fixed in https://github.com/huggingface/datasets/pull/6247. You can find the fix in the `main` version of the docs", "Ah great, thanks :)" ]

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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) | 0.009286 / 0.011353 (-0.002067) | 0.005478 / 0.011008 (-0.005530) | 0.109768 / 0.038508 (0.071260) | 0.088460 / 0.023109 (0.065351) | 0.387664 / 0.275898 (0.111766) | 0.457379 / 0.323480 (0.133899) | 0.006517 / 0.007986 (-0.001469) | 0.004037 / 0.004328 (-0.000292) | 0.083911 / 0.004250 (0.079661) | 0.071658 / 0.037052 (0.034605) | 0.385065 / 0.258489 (0.126576) | 0.460928 / 0.293841 (0.167087) | 0.048062 / 0.128546 (-0.080484) | 0.016343 / 0.075646 (-0.059303) | 0.373675 / 0.419271 (-0.045597) | 0.067640 / 0.043533 (0.024108) | 0.391730 / 0.255139 (0.136591) | 0.432908 / 0.283200 (0.149708) | 0.035748 / 0.141683 (-0.105935) | 1.767625 / 1.452155 (0.315471) | 1.965606 / 1.492716 (0.472889) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.277405 / 0.018006 (0.259399) | 0.538448 / 0.000490 (0.537958) | 0.013795 / 0.000200 (0.013595) | 0.000518 / 0.000054 (0.000464) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.043962 / 0.037411 (0.006550) | 0.115305 / 0.014526 (0.100780) | 0.117572 / 0.176557 (-0.058985) | 0.182168 / 0.737135 (-0.554968) | 0.114833 / 0.296338 (-0.181505) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.604209 / 0.215209 (0.389000) | 6.186113 / 2.077655 (4.108458) | 2.771067 / 1.504120 (1.266947) | 2.425420 / 1.541195 (0.884226) | 2.475200 / 1.468490 (1.006710) | 0.887096 / 4.584777 (-3.697681) | 5.214349 / 3.745712 (1.468637) | 4.989606 / 5.269862 (-0.280256) | 3.092135 / 4.565676 (-1.473541) | 0.104464 / 0.424275 (-0.319811) | 0.008994 / 0.007607 (0.001387) | 0.732819 / 0.226044 (0.506775) | 7.396007 / 2.268929 (5.127078) | 3.371167 / 55.444624 (-52.073457) | 2.645475 / 6.876477 (-4.231001) | 2.704215 / 2.142072 (0.562143) | 1.034724 / 4.805227 (-3.770504) | 0.219063 / 6.500664 (-6.281601) | 0.073863 / 0.075469 (-0.001606) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.625020 / 1.841788 (-0.216768) | 23.369980 / 8.074308 (15.295671) | 22.480951 / 10.191392 (12.289559) | 0.228219 / 0.680424 (-0.452204) | 0.026981 / 0.534201 (-0.507220) | 0.487670 / 0.579283 (-0.091613) | 0.582310 / 0.434364 (0.147946) | 0.539182 / 0.540337 (-0.001156) | 0.791962 / 1.386936 (-0.594974) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008657 / 0.011353 (-0.002696) | 0.004971 / 0.011008 (-0.006037) | 0.089499 / 0.038508 (0.050991) | 0.075963 / 0.023109 (0.052854) | 0.497719 / 0.275898 (0.221821) | 0.507912 / 0.323480 (0.184432) | 0.006067 / 0.007986 (-0.001919) | 0.004118 / 0.004328 (-0.000210) | 0.079397 / 0.004250 (0.075146) | 0.059181 / 0.037052 (0.022129) | 0.501108 / 0.258489 (0.242619) | 0.565792 / 0.293841 (0.271951) | 0.048818 / 0.128546 (-0.079729) | 0.014813 / 0.075646 (-0.060833) | 0.093863 / 0.419271 (-0.325409) | 0.060824 / 0.043533 (0.017292) | 0.489289 / 0.255139 (0.234150) | 0.533624 / 0.283200 (0.250425) | 0.034997 / 0.141683 (-0.106685) | 1.770574 / 1.452155 (0.318419) | 1.837213 / 1.492716 (0.344496) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.237319 / 0.018006 (0.219313) | 0.594976 / 0.000490 (0.594486) | 0.008888 / 0.000200 (0.008688) | 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) | 0.036955 / 0.037411 (-0.000456) | 0.097825 / 0.014526 (0.083299) | 0.111139 / 0.176557 (-0.065418) | 0.174776 / 0.737135 (-0.562359) | 0.117755 / 0.296338 (-0.178584) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.606498 / 0.215209 (0.391289) | 6.089874 / 2.077655 (4.012219) | 2.811135 / 1.504120 (1.307015) | 2.428486 / 1.541195 (0.887292) | 2.399512 / 1.468490 (0.931022) | 0.823492 / 4.584777 (-3.761285) | 4.897107 / 3.745712 (1.151395) | 4.407589 / 5.269862 (-0.862272) | 2.868442 / 4.565676 (-1.697235) | 0.098774 / 0.424275 (-0.325502) | 0.007998 / 0.007607 (0.000391) | 0.699489 / 0.226044 (0.473445) | 7.139214 / 2.268929 (4.870285) | 3.511158 / 55.444624 (-51.933466) | 2.775459 / 6.876477 (-4.101018) | 2.951549 / 2.142072 (0.809477) | 1.006921 / 4.805227 (-3.798306) | 0.200105 / 6.500664 (-6.300559) | 0.071064 / 0.075469 (-0.004405) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.680599 / 1.841788 (-0.161189) | 23.399777 / 8.074308 (15.325469) | 21.776357 / 10.191392 (11.584965) | 0.264697 / 0.680424 (-0.415726) | 0.034272 / 0.534201 (-0.499929) | 0.506984 / 0.579283 (-0.072299) | 0.609556 / 0.434364 (0.175192) | 0.599014 / 0.540337 (0.058677) | 0.824068 / 1.386936 (-0.562868) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3ab9de69420de8bd5d057579d71d07187b3a2c60 \"CML watermark\")\n" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6344). 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) | 0.008237 / 0.011353 (-0.003116) | 0.004658 / 0.011008 (-0.006351) | 0.105902 / 0.038508 (0.067394) | 0.082690 / 0.023109 (0.059581) | 0.471745 / 0.275898 (0.195847) | 0.464772 / 0.323480 (0.141292) | 0.006373 / 0.007986 (-0.001613) | 0.003823 / 0.004328 (-0.000505) | 0.077721 / 0.004250 (0.073471) | 0.068371 / 0.037052 (0.031318) | 0.457004 / 0.258489 (0.198515) | 0.500989 / 0.293841 (0.207148) | 0.036688 / 0.128546 (-0.091858) | 0.010004 / 0.075646 (-0.065643) | 0.363398 / 0.419271 (-0.055874) | 0.065354 / 0.043533 (0.021821) | 0.440326 / 0.255139 (0.185187) | 0.475314 / 0.283200 (0.192115) | 0.029024 / 0.141683 (-0.112659) | 1.851005 / 1.452155 (0.398851) | 1.939997 / 1.492716 (0.447281) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.269739 / 0.018006 (0.251732) | 0.510411 / 0.000490 (0.509922) | 0.013423 / 0.000200 (0.013223) | 0.000513 / 0.000054 (0.000458) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032912 / 0.037411 (-0.004499) | 0.097497 / 0.014526 (0.082971) | 0.111945 / 0.176557 (-0.064612) | 0.179264 / 0.737135 (-0.557871) | 0.111901 / 0.296338 (-0.184437) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.480994 / 0.215209 (0.265785) | 4.800969 / 2.077655 (2.723314) | 2.467390 / 1.504120 (0.963270) | 2.283219 / 1.541195 (0.742024) | 2.407735 / 1.468490 (0.939245) | 0.573862 / 4.584777 (-4.010915) | 4.213394 / 3.745712 (0.467682) | 4.120092 / 5.269862 (-1.149770) | 2.479549 / 4.565676 (-2.086128) | 0.077204 / 0.424275 (-0.347071) | 0.009165 / 0.007607 (0.001558) | 0.583887 / 0.226044 (0.357842) | 5.760759 / 2.268929 (3.491830) | 3.089220 / 55.444624 (-52.355404) | 2.652330 / 6.876477 (-4.224146) | 2.746255 / 2.142072 (0.604182) | 0.689010 / 4.805227 (-4.116217) | 0.158042 / 6.500664 (-6.342622) | 0.072789 / 0.075469 (-0.002680) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.658877 / 1.841788 (-0.182911) | 22.928756 / 8.074308 (14.854448) | 17.231823 / 10.191392 (7.040431) | 0.201475 / 0.680424 (-0.478949) | 0.025533 / 0.534201 (-0.508668) | 0.467023 / 0.579283 (-0.112260) | 0.470779 / 0.434364 (0.036415) | 0.643192 / 0.540337 (0.102855) | 0.822006 / 1.386936 (-0.564930) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008096 / 0.011353 (-0.003257) | 0.004708 / 0.011008 (-0.006300) | 0.076607 / 0.038508 (0.038099) | 0.086278 / 0.023109 (0.063168) | 0.478027 / 0.275898 (0.202129) | 0.533121 / 0.323480 (0.209641) | 0.006331 / 0.007986 (-0.001654) | 0.004005 / 0.004328 (-0.000324) | 0.076018 / 0.004250 (0.071767) | 0.067240 / 0.037052 (0.030188) | 0.484882 / 0.258489 (0.226393) | 0.536924 / 0.293841 (0.243083) | 0.045064 / 0.128546 (-0.083482) | 0.010071 / 0.075646 (-0.065575) | 0.084319 / 0.419271 (-0.334953) | 0.066267 / 0.043533 (0.022734) | 0.479283 / 0.255139 (0.224144) | 0.507832 / 0.283200 (0.224633) | 0.026436 / 0.141683 (-0.115247) | 1.820043 / 1.452155 (0.367889) | 1.954663 / 1.492716 (0.461947) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.292672 / 0.018006 (0.274666) | 0.495523 / 0.000490 (0.495033) | 0.020836 / 0.000200 (0.020636) | 0.000143 / 0.000054 (0.000088) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.038326 / 0.037411 (0.000915) | 0.114629 / 0.014526 (0.100103) | 0.126036 / 0.176557 (-0.050521) | 0.191498 / 0.737135 (-0.545638) | 0.128763 / 0.296338 (-0.167575) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.507657 / 0.215209 (0.292448) | 5.062056 / 2.077655 (2.984401) | 2.765895 / 1.504120 (1.261775) | 2.590335 / 1.541195 (1.049141) | 2.790912 / 1.468490 (1.322422) | 0.582819 / 4.584777 (-4.001958) | 4.350034 / 3.745712 (0.604322) | 3.899466 / 5.269862 (-1.370396) | 2.499655 / 4.565676 (-2.066021) | 0.068909 / 0.424275 (-0.355366) | 0.008633 / 0.007607 (0.001026) | 0.593597 / 0.226044 (0.367553) | 5.934398 / 2.268929 (3.665470) | 3.358549 / 55.444624 (-52.086075) | 3.145686 / 6.876477 (-3.730791) | 3.232153 / 2.142072 (1.090080) | 0.753039 / 4.805227 (-4.052188) | 0.164043 / 6.500664 (-6.336621) | 0.072084 / 0.075469 (-0.003385) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.632702 / 1.841788 (-0.209086) | 23.411084 / 8.074308 (15.336776) | 17.035726 / 10.191392 (6.844334) | 0.223460 / 0.680424 (-0.456964) | 0.023723 / 0.534201 (-0.510478) | 0.474160 / 0.579283 (-0.105124) | 0.538638 / 0.434364 (0.104274) | 0.595591 / 0.540337 (0.055254) | 0.803324 / 1.386936 (-0.583612) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#84855c8ddc8d3e33b516f04b687e01d498d0906e \"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) | 0.008300 / 0.011353 (-0.003053) | 0.004667 / 0.011008 (-0.006341) | 0.101028 / 0.038508 (0.062520) | 0.100269 / 0.023109 (0.077160) | 0.418651 / 0.275898 (0.142752) | 0.459061 / 0.323480 (0.135581) | 0.006786 / 0.007986 (-0.001199) | 0.003926 / 0.004328 (-0.000403) | 0.076682 / 0.004250 (0.072432) | 0.066173 / 0.037052 (0.029120) | 0.430644 / 0.258489 (0.172155) | 0.466244 / 0.293841 (0.172403) | 0.040601 / 0.128546 (-0.087946) | 0.009856 / 0.075646 (-0.065790) | 0.351467 / 0.419271 (-0.067805) | 0.068727 / 0.043533 (0.025194) | 0.419527 / 0.255139 (0.164388) | 0.431245 / 0.283200 (0.148045) | 0.028933 / 0.141683 (-0.112750) | 1.749540 / 1.452155 (0.297386) | 1.829076 / 1.492716 (0.336360) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.282248 / 0.018006 (0.264242) | 0.587293 / 0.000490 (0.586803) | 0.014497 / 0.000200 (0.014297) | 0.000383 / 0.000054 (0.000329) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031861 / 0.037411 (-0.005550) | 0.097395 / 0.014526 (0.082869) | 0.113610 / 0.176557 (-0.062946) | 0.181208 / 0.737135 (-0.555927) | 0.115340 / 0.296338 (-0.180999) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.459746 / 0.215209 (0.244537) | 4.582387 / 2.077655 (2.504733) | 2.247968 / 1.504120 (0.743848) | 2.032340 / 1.541195 (0.491145) | 2.151766 / 1.468490 (0.683276) | 0.567664 / 4.584777 (-4.017113) | 4.491732 / 3.745712 (0.746020) | 4.000651 / 5.269862 (-1.269211) | 2.429113 / 4.565676 (-2.136564) | 0.067052 / 0.424275 (-0.357223) | 0.009095 / 0.007607 (0.001488) | 0.546461 / 0.226044 (0.320417) | 5.473524 / 2.268929 (3.204595) | 2.902091 / 55.444624 (-52.542533) | 2.517510 / 6.876477 (-4.358966) | 2.572537 / 2.142072 (0.430464) | 0.683499 / 4.805227 (-4.121728) | 0.154863 / 6.500664 (-6.345801) | 0.071298 / 0.075469 (-0.004171) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.625236 / 1.841788 (-0.216552) | 23.531541 / 8.074308 (15.457233) | 16.762514 / 10.191392 (6.571122) | 0.215922 / 0.680424 (-0.464502) | 0.021928 / 0.534201 (-0.512273) | 0.466055 / 0.579283 (-0.113228) | 0.553036 / 0.434364 (0.118672) | 0.590063 / 0.540337 (0.049725) | 0.789959 / 1.386936 (-0.596977) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008240 / 0.011353 (-0.003113) | 0.004151 / 0.011008 (-0.006858) | 0.077988 / 0.038508 (0.039479) | 0.092865 / 0.023109 (0.069756) | 0.468238 / 0.275898 (0.192340) | 0.512882 / 0.323480 (0.189402) | 0.006632 / 0.007986 (-0.001354) | 0.003879 / 0.004328 (-0.000450) | 0.076238 / 0.004250 (0.071988) | 0.069372 / 0.037052 (0.032319) | 0.481040 / 0.258489 (0.222550) | 0.526332 / 0.293841 (0.232491) | 0.036768 / 0.128546 (-0.091778) | 0.009891 / 0.075646 (-0.065756) | 0.084426 / 0.419271 (-0.334846) | 0.062382 / 0.043533 (0.018849) | 0.480667 / 0.255139 (0.225528) | 0.509001 / 0.283200 (0.225802) | 0.029215 / 0.141683 (-0.112468) | 1.776075 / 1.452155 (0.323920) | 1.948558 / 1.492716 (0.455841) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.257879 / 0.018006 (0.239873) | 0.471038 / 0.000490 (0.470548) | 0.009273 / 0.000200 (0.009073) | 0.000208 / 0.000054 (0.000154) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.039249 / 0.037411 (0.001838) | 0.133281 / 0.014526 (0.118755) | 0.138261 / 0.176557 (-0.038296) | 0.191051 / 0.737135 (-0.546084) | 0.134493 / 0.296338 (-0.161845) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.507165 / 0.215209 (0.291955) | 5.081018 / 2.077655 (3.003364) | 2.747633 / 1.504120 (1.243513) | 2.558265 / 1.541195 (1.017070) | 2.710839 / 1.468490 (1.242348) | 0.579913 / 4.584777 (-4.004864) | 4.843657 / 3.745712 (1.097945) | 3.942503 / 5.269862 (-1.327358) | 2.529641 / 4.565676 (-2.036036) | 0.068826 / 0.424275 (-0.355449) | 0.008847 / 0.007607 (0.001240) | 0.605332 / 0.226044 (0.379287) | 6.039574 / 2.268929 (3.770646) | 3.437291 / 55.444624 (-52.007333) | 3.086631 / 6.876477 (-3.789846) | 3.189340 / 2.142072 (1.047267) | 0.702650 / 4.805227 (-4.102578) | 0.157403 / 6.500664 (-6.343261) | 0.074637 / 0.075469 (-0.000832) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.816532 / 1.841788 (-0.025256) | 24.526675 / 8.074308 (16.452367) | 17.371691 / 10.191392 (7.180299) | 0.236044 / 0.680424 (-0.444380) | 0.024759 / 0.534201 (-0.509442) | 0.530578 / 0.579283 (-0.048705) | 0.527424 / 0.434364 (0.093060) | 0.620267 / 0.540337 (0.079929) | 0.791159 / 1.386936 (-0.595777) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#78cfce823b98b6cce79a9297fe6fa9e8f80a869c \"CML watermark\")\n" ]

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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) | 0.006584 / 0.011353 (-0.004769) | 0.004197 / 0.011008 (-0.006812) | 0.083598 / 0.038508 (0.045090) | 0.075502 / 0.023109 (0.052392) | 0.312986 / 0.275898 (0.037088) | 0.344630 / 0.323480 (0.021150) | 0.005394 / 0.007986 (-0.002591) | 0.003485 / 0.004328 (-0.000843) | 0.064529 / 0.004250 (0.060279) | 0.055003 / 0.037052 (0.017950) | 0.320522 / 0.258489 (0.062033) | 0.362623 / 0.293841 (0.068782) | 0.030900 / 0.128546 (-0.097646) | 0.008459 / 0.075646 (-0.067187) | 0.286986 / 0.419271 (-0.132285) | 0.052310 / 0.043533 (0.008777) | 0.315873 / 0.255139 (0.060734) | 0.333962 / 0.283200 (0.050762) | 0.023836 / 0.141683 (-0.117847) | 1.481806 / 1.452155 (0.029651) | 1.567926 / 1.492716 (0.075209) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.268188 / 0.018006 (0.250182) | 0.520542 / 0.000490 (0.520052) | 0.017617 / 0.000200 (0.017417) | 0.000631 / 0.000054 (0.000577) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028828 / 0.037411 (-0.008584) | 0.083028 / 0.014526 (0.068502) | 0.099808 / 0.176557 (-0.076748) | 0.154282 / 0.737135 (-0.582853) | 0.098590 / 0.296338 (-0.197748) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.407548 / 0.215209 (0.192339) | 4.066128 / 2.077655 (1.988474) | 2.036757 / 1.504120 (0.532637) | 1.870130 / 1.541195 (0.328935) | 1.949031 / 1.468490 (0.480541) | 0.489263 / 4.584777 (-4.095514) | 3.506269 / 3.745712 (-0.239443) | 3.457232 / 5.269862 (-1.812629) | 2.060097 / 4.565676 (-2.505580) | 0.057252 / 0.424275 (-0.367024) | 0.007727 / 0.007607 (0.000120) | 0.480229 / 0.226044 (0.254185) | 4.807064 / 2.268929 (2.538135) | 2.495438 / 55.444624 (-52.949186) | 2.186194 / 6.876477 (-4.690283) | 2.243372 / 2.142072 (0.101300) | 0.580550 / 4.805227 (-4.224678) | 0.135398 / 6.500664 (-6.365266) | 0.061878 / 0.075469 (-0.013591) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.305635 / 1.841788 (-0.536152) | 19.194421 / 8.074308 (11.120113) | 14.531699 / 10.191392 (4.340307) | 0.167144 / 0.680424 (-0.513280) | 0.018270 / 0.534201 (-0.515931) | 0.393702 / 0.579283 (-0.185581) | 0.406518 / 0.434364 (-0.027846) | 0.458126 / 0.540337 (-0.082211) | 0.639839 / 1.386936 (-0.747097) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006742 / 0.011353 (-0.004611) | 0.004092 / 0.011008 (-0.006916) | 0.065547 / 0.038508 (0.027039) | 0.076293 / 0.023109 (0.053184) | 0.389701 / 0.275898 (0.113803) | 0.429158 / 0.323480 (0.105678) | 0.005606 / 0.007986 (-0.002380) | 0.003491 / 0.004328 (-0.000837) | 0.065903 / 0.004250 (0.061653) | 0.057346 / 0.037052 (0.020293) | 0.393233 / 0.258489 (0.134744) | 0.433106 / 0.293841 (0.139265) | 0.032612 / 0.128546 (-0.095934) | 0.008777 / 0.075646 (-0.066869) | 0.073135 / 0.419271 (-0.346137) | 0.048167 / 0.043533 (0.004635) | 0.389309 / 0.255139 (0.134170) | 0.416442 / 0.283200 (0.133242) | 0.022839 / 0.141683 (-0.118844) | 1.531607 / 1.452155 (0.079453) | 1.598950 / 1.492716 (0.106234) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.254856 / 0.018006 (0.236850) | 0.528186 / 0.000490 (0.527697) | 0.006975 / 0.000200 (0.006775) | 0.000102 / 0.000054 (0.000048) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032377 / 0.037411 (-0.005034) | 0.092706 / 0.014526 (0.078180) | 0.107618 / 0.176557 (-0.068939) | 0.160103 / 0.737135 (-0.577032) | 0.107226 / 0.296338 (-0.189112) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.430922 / 0.215209 (0.215713) | 4.312556 / 2.077655 (2.234901) | 2.287686 / 1.504120 (0.783567) | 2.111103 / 1.541195 (0.569908) | 2.284105 / 1.468490 (0.815614) | 0.485987 / 4.584777 (-4.098790) | 3.557320 / 3.745712 (-0.188392) | 3.341150 / 5.269862 (-1.928711) | 2.056705 / 4.565676 (-2.508972) | 0.057265 / 0.424275 (-0.367010) | 0.007264 / 0.007607 (-0.000344) | 0.505191 / 0.226044 (0.279146) | 5.045379 / 2.268929 (2.776450) | 2.732357 / 55.444624 (-52.712267) | 2.390256 / 6.876477 (-4.486220) | 2.643676 / 2.142072 (0.501604) | 0.584630 / 4.805227 (-4.220597) | 0.132402 / 6.500664 (-6.368262) | 0.061387 / 0.075469 (-0.014082) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.340721 / 1.841788 (-0.501066) | 19.744145 / 8.074308 (11.669837) | 14.694482 / 10.191392 (4.503090) | 0.166294 / 0.680424 (-0.514129) | 0.020691 / 0.534201 (-0.513510) | 0.398359 / 0.579283 (-0.180924) | 0.423831 / 0.434364 (-0.010533) | 0.474365 / 0.540337 (-0.065972) | 0.649410 / 1.386936 (-0.737526) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b29bc9cef6237eb0d18f77c56686705f468bed25 \"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) | 0.004369 / 0.011353 (-0.006984) | 0.002728 / 0.011008 (-0.008280) | 0.063754 / 0.038508 (0.025246) | 0.029396 / 0.023109 (0.006287) | 0.269409 / 0.275898 (-0.006489) | 0.287654 / 0.323480 (-0.035826) | 0.003926 / 0.007986 (-0.004060) | 0.002366 / 0.004328 (-0.001963) | 0.048910 / 0.004250 (0.044660) | 0.043126 / 0.037052 (0.006074) | 0.260774 / 0.258489 (0.002285) | 0.299996 / 0.293841 (0.006155) | 0.023359 / 0.128546 (-0.105187) | 0.007259 / 0.075646 (-0.068388) | 0.211412 / 0.419271 (-0.207860) | 0.053883 / 0.043533 (0.010350) | 0.268946 / 0.255139 (0.013807) | 0.287664 / 0.283200 (0.004465) | 0.017600 / 0.141683 (-0.124083) | 1.096478 / 1.452155 (-0.355676) | 1.193063 / 1.492716 (-0.299653) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.090985 / 0.018006 (0.072979) | 0.287168 / 0.000490 (0.286678) | 0.000208 / 0.000200 (0.000009) | 0.000052 / 0.000054 (-0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019238 / 0.037411 (-0.018173) | 0.062660 / 0.014526 (0.048134) | 0.073414 / 0.176557 (-0.103143) | 0.120842 / 0.737135 (-0.616294) | 0.077658 / 0.296338 (-0.218681) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.280285 / 0.215209 (0.065076) | 2.729807 / 2.077655 (0.652152) | 1.430686 / 1.504120 (-0.073434) | 1.307260 / 1.541195 (-0.233935) | 1.321013 / 1.468490 (-0.147477) | 0.387253 / 4.584777 (-4.197524) | 2.415635 / 3.745712 (-1.330077) | 2.557206 / 5.269862 (-2.712656) | 1.553224 / 4.565676 (-3.012453) | 0.045402 / 0.424275 (-0.378873) | 0.004798 / 0.007607 (-0.002809) | 0.330493 / 0.226044 (0.104449) | 3.226835 / 2.268929 (0.957906) | 1.739068 / 55.444624 (-53.705557) | 1.494841 / 6.876477 (-5.381636) | 1.528253 / 2.142072 (-0.613820) | 0.451525 / 4.805227 (-4.353702) | 0.096620 / 6.500664 (-6.404044) | 0.041176 / 0.075469 (-0.034293) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.930892 / 1.841788 (-0.910896) | 11.343351 / 8.074308 (3.269043) | 10.420327 / 10.191392 (0.228935) | 0.137629 / 0.680424 (-0.542795) | 0.013907 / 0.534201 (-0.520293) | 0.267778 / 0.579283 (-0.311505) | 0.260774 / 0.434364 (-0.173590) | 0.308213 / 0.540337 (-0.232124) | 0.419659 / 1.386936 (-0.967277) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004867 / 0.011353 (-0.006486) | 0.002830 / 0.011008 (-0.008178) | 0.048506 / 0.038508 (0.009998) | 0.048190 / 0.023109 (0.025080) | 0.279995 / 0.275898 (0.004097) | 0.296396 / 0.323480 (-0.027083) | 0.004700 / 0.007986 (-0.003285) | 0.003546 / 0.004328 (-0.000782) | 0.048237 / 0.004250 (0.043987) | 0.037102 / 0.037052 (0.000050) | 0.284582 / 0.258489 (0.026093) | 0.315896 / 0.293841 (0.022055) | 0.024699 / 0.128546 (-0.103848) | 0.007077 / 0.075646 (-0.068569) | 0.054471 / 0.419271 (-0.364800) | 0.032537 / 0.043533 (-0.010996) | 0.276761 / 0.255139 (0.021622) | 0.294741 / 0.283200 (0.011542) | 0.017766 / 0.141683 (-0.123917) | 1.118377 / 1.452155 (-0.333778) | 1.186617 / 1.492716 (-0.306100) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.088981 / 0.018006 (0.070975) | 0.297793 / 0.000490 (0.297303) | 0.000220 / 0.000200 (0.000020) | 0.000050 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021300 / 0.037411 (-0.016111) | 0.070059 / 0.014526 (0.055533) | 0.080452 / 0.176557 (-0.096104) | 0.118461 / 0.737135 (-0.618674) | 0.081099 / 0.296338 (-0.215240) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.300560 / 0.215209 (0.085351) | 2.951461 / 2.077655 (0.873806) | 1.621978 / 1.504120 (0.117858) | 1.478871 / 1.541195 (-0.062324) | 1.520732 / 1.468490 (0.052242) | 0.408625 / 4.584777 (-4.176152) | 2.407253 / 3.745712 (-1.338459) | 2.546000 / 5.269862 (-2.723861) | 1.525920 / 4.565676 (-3.039757) | 0.046817 / 0.424275 (-0.377458) | 0.004880 / 0.007607 (-0.002727) | 0.350866 / 0.226044 (0.124821) | 3.489379 / 2.268929 (1.220451) | 1.967197 / 55.444624 (-53.477427) | 1.686083 / 6.876477 (-5.190394) | 1.699307 / 2.142072 (-0.442766) | 0.479659 / 4.805227 (-4.325568) | 0.098853 / 6.500664 (-6.401811) | 0.040718 / 0.075469 (-0.034751) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.018352 / 1.841788 (-0.823436) | 12.022551 / 8.074308 (3.948243) | 10.841890 / 10.191392 (0.650498) | 0.130732 / 0.680424 (-0.549692) | 0.016334 / 0.534201 (-0.517867) | 0.271984 / 0.579283 (-0.307299) | 0.276733 / 0.434364 (-0.157631) | 0.308049 / 0.540337 (-0.232289) | 0.415428 / 1.386936 (-0.971508) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#31d95717e4e5fc6dd7699878720f063d51f1d595 \"CML watermark\")\n" ]

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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) | 0.007051 / 0.011353 (-0.004302) | 0.004291 / 0.011008 (-0.006717) | 0.085557 / 0.038508 (0.047048) | 0.087919 / 0.023109 (0.064810) | 0.356912 / 0.275898 (0.081014) | 0.394835 / 0.323480 (0.071355) | 0.004464 / 0.007986 (-0.003522) | 0.003688 / 0.004328 (-0.000640) | 0.065437 / 0.004250 (0.061186) | 0.060156 / 0.037052 (0.023103) | 0.361807 / 0.258489 (0.103318) | 0.420917 / 0.293841 (0.127076) | 0.031704 / 0.128546 (-0.096842) | 0.008921 / 0.075646 (-0.066726) | 0.287828 / 0.419271 (-0.131443) | 0.053600 / 0.043533 (0.010067) | 0.361833 / 0.255139 (0.106694) | 0.396732 / 0.283200 (0.113532) | 0.025874 / 0.141683 (-0.115809) | 1.474926 / 1.452155 (0.022771) | 1.563186 / 1.492716 (0.070469) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.316823 / 0.018006 (0.298817) | 0.604085 / 0.000490 (0.603595) | 0.020828 / 0.000200 (0.020628) | 0.000351 / 0.000054 (0.000297) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030468 / 0.037411 (-0.006943) | 0.083904 / 0.014526 (0.069378) | 0.103019 / 0.176557 (-0.073537) | 0.159018 / 0.737135 (-0.578117) | 0.102737 / 0.296338 (-0.193602) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.405311 / 0.215209 (0.190102) | 4.029060 / 2.077655 (1.951406) | 2.046590 / 1.504120 (0.542470) | 1.919335 / 1.541195 (0.378140) | 2.030371 / 1.468490 (0.561881) | 0.484209 / 4.584777 (-4.100568) | 3.486888 / 3.745712 (-0.258824) | 3.390777 / 5.269862 (-1.879084) | 2.110744 / 4.565676 (-2.454933) | 0.056587 / 0.424275 (-0.367688) | 0.007766 / 0.007607 (0.000159) | 0.488217 / 0.226044 (0.262173) | 4.853904 / 2.268929 (2.584976) | 2.595122 / 55.444624 (-52.849502) | 2.217712 / 6.876477 (-4.658765) | 2.500368 / 2.142072 (0.358296) | 0.580843 / 4.805227 (-4.224384) | 0.132719 / 6.500664 (-6.367945) | 0.060202 / 0.075469 (-0.015267) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.260748 / 1.841788 (-0.581040) | 20.148848 / 8.074308 (12.074540) | 14.738779 / 10.191392 (4.547387) | 0.167562 / 0.680424 (-0.512862) | 0.018944 / 0.534201 (-0.515257) | 0.394314 / 0.579283 (-0.184969) | 0.409345 / 0.434364 (-0.025019) | 0.458743 / 0.540337 (-0.081594) | 0.638175 / 1.386936 (-0.748761) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007097 / 0.011353 (-0.004256) | 0.004304 / 0.011008 (-0.006705) | 0.065539 / 0.038508 (0.027030) | 0.094078 / 0.023109 (0.070969) | 0.412411 / 0.275898 (0.136513) | 0.441900 / 0.323480 (0.118420) | 0.006038 / 0.007986 (-0.001948) | 0.003647 / 0.004328 (-0.000682) | 0.065298 / 0.004250 (0.061048) | 0.062571 / 0.037052 (0.025518) | 0.405156 / 0.258489 (0.146667) | 0.443779 / 0.293841 (0.149938) | 0.034470 / 0.128546 (-0.094077) | 0.008858 / 0.075646 (-0.066789) | 0.071840 / 0.419271 (-0.347431) | 0.050468 / 0.043533 (0.006935) | 0.404198 / 0.255139 (0.149059) | 0.430196 / 0.283200 (0.146997) | 0.025710 / 0.141683 (-0.115973) | 1.525374 / 1.452155 (0.073219) | 1.591830 / 1.492716 (0.099114) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.294330 / 0.018006 (0.276324) | 0.516943 / 0.000490 (0.516453) | 0.004807 / 0.000200 (0.004607) | 0.000103 / 0.000054 (0.000048) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034505 / 0.037411 (-0.002907) | 0.096645 / 0.014526 (0.082119) | 0.111926 / 0.176557 (-0.064630) | 0.165241 / 0.737135 (-0.571894) | 0.111834 / 0.296338 (-0.184504) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.436370 / 0.215209 (0.221161) | 4.357568 / 2.077655 (2.279913) | 2.360529 / 1.504120 (0.856409) | 2.196375 / 1.541195 (0.655180) | 2.307481 / 1.468490 (0.838991) | 0.494072 / 4.584777 (-4.090705) | 3.565078 / 3.745712 (-0.180634) | 3.405174 / 5.269862 (-1.864688) | 2.203307 / 4.565676 (-2.362369) | 0.058582 / 0.424275 (-0.365693) | 0.007410 / 0.007607 (-0.000197) | 0.514323 / 0.226044 (0.288279) | 5.139834 / 2.268929 (2.870905) | 2.884111 / 55.444624 (-52.560513) | 2.589021 / 6.876477 (-4.287456) | 2.787577 / 2.142072 (0.645504) | 0.590765 / 4.805227 (-4.214462) | 0.135237 / 6.500664 (-6.365427) | 0.061078 / 0.075469 (-0.014391) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.346938 / 1.841788 (-0.494850) | 21.009948 / 8.074308 (12.935640) | 15.203281 / 10.191392 (5.011889) | 0.166208 / 0.680424 (-0.514216) | 0.020634 / 0.534201 (-0.513567) | 0.413825 / 0.579283 (-0.165458) | 0.416477 / 0.434364 (-0.017887) | 0.485888 / 0.540337 (-0.054449) | 0.664941 / 1.386936 (-0.721995) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#395b30ee2c0f6088e28fe78a3e61b591e40a4668 \"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) | 0.005927 / 0.011353 (-0.005425) | 0.003622 / 0.011008 (-0.007386) | 0.081414 / 0.038508 (0.042906) | 0.061031 / 0.023109 (0.037922) | 0.358323 / 0.275898 (0.082425) | 0.394192 / 0.323480 (0.070712) | 0.003471 / 0.007986 (-0.004515) | 0.002930 / 0.004328 (-0.001399) | 0.064215 / 0.004250 (0.059964) | 0.048678 / 0.037052 (0.011625) | 0.367966 / 0.258489 (0.109477) | 0.412618 / 0.293841 (0.118777) | 0.027192 / 0.128546 (-0.101355) | 0.007921 / 0.075646 (-0.067725) | 0.262213 / 0.419271 (-0.157059) | 0.044750 / 0.043533 (0.001217) | 0.351573 / 0.255139 (0.096434) | 0.389000 / 0.283200 (0.105800) | 0.020842 / 0.141683 (-0.120840) | 1.448925 / 1.452155 (-0.003229) | 1.530478 / 1.492716 (0.037761) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.227787 / 0.018006 (0.209780) | 0.423161 / 0.000490 (0.422671) | 0.007557 / 0.000200 (0.007357) | 0.000205 / 0.000054 (0.000150) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024703 / 0.037411 (-0.012709) | 0.074044 / 0.014526 (0.059518) | 0.085520 / 0.176557 (-0.091037) | 0.146132 / 0.737135 (-0.591003) | 0.085637 / 0.296338 (-0.210701) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.393177 / 0.215209 (0.177968) | 3.926740 / 2.077655 (1.849085) | 1.892420 / 1.504120 (0.388300) | 1.716844 / 1.541195 (0.175650) | 1.784040 / 1.468490 (0.315550) | 0.499570 / 4.584777 (-4.085207) | 3.057764 / 3.745712 (-0.687948) | 2.885463 / 5.269862 (-2.384399) | 1.905206 / 4.565676 (-2.660471) | 0.058216 / 0.424275 (-0.366059) | 0.006805 / 0.007607 (-0.000802) | 0.465406 / 0.226044 (0.239361) | 4.658569 / 2.268929 (2.389641) | 2.461737 / 55.444624 (-52.982887) | 2.170620 / 6.876477 (-4.705856) | 2.373715 / 2.142072 (0.231643) | 0.592818 / 4.805227 (-4.212409) | 0.127960 / 6.500664 (-6.372704) | 0.061696 / 0.075469 (-0.013773) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.229073 / 1.841788 (-0.612715) | 17.832087 / 8.074308 (9.757778) | 13.889485 / 10.191392 (3.698093) | 0.142237 / 0.680424 (-0.538187) | 0.016752 / 0.534201 (-0.517449) | 0.338342 / 0.579283 (-0.240941) | 0.383933 / 0.434364 (-0.050431) | 0.393017 / 0.540337 (-0.147320) | 0.557621 / 1.386936 (-0.829315) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006218 / 0.011353 (-0.005135) | 0.003679 / 0.011008 (-0.007329) | 0.062934 / 0.038508 (0.024426) | 0.066764 / 0.023109 (0.043655) | 0.482737 / 0.275898 (0.206839) | 0.483241 / 0.323480 (0.159761) | 0.004828 / 0.007986 (-0.003158) | 0.002880 / 0.004328 (-0.001448) | 0.063111 / 0.004250 (0.058861) | 0.049500 / 0.037052 (0.012448) | 0.453155 / 0.258489 (0.194666) | 0.488776 / 0.293841 (0.194935) | 0.028568 / 0.128546 (-0.099978) | 0.008490 / 0.075646 (-0.067157) | 0.068202 / 0.419271 (-0.351069) | 0.040695 / 0.043533 (-0.002838) | 0.457473 / 0.255139 (0.202334) | 0.471968 / 0.283200 (0.188768) | 0.021261 / 0.141683 (-0.120422) | 1.476304 / 1.452155 (0.024150) | 1.503433 / 1.492716 (0.010716) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.227108 / 0.018006 (0.209102) | 0.428330 / 0.000490 (0.427840) | 0.004637 / 0.000200 (0.004437) | 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) | 0.027253 / 0.037411 (-0.010158) | 0.081990 / 0.014526 (0.067464) | 0.092763 / 0.176557 (-0.083794) | 0.146155 / 0.737135 (-0.590981) | 0.093175 / 0.296338 (-0.203164) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.464585 / 0.215209 (0.249376) | 4.630704 / 2.077655 (2.553050) | 2.583272 / 1.504120 (1.079152) | 2.393810 / 1.541195 (0.852615) | 2.463255 / 1.468490 (0.994765) | 0.507045 / 4.584777 (-4.077732) | 3.181972 / 3.745712 (-0.563740) | 2.902321 / 5.269862 (-2.367541) | 1.905431 / 4.565676 (-2.660246) | 0.059427 / 0.424275 (-0.364848) | 0.006387 / 0.007607 (-0.001220) | 0.542247 / 0.226044 (0.316203) | 5.426868 / 2.268929 (3.157939) | 3.073489 / 55.444624 (-52.371136) | 2.719620 / 6.876477 (-4.156857) | 2.861865 / 2.142072 (0.719793) | 0.593757 / 4.805227 (-4.211471) | 0.125439 / 6.500664 (-6.375225) | 0.060901 / 0.075469 (-0.014568) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.359938 / 1.841788 (-0.481850) | 18.484867 / 8.074308 (10.410559) | 14.685645 / 10.191392 (4.494253) | 0.164098 / 0.680424 (-0.516325) | 0.018090 / 0.534201 (-0.516111) | 0.339760 / 0.579283 (-0.239523) | 0.376668 / 0.434364 (-0.057696) | 0.396963 / 0.540337 (-0.143374) | 0.549305 / 1.386936 (-0.837631) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0c896f4195ec8a91e09f8bb9a57950bcec8b8450 \"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) | 0.006052 / 0.011353 (-0.005301) | 0.003715 / 0.011008 (-0.007293) | 0.079646 / 0.038508 (0.041138) | 0.059053 / 0.023109 (0.035944) | 0.393016 / 0.275898 (0.117118) | 0.424758 / 0.323480 (0.101278) | 0.005407 / 0.007986 (-0.002578) | 0.002920 / 0.004328 (-0.001408) | 0.062145 / 0.004250 (0.057894) | 0.047289 / 0.037052 (0.010237) | 0.399848 / 0.258489 (0.141359) | 0.434239 / 0.293841 (0.140398) | 0.027388 / 0.128546 (-0.101158) | 0.007967 / 0.075646 (-0.067680) | 0.262546 / 0.419271 (-0.156725) | 0.045014 / 0.043533 (0.001482) | 0.398086 / 0.255139 (0.142947) | 0.414615 / 0.283200 (0.131415) | 0.020410 / 0.141683 (-0.121272) | 1.447276 / 1.452155 (-0.004879) | 1.512390 / 1.492716 (0.019673) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.224854 / 0.018006 (0.206847) | 0.434173 / 0.000490 (0.433683) | 0.010091 / 0.000200 (0.009891) | 0.000259 / 0.000054 (0.000205) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025316 / 0.037411 (-0.012095) | 0.073284 / 0.014526 (0.058758) | 0.085177 / 0.176557 (-0.091379) | 0.148905 / 0.737135 (-0.588230) | 0.084696 / 0.296338 (-0.211642) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.438259 / 0.215209 (0.223050) | 4.380679 / 2.077655 (2.303025) | 2.310329 / 1.504120 (0.806209) | 2.144002 / 1.541195 (0.602807) | 2.203761 / 1.468490 (0.735270) | 0.500559 / 4.584777 (-4.084218) | 3.031172 / 3.745712 (-0.714540) | 2.839425 / 5.269862 (-2.430436) | 1.878391 / 4.565676 (-2.687285) | 0.057325 / 0.424275 (-0.366950) | 0.006719 / 0.007607 (-0.000888) | 0.510122 / 0.226044 (0.284078) | 5.108632 / 2.268929 (2.839704) | 2.805716 / 55.444624 (-52.638909) | 2.422183 / 6.876477 (-4.454293) | 2.635280 / 2.142072 (0.493207) | 0.589351 / 4.805227 (-4.215876) | 0.125416 / 6.500664 (-6.375248) | 0.061142 / 0.075469 (-0.014327) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.234997 / 1.841788 (-0.606791) | 17.731828 / 8.074308 (9.657520) | 13.858081 / 10.191392 (3.666689) | 0.145975 / 0.680424 (-0.534449) | 0.016827 / 0.534201 (-0.517374) | 0.335701 / 0.579283 (-0.243582) | 0.361867 / 0.434364 (-0.072497) | 0.394620 / 0.540337 (-0.145718) | 0.532146 / 1.386936 (-0.854790) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006091 / 0.011353 (-0.005262) | 0.003663 / 0.011008 (-0.007345) | 0.062596 / 0.038508 (0.024088) | 0.061649 / 0.023109 (0.038539) | 0.440647 / 0.275898 (0.164749) | 0.472974 / 0.323480 (0.149494) | 0.005009 / 0.007986 (-0.002976) | 0.002879 / 0.004328 (-0.001449) | 0.062815 / 0.004250 (0.058565) | 0.049000 / 0.037052 (0.011947) | 0.442990 / 0.258489 (0.184501) | 0.477622 / 0.293841 (0.183781) | 0.028512 / 0.128546 (-0.100034) | 0.008031 / 0.075646 (-0.067615) | 0.067853 / 0.419271 (-0.351418) | 0.040823 / 0.043533 (-0.002710) | 0.437811 / 0.255139 (0.182672) | 0.464615 / 0.283200 (0.181416) | 0.021348 / 0.141683 (-0.120334) | 1.479230 / 1.452155 (0.027075) | 1.544053 / 1.492716 (0.051337) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.210697 / 0.018006 (0.192691) | 0.436450 / 0.000490 (0.435960) | 0.003413 / 0.000200 (0.003213) | 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) | 0.027190 / 0.037411 (-0.010222) | 0.083254 / 0.014526 (0.068728) | 0.092936 / 0.176557 (-0.083620) | 0.147261 / 0.737135 (-0.589874) | 0.092910 / 0.296338 (-0.203429) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.454195 / 0.215209 (0.238986) | 4.569122 / 2.077655 (2.491468) | 2.497198 / 1.504120 (0.993079) | 2.314337 / 1.541195 (0.773142) | 2.378471 / 1.468490 (0.909981) | 0.515402 / 4.584777 (-4.069375) | 3.199374 / 3.745712 (-0.546338) | 2.899300 / 5.269862 (-2.370562) | 1.873314 / 4.565676 (-2.692362) | 0.058820 / 0.424275 (-0.365455) | 0.006651 / 0.007607 (-0.000957) | 0.526681 / 0.226044 (0.300636) | 5.275232 / 2.268929 (3.006303) | 2.969107 / 55.444624 (-52.475517) | 2.600959 / 6.876477 (-4.275518) | 2.762930 / 2.142072 (0.620858) | 0.605726 / 4.805227 (-4.199501) | 0.127618 / 6.500664 (-6.373046) | 0.062840 / 0.075469 (-0.012629) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.367276 / 1.841788 (-0.474512) | 18.069385 / 8.074308 (9.995077) | 14.691945 / 10.191392 (4.500553) | 0.147203 / 0.680424 (-0.533221) | 0.018484 / 0.534201 (-0.515717) | 0.333759 / 0.579283 (-0.245524) | 0.395503 / 0.434364 (-0.038861) | 0.387031 / 0.540337 (-0.153306) | 0.550428 / 1.386936 (-0.836508) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4c8f7eb79dff66dd03211321dcb55f7a7a05ef38 \"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) | 0.007675 / 0.011353 (-0.003678) | 0.004532 / 0.011008 (-0.006476) | 0.088176 / 0.038508 (0.049668) | 0.103257 / 0.023109 (0.080148) | 0.314785 / 0.275898 (0.038887) | 0.354280 / 0.323480 (0.030800) | 0.004638 / 0.007986 (-0.003348) | 0.003736 / 0.004328 (-0.000592) | 0.066744 / 0.004250 (0.062493) | 0.064647 / 0.037052 (0.027595) | 0.320227 / 0.258489 (0.061738) | 0.369581 / 0.293841 (0.075740) | 0.032347 / 0.128546 (-0.096199) | 0.009226 / 0.075646 (-0.066421) | 0.292966 / 0.419271 (-0.126306) | 0.055738 / 0.043533 (0.012206) | 0.316537 / 0.255139 (0.061398) | 0.334699 / 0.283200 (0.051499) | 0.027401 / 0.141683 (-0.114282) | 1.482390 / 1.452155 (0.030236) | 1.594771 / 1.492716 (0.102055) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.322181 / 0.018006 (0.304175) | 0.577701 / 0.000490 (0.577212) | 0.014565 / 0.000200 (0.014365) | 0.000393 / 0.000054 (0.000338) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033255 / 0.037411 (-0.004156) | 0.094271 / 0.014526 (0.079745) | 0.105360 / 0.176557 (-0.071197) | 0.163699 / 0.737135 (-0.573436) | 0.105620 / 0.296338 (-0.190719) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.383449 / 0.215209 (0.168240) | 3.824292 / 2.077655 (1.746637) | 1.861809 / 1.504120 (0.357689) | 1.698153 / 1.541195 (0.156958) | 1.819460 / 1.468490 (0.350970) | 0.488277 / 4.584777 (-4.096500) | 3.622772 / 3.745712 (-0.122940) | 3.486041 / 5.269862 (-1.783821) | 2.211679 / 4.565676 (-2.353998) | 0.057637 / 0.424275 (-0.366638) | 0.008028 / 0.007607 (0.000421) | 0.461917 / 0.226044 (0.235873) | 4.626493 / 2.268929 (2.357565) | 2.374846 / 55.444624 (-53.069779) | 1.976003 / 6.876477 (-4.900473) | 2.325342 / 2.142072 (0.183269) | 0.582538 / 4.805227 (-4.222689) | 0.133575 / 6.500664 (-6.367089) | 0.061696 / 0.075469 (-0.013773) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.271846 / 1.841788 (-0.569941) | 20.944702 / 8.074308 (12.870394) | 15.438119 / 10.191392 (5.246727) | 0.167334 / 0.680424 (-0.513090) | 0.019538 / 0.534201 (-0.514663) | 0.401467 / 0.579283 (-0.177816) | 0.428222 / 0.434364 (-0.006142) | 0.466108 / 0.540337 (-0.074229) | 0.645326 / 1.386936 (-0.741610) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007096 / 0.011353 (-0.004257) | 0.004398 / 0.011008 (-0.006610) | 0.066253 / 0.038508 (0.027745) | 0.089415 / 0.023109 (0.066306) | 0.395760 / 0.275898 (0.119862) | 0.436058 / 0.323480 (0.112579) | 0.005944 / 0.007986 (-0.002042) | 0.003821 / 0.004328 (-0.000507) | 0.065286 / 0.004250 (0.061036) | 0.060990 / 0.037052 (0.023937) | 0.394674 / 0.258489 (0.136185) | 0.437672 / 0.293841 (0.143831) | 0.032370 / 0.128546 (-0.096177) | 0.009025 / 0.075646 (-0.066622) | 0.071365 / 0.419271 (-0.347906) | 0.048232 / 0.043533 (0.004699) | 0.395677 / 0.255139 (0.140538) | 0.415869 / 0.283200 (0.132669) | 0.024632 / 0.141683 (-0.117051) | 1.511386 / 1.452155 (0.059231) | 1.604475 / 1.492716 (0.111759) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.312864 / 0.018006 (0.294858) | 0.535432 / 0.000490 (0.534943) | 0.005195 / 0.000200 (0.004995) | 0.000101 / 0.000054 (0.000047) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035827 / 0.037411 (-0.001584) | 0.099353 / 0.014526 (0.084827) | 0.110796 / 0.176557 (-0.065761) | 0.165224 / 0.737135 (-0.571911) | 0.112111 / 0.296338 (-0.184228) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.428873 / 0.215209 (0.213664) | 4.284264 / 2.077655 (2.206609) | 2.303966 / 1.504120 (0.799847) | 2.153868 / 1.541195 (0.612674) | 2.275669 / 1.468490 (0.807179) | 0.495452 / 4.584777 (-4.089325) | 3.706773 / 3.745712 (-0.038939) | 3.471988 / 5.269862 (-1.797874) | 2.194851 / 4.565676 (-2.370825) | 0.058998 / 0.424275 (-0.365277) | 0.007522 / 0.007607 (-0.000085) | 0.511222 / 0.226044 (0.285177) | 5.097058 / 2.268929 (2.828130) | 2.856793 / 55.444624 (-52.587832) | 2.521907 / 6.876477 (-4.354569) | 2.783133 / 2.142072 (0.641060) | 0.600511 / 4.805227 (-4.204717) | 0.134130 / 6.500664 (-6.366534) | 0.061726 / 0.075469 (-0.013743) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.385272 / 1.841788 (-0.456516) | 21.149260 / 8.074308 (13.074952) | 15.548746 / 10.191392 (5.357354) | 0.167506 / 0.680424 (-0.512918) | 0.020494 / 0.534201 (-0.513707) | 0.400697 / 0.579283 (-0.178586) | 0.427386 / 0.434364 (-0.006978) | 0.478514 / 0.540337 (-0.061824) | 0.655753 / 1.386936 (-0.731183) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4c8f7eb79dff66dd03211321dcb55f7a7a05ef38 \"CML watermark\")\n" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6340). All of your documentation changes will be reflected on that endpoint." ]

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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) | 0.006572 / 0.011353 (-0.004780) | 0.004019 / 0.011008 (-0.006989) | 0.084080 / 0.038508 (0.045572) | 0.070111 / 0.023109 (0.047002) | 0.340440 / 0.275898 (0.064542) | 0.358839 / 0.323480 (0.035359) | 0.005254 / 0.007986 (-0.002732) | 0.003296 / 0.004328 (-0.001032) | 0.064368 / 0.004250 (0.060117) | 0.054549 / 0.037052 (0.017497) | 0.343817 / 0.258489 (0.085328) | 0.369871 / 0.293841 (0.076030) | 0.030621 / 0.128546 (-0.097925) | 0.008457 / 0.075646 (-0.067189) | 0.287839 / 0.419271 (-0.131432) | 0.051700 / 0.043533 (0.008167) | 0.331602 / 0.255139 (0.076463) | 0.339836 / 0.283200 (0.056636) | 0.023224 / 0.141683 (-0.118459) | 1.494597 / 1.452155 (0.042443) | 1.578640 / 1.492716 (0.085924) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.236985 / 0.018006 (0.218979) | 0.506153 / 0.000490 (0.505664) | 0.009753 / 0.000200 (0.009553) | 0.000345 / 0.000054 (0.000291) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028355 / 0.037411 (-0.009056) | 0.082104 / 0.014526 (0.067578) | 0.095141 / 0.176557 (-0.081415) | 0.151054 / 0.737135 (-0.586081) | 0.095139 / 0.296338 (-0.201200) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.403773 / 0.215209 (0.188564) | 4.025567 / 2.077655 (1.947912) | 2.024641 / 1.504120 (0.520521) | 1.857039 / 1.541195 (0.315845) | 1.957346 / 1.468490 (0.488856) | 0.481486 / 4.584777 (-4.103291) | 3.574463 / 3.745712 (-0.171249) | 3.399311 / 5.269862 (-1.870551) | 1.996806 / 4.565676 (-2.568870) | 0.056644 / 0.424275 (-0.367631) | 0.007503 / 0.007607 (-0.000104) | 0.479480 / 0.226044 (0.253435) | 4.793686 / 2.268929 (2.524757) | 2.481011 / 55.444624 (-52.963613) | 2.176473 / 6.876477 (-4.700004) | 2.203192 / 2.142072 (0.061120) | 0.574071 / 4.805227 (-4.231156) | 0.131852 / 6.500664 (-6.368812) | 0.058883 / 0.075469 (-0.016586) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.249945 / 1.841788 (-0.591842) | 18.439267 / 8.074308 (10.364959) | 14.100934 / 10.191392 (3.909542) | 0.164191 / 0.680424 (-0.516233) | 0.018086 / 0.534201 (-0.516115) | 0.390821 / 0.579283 (-0.188462) | 0.414166 / 0.434364 (-0.020198) | 0.460073 / 0.540337 (-0.080265) | 0.636299 / 1.386936 (-0.750637) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006606 / 0.011353 (-0.004747) | 0.003987 / 0.011008 (-0.007021) | 0.064616 / 0.038508 (0.026108) | 0.070830 / 0.023109 (0.047721) | 0.397340 / 0.275898 (0.121442) | 0.426823 / 0.323480 (0.103343) | 0.005345 / 0.007986 (-0.002641) | 0.003264 / 0.004328 (-0.001065) | 0.064728 / 0.004250 (0.060477) | 0.055763 / 0.037052 (0.018711) | 0.405347 / 0.258489 (0.146858) | 0.433163 / 0.293841 (0.139322) | 0.032394 / 0.128546 (-0.096153) | 0.008474 / 0.075646 (-0.067172) | 0.071583 / 0.419271 (-0.347689) | 0.048424 / 0.043533 (0.004892) | 0.400582 / 0.255139 (0.145443) | 0.418111 / 0.283200 (0.134911) | 0.022257 / 0.141683 (-0.119426) | 1.495521 / 1.452155 (0.043366) | 1.554626 / 1.492716 (0.061910) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.218249 / 0.018006 (0.200242) | 0.438527 / 0.000490 (0.438037) | 0.005406 / 0.000200 (0.005206) | 0.000098 / 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) | 0.031600 / 0.037411 (-0.005812) | 0.090836 / 0.014526 (0.076310) | 0.105000 / 0.176557 (-0.071556) | 0.157648 / 0.737135 (-0.579487) | 0.103827 / 0.296338 (-0.192512) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.426428 / 0.215209 (0.211219) | 4.259435 / 2.077655 (2.181780) | 2.300795 / 1.504120 (0.796675) | 2.121302 / 1.541195 (0.580108) | 2.145602 / 1.468490 (0.677112) | 0.486856 / 4.584777 (-4.097921) | 3.673568 / 3.745712 (-0.072144) | 3.278619 / 5.269862 (-1.991243) | 2.037760 / 4.565676 (-2.527917) | 0.057699 / 0.424275 (-0.366576) | 0.007269 / 0.007607 (-0.000338) | 0.499549 / 0.226044 (0.273505) | 4.996214 / 2.268929 (2.727285) | 2.766480 / 55.444624 (-52.678144) | 2.417308 / 6.876477 (-4.459168) | 2.581026 / 2.142072 (0.438953) | 0.589463 / 4.805227 (-4.215765) | 0.134820 / 6.500664 (-6.365844) | 0.061699 / 0.075469 (-0.013770) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.353704 / 1.841788 (-0.488084) | 19.104167 / 8.074308 (11.029859) | 14.652166 / 10.191392 (4.460774) | 0.171885 / 0.680424 (-0.508539) | 0.020222 / 0.534201 (-0.513978) | 0.396777 / 0.579283 (-0.182506) | 0.426304 / 0.434364 (-0.008060) | 0.471347 / 0.540337 (-0.068991) | 0.635887 / 1.386936 (-0.751049) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ce19ec527c581eddec306a03ad1db554223cc94a \"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) | 0.004686 / 0.011353 (-0.006667) | 0.002998 / 0.011008 (-0.008010) | 0.063604 / 0.038508 (0.025096) | 0.048927 / 0.023109 (0.025818) | 0.247238 / 0.275898 (-0.028660) | 0.272409 / 0.323480 (-0.051071) | 0.003909 / 0.007986 (-0.004077) | 0.002469 / 0.004328 (-0.001859) | 0.048473 / 0.004250 (0.044223) | 0.037514 / 0.037052 (0.000462) | 0.257292 / 0.258489 (-0.001197) | 0.285203 / 0.293841 (-0.008638) | 0.023131 / 0.128546 (-0.105415) | 0.006803 / 0.075646 (-0.068843) | 0.202920 / 0.419271 (-0.216351) | 0.035653 / 0.043533 (-0.007880) | 0.254791 / 0.255139 (-0.000348) | 0.272973 / 0.283200 (-0.010226) | 0.017707 / 0.141683 (-0.123976) | 1.091606 / 1.452155 (-0.360549) | 1.151453 / 1.492716 (-0.341263) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093701 / 0.018006 (0.075695) | 0.304199 / 0.000490 (0.303709) | 0.000223 / 0.000200 (0.000023) | 0.000051 / 0.000054 (-0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019291 / 0.037411 (-0.018120) | 0.062168 / 0.014526 (0.047642) | 0.073273 / 0.176557 (-0.103284) | 0.119497 / 0.737135 (-0.617638) | 0.075008 / 0.296338 (-0.221331) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.279983 / 0.215209 (0.064774) | 2.774413 / 2.077655 (0.696758) | 1.476678 / 1.504120 (-0.027441) | 1.336273 / 1.541195 (-0.204922) | 1.332349 / 1.468490 (-0.136142) | 0.403150 / 4.584777 (-4.181627) | 2.390026 / 3.745712 (-1.355686) | 2.619151 / 5.269862 (-2.650711) | 1.578607 / 4.565676 (-2.987069) | 0.046632 / 0.424275 (-0.377643) | 0.007352 / 0.007607 (-0.000255) | 0.333419 / 0.226044 (0.107375) | 3.288734 / 2.268929 (1.019805) | 1.843677 / 55.444624 (-53.600947) | 1.536746 / 6.876477 (-5.339731) | 1.573005 / 2.142072 (-0.569067) | 0.475699 / 4.805227 (-4.329529) | 0.104742 / 6.500664 (-6.395922) | 0.042450 / 0.075469 (-0.033019) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.949039 / 1.841788 (-0.892749) | 11.895928 / 8.074308 (3.821620) | 10.650521 / 10.191392 (0.459129) | 0.142308 / 0.680424 (-0.538116) | 0.014207 / 0.534201 (-0.519994) | 0.274011 / 0.579283 (-0.305272) | 0.288259 / 0.434364 (-0.146105) | 0.327729 / 0.540337 (-0.212609) | 0.395728 / 1.386936 (-0.991208) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004830 / 0.011353 (-0.006523) | 0.002978 / 0.011008 (-0.008030) | 0.048623 / 0.038508 (0.010114) | 0.055040 / 0.023109 (0.031930) | 0.276436 / 0.275898 (0.000538) | 0.302403 / 0.323480 (-0.021076) | 0.004080 / 0.007986 (-0.003905) | 0.002479 / 0.004328 (-0.001849) | 0.048078 / 0.004250 (0.043827) | 0.039680 / 0.037052 (0.002627) | 0.279095 / 0.258489 (0.020606) | 0.307399 / 0.293841 (0.013558) | 0.024533 / 0.128546 (-0.104013) | 0.007196 / 0.075646 (-0.068450) | 0.053879 / 0.419271 (-0.365393) | 0.032545 / 0.043533 (-0.010988) | 0.275501 / 0.255139 (0.020362) | 0.298530 / 0.283200 (0.015330) | 0.017992 / 0.141683 (-0.123691) | 1.144191 / 1.452155 (-0.307963) | 1.208309 / 1.492716 (-0.284408) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.095690 / 0.018006 (0.077684) | 0.304932 / 0.000490 (0.304442) | 0.000223 / 0.000200 (0.000023) | 0.000055 / 0.000054 (0.000000) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021409 / 0.037411 (-0.016003) | 0.069861 / 0.014526 (0.055335) | 0.080959 / 0.176557 (-0.095597) | 0.119432 / 0.737135 (-0.617703) | 0.083649 / 0.296338 (-0.212690) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.297243 / 0.215209 (0.082034) | 2.909288 / 2.077655 (0.831634) | 1.571512 / 1.504120 (0.067392) | 1.452403 / 1.541195 (-0.088792) | 1.481290 / 1.468490 (0.012800) | 0.405795 / 4.584777 (-4.178982) | 2.452923 / 3.745712 (-1.292789) | 2.513371 / 5.269862 (-2.756490) | 1.593216 / 4.565676 (-2.972460) | 0.048073 / 0.424275 (-0.376202) | 0.005312 / 0.007607 (-0.002296) | 0.355783 / 0.226044 (0.129738) | 3.494062 / 2.268929 (1.225133) | 1.947388 / 55.444624 (-53.497236) | 1.651724 / 6.876477 (-5.224753) | 1.789007 / 2.142072 (-0.353065) | 0.487073 / 4.805227 (-4.318154) | 0.100271 / 6.500664 (-6.400393) | 0.041571 / 0.075469 (-0.033898) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.983766 / 1.841788 (-0.858021) | 12.384778 / 8.074308 (4.310469) | 10.669519 / 10.191392 (0.478127) | 0.133105 / 0.680424 (-0.547318) | 0.016665 / 0.534201 (-0.517536) | 0.269479 / 0.579283 (-0.309804) | 0.276498 / 0.434364 (-0.157866) | 0.302105 / 0.540337 (-0.238233) | 0.391204 / 1.386936 (-0.995732) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#12ebe695b4748c5a26e08b44ed51955f74f5801d \"CML watermark\")\n" ]

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[ "closing in favor of https://github.com/huggingface/datasets/pull/6337", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6338). All of your documentation changes will be reflected on that endpoint." ]

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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) | 0.006915 / 0.011353 (-0.004438) | 0.004110 / 0.011008 (-0.006898) | 0.084392 / 0.038508 (0.045884) | 0.079649 / 0.023109 (0.056540) | 0.305760 / 0.275898 (0.029862) | 0.343968 / 0.323480 (0.020488) | 0.005402 / 0.007986 (-0.002584) | 0.003342 / 0.004328 (-0.000986) | 0.064774 / 0.004250 (0.060523) | 0.055919 / 0.037052 (0.018866) | 0.315194 / 0.258489 (0.056705) | 0.355014 / 0.293841 (0.061173) | 0.032140 / 0.128546 (-0.096406) | 0.008865 / 0.075646 (-0.066781) | 0.287684 / 0.419271 (-0.131588) | 0.053504 / 0.043533 (0.009971) | 0.306852 / 0.255139 (0.051713) | 0.331125 / 0.283200 (0.047925) | 0.023476 / 0.141683 (-0.118207) | 1.506590 / 1.452155 (0.054435) | 1.574508 / 1.492716 (0.081792) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.239987 / 0.018006 (0.221981) | 0.459144 / 0.000490 (0.458654) | 0.008509 / 0.000200 (0.008309) | 0.000335 / 0.000054 (0.000280) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028353 / 0.037411 (-0.009058) | 0.082345 / 0.014526 (0.067819) | 0.499524 / 0.176557 (0.322967) | 0.152896 / 0.737135 (-0.584239) | 0.096978 / 0.296338 (-0.199360) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.404855 / 0.215209 (0.189646) | 4.053103 / 2.077655 (1.975448) | 2.069638 / 1.504120 (0.565518) | 1.917354 / 1.541195 (0.376159) | 2.035816 / 1.468490 (0.567326) | 0.480358 / 4.584777 (-4.104419) | 3.594316 / 3.745712 (-0.151396) | 3.582952 / 5.269862 (-1.686910) | 2.101142 / 4.565676 (-2.464535) | 0.057004 / 0.424275 (-0.367271) | 0.007715 / 0.007607 (0.000108) | 0.487417 / 0.226044 (0.261372) | 4.863100 / 2.268929 (2.594172) | 2.569038 / 55.444624 (-52.875587) | 2.187167 / 6.876477 (-4.689310) | 2.270034 / 2.142072 (0.127962) | 0.578095 / 4.805227 (-4.227132) | 0.133283 / 6.500664 (-6.367381) | 0.060164 / 0.075469 (-0.015305) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.269120 / 1.841788 (-0.572667) | 19.493072 / 8.074308 (11.418764) | 14.560576 / 10.191392 (4.369184) | 0.167440 / 0.680424 (-0.512984) | 0.018493 / 0.534201 (-0.515708) | 0.392774 / 0.579283 (-0.186509) | 0.420903 / 0.434364 (-0.013461) | 0.461904 / 0.540337 (-0.078433) | 0.643104 / 1.386936 (-0.743832) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006985 / 0.011353 (-0.004368) | 0.004246 / 0.011008 (-0.006762) | 0.066246 / 0.038508 (0.027738) | 0.080757 / 0.023109 (0.057648) | 0.391774 / 0.275898 (0.115876) | 0.424957 / 0.323480 (0.101478) | 0.005575 / 0.007986 (-0.002411) | 0.003447 / 0.004328 (-0.000881) | 0.066565 / 0.004250 (0.062315) | 0.057597 / 0.037052 (0.020544) | 0.394663 / 0.258489 (0.136174) | 0.430310 / 0.293841 (0.136469) | 0.032746 / 0.128546 (-0.095800) | 0.008783 / 0.075646 (-0.066863) | 0.071940 / 0.419271 (-0.347331) | 0.048877 / 0.043533 (0.005344) | 0.390269 / 0.255139 (0.135130) | 0.411867 / 0.283200 (0.128668) | 0.024101 / 0.141683 (-0.117582) | 1.507370 / 1.452155 (0.055215) | 1.585810 / 1.492716 (0.093093) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.222796 / 0.018006 (0.204790) | 0.459035 / 0.000490 (0.458546) | 0.005322 / 0.000200 (0.005122) | 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) | 0.033237 / 0.037411 (-0.004174) | 0.098244 / 0.014526 (0.083718) | 0.106654 / 0.176557 (-0.069903) | 0.159675 / 0.737135 (-0.577460) | 0.108470 / 0.296338 (-0.187869) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.429085 / 0.215209 (0.213876) | 4.281206 / 2.077655 (2.203551) | 2.320492 / 1.504120 (0.816372) | 2.153218 / 1.541195 (0.612024) | 2.287122 / 1.468490 (0.818632) | 0.497307 / 4.584777 (-4.087470) | 3.799541 / 3.745712 (0.053828) | 3.380053 / 5.269862 (-1.889809) | 2.100009 / 4.565676 (-2.465667) | 0.057988 / 0.424275 (-0.366287) | 0.007381 / 0.007607 (-0.000226) | 0.506843 / 0.226044 (0.280798) | 5.071286 / 2.268929 (2.802357) | 2.750487 / 55.444624 (-52.694137) | 2.415613 / 6.876477 (-4.460864) | 2.667144 / 2.142072 (0.525072) | 0.624889 / 4.805227 (-4.180338) | 0.134191 / 6.500664 (-6.366473) | 0.060704 / 0.075469 (-0.014765) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.353074 / 1.841788 (-0.488714) | 20.507074 / 8.074308 (12.432766) | 14.911788 / 10.191392 (4.720396) | 0.149248 / 0.680424 (-0.531176) | 0.020593 / 0.534201 (-0.513608) | 0.398458 / 0.579283 (-0.180825) | 0.434846 / 0.434364 (0.000482) | 0.478853 / 0.540337 (-0.061484) | 0.648072 / 1.386936 (-0.738864) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b30c72a2d3d9c191a590e0f0a6b3a6363ab15e8f \"CML watermark\")\n", "In particular I expect fsspec to do another breaking change in the next release (switch to glob.glob)", "_The documentation is not available anymore as the PR was closed or merged._", "see https://github.com/huggingface/datasets/pull/6338", "Yes, unfortunately breaking changes are quite usual from their part.", "<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) | 0.006099 / 0.011353 (-0.005253) | 0.003672 / 0.011008 (-0.007336) | 0.083095 / 0.038508 (0.044587) | 0.059607 / 0.023109 (0.036498) | 0.319591 / 0.275898 (0.043693) | 0.351945 / 0.323480 (0.028465) | 0.004785 / 0.007986 (-0.003201) | 0.002965 / 0.004328 (-0.001364) | 0.062907 / 0.004250 (0.058657) | 0.049122 / 0.037052 (0.012070) | 0.344641 / 0.258489 (0.086152) | 0.361519 / 0.293841 (0.067678) | 0.027254 / 0.128546 (-0.101292) | 0.008081 / 0.075646 (-0.067565) | 0.261569 / 0.419271 (-0.157702) | 0.045101 / 0.043533 (0.001568) | 0.313645 / 0.255139 (0.058506) | 0.337843 / 0.283200 (0.054644) | 0.020968 / 0.141683 (-0.120715) | 1.438450 / 1.452155 (-0.013705) | 1.507567 / 1.492716 (0.014850) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.230826 / 0.018006 (0.212820) | 0.434363 / 0.000490 (0.433873) | 0.008210 / 0.000200 (0.008010) | 0.000212 / 0.000054 (0.000157) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025278 / 0.037411 (-0.012133) | 0.073659 / 0.014526 (0.059133) | 0.085147 / 0.176557 (-0.091409) | 0.145451 / 0.737135 (-0.591684) | 0.086400 / 0.296338 (-0.209939) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.429887 / 0.215209 (0.214678) | 4.292626 / 2.077655 (2.214971) | 2.266824 / 1.504120 (0.762704) | 2.090472 / 1.541195 (0.549277) | 2.186477 / 1.468490 (0.717987) | 0.503684 / 4.584777 (-4.081093) | 3.100791 / 3.745712 (-0.644921) | 3.008938 / 5.269862 (-2.260923) | 1.885559 / 4.565676 (-2.680118) | 0.057434 / 0.424275 (-0.366841) | 0.006639 / 0.007607 (-0.000969) | 0.506579 / 0.226044 (0.280535) | 5.058905 / 2.268929 (2.789977) | 2.708321 / 55.444624 (-52.736304) | 2.367388 / 6.876477 (-4.509089) | 2.422660 / 2.142072 (0.280587) | 0.587562 / 4.805227 (-4.217665) | 0.125260 / 6.500664 (-6.375404) | 0.061856 / 0.075469 (-0.013613) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.280495 / 1.841788 (-0.561292) | 17.968873 / 8.074308 (9.894565) | 13.922838 / 10.191392 (3.731446) | 0.149907 / 0.680424 (-0.530517) | 0.016736 / 0.534201 (-0.517465) | 0.333417 / 0.579283 (-0.245866) | 0.367710 / 0.434364 (-0.066654) | 0.389648 / 0.540337 (-0.150690) | 0.535625 / 1.386936 (-0.851311) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006237 / 0.011353 (-0.005116) | 0.003787 / 0.011008 (-0.007221) | 0.062536 / 0.038508 (0.024028) | 0.062335 / 0.023109 (0.039226) | 0.455209 / 0.275898 (0.179311) | 0.488961 / 0.323480 (0.165482) | 0.004875 / 0.007986 (-0.003111) | 0.002961 / 0.004328 (-0.001368) | 0.063045 / 0.004250 (0.058795) | 0.048624 / 0.037052 (0.011571) | 0.455743 / 0.258489 (0.197254) | 0.494024 / 0.293841 (0.200183) | 0.028690 / 0.128546 (-0.099856) | 0.008147 / 0.075646 (-0.067499) | 0.069479 / 0.419271 (-0.349792) | 0.041613 / 0.043533 (-0.001919) | 0.460472 / 0.255139 (0.205333) | 0.475606 / 0.283200 (0.192406) | 0.020600 / 0.141683 (-0.121083) | 1.464960 / 1.452155 (0.012805) | 1.540942 / 1.492716 (0.048226) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.214558 / 0.018006 (0.196552) | 0.410482 / 0.000490 (0.409992) | 0.005539 / 0.000200 (0.005339) | 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) | 0.027044 / 0.037411 (-0.010367) | 0.081512 / 0.014526 (0.066986) | 0.101963 / 0.176557 (-0.074593) | 0.146686 / 0.737135 (-0.590449) | 0.092676 / 0.296338 (-0.203663) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.468766 / 0.215209 (0.253557) | 4.680514 / 2.077655 (2.602859) | 2.562454 / 1.504120 (1.058334) | 2.383692 / 1.541195 (0.842497) | 2.481820 / 1.468490 (1.013330) | 0.509122 / 4.584777 (-4.075655) | 3.201597 / 3.745712 (-0.544115) | 2.853539 / 5.269862 (-2.416323) | 1.891535 / 4.565676 (-2.674141) | 0.058594 / 0.424275 (-0.365681) | 0.006448 / 0.007607 (-0.001159) | 0.535950 / 0.226044 (0.309906) | 5.388239 / 2.268929 (3.119311) | 2.999986 / 55.444624 (-52.444638) | 2.733291 / 6.876477 (-4.143186) | 2.841548 / 2.142072 (0.699475) | 0.602388 / 4.805227 (-4.202840) | 0.126369 / 6.500664 (-6.374295) | 0.061519 / 0.075469 (-0.013951) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.322746 / 1.841788 (-0.519042) | 17.940825 / 8.074308 (9.866517) | 14.679559 / 10.191392 (4.488167) | 0.146481 / 0.680424 (-0.533943) | 0.018060 / 0.534201 (-0.516141) | 0.334924 / 0.579283 (-0.244359) | 0.384735 / 0.434364 (-0.049629) | 0.391834 / 0.540337 (-0.148503) | 0.540011 / 1.386936 (-0.846925) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d82f3c2264436ef60fac8c397fb11c80175c5132 \"CML watermark\")\n" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6336). 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) | 0.006202 / 0.011353 (-0.005151) | 0.003627 / 0.011008 (-0.007381) | 0.080643 / 0.038508 (0.042135) | 0.057135 / 0.023109 (0.034026) | 0.315853 / 0.275898 (0.039955) | 0.348503 / 0.323480 (0.025023) | 0.004762 / 0.007986 (-0.003224) | 0.002884 / 0.004328 (-0.001445) | 0.063208 / 0.004250 (0.058958) | 0.046777 / 0.037052 (0.009725) | 0.321426 / 0.258489 (0.062937) | 0.362128 / 0.293841 (0.068287) | 0.027494 / 0.128546 (-0.101052) | 0.007931 / 0.075646 (-0.067715) | 0.262262 / 0.419271 (-0.157009) | 0.044330 / 0.043533 (0.000797) | 0.310504 / 0.255139 (0.055366) | 0.339409 / 0.283200 (0.056209) | 0.021030 / 0.141683 (-0.120652) | 1.405333 / 1.452155 (-0.046822) | 1.493497 / 1.492716 (0.000781) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.225431 / 0.018006 (0.207425) | 0.451723 / 0.000490 (0.451233) | 0.007763 / 0.000200 (0.007563) | 0.000310 / 0.000054 (0.000256) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023381 / 0.037411 (-0.014031) | 0.074183 / 0.014526 (0.059657) | 0.084003 / 0.176557 (-0.092553) | 0.143628 / 0.737135 (-0.593507) | 0.084543 / 0.296338 (-0.211796) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.393062 / 0.215209 (0.177853) | 3.905649 / 2.077655 (1.827994) | 1.923155 / 1.504120 (0.419035) | 1.751554 / 1.541195 (0.210359) | 1.816141 / 1.468490 (0.347651) | 0.502789 / 4.584777 (-4.081988) | 3.006149 / 3.745712 (-0.739564) | 2.979645 / 5.269862 (-2.290216) | 1.877408 / 4.565676 (-2.688269) | 0.057544 / 0.424275 (-0.366731) | 0.006733 / 0.007607 (-0.000874) | 0.468469 / 0.226044 (0.242425) | 4.695595 / 2.268929 (2.426667) | 2.367238 / 55.444624 (-53.077387) | 2.041035 / 6.876477 (-4.835442) | 2.087396 / 2.142072 (-0.054676) | 0.586866 / 4.805227 (-4.218361) | 0.125616 / 6.500664 (-6.375049) | 0.060535 / 0.075469 (-0.014934) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.244753 / 1.841788 (-0.597035) | 17.652902 / 8.074308 (9.578594) | 13.733195 / 10.191392 (3.541803) | 0.143741 / 0.680424 (-0.536683) | 0.016775 / 0.534201 (-0.517426) | 0.335487 / 0.579283 (-0.243797) | 0.350292 / 0.434364 (-0.084072) | 0.388744 / 0.540337 (-0.151594) | 0.536630 / 1.386936 (-0.850306) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006008 / 0.011353 (-0.005345) | 0.003708 / 0.011008 (-0.007301) | 0.062504 / 0.038508 (0.023996) | 0.058570 / 0.023109 (0.035461) | 0.450549 / 0.275898 (0.174651) | 0.467768 / 0.323480 (0.144288) | 0.004955 / 0.007986 (-0.003031) | 0.002903 / 0.004328 (-0.001426) | 0.062778 / 0.004250 (0.058528) | 0.048750 / 0.037052 (0.011698) | 0.439848 / 0.258489 (0.181359) | 0.471780 / 0.293841 (0.177939) | 0.028472 / 0.128546 (-0.100074) | 0.008221 / 0.075646 (-0.067425) | 0.068325 / 0.419271 (-0.350946) | 0.040612 / 0.043533 (-0.002921) | 0.435530 / 0.255139 (0.180391) | 0.458992 / 0.283200 (0.175792) | 0.020143 / 0.141683 (-0.121539) | 1.479101 / 1.452155 (0.026947) | 1.507408 / 1.492716 (0.014692) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.207723 / 0.018006 (0.189717) | 0.406596 / 0.000490 (0.406106) | 0.004431 / 0.000200 (0.004231) | 0.000078 / 0.000054 (0.000024) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027037 / 0.037411 (-0.010374) | 0.081576 / 0.014526 (0.067050) | 0.091177 / 0.176557 (-0.085379) | 0.146191 / 0.737135 (-0.590944) | 0.092485 / 0.296338 (-0.203854) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.456676 / 0.215209 (0.241467) | 4.556214 / 2.077655 (2.478559) | 2.500146 / 1.504120 (0.996026) | 2.325175 / 1.541195 (0.783981) | 2.421023 / 1.468490 (0.952533) | 0.512135 / 4.584777 (-4.072641) | 3.167070 / 3.745712 (-0.578642) | 2.897697 / 5.269862 (-2.372165) | 1.881974 / 4.565676 (-2.683702) | 0.058453 / 0.424275 (-0.365823) | 0.006515 / 0.007607 (-0.001092) | 0.530742 / 0.226044 (0.304698) | 5.304943 / 2.268929 (3.036014) | 2.928824 / 55.444624 (-52.515800) | 2.598023 / 6.876477 (-4.278454) | 2.758496 / 2.142072 (0.616423) | 0.601777 / 4.805227 (-4.203450) | 0.126701 / 6.500664 (-6.373964) | 0.061808 / 0.075469 (-0.013661) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.357844 / 1.841788 (-0.483943) | 17.887666 / 8.074308 (9.813358) | 14.561904 / 10.191392 (4.370512) | 0.146788 / 0.680424 (-0.533636) | 0.018277 / 0.534201 (-0.515924) | 0.343168 / 0.579283 (-0.236115) | 0.382220 / 0.434364 (-0.052144) | 0.401234 / 0.540337 (-0.139104) | 0.546246 / 1.386936 (-0.840690) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b0980a74d58098b8b1738e2411f1212161a211b8 \"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) | 0.008919 / 0.011353 (-0.002434) | 0.006110 / 0.011008 (-0.004898) | 0.110554 / 0.038508 (0.072046) | 0.075705 / 0.023109 (0.052596) | 0.391235 / 0.275898 (0.115336) | 0.458331 / 0.323480 (0.134851) | 0.007489 / 0.007986 (-0.000497) | 0.003744 / 0.004328 (-0.000585) | 0.078124 / 0.004250 (0.073874) | 0.057244 / 0.037052 (0.020192) | 0.393251 / 0.258489 (0.134762) | 0.460153 / 0.293841 (0.166312) | 0.047245 / 0.128546 (-0.081301) | 0.014086 / 0.075646 (-0.061560) | 0.421272 / 0.419271 (0.002001) | 0.067668 / 0.043533 (0.024135) | 0.397325 / 0.255139 (0.142186) | 0.432683 / 0.283200 (0.149483) | 0.039086 / 0.141683 (-0.102596) | 1.764898 / 1.452155 (0.312744) | 1.848820 / 1.492716 (0.356104) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.258163 / 0.018006 (0.240156) | 0.498655 / 0.000490 (0.498165) | 0.014959 / 0.000200 (0.014759) | 0.000465 / 0.000054 (0.000410) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028889 / 0.037411 (-0.008522) | 0.091568 / 0.014526 (0.077042) | 0.102700 / 0.176557 (-0.073857) | 0.173580 / 0.737135 (-0.563555) | 0.108763 / 0.296338 (-0.187576) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.610147 / 0.215209 (0.394938) | 5.851239 / 2.077655 (3.773584) | 2.467471 / 1.504120 (0.963351) | 2.117189 / 1.541195 (0.575995) | 2.197947 / 1.468490 (0.729457) | 0.851736 / 4.584777 (-3.733041) | 5.163183 / 3.745712 (1.417471) | 5.039564 / 5.269862 (-0.230297) | 3.067215 / 4.565676 (-1.498462) | 0.098593 / 0.424275 (-0.325682) | 0.008646 / 0.007607 (0.001038) | 0.788397 / 0.226044 (0.562352) | 7.340837 / 2.268929 (5.071909) | 3.511611 / 55.444624 (-51.933013) | 2.767479 / 6.876477 (-4.108998) | 2.687368 / 2.142072 (0.545296) | 1.046387 / 4.805227 (-3.758841) | 0.215902 / 6.500664 (-6.284763) | 0.072939 / 0.075469 (-0.002530) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.512795 / 1.841788 (-0.328992) | 22.086131 / 8.074308 (14.011823) | 20.235550 / 10.191392 (10.044158) | 0.240381 / 0.680424 (-0.440043) | 0.029171 / 0.534201 (-0.505030) | 0.465123 / 0.579283 (-0.114160) | 0.569260 / 0.434364 (0.134896) | 0.540967 / 0.540337 (0.000629) | 0.764006 / 1.386936 (-0.622930) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.011024 / 0.011353 (-0.000329) | 0.005915 / 0.011008 (-0.005094) | 0.076455 / 0.038508 (0.037947) | 0.087842 / 0.023109 (0.064733) | 0.471732 / 0.275898 (0.195834) | 0.513666 / 0.323480 (0.190186) | 0.007062 / 0.007986 (-0.000924) | 0.004013 / 0.004328 (-0.000315) | 0.076016 / 0.004250 (0.071766) | 0.061296 / 0.037052 (0.024244) | 0.487277 / 0.258489 (0.228788) | 0.508185 / 0.293841 (0.214344) | 0.049963 / 0.128546 (-0.078583) | 0.013774 / 0.075646 (-0.061873) | 0.089376 / 0.419271 (-0.329895) | 0.067502 / 0.043533 (0.023969) | 0.471283 / 0.255139 (0.216144) | 0.507365 / 0.283200 (0.224165) | 0.033638 / 0.141683 (-0.108045) | 1.785544 / 1.452155 (0.333390) | 1.878765 / 1.492716 (0.386048) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.230462 / 0.018006 (0.212456) | 0.502458 / 0.000490 (0.501968) | 0.005987 / 0.000200 (0.005787) | 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) | 0.031588 / 0.037411 (-0.005824) | 0.113566 / 0.014526 (0.099040) | 0.115734 / 0.176557 (-0.060822) | 0.174162 / 0.737135 (-0.562974) | 0.121574 / 0.296338 (-0.174764) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.662837 / 0.215209 (0.447628) | 6.420327 / 2.077655 (4.342672) | 3.033522 / 1.504120 (1.529402) | 2.728294 / 1.541195 (1.187099) | 2.790621 / 1.468490 (1.322131) | 0.852478 / 4.584777 (-3.732299) | 5.033637 / 3.745712 (1.287925) | 4.543152 / 5.269862 (-0.726709) | 2.980261 / 4.565676 (-1.585415) | 0.102444 / 0.424275 (-0.321831) | 0.008362 / 0.007607 (0.000755) | 0.786868 / 0.226044 (0.560823) | 7.887665 / 2.268929 (5.618737) | 4.010614 / 55.444624 (-51.434010) | 3.220715 / 6.876477 (-3.655762) | 3.317316 / 2.142072 (1.175244) | 1.098137 / 4.805227 (-3.707090) | 0.218309 / 6.500664 (-6.282355) | 0.078182 / 0.075469 (0.002713) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.696740 / 1.841788 (-0.145047) | 23.762454 / 8.074308 (15.688146) | 21.802645 / 10.191392 (11.611253) | 0.233654 / 0.680424 (-0.446770) | 0.032911 / 0.534201 (-0.501290) | 0.511760 / 0.579283 (-0.067524) | 0.586299 / 0.434364 (0.151935) | 0.583704 / 0.540337 (0.043367) | 0.780762 / 1.386936 (-0.606174) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0a94aa2f738075bbc08291583f1b153220d5e6e7 \"CML watermark\")\n" ]

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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) | 0.006013 / 0.011353 (-0.005340) | 0.003647 / 0.011008 (-0.007362) | 0.081781 / 0.038508 (0.043273) | 0.059020 / 0.023109 (0.035911) | 0.321823 / 0.275898 (0.045925) | 0.350159 / 0.323480 (0.026679) | 0.003599 / 0.007986 (-0.004386) | 0.002877 / 0.004328 (-0.001452) | 0.063941 / 0.004250 (0.059690) | 0.049460 / 0.037052 (0.012408) | 0.330185 / 0.258489 (0.071696) | 0.362220 / 0.293841 (0.068379) | 0.027613 / 0.128546 (-0.100934) | 0.007976 / 0.075646 (-0.067670) | 0.263386 / 0.419271 (-0.155885) | 0.045504 / 0.043533 (0.001971) | 0.321172 / 0.255139 (0.066033) | 0.345291 / 0.283200 (0.062091) | 0.023133 / 0.141683 (-0.118550) | 1.435816 / 1.452155 (-0.016339) | 1.557241 / 1.492716 (0.064524) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.222228 / 0.018006 (0.204222) | 0.420008 / 0.000490 (0.419518) | 0.008598 / 0.000200 (0.008398) | 0.000343 / 0.000054 (0.000288) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023725 / 0.037411 (-0.013686) | 0.073023 / 0.014526 (0.058497) | 0.814888 / 0.176557 (0.638332) | 0.294122 / 0.737135 (-0.443013) | 0.088945 / 0.296338 (-0.207393) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.393561 / 0.215209 (0.178352) | 3.946544 / 2.077655 (1.868890) | 1.916476 / 1.504120 (0.412356) | 1.721544 / 1.541195 (0.180349) | 1.768583 / 1.468490 (0.300093) | 0.508067 / 4.584777 (-4.076710) | 3.047832 / 3.745712 (-0.697880) | 2.952842 / 5.269862 (-2.317020) | 1.869337 / 4.565676 (-2.696339) | 0.057812 / 0.424275 (-0.366463) | 0.006694 / 0.007607 (-0.000913) | 0.463007 / 0.226044 (0.236963) | 4.635087 / 2.268929 (2.366158) | 2.419833 / 55.444624 (-53.024792) | 2.018519 / 6.876477 (-4.857958) | 2.043430 / 2.142072 (-0.098643) | 0.590895 / 4.805227 (-4.214333) | 0.126113 / 6.500664 (-6.374552) | 0.061045 / 0.075469 (-0.014424) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.226850 / 1.841788 (-0.614937) | 17.336630 / 8.074308 (9.262322) | 13.651049 / 10.191392 (3.459656) | 0.143308 / 0.680424 (-0.537116) | 0.016938 / 0.534201 (-0.517263) | 0.332829 / 0.579283 (-0.246454) | 0.368684 / 0.434364 (-0.065680) | 0.385848 / 0.540337 (-0.154489) | 0.546391 / 1.386936 (-0.840545) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006149 / 0.011353 (-0.005204) | 0.003818 / 0.011008 (-0.007191) | 0.064012 / 0.038508 (0.025504) | 0.059846 / 0.023109 (0.036737) | 0.455928 / 0.275898 (0.180030) | 0.480736 / 0.323480 (0.157256) | 0.004874 / 0.007986 (-0.003111) | 0.002877 / 0.004328 (-0.001451) | 0.064195 / 0.004250 (0.059944) | 0.048146 / 0.037052 (0.011094) | 0.452638 / 0.258489 (0.194149) | 0.484339 / 0.293841 (0.190499) | 0.028832 / 0.128546 (-0.099715) | 0.008162 / 0.075646 (-0.067485) | 0.069855 / 0.419271 (-0.349417) | 0.041429 / 0.043533 (-0.002104) | 0.453282 / 0.255139 (0.198143) | 0.473812 / 0.283200 (0.190613) | 0.021186 / 0.141683 (-0.120497) | 1.465207 / 1.452155 (0.013052) | 1.508216 / 1.492716 (0.015500) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.242491 / 0.018006 (0.224485) | 0.421219 / 0.000490 (0.420730) | 0.011201 / 0.000200 (0.011001) | 0.000083 / 0.000054 (0.000028) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027015 / 0.037411 (-0.010396) | 0.080465 / 0.014526 (0.065939) | 0.092622 / 0.176557 (-0.083934) | 0.146111 / 0.737135 (-0.591024) | 0.091546 / 0.296338 (-0.204793) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.458351 / 0.215209 (0.243142) | 4.591454 / 2.077655 (2.513799) | 2.508156 / 1.504120 (1.004037) | 2.328771 / 1.541195 (0.787576) | 2.423251 / 1.468490 (0.954761) | 0.508504 / 4.584777 (-4.076273) | 3.133789 / 3.745712 (-0.611923) | 2.862777 / 5.269862 (-2.407084) | 1.886327 / 4.565676 (-2.679350) | 0.058017 / 0.424275 (-0.366258) | 0.006496 / 0.007607 (-0.001111) | 0.529629 / 0.226044 (0.303585) | 5.310338 / 2.268929 (3.041409) | 2.973075 / 55.444624 (-52.471549) | 2.601313 / 6.876477 (-4.275163) | 2.777348 / 2.142072 (0.635275) | 0.593711 / 4.805227 (-4.211516) | 0.125453 / 6.500664 (-6.375211) | 0.061034 / 0.075469 (-0.014435) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.374391 / 1.841788 (-0.467397) | 18.768026 / 8.074308 (10.693718) | 15.053637 / 10.191392 (4.862245) | 0.158253 / 0.680424 (-0.522171) | 0.018126 / 0.534201 (-0.516075) | 0.337427 / 0.579283 (-0.241856) | 0.391678 / 0.434364 (-0.042686) | 0.398524 / 0.540337 (-0.141813) | 0.558629 / 1.386936 (-0.828307) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e0b79660f180c88517884f831eca620bc46a0957 \"CML watermark\")\n", "I think https://github.com/huggingface/datasets/pull/6334 fixes it already no ?", "<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) | 0.006432 / 0.011353 (-0.004921) | 0.003861 / 0.011008 (-0.007147) | 0.084132 / 0.038508 (0.045624) | 0.069391 / 0.023109 (0.046282) | 0.341081 / 0.275898 (0.065183) | 0.375975 / 0.323480 (0.052495) | 0.003962 / 0.007986 (-0.004024) | 0.003235 / 0.004328 (-0.001094) | 0.064927 / 0.004250 (0.060677) | 0.054190 / 0.037052 (0.017137) | 0.350719 / 0.258489 (0.092230) | 0.393216 / 0.293841 (0.099375) | 0.031002 / 0.128546 (-0.097544) | 0.008416 / 0.075646 (-0.067230) | 0.289268 / 0.419271 (-0.130003) | 0.052167 / 0.043533 (0.008634) | 0.347559 / 0.255139 (0.092420) | 0.370908 / 0.283200 (0.087709) | 0.022540 / 0.141683 (-0.119142) | 1.486297 / 1.452155 (0.034143) | 1.576968 / 1.492716 (0.084252) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.237048 / 0.018006 (0.219042) | 0.452065 / 0.000490 (0.451575) | 0.013963 / 0.000200 (0.013763) | 0.000242 / 0.000054 (0.000188) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028084 / 0.037411 (-0.009327) | 0.081271 / 0.014526 (0.066745) | 0.096490 / 0.176557 (-0.080067) | 0.152106 / 0.737135 (-0.585030) | 0.096174 / 0.296338 (-0.200164) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.386585 / 0.215209 (0.171375) | 3.854996 / 2.077655 (1.777342) | 1.832898 / 1.504120 (0.328778) | 1.662832 / 1.541195 (0.121638) | 1.730753 / 1.468490 (0.262263) | 0.485286 / 4.584777 (-4.099491) | 3.571410 / 3.745712 (-0.174302) | 3.373035 / 5.269862 (-1.896826) | 1.995570 / 4.565676 (-2.570107) | 0.056711 / 0.424275 (-0.367564) | 0.007447 / 0.007607 (-0.000160) | 0.462985 / 0.226044 (0.236941) | 4.617186 / 2.268929 (2.348257) | 2.313915 / 55.444624 (-53.130709) | 1.961697 / 6.876477 (-4.914780) | 1.990410 / 2.142072 (-0.151662) | 0.580536 / 4.805227 (-4.224692) | 0.146275 / 6.500664 (-6.354389) | 0.059458 / 0.075469 (-0.016011) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.274841 / 1.841788 (-0.566947) | 18.641853 / 8.074308 (10.567545) | 13.977525 / 10.191392 (3.786133) | 0.151469 / 0.680424 (-0.528955) | 0.018111 / 0.534201 (-0.516090) | 0.393243 / 0.579283 (-0.186040) | 0.412310 / 0.434364 (-0.022054) | 0.461646 / 0.540337 (-0.078692) | 0.633016 / 1.386936 (-0.753920) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006496 / 0.011353 (-0.004857) | 0.003973 / 0.011008 (-0.007035) | 0.064527 / 0.038508 (0.026019) | 0.069390 / 0.023109 (0.046281) | 0.401162 / 0.275898 (0.125264) | 0.431031 / 0.323480 (0.107551) | 0.005244 / 0.007986 (-0.002741) | 0.003283 / 0.004328 (-0.001046) | 0.064931 / 0.004250 (0.060680) | 0.054402 / 0.037052 (0.017350) | 0.397917 / 0.258489 (0.139428) | 0.436728 / 0.293841 (0.142887) | 0.031932 / 0.128546 (-0.096614) | 0.008557 / 0.075646 (-0.067089) | 0.073336 / 0.419271 (-0.345935) | 0.047559 / 0.043533 (0.004026) | 0.395825 / 0.255139 (0.140686) | 0.423002 / 0.283200 (0.139802) | 0.021708 / 0.141683 (-0.119975) | 1.501140 / 1.452155 (0.048985) | 1.558376 / 1.492716 (0.065660) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.289522 / 0.018006 (0.271516) | 0.449078 / 0.000490 (0.448589) | 0.034174 / 0.000200 (0.033974) | 0.000396 / 0.000054 (0.000342) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032533 / 0.037411 (-0.004878) | 0.093398 / 0.014526 (0.078872) | 0.106930 / 0.176557 (-0.069626) | 0.158743 / 0.737135 (-0.578393) | 0.106904 / 0.296338 (-0.189435) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.427479 / 0.215209 (0.212270) | 4.271758 / 2.077655 (2.194103) | 2.298770 / 1.504120 (0.794650) | 2.134906 / 1.541195 (0.593712) | 2.220487 / 1.468490 (0.751996) | 0.490506 / 4.584777 (-4.094270) | 3.593876 / 3.745712 (-0.151836) | 3.225656 / 5.269862 (-2.044205) | 2.004434 / 4.565676 (-2.561243) | 0.058015 / 0.424275 (-0.366260) | 0.007221 / 0.007607 (-0.000387) | 0.504928 / 0.226044 (0.278884) | 5.049547 / 2.268929 (2.780618) | 2.743843 / 55.444624 (-52.700781) | 2.398399 / 6.876477 (-4.478078) | 2.562939 / 2.142072 (0.420867) | 0.597229 / 4.805227 (-4.207998) | 0.134664 / 6.500664 (-6.366001) | 0.059612 / 0.075469 (-0.015857) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.369692 / 1.841788 (-0.472095) | 19.065326 / 8.074308 (10.991018) | 14.404508 / 10.191392 (4.213116) | 0.175809 / 0.680424 (-0.504615) | 0.020137 / 0.534201 (-0.514064) | 0.394043 / 0.579283 (-0.185240) | 0.424772 / 0.434364 (-0.009592) | 0.475587 / 0.540337 (-0.064751) | 0.644275 / 1.386936 (-0.742661) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#224977971accd63d97ba0a90cc108c4754055ebb \"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) | 0.007259 / 0.011353 (-0.004094) | 0.004396 / 0.011008 (-0.006612) | 0.096456 / 0.038508 (0.057948) | 0.078752 / 0.023109 (0.055643) | 0.359215 / 0.275898 (0.083317) | 0.396927 / 0.323480 (0.073448) | 0.005611 / 0.007986 (-0.002375) | 0.003687 / 0.004328 (-0.000641) | 0.072794 / 0.004250 (0.068544) | 0.059794 / 0.037052 (0.022741) | 0.372352 / 0.258489 (0.113863) | 0.414038 / 0.293841 (0.120197) | 0.034490 / 0.128546 (-0.094056) | 0.009790 / 0.075646 (-0.065857) | 0.326338 / 0.419271 (-0.092934) | 0.058582 / 0.043533 (0.015049) | 0.354221 / 0.255139 (0.099082) | 0.386669 / 0.283200 (0.103469) | 0.025356 / 0.141683 (-0.116327) | 1.664104 / 1.452155 (0.211950) | 1.766825 / 1.492716 (0.274108) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.251107 / 0.018006 (0.233101) | 0.478833 / 0.000490 (0.478344) | 0.010776 / 0.000200 (0.010577) | 0.000292 / 0.000054 (0.000238) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032869 / 0.037411 (-0.004543) | 0.098449 / 0.014526 (0.083923) | 0.109954 / 0.176557 (-0.066602) | 0.176786 / 0.737135 (-0.560350) | 0.113477 / 0.296338 (-0.182862) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.431169 / 0.215209 (0.215960) | 4.303239 / 2.077655 (2.225585) | 2.088885 / 1.504120 (0.584765) | 1.895900 / 1.541195 (0.354706) | 1.997442 / 1.468490 (0.528952) | 0.541840 / 4.584777 (-4.042937) | 3.991982 / 3.745712 (0.246270) | 3.842421 / 5.269862 (-1.427440) | 2.281150 / 4.565676 (-2.284526) | 0.063851 / 0.424275 (-0.360425) | 0.008470 / 0.007607 (0.000863) | 0.515886 / 0.226044 (0.289841) | 5.202908 / 2.268929 (2.933980) | 2.662789 / 55.444624 (-52.781835) | 2.266731 / 6.876477 (-4.609746) | 2.343760 / 2.142072 (0.201688) | 0.641050 / 4.805227 (-4.164177) | 0.148236 / 6.500664 (-6.352428) | 0.067422 / 0.075469 (-0.008047) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.475729 / 1.841788 (-0.366059) | 22.401583 / 8.074308 (14.327274) | 15.886237 / 10.191392 (5.694845) | 0.171828 / 0.680424 (-0.508595) | 0.022161 / 0.534201 (-0.512040) | 0.465873 / 0.579283 (-0.113411) | 0.476386 / 0.434364 (0.042022) | 0.538317 / 0.540337 (-0.002020) | 0.754375 / 1.386936 (-0.632561) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007429 / 0.011353 (-0.003924) | 0.004592 / 0.011008 (-0.006416) | 0.072315 / 0.038508 (0.033807) | 0.080806 / 0.023109 (0.057697) | 0.444607 / 0.275898 (0.168709) | 0.476970 / 0.323480 (0.153490) | 0.006030 / 0.007986 (-0.001956) | 0.003755 / 0.004328 (-0.000573) | 0.074602 / 0.004250 (0.070352) | 0.061846 / 0.037052 (0.024794) | 0.450928 / 0.258489 (0.192439) | 0.493932 / 0.293841 (0.200091) | 0.037398 / 0.128546 (-0.091148) | 0.009807 / 0.075646 (-0.065840) | 0.080531 / 0.419271 (-0.338741) | 0.054052 / 0.043533 (0.010519) | 0.453034 / 0.255139 (0.197895) | 0.464959 / 0.283200 (0.181760) | 0.024718 / 0.141683 (-0.116965) | 1.687552 / 1.452155 (0.235397) | 1.765746 / 1.492716 (0.273029) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.266998 / 0.018006 (0.248992) | 0.479832 / 0.000490 (0.479342) | 0.005429 / 0.000200 (0.005229) | 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) | 0.038885 / 0.037411 (0.001474) | 0.105931 / 0.014526 (0.091405) | 0.120880 / 0.176557 (-0.055677) | 0.184006 / 0.737135 (-0.553130) | 0.120750 / 0.296338 (-0.175589) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.478626 / 0.215209 (0.263417) | 4.797355 / 2.077655 (2.719700) | 2.582758 / 1.504120 (1.078638) | 2.396488 / 1.541195 (0.855293) | 2.515597 / 1.468490 (1.047107) | 0.544541 / 4.584777 (-4.040236) | 4.150702 / 3.745712 (0.404990) | 3.676837 / 5.269862 (-1.593024) | 2.287275 / 4.565676 (-2.278402) | 0.064602 / 0.424275 (-0.359673) | 0.008253 / 0.007607 (0.000646) | 0.576201 / 0.226044 (0.350157) | 5.859839 / 2.268929 (3.590910) | 3.248603 / 55.444624 (-52.196021) | 2.841959 / 6.876477 (-4.034518) | 2.991120 / 2.142072 (0.849047) | 0.667755 / 4.805227 (-4.137472) | 0.151219 / 6.500664 (-6.349445) | 0.068990 / 0.075469 (-0.006479) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.572359 / 1.841788 (-0.269429) | 21.890279 / 8.074308 (13.815971) | 15.927473 / 10.191392 (5.736081) | 0.170388 / 0.680424 (-0.510036) | 0.023282 / 0.534201 (-0.510919) | 0.459371 / 0.579283 (-0.119912) | 0.468838 / 0.434364 (0.034475) | 0.546438 / 0.540337 (0.006101) | 0.746912 / 1.386936 (-0.640024) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8197ce872d2e24bd1ffbb07213faea25078f1386 \"CML watermark\")\n", "Yes, @lhoestq, you are right. I think we cross-send fixing PRs in a 15 minute interval... :sweat_smile: \r\n\r\nI would say the code in this PR is simpler and easier to understand, but feel free to ignore it.", "I think the correct way it to check if \"file\" in in the tuple if it's a tuple (in case someone adds another protocol name for the local filesystem)" ]

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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) | 0.006648 / 0.011353 (-0.004705) | 0.004104 / 0.011008 (-0.006904) | 0.084718 / 0.038508 (0.046210) | 0.075342 / 0.023109 (0.052232) | 0.332624 / 0.275898 (0.056726) | 0.376758 / 0.323480 (0.053278) | 0.005371 / 0.007986 (-0.002614) | 0.003317 / 0.004328 (-0.001011) | 0.065153 / 0.004250 (0.060902) | 0.055270 / 0.037052 (0.018218) | 0.342410 / 0.258489 (0.083920) | 0.397484 / 0.293841 (0.103643) | 0.031168 / 0.128546 (-0.097379) | 0.008545 / 0.075646 (-0.067101) | 0.297641 / 0.419271 (-0.121631) | 0.052404 / 0.043533 (0.008871) | 0.327633 / 0.255139 (0.072494) | 0.362177 / 0.283200 (0.078977) | 0.025056 / 0.141683 (-0.116627) | 1.459023 / 1.452155 (0.006868) | 1.529651 / 1.492716 (0.036935) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.242838 / 0.018006 (0.224832) | 0.451007 / 0.000490 (0.450517) | 0.013732 / 0.000200 (0.013532) | 0.000345 / 0.000054 (0.000290) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028068 / 0.037411 (-0.009343) | 0.081970 / 0.014526 (0.067444) | 0.096148 / 0.176557 (-0.080409) | 0.151758 / 0.737135 (-0.585377) | 0.095617 / 0.296338 (-0.200721) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.389188 / 0.215209 (0.173979) | 3.867506 / 2.077655 (1.789852) | 1.941912 / 1.504120 (0.437792) | 1.759270 / 1.541195 (0.218076) | 1.774714 / 1.468490 (0.306224) | 0.476587 / 4.584777 (-4.108190) | 3.539342 / 3.745712 (-0.206370) | 3.434389 / 5.269862 (-1.835472) | 2.047581 / 4.565676 (-2.518096) | 0.056322 / 0.424275 (-0.367954) | 0.007286 / 0.007607 (-0.000321) | 0.461826 / 0.226044 (0.235781) | 4.604179 / 2.268929 (2.335251) | 2.405267 / 55.444624 (-53.039357) | 2.133998 / 6.876477 (-4.742479) | 2.187724 / 2.142072 (0.045652) | 0.566578 / 4.805227 (-4.238650) | 0.130007 / 6.500664 (-6.370657) | 0.059685 / 0.075469 (-0.015784) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.256204 / 1.841788 (-0.585584) | 18.829475 / 8.074308 (10.755167) | 13.937879 / 10.191392 (3.746487) | 0.163948 / 0.680424 (-0.516475) | 0.018118 / 0.534201 (-0.516083) | 0.389369 / 0.579283 (-0.189914) | 0.399988 / 0.434364 (-0.034376) | 0.459504 / 0.540337 (-0.080834) | 0.674696 / 1.386936 (-0.712240) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006806 / 0.011353 (-0.004547) | 0.004103 / 0.011008 (-0.006905) | 0.064477 / 0.038508 (0.025969) | 0.079514 / 0.023109 (0.056405) | 0.391657 / 0.275898 (0.115759) | 0.422997 / 0.323480 (0.099517) | 0.005485 / 0.007986 (-0.002501) | 0.003461 / 0.004328 (-0.000868) | 0.064621 / 0.004250 (0.060371) | 0.057686 / 0.037052 (0.020633) | 0.396885 / 0.258489 (0.138396) | 0.431508 / 0.293841 (0.137667) | 0.032305 / 0.128546 (-0.096241) | 0.008617 / 0.075646 (-0.067030) | 0.071577 / 0.419271 (-0.347694) | 0.047769 / 0.043533 (0.004236) | 0.394037 / 0.255139 (0.138898) | 0.412593 / 0.283200 (0.129393) | 0.023800 / 0.141683 (-0.117883) | 1.479114 / 1.452155 (0.026959) | 1.562422 / 1.492716 (0.069706) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.229822 / 0.018006 (0.211816) | 0.452465 / 0.000490 (0.451975) | 0.005877 / 0.000200 (0.005677) | 0.000097 / 0.000054 (0.000043) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033528 / 0.037411 (-0.003884) | 0.091819 / 0.014526 (0.077294) | 0.106188 / 0.176557 (-0.070368) | 0.159480 / 0.737135 (-0.577655) | 0.106326 / 0.296338 (-0.190013) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.427396 / 0.215209 (0.212187) | 4.275196 / 2.077655 (2.197541) | 2.287446 / 1.504120 (0.783326) | 2.137089 / 1.541195 (0.595894) | 2.198439 / 1.468490 (0.729949) | 0.491006 / 4.584777 (-4.093771) | 3.531067 / 3.745712 (-0.214645) | 3.264357 / 5.269862 (-2.005505) | 2.047760 / 4.565676 (-2.517916) | 0.057982 / 0.424275 (-0.366293) | 0.007278 / 0.007607 (-0.000329) | 0.507471 / 0.226044 (0.281426) | 5.073901 / 2.268929 (2.804973) | 2.781799 / 55.444624 (-52.662825) | 2.410759 / 6.876477 (-4.465718) | 2.623331 / 2.142072 (0.481258) | 0.601601 / 4.805227 (-4.203626) | 0.131461 / 6.500664 (-6.369204) | 0.060045 / 0.075469 (-0.015424) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.372946 / 1.841788 (-0.468842) | 19.560818 / 8.074308 (11.486509) | 14.388468 / 10.191392 (4.197076) | 0.177310 / 0.680424 (-0.503114) | 0.020233 / 0.534201 (-0.513967) | 0.395938 / 0.579283 (-0.183345) | 0.418336 / 0.434364 (-0.016028) | 0.471731 / 0.540337 (-0.068607) | 0.684679 / 1.386936 (-0.702257) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4bedb7dcbaedd292ae5764f0fe6d44c16e1c2c10 \"CML watermark\")\n", "We did a patch release containing your fix @ap-- !" ]

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[ "Hi @albertvillanova @lhoestq \r\n\r\nI believe the pull request that pins the fsspec version (https://github.com/huggingface/datasets/pull/6331) was merged by mistake. Another fix for the issue was merged on the same day an hour apart. See https://github.com/huggingface/datasets/pull/6334\r\n\r\nI'm now having an issue in my project where I can't use newer versions of fsspec.\r\n\r\nCan we remove the pin?\r\n\r\nHave a nice day! :)", "Hi @tomscholz,\r\n\r\nThanks for pointing this out. I think you are right.\r\n\r\nI am doing some cross-checks and fixing it. ", "Hi again, @tomscholz.\r\n\r\nAfter a more cautious investigation, I think the pin is OK because there are other reasons for it. Chronologically:\r\n- #6331 \r\n- #6334\r\n- #6336 \r\n- #6337 \r\n\r\nThe reason is that after version 2023.10.0, they changed again the behavior of their `glob` function. See: https://github.com/huggingface/datasets/pull/6337#issuecomment-1774930135\r\nWe are working on our side to support both previous and new glob behavior.\r\n\r\nNote:\r\n- First pin was < 2023.10.0\r\n- Last pin is <= 2023.10.0", "Fixed by #6334 and #6336." ]

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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) | 0.006884 / 0.011353 (-0.004469) | 0.004132 / 0.011008 (-0.006877) | 0.085993 / 0.038508 (0.047485) | 0.084049 / 0.023109 (0.060940) | 0.346194 / 0.275898 (0.070296) | 0.386999 / 0.323480 (0.063519) | 0.004185 / 0.007986 (-0.003801) | 0.004354 / 0.004328 (0.000026) | 0.065137 / 0.004250 (0.060886) | 0.057629 / 0.037052 (0.020577) | 0.353639 / 0.258489 (0.095150) | 0.400815 / 0.293841 (0.106974) | 0.031370 / 0.128546 (-0.097176) | 0.008719 / 0.075646 (-0.066927) | 0.289579 / 0.419271 (-0.129693) | 0.052826 / 0.043533 (0.009293) | 0.351110 / 0.255139 (0.095971) | 0.375663 / 0.283200 (0.092464) | 0.025892 / 0.141683 (-0.115791) | 1.481943 / 1.452155 (0.029789) | 1.541494 / 1.492716 (0.048778) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.240007 / 0.018006 (0.222000) | 0.456216 / 0.000490 (0.455726) | 0.009348 / 0.000200 (0.009148) | 0.000370 / 0.000054 (0.000315) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029541 / 0.037411 (-0.007870) | 0.088394 / 0.014526 (0.073868) | 0.098460 / 0.176557 (-0.078096) | 0.154053 / 0.737135 (-0.583083) | 0.098821 / 0.296338 (-0.197518) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.386751 / 0.215209 (0.171542) | 3.809818 / 2.077655 (1.732164) | 1.833439 / 1.504120 (0.329319) | 1.686924 / 1.541195 (0.145729) | 1.796882 / 1.468490 (0.328392) | 0.488853 / 4.584777 (-4.095924) | 3.606369 / 3.745712 (-0.139343) | 3.460003 / 5.269862 (-1.809858) | 2.087493 / 4.565676 (-2.478184) | 0.056838 / 0.424275 (-0.367437) | 0.007679 / 0.007607 (0.000072) | 0.455080 / 0.226044 (0.229036) | 4.539227 / 2.268929 (2.270299) | 2.337245 / 55.444624 (-53.107379) | 1.988195 / 6.876477 (-4.888281) | 2.067473 / 2.142072 (-0.074600) | 0.576640 / 4.805227 (-4.228587) | 0.132140 / 6.500664 (-6.368525) | 0.060737 / 0.075469 (-0.014732) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.268866 / 1.841788 (-0.572922) | 19.695296 / 8.074308 (11.620988) | 14.431254 / 10.191392 (4.239862) | 0.166779 / 0.680424 (-0.513645) | 0.018262 / 0.534201 (-0.515939) | 0.390406 / 0.579283 (-0.188877) | 0.411284 / 0.434364 (-0.023080) | 0.456696 / 0.540337 (-0.083642) | 0.629660 / 1.386936 (-0.757276) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007210 / 0.011353 (-0.004143) | 0.004124 / 0.011008 (-0.006884) | 0.065877 / 0.038508 (0.027368) | 0.086242 / 0.023109 (0.063133) | 0.420087 / 0.275898 (0.144189) | 0.454327 / 0.323480 (0.130847) | 0.005586 / 0.007986 (-0.002399) | 0.003465 / 0.004328 (-0.000863) | 0.065153 / 0.004250 (0.060902) | 0.059337 / 0.037052 (0.022285) | 0.420913 / 0.258489 (0.162424) | 0.458552 / 0.293841 (0.164711) | 0.032335 / 0.128546 (-0.096211) | 0.008672 / 0.075646 (-0.066974) | 0.072029 / 0.419271 (-0.347242) | 0.048148 / 0.043533 (0.004615) | 0.423334 / 0.255139 (0.168196) | 0.440616 / 0.283200 (0.157416) | 0.023761 / 0.141683 (-0.117922) | 1.487022 / 1.452155 (0.034868) | 1.554028 / 1.492716 (0.061312) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.216693 / 0.018006 (0.198687) | 0.446359 / 0.000490 (0.445869) | 0.005294 / 0.000200 (0.005094) | 0.000100 / 0.000054 (0.000045) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034655 / 0.037411 (-0.002756) | 0.099479 / 0.014526 (0.084953) | 0.111822 / 0.176557 (-0.064735) | 0.160675 / 0.737135 (-0.576461) | 0.108718 / 0.296338 (-0.187621) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.440270 / 0.215209 (0.225061) | 4.389013 / 2.077655 (2.311358) | 2.408007 / 1.504120 (0.903887) | 2.237233 / 1.541195 (0.696038) | 2.344131 / 1.468490 (0.875641) | 0.493143 / 4.584777 (-4.091634) | 3.620024 / 3.745712 (-0.125688) | 3.335810 / 5.269862 (-1.934052) | 2.079256 / 4.565676 (-2.486420) | 0.058324 / 0.424275 (-0.365951) | 0.007410 / 0.007607 (-0.000197) | 0.512057 / 0.226044 (0.286013) | 5.120629 / 2.268929 (2.851701) | 2.913268 / 55.444624 (-52.531356) | 2.558214 / 6.876477 (-4.318262) | 2.784146 / 2.142072 (0.642074) | 0.593308 / 4.805227 (-4.211920) | 0.134941 / 6.500664 (-6.365723) | 0.062292 / 0.075469 (-0.013177) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.351795 / 1.841788 (-0.489993) | 20.489559 / 8.074308 (12.415251) | 15.046116 / 10.191392 (4.854724) | 0.166339 / 0.680424 (-0.514085) | 0.020449 / 0.534201 (-0.513752) | 0.406570 / 0.579283 (-0.172713) | 0.423405 / 0.434364 (-0.010959) | 0.474541 / 0.540337 (-0.065796) | 0.653280 / 1.386936 (-0.733656) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3bde0f0f0e556e55b95c72b0f83bdcf7145c813c \"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) | 0.006362 / 0.011353 (-0.004991) | 0.003990 / 0.011008 (-0.007018) | 0.084020 / 0.038508 (0.045512) | 0.072198 / 0.023109 (0.049089) | 0.335992 / 0.275898 (0.060094) | 0.362056 / 0.323480 (0.038576) | 0.005298 / 0.007986 (-0.002688) | 0.003421 / 0.004328 (-0.000908) | 0.065343 / 0.004250 (0.061092) | 0.053310 / 0.037052 (0.016258) | 0.344855 / 0.258489 (0.086366) | 0.385524 / 0.293841 (0.091683) | 0.030209 / 0.128546 (-0.098337) | 0.008465 / 0.075646 (-0.067181) | 0.287359 / 0.419271 (-0.131912) | 0.051371 / 0.043533 (0.007838) | 0.338716 / 0.255139 (0.083577) | 0.351730 / 0.283200 (0.068530) | 0.023581 / 0.141683 (-0.118102) | 1.473772 / 1.452155 (0.021617) | 1.560594 / 1.492716 (0.067878) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.309019 / 0.018006 (0.291013) | 0.561428 / 0.000490 (0.560939) | 0.007237 / 0.000200 (0.007038) | 0.000266 / 0.000054 (0.000212) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028172 / 0.037411 (-0.009239) | 0.081050 / 0.014526 (0.066524) | 0.095952 / 0.176557 (-0.080604) | 0.151796 / 0.737135 (-0.585340) | 0.096132 / 0.296338 (-0.200206) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.384287 / 0.215209 (0.169078) | 3.840797 / 2.077655 (1.763142) | 1.891120 / 1.504120 (0.387000) | 1.743498 / 1.541195 (0.202303) | 1.821037 / 1.468490 (0.352547) | 0.484946 / 4.584777 (-4.099831) | 3.586053 / 3.745712 (-0.159659) | 3.446215 / 5.269862 (-1.823647) | 2.054352 / 4.565676 (-2.511325) | 0.057315 / 0.424275 (-0.366960) | 0.007541 / 0.007607 (-0.000066) | 0.464088 / 0.226044 (0.238044) | 4.634005 / 2.268929 (2.365076) | 2.355818 / 55.444624 (-53.088806) | 2.045584 / 6.876477 (-4.830893) | 2.039455 / 2.142072 (-0.102617) | 0.576137 / 4.805227 (-4.229090) | 0.132071 / 6.500664 (-6.368593) | 0.059611 / 0.075469 (-0.015858) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.280078 / 1.841788 (-0.561710) | 19.054079 / 8.074308 (10.979771) | 14.291090 / 10.191392 (4.099698) | 0.170607 / 0.680424 (-0.509817) | 0.018489 / 0.534201 (-0.515712) | 0.391802 / 0.579283 (-0.187481) | 0.418945 / 0.434364 (-0.015419) | 0.464084 / 0.540337 (-0.076254) | 0.638099 / 1.386936 (-0.748837) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006735 / 0.011353 (-0.004618) | 0.004133 / 0.011008 (-0.006876) | 0.064620 / 0.038508 (0.026112) | 0.076395 / 0.023109 (0.053286) | 0.399659 / 0.275898 (0.123761) | 0.426821 / 0.323480 (0.103341) | 0.006407 / 0.007986 (-0.001578) | 0.003472 / 0.004328 (-0.000857) | 0.064922 / 0.004250 (0.060671) | 0.058312 / 0.037052 (0.021260) | 0.403286 / 0.258489 (0.144797) | 0.437772 / 0.293841 (0.143931) | 0.032323 / 0.128546 (-0.096223) | 0.008727 / 0.075646 (-0.066919) | 0.071344 / 0.419271 (-0.347927) | 0.048673 / 0.043533 (0.005141) | 0.400693 / 0.255139 (0.145554) | 0.418668 / 0.283200 (0.135468) | 0.022871 / 0.141683 (-0.118812) | 1.517691 / 1.452155 (0.065536) | 1.552021 / 1.492716 (0.059305) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.305279 / 0.018006 (0.287272) | 0.520054 / 0.000490 (0.519564) | 0.007247 / 0.000200 (0.007047) | 0.000098 / 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) | 0.032001 / 0.037411 (-0.005410) | 0.091273 / 0.014526 (0.076747) | 0.106480 / 0.176557 (-0.070077) | 0.163122 / 0.737135 (-0.574014) | 0.105244 / 0.296338 (-0.191094) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.432207 / 0.215209 (0.216998) | 4.304856 / 2.077655 (2.227202) | 2.326790 / 1.504120 (0.822670) | 2.150081 / 1.541195 (0.608886) | 2.150558 / 1.468490 (0.682068) | 0.488808 / 4.584777 (-4.095969) | 3.690435 / 3.745712 (-0.055277) | 3.302625 / 5.269862 (-1.967236) | 2.044193 / 4.565676 (-2.521483) | 0.057520 / 0.424275 (-0.366755) | 0.007281 / 0.007607 (-0.000326) | 0.521078 / 0.226044 (0.295034) | 5.162620 / 2.268929 (2.893691) | 2.744041 / 55.444624 (-52.700583) | 2.407211 / 6.876477 (-4.469266) | 2.606290 / 2.142072 (0.464217) | 0.586412 / 4.805227 (-4.218815) | 0.132152 / 6.500664 (-6.368512) | 0.059424 / 0.075469 (-0.016045) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.351879 / 1.841788 (-0.489908) | 19.460608 / 8.074308 (11.386299) | 14.643413 / 10.191392 (4.452021) | 0.168062 / 0.680424 (-0.512362) | 0.020396 / 0.534201 (-0.513805) | 0.395885 / 0.579283 (-0.183398) | 0.439551 / 0.434364 (0.005187) | 0.473051 / 0.540337 (-0.067286) | 0.644614 / 1.386936 (-0.742322) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#732b2ed47728fffc8d74f92691c21de8ac7423fe \"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) | 0.014708 / 0.011353 (0.003355) | 0.008309 / 0.011008 (-0.002699) | 0.138986 / 0.038508 (0.100478) | 0.121781 / 0.023109 (0.098671) | 0.495536 / 0.275898 (0.219637) | 0.565195 / 0.323480 (0.241715) | 0.008018 / 0.007986 (0.000032) | 0.004904 / 0.004328 (0.000575) | 0.080622 / 0.004250 (0.076371) | 0.078917 / 0.037052 (0.041865) | 0.489424 / 0.258489 (0.230935) | 0.540496 / 0.293841 (0.246656) | 0.061110 / 0.128546 (-0.067437) | 0.021443 / 0.075646 (-0.054203) | 0.395789 / 0.419271 (-0.023482) | 0.076727 / 0.043533 (0.033194) | 0.427808 / 0.255139 (0.172669) | 0.519672 / 0.283200 (0.236473) | 0.041607 / 0.141683 (-0.100076) | 2.098675 / 1.452155 (0.646520) | 2.175123 / 1.492716 (0.682407) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.275784 / 0.018006 (0.257777) | 0.707103 / 0.000490 (0.706613) | 0.011524 / 0.000200 (0.011324) | 0.000390 / 0.000054 (0.000336) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032897 / 0.037411 (-0.004514) | 0.123239 / 0.014526 (0.108713) | 0.151815 / 0.176557 (-0.024741) | 0.214790 / 0.737135 (-0.522345) | 0.139166 / 0.296338 (-0.157173) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.740662 / 0.215209 (0.525453) | 7.540376 / 2.077655 (5.462721) | 3.168207 / 1.504120 (1.664087) | 2.745663 / 1.541195 (1.204468) | 2.714020 / 1.468490 (1.245530) | 1.182632 / 4.584777 (-3.402145) | 6.365807 / 3.745712 (2.620095) | 6.317228 / 5.269862 (1.047366) | 4.061107 / 4.565676 (-0.504569) | 0.146939 / 0.424275 (-0.277336) | 0.011765 / 0.007607 (0.004158) | 0.910564 / 0.226044 (0.684519) | 9.020618 / 2.268929 (6.751689) | 4.180748 / 55.444624 (-51.263876) | 3.290257 / 6.876477 (-3.586220) | 3.363172 / 2.142072 (1.221099) | 1.239142 / 4.805227 (-3.566086) | 0.294965 / 6.500664 (-6.205699) | 0.088520 / 0.075469 (0.013051) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.867528 / 1.841788 (0.025741) | 29.494058 / 8.074308 (21.419750) | 31.386703 / 10.191392 (21.195311) | 0.302488 / 0.680424 (-0.377936) | 0.036116 / 0.534201 (-0.498085) | 0.622112 / 0.579283 (0.042829) | 0.775658 / 0.434364 (0.341294) | 0.632452 / 0.540337 (0.092115) | 0.909424 / 1.386936 (-0.477512) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.016002 / 0.011353 (0.004649) | 0.007007 / 0.011008 (-0.004002) | 0.100463 / 0.038508 (0.061955) | 0.124423 / 0.023109 (0.101314) | 0.556014 / 0.275898 (0.280116) | 0.600909 / 0.323480 (0.277429) | 0.007272 / 0.007986 (-0.000714) | 0.006743 / 0.004328 (0.002415) | 0.088575 / 0.004250 (0.084324) | 0.066003 / 0.037052 (0.028951) | 0.580080 / 0.258489 (0.321591) | 0.655567 / 0.293841 (0.361726) | 0.065295 / 0.128546 (-0.063252) | 0.021105 / 0.075646 (-0.054541) | 0.120044 / 0.419271 (-0.299227) | 0.081133 / 0.043533 (0.037600) | 0.570322 / 0.255139 (0.315183) | 0.581134 / 0.283200 (0.297934) | 0.046298 / 0.141683 (-0.095385) | 2.113200 / 1.452155 (0.661045) | 2.344187 / 1.492716 (0.851471) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.284517 / 0.018006 (0.266511) | 0.611834 / 0.000490 (0.611345) | 0.005581 / 0.000200 (0.005381) | 0.000153 / 0.000054 (0.000098) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.042162 / 0.037411 (0.004750) | 0.114496 / 0.014526 (0.099970) | 0.134034 / 0.176557 (-0.042523) | 0.201649 / 0.737135 (-0.535486) | 0.143235 / 0.296338 (-0.153103) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.764863 / 0.215209 (0.549654) | 7.603076 / 2.077655 (5.525421) | 3.318911 / 1.504120 (1.814791) | 2.939815 / 1.541195 (1.398620) | 2.870911 / 1.468490 (1.402421) | 1.171978 / 4.584777 (-3.412799) | 6.479933 / 3.745712 (2.734221) | 5.944387 / 5.269862 (0.674526) | 4.282625 / 4.565676 (-0.283051) | 0.123672 / 0.424275 (-0.300603) | 0.009666 / 0.007607 (0.002059) | 0.870683 / 0.226044 (0.644638) | 9.187788 / 2.268929 (6.918859) | 4.431818 / 55.444624 (-51.012807) | 3.460457 / 6.876477 (-3.416020) | 3.708198 / 2.142072 (1.566126) | 1.353673 / 4.805227 (-3.451554) | 0.264274 / 6.500664 (-6.236390) | 0.074943 / 0.075469 (-0.000526) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 2.073810 / 1.841788 (0.232023) | 29.182464 / 8.074308 (21.108156) | 30.527040 / 10.191392 (20.335648) | 0.307561 / 0.680424 (-0.372863) | 0.047384 / 0.534201 (-0.486817) | 0.662760 / 0.579283 (0.083477) | 0.768321 / 0.434364 (0.333957) | 0.692296 / 0.540337 (0.151959) | 0.955197 / 1.386936 (-0.431739) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1e82d6f017c7fc0ab6b65847c1e34772c880d3b7 \"CML watermark\")\n" ]

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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) | 0.009605 / 0.011353 (-0.001747) | 0.004864 / 0.011008 (-0.006144) | 0.114605 / 0.038508 (0.076097) | 0.090874 / 0.023109 (0.067765) | 0.429203 / 0.275898 (0.153305) | 0.489888 / 0.323480 (0.166408) | 0.006542 / 0.007986 (-0.001443) | 0.004585 / 0.004328 (0.000257) | 0.090251 / 0.004250 (0.086001) | 0.066612 / 0.037052 (0.029560) | 0.437491 / 0.258489 (0.179002) | 0.515196 / 0.293841 (0.221355) | 0.047756 / 0.128546 (-0.080791) | 0.013587 / 0.075646 (-0.062059) | 0.376960 / 0.419271 (-0.042311) | 0.069701 / 0.043533 (0.026168) | 0.430850 / 0.255139 (0.175711) | 0.475061 / 0.283200 (0.191861) | 0.034800 / 0.141683 (-0.106883) | 1.799947 / 1.452155 (0.347793) | 1.941863 / 1.492716 (0.449147) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.316685 / 0.018006 (0.298679) | 0.595098 / 0.000490 (0.594608) | 0.015447 / 0.000200 (0.015247) | 0.000463 / 0.000054 (0.000409) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.039364 / 0.037411 (0.001953) | 0.091295 / 0.014526 (0.076769) | 0.109380 / 0.176557 (-0.067177) | 0.185454 / 0.737135 (-0.551681) | 0.104476 / 0.296338 (-0.191862) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.626291 / 0.215209 (0.411082) | 5.869948 / 2.077655 (3.792293) | 2.466267 / 1.504120 (0.962147) | 2.183572 / 1.541195 (0.642377) | 2.208286 / 1.468490 (0.739796) | 0.817175 / 4.584777 (-3.767602) | 5.255141 / 3.745712 (1.509429) | 4.878668 / 5.269862 (-0.391193) | 2.917020 / 4.565676 (-1.648657) | 0.104995 / 0.424275 (-0.319280) | 0.008687 / 0.007607 (0.001080) | 0.678993 / 0.226044 (0.452948) | 7.004983 / 2.268929 (4.736054) | 3.444040 / 55.444624 (-52.000584) | 2.745075 / 6.876477 (-4.131402) | 2.720151 / 2.142072 (0.578078) | 0.995803 / 4.805227 (-3.809424) | 0.205928 / 6.500664 (-6.294736) | 0.077053 / 0.075469 (0.001584) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.587354 / 1.841788 (-0.254434) | 23.843227 / 8.074308 (15.768919) | 21.355771 / 10.191392 (11.164379) | 0.225593 / 0.680424 (-0.454831) | 0.029054 / 0.534201 (-0.505147) | 0.469676 / 0.579283 (-0.109607) | 0.582619 / 0.434364 (0.148255) | 0.576932 / 0.540337 (0.036594) | 0.946182 / 1.386936 (-0.440754) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009819 / 0.011353 (-0.001534) | 0.005562 / 0.011008 (-0.005446) | 0.075512 / 0.038508 (0.037004) | 0.084294 / 0.023109 (0.061185) | 0.549516 / 0.275898 (0.273618) | 0.550364 / 0.323480 (0.226884) | 0.006603 / 0.007986 (-0.001383) | 0.004587 / 0.004328 (0.000259) | 0.084040 / 0.004250 (0.079789) | 0.066815 / 0.037052 (0.029762) | 0.549224 / 0.258489 (0.290735) | 0.556213 / 0.293841 (0.262372) | 0.048538 / 0.128546 (-0.080008) | 0.014050 / 0.075646 (-0.061596) | 0.088955 / 0.419271 (-0.330317) | 0.062393 / 0.043533 (0.018860) | 0.528770 / 0.255139 (0.273631) | 0.564854 / 0.283200 (0.281655) | 0.033976 / 0.141683 (-0.107707) | 1.858558 / 1.452155 (0.406403) | 1.894616 / 1.492716 (0.401899) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.378597 / 0.018006 (0.360591) | 0.650586 / 0.000490 (0.650097) | 0.033179 / 0.000200 (0.032979) | 0.000477 / 0.000054 (0.000423) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031779 / 0.037411 (-0.005632) | 0.103393 / 0.014526 (0.088867) | 0.119810 / 0.176557 (-0.056747) | 0.192188 / 0.737135 (-0.544948) | 0.114545 / 0.296338 (-0.181794) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.623571 / 0.215209 (0.408362) | 6.350249 / 2.077655 (4.272594) | 3.207773 / 1.504120 (1.703653) | 2.861118 / 1.541195 (1.319923) | 2.864445 / 1.468490 (1.395955) | 0.827451 / 4.584777 (-3.757326) | 5.323860 / 3.745712 (1.578148) | 4.569197 / 5.269862 (-0.700665) | 2.967595 / 4.565676 (-1.598081) | 0.090926 / 0.424275 (-0.333349) | 0.007820 / 0.007607 (0.000213) | 0.731610 / 0.226044 (0.505565) | 7.342651 / 2.268929 (5.073723) | 3.781727 / 55.444624 (-51.662897) | 3.222100 / 6.876477 (-3.654377) | 3.546145 / 2.142072 (1.404073) | 1.030500 / 4.805227 (-3.774728) | 0.226563 / 6.500664 (-6.274101) | 0.078633 / 0.075469 (0.003164) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.733677 / 1.841788 (-0.108111) | 24.650616 / 8.074308 (16.576308) | 22.033745 / 10.191392 (11.842353) | 0.211055 / 0.680424 (-0.469369) | 0.031658 / 0.534201 (-0.502543) | 0.467190 / 0.579283 (-0.112094) | 0.598303 / 0.434364 (0.163939) | 0.569318 / 0.540337 (0.028981) | 0.825984 / 1.386936 (-0.560952) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7eef7749031232d0b29f7ca10e3fa9f997b19ef7 \"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) | 0.006873 / 0.011353 (-0.004479) | 0.004174 / 0.011008 (-0.006835) | 0.085874 / 0.038508 (0.047366) | 0.074207 / 0.023109 (0.051098) | 0.307342 / 0.275898 (0.031444) | 0.339972 / 0.323480 (0.016493) | 0.005522 / 0.007986 (-0.002463) | 0.003576 / 0.004328 (-0.000753) | 0.065680 / 0.004250 (0.061430) | 0.056274 / 0.037052 (0.019222) | 0.313121 / 0.258489 (0.054632) | 0.364699 / 0.293841 (0.070858) | 0.031297 / 0.128546 (-0.097249) | 0.008652 / 0.075646 (-0.066994) | 0.288431 / 0.419271 (-0.130840) | 0.053081 / 0.043533 (0.009548) | 0.309076 / 0.255139 (0.053937) | 0.329251 / 0.283200 (0.046052) | 0.024840 / 0.141683 (-0.116843) | 1.484155 / 1.452155 (0.032001) | 1.598665 / 1.492716 (0.105949) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.270933 / 0.018006 (0.252927) | 0.565867 / 0.000490 (0.565377) | 0.006964 / 0.000200 (0.006764) | 0.000298 / 0.000054 (0.000244) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028393 / 0.037411 (-0.009018) | 0.081756 / 0.014526 (0.067230) | 0.095733 / 0.176557 (-0.080823) | 0.152426 / 0.737135 (-0.584710) | 0.096655 / 0.296338 (-0.199683) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.403992 / 0.215209 (0.188783) | 4.027230 / 2.077655 (1.949576) | 2.031102 / 1.504120 (0.526982) | 1.843727 / 1.541195 (0.302532) | 1.898342 / 1.468490 (0.429852) | 0.479186 / 4.584777 (-4.105591) | 3.488153 / 3.745712 (-0.257559) | 3.523953 / 5.269862 (-1.745909) | 2.078392 / 4.565676 (-2.487284) | 0.056104 / 0.424275 (-0.368171) | 0.007368 / 0.007607 (-0.000239) | 0.479630 / 0.226044 (0.253585) | 4.787400 / 2.268929 (2.518471) | 2.488268 / 55.444624 (-52.956356) | 2.229955 / 6.876477 (-4.646522) | 2.260468 / 2.142072 (0.118396) | 0.587934 / 4.805227 (-4.217294) | 0.147124 / 6.500664 (-6.353540) | 0.059954 / 0.075469 (-0.015515) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.283155 / 1.841788 (-0.558632) | 19.013574 / 8.074308 (10.939266) | 13.915188 / 10.191392 (3.723796) | 0.174101 / 0.680424 (-0.506323) | 0.018172 / 0.534201 (-0.516029) | 0.390322 / 0.579283 (-0.188961) | 0.405493 / 0.434364 (-0.028871) | 0.456914 / 0.540337 (-0.083424) | 0.635213 / 1.386936 (-0.751723) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006622 / 0.011353 (-0.004731) | 0.003997 / 0.011008 (-0.007011) | 0.064542 / 0.038508 (0.026034) | 0.074165 / 0.023109 (0.051056) | 0.392285 / 0.275898 (0.116387) | 0.423522 / 0.323480 (0.100042) | 0.006361 / 0.007986 (-0.001625) | 0.003463 / 0.004328 (-0.000866) | 0.064891 / 0.004250 (0.060641) | 0.058485 / 0.037052 (0.021433) | 0.425217 / 0.258489 (0.166728) | 0.435907 / 0.293841 (0.142066) | 0.031501 / 0.128546 (-0.097045) | 0.008575 / 0.075646 (-0.067071) | 0.072094 / 0.419271 (-0.347178) | 0.047904 / 0.043533 (0.004371) | 0.397174 / 0.255139 (0.142035) | 0.417940 / 0.283200 (0.134741) | 0.023324 / 0.141683 (-0.118358) | 1.517245 / 1.452155 (0.065090) | 1.586497 / 1.492716 (0.093781) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.268311 / 0.018006 (0.250305) | 0.561118 / 0.000490 (0.560628) | 0.004352 / 0.000200 (0.004152) | 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) | 0.033039 / 0.037411 (-0.004373) | 0.091596 / 0.014526 (0.077071) | 0.111520 / 0.176557 (-0.065036) | 0.161755 / 0.737135 (-0.575381) | 0.107681 / 0.296338 (-0.188657) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.427170 / 0.215209 (0.211961) | 4.252648 / 2.077655 (2.174994) | 2.257623 / 1.504120 (0.753503) | 2.098446 / 1.541195 (0.557251) | 2.128544 / 1.468490 (0.660054) | 0.496639 / 4.584777 (-4.088138) | 3.593385 / 3.745712 (-0.152328) | 3.396367 / 5.269862 (-1.873494) | 2.073369 / 4.565676 (-2.492308) | 0.058386 / 0.424275 (-0.365889) | 0.007515 / 0.007607 (-0.000093) | 0.502358 / 0.226044 (0.276313) | 5.015224 / 2.268929 (2.746296) | 2.735740 / 55.444624 (-52.708885) | 2.388368 / 6.876477 (-4.488109) | 2.682857 / 2.142072 (0.540785) | 0.595003 / 4.805227 (-4.210225) | 0.135419 / 6.500664 (-6.365245) | 0.062824 / 0.075469 (-0.012645) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.367507 / 1.841788 (-0.474281) | 19.569288 / 8.074308 (11.494979) | 14.748693 / 10.191392 (4.557301) | 0.198659 / 0.680424 (-0.481765) | 0.020954 / 0.534201 (-0.513247) | 0.414858 / 0.579283 (-0.164426) | 0.421226 / 0.434364 (-0.013138) | 0.477774 / 0.540337 (-0.062563) | 0.676173 / 1.386936 (-0.710763) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#87ed467af77d87ad7e38279cf9a24c341545cbac \"CML watermark\")\n" ]

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[ "I also encountered a similar error below.\r\nAppreciate the team could shed some light on this issue.\r\n\r\n```\r\n---------------------------------------------------------------------------\r\nNotImplementedError Traceback (most recent call last)\r\n[/home/ubuntu/work/EveryDream2trainer/prepare_dataset.ipynb](https://vscode-remote+ssh-002dremote-002braspberry-002dg5-002e4x.vscode-resource.vscode-cdn.net/home/ubuntu/work/EveryDream2trainer/prepare_dataset.ipynb) Cell 1 line 4\r\n [1](vscode-notebook-cell://ssh-remote%2Braspberry-g5.4x/home/ubuntu/work/EveryDream2trainer/prepare_dataset.ipynb#W0sdnNjb2RlLXJlbW90ZQ%3D%3D?line=0) from datasets import load_dataset, load_dataset\r\n [3](vscode-notebook-cell://ssh-remote%2Braspberry-g5.4x/home/ubuntu/work/EveryDream2trainer/prepare_dataset.ipynb#W0sdnNjb2RlLXJlbW90ZQ%3D%3D?line=2) # ds = load_dataset(\"parquet\", data_dir=\"/home/ubuntu/work/EveryDream2trainer/datasets/monse_v1/data\")\r\n----> [4](vscode-notebook-cell://ssh-remote%2Braspberry-g5.4x/home/ubuntu/work/EveryDream2trainer/prepare_dataset.ipynb#W0sdnNjb2RlLXJlbW90ZQ%3D%3D?line=3) ds = load_dataset(\"Raspberry-ai/monse-v1\")\r\n\r\nFile [/opt/conda/envs/everydream/lib/python3.10/site-packages/datasets/load.py:1804](https://vscode-remote+ssh-002dremote-002braspberry-002dg5-002e4x.vscode-resource.vscode-cdn.net/opt/conda/envs/everydream/lib/python3.10/site-packages/datasets/load.py:1804), in load_dataset(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, use_auth_token, task, streaming, num_proc, storage_options, **config_kwargs)\r\n 1800 # Build dataset for splits\r\n 1801 keep_in_memory = (\r\n 1802 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size)\r\n 1803 )\r\n-> 1804 ds = builder_instance.as_dataset(split=split, verification_mode=verification_mode, in_memory=keep_in_memory)\r\n 1805 # Rename and cast features to match task schema\r\n 1806 if task is not None:\r\n\r\nFile [/opt/conda/envs/everydream/lib/python3.10/site-packages/datasets/builder.py:1108](https://vscode-remote+ssh-002dremote-002braspberry-002dg5-002e4x.vscode-resource.vscode-cdn.net/opt/conda/envs/everydream/lib/python3.10/site-packages/datasets/builder.py:1108), in DatasetBuilder.as_dataset(self, split, run_post_process, verification_mode, ignore_verifications, in_memory)\r\n 1106 is_local = not is_remote_filesystem(self._fs)\r\n 1107 if not is_local:\r\n-> 1108 raise NotImplementedError(f\"Loading a dataset cached in a {type(self._fs).__name__} is not supported.\")\r\n 1109 if not os.path.exists(self._output_dir):\r\n 1110 raise FileNotFoundError(\r\n 1111 f\"Dataset {self.name}: could not find data in {self._output_dir}. Please make sure to call \"\r\n 1112 \"builder.download_and_prepare(), or use \"\r\n 1113 \"datasets.load_dataset() before trying to access the Dataset object.\"\r\n 1114 )\r\n\r\nNotImplementedError: Loading a dataset cached in a LocalFileSystem is not supported.\r\n```\r\n\r\nCode to reproduce the issue:\r\n\r\n```\r\nfrom datasets import load_dataset\r\n\r\nds = load_dataset(\"Raspberry-ai/monse-v1\")\r\n```\r\n\r\n\r\nDependencies:\r\n```\r\nPackage Version\r\n------------------------- ------------\r\nabsl-py 2.0.0\r\naccelerate 0.23.0\r\naiohttp 3.8.4\r\naiosignal 1.3.1\r\nantlr4-python3-runtime 4.9.3\r\nanyio 4.0.0\r\nappdirs 1.4.4\r\nargon2-cffi 23.1.0\r\nargon2-cffi-bindings 21.2.0\r\narrow 1.3.0\r\nasttokens 2.4.0\r\nasync-lru 2.0.4\r\nasync-timeout 4.0.3\r\nattrs 23.1.0\r\nBabel 2.13.0\r\nbackcall 0.2.0\r\nbeautifulsoup4 4.12.2\r\nbitsandbytes 0.41.1\r\nbleach 6.1.0\r\nbraceexpand 0.1.7\r\ncachetools 5.3.1\r\ncertifi 2023.7.22\r\ncffi 1.16.0\r\ncharset-normalizer 3.3.1\r\nclick 8.1.7\r\ncmake 3.27.7\r\ncolorama 0.4.6\r\ncomm 0.1.4\r\ncompel 1.1.6\r\ndatasets 2.11.0\r\ndebugpy 1.8.0\r\ndecorator 5.1.1\r\ndefusedxml 0.7.1\r\ndiffusers 0.18.0\r\ndill 0.3.6\r\ndocker-pycreds 0.4.0\r\ndowg 0.3.1\r\neinops 0.7.0\r\neinops-exts 0.0.4\r\nexceptiongroup 1.1.3\r\nexecuting 2.0.0\r\nfastjsonschema 2.18.1\r\nfilelock 3.12.4\r\nfqdn 1.5.1\r\nfrozenlist 1.4.0\r\nfsspec 2023.10.0\r\nftfy 6.1.1\r\ngitdb 4.0.11\r\nGitPython 3.1.40\r\ngoogle-auth 2.23.3\r\ngoogle-auth-oauthlib 1.1.0\r\ngrpcio 1.59.0\r\nhuggingface-hub 0.18.0\r\nidna 3.4\r\nimportlib-metadata 6.8.0\r\ninflection 0.5.1\r\nipykernel 6.25.2\r\nipython 8.16.1\r\nisoduration 20.11.0\r\njedi 0.19.1\r\nJinja2 3.1.2\r\njoblib 1.3.2\r\njson5 0.9.14\r\njsonpointer 2.4\r\njsonschema 4.19.1\r\njsonschema-specifications 2023.7.1\r\njupyter_client 8.4.0\r\njupyter_core 5.4.0\r\njupyter-events 0.8.0\r\njupyter-lsp 2.2.0\r\njupyter_server 2.8.0\r\njupyter_server_terminals 0.4.4\r\njupyterlab 4.0.7\r\njupyterlab-pygments 0.2.2\r\njupyterlab_server 2.25.0\r\nlightning-utilities 0.9.0\r\nlion-pytorch 0.1.2\r\nlit 17.0.3\r\nMarkdown 3.5\r\nMarkupSafe 2.1.3\r\nmatplotlib-inline 0.1.6\r\nmistune 3.0.2\r\nmore-itertools 10.1.0\r\nmpmath 1.3.0\r\nmultidict 6.0.4\r\nmultiprocess 0.70.14\r\nmypy-extensions 1.0.0\r\nnbclient 0.8.0\r\nnbconvert 7.9.2\r\nnbformat 5.9.2\r\nnest-asyncio 1.5.8\r\nnetworkx 3.2\r\nnltk 3.8.1\r\nnotebook_shim 0.2.3\r\nnumpy 1.23.5\r\noauthlib 3.2.2\r\nomegaconf 2.2.3\r\nopen-clip-torch 2.22.0\r\nopen-flamingo 2.0.0\r\noverrides 7.4.0\r\npackaging 23.2\r\npandas 2.1.1\r\npandocfilters 1.5.0\r\nparso 0.8.3\r\npathtools 0.1.2\r\npexpect 4.8.0\r\npickleshare 0.7.5\r\nPillow 10.1.0\r\npip 23.3.1\r\nplatformdirs 3.11.0\r\nprometheus-client 0.17.1\r\nprompt-toolkit 3.0.39\r\nprotobuf 3.20.1\r\npsutil 5.9.6\r\nptyprocess 0.7.0\r\npure-eval 0.2.2\r\npyarrow 13.0.0\r\npyasn1 0.5.0\r\npyasn1-modules 0.3.0\r\npycparser 2.21\r\npyDeprecate 0.3.2\r\nPygments 2.16.1\r\npynvml 11.4.1\r\npyparsing 3.1.1\r\npyre-extensions 0.0.29\r\npython-dateutil 2.8.2\r\npython-json-logger 2.0.7\r\npytorch-lightning 1.6.5\r\npytz 2023.3.post1\r\nPyYAML 6.0.1\r\npyzmq 25.1.1\r\nreferencing 0.30.2\r\nregex 2023.10.3\r\nrequests 2.31.0\r\nrequests-oauthlib 1.3.1\r\nresponses 0.18.0\r\nrfc3339-validator 0.1.4\r\nrfc3986-validator 0.1.1\r\nrpds-py 0.10.6\r\nrsa 4.9\r\nsafetensors 0.4.0\r\nscipy 1.11.3\r\nSend2Trash 1.8.2\r\nsentencepiece 0.1.98\r\nsentry-sdk 1.32.0\r\nsetproctitle 1.3.3\r\nsetuptools 68.2.2\r\nsix 1.16.0\r\nsmmap 5.0.1\r\nsniffio 1.3.0\r\nsoupsieve 2.5\r\nstack-data 0.6.3\r\nsympy 1.12\r\ntensorboard 2.15.0\r\ntensorboard-data-server 0.7.1\r\nterminado 0.17.1\r\ntimm 0.9.8\r\ntinycss2 1.2.1\r\ntokenizers 0.13.3\r\ntomli 2.0.1\r\ntorch 2.0.1+cu118\r\ntorchmetrics 1.2.0\r\ntorchvision 0.15.2+cu118\r\ntornado 6.3.3\r\ntqdm 4.66.1\r\ntraitlets 5.11.2\r\ntransformers 4.29.2\r\ntriton 2.0.0\r\ntypes-python-dateutil 2.8.19.14\r\ntyping_extensions 4.8.0\r\ntyping-inspect 0.9.0\r\ntzdata 2023.3\r\nuri-template 1.3.0\r\nurllib3 2.0.7\r\nwandb 0.15.12\r\nwcwidth 0.2.8\r\nwebcolors 1.13\r\nwebdataset 0.2.62\r\nwebencodings 0.5.1\r\nwebsocket-client 1.6.4\r\nWerkzeug 3.0.0\r\nwheel 0.41.2\r\nxformers 0.0.20\r\nxxhash 3.4.1\r\nyarl 1.9.2\r\nzipp 3.17.0\r\n```", "@humpydonkey FWIW setting fsspec down to 2023.9.2 fixed the issue\r\n\r\n`pip install fsspec==2023.9.2`", "got it, thanks @ZachNagengast ", "Thanks for reporting and for the investigation, @ZachNagengast! :hugs: \r\n\r\nWe are investigating the root cause of the issue. In the meantime, we are going to pin fsspec < 2023.10.0. ", "https://stackoverflow.com/questions/77433096/notimplementederror-loading-a-dataset-cached-in-a-localfilesystem-is-not-suppor/77433141#77433141", "You can also update `datasets`:\r\n\r\n```\r\npip install -U datasets\r\n```\r\n\r\nIt will also update `fsspec` to use the right version" ]

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[ "شبکه های متن به گفتار ابتدا متن داده شده را به بازنمایی میانی" ]

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[ "You can clone the `togethercomputer/RedPajama-Data-1T-Sample` repo and load the dataset with `load_dataset(\"path/to/cloned_repo\")` to use it offline.", "@mariosasko Thank you for your kind reply! I'll try it as a workaround.\r\nDoes that mean that currently it's not supported to simply load with a short name?", "It is, but manually downloading repo files to the cache can easily lead to failure (the HF cache is not meant to be modified by a user besides deleting the files 🙂), as in your case. Hence, the clone + `load_dataset(\"path/to/cloned_repo\")` workflow should be used instead." ]

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[ "Unlike Pandas, Arrow is strict with types, so converting the problematic strings to ints (or ints to strings) to ensure all the values have the same type is the only fix. \r\n\r\nJSON support has been requested in Arrow [here](https://github.com/apache/arrow/issues/32538), but I don't expect this to be implemented soon. \r\n\r\nAlso, this type could be represented with the Arrow Union type. However, due to low usage, the Union type has limited support in the Arrow ecosystem (e.g., IIRC Parquet still does not support it). So, we should probably wait a bit more before adding support for it in `datasets`", "> Unlike Pandas, Arrow is strict with types, so converting the problematic strings to ints (or ints to strings) to ensure all the values have the same type is the only fix.\r\n> \r\n> JSON support has been requested in Arrow [here](https://github.com/apache/arrow/issues/32538), but I don't expect this to be implemented soon.\r\n> \r\n> Also, this type could be represented with the Arrow Union type. However, due to low usage, the Union type has limited support in the Arrow ecosystem (e.g., IIRC Parquet still does not support it). So, we should probably wait a bit more before adding support for it in `datasets`\r\n\r\nOk many thanks, I was able to mitigate the problem by manually checking and converting all problematic fields now." ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "> The reason why I used the the glob_pattern_to_regex in the entire pattern is because otherwise I got an error for Windows local paths: a base_path like 'C:\\\\Users\\\\runneradmin... made the function string_to_dict raise re.error: incomplete escape \\U at position 2\r\n\r\nWhat is the expected inputs and outputs for the windows `base_path`\r\n\r\n> That issue was fixed once we pass the base_path as POSIX.\r\n\r\nI'm not sure what you meant by that, are there still changes needed?\r\n", "We took the liberty of continuing this PR to include it in today's patch release :)\r\nI hope you don't mind", "<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) | 0.007109 / 0.011353 (-0.004244) | 0.004209 / 0.011008 (-0.006799) | 0.097401 / 0.038508 (0.058892) | 0.079532 / 0.023109 (0.056423) | 0.341300 / 0.275898 (0.065402) | 0.402165 / 0.323480 (0.078685) | 0.005838 / 0.007986 (-0.002148) | 0.003310 / 0.004328 (-0.001018) | 0.072804 / 0.004250 (0.068553) | 0.059418 / 0.037052 (0.022366) | 0.339277 / 0.258489 (0.080788) | 0.418495 / 0.293841 (0.124654) | 0.035975 / 0.128546 (-0.092571) | 0.008101 / 0.075646 (-0.067546) | 0.339236 / 0.419271 (-0.080035) | 0.059326 / 0.043533 (0.015794) | 0.326880 / 0.255139 (0.071741) | 0.393614 / 0.283200 (0.110414) | 0.025830 / 0.141683 (-0.115852) | 1.657726 / 1.452155 (0.205571) | 1.817250 / 1.492716 (0.324534) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.256015 / 0.018006 (0.238008) | 0.482447 / 0.000490 (0.481957) | 0.012166 / 0.000200 (0.011966) | 0.000343 / 0.000054 (0.000288) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029898 / 0.037411 (-0.007514) | 0.088218 / 0.014526 (0.073692) | 0.102353 / 0.176557 (-0.074203) | 0.165863 / 0.737135 (-0.571272) | 0.100342 / 0.296338 (-0.195996) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.429362 / 0.215209 (0.214153) | 4.147327 / 2.077655 (2.069672) | 2.014653 / 1.504120 (0.510533) | 1.824394 / 1.541195 (0.283199) | 1.936408 / 1.468490 (0.467917) | 0.542960 / 4.584777 (-4.041817) | 3.917215 / 3.745712 (0.171503) | 3.714825 / 5.269862 (-1.555036) | 2.180279 / 4.565676 (-2.385398) | 0.057808 / 0.424275 (-0.366467) | 0.008426 / 0.007607 (0.000819) | 0.472372 / 0.226044 (0.246327) | 4.879656 / 2.268929 (2.610728) | 2.602729 / 55.444624 (-52.841896) | 2.142593 / 6.876477 (-4.733884) | 2.206070 / 2.142072 (0.063997) | 0.635591 / 4.805227 (-4.169636) | 0.140928 / 6.500664 (-6.359736) | 0.065119 / 0.075469 (-0.010350) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.455909 / 1.841788 (-0.385879) | 20.803592 / 8.074308 (12.729284) | 14.788713 / 10.191392 (4.597321) | 0.170546 / 0.680424 (-0.509878) | 0.021189 / 0.534201 (-0.513012) | 0.432368 / 0.579283 (-0.146915) | 0.444664 / 0.434364 (0.010300) | 0.517744 / 0.540337 (-0.022593) | 0.699265 / 1.386936 (-0.687671) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007592 / 0.011353 (-0.003760) | 0.004045 / 0.011008 (-0.006964) | 0.073434 / 0.038508 (0.034926) | 0.076962 / 0.023109 (0.053853) | 0.468873 / 0.275898 (0.192975) | 0.479968 / 0.323480 (0.156488) | 0.006270 / 0.007986 (-0.001716) | 0.003652 / 0.004328 (-0.000677) | 0.069893 / 0.004250 (0.065643) | 0.061902 / 0.037052 (0.024850) | 0.443379 / 0.258489 (0.184890) | 0.492627 / 0.293841 (0.198786) | 0.035967 / 0.128546 (-0.092579) | 0.009276 / 0.075646 (-0.066370) | 0.083060 / 0.419271 (-0.336212) | 0.050870 / 0.043533 (0.007337) | 0.438246 / 0.255139 (0.183107) | 0.472074 / 0.283200 (0.188874) | 0.023724 / 0.141683 (-0.117959) | 1.677178 / 1.452155 (0.225023) | 1.732273 / 1.492716 (0.239557) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.244693 / 0.018006 (0.226687) | 0.470067 / 0.000490 (0.469577) | 0.005574 / 0.000200 (0.005374) | 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) | 0.036242 / 0.037411 (-0.001169) | 0.099166 / 0.014526 (0.084641) | 0.116785 / 0.176557 (-0.059772) | 0.174986 / 0.737135 (-0.562149) | 0.118130 / 0.296338 (-0.178209) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.475907 / 0.215209 (0.260698) | 4.708125 / 2.077655 (2.630470) | 2.600855 / 1.504120 (1.096735) | 2.446498 / 1.541195 (0.905303) | 2.538786 / 1.468490 (1.070296) | 0.566787 / 4.584777 (-4.017990) | 4.066187 / 3.745712 (0.320475) | 3.743632 / 5.269862 (-1.526229) | 2.337737 / 4.565676 (-2.227939) | 0.068402 / 0.424275 (-0.355873) | 0.008674 / 0.007607 (0.001067) | 0.593428 / 0.226044 (0.367384) | 5.840687 / 2.268929 (3.571759) | 3.194937 / 55.444624 (-52.249688) | 2.899033 / 6.876477 (-3.977444) | 2.977870 / 2.142072 (0.835797) | 0.683673 / 4.805227 (-4.121554) | 0.154933 / 6.500664 (-6.345731) | 0.071619 / 0.075469 (-0.003850) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.501895 / 1.841788 (-0.339893) | 21.709792 / 8.074308 (13.635484) | 15.679556 / 10.191392 (5.488164) | 0.188028 / 0.680424 (-0.492396) | 0.022555 / 0.534201 (-0.511646) | 0.439840 / 0.579283 (-0.139443) | 0.452140 / 0.434364 (0.017776) | 0.526421 / 0.540337 (-0.013916) | 0.731692 / 1.386936 (-0.655244) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#02ecc84a2e2ed664f574ccbcab0e525a7377a01d \"CML watermark\")\n" ]

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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) | 0.007809 / 0.011353 (-0.003544) | 0.004573 / 0.011008 (-0.006435) | 0.101201 / 0.038508 (0.062693) | 0.089703 / 0.023109 (0.066594) | 0.416502 / 0.275898 (0.140604) | 0.463352 / 0.323480 (0.139872) | 0.006101 / 0.007986 (-0.001885) | 0.003783 / 0.004328 (-0.000545) | 0.076531 / 0.004250 (0.072281) | 0.064017 / 0.037052 (0.026964) | 0.422453 / 0.258489 (0.163964) | 0.485926 / 0.293841 (0.192085) | 0.036797 / 0.128546 (-0.091749) | 0.010172 / 0.075646 (-0.065474) | 0.344442 / 0.419271 (-0.074829) | 0.062240 / 0.043533 (0.018707) | 0.422685 / 0.255139 (0.167546) | 0.451457 / 0.283200 (0.168257) | 0.027831 / 0.141683 (-0.113852) | 1.737187 / 1.452155 (0.285033) | 1.847631 / 1.492716 (0.354915) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.270336 / 0.018006 (0.252330) | 0.500540 / 0.000490 (0.500050) | 0.017042 / 0.000200 (0.016842) | 0.000704 / 0.000054 (0.000650) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033450 / 0.037411 (-0.003962) | 0.100314 / 0.014526 (0.085788) | 0.117216 / 0.176557 (-0.059340) | 0.182352 / 0.737135 (-0.554784) | 0.114903 / 0.296338 (-0.181436) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.458562 / 0.215209 (0.243353) | 4.570492 / 2.077655 (2.492837) | 2.230286 / 1.504120 (0.726167) | 2.032229 / 1.541195 (0.491034) | 2.130431 / 1.468490 (0.661941) | 0.563254 / 4.584777 (-4.021523) | 4.108455 / 3.745712 (0.362743) | 3.994059 / 5.269862 (-1.275802) | 2.424589 / 4.565676 (-2.141087) | 0.067534 / 0.424275 (-0.356741) | 0.008774 / 0.007607 (0.001167) | 0.546356 / 0.226044 (0.320312) | 5.527772 / 2.268929 (3.258843) | 2.934410 / 55.444624 (-52.510215) | 2.536871 / 6.876477 (-4.339605) | 2.598704 / 2.142072 (0.456632) | 0.676721 / 4.805227 (-4.128506) | 0.155904 / 6.500664 (-6.344760) | 0.073274 / 0.075469 (-0.002195) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.559170 / 1.841788 (-0.282618) | 23.228524 / 8.074308 (15.154216) | 16.743246 / 10.191392 (6.551854) | 0.184113 / 0.680424 (-0.496310) | 0.021804 / 0.534201 (-0.512397) | 0.466158 / 0.579283 (-0.113125) | 0.539911 / 0.434364 (0.105547) | 0.544377 / 0.540337 (0.004040) | 0.765779 / 1.386936 (-0.621157) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008249 / 0.011353 (-0.003104) | 0.004734 / 0.011008 (-0.006275) | 0.077083 / 0.038508 (0.038575) | 0.096959 / 0.023109 (0.073850) | 0.497501 / 0.275898 (0.221603) | 0.530687 / 0.323480 (0.207207) | 0.006379 / 0.007986 (-0.001607) | 0.003899 / 0.004328 (-0.000430) | 0.076165 / 0.004250 (0.071915) | 0.069406 / 0.037052 (0.032354) | 0.515847 / 0.258489 (0.257358) | 0.540639 / 0.293841 (0.246798) | 0.038334 / 0.128546 (-0.090213) | 0.010112 / 0.075646 (-0.065534) | 0.084918 / 0.419271 (-0.334353) | 0.056866 / 0.043533 (0.013333) | 0.495555 / 0.255139 (0.240416) | 0.518988 / 0.283200 (0.235789) | 0.028556 / 0.141683 (-0.113127) | 1.799320 / 1.452155 (0.347165) | 1.874647 / 1.492716 (0.381931) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.264283 / 0.018006 (0.246277) | 0.510278 / 0.000490 (0.509788) | 0.015219 / 0.000200 (0.015019) | 0.000160 / 0.000054 (0.000105) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.038462 / 0.037411 (0.001051) | 0.115420 / 0.014526 (0.100894) | 0.124250 / 0.176557 (-0.052306) | 0.187724 / 0.737135 (-0.549411) | 0.126674 / 0.296338 (-0.169664) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.499345 / 0.215209 (0.284136) | 4.983924 / 2.077655 (2.906269) | 2.705099 / 1.504120 (1.200980) | 2.516344 / 1.541195 (0.975149) | 2.621103 / 1.468490 (1.152613) | 0.583254 / 4.584777 (-4.001523) | 4.231215 / 3.745712 (0.485503) | 4.028326 / 5.269862 (-1.241536) | 2.459171 / 4.565676 (-2.106505) | 0.069194 / 0.424275 (-0.355081) | 0.008850 / 0.007607 (0.001243) | 0.593878 / 0.226044 (0.367834) | 5.926478 / 2.268929 (3.657549) | 3.287435 / 55.444624 (-52.157189) | 2.902104 / 6.876477 (-3.974372) | 3.151307 / 2.142072 (1.009234) | 0.696922 / 4.805227 (-4.108306) | 0.161140 / 6.500664 (-6.339524) | 0.073728 / 0.075469 (-0.001741) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.636456 / 1.841788 (-0.205331) | 23.884606 / 8.074308 (15.810298) | 17.180875 / 10.191392 (6.989483) | 0.176782 / 0.680424 (-0.503642) | 0.023731 / 0.534201 (-0.510470) | 0.475191 / 0.579283 (-0.104092) | 0.506603 / 0.434364 (0.072239) | 0.571976 / 0.540337 (0.031638) | 0.826935 / 1.386936 (-0.560002) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2b19f6b30f49e09b0d1f0c4a38b10d76f35ac483 \"CML watermark\")\n" ]

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[ "The expression \"train+test\" concatenates the splits.\r\n\r\nThe individual splits as separate datasets can be obtained as follows:\r\n```python\r\ntrain_ds, test_ds = load_dataset(\"<dataset_name>\", split=[\"train\", \"test\"])\r\ntrain_10pct_ds, test_10pct_ds = load_dataset(\"<dataset_name>\", split=[\"train[:10%]\", \"test[:%10]\"])\r\n```" ]

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[ "Hi! Instead of processing a single example at a time, you should use the batched `map` for the best performance (with `num_proc=1`) - the fast tokenizers can process a batch's samples in parallel in that scenario.\r\n\r\nE.g., the following code in Colab takes an hour to complete:\r\n```python\r\n# !pip install datasets transformers\r\nfrom datasets import load_dataset\r\nfrom transformers import AutoTokenizer\r\ntokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")\r\ndataset = dataset.map(lambda ex: tokenizer(ex[\"text\"]), batched=True, remove_columns=[\"text\", \"meta\"])\r\n```", "Batched is far worse. A single batch of 1000 took hours and that was only 1%\r\n\r\n\r\nOn Thu, Oct 19, 2023, 2:26 PM Mario Šaško ***@***.***> wrote:\r\n\r\n> Hi! You should use the batched map for the best performance (with\r\n> num_proc=1) - the fast tokenizers can process a batch's samples in\r\n> parallel.\r\n>\r\n> E.g., the following code in Colab takes an hour to complete:\r\n>\r\n> # !pip install datasets transformersfrom datasets import load_datasetfrom transformers import AutoTokenizertokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")dataset = dataset.map(lambda ex: tokenizer(ex[\"text\"]), batched=True, remove_columns=[\"text\", \"meta\"])\r\n>\r\n> —\r\n> Reply to this email directly, view it on GitHub\r\n> <https://github.com/huggingface/datasets/issues/6319#issuecomment-1771503757>,\r\n> or unsubscribe\r\n> <https://github.com/notifications/unsubscribe-auth/ABDD3ZJHPSRVDEXFNMXR2N3YAFWFZAVCNFSM6AAAAAA6HDKPSCVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMYTONZRGUYDGNZVG4>\r\n> .\r\n> You are receiving this because you authored the thread.Message ID:\r\n> ***@***.***>\r\n>\r\n", "Can you please provide a self-contained reproducer?", "Which specific version of datasets are you using?\r\n\r\nWhat is the architecture of your colab setup? Ram? Cores? OS?\r\n\r\n\r\nOn Thu, Oct 19, 2023, 2:27 PM pensive introvert ***@***.***>\r\nwrote:\r\n\r\n> Batched is far worse. A single batch of 1000 took hours and that was only\r\n> 1%\r\n>\r\n>\r\n> On Thu, Oct 19, 2023, 2:26 PM Mario Šaško ***@***.***>\r\n> wrote:\r\n>\r\n>> Hi! You should use the batched map for the best performance (with\r\n>> num_proc=1) - the fast tokenizers can process a batch's samples in\r\n>> parallel.\r\n>>\r\n>> E.g., the following code in Colab takes an hour to complete:\r\n>>\r\n>> # !pip install datasets transformersfrom datasets import load_datasetfrom transformers import AutoTokenizertokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")dataset = dataset.map(lambda ex: tokenizer(ex[\"text\"]), batched=True, remove_columns=[\"text\", \"meta\"])\r\n>>\r\n>> —\r\n>> Reply to this email directly, view it on GitHub\r\n>> <https://github.com/huggingface/datasets/issues/6319#issuecomment-1771503757>,\r\n>> or unsubscribe\r\n>> <https://github.com/notifications/unsubscribe-auth/ABDD3ZJHPSRVDEXFNMXR2N3YAFWFZAVCNFSM6AAAAAA6HDKPSCVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMYTONZRGUYDGNZVG4>\r\n>> .\r\n>> You are receiving this because you authored the thread.Message ID:\r\n>> ***@***.***>\r\n>>\r\n>\r\n", "from functools import partial\r\nimport transformers\r\nfrom datasets import load_dataset, concatenate_datasets, load_from_disk\r\n\r\nmodel_name_or_path=\"/opt/data/data/daryl149/llama-2-7b-chat-hf\"\r\noutput_dir=\"/opt/data/data/LongLoRA/checkpoints\"\r\ncache_dir=\"/opt/data/data/LongLoRA/cache\"\r\nmodel_max_length=16384\r\n\r\nIGNORE_INDEX = -100\r\nDEFAULT_PAD_TOKEN = \"[PAD]\"\r\nDEFAULT_EOS_TOKEN = \"</s>\"\r\nDEFAULT_BOS_TOKEN = \"<s>\"\r\nDEFAULT_UNK_TOKEN = \"<unk>\"\r\n\r\n\r\ntokenizer = transformers.LlamaTokenizerFast.from_pretrained(\r\n model_name_or_path,\r\n cache_dir=cache_dir,\r\n model_max_length=model_max_length,\r\n padding_side=\"right\",\r\n use_fast=True,\r\n #use_fast=False\r\n)\r\n\r\nspecial_tokens_dict = dict()\r\nif tokenizer.pad_token is None:\r\n special_tokens_dict[\"pad_token\"] = DEFAULT_PAD_TOKEN\r\nif tokenizer.eos_token is None:\r\n special_tokens_dict[\"eos_token\"] = DEFAULT_EOS_TOKEN\r\nif tokenizer.bos_token is None:\r\n special_tokens_dict[\"bos_token\"] = DEFAULT_BOS_TOKEN\r\nif tokenizer.unk_token is None:\r\n special_tokens_dict[\"unk_token\"] = DEFAULT_UNK_TOKEN\r\n\r\ntokenizer.add_special_tokens(special_tokens_dict)\r\n\r\ndef tokenize_fn(tokenizer, example):\r\n context_length = tokenizer.model_max_length\r\n outputs = tokenizer(\r\n tokenizer.eos_token.join(example[\"text\"]),\r\n #truncation=False,\r\n truncation=True,\r\n return_tensors=\"pt\",\r\n #return_tensors=\"np\",\r\n pad_to_multiple_of=context_length,\r\n padding=True,\r\n )\r\n return {\"input_ids\": outputs[\"input_ids\"].view(-1, context_length)}\r\n\r\nfor idx in range(100):\r\n dataset = load_dataset(\"togethercomputer/RedPajama-Data-1T-Sample\",\r\ncache_dir=cache_dir, split=f'train[{idx}%:{idx+1}%]')\r\n dataset = dataset.map(partial(tokenize_fn, tokenizer), batched=False,\r\nnum_proc=16, remove_columns=[\"text\", \"meta\"])\r\n dataset.save_to_disk(training_args.cache_dir + f\"/training_data_{idx}\")\r\n\r\n\r\nOn Thu, Oct 19, 2023 at 2:30 PM Mario Šaško ***@***.***>\r\nwrote:\r\n\r\n> Can you please provide a self-contained reproducer?\r\n>\r\n> —\r\n> Reply to this email directly, view it on GitHub\r\n> <https://github.com/huggingface/datasets/issues/6319#issuecomment-1771509229>,\r\n> or unsubscribe\r\n> <https://github.com/notifications/unsubscribe-auth/ABDD3ZNBZ3BE7Q4EQZZK6MLYAFWURAVCNFSM6AAAAAA6HDKPSCVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMYTONZRGUYDSMRSHE>\r\n> .\r\n> You are receiving this because you authored the thread.Message ID:\r\n> ***@***.***>\r\n>\r\n", "I changed the tokenizer to one without \"Fast suffix, and something changed.\r\nThe fraction, although still slowed a lot at 80% was able to get over the\r\nfinish line of 100%\r\n\r\nI have to do more testng, see if the whole set can be processed\r\n\r\n\r\n\r\nOn Thu, Oct 19, 2023 at 3:03 PM pensive introvert <\r\n***@***.***> wrote:\r\n\r\n> from functools import partial\r\n> import transformers\r\n> from datasets import load_dataset, concatenate_datasets, load_from_disk\r\n>\r\n> model_name_or_path=\"/opt/data/data/daryl149/llama-2-7b-chat-hf\"\r\n> output_dir=\"/opt/data/data/LongLoRA/checkpoints\"\r\n> cache_dir=\"/opt/data/data/LongLoRA/cache\"\r\n> model_max_length=16384\r\n>\r\n> IGNORE_INDEX = -100\r\n> DEFAULT_PAD_TOKEN = \"[PAD]\"\r\n> DEFAULT_EOS_TOKEN = \"</s>\"\r\n> DEFAULT_BOS_TOKEN = \"<s>\"\r\n> DEFAULT_UNK_TOKEN = \"<unk>\"\r\n>\r\n>\r\n> tokenizer = transformers.LlamaTokenizerFast.from_pretrained(\r\n> model_name_or_path,\r\n> cache_dir=cache_dir,\r\n> model_max_length=model_max_length,\r\n> padding_side=\"right\",\r\n> use_fast=True,\r\n> #use_fast=False\r\n> )\r\n>\r\n> special_tokens_dict = dict()\r\n> if tokenizer.pad_token is None:\r\n> special_tokens_dict[\"pad_token\"] = DEFAULT_PAD_TOKEN\r\n> if tokenizer.eos_token is None:\r\n> special_tokens_dict[\"eos_token\"] = DEFAULT_EOS_TOKEN\r\n> if tokenizer.bos_token is None:\r\n> special_tokens_dict[\"bos_token\"] = DEFAULT_BOS_TOKEN\r\n> if tokenizer.unk_token is None:\r\n> special_tokens_dict[\"unk_token\"] = DEFAULT_UNK_TOKEN\r\n>\r\n> tokenizer.add_special_tokens(special_tokens_dict)\r\n>\r\n> def tokenize_fn(tokenizer, example):\r\n> context_length = tokenizer.model_max_length\r\n> outputs = tokenizer(\r\n> tokenizer.eos_token.join(example[\"text\"]),\r\n> #truncation=False,\r\n> truncation=True,\r\n> return_tensors=\"pt\",\r\n> #return_tensors=\"np\",\r\n> pad_to_multiple_of=context_length,\r\n> padding=True,\r\n> )\r\n> return {\"input_ids\": outputs[\"input_ids\"].view(-1, context_length)}\r\n>\r\n> for idx in range(100):\r\n> dataset = load_dataset(\"togethercomputer/RedPajama-Data-1T-Sample\",\r\n> cache_dir=cache_dir, split=f'train[{idx}%:{idx+1}%]')\r\n> dataset = dataset.map(partial(tokenize_fn, tokenizer), batched=False,\r\n> num_proc=16, remove_columns=[\"text\", \"meta\"])\r\n> dataset.save_to_disk(training_args.cache_dir + f\"/training_data_{idx}\")\r\n>\r\n>\r\n> On Thu, Oct 19, 2023 at 2:30 PM Mario Šaško ***@***.***>\r\n> wrote:\r\n>\r\n>> Can you please provide a self-contained reproducer?\r\n>>\r\n>> —\r\n>> Reply to this email directly, view it on GitHub\r\n>> <https://github.com/huggingface/datasets/issues/6319#issuecomment-1771509229>,\r\n>> or unsubscribe\r\n>> <https://github.com/notifications/unsubscribe-auth/ABDD3ZNBZ3BE7Q4EQZZK6MLYAFWURAVCNFSM6AAAAAA6HDKPSCVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMYTONZRGUYDSMRSHE>\r\n>> .\r\n>> You are receiving this because you authored the thread.Message ID:\r\n>> ***@***.***>\r\n>>\r\n>\r\n", "So, using LlamaTokenizerFast was the problem. Changing it to LlamaTokenizer\r\nfixed things,\r\n\r\nOn Thu, Oct 19, 2023 at 4:04 PM pensive introvert <\r\n***@***.***> wrote:\r\n\r\n> I changed the tokenizer to one without \"Fast suffix, and something\r\n> changed. The fraction, although still slowed a lot at 80% was able to get\r\n> over the finish line of 100%\r\n>\r\n> I have to do more testng, see if the whole set can be processed\r\n>\r\n>\r\n>\r\n> On Thu, Oct 19, 2023 at 3:03 PM pensive introvert <\r\n> ***@***.***> wrote:\r\n>\r\n>> from functools import partial\r\n>> import transformers\r\n>> from datasets import load_dataset, concatenate_datasets, load_from_disk\r\n>>\r\n>> model_name_or_path=\"/opt/data/data/daryl149/llama-2-7b-chat-hf\"\r\n>> output_dir=\"/opt/data/data/LongLoRA/checkpoints\"\r\n>> cache_dir=\"/opt/data/data/LongLoRA/cache\"\r\n>> model_max_length=16384\r\n>>\r\n>> IGNORE_INDEX = -100\r\n>> DEFAULT_PAD_TOKEN = \"[PAD]\"\r\n>> DEFAULT_EOS_TOKEN = \"</s>\"\r\n>> DEFAULT_BOS_TOKEN = \"<s>\"\r\n>> DEFAULT_UNK_TOKEN = \"<unk>\"\r\n>>\r\n>>\r\n>> tokenizer = transformers.LlamaTokenizerFast.from_pretrained(\r\n>> model_name_or_path,\r\n>> cache_dir=cache_dir,\r\n>> model_max_length=model_max_length,\r\n>> padding_side=\"right\",\r\n>> use_fast=True,\r\n>> #use_fast=False\r\n>> )\r\n>>\r\n>> special_tokens_dict = dict()\r\n>> if tokenizer.pad_token is None:\r\n>> special_tokens_dict[\"pad_token\"] = DEFAULT_PAD_TOKEN\r\n>> if tokenizer.eos_token is None:\r\n>> special_tokens_dict[\"eos_token\"] = DEFAULT_EOS_TOKEN\r\n>> if tokenizer.bos_token is None:\r\n>> special_tokens_dict[\"bos_token\"] = DEFAULT_BOS_TOKEN\r\n>> if tokenizer.unk_token is None:\r\n>> special_tokens_dict[\"unk_token\"] = DEFAULT_UNK_TOKEN\r\n>>\r\n>> tokenizer.add_special_tokens(special_tokens_dict)\r\n>>\r\n>> def tokenize_fn(tokenizer, example):\r\n>> context_length = tokenizer.model_max_length\r\n>> outputs = tokenizer(\r\n>> tokenizer.eos_token.join(example[\"text\"]),\r\n>> #truncation=False,\r\n>> truncation=True,\r\n>> return_tensors=\"pt\",\r\n>> #return_tensors=\"np\",\r\n>> pad_to_multiple_of=context_length,\r\n>> padding=True,\r\n>> )\r\n>> return {\"input_ids\": outputs[\"input_ids\"].view(-1, context_length)}\r\n>>\r\n>> for idx in range(100):\r\n>> dataset = load_dataset(\"togethercomputer/RedPajama-Data-1T-Sample\",\r\n>> cache_dir=cache_dir, split=f'train[{idx}%:{idx+1}%]')\r\n>> dataset = dataset.map(partial(tokenize_fn, tokenizer), batched=False,\r\n>> num_proc=16, remove_columns=[\"text\", \"meta\"])\r\n>> dataset.save_to_disk(training_args.cache_dir +\r\n>> f\"/training_data_{idx}\")\r\n>>\r\n>>\r\n>> On Thu, Oct 19, 2023 at 2:30 PM Mario Šaško ***@***.***>\r\n>> wrote:\r\n>>\r\n>>> Can you please provide a self-contained reproducer?\r\n>>>\r\n>>> —\r\n>>> Reply to this email directly, view it on GitHub\r\n>>> <https://github.com/huggingface/datasets/issues/6319#issuecomment-1771509229>,\r\n>>> or unsubscribe\r\n>>> <https://github.com/notifications/unsubscribe-auth/ABDD3ZNBZ3BE7Q4EQZZK6MLYAFWURAVCNFSM6AAAAAA6HDKPSCVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMYTONZRGUYDSMRSHE>\r\n>>> .\r\n>>> You are receiving this because you authored the thread.Message ID:\r\n>>> ***@***.***>\r\n>>>\r\n>>\r\n", "Indeed, the tokenizer is super slow. Perhaps @ArthurZucker knows the reason why.\r\n\r\n([This](https://colab.research.google.com/drive/1VgeurX-4Fl2X6aBQTwh_X4kuQKZ6K9L1?usp=sharing) simplified Colab can be used to reproduce the behavior)", "same issue here\r\nsample to reproduce: https://github.com/philschmid/document-ai-transformers/blob/main/training/donut_sroie.ipynb\r\nwith following map line\r\nhttps://github.com/philschmid/document-ai-transformers/blob/main/training/donut_sroie.ipynb\r\n\r\nIf I directly iterate over the dataset and call the mapping method, it is very fast\r\n```py\r\nfor sample in dataset:\r\n def preprocess_documents_for_donut(sample):\r\n```\r\n\r\nif i removed `.convert('RGB')` It can run to completion without getting stuck. I suspect it has something to do with the Image.\r\n\r\nIf I use batch, it's even slower.", "@ewfian \r\n\r\n> If I directly iterate over the dataset and call the mapping method, it is very fast\r\n\r\n`Dataset.map` must also convert the images into bytes to write them to an Arrow file (the write itself takes some time, too). \r\n\r\nYou can make the `map` faster by manually converting the images into an \"arrow-compatible\" representation. Otherwise, the Pillow defaults are used when saving an image, which seems particularly slow for the notebook's case.\r\n\r\n```python\r\ndef preprocess_documents_for_donut(sample):\r\n text = json.loads(sample[\"text\"])\r\n d_doc = task_start_token + json2token(text) + eos_token\r\n image = sample[\"image\"].convert('RGB')\r\n # convert image to bytes\r\n buffer = io.BytesIO()\r\n image.save(buffer, format=\"PNG\", compress_level=1)\r\n return {\"image\": {\"bytes\": buffer.getvalue()}, \"text\": d_doc}\r\n\r\nproc_dataset = dataset.map(preprocess_documents_for_donut, writer_batch_size=50)\r\n```", "The problem I had was to do with map using fork and copying locks from the\r\nparent process in acquired state. I ended up changing the context to use\r\nforkserver instead.\r\n\r\n\r\nOn Wed, Nov 29, 2023, 10:04 PM Mario Šaško ***@***.***> wrote:\r\n\r\n> @ewfian <https://github.com/ewfian>\r\n>\r\n> If I directly iterate over the dataset and call the mapping method, it is\r\n> very fast\r\n>\r\n> Dataset.map must also convert the images into bytes to write them to an\r\n> Arrow file (the write itself takes some time, too).\r\n>\r\n> You can make the map faster by manually converting the images into an\r\n> \"arrow-compatible\" representation. Otherwise, the Pillow defaults are used\r\n> when saving an image, which seems particularly slow for the notebook's case.\r\n>\r\n> def preprocess_documents_for_donut(sample):\r\n> text = json.loads(sample[\"text\"])\r\n> d_doc = task_start_token + json2token(text) + eos_token\r\n> image = sample[\"image\"].convert('RGB')\r\n> # convert image to bytes\r\n> buffer = io.BytesIO()\r\n> image.save(buffer, format=\"PNG\", compress_level=1)\r\n> return {\"image\": {\"bytes\": buffer.getvalue()}, \"text\": d_doc}\r\n> proc_dataset = dataset.map(preprocess_documents_for_donut, writer_batch_size=50)\r\n>\r\n> —\r\n> Reply to this email directly, view it on GitHub\r\n> <https://github.com/huggingface/datasets/issues/6319#issuecomment-1833033973>,\r\n> or unsubscribe\r\n> <https://github.com/notifications/unsubscribe-auth/ABDD3ZKKEKJVWBFH7QHLRJ3YG7ZUJAVCNFSM6AAAAAA6HDKPSCVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMYTQMZTGAZTGOJXGM>\r\n> .\r\n> You are receiving this because you authored the thread.Message ID:\r\n> ***@***.***>\r\n>\r\n" ]

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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) | 0.006827 / 0.011353 (-0.004526) | 0.004468 / 0.011008 (-0.006540) | 0.088687 / 0.038508 (0.050179) | 0.072560 / 0.023109 (0.049451) | 0.333421 / 0.275898 (0.057523) | 0.374977 / 0.323480 (0.051497) | 0.005829 / 0.007986 (-0.002156) | 0.003284 / 0.004328 (-0.001045) | 0.068929 / 0.004250 (0.064678) | 0.057212 / 0.037052 (0.020160) | 0.328911 / 0.258489 (0.070422) | 0.389107 / 0.293841 (0.095266) | 0.033518 / 0.128546 (-0.095029) | 0.009919 / 0.075646 (-0.065728) | 0.308100 / 0.419271 (-0.111171) | 0.059380 / 0.043533 (0.015847) | 0.345587 / 0.255139 (0.090448) | 0.353703 / 0.283200 (0.070503) | 0.026454 / 0.141683 (-0.115229) | 1.573309 / 1.452155 (0.121155) | 1.663812 / 1.492716 (0.171095) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.255081 / 0.018006 (0.237075) | 0.472613 / 0.000490 (0.472123) | 0.016120 / 0.000200 (0.015920) | 0.000383 / 0.000054 (0.000328) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028219 / 0.037411 (-0.009192) | 0.086600 / 0.014526 (0.072074) | 0.099484 / 0.176557 (-0.077073) | 0.154604 / 0.737135 (-0.582531) | 0.099168 / 0.296338 (-0.197171) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.421703 / 0.215209 (0.206494) | 4.188600 / 2.077655 (2.110945) | 2.037575 / 1.504120 (0.533456) | 1.843389 / 1.541195 (0.302194) | 1.912554 / 1.468490 (0.444064) | 0.517452 / 4.584777 (-4.067325) | 3.838002 / 3.745712 (0.092290) | 3.698899 / 5.269862 (-1.570963) | 2.175393 / 4.565676 (-2.390283) | 0.066059 / 0.424275 (-0.358216) | 0.008455 / 0.007607 (0.000848) | 0.506813 / 0.226044 (0.280768) | 4.826994 / 2.268929 (2.558066) | 2.544437 / 55.444624 (-52.900187) | 2.164938 / 6.876477 (-4.711539) | 2.171725 / 2.142072 (0.029652) | 0.603757 / 4.805227 (-4.201470) | 0.149113 / 6.500664 (-6.351551) | 0.065093 / 0.075469 (-0.010376) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.366887 / 1.841788 (-0.474901) | 20.508089 / 8.074308 (12.433780) | 14.836531 / 10.191392 (4.645139) | 0.167418 / 0.680424 (-0.513006) | 0.019707 / 0.534201 (-0.514494) | 0.409897 / 0.579283 (-0.169387) | 0.439412 / 0.434364 (0.005048) | 0.495784 / 0.540337 (-0.044553) | 0.685367 / 1.386936 (-0.701569) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007604 / 0.011353 (-0.003749) | 0.004368 / 0.011008 (-0.006640) | 0.072628 / 0.038508 (0.034120) | 0.084187 / 0.023109 (0.061077) | 0.461396 / 0.275898 (0.185498) | 0.481429 / 0.323480 (0.157949) | 0.005894 / 0.007986 (-0.002092) | 0.003472 / 0.004328 (-0.000857) | 0.068717 / 0.004250 (0.064466) | 0.061066 / 0.037052 (0.024014) | 0.464217 / 0.258489 (0.205728) | 0.498061 / 0.293841 (0.204220) | 0.035458 / 0.128546 (-0.093089) | 0.009474 / 0.075646 (-0.066173) | 0.079633 / 0.419271 (-0.339639) | 0.053966 / 0.043533 (0.010433) | 0.454911 / 0.255139 (0.199772) | 0.470837 / 0.283200 (0.187637) | 0.026358 / 0.141683 (-0.115325) | 1.665131 / 1.452155 (0.212976) | 1.730365 / 1.492716 (0.237648) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.234810 / 0.018006 (0.216804) | 0.453672 / 0.000490 (0.453183) | 0.004620 / 0.000200 (0.004420) | 0.000119 / 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) | 0.035310 / 0.037411 (-0.002101) | 0.100379 / 0.014526 (0.085853) | 0.118802 / 0.176557 (-0.057754) | 0.173853 / 0.737135 (-0.563282) | 0.115714 / 0.296338 (-0.180624) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.466797 / 0.215209 (0.251588) | 4.698324 / 2.077655 (2.620670) | 2.446897 / 1.504120 (0.942777) | 2.277346 / 1.541195 (0.736151) | 2.347211 / 1.468490 (0.878721) | 0.514377 / 4.584777 (-4.070400) | 3.931269 / 3.745712 (0.185557) | 3.573575 / 5.269862 (-1.696286) | 2.208122 / 4.565676 (-2.357554) | 0.061081 / 0.424275 (-0.363194) | 0.007803 / 0.007607 (0.000196) | 0.544376 / 0.226044 (0.318332) | 5.440003 / 2.268929 (3.171074) | 3.012559 / 55.444624 (-52.432065) | 2.617286 / 6.876477 (-4.259191) | 2.863978 / 2.142072 (0.721906) | 0.610024 / 4.805227 (-4.195203) | 0.133643 / 6.500664 (-6.367021) | 0.064766 / 0.075469 (-0.010703) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.465225 / 1.841788 (-0.376563) | 21.308351 / 8.074308 (13.234043) | 15.176634 / 10.191392 (4.985242) | 0.172701 / 0.680424 (-0.507723) | 0.020345 / 0.534201 (-0.513855) | 0.433923 / 0.579283 (-0.145360) | 0.450183 / 0.434364 (0.015819) | 0.514048 / 0.540337 (-0.026289) | 0.736302 / 1.386936 (-0.650634) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7f1a7d621fff3b08ace02643466097654a5e010f \"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) | 0.008305 / 0.011353 (-0.003048) | 0.006007 / 0.011008 (-0.005001) | 0.103521 / 0.038508 (0.065013) | 0.075776 / 0.023109 (0.052666) | 0.378888 / 0.275898 (0.102990) | 0.405245 / 0.323480 (0.081765) | 0.004596 / 0.007986 (-0.003390) | 0.003687 / 0.004328 (-0.000641) | 0.079043 / 0.004250 (0.074792) | 0.055895 / 0.037052 (0.018843) | 0.406565 / 0.258489 (0.148076) | 0.433869 / 0.293841 (0.140028) | 0.045321 / 0.128546 (-0.083226) | 0.014317 / 0.075646 (-0.061329) | 0.345312 / 0.419271 (-0.073960) | 0.064485 / 0.043533 (0.020953) | 0.381744 / 0.255139 (0.126605) | 0.401162 / 0.283200 (0.117962) | 0.035973 / 0.141683 (-0.105709) | 1.829616 / 1.452155 (0.377461) | 1.868487 / 1.492716 (0.375771) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.245432 / 0.018006 (0.227426) | 0.494249 / 0.000490 (0.493759) | 0.010878 / 0.000200 (0.010678) | 0.000492 / 0.000054 (0.000437) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032778 / 0.037411 (-0.004633) | 0.103418 / 0.014526 (0.088892) | 0.108010 / 0.176557 (-0.068547) | 0.176477 / 0.737135 (-0.560658) | 0.107732 / 0.296338 (-0.188606) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.572471 / 0.215209 (0.357262) | 5.647039 / 2.077655 (3.569384) | 2.385069 / 1.504120 (0.880949) | 2.048928 / 1.541195 (0.507733) | 2.108538 / 1.468490 (0.640048) | 0.861436 / 4.584777 (-3.723341) | 4.933452 / 3.745712 (1.187739) | 4.735219 / 5.269862 (-0.534642) | 2.926971 / 4.565676 (-1.638705) | 0.097687 / 0.424275 (-0.326588) | 0.008346 / 0.007607 (0.000739) | 0.677754 / 0.226044 (0.451709) | 6.798433 / 2.268929 (4.529504) | 3.129862 / 55.444624 (-52.314762) | 2.454033 / 6.876477 (-4.422444) | 2.464590 / 2.142072 (0.322517) | 1.034497 / 4.805227 (-3.770730) | 0.205753 / 6.500664 (-6.294911) | 0.076618 / 0.075469 (0.001149) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.617569 / 1.841788 (-0.224219) | 22.091489 / 8.074308 (14.017181) | 20.406312 / 10.191392 (10.214920) | 0.222012 / 0.680424 (-0.458411) | 0.027787 / 0.534201 (-0.506414) | 0.441669 / 0.579283 (-0.137615) | 0.564773 / 0.434364 (0.130409) | 0.510389 / 0.540337 (-0.029948) | 0.753672 / 1.386936 (-0.633264) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.011107 / 0.011353 (-0.000246) | 0.004973 / 0.011008 (-0.006035) | 0.078331 / 0.038508 (0.039823) | 0.083964 / 0.023109 (0.060855) | 0.518980 / 0.275898 (0.243082) | 0.528264 / 0.323480 (0.204784) | 0.007452 / 0.007986 (-0.000534) | 0.003931 / 0.004328 (-0.000397) | 0.079724 / 0.004250 (0.075474) | 0.061739 / 0.037052 (0.024686) | 0.517804 / 0.258489 (0.259315) | 0.582764 / 0.293841 (0.288923) | 0.049674 / 0.128546 (-0.078873) | 0.014540 / 0.075646 (-0.061106) | 0.093130 / 0.419271 (-0.326141) | 0.060647 / 0.043533 (0.017114) | 0.492628 / 0.255139 (0.237489) | 0.549761 / 0.283200 (0.266562) | 0.034313 / 0.141683 (-0.107369) | 1.824574 / 1.452155 (0.372419) | 2.013664 / 1.492716 (0.520947) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.231335 / 0.018006 (0.213329) | 0.521477 / 0.000490 (0.520987) | 0.011314 / 0.000200 (0.011114) | 0.000397 / 0.000054 (0.000343) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033303 / 0.037411 (-0.004108) | 0.098238 / 0.014526 (0.083712) | 0.119527 / 0.176557 (-0.057030) | 0.169163 / 0.737135 (-0.567972) | 0.114536 / 0.296338 (-0.181803) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.578401 / 0.215209 (0.363191) | 5.966438 / 2.077655 (3.888783) | 2.646370 / 1.504120 (1.142250) | 2.361833 / 1.541195 (0.820638) | 2.476573 / 1.468490 (1.008083) | 0.777411 / 4.584777 (-3.807366) | 4.811070 / 3.745712 (1.065357) | 4.314221 / 5.269862 (-0.955641) | 2.743317 / 4.565676 (-1.822359) | 0.110394 / 0.424275 (-0.313881) | 0.008333 / 0.007607 (0.000726) | 0.729588 / 0.226044 (0.503543) | 7.743226 / 2.268929 (5.474298) | 3.606294 / 55.444624 (-51.838330) | 2.838069 / 6.876477 (-4.038408) | 3.087494 / 2.142072 (0.945421) | 1.053341 / 4.805227 (-3.751886) | 0.205105 / 6.500664 (-6.295559) | 0.075204 / 0.075469 (-0.000265) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.561959 / 1.841788 (-0.279829) | 21.407849 / 8.074308 (13.333541) | 19.084263 / 10.191392 (8.892871) | 0.226129 / 0.680424 (-0.454295) | 0.029695 / 0.534201 (-0.504506) | 0.427035 / 0.579283 (-0.152248) | 0.565353 / 0.434364 (0.130989) | 0.526789 / 0.540337 (-0.013548) | 0.734820 / 1.386936 (-0.652116) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5b52536f4e39df3b98f7e0b03ee71b24c4fff49a \"CML watermark\")\n" ]

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[ "Thanks for reporting. We are investigating the issue.", "We have opened an issue in the corresponding Hub dataset: https://huggingface.co/datasets/sentiment140/discussions/3\r\n\r\nLet's continue the discussion there." ]

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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) | 0.008896 / 0.011353 (-0.002456) | 0.005811 / 0.011008 (-0.005197) | 0.108582 / 0.038508 (0.070074) | 0.096509 / 0.023109 (0.073399) | 0.481725 / 0.275898 (0.205827) | 0.534743 / 0.323480 (0.211263) | 0.005517 / 0.007986 (-0.002468) | 0.006479 / 0.004328 (0.002151) | 0.081313 / 0.004250 (0.077062) | 0.063578 / 0.037052 (0.026525) | 0.493977 / 0.258489 (0.235488) | 0.551897 / 0.293841 (0.258056) | 0.051835 / 0.128546 (-0.076711) | 0.014105 / 0.075646 (-0.061541) | 0.385866 / 0.419271 (-0.033405) | 0.069131 / 0.043533 (0.025598) | 0.484780 / 0.255139 (0.229641) | 0.493221 / 0.283200 (0.210021) | 0.039560 / 0.141683 (-0.102123) | 1.782331 / 1.452155 (0.330176) | 1.899193 / 1.492716 (0.406477) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.329978 / 0.018006 (0.311972) | 0.600839 / 0.000490 (0.600349) | 0.013187 / 0.000200 (0.012987) | 0.000499 / 0.000054 (0.000444) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031835 / 0.037411 (-0.005576) | 0.103740 / 0.014526 (0.089214) | 0.115875 / 0.176557 (-0.060681) | 0.189880 / 0.737135 (-0.547255) | 0.132614 / 0.296338 (-0.163725) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.596255 / 0.215209 (0.381046) | 5.967993 / 2.077655 (3.890339) | 2.612675 / 1.504120 (1.108555) | 2.251461 / 1.541195 (0.710266) | 2.308585 / 1.468490 (0.840095) | 0.816516 / 4.584777 (-3.768261) | 5.241791 / 3.745712 (1.496079) | 4.680745 / 5.269862 (-0.589117) | 2.997370 / 4.565676 (-1.568307) | 0.098632 / 0.424275 (-0.325643) | 0.010912 / 0.007607 (0.003305) | 0.659092 / 0.226044 (0.433047) | 6.825562 / 2.268929 (4.556634) | 3.323844 / 55.444624 (-52.120780) | 2.796203 / 6.876477 (-4.080274) | 2.946994 / 2.142072 (0.804922) | 1.002814 / 4.805227 (-3.802413) | 0.202613 / 6.500664 (-6.298051) | 0.072011 / 0.075469 (-0.003459) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.613873 / 1.841788 (-0.227914) | 24.500990 / 8.074308 (16.426682) | 21.941599 / 10.191392 (11.750207) | 0.214450 / 0.680424 (-0.465974) | 0.031227 / 0.534201 (-0.502974) | 0.498297 / 0.579283 (-0.080986) | 0.597460 / 0.434364 (0.163096) | 0.558152 / 0.540337 (0.017815) | 0.789693 / 1.386936 (-0.597243) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.011299 / 0.011353 (-0.000053) | 0.005103 / 0.011008 (-0.005905) | 0.083161 / 0.038508 (0.044653) | 0.094201 / 0.023109 (0.071092) | 0.560457 / 0.275898 (0.284559) | 0.590459 / 0.323480 (0.266980) | 0.007059 / 0.007986 (-0.000926) | 0.004418 / 0.004328 (0.000090) | 0.081343 / 0.004250 (0.077093) | 0.067069 / 0.037052 (0.030016) | 0.538137 / 0.258489 (0.279648) | 0.600416 / 0.293841 (0.306575) | 0.049046 / 0.128546 (-0.079500) | 0.014299 / 0.075646 (-0.061347) | 0.093631 / 0.419271 (-0.325641) | 0.062536 / 0.043533 (0.019003) | 0.557238 / 0.255139 (0.302099) | 0.571050 / 0.283200 (0.287850) | 0.035881 / 0.141683 (-0.105802) | 1.918487 / 1.452155 (0.466332) | 2.013979 / 1.492716 (0.521263) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.400995 / 0.018006 (0.382989) | 0.634898 / 0.000490 (0.634408) | 0.041809 / 0.000200 (0.041609) | 0.000279 / 0.000054 (0.000224) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034160 / 0.037411 (-0.003251) | 0.109996 / 0.014526 (0.095470) | 0.124335 / 0.176557 (-0.052222) | 0.188100 / 0.737135 (-0.549035) | 0.135897 / 0.296338 (-0.160442) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.639751 / 0.215209 (0.424542) | 6.403312 / 2.077655 (4.325657) | 3.146453 / 1.504120 (1.642333) | 2.840358 / 1.541195 (1.299164) | 2.908667 / 1.468490 (1.440177) | 0.818767 / 4.584777 (-3.766010) | 5.416939 / 3.745712 (1.671227) | 4.853498 / 5.269862 (-0.416364) | 3.023526 / 4.565676 (-1.542150) | 0.110850 / 0.424275 (-0.313425) | 0.013103 / 0.007607 (0.005496) | 0.799720 / 0.226044 (0.573676) | 7.837704 / 2.268929 (5.568775) | 4.016526 / 55.444624 (-51.428099) | 3.338965 / 6.876477 (-3.537512) | 3.715721 / 2.142072 (1.573648) | 1.088340 / 4.805227 (-3.716887) | 0.213610 / 6.500664 (-6.287054) | 0.079244 / 0.075469 (0.003775) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.833175 / 1.841788 (-0.008612) | 25.307218 / 8.074308 (17.232910) | 23.716075 / 10.191392 (13.524683) | 0.259114 / 0.680424 (-0.421310) | 0.035171 / 0.534201 (-0.499029) | 0.530128 / 0.579283 (-0.049155) | 0.651484 / 0.434364 (0.217120) | 0.589414 / 0.540337 (0.049077) | 0.862691 / 1.386936 (-0.524245) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1bdfba93b8a739b9d885b8fb1909d47ff689bbc2 \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "Me too, I thought the same... quite surprised... :open_mouth: ", "<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) | 0.006929 / 0.011353 (-0.004423) | 0.004345 / 0.011008 (-0.006663) | 0.085522 / 0.038508 (0.047014) | 0.083380 / 0.023109 (0.060271) | 0.310332 / 0.275898 (0.034434) | 0.350525 / 0.323480 (0.027045) | 0.004367 / 0.007986 (-0.003618) | 0.005503 / 0.004328 (0.001175) | 0.066311 / 0.004250 (0.062061) | 0.059545 / 0.037052 (0.022492) | 0.314090 / 0.258489 (0.055601) | 0.366661 / 0.293841 (0.072821) | 0.031581 / 0.128546 (-0.096965) | 0.008852 / 0.075646 (-0.066794) | 0.289312 / 0.419271 (-0.129960) | 0.052960 / 0.043533 (0.009427) | 0.308134 / 0.255139 (0.052995) | 0.330342 / 0.283200 (0.047142) | 0.026157 / 0.141683 (-0.115526) | 1.488463 / 1.452155 (0.036308) | 1.561441 / 1.492716 (0.068725) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.327735 / 0.018006 (0.309729) | 0.568162 / 0.000490 (0.567672) | 0.012097 / 0.000200 (0.011897) | 0.000438 / 0.000054 (0.000383) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029503 / 0.037411 (-0.007909) | 0.084327 / 0.014526 (0.069801) | 0.102065 / 0.176557 (-0.074492) | 0.157392 / 0.737135 (-0.579744) | 0.101428 / 0.296338 (-0.194910) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.386767 / 0.215209 (0.171558) | 3.870757 / 2.077655 (1.793102) | 1.870048 / 1.504120 (0.365928) | 1.678221 / 1.541195 (0.137026) | 1.799423 / 1.468490 (0.330933) | 0.477718 / 4.584777 (-4.107059) | 3.618351 / 3.745712 (-0.127361) | 3.577921 / 5.269862 (-1.691941) | 2.146217 / 4.565676 (-2.419459) | 0.056290 / 0.424275 (-0.367985) | 0.007378 / 0.007607 (-0.000229) | 0.460678 / 0.226044 (0.234633) | 4.606243 / 2.268929 (2.337314) | 2.303460 / 55.444624 (-53.141164) | 1.982662 / 6.876477 (-4.893814) | 2.103891 / 2.142072 (-0.038182) | 0.570700 / 4.805227 (-4.234527) | 0.131747 / 6.500664 (-6.368918) | 0.060915 / 0.075469 (-0.014554) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.286364 / 1.841788 (-0.555424) | 20.106330 / 8.074308 (12.032022) | 14.780833 / 10.191392 (4.589441) | 0.164301 / 0.680424 (-0.516123) | 0.018730 / 0.534201 (-0.515471) | 0.398530 / 0.579283 (-0.180754) | 0.418084 / 0.434364 (-0.016280) | 0.468735 / 0.540337 (-0.071602) | 0.690122 / 1.386936 (-0.696814) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007262 / 0.011353 (-0.004091) | 0.004228 / 0.011008 (-0.006780) | 0.065866 / 0.038508 (0.027358) | 0.096151 / 0.023109 (0.073042) | 0.409352 / 0.275898 (0.133454) | 0.441234 / 0.323480 (0.117754) | 0.005946 / 0.007986 (-0.002039) | 0.003630 / 0.004328 (-0.000698) | 0.066271 / 0.004250 (0.062020) | 0.061567 / 0.037052 (0.024515) | 0.409097 / 0.258489 (0.150608) | 0.447675 / 0.293841 (0.153834) | 0.032804 / 0.128546 (-0.095743) | 0.008793 / 0.075646 (-0.066853) | 0.070790 / 0.419271 (-0.348482) | 0.048650 / 0.043533 (0.005117) | 0.411021 / 0.255139 (0.155882) | 0.421398 / 0.283200 (0.138198) | 0.025305 / 0.141683 (-0.116378) | 1.494826 / 1.452155 (0.042671) | 1.580441 / 1.492716 (0.087724) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.321871 / 0.018006 (0.303865) | 0.526471 / 0.000490 (0.525982) | 0.006913 / 0.000200 (0.006713) | 0.000108 / 0.000054 (0.000054) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034889 / 0.037411 (-0.002522) | 0.096096 / 0.014526 (0.081570) | 0.111920 / 0.176557 (-0.064636) | 0.166103 / 0.737135 (-0.571032) | 0.111162 / 0.296338 (-0.185176) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.428037 / 0.215209 (0.212828) | 4.294150 / 2.077655 (2.216495) | 2.270331 / 1.504120 (0.766211) | 2.108235 / 1.541195 (0.567041) | 2.242560 / 1.468490 (0.774070) | 0.489941 / 4.584777 (-4.094836) | 3.688111 / 3.745712 (-0.057601) | 3.450180 / 5.269862 (-1.819681) | 2.175106 / 4.565676 (-2.390570) | 0.057657 / 0.424275 (-0.366619) | 0.007478 / 0.007607 (-0.000130) | 0.505242 / 0.226044 (0.279198) | 5.047817 / 2.268929 (2.778888) | 2.724125 / 55.444624 (-52.720500) | 2.419765 / 6.876477 (-4.456711) | 2.723231 / 2.142072 (0.581159) | 0.602382 / 4.805227 (-4.202846) | 0.132362 / 6.500664 (-6.368302) | 0.060600 / 0.075469 (-0.014869) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.363356 / 1.841788 (-0.478431) | 21.446474 / 8.074308 (13.372165) | 15.074732 / 10.191392 (4.883340) | 0.191837 / 0.680424 (-0.488587) | 0.020565 / 0.534201 (-0.513636) | 0.396692 / 0.579283 (-0.182591) | 0.432390 / 0.434364 (-0.001974) | 0.491747 / 0.540337 (-0.048591) | 0.699203 / 1.386936 (-0.687733) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c3a8a87c841426495d3a7ed1863c26660a6a551f \"CML watermark\")\n" ]

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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) | 0.006760 / 0.011353 (-0.004593) | 0.003918 / 0.011008 (-0.007091) | 0.084016 / 0.038508 (0.045508) | 0.069927 / 0.023109 (0.046818) | 0.307898 / 0.275898 (0.032000) | 0.337453 / 0.323480 (0.013973) | 0.004132 / 0.007986 (-0.003854) | 0.003248 / 0.004328 (-0.001081) | 0.064526 / 0.004250 (0.060275) | 0.056424 / 0.037052 (0.019371) | 0.316313 / 0.258489 (0.057824) | 0.356302 / 0.293841 (0.062461) | 0.030634 / 0.128546 (-0.097912) | 0.008467 / 0.075646 (-0.067180) | 0.286676 / 0.419271 (-0.132595) | 0.051813 / 0.043533 (0.008280) | 0.309874 / 0.255139 (0.054735) | 0.332513 / 0.283200 (0.049313) | 0.023919 / 0.141683 (-0.117764) | 1.509033 / 1.452155 (0.056878) | 1.549636 / 1.492716 (0.056920) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.221464 / 0.018006 (0.203458) | 0.447873 / 0.000490 (0.447384) | 0.002408 / 0.000200 (0.002208) | 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) | 0.027634 / 0.037411 (-0.009777) | 0.081802 / 0.014526 (0.067276) | 0.781489 / 0.176557 (0.604933) | 0.165184 / 0.737135 (-0.571951) | 0.121526 / 0.296338 (-0.174813) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.408215 / 0.215209 (0.193006) | 4.091192 / 2.077655 (2.013538) | 2.062608 / 1.504120 (0.558488) | 1.895747 / 1.541195 (0.354552) | 1.873682 / 1.468490 (0.405192) | 0.484184 / 4.584777 (-4.100593) | 3.469096 / 3.745712 (-0.276616) | 3.365325 / 5.269862 (-1.904537) | 2.000333 / 4.565676 (-2.565343) | 0.056661 / 0.424275 (-0.367614) | 0.007100 / 0.007607 (-0.000507) | 0.478587 / 0.226044 (0.252542) | 4.768703 / 2.268929 (2.499774) | 2.472432 / 55.444624 (-52.972192) | 2.133611 / 6.876477 (-4.742865) | 2.154296 / 2.142072 (0.012223) | 0.582293 / 4.805227 (-4.222934) | 0.131932 / 6.500664 (-6.368732) | 0.060259 / 0.075469 (-0.015211) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.259167 / 1.841788 (-0.582620) | 18.465604 / 8.074308 (10.391296) | 14.024528 / 10.191392 (3.833136) | 0.162320 / 0.680424 (-0.518104) | 0.018144 / 0.534201 (-0.516057) | 0.389931 / 0.579283 (-0.189352) | 0.396456 / 0.434364 (-0.037908) | 0.454734 / 0.540337 (-0.085603) | 0.636406 / 1.386936 (-0.750530) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006565 / 0.011353 (-0.004788) | 0.004008 / 0.011008 (-0.007000) | 0.064526 / 0.038508 (0.026018) | 0.071963 / 0.023109 (0.048854) | 0.415456 / 0.275898 (0.139557) | 0.441199 / 0.323480 (0.117719) | 0.005619 / 0.007986 (-0.002366) | 0.003261 / 0.004328 (-0.001067) | 0.064817 / 0.004250 (0.060567) | 0.055349 / 0.037052 (0.018296) | 0.425172 / 0.258489 (0.166683) | 0.452629 / 0.293841 (0.158788) | 0.031676 / 0.128546 (-0.096870) | 0.008432 / 0.075646 (-0.067214) | 0.071752 / 0.419271 (-0.347519) | 0.047176 / 0.043533 (0.003643) | 0.408641 / 0.255139 (0.153502) | 0.428579 / 0.283200 (0.145380) | 0.021548 / 0.141683 (-0.120135) | 1.495153 / 1.452155 (0.042999) | 1.557933 / 1.492716 (0.065217) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.212749 / 0.018006 (0.194743) | 0.441263 / 0.000490 (0.440773) | 0.005831 / 0.000200 (0.005631) | 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) | 0.031844 / 0.037411 (-0.005567) | 0.091590 / 0.014526 (0.077064) | 0.102859 / 0.176557 (-0.073697) | 0.155859 / 0.737135 (-0.581276) | 0.104717 / 0.296338 (-0.191622) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.425924 / 0.215209 (0.210715) | 4.292829 / 2.077655 (2.215174) | 2.314350 / 1.504120 (0.810230) | 2.163087 / 1.541195 (0.621892) | 2.217310 / 1.468490 (0.748820) | 0.490889 / 4.584777 (-4.093887) | 3.498287 / 3.745712 (-0.247425) | 3.224980 / 5.269862 (-2.044881) | 1.987739 / 4.565676 (-2.577938) | 0.057486 / 0.424275 (-0.366790) | 0.007199 / 0.007607 (-0.000408) | 0.501194 / 0.226044 (0.275149) | 5.015202 / 2.268929 (2.746273) | 2.816307 / 55.444624 (-52.628318) | 2.474593 / 6.876477 (-4.401884) | 2.649510 / 2.142072 (0.507437) | 0.597167 / 4.805227 (-4.208060) | 0.131199 / 6.500664 (-6.369465) | 0.059532 / 0.075469 (-0.015938) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.384053 / 1.841788 (-0.457734) | 18.964201 / 8.074308 (10.889893) | 14.336209 / 10.191392 (4.144817) | 0.187522 / 0.680424 (-0.492902) | 0.020201 / 0.534201 (-0.514000) | 0.394778 / 0.579283 (-0.184505) | 0.408393 / 0.434364 (-0.025971) | 0.470965 / 0.540337 (-0.069373) | 0.667974 / 1.386936 (-0.718962) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3b3333d790800ddaa3bf386ee71dc800258c921c \"CML watermark\")\n" ]

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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) | 0.006209 / 0.011353 (-0.005144) | 0.003708 / 0.011008 (-0.007300) | 0.080435 / 0.038508 (0.041926) | 0.060105 / 0.023109 (0.036995) | 0.392962 / 0.275898 (0.117064) | 0.429381 / 0.323480 (0.105902) | 0.003596 / 0.007986 (-0.004390) | 0.003849 / 0.004328 (-0.000480) | 0.062377 / 0.004250 (0.058127) | 0.048718 / 0.037052 (0.011666) | 0.400906 / 0.258489 (0.142417) | 0.440335 / 0.293841 (0.146494) | 0.027807 / 0.128546 (-0.100739) | 0.008066 / 0.075646 (-0.067580) | 0.262542 / 0.419271 (-0.156730) | 0.045513 / 0.043533 (0.001980) | 0.399608 / 0.255139 (0.144469) | 0.418007 / 0.283200 (0.134807) | 0.023475 / 0.141683 (-0.118208) | 1.476563 / 1.452155 (0.024409) | 1.528898 / 1.492716 (0.036182) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.223798 / 0.018006 (0.205792) | 0.430526 / 0.000490 (0.430036) | 0.009232 / 0.000200 (0.009032) | 0.000082 / 0.000054 (0.000028) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024921 / 0.037411 (-0.012490) | 0.077692 / 0.014526 (0.063166) | 0.085382 / 0.176557 (-0.091174) | 0.146220 / 0.737135 (-0.590915) | 0.086396 / 0.296338 (-0.209943) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.439986 / 0.215209 (0.224777) | 4.384552 / 2.077655 (2.306897) | 2.373697 / 1.504120 (0.869577) | 2.176138 / 1.541195 (0.634943) | 2.225914 / 1.468490 (0.757424) | 0.505776 / 4.584777 (-4.079001) | 3.053744 / 3.745712 (-0.691968) | 3.080443 / 5.269862 (-2.189419) | 1.904392 / 4.565676 (-2.661285) | 0.058112 / 0.424275 (-0.366163) | 0.006631 / 0.007607 (-0.000976) | 0.503409 / 0.226044 (0.277365) | 5.053375 / 2.268929 (2.784447) | 2.789963 / 55.444624 (-52.654661) | 2.452659 / 6.876477 (-4.423818) | 2.512353 / 2.142072 (0.370280) | 0.590095 / 4.805227 (-4.215132) | 0.126267 / 6.500664 (-6.374397) | 0.061246 / 0.075469 (-0.014223) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.249884 / 1.841788 (-0.591903) | 17.684730 / 8.074308 (9.610422) | 13.967467 / 10.191392 (3.776075) | 0.144202 / 0.680424 (-0.536222) | 0.017004 / 0.534201 (-0.517197) | 0.333634 / 0.579283 (-0.245649) | 0.387251 / 0.434364 (-0.047113) | 0.390189 / 0.540337 (-0.150148) | 0.535662 / 1.386936 (-0.851274) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006379 / 0.011353 (-0.004974) | 0.003681 / 0.011008 (-0.007327) | 0.063005 / 0.038508 (0.024497) | 0.064221 / 0.023109 (0.041112) | 0.446074 / 0.275898 (0.170176) | 0.471997 / 0.323480 (0.148517) | 0.005074 / 0.007986 (-0.002911) | 0.002945 / 0.004328 (-0.001383) | 0.063305 / 0.004250 (0.059054) | 0.050608 / 0.037052 (0.013556) | 0.443260 / 0.258489 (0.184771) | 0.478497 / 0.293841 (0.184656) | 0.028980 / 0.128546 (-0.099566) | 0.008145 / 0.075646 (-0.067502) | 0.068412 / 0.419271 (-0.350859) | 0.041552 / 0.043533 (-0.001980) | 0.436649 / 0.255139 (0.181510) | 0.462397 / 0.283200 (0.179198) | 0.019929 / 0.141683 (-0.121753) | 1.530248 / 1.452155 (0.078093) | 1.611117 / 1.492716 (0.118401) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.232894 / 0.018006 (0.214888) | 0.421451 / 0.000490 (0.420961) | 0.003984 / 0.000200 (0.003784) | 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) | 0.027776 / 0.037411 (-0.009635) | 0.081632 / 0.014526 (0.067106) | 0.094031 / 0.176557 (-0.082526) | 0.147930 / 0.737135 (-0.589206) | 0.094226 / 0.296338 (-0.202112) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.471722 / 0.215209 (0.256513) | 4.713241 / 2.077655 (2.635587) | 2.662660 / 1.504120 (1.158540) | 2.490778 / 1.541195 (0.949583) | 2.555786 / 1.468490 (1.087296) | 0.512209 / 4.584777 (-4.072568) | 3.210612 / 3.745712 (-0.535100) | 2.863346 / 5.269862 (-2.406516) | 1.884664 / 4.565676 (-2.681012) | 0.058514 / 0.424275 (-0.365761) | 0.006473 / 0.007607 (-0.001134) | 0.543279 / 0.226044 (0.317235) | 5.441485 / 2.268929 (3.172556) | 3.145398 / 55.444624 (-52.299226) | 2.749603 / 6.876477 (-4.126874) | 2.925738 / 2.142072 (0.783666) | 0.598725 / 4.805227 (-4.206502) | 0.125616 / 6.500664 (-6.375048) | 0.061314 / 0.075469 (-0.014155) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.384270 / 1.841788 (-0.457518) | 18.307618 / 8.074308 (10.233310) | 14.635768 / 10.191392 (4.444376) | 0.148787 / 0.680424 (-0.531637) | 0.018191 / 0.534201 (-0.516010) | 0.333166 / 0.579283 (-0.246117) | 0.405116 / 0.434364 (-0.029247) | 0.392798 / 0.540337 (-0.147540) | 0.582299 / 1.386936 (-0.804637) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7004f0f2ec59832fe53af033efdca10d00377760 \"CML watermark\")\n" ]

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[ "Thanks for reporting! We've spotted the bugs with the `array.values` handling and are fixing them in https://github.com/huggingface/datasets/pull/6283 (should be part of the next release).", "> Thanks for reporting! We've spotted the bugs with the `array.values` handling and are fixing them in #6283 (should be part of the next release).\r\n\r\ni encounter another exception while cast_column to type `Sequence(feature={\"points\": Array2D(shape=(-1, 2), dtype=\"int64\"), \"label\": ClassLabel(num_classes=num_classes, names=names)})`\r\n\r\nwhile my data like this: '{\"points\": [[0.6,0.6], [0.7,0.7], [0.8,0.8]], \"label\": \"A1\"}'\r\n\r\nhere is the backtrace info:\r\n\r\n```\r\n out = func(dataset, *args, **kwargs)\r\n File \"/home/protoss.gao/.local/lib/python3.9/site-packages/datasets/arrow_dataset.py\", line 2110, in cast_column\r\n return self.cast(features)\r\n File \"/home/protoss.gao/.local/lib/python3.9/site-packages/datasets/arrow_dataset.py\", line 2055, in cast\r\n dataset = dataset.map(\r\n File \"/home/protoss.gao/.local/lib/python3.9/site-packages/datasets/arrow_dataset.py\", line 592, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n File \"/home/protoss.gao/.local/lib/python3.9/site-packages/datasets/arrow_dataset.py\", line 557, in wrapper\r\n out: Union[\"Dataset\", \"DatasetDict\"] = func(self, *args, **kwargs)\r\n File \"/home/protoss.gao/.local/lib/python3.9/site-packages/datasets/arrow_dataset.py\", line 3097, in map\r\n for rank, done, content in Dataset._map_single(**dataset_kwargs):\r\n File \"/home/protoss.gao/.local/lib/python3.9/site-packages/datasets/arrow_dataset.py\", line 3474, in _map_single\r\n batch = apply_function_on_filtered_inputs(\r\n File \"/home/protoss.gao/.local/lib/python3.9/site-packages/datasets/arrow_dataset.py\", line 3353, in apply_function_on_filtered_inputs\r\n processed_inputs = function(*fn_args, *additional_args, **fn_kwargs)\r\n File \"/home/protoss.gao/.local/lib/python3.9/site-packages/datasets/table.py\", line 2329, in table_cast\r\n return cast_table_to_schema(table, schema)\r\n File \"/home/protoss.gao/.local/lib/python3.9/site-packages/datasets/table.py\", line 2288, in cast_table_to_schema\r\n arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]\r\n File \"/home/protoss.gao/.local/lib/python3.9/site-packages/datasets/table.py\", line 2288, in <listcomp>\r\n arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]\r\n File \"/home/protoss.gao/.local/lib/python3.9/site-packages/datasets/table.py\", line 1831, in wrapper\r\n return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])\r\n File \"/home/protoss.gao/.local/lib/python3.9/site-packages/datasets/table.py\", line 1831, in <listcomp>\r\n return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])\r\n File \"/home/protoss.gao/.local/lib/python3.9/site-packages/datasets/table.py\", line 2073, in cast_array_to_feature\r\n arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]\r\n File \"/home/protoss.gao/.local/lib/python3.9/site-packages/datasets/table.py\", line 2073, in <listcomp>\r\n arrays = [_c(array.field(name), subfeature) for name, subfeature in feature.items()]\r\n File \"/home/protoss.gao/.local/lib/python3.9/site-packages/datasets/table.py\", line 1833, in wrapper\r\n return func(array, *args, **kwargs)\r\n File \"/home/protoss.gao/.local/lib/python3.9/site-packages/datasets/table.py\", line 2095, in cast_array_to_feature\r\n casted_values = _c(array.values, feature.feature)\r\n File \"/home/protoss.gao/.local/lib/python3.9/site-packages/datasets/table.py\", line 1833, in wrapper\r\n return func(array, *args, **kwargs)\r\n File \"/home/protoss.gao/.local/lib/python3.9/site-packages/datasets/table.py\", line 2144, in cast_array_to_feature\r\n return array_cast(array, feature(), allow_number_to_str=allow_number_to_str)\r\n File \"/home/protoss.gao/.local/lib/python3.9/site-packages/datasets/table.py\", line 1833, in wrapper\r\n return func(array, *args, **kwargs)\r\n File \"/home/protoss.gao/.local/lib/python3.9/site-packages/datasets/table.py\", line 1967, in array_cast\r\n return pa_type.wrap_array(array)\r\n File \"pyarrow/types.pxi\", line 1369, in pyarrow.lib.BaseExtensionType.wrap_array\r\nTypeError: Incompatible storage type for extension<arrow.py_extension_type<Array2DExtensionType>>: expected list<item: list<item: double>>, got list<item: double>\r\n```\r\nand i print(array) in datasets/table.py:1967 indeed get 2D list. is that same issue in #6283 ?\r\n\r\nbesides this, hugging face datasets seems don't naturally support multi-labels which means `Sequence(ClassLabel)` illegal if data is [\"label1\", \"label2\"]. so i have to define a class derived from `ClassLabel`, like this:\r\n\r\n```\r\nclass AisClassLabels(ClassLabel):\r\n def encode_example(self, example_data):\r\n if self.num_classes is None:\r\n raise ValueError(\r\n \"Trying to use ClassLabel feature with undefined number of class. \"\r\n \"Please set ClassLabel.names or num_classes.\"\r\n )\r\n if not isinstance(example_data, list):\r\n example_data = [example_data]\r\n\r\n for i in range(len(example_data)):\r\n if isinstance(example_data[i], str):\r\n example_data[i] = self.str2int(example_data[i])\r\n if not -1 <= example_data[i] < self.num_classes:\r\n raise ValueError(f\"Class label {example_data:d} greater than configured num_classes {self.num_classes}\")\r\n return example_data\r\n```\r\nand it works well in my case. but is there any recommend way to implement multi-labels?", "`Incompatible storage type for extension<arrow.py_extension_type<Array2DExtensionType>>: expected list<item: list<item: double>>, got list<item: double>`\r\nif i change `Array2D(shape=(-1, 2), dtype=\"int64\")` to `Sequence(Value(\"int64\"))` , every thing goes well. but my data is 2D int list", "i test Sequence(ClassLabel) is ok if one column is label list. but it is not ok in nested column such as `Sequence(feature= {\"points\": Sequence(Value(\"int32\")), \"label\": Sequence(ClassLabel(num_classes....)))`. in this case i need override ClassLabels. encode_example as i given above." ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6310). All of your documentation changes will be reflected on that endpoint.", "> Thanks for the change !\r\n> \r\n> Since `return` in python often refers to what is actually returned by the function (here `load_dataset`), I think we can use another word for the parameter. Maybe name it `with_file_names`?\r\n> \r\n> cc @mariosasko in case you have an opinion\r\n\r\nI changed the argument name to your suggestion, I agree that it should be less confusing :)", "> Thanks! I've left some comments.\r\n> \r\n> @lhoestq WDYT about returning a data file's name (the last part) instead of the full path? This way we could have the same values in the streaming and the non-streaming mode. (In the non-streaming mode, we would also have to iterate over remote files to not output the files' hash (from the HF cache))\r\n\r\nConcerning the last part of the file name, do you have suggestions on how to do that? Because it can happen that the files are located in different folders with the same name so I am wondering what would be the way to go." ]

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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) | 0.006461 / 0.011353 (-0.004891) | 0.004035 / 0.011008 (-0.006973) | 0.085037 / 0.038508 (0.046529) | 0.072434 / 0.023109 (0.049325) | 0.308565 / 0.275898 (0.032667) | 0.330455 / 0.323480 (0.006975) | 0.003782 / 0.007986 (-0.004204) | 0.004363 / 0.004328 (0.000034) | 0.065242 / 0.004250 (0.060991) | 0.056111 / 0.037052 (0.019058) | 0.318008 / 0.258489 (0.059519) | 0.357904 / 0.293841 (0.064063) | 0.030702 / 0.128546 (-0.097844) | 0.008741 / 0.075646 (-0.066905) | 0.287666 / 0.419271 (-0.131605) | 0.052281 / 0.043533 (0.008748) | 0.306894 / 0.255139 (0.051755) | 0.335739 / 0.283200 (0.052540) | 0.023712 / 0.141683 (-0.117971) | 1.492304 / 1.452155 (0.040149) | 1.544540 / 1.492716 (0.051823) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.299419 / 0.018006 (0.281413) | 0.547195 / 0.000490 (0.546705) | 0.011571 / 0.000200 (0.011371) | 0.000223 / 0.000054 (0.000168) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028364 / 0.037411 (-0.009048) | 0.081445 / 0.014526 (0.066919) | 0.626670 / 0.176557 (0.450114) | 0.159964 / 0.737135 (-0.577171) | 0.100528 / 0.296338 (-0.195811) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.409915 / 0.215209 (0.194705) | 4.108689 / 2.077655 (2.031034) | 2.046247 / 1.504120 (0.542127) | 1.851081 / 1.541195 (0.309887) | 1.857857 / 1.468490 (0.389367) | 0.493246 / 4.584777 (-4.091531) | 3.581557 / 3.745712 (-0.164155) | 3.456708 / 5.269862 (-1.813153) | 2.051054 / 4.565676 (-2.514623) | 0.057553 / 0.424275 (-0.366722) | 0.007287 / 0.007607 (-0.000320) | 0.493094 / 0.226044 (0.267050) | 4.873051 / 2.268929 (2.604122) | 2.515266 / 55.444624 (-52.929358) | 2.144743 / 6.876477 (-4.731733) | 2.159412 / 2.142072 (0.017340) | 0.595627 / 4.805227 (-4.209601) | 0.133773 / 6.500664 (-6.366891) | 0.059965 / 0.075469 (-0.015504) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.259625 / 1.841788 (-0.582163) | 19.030742 / 8.074308 (10.956434) | 14.039246 / 10.191392 (3.847854) | 0.168116 / 0.680424 (-0.512308) | 0.018168 / 0.534201 (-0.516033) | 0.391187 / 0.579283 (-0.188096) | 0.420901 / 0.434364 (-0.013463) | 0.465827 / 0.540337 (-0.074511) | 0.718373 / 1.386936 (-0.668563) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006616 / 0.011353 (-0.004737) | 0.004048 / 0.011008 (-0.006960) | 0.064568 / 0.038508 (0.026060) | 0.075933 / 0.023109 (0.052824) | 0.396353 / 0.275898 (0.120455) | 0.424159 / 0.323480 (0.100679) | 0.005446 / 0.007986 (-0.002540) | 0.003393 / 0.004328 (-0.000935) | 0.064673 / 0.004250 (0.060422) | 0.056983 / 0.037052 (0.019930) | 0.402478 / 0.258489 (0.143989) | 0.433240 / 0.293841 (0.139399) | 0.032100 / 0.128546 (-0.096446) | 0.008664 / 0.075646 (-0.066983) | 0.070502 / 0.419271 (-0.348770) | 0.047800 / 0.043533 (0.004267) | 0.399506 / 0.255139 (0.144367) | 0.418376 / 0.283200 (0.135176) | 0.022654 / 0.141683 (-0.119029) | 1.487280 / 1.452155 (0.035125) | 1.543733 / 1.492716 (0.051017) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.317660 / 0.018006 (0.299654) | 0.523922 / 0.000490 (0.523432) | 0.007086 / 0.000200 (0.006886) | 0.000109 / 0.000054 (0.000055) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032381 / 0.037411 (-0.005030) | 0.091636 / 0.014526 (0.077110) | 0.104743 / 0.176557 (-0.071814) | 0.158793 / 0.737135 (-0.578342) | 0.103164 / 0.296338 (-0.193175) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.434081 / 0.215209 (0.218872) | 4.329448 / 2.077655 (2.251794) | 2.335855 / 1.504120 (0.831735) | 2.177513 / 1.541195 (0.636319) | 2.205406 / 1.468490 (0.736916) | 0.500117 / 4.584777 (-4.084660) | 3.693715 / 3.745712 (-0.051997) | 3.305803 / 5.269862 (-1.964059) | 2.048283 / 4.565676 (-2.517394) | 0.058301 / 0.424275 (-0.365974) | 0.007196 / 0.007607 (-0.000411) | 0.512917 / 0.226044 (0.286873) | 5.129283 / 2.268929 (2.860355) | 2.836200 / 55.444624 (-52.608425) | 2.499022 / 6.876477 (-4.377455) | 2.652305 / 2.142072 (0.510232) | 0.604219 / 4.805227 (-4.201008) | 0.137310 / 6.500664 (-6.363354) | 0.060880 / 0.075469 (-0.014589) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.346948 / 1.841788 (-0.494839) | 19.499516 / 8.074308 (11.425208) | 14.701500 / 10.191392 (4.510108) | 0.168626 / 0.680424 (-0.511798) | 0.020002 / 0.534201 (-0.514199) | 0.394729 / 0.579283 (-0.184554) | 0.428323 / 0.434364 (-0.006040) | 0.481202 / 0.540337 (-0.059136) | 0.684768 / 1.386936 (-0.702169) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#fed9c07458afc73870e8ec9846bf1fc5cac0b378 \"CML watermark\")\n", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6309). All of your documentation changes will be reflected on that endpoint.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007033 / 0.011353 (-0.004320) | 0.004411 / 0.011008 (-0.006597) | 0.086146 / 0.038508 (0.047638) | 0.086669 / 0.023109 (0.063560) | 0.329145 / 0.275898 (0.053247) | 0.348728 / 0.323480 (0.025248) | 0.004404 / 0.007986 (-0.003582) | 0.003656 / 0.004328 (-0.000673) | 0.066120 / 0.004250 (0.061869) | 0.059157 / 0.037052 (0.022105) | 0.316537 / 0.258489 (0.058048) | 0.369065 / 0.293841 (0.075224) | 0.031921 / 0.128546 (-0.096625) | 0.008877 / 0.075646 (-0.066770) | 0.290068 / 0.419271 (-0.129204) | 0.054007 / 0.043533 (0.010475) | 0.308823 / 0.255139 (0.053684) | 0.331189 / 0.283200 (0.047989) | 0.027313 / 0.141683 (-0.114370) | 1.486772 / 1.452155 (0.034617) | 1.570359 / 1.492716 (0.077643) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.315991 / 0.018006 (0.297985) | 0.577876 / 0.000490 (0.577386) | 0.011207 / 0.000200 (0.011007) | 0.000089 / 0.000054 (0.000035) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031753 / 0.037411 (-0.005658) | 0.089270 / 0.014526 (0.074744) | 0.102518 / 0.176557 (-0.074038) | 0.160260 / 0.737135 (-0.576875) | 0.103365 / 0.296338 (-0.192973) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.405789 / 0.215209 (0.190580) | 4.052740 / 2.077655 (1.975085) | 2.052076 / 1.504120 (0.547956) | 1.873966 / 1.541195 (0.332771) | 1.997156 / 1.468490 (0.528665) | 0.494975 / 4.584777 (-4.089802) | 3.600007 / 3.745712 (-0.145705) | 3.626459 / 5.269862 (-1.643403) | 2.176927 / 4.565676 (-2.388750) | 0.057894 / 0.424275 (-0.366381) | 0.007469 / 0.007607 (-0.000138) | 0.487422 / 0.226044 (0.261377) | 4.868744 / 2.268929 (2.599815) | 2.528707 / 55.444624 (-52.915918) | 2.149520 / 6.876477 (-4.726956) | 2.275491 / 2.142072 (0.133419) | 0.589112 / 4.805227 (-4.216115) | 0.136644 / 6.500664 (-6.364020) | 0.062144 / 0.075469 (-0.013325) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.286625 / 1.841788 (-0.555163) | 20.528128 / 8.074308 (12.453819) | 15.290866 / 10.191392 (5.099474) | 0.168380 / 0.680424 (-0.512044) | 0.018908 / 0.534201 (-0.515293) | 0.397210 / 0.579283 (-0.182073) | 0.426133 / 0.434364 (-0.008231) | 0.471754 / 0.540337 (-0.068584) | 0.653343 / 1.386936 (-0.733593) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007599 / 0.011353 (-0.003754) | 0.004499 / 0.011008 (-0.006509) | 0.066248 / 0.038508 (0.027740) | 0.097704 / 0.023109 (0.074595) | 0.414558 / 0.275898 (0.138660) | 0.451088 / 0.323480 (0.127609) | 0.005932 / 0.007986 (-0.002054) | 0.003698 / 0.004328 (-0.000630) | 0.065784 / 0.004250 (0.061534) | 0.064777 / 0.037052 (0.027725) | 0.443318 / 0.258489 (0.184829) | 0.456896 / 0.293841 (0.163055) | 0.033436 / 0.128546 (-0.095111) | 0.008977 / 0.075646 (-0.066669) | 0.072067 / 0.419271 (-0.347205) | 0.049571 / 0.043533 (0.006038) | 0.420325 / 0.255139 (0.165186) | 0.443588 / 0.283200 (0.160388) | 0.026723 / 0.141683 (-0.114960) | 1.512566 / 1.452155 (0.060411) | 1.647591 / 1.492716 (0.154875) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.326410 / 0.018006 (0.308404) | 0.532878 / 0.000490 (0.532388) | 0.006257 / 0.000200 (0.006057) | 0.000104 / 0.000054 (0.000049) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.037289 / 0.037411 (-0.000122) | 0.104940 / 0.014526 (0.090414) | 0.113597 / 0.176557 (-0.062960) | 0.170562 / 0.737135 (-0.566573) | 0.114583 / 0.296338 (-0.181755) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.435530 / 0.215209 (0.220321) | 4.332659 / 2.077655 (2.255005) | 2.343576 / 1.504120 (0.839456) | 2.190517 / 1.541195 (0.649322) | 2.323101 / 1.468490 (0.854611) | 0.493019 / 4.584777 (-4.091758) | 3.686726 / 3.745712 (-0.058986) | 3.437143 / 5.269862 (-1.832719) | 2.167193 / 4.565676 (-2.398483) | 0.059636 / 0.424275 (-0.364639) | 0.007696 / 0.007607 (0.000089) | 0.511159 / 0.226044 (0.285115) | 5.119358 / 2.268929 (2.850429) | 2.814934 / 55.444624 (-52.629690) | 2.477871 / 6.876477 (-4.398606) | 2.774473 / 2.142072 (0.632401) | 0.590258 / 4.805227 (-4.214969) | 0.135923 / 6.500664 (-6.364741) | 0.062793 / 0.075469 (-0.012676) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.350192 / 1.841788 (-0.491596) | 21.382135 / 8.074308 (13.307827) | 16.024198 / 10.191392 (5.832806) | 0.163623 / 0.680424 (-0.516801) | 0.020749 / 0.534201 (-0.513452) | 0.402578 / 0.579283 (-0.176705) | 0.436569 / 0.434364 (0.002205) | 0.477217 / 0.540337 (-0.063121) | 0.682929 / 1.386936 (-0.704007) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#fa36173f2e8c6f266efd236933eff3a95af0382c \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006671 / 0.011353 (-0.004681) | 0.004176 / 0.011008 (-0.006832) | 0.084095 / 0.038508 (0.045587) | 0.076345 / 0.023109 (0.053236) | 0.341201 / 0.275898 (0.065303) | 0.381920 / 0.323480 (0.058440) | 0.005578 / 0.007986 (-0.002408) | 0.003535 / 0.004328 (-0.000794) | 0.065227 / 0.004250 (0.060976) | 0.054983 / 0.037052 (0.017931) | 0.345938 / 0.258489 (0.087449) | 0.398708 / 0.293841 (0.104867) | 0.031029 / 0.128546 (-0.097518) | 0.008643 / 0.075646 (-0.067004) | 0.287286 / 0.419271 (-0.131985) | 0.052424 / 0.043533 (0.008892) | 0.342914 / 0.255139 (0.087775) | 0.366982 / 0.283200 (0.083782) | 0.024511 / 0.141683 (-0.117172) | 1.510575 / 1.452155 (0.058421) | 1.593214 / 1.492716 (0.100497) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.272703 / 0.018006 (0.254697) | 0.583235 / 0.000490 (0.582746) | 0.008467 / 0.000200 (0.008267) | 0.000295 / 0.000054 (0.000240) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029654 / 0.037411 (-0.007757) | 0.085078 / 0.014526 (0.070552) | 0.106391 / 0.176557 (-0.070165) | 0.155790 / 0.737135 (-0.581345) | 0.104835 / 0.296338 (-0.191503) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.408584 / 0.215209 (0.193375) | 4.082557 / 2.077655 (2.004902) | 2.054001 / 1.504120 (0.549881) | 1.868470 / 1.541195 (0.327275) | 1.950600 / 1.468490 (0.482110) | 0.492572 / 4.584777 (-4.092205) | 3.497105 / 3.745712 (-0.248607) | 3.464596 / 5.269862 (-1.805265) | 2.106399 / 4.565676 (-2.459278) | 0.057413 / 0.424275 (-0.366862) | 0.007449 / 0.007607 (-0.000158) | 0.482900 / 0.226044 (0.256856) | 4.844152 / 2.268929 (2.575223) | 2.499930 / 55.444624 (-52.944695) | 2.180396 / 6.876477 (-4.696081) | 2.282830 / 2.142072 (0.140758) | 0.581371 / 4.805227 (-4.223857) | 0.134641 / 6.500664 (-6.366023) | 0.063137 / 0.075469 (-0.012332) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.274291 / 1.841788 (-0.567496) | 19.426189 / 8.074308 (11.351881) | 14.292833 / 10.191392 (4.101441) | 0.166321 / 0.680424 (-0.514102) | 0.018419 / 0.534201 (-0.515782) | 0.392433 / 0.579283 (-0.186850) | 0.415128 / 0.434364 (-0.019236) | 0.459274 / 0.540337 (-0.081063) | 0.714668 / 1.386936 (-0.672268) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006740 / 0.011353 (-0.004613) | 0.004283 / 0.011008 (-0.006725) | 0.063845 / 0.038508 (0.025337) | 0.077037 / 0.023109 (0.053927) | 0.425103 / 0.275898 (0.149205) | 0.445525 / 0.323480 (0.122046) | 0.005755 / 0.007986 (-0.002230) | 0.003589 / 0.004328 (-0.000739) | 0.064515 / 0.004250 (0.060265) | 0.057398 / 0.037052 (0.020346) | 0.424781 / 0.258489 (0.166292) | 0.452162 / 0.293841 (0.158321) | 0.032164 / 0.128546 (-0.096382) | 0.008660 / 0.075646 (-0.066986) | 0.069873 / 0.419271 (-0.349399) | 0.048100 / 0.043533 (0.004567) | 0.409097 / 0.255139 (0.153958) | 0.441533 / 0.283200 (0.158333) | 0.024122 / 0.141683 (-0.117560) | 1.503431 / 1.452155 (0.051277) | 1.577518 / 1.492716 (0.084802) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.264433 / 0.018006 (0.246426) | 0.553631 / 0.000490 (0.553141) | 0.006354 / 0.000200 (0.006154) | 0.000106 / 0.000054 (0.000051) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033259 / 0.037411 (-0.004152) | 0.094908 / 0.014526 (0.080382) | 0.108238 / 0.176557 (-0.068318) | 0.161354 / 0.737135 (-0.575781) | 0.109073 / 0.296338 (-0.187265) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.434450 / 0.215209 (0.219241) | 4.347501 / 2.077655 (2.269847) | 2.362225 / 1.504120 (0.858105) | 2.189285 / 1.541195 (0.648090) | 2.288797 / 1.468490 (0.820307) | 0.487782 / 4.584777 (-4.096995) | 3.598732 / 3.745712 (-0.146980) | 3.343263 / 5.269862 (-1.926599) | 2.086256 / 4.565676 (-2.479420) | 0.057838 / 0.424275 (-0.366437) | 0.007412 / 0.007607 (-0.000195) | 0.510098 / 0.226044 (0.284054) | 5.088743 / 2.268929 (2.819814) | 2.809105 / 55.444624 (-52.635519) | 2.476005 / 6.876477 (-4.400471) | 2.753785 / 2.142072 (0.611712) | 0.585045 / 4.805227 (-4.220182) | 0.131162 / 6.500664 (-6.369502) | 0.060431 / 0.075469 (-0.015038) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.342149 / 1.841788 (-0.499639) | 20.602369 / 8.074308 (12.528061) | 14.973301 / 10.191392 (4.781909) | 0.151655 / 0.680424 (-0.528769) | 0.020793 / 0.534201 (-0.513408) | 0.401657 / 0.579283 (-0.177626) | 0.419845 / 0.434364 (-0.014519) | 0.467225 / 0.540337 (-0.073113) | 0.672469 / 1.386936 (-0.714467) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#474beafbc1c2735ff4747f5675855583be2ede06 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007006 / 0.011353 (-0.004346) | 0.004282 / 0.011008 (-0.006726) | 0.085413 / 0.038508 (0.046905) | 0.085148 / 0.023109 (0.062038) | 0.336543 / 0.275898 (0.060645) | 0.367959 / 0.323480 (0.044479) | 0.004337 / 0.007986 (-0.003648) | 0.004535 / 0.004328 (0.000207) | 0.065379 / 0.004250 (0.061128) | 0.059993 / 0.037052 (0.022941) | 0.343162 / 0.258489 (0.084673) | 0.383766 / 0.293841 (0.089925) | 0.031520 / 0.128546 (-0.097026) | 0.008605 / 0.075646 (-0.067042) | 0.288620 / 0.419271 (-0.130651) | 0.053617 / 0.043533 (0.010084) | 0.339389 / 0.255139 (0.084250) | 0.350842 / 0.283200 (0.067642) | 0.027816 / 0.141683 (-0.113867) | 1.505500 / 1.452155 (0.053346) | 1.566511 / 1.492716 (0.073795) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.272203 / 0.018006 (0.254197) | 0.569729 / 0.000490 (0.569240) | 0.010061 / 0.000200 (0.009861) | 0.000328 / 0.000054 (0.000273) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030015 / 0.037411 (-0.007396) | 0.083991 / 0.014526 (0.069465) | 0.099796 / 0.176557 (-0.076761) | 0.159131 / 0.737135 (-0.578004) | 0.099102 / 0.296338 (-0.197237) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.390076 / 0.215209 (0.174867) | 3.897157 / 2.077655 (1.819502) | 1.935912 / 1.504120 (0.431793) | 1.815109 / 1.541195 (0.273915) | 1.875041 / 1.468490 (0.406551) | 0.482168 / 4.584777 (-4.102609) | 3.556140 / 3.745712 (-0.189572) | 3.528889 / 5.269862 (-1.740972) | 2.132767 / 4.565676 (-2.432909) | 0.057761 / 0.424275 (-0.366514) | 0.007353 / 0.007607 (-0.000254) | 0.464801 / 0.226044 (0.238757) | 4.637301 / 2.268929 (2.368372) | 2.362239 / 55.444624 (-53.082386) | 2.049811 / 6.876477 (-4.826665) | 2.143485 / 2.142072 (0.001412) | 0.580929 / 4.805227 (-4.224299) | 0.140252 / 6.500664 (-6.360412) | 0.061352 / 0.075469 (-0.014117) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.257487 / 1.841788 (-0.584301) | 19.453319 / 8.074308 (11.379011) | 14.276332 / 10.191392 (4.084940) | 0.166772 / 0.680424 (-0.513652) | 0.018339 / 0.534201 (-0.515862) | 0.393008 / 0.579283 (-0.186275) | 0.420960 / 0.434364 (-0.013404) | 0.464331 / 0.540337 (-0.076007) | 0.717973 / 1.386936 (-0.668963) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007255 / 0.011353 (-0.004098) | 0.004230 / 0.011008 (-0.006778) | 0.065191 / 0.038508 (0.026683) | 0.085765 / 0.023109 (0.062655) | 0.412464 / 0.275898 (0.136566) | 0.446067 / 0.323480 (0.122587) | 0.005875 / 0.007986 (-0.002110) | 0.003700 / 0.004328 (-0.000628) | 0.065430 / 0.004250 (0.061179) | 0.060284 / 0.037052 (0.023231) | 0.419984 / 0.258489 (0.161495) | 0.453779 / 0.293841 (0.159938) | 0.032595 / 0.128546 (-0.095952) | 0.008873 / 0.075646 (-0.066773) | 0.072124 / 0.419271 (-0.347148) | 0.048072 / 0.043533 (0.004539) | 0.408725 / 0.255139 (0.153586) | 0.432485 / 0.283200 (0.149285) | 0.024662 / 0.141683 (-0.117021) | 1.540434 / 1.452155 (0.088279) | 1.624768 / 1.492716 (0.132051) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.253220 / 0.018006 (0.235214) | 0.555469 / 0.000490 (0.554980) | 0.007765 / 0.000200 (0.007565) | 0.000101 / 0.000054 (0.000046) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032666 / 0.037411 (-0.004745) | 0.094786 / 0.014526 (0.080260) | 0.108219 / 0.176557 (-0.068337) | 0.161546 / 0.737135 (-0.575589) | 0.109828 / 0.296338 (-0.186510) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.437024 / 0.215209 (0.221815) | 4.354065 / 2.077655 (2.276411) | 2.336832 / 1.504120 (0.832713) | 2.161959 / 1.541195 (0.620764) | 2.257214 / 1.468490 (0.788724) | 0.501576 / 4.584777 (-4.083201) | 3.654292 / 3.745712 (-0.091420) | 3.349504 / 5.269862 (-1.920357) | 2.092998 / 4.565676 (-2.472679) | 0.058740 / 0.424275 (-0.365535) | 0.007420 / 0.007607 (-0.000187) | 0.513443 / 0.226044 (0.287399) | 5.151247 / 2.268929 (2.882319) | 2.816036 / 55.444624 (-52.628589) | 2.451863 / 6.876477 (-4.424613) | 2.709908 / 2.142072 (0.567836) | 0.597834 / 4.805227 (-4.207394) | 0.136547 / 6.500664 (-6.364117) | 0.062030 / 0.075469 (-0.013439) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.371412 / 1.841788 (-0.470375) | 20.398981 / 8.074308 (12.324673) | 14.932307 / 10.191392 (4.740915) | 0.167796 / 0.680424 (-0.512628) | 0.020740 / 0.534201 (-0.513461) | 0.397162 / 0.579283 (-0.182121) | 0.435493 / 0.434364 (0.001129) | 0.477074 / 0.540337 (-0.063264) | 0.697546 / 1.386936 (-0.689390) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#017cefbc832bfe662afd87d9d1241104bf67c53e \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007388 / 0.011353 (-0.003964) | 0.004408 / 0.011008 (-0.006600) | 0.098225 / 0.038508 (0.059717) | 0.079368 / 0.023109 (0.056259) | 0.381866 / 0.275898 (0.105968) | 0.425942 / 0.323480 (0.102462) | 0.005978 / 0.007986 (-0.002007) | 0.003677 / 0.004328 (-0.000651) | 0.075488 / 0.004250 (0.071238) | 0.061725 / 0.037052 (0.024672) | 0.389126 / 0.258489 (0.130637) | 0.444099 / 0.293841 (0.150258) | 0.036222 / 0.128546 (-0.092324) | 0.009926 / 0.075646 (-0.065720) | 0.336632 / 0.419271 (-0.082640) | 0.060867 / 0.043533 (0.017335) | 0.385437 / 0.255139 (0.130298) | 0.416599 / 0.283200 (0.133399) | 0.025118 / 0.141683 (-0.116565) | 1.728073 / 1.452155 (0.275919) | 1.847750 / 1.492716 (0.355033) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.263774 / 0.018006 (0.245768) | 0.491242 / 0.000490 (0.490752) | 0.013621 / 0.000200 (0.013421) | 0.000333 / 0.000054 (0.000279) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032911 / 0.037411 (-0.004500) | 0.095738 / 0.014526 (0.081212) | 0.110482 / 0.176557 (-0.066075) | 0.175533 / 0.737135 (-0.561603) | 0.109240 / 0.296338 (-0.187098) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.453967 / 0.215209 (0.238758) | 4.489384 / 2.077655 (2.411730) | 2.185496 / 1.504120 (0.681376) | 1.979126 / 1.541195 (0.437931) | 2.016364 / 1.468490 (0.547874) | 0.565539 / 4.584777 (-4.019238) | 4.106561 / 3.745712 (0.360849) | 3.906402 / 5.269862 (-1.363460) | 2.342186 / 4.565676 (-2.223491) | 0.067815 / 0.424275 (-0.356460) | 0.008663 / 0.007607 (0.001056) | 0.543841 / 0.226044 (0.317796) | 5.433491 / 2.268929 (3.164563) | 2.785723 / 55.444624 (-52.658901) | 2.355716 / 6.876477 (-4.520760) | 2.397563 / 2.142072 (0.255491) | 0.682587 / 4.805227 (-4.122641) | 0.156548 / 6.500664 (-6.344116) | 0.070654 / 0.075469 (-0.004815) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.475183 / 1.841788 (-0.366605) | 21.353030 / 8.074308 (13.278722) | 15.938324 / 10.191392 (5.746932) | 0.167010 / 0.680424 (-0.513413) | 0.020931 / 0.534201 (-0.513270) | 0.464376 / 0.579283 (-0.114907) | 0.472546 / 0.434364 (0.038182) | 0.544645 / 0.540337 (0.004308) | 0.752940 / 1.386936 (-0.633996) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007359 / 0.011353 (-0.003994) | 0.004276 / 0.011008 (-0.006732) | 0.075345 / 0.038508 (0.036837) | 0.080105 / 0.023109 (0.056995) | 0.480456 / 0.275898 (0.204558) | 0.514974 / 0.323480 (0.191494) | 0.006087 / 0.007986 (-0.001899) | 0.003717 / 0.004328 (-0.000611) | 0.075067 / 0.004250 (0.070816) | 0.063739 / 0.037052 (0.026686) | 0.487569 / 0.258489 (0.229080) | 0.530198 / 0.293841 (0.236357) | 0.036056 / 0.128546 (-0.092491) | 0.009606 / 0.075646 (-0.066041) | 0.082343 / 0.419271 (-0.336929) | 0.055488 / 0.043533 (0.011956) | 0.484789 / 0.255139 (0.229650) | 0.501918 / 0.283200 (0.218718) | 0.025340 / 0.141683 (-0.116342) | 1.784417 / 1.452155 (0.332262) | 1.854202 / 1.492716 (0.361486) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.252476 / 0.018006 (0.234470) | 0.484967 / 0.000490 (0.484478) | 0.005471 / 0.000200 (0.005271) | 0.000111 / 0.000054 (0.000057) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.037084 / 0.037411 (-0.000327) | 0.106648 / 0.014526 (0.092122) | 0.123393 / 0.176557 (-0.053164) | 0.183088 / 0.737135 (-0.554047) | 0.122572 / 0.296338 (-0.173767) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.516003 / 0.215209 (0.300793) | 5.107748 / 2.077655 (3.030093) | 2.778044 / 1.504120 (1.273924) | 2.589944 / 1.541195 (1.048749) | 2.649921 / 1.468490 (1.181431) | 0.572783 / 4.584777 (-4.011994) | 4.211331 / 3.745712 (0.465619) | 3.738859 / 5.269862 (-1.531003) | 2.331628 / 4.565676 (-2.234048) | 0.067347 / 0.424275 (-0.356928) | 0.008513 / 0.007607 (0.000905) | 0.601056 / 0.226044 (0.375012) | 5.990921 / 2.268929 (3.721992) | 3.311544 / 55.444624 (-52.133081) | 2.929850 / 6.876477 (-3.946627) | 3.118741 / 2.142072 (0.976669) | 0.685975 / 4.805227 (-4.119253) | 0.155105 / 6.500664 (-6.345559) | 0.069629 / 0.075469 (-0.005840) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.602367 / 1.841788 (-0.239421) | 22.577072 / 8.074308 (14.502764) | 17.049655 / 10.191392 (6.858263) | 0.182412 / 0.680424 (-0.498011) | 0.023137 / 0.534201 (-0.511064) | 0.466988 / 0.579283 (-0.112295) | 0.483887 / 0.434364 (0.049523) | 0.556099 / 0.540337 (0.015761) | 0.798332 / 1.386936 (-0.588604) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3e6d8318bd73a91852c22d14f1d788ac6dc8ae90 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009086 / 0.011353 (-0.002267) | 0.004755 / 0.011008 (-0.006253) | 0.128866 / 0.038508 (0.090358) | 0.086099 / 0.023109 (0.062990) | 0.378079 / 0.275898 (0.102181) | 0.487431 / 0.323480 (0.163951) | 0.004712 / 0.007986 (-0.003274) | 0.003622 / 0.004328 (-0.000706) | 0.081214 / 0.004250 (0.076963) | 0.057226 / 0.037052 (0.020174) | 0.407655 / 0.258489 (0.149166) | 0.448630 / 0.293841 (0.154789) | 0.049051 / 0.128546 (-0.079495) | 0.014537 / 0.075646 (-0.061110) | 0.467343 / 0.419271 (0.048071) | 0.070482 / 0.043533 (0.026949) | 0.379664 / 0.255139 (0.124525) | 0.464181 / 0.283200 (0.180981) | 0.039973 / 0.141683 (-0.101710) | 1.731164 / 1.452155 (0.279010) | 1.886895 / 1.492716 (0.394178) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.251327 / 0.018006 (0.233321) | 0.502670 / 0.000490 (0.502180) | 0.012183 / 0.000200 (0.011984) | 0.000111 / 0.000054 (0.000057) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028892 / 0.037411 (-0.008519) | 0.093789 / 0.014526 (0.079263) | 0.104255 / 0.176557 (-0.072301) | 0.170257 / 0.737135 (-0.566879) | 0.115430 / 0.296338 (-0.180909) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.573745 / 0.215209 (0.358536) | 5.873732 / 2.077655 (3.796077) | 2.485188 / 1.504120 (0.981068) | 2.018476 / 1.541195 (0.477282) | 2.062765 / 1.468490 (0.594275) | 0.913816 / 4.584777 (-3.670961) | 5.362338 / 3.745712 (1.616626) | 4.698758 / 5.269862 (-0.571103) | 3.132973 / 4.565676 (-1.432703) | 0.093594 / 0.424275 (-0.330681) | 0.008359 / 0.007607 (0.000751) | 0.693997 / 0.226044 (0.467953) | 7.042645 / 2.268929 (4.773717) | 3.196180 / 55.444624 (-52.248445) | 2.384585 / 6.876477 (-4.491892) | 2.301256 / 2.142072 (0.159183) | 1.048025 / 4.805227 (-3.757202) | 0.206931 / 6.500664 (-6.293733) | 0.069401 / 0.075469 (-0.006068) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.598898 / 1.841788 (-0.242889) | 22.963667 / 8.074308 (14.889359) | 20.373688 / 10.191392 (10.182296) | 0.239716 / 0.680424 (-0.440707) | 0.040213 / 0.534201 (-0.493988) | 0.503268 / 0.579283 (-0.076015) | 0.630750 / 0.434364 (0.196386) | 0.578007 / 0.540337 (0.037669) | 0.789564 / 1.386936 (-0.597372) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009129 / 0.011353 (-0.002224) | 0.005453 / 0.011008 (-0.005555) | 0.101040 / 0.038508 (0.062532) | 0.099172 / 0.023109 (0.076062) | 0.508453 / 0.275898 (0.232555) | 0.570858 / 0.323480 (0.247378) | 0.006584 / 0.007986 (-0.001401) | 0.003800 / 0.004328 (-0.000528) | 0.094349 / 0.004250 (0.090098) | 0.064642 / 0.037052 (0.027590) | 0.563008 / 0.258489 (0.304518) | 0.625560 / 0.293841 (0.331719) | 0.050121 / 0.128546 (-0.078426) | 0.014183 / 0.075646 (-0.061463) | 0.106564 / 0.419271 (-0.312707) | 0.061030 / 0.043533 (0.017498) | 0.522311 / 0.255139 (0.267172) | 0.598356 / 0.283200 (0.315156) | 0.042008 / 0.141683 (-0.099675) | 1.879999 / 1.452155 (0.427844) | 1.963879 / 1.492716 (0.471162) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.270573 / 0.018006 (0.252567) | 0.554356 / 0.000490 (0.553866) | 0.008145 / 0.000200 (0.007945) | 0.000218 / 0.000054 (0.000163) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031089 / 0.037411 (-0.006322) | 0.099568 / 0.014526 (0.085043) | 0.118304 / 0.176557 (-0.058253) | 0.182991 / 0.737135 (-0.554144) | 0.115874 / 0.296338 (-0.180465) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.615020 / 0.215209 (0.399811) | 6.279740 / 2.077655 (4.202085) | 2.882094 / 1.504120 (1.377974) | 2.559265 / 1.541195 (1.018070) | 2.639259 / 1.468490 (1.170769) | 0.903727 / 4.584777 (-3.681050) | 5.248555 / 3.745712 (1.502843) | 4.817340 / 5.269862 (-0.452522) | 3.056880 / 4.565676 (-1.508797) | 0.096602 / 0.424275 (-0.327673) | 0.008660 / 0.007607 (0.001053) | 0.794347 / 0.226044 (0.568303) | 7.625127 / 2.268929 (5.356198) | 3.766826 / 55.444624 (-51.677798) | 2.968254 / 6.876477 (-3.908223) | 3.260595 / 2.142072 (1.118523) | 1.066228 / 4.805227 (-3.739000) | 0.207158 / 6.500664 (-6.293506) | 0.076920 / 0.075469 (0.001451) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.741442 / 1.841788 (-0.100345) | 23.499552 / 8.074308 (15.425244) | 22.064966 / 10.191392 (11.873574) | 0.239173 / 0.680424 (-0.441251) | 0.032105 / 0.534201 (-0.502096) | 0.484709 / 0.579283 (-0.094574) | 0.583632 / 0.434364 (0.149268) | 0.569018 / 0.540337 (0.028681) | 0.815764 / 1.386936 (-0.571172) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3aeb078ba1afd713e901df43343c160877403d07 \"CML watermark\")\n" ]

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[ "This (Windows) issue was fixed in `fsspec` in https://github.com/fsspec/filesystem_spec/pull/1275. So, to avoid the error, update the `fsspec` installation with `pip install -U fsspec`.", "> This (Windows) issue was fixed in `fsspec` in [fsspec/filesystem_spec#1275](https://github.com/fsspec/filesystem_spec/pull/1275). So, to avoid the error, update the `fsspec` installation with `pip install -U fsspec`.\r\n\r\nafter I run `pip install -U fsspec`\r\n\r\nit occurs a new error:\r\n```\r\nERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflict\r\ns.\r\ndatasets 2.14.5 requires fsspec[http]<2023.9.0,>=2023.1.0, but you have fsspec 2023.9.2 which is incompatible.\r\n\r\n```", "The `fsspec<2023.9.0` upper bound will be removed in the next release. The `ResourceError` fix is also present in version 2023.6.0, so use that version in the meantime (`pip install fsspec==2023.6.0`).", "> The `fsspec<2023.9.0` upper bound will be removed in the next release. The `ResourceError` fix is also present in version 2023.6.0, so use that version in the meantime (`pip install fsspec==2023.6.0`).\r\n\r\nthanks for reply！" ]

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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) | 0.011548 / 0.011353 (0.000196) | 0.004630 / 0.011008 (-0.006378) | 0.105349 / 0.038508 (0.066841) | 0.110557 / 0.023109 (0.087448) | 0.395463 / 0.275898 (0.119565) | 0.448391 / 0.323480 (0.124912) | 0.005112 / 0.007986 (-0.002873) | 0.003854 / 0.004328 (-0.000474) | 0.088513 / 0.004250 (0.084263) | 0.073081 / 0.037052 (0.036028) | 0.391572 / 0.258489 (0.133083) | 0.459543 / 0.293841 (0.165702) | 0.040424 / 0.128546 (-0.088122) | 0.010306 / 0.075646 (-0.065340) | 0.365493 / 0.419271 (-0.053778) | 0.068154 / 0.043533 (0.024622) | 0.397675 / 0.255139 (0.142536) | 0.447147 / 0.283200 (0.163947) | 0.033482 / 0.141683 (-0.108201) | 1.857087 / 1.452155 (0.404932) | 1.973311 / 1.492716 (0.480595) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.257938 / 0.018006 (0.239932) | 0.569572 / 0.000490 (0.569083) | 0.012155 / 0.000200 (0.011955) | 0.000112 / 0.000054 (0.000058) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033094 / 0.037411 (-0.004318) | 0.102370 / 0.014526 (0.087844) | 0.122421 / 0.176557 (-0.054136) | 0.189983 / 0.737135 (-0.547152) | 0.117902 / 0.296338 (-0.178437) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.468419 / 0.215209 (0.253210) | 4.671410 / 2.077655 (2.593755) | 2.371136 / 1.504120 (0.867016) | 2.191877 / 1.541195 (0.650682) | 2.301894 / 1.468490 (0.833404) | 0.572260 / 4.584777 (-4.012517) | 4.302031 / 3.745712 (0.556319) | 4.128431 / 5.269862 (-1.141431) | 2.464543 / 4.565676 (-2.101133) | 0.067663 / 0.424275 (-0.356612) | 0.008947 / 0.007607 (0.001340) | 0.570063 / 0.226044 (0.344018) | 5.684460 / 2.268929 (3.415531) | 2.969708 / 55.444624 (-52.474916) | 2.573568 / 6.876477 (-4.302909) | 2.666074 / 2.142072 (0.524001) | 0.710098 / 4.805227 (-4.095129) | 0.158413 / 6.500664 (-6.342251) | 0.072776 / 0.075469 (-0.002693) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.564166 / 1.841788 (-0.277622) | 23.612774 / 8.074308 (15.538465) | 17.725070 / 10.191392 (7.533678) | 0.178982 / 0.680424 (-0.501442) | 0.021615 / 0.534201 (-0.512586) | 0.467090 / 0.579283 (-0.112193) | 0.472648 / 0.434364 (0.038284) | 0.578820 / 0.540337 (0.038483) | 0.783533 / 1.386936 (-0.603403) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008895 / 0.011353 (-0.002458) | 0.004617 / 0.011008 (-0.006392) | 0.077677 / 0.038508 (0.039169) | 0.090283 / 0.023109 (0.067174) | 0.491115 / 0.275898 (0.215217) | 0.525189 / 0.323480 (0.201709) | 0.007845 / 0.007986 (-0.000141) | 0.003742 / 0.004328 (-0.000586) | 0.077856 / 0.004250 (0.073606) | 0.067447 / 0.037052 (0.030394) | 0.488423 / 0.258489 (0.229933) | 0.532938 / 0.293841 (0.239097) | 0.041035 / 0.128546 (-0.087511) | 0.009917 / 0.075646 (-0.065730) | 0.085313 / 0.419271 (-0.333958) | 0.063374 / 0.043533 (0.019841) | 0.472287 / 0.255139 (0.217148) | 0.509773 / 0.283200 (0.226573) | 0.028706 / 0.141683 (-0.112977) | 1.775558 / 1.452155 (0.323403) | 1.967778 / 1.492716 (0.475061) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.249834 / 0.018006 (0.231828) | 0.467266 / 0.000490 (0.466776) | 0.005837 / 0.000200 (0.005637) | 0.000128 / 0.000054 (0.000074) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.038759 / 0.037411 (0.001347) | 0.113156 / 0.014526 (0.098630) | 0.123936 / 0.176557 (-0.052621) | 0.186831 / 0.737135 (-0.550304) | 0.125195 / 0.296338 (-0.171143) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.545666 / 0.215209 (0.330457) | 5.465713 / 2.077655 (3.388058) | 2.941279 / 1.504120 (1.437159) | 2.688377 / 1.541195 (1.147182) | 2.619501 / 1.468490 (1.151010) | 0.577974 / 4.584777 (-4.006803) | 4.300966 / 3.745712 (0.555254) | 3.879552 / 5.269862 (-1.390310) | 2.454932 / 4.565676 (-2.110745) | 0.069233 / 0.424275 (-0.355043) | 0.009729 / 0.007607 (0.002122) | 0.595290 / 0.226044 (0.369245) | 5.945445 / 2.268929 (3.676516) | 3.314607 / 55.444624 (-52.130017) | 2.894474 / 6.876477 (-3.982002) | 3.140790 / 2.142072 (0.998718) | 0.695808 / 4.805227 (-4.109419) | 0.158087 / 6.500664 (-6.342577) | 0.071374 / 0.075469 (-0.004095) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.706482 / 1.841788 (-0.135306) | 24.022666 / 8.074308 (15.948358) | 17.658003 / 10.191392 (7.466611) | 0.196771 / 0.680424 (-0.483653) | 0.023928 / 0.534201 (-0.510273) | 0.471992 / 0.579283 (-0.107291) | 0.510463 / 0.434364 (0.076099) | 0.621250 / 0.540337 (0.080912) | 0.807670 / 1.386936 (-0.579266) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f77539cbd88d00ec1ab2b9d4edfd01d5a58ef88a \"CML watermark\")\n" ]

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[ "more information：\r\n``` \r\nFile \"text2vec\\__init__.py\", line 8, in <module>\r\nFile \"<frozen importlib._bootstrap>\", line 1027, in _find_and_load\r\nFile \"<frozen importlib._bootstrap>\", line 1006, in _find_and_load_unlocked\r\nFile \"<frozen importlib._bootstrap>\", line 688, in _load_unlocked\r\nFile \"PyInstaller\\loader\\pyimod02_importers.py\", line 499, in exec_module\r\nFile \"text2vec\\bertmatching_model.py\", line 19, in <module>\r\nFile \"<frozen importlib._bootstrap>\", line 1027, in _find_and_load\r\nFile \"<frozen importlib._bootstrap>\", line 1006, in _find_and_load_unlocked\r\nFile \"<frozen importlib._bootstrap>\", line 688, in _load_unlocked\r\nFile \"PyInstaller\\loader\\pyimod02_importers.py\", line 499, in exec_module\r\nFile \"text2vec\\bertmatching_dataset.py\", line 7, in <module>\r\nFile \"<frozen importlib._bootstrap>\", line 1027, in _find_and_load\r\nFile \"<frozen importlib._bootstrap>\", line 1006, in _find_and_load_unlocked\r\nFile \"<frozen importlib._bootstrap>\", line 688, in _load_unlocked\r\nFile \"PyInstaller\\loader\\pyimod02_importers.py\", line 499, in exec_module\r\nFile \"datasets\\__init__.py\", line 52, in <module>\r\nFile \"<frozen importlib._bootstrap>\", line 1027, in _find_and_load\r\nFile \"<frozen importlib._bootstrap>\", line 1006, in _find_and_load_unlocked\r\nFile \"<frozen importlib._bootstrap>\", line 688, in _load_unlocked\r\nFile \"PyInstaller\\loader\\pyimod02_importers.py\", line 499, in exec_module\r\nFile \"datasets\\inspect.py\", line 30, in <module>\r\nFile \"<frozen importlib._bootstrap>\", line 1027, in _find_and_load\r\nFile \"<frozen importlib._bootstrap>\", line 1006, in _find_and_load_unlocked\r\nFile \"<frozen importlib._bootstrap>\", line 688, in _load_unlocked\r\nFile \"PyInstaller\\loader\\pyimod02_importers.py\", line 499, in exec_module\r\nFile \"datasets\\load.py\", line 58, in <module>\r\nFile \"<frozen importlib._bootstrap>\", line 1027, in _find_and_load\r\nFile \"<frozen importlib._bootstrap>\", line 1006, in _find_and_load_unlocked\r\nFile \"<frozen importlib._bootstrap>\", line 688, in _load_unlocked\r\nFile \"PyInstaller\\loader\\pyimod02_importers.py\", line 499, in exec_module\r\nFile \"datasets\\packaged_modules\\__init__.py\", line 31, in <module>\r\nFile \"inspect.py\", line 1147, in getsource\r\nFile \"inspect.py\", line 1129, in getsourcelines\r\nFile \"inspect.py\", line 958, in findsource\r\nOSError: could not get source code\r\n```\r\n", "Can you share a reproducer? I haven't been able to reproduce the error myself.", "> '\r\n\r\nthanks,I solve it.it's about pyinstaller.", "1", "> > '\r\n> \r\n> thanks,I solve it.it's about pyinstaller.\r\n\r\nI encountered the same error, how to solve it?" ]

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[ "Thanks for reporting, @finiteautomata.\r\n\r\nWe are investigating it. ", "There is a bug in `datasets`. You can see our proposed fix:\r\n- #6309 " ]

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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) | 0.006678 / 0.011353 (-0.004675) | 0.004013 / 0.011008 (-0.006995) | 0.083372 / 0.038508 (0.044864) | 0.070339 / 0.023109 (0.047230) | 0.339026 / 0.275898 (0.063128) | 0.370945 / 0.323480 (0.047465) | 0.004050 / 0.007986 (-0.003935) | 0.003283 / 0.004328 (-0.001046) | 0.064956 / 0.004250 (0.060705) | 0.055427 / 0.037052 (0.018374) | 0.341787 / 0.258489 (0.083297) | 0.385030 / 0.293841 (0.091189) | 0.031791 / 0.128546 (-0.096755) | 0.008511 / 0.075646 (-0.067135) | 0.286538 / 0.419271 (-0.132734) | 0.052893 / 0.043533 (0.009360) | 0.338522 / 0.255139 (0.083383) | 0.371821 / 0.283200 (0.088622) | 0.023731 / 0.141683 (-0.117951) | 1.485857 / 1.452155 (0.033702) | 1.515218 / 1.492716 (0.022502) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.232798 / 0.018006 (0.214792) | 0.446783 / 0.000490 (0.446293) | 0.007395 / 0.000200 (0.007195) | 0.000385 / 0.000054 (0.000330) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028866 / 0.037411 (-0.008545) | 0.081653 / 0.014526 (0.067127) | 0.094457 / 0.176557 (-0.082099) | 0.151761 / 0.737135 (-0.585375) | 0.095579 / 0.296338 (-0.200760) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.379926 / 0.215209 (0.164717) | 3.801839 / 2.077655 (1.724184) | 1.830302 / 1.504120 (0.326182) | 1.686912 / 1.541195 (0.145717) | 1.803418 / 1.468490 (0.334928) | 0.484431 / 4.584777 (-4.100346) | 3.592748 / 3.745712 (-0.152964) | 3.402578 / 5.269862 (-1.867284) | 2.043434 / 4.565676 (-2.522242) | 0.057274 / 0.424275 (-0.367001) | 0.007211 / 0.007607 (-0.000396) | 0.462611 / 0.226044 (0.236567) | 4.610703 / 2.268929 (2.341775) | 2.397668 / 55.444624 (-53.046956) | 2.149983 / 6.876477 (-4.726494) | 2.199100 / 2.142072 (0.057028) | 0.575883 / 4.805227 (-4.229344) | 0.133421 / 6.500664 (-6.367243) | 0.061168 / 0.075469 (-0.014301) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.246792 / 1.841788 (-0.594995) | 18.974385 / 8.074308 (10.900077) | 14.268859 / 10.191392 (4.077467) | 0.166340 / 0.680424 (-0.514084) | 0.018227 / 0.534201 (-0.515974) | 0.389646 / 0.579283 (-0.189637) | 0.418780 / 0.434364 (-0.015584) | 0.458063 / 0.540337 (-0.082275) | 0.635156 / 1.386936 (-0.751780) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006613 / 0.011353 (-0.004740) | 0.003977 / 0.011008 (-0.007031) | 0.064609 / 0.038508 (0.026101) | 0.070418 / 0.023109 (0.047308) | 0.395814 / 0.275898 (0.119916) | 0.424803 / 0.323480 (0.101323) | 0.005342 / 0.007986 (-0.002644) | 0.003252 / 0.004328 (-0.001076) | 0.065177 / 0.004250 (0.060927) | 0.055299 / 0.037052 (0.018247) | 0.403983 / 0.258489 (0.145494) | 0.438522 / 0.293841 (0.144681) | 0.032336 / 0.128546 (-0.096210) | 0.008524 / 0.075646 (-0.067122) | 0.071645 / 0.419271 (-0.347627) | 0.048137 / 0.043533 (0.004604) | 0.395170 / 0.255139 (0.140031) | 0.421727 / 0.283200 (0.138528) | 0.023028 / 0.141683 (-0.118655) | 1.500739 / 1.452155 (0.048584) | 1.568887 / 1.492716 (0.076170) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.227542 / 0.018006 (0.209536) | 0.447882 / 0.000490 (0.447393) | 0.005416 / 0.000200 (0.005216) | 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) | 0.032954 / 0.037411 (-0.004457) | 0.091994 / 0.014526 (0.077468) | 0.105957 / 0.176557 (-0.070600) | 0.158728 / 0.737135 (-0.578407) | 0.104734 / 0.296338 (-0.191605) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.436275 / 0.215209 (0.221066) | 4.344864 / 2.077655 (2.267209) | 2.304949 / 1.504120 (0.800829) | 2.123963 / 1.541195 (0.582768) | 2.189099 / 1.468490 (0.720609) | 0.492662 / 4.584777 (-4.092115) | 3.633662 / 3.745712 (-0.112051) | 3.251338 / 5.269862 (-2.018524) | 2.061378 / 4.565676 (-2.504299) | 0.058100 / 0.424275 (-0.366175) | 0.007311 / 0.007607 (-0.000297) | 0.516227 / 0.226044 (0.290183) | 5.184228 / 2.268929 (2.915300) | 2.780343 / 55.444624 (-52.664281) | 2.423428 / 6.876477 (-4.453048) | 2.617371 / 2.142072 (0.475298) | 0.590455 / 4.805227 (-4.214772) | 0.131728 / 6.500664 (-6.368936) | 0.059994 / 0.075469 (-0.015475) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.354920 / 1.841788 (-0.486868) | 19.427822 / 8.074308 (11.353514) | 15.289037 / 10.191392 (5.097645) | 0.170437 / 0.680424 (-0.509987) | 0.020242 / 0.534201 (-0.513959) | 0.394921 / 0.579283 (-0.184362) | 0.426447 / 0.434364 (-0.007917) | 0.468321 / 0.540337 (-0.072017) | 0.671052 / 1.386936 (-0.715884) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#bade7af74437347a760830466eb74f7a8ce0d799 \"CML watermark\")\n" ]

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[ "You can find the reasoning behind this naming scheme [here](https://github.com/huggingface/transformers/pull/16343#discussion_r931182168).\r\n\r\nThis point has been raised several times, so I'd be okay with starting with `00001-` (also to be consistent with the `transformers` sharding), but I'm not sure @lhoestq agrees.", "We start at 0 in `datasets` for consistency with Apache Spark, Apache Beam, Dask and others.\r\n\r\nAlso note `transformers` isn't a good reference on this topic. I talked with the maintainers when they added shards but it was already released this way. Though we found that there is a backward-compatible way in `transformers` to start at 0, but no request from `transformers` users to changes this AFAIK.", "not sure it would be a good idea to break the consistency now, IMO", "Makes sense to start at 0 for plenty of good reasons so I'm on board.\r\n\r\nWhat about the second part `-of-0000X`? With single commit PR #6269 just getting merged, there was a note about issues with 100+ file edits https://github.com/huggingface/datasets/pull/6269#issuecomment-1755428581.\r\n\r\nThat would be my last remaining concern in the context of the `push_to_hub(..., append=True)` work to be done, where appending a single file to the full dataset will require renaming every other existing file in the dataset. If it doesn't seem like a big issue for this work then all the better 👍" ]

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[ "`writer._num_bytes` is updated every `writer_batch_size`-th call to the `write` method (default `writer_batch_size` is 1000 (examples)). You should be able to see the update by passing a smaller `writer_batch_size` to the `load_dataset_builder`.\r\n\r\nWe could improve this by supporting the string `writer_batch_size` version as we do with `max_shard_size`, and capping `writer_batch_size` to `max_shard_size` in scenarios where the default `writer_batch_size` > `max_shard_size`. ", "Thanks, reducing `writer_batch_size` solved my problem :)" ]

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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) | 0.006663 / 0.011353 (-0.004690) | 0.004091 / 0.011008 (-0.006918) | 0.084954 / 0.038508 (0.046445) | 0.071869 / 0.023109 (0.048760) | 0.314706 / 0.275898 (0.038808) | 0.352794 / 0.323480 (0.029314) | 0.004027 / 0.007986 (-0.003959) | 0.003371 / 0.004328 (-0.000957) | 0.065456 / 0.004250 (0.061205) | 0.055828 / 0.037052 (0.018775) | 0.316502 / 0.258489 (0.058013) | 0.377979 / 0.293841 (0.084138) | 0.030870 / 0.128546 (-0.097676) | 0.008616 / 0.075646 (-0.067030) | 0.288625 / 0.419271 (-0.130646) | 0.052314 / 0.043533 (0.008781) | 0.322725 / 0.255139 (0.067586) | 0.351810 / 0.283200 (0.068611) | 0.025726 / 0.141683 (-0.115957) | 1.439308 / 1.452155 (-0.012847) | 1.524484 / 1.492716 (0.031768) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.235212 / 0.018006 (0.217206) | 0.444926 / 0.000490 (0.444437) | 0.009887 / 0.000200 (0.009687) | 0.000402 / 0.000054 (0.000347) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028956 / 0.037411 (-0.008455) | 0.084401 / 0.014526 (0.069875) | 0.339686 / 0.176557 (0.163130) | 0.186785 / 0.737135 (-0.550350) | 0.195017 / 0.296338 (-0.101322) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.405480 / 0.215209 (0.190271) | 4.024315 / 2.077655 (1.946661) | 2.056398 / 1.504120 (0.552278) | 1.912099 / 1.541195 (0.370904) | 1.950119 / 1.468490 (0.481629) | 0.486071 / 4.584777 (-4.098706) | 3.578501 / 3.745712 (-0.167211) | 3.268980 / 5.269862 (-2.000881) | 2.018114 / 4.565676 (-2.547563) | 0.057440 / 0.424275 (-0.366835) | 0.007281 / 0.007607 (-0.000326) | 0.474760 / 0.226044 (0.248716) | 4.746908 / 2.268929 (2.477979) | 2.550111 / 55.444624 (-52.894513) | 2.171932 / 6.876477 (-4.704544) | 2.392235 / 2.142072 (0.250162) | 0.585940 / 4.805227 (-4.219287) | 0.136445 / 6.500664 (-6.364219) | 0.062125 / 0.075469 (-0.013344) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.270763 / 1.841788 (-0.571025) | 19.213516 / 8.074308 (11.139208) | 13.992620 / 10.191392 (3.801228) | 0.167356 / 0.680424 (-0.513068) | 0.018261 / 0.534201 (-0.515940) | 0.392489 / 0.579283 (-0.186794) | 0.418845 / 0.434364 (-0.015519) | 0.461824 / 0.540337 (-0.078513) | 0.649661 / 1.386936 (-0.737275) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006675 / 0.011353 (-0.004678) | 0.003913 / 0.011008 (-0.007096) | 0.064943 / 0.038508 (0.026435) | 0.072426 / 0.023109 (0.049317) | 0.400785 / 0.275898 (0.124887) | 0.434359 / 0.323480 (0.110879) | 0.005370 / 0.007986 (-0.002616) | 0.003290 / 0.004328 (-0.001038) | 0.065035 / 0.004250 (0.060785) | 0.054924 / 0.037052 (0.017872) | 0.404442 / 0.258489 (0.145953) | 0.439027 / 0.293841 (0.145186) | 0.032467 / 0.128546 (-0.096080) | 0.008565 / 0.075646 (-0.067081) | 0.070653 / 0.419271 (-0.348619) | 0.048034 / 0.043533 (0.004501) | 0.400869 / 0.255139 (0.145730) | 0.423048 / 0.283200 (0.139848) | 0.022757 / 0.141683 (-0.118926) | 1.516956 / 1.452155 (0.064801) | 1.581599 / 1.492716 (0.088883) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.214761 / 0.018006 (0.196755) | 0.440921 / 0.000490 (0.440431) | 0.007538 / 0.000200 (0.007338) | 0.000087 / 0.000054 (0.000033) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032313 / 0.037411 (-0.005099) | 0.091365 / 0.014526 (0.076839) | 0.106665 / 0.176557 (-0.069891) | 0.158637 / 0.737135 (-0.578498) | 0.104894 / 0.296338 (-0.191445) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.432995 / 0.215209 (0.217786) | 4.339911 / 2.077655 (2.262256) | 2.313139 / 1.504120 (0.809019) | 2.142552 / 1.541195 (0.601357) | 2.279275 / 1.468490 (0.810785) | 0.501133 / 4.584777 (-4.083644) | 3.696160 / 3.745712 (-0.049552) | 3.341886 / 5.269862 (-1.927976) | 2.105972 / 4.565676 (-2.459705) | 0.059268 / 0.424275 (-0.365008) | 0.007568 / 0.007607 (-0.000039) | 0.512546 / 0.226044 (0.286502) | 5.130219 / 2.268929 (2.861290) | 2.808292 / 55.444624 (-52.636332) | 2.478721 / 6.876477 (-4.397755) | 2.679341 / 2.142072 (0.537269) | 0.599022 / 4.805227 (-4.206206) | 0.143761 / 6.500664 (-6.356903) | 0.062061 / 0.075469 (-0.013409) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.430507 / 1.841788 (-0.411281) | 20.458085 / 8.074308 (12.383777) | 15.268356 / 10.191392 (5.076964) | 0.163359 / 0.680424 (-0.517065) | 0.020908 / 0.534201 (-0.513293) | 0.396870 / 0.579283 (-0.182413) | 0.432630 / 0.434364 (-0.001733) | 0.475909 / 0.540337 (-0.064429) | 0.681031 / 1.386936 (-0.705905) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#fd1dd6aa4c7fa7744c1c1f877573ff59f1529292 \"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) | 0.005815 / 0.011353 (-0.005538) | 0.003419 / 0.011008 (-0.007589) | 0.080286 / 0.038508 (0.041778) | 0.056487 / 0.023109 (0.033377) | 0.304414 / 0.275898 (0.028516) | 0.341039 / 0.323480 (0.017559) | 0.004392 / 0.007986 (-0.003594) | 0.002852 / 0.004328 (-0.001477) | 0.062339 / 0.004250 (0.058089) | 0.044683 / 0.037052 (0.007630) | 0.311651 / 0.258489 (0.053162) | 0.357249 / 0.293841 (0.063409) | 0.027300 / 0.128546 (-0.101246) | 0.007963 / 0.075646 (-0.067683) | 0.261948 / 0.419271 (-0.157323) | 0.044952 / 0.043533 (0.001419) | 0.309990 / 0.255139 (0.054851) | 0.340735 / 0.283200 (0.057536) | 0.020786 / 0.141683 (-0.120897) | 1.471378 / 1.452155 (0.019224) | 1.517260 / 1.492716 (0.024543) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.245447 / 0.018006 (0.227441) | 0.418967 / 0.000490 (0.418477) | 0.007039 / 0.000200 (0.006840) | 0.000196 / 0.000054 (0.000142) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022880 / 0.037411 (-0.014532) | 0.071862 / 0.014526 (0.057337) | 0.083009 / 0.176557 (-0.093547) | 0.143414 / 0.737135 (-0.593722) | 0.082896 / 0.296338 (-0.213442) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.390645 / 0.215209 (0.175436) | 3.888104 / 2.077655 (1.810450) | 1.859572 / 1.504120 (0.355452) | 1.683803 / 1.541195 (0.142608) | 1.697902 / 1.468490 (0.229412) | 0.499537 / 4.584777 (-4.085239) | 3.015832 / 3.745712 (-0.729881) | 2.805696 / 5.269862 (-2.464166) | 1.830408 / 4.565676 (-2.735268) | 0.058191 / 0.424275 (-0.366085) | 0.006357 / 0.007607 (-0.001250) | 0.462486 / 0.226044 (0.236442) | 4.634951 / 2.268929 (2.366022) | 2.309364 / 55.444624 (-53.135260) | 1.979521 / 6.876477 (-4.896956) | 2.080011 / 2.142072 (-0.062062) | 0.593086 / 4.805227 (-4.212141) | 0.124856 / 6.500664 (-6.375808) | 0.060172 / 0.075469 (-0.015297) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.251439 / 1.841788 (-0.590349) | 17.068999 / 8.074308 (8.994691) | 13.527209 / 10.191392 (3.335817) | 0.146636 / 0.680424 (-0.533788) | 0.016866 / 0.534201 (-0.517335) | 0.333202 / 0.579283 (-0.246081) | 0.360444 / 0.434364 (-0.073920) | 0.388378 / 0.540337 (-0.151959) | 0.530519 / 1.386936 (-0.856417) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006043 / 0.011353 (-0.005310) | 0.003612 / 0.011008 (-0.007396) | 0.062644 / 0.038508 (0.024135) | 0.056104 / 0.023109 (0.032995) | 0.446328 / 0.275898 (0.170430) | 0.478044 / 0.323480 (0.154564) | 0.004641 / 0.007986 (-0.003345) | 0.002896 / 0.004328 (-0.001432) | 0.062344 / 0.004250 (0.058093) | 0.046339 / 0.037052 (0.009287) | 0.454866 / 0.258489 (0.196377) | 0.484242 / 0.293841 (0.190401) | 0.028602 / 0.128546 (-0.099944) | 0.008075 / 0.075646 (-0.067571) | 0.067980 / 0.419271 (-0.351291) | 0.041339 / 0.043533 (-0.002194) | 0.452911 / 0.255139 (0.197772) | 0.474180 / 0.283200 (0.190981) | 0.019395 / 0.141683 (-0.122288) | 1.432161 / 1.452155 (-0.019993) | 1.505800 / 1.492716 (0.013083) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.216983 / 0.018006 (0.198977) | 0.406232 / 0.000490 (0.405743) | 0.005101 / 0.000200 (0.004902) | 0.000077 / 0.000054 (0.000022) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026295 / 0.037411 (-0.011116) | 0.080490 / 0.014526 (0.065964) | 0.088105 / 0.176557 (-0.088451) | 0.143294 / 0.737135 (-0.593841) | 0.089125 / 0.296338 (-0.207213) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.465512 / 0.215209 (0.250302) | 4.648656 / 2.077655 (2.571002) | 2.598225 / 1.504120 (1.094105) | 2.409588 / 1.541195 (0.868393) | 2.513745 / 1.468490 (1.045255) | 0.507425 / 4.584777 (-4.077352) | 3.130164 / 3.745712 (-0.615548) | 2.836817 / 5.269862 (-2.433045) | 1.836029 / 4.565676 (-2.729647) | 0.058829 / 0.424275 (-0.365446) | 0.006551 / 0.007607 (-0.001056) | 0.537892 / 0.226044 (0.311848) | 5.401079 / 2.268929 (3.132150) | 3.019817 / 55.444624 (-52.424807) | 2.695131 / 6.876477 (-4.181346) | 2.805321 / 2.142072 (0.663248) | 0.595681 / 4.805227 (-4.209546) | 0.124368 / 6.500664 (-6.376296) | 0.060712 / 0.075469 (-0.014757) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.361508 / 1.841788 (-0.480279) | 17.811373 / 8.074308 (9.737065) | 14.482705 / 10.191392 (4.291313) | 0.153193 / 0.680424 (-0.527231) | 0.018347 / 0.534201 (-0.515854) | 0.330900 / 0.579283 (-0.248383) | 0.374948 / 0.434364 (-0.059416) | 0.385615 / 0.540337 (-0.154722) | 0.568077 / 1.386936 (-0.818859) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#18ef408c21f8efbb2142f050a691b5c916455af3 \"CML watermark\")\n" ]

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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) | 0.008410 / 0.011353 (-0.002943) | 0.004888 / 0.011008 (-0.006120) | 0.103342 / 0.038508 (0.064834) | 0.103697 / 0.023109 (0.080587) | 0.416445 / 0.275898 (0.140547) | 0.454604 / 0.323480 (0.131124) | 0.004976 / 0.007986 (-0.003010) | 0.003957 / 0.004328 (-0.000371) | 0.077398 / 0.004250 (0.073148) | 0.069026 / 0.037052 (0.031973) | 0.420484 / 0.258489 (0.161995) | 0.471828 / 0.293841 (0.177987) | 0.037133 / 0.128546 (-0.091413) | 0.010009 / 0.075646 (-0.065637) | 0.349573 / 0.419271 (-0.069698) | 0.063240 / 0.043533 (0.019708) | 0.421554 / 0.255139 (0.166415) | 0.433548 / 0.283200 (0.150348) | 0.029397 / 0.141683 (-0.112286) | 1.716860 / 1.452155 (0.264705) | 1.851264 / 1.492716 (0.358547) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.269733 / 0.018006 (0.251727) | 0.493313 / 0.000490 (0.492823) | 0.010438 / 0.000200 (0.010238) | 0.000401 / 0.000054 (0.000347) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034690 / 0.037411 (-0.002722) | 0.105304 / 0.014526 (0.090778) | 0.115831 / 0.176557 (-0.060726) | 0.185017 / 0.737135 (-0.552118) | 0.117480 / 0.296338 (-0.178859) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.479414 / 0.215209 (0.264205) | 4.785526 / 2.077655 (2.707871) | 2.388412 / 1.504120 (0.884292) | 2.178222 / 1.541195 (0.637027) | 2.248214 / 1.468490 (0.779723) | 0.571723 / 4.584777 (-4.013054) | 4.721250 / 3.745712 (0.975538) | 4.073893 / 5.269862 (-1.195969) | 2.618131 / 4.565676 (-1.947546) | 0.068406 / 0.424275 (-0.355869) | 0.008890 / 0.007607 (0.001283) | 0.564224 / 0.226044 (0.338180) | 5.631412 / 2.268929 (3.362483) | 3.072212 / 55.444624 (-52.372412) | 2.760574 / 6.876477 (-4.115903) | 2.963060 / 2.142072 (0.820987) | 0.708150 / 4.805227 (-4.097077) | 0.160324 / 6.500664 (-6.340340) | 0.075402 / 0.075469 (-0.000067) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.649965 / 1.841788 (-0.191823) | 24.297517 / 8.074308 (16.223209) | 17.658675 / 10.191392 (7.467283) | 0.171399 / 0.680424 (-0.509025) | 0.021172 / 0.534201 (-0.513029) | 0.477196 / 0.579283 (-0.102087) | 0.503900 / 0.434364 (0.069536) | 0.555858 / 0.540337 (0.015520) | 0.824302 / 1.386936 (-0.562634) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008613 / 0.011353 (-0.002740) | 0.004848 / 0.011008 (-0.006160) | 0.078344 / 0.038508 (0.039836) | 0.098976 / 0.023109 (0.075867) | 0.520713 / 0.275898 (0.244815) | 0.566350 / 0.323480 (0.242870) | 0.006658 / 0.007986 (-0.001327) | 0.004043 / 0.004328 (-0.000285) | 0.077881 / 0.004250 (0.073631) | 0.070731 / 0.037052 (0.033678) | 0.519717 / 0.258489 (0.261228) | 0.575623 / 0.293841 (0.281782) | 0.038542 / 0.128546 (-0.090004) | 0.010277 / 0.075646 (-0.065369) | 0.084269 / 0.419271 (-0.335002) | 0.058088 / 0.043533 (0.014555) | 0.541790 / 0.255139 (0.286651) | 0.534915 / 0.283200 (0.251715) | 0.027851 / 0.141683 (-0.113831) | 1.814827 / 1.452155 (0.362672) | 1.898208 / 1.492716 (0.405492) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.244162 / 0.018006 (0.226156) | 0.482895 / 0.000490 (0.482405) | 0.005734 / 0.000200 (0.005534) | 0.000127 / 0.000054 (0.000072) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.039328 / 0.037411 (0.001917) | 0.119795 / 0.014526 (0.105269) | 0.128570 / 0.176557 (-0.047986) | 0.191207 / 0.737135 (-0.545929) | 0.127147 / 0.296338 (-0.169192) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.533545 / 0.215209 (0.318336) | 5.320135 / 2.077655 (3.242480) | 2.924573 / 1.504120 (1.420453) | 2.741351 / 1.541195 (1.200156) | 2.824217 / 1.468490 (1.355727) | 0.595842 / 4.584777 (-3.988935) | 4.343499 / 3.745712 (0.597787) | 3.976546 / 5.269862 (-1.293316) | 2.532541 / 4.565676 (-2.033135) | 0.070480 / 0.424275 (-0.353795) | 0.008868 / 0.007607 (0.001260) | 0.634297 / 0.226044 (0.408253) | 6.327314 / 2.268929 (4.058386) | 3.530741 / 55.444624 (-51.913883) | 3.121435 / 6.876477 (-3.755042) | 3.344473 / 2.142072 (1.202401) | 0.719413 / 4.805227 (-4.085814) | 0.162348 / 6.500664 (-6.338316) | 0.074964 / 0.075469 (-0.000505) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.679095 / 1.841788 (-0.162693) | 25.071620 / 8.074308 (16.997312) | 18.422398 / 10.191392 (8.231006) | 0.223981 / 0.680424 (-0.456443) | 0.026537 / 0.534201 (-0.507664) | 0.513867 / 0.579283 (-0.065416) | 0.535874 / 0.434364 (0.101510) | 0.567971 / 0.540337 (0.027634) | 0.842545 / 1.386936 (-0.544391) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d8b871016c25cb3b90ac1ff65a4e54f0454f525e \"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) | 0.006445 / 0.011353 (-0.004908) | 0.003978 / 0.011008 (-0.007030) | 0.084542 / 0.038508 (0.046034) | 0.069231 / 0.023109 (0.046122) | 0.308794 / 0.275898 (0.032896) | 0.339246 / 0.323480 (0.015766) | 0.005269 / 0.007986 (-0.002716) | 0.003285 / 0.004328 (-0.001043) | 0.065336 / 0.004250 (0.061086) | 0.053480 / 0.037052 (0.016428) | 0.316775 / 0.258489 (0.058286) | 0.357885 / 0.293841 (0.064044) | 0.031309 / 0.128546 (-0.097237) | 0.008450 / 0.075646 (-0.067196) | 0.287911 / 0.419271 (-0.131361) | 0.052756 / 0.043533 (0.009223) | 0.321516 / 0.255139 (0.066377) | 0.331998 / 0.283200 (0.048799) | 0.024129 / 0.141683 (-0.117553) | 1.507718 / 1.452155 (0.055563) | 1.571400 / 1.492716 (0.078683) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.237536 / 0.018006 (0.219530) | 0.499691 / 0.000490 (0.499201) | 0.007644 / 0.000200 (0.007444) | 0.000284 / 0.000054 (0.000230) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028243 / 0.037411 (-0.009168) | 0.081556 / 0.014526 (0.067030) | 0.096877 / 0.176557 (-0.079680) | 0.149985 / 0.737135 (-0.587150) | 0.095556 / 0.296338 (-0.200783) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.383215 / 0.215209 (0.168006) | 3.815800 / 2.077655 (1.738145) | 1.832227 / 1.504120 (0.328107) | 1.664001 / 1.541195 (0.122806) | 1.698786 / 1.468490 (0.230296) | 0.487594 / 4.584777 (-4.097183) | 3.569767 / 3.745712 (-0.175945) | 3.262387 / 5.269862 (-2.007475) | 2.017105 / 4.565676 (-2.548572) | 0.057555 / 0.424275 (-0.366720) | 0.007170 / 0.007607 (-0.000437) | 0.460134 / 0.226044 (0.234090) | 4.629800 / 2.268929 (2.360871) | 2.357126 / 55.444624 (-53.087499) | 1.970144 / 6.876477 (-4.906332) | 2.123520 / 2.142072 (-0.018552) | 0.613058 / 4.805227 (-4.192169) | 0.135869 / 6.500664 (-6.364795) | 0.061292 / 0.075469 (-0.014177) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.311294 / 1.841788 (-0.530494) | 18.640807 / 8.074308 (10.566499) | 13.946834 / 10.191392 (3.755442) | 0.163976 / 0.680424 (-0.516448) | 0.018527 / 0.534201 (-0.515674) | 0.390530 / 0.579283 (-0.188753) | 0.412661 / 0.434364 (-0.021703) | 0.459514 / 0.540337 (-0.080823) | 0.635026 / 1.386936 (-0.751910) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006645 / 0.011353 (-0.004708) | 0.003943 / 0.011008 (-0.007066) | 0.064470 / 0.038508 (0.025962) | 0.069895 / 0.023109 (0.046786) | 0.411091 / 0.275898 (0.135193) | 0.437628 / 0.323480 (0.114148) | 0.005214 / 0.007986 (-0.002772) | 0.003281 / 0.004328 (-0.001047) | 0.064434 / 0.004250 (0.060183) | 0.054294 / 0.037052 (0.017241) | 0.413576 / 0.258489 (0.155087) | 0.448793 / 0.293841 (0.154952) | 0.031754 / 0.128546 (-0.096793) | 0.008530 / 0.075646 (-0.067117) | 0.069950 / 0.419271 (-0.349322) | 0.047747 / 0.043533 (0.004214) | 0.411241 / 0.255139 (0.156102) | 0.430076 / 0.283200 (0.146876) | 0.023462 / 0.141683 (-0.118220) | 1.519501 / 1.452155 (0.067346) | 1.575782 / 1.492716 (0.083066) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.231816 / 0.018006 (0.213810) | 0.442802 / 0.000490 (0.442312) | 0.005738 / 0.000200 (0.005539) | 0.000087 / 0.000054 (0.000032) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031426 / 0.037411 (-0.005985) | 0.090758 / 0.014526 (0.076233) | 0.103414 / 0.176557 (-0.073142) | 0.156409 / 0.737135 (-0.580726) | 0.103900 / 0.296338 (-0.192439) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.438897 / 0.215209 (0.223688) | 4.385318 / 2.077655 (2.307663) | 2.352042 / 1.504120 (0.847923) | 2.182228 / 1.541195 (0.641033) | 2.266256 / 1.468490 (0.797766) | 0.492780 / 4.584777 (-4.091997) | 3.665787 / 3.745712 (-0.079925) | 3.315329 / 5.269862 (-1.954533) | 2.027993 / 4.565676 (-2.537684) | 0.058220 / 0.424275 (-0.366055) | 0.007429 / 0.007607 (-0.000178) | 0.508790 / 0.226044 (0.282746) | 5.107093 / 2.268929 (2.838164) | 2.799789 / 55.444624 (-52.644836) | 2.462828 / 6.876477 (-4.413649) | 2.610193 / 2.142072 (0.468120) | 0.588133 / 4.805227 (-4.217094) | 0.133418 / 6.500664 (-6.367246) | 0.059793 / 0.075469 (-0.015676) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.363358 / 1.841788 (-0.478430) | 19.258372 / 8.074308 (11.184064) | 14.730977 / 10.191392 (4.539584) | 0.169493 / 0.680424 (-0.510931) | 0.020462 / 0.534201 (-0.513739) | 0.397980 / 0.579283 (-0.181303) | 0.426638 / 0.434364 (-0.007726) | 0.474249 / 0.540337 (-0.066088) | 0.677640 / 1.386936 (-0.709296) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#90b3d2619ecb8f01dd12283c30f04dfe6e443795 \"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) | 0.006536 / 0.011353 (-0.004817) | 0.003827 / 0.011008 (-0.007181) | 0.084394 / 0.038508 (0.045886) | 0.073166 / 0.023109 (0.050056) | 0.309380 / 0.275898 (0.033482) | 0.338501 / 0.323480 (0.015021) | 0.005346 / 0.007986 (-0.002640) | 0.003273 / 0.004328 (-0.001056) | 0.064606 / 0.004250 (0.060356) | 0.053500 / 0.037052 (0.016447) | 0.313143 / 0.258489 (0.054654) | 0.354364 / 0.293841 (0.060523) | 0.030919 / 0.128546 (-0.097627) | 0.008512 / 0.075646 (-0.067134) | 0.292774 / 0.419271 (-0.126498) | 0.052441 / 0.043533 (0.008908) | 0.310503 / 0.255139 (0.055364) | 0.341211 / 0.283200 (0.058011) | 0.023608 / 0.141683 (-0.118074) | 1.456220 / 1.452155 (0.004065) | 1.540189 / 1.492716 (0.047473) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.234321 / 0.018006 (0.216315) | 0.451809 / 0.000490 (0.451319) | 0.008560 / 0.000200 (0.008360) | 0.000085 / 0.000054 (0.000031) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028165 / 0.037411 (-0.009246) | 0.082548 / 0.014526 (0.068023) | 0.752621 / 0.176557 (0.576065) | 0.263949 / 0.737135 (-0.473187) | 0.097635 / 0.296338 (-0.198704) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.386611 / 0.215209 (0.171402) | 3.847528 / 2.077655 (1.769873) | 1.859173 / 1.504120 (0.355053) | 1.685269 / 1.541195 (0.144074) | 1.715823 / 1.468490 (0.247333) | 0.485272 / 4.584777 (-4.099505) | 3.500724 / 3.745712 (-0.244988) | 3.252149 / 5.269862 (-2.017713) | 2.052914 / 4.565676 (-2.512762) | 0.056794 / 0.424275 (-0.367481) | 0.007317 / 0.007607 (-0.000291) | 0.457924 / 0.226044 (0.231879) | 4.570092 / 2.268929 (2.301163) | 2.328829 / 55.444624 (-53.115796) | 1.986502 / 6.876477 (-4.889975) | 2.164645 / 2.142072 (0.022573) | 0.580455 / 4.805227 (-4.224772) | 0.134415 / 6.500664 (-6.366249) | 0.060506 / 0.075469 (-0.014963) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.267423 / 1.841788 (-0.574364) | 18.653450 / 8.074308 (10.579142) | 13.919682 / 10.191392 (3.728290) | 0.144001 / 0.680424 (-0.536423) | 0.018218 / 0.534201 (-0.515983) | 0.389933 / 0.579283 (-0.189350) | 0.418366 / 0.434364 (-0.015998) | 0.456341 / 0.540337 (-0.083997) | 0.631401 / 1.386936 (-0.755535) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006838 / 0.011353 (-0.004515) | 0.003973 / 0.011008 (-0.007036) | 0.065217 / 0.038508 (0.026709) | 0.068357 / 0.023109 (0.045248) | 0.407960 / 0.275898 (0.132062) | 0.437794 / 0.323480 (0.114314) | 0.005398 / 0.007986 (-0.002587) | 0.003360 / 0.004328 (-0.000969) | 0.065503 / 0.004250 (0.061253) | 0.055676 / 0.037052 (0.018623) | 0.411381 / 0.258489 (0.152892) | 0.446902 / 0.293841 (0.153061) | 0.032156 / 0.128546 (-0.096390) | 0.008702 / 0.075646 (-0.066944) | 0.072295 / 0.419271 (-0.346976) | 0.047722 / 0.043533 (0.004189) | 0.406125 / 0.255139 (0.150986) | 0.428359 / 0.283200 (0.145160) | 0.021901 / 0.141683 (-0.119782) | 1.464186 / 1.452155 (0.012032) | 1.532809 / 1.492716 (0.040093) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.218505 / 0.018006 (0.200499) | 0.447450 / 0.000490 (0.446961) | 0.006509 / 0.000200 (0.006309) | 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) | 0.031789 / 0.037411 (-0.005622) | 0.091100 / 0.014526 (0.076574) | 0.102812 / 0.176557 (-0.073745) | 0.155988 / 0.737135 (-0.581147) | 0.103983 / 0.296338 (-0.192355) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.436431 / 0.215209 (0.221222) | 4.336072 / 2.077655 (2.258417) | 2.344613 / 1.504120 (0.840493) | 2.173513 / 1.541195 (0.632319) | 2.313134 / 1.468490 (0.844644) | 0.493651 / 4.584777 (-4.091126) | 3.657541 / 3.745712 (-0.088171) | 3.289933 / 5.269862 (-1.979928) | 2.040271 / 4.565676 (-2.525406) | 0.058092 / 0.424275 (-0.366183) | 0.007348 / 0.007607 (-0.000259) | 0.507506 / 0.226044 (0.281462) | 5.093477 / 2.268929 (2.824548) | 2.770579 / 55.444624 (-52.674046) | 2.449507 / 6.876477 (-4.426970) | 2.645470 / 2.142072 (0.503397) | 0.590799 / 4.805227 (-4.214429) | 0.133411 / 6.500664 (-6.367253) | 0.059507 / 0.075469 (-0.015962) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.381148 / 1.841788 (-0.460639) | 19.188716 / 8.074308 (11.114408) | 14.709111 / 10.191392 (4.517719) | 0.191104 / 0.680424 (-0.489320) | 0.019862 / 0.534201 (-0.514339) | 0.395380 / 0.579283 (-0.183903) | 0.424757 / 0.434364 (-0.009607) | 0.468810 / 0.540337 (-0.071527) | 0.687058 / 1.386936 (-0.699878) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#407169e1ea91ae31f79ff29c4115b04a461279ab \"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) | 0.008872 / 0.011353 (-0.002481) | 0.004824 / 0.011008 (-0.006184) | 0.097012 / 0.038508 (0.058504) | 0.074728 / 0.023109 (0.051619) | 0.400604 / 0.275898 (0.124706) | 0.434316 / 0.323480 (0.110836) | 0.006025 / 0.007986 (-0.001961) | 0.004153 / 0.004328 (-0.000176) | 0.074093 / 0.004250 (0.069842) | 0.057239 / 0.037052 (0.020187) | 0.420611 / 0.258489 (0.162122) | 0.457779 / 0.293841 (0.163938) | 0.047610 / 0.128546 (-0.080936) | 0.014577 / 0.075646 (-0.061069) | 0.414351 / 0.419271 (-0.004921) | 0.063072 / 0.043533 (0.019539) | 0.426141 / 0.255139 (0.171002) | 0.429844 / 0.283200 (0.146644) | 0.034754 / 0.141683 (-0.106929) | 1.620946 / 1.452155 (0.168792) | 1.725831 / 1.492716 (0.233115) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.304712 / 0.018006 (0.286706) | 0.646924 / 0.000490 (0.646434) | 0.014486 / 0.000200 (0.014286) | 0.000626 / 0.000054 (0.000572) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034935 / 0.037411 (-0.002477) | 0.085788 / 0.014526 (0.071262) | 0.107749 / 0.176557 (-0.068807) | 0.170924 / 0.737135 (-0.566211) | 0.134985 / 0.296338 (-0.161354) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.602913 / 0.215209 (0.387704) | 6.041700 / 2.077655 (3.964045) | 2.539970 / 1.504120 (1.035850) | 2.184166 / 1.541195 (0.642972) | 2.241783 / 1.468490 (0.773293) | 0.864601 / 4.584777 (-3.720176) | 5.246955 / 3.745712 (1.501243) | 4.850458 / 5.269862 (-0.419404) | 3.101497 / 4.565676 (-1.464179) | 0.098591 / 0.424275 (-0.325684) | 0.008902 / 0.007607 (0.001295) | 0.732278 / 0.226044 (0.506234) | 7.163557 / 2.268929 (4.894629) | 3.226444 / 55.444624 (-52.218180) | 2.578737 / 6.876477 (-4.297740) | 2.850212 / 2.142072 (0.708140) | 1.026390 / 4.805227 (-3.778837) | 0.217077 / 6.500664 (-6.283587) | 0.080344 / 0.075469 (0.004875) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.687488 / 1.841788 (-0.154300) | 24.686337 / 8.074308 (16.612029) | 21.315989 / 10.191392 (11.124597) | 0.226176 / 0.680424 (-0.454248) | 0.035774 / 0.534201 (-0.498427) | 0.477807 / 0.579283 (-0.101476) | 0.636305 / 0.434364 (0.201941) | 0.553341 / 0.540337 (0.013003) | 0.797267 / 1.386936 (-0.589669) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008955 / 0.011353 (-0.002398) | 0.006099 / 0.011008 (-0.004909) | 0.086306 / 0.038508 (0.047798) | 0.090783 / 0.023109 (0.067674) | 0.554802 / 0.275898 (0.278904) | 0.598778 / 0.323480 (0.275299) | 0.008656 / 0.007986 (0.000670) | 0.004487 / 0.004328 (0.000159) | 0.084194 / 0.004250 (0.079943) | 0.076048 / 0.037052 (0.038996) | 0.533212 / 0.258489 (0.274723) | 0.584029 / 0.293841 (0.290188) | 0.051913 / 0.128546 (-0.076634) | 0.014253 / 0.075646 (-0.061393) | 0.100500 / 0.419271 (-0.318772) | 0.061092 / 0.043533 (0.017560) | 0.516955 / 0.255139 (0.261816) | 0.562754 / 0.283200 (0.279554) | 0.036673 / 0.141683 (-0.105010) | 1.853655 / 1.452155 (0.401501) | 1.968358 / 1.492716 (0.475642) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.308258 / 0.018006 (0.290252) | 0.630492 / 0.000490 (0.630002) | 0.010575 / 0.000200 (0.010375) | 0.000271 / 0.000054 (0.000217) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034762 / 0.037411 (-0.002649) | 0.107314 / 0.014526 (0.092788) | 0.132160 / 0.176557 (-0.044396) | 0.178737 / 0.737135 (-0.558398) | 0.125988 / 0.296338 (-0.170351) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.730738 / 0.215209 (0.515528) | 7.240393 / 2.077655 (5.162738) | 3.557665 / 1.504120 (2.053545) | 3.541425 / 1.541195 (2.000230) | 3.103849 / 1.468490 (1.635359) | 0.926843 / 4.584777 (-3.657934) | 5.818264 / 3.745712 (2.072552) | 5.012984 / 5.269862 (-0.256878) | 3.286085 / 4.565676 (-1.279591) | 0.104879 / 0.424275 (-0.319396) | 0.009010 / 0.007607 (0.001403) | 0.806145 / 0.226044 (0.580101) | 8.263655 / 2.268929 (5.994727) | 4.108932 / 55.444624 (-51.335693) | 3.454613 / 6.876477 (-3.421864) | 3.629045 / 2.142072 (1.486973) | 1.062325 / 4.805227 (-3.742902) | 0.220482 / 6.500664 (-6.280182) | 0.081440 / 0.075469 (0.005970) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.665587 / 1.841788 (-0.176201) | 23.695299 / 8.074308 (15.620991) | 22.917493 / 10.191392 (12.726101) | 0.259033 / 0.680424 (-0.421391) | 0.040118 / 0.534201 (-0.494083) | 0.487329 / 0.579283 (-0.091954) | 0.607482 / 0.434364 (0.173118) | 0.568383 / 0.540337 (0.028045) | 0.824486 / 1.386936 (-0.562450) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#53592bb8f635a1d6ea3e77acc290efdfb28fcbd7 \"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) | 0.007095 / 0.011353 (-0.004258) | 0.004260 / 0.011008 (-0.006748) | 0.084729 / 0.038508 (0.046221) | 0.076498 / 0.023109 (0.053389) | 0.325981 / 0.275898 (0.050083) | 0.357140 / 0.323480 (0.033661) | 0.004325 / 0.007986 (-0.003660) | 0.003632 / 0.004328 (-0.000696) | 0.065075 / 0.004250 (0.060824) | 0.059058 / 0.037052 (0.022006) | 0.331895 / 0.258489 (0.073406) | 0.370782 / 0.293841 (0.076941) | 0.031886 / 0.128546 (-0.096660) | 0.008782 / 0.075646 (-0.066864) | 0.288159 / 0.419271 (-0.131113) | 0.053012 / 0.043533 (0.009479) | 0.319992 / 0.255139 (0.064853) | 0.347061 / 0.283200 (0.063861) | 0.026365 / 0.141683 (-0.115317) | 1.486112 / 1.452155 (0.033958) | 1.570150 / 1.492716 (0.077434) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.277155 / 0.018006 (0.259149) | 0.573507 / 0.000490 (0.573017) | 0.010122 / 0.000200 (0.009922) | 0.000322 / 0.000054 (0.000268) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029076 / 0.037411 (-0.008335) | 0.082517 / 0.014526 (0.067991) | 0.100710 / 0.176557 (-0.075847) | 0.154529 / 0.737135 (-0.582606) | 0.099531 / 0.296338 (-0.196807) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.382058 / 0.215209 (0.166849) | 3.803307 / 2.077655 (1.725652) | 1.834107 / 1.504120 (0.329987) | 1.665703 / 1.541195 (0.124508) | 1.739520 / 1.468490 (0.271030) | 0.490544 / 4.584777 (-4.094233) | 3.577874 / 3.745712 (-0.167838) | 3.327631 / 5.269862 (-1.942231) | 2.056634 / 4.565676 (-2.509043) | 0.057871 / 0.424275 (-0.366404) | 0.007326 / 0.007607 (-0.000281) | 0.453993 / 0.226044 (0.227949) | 4.549179 / 2.268929 (2.280250) | 2.320304 / 55.444624 (-53.124321) | 1.966082 / 6.876477 (-4.910395) | 2.189979 / 2.142072 (0.047907) | 0.586678 / 4.805227 (-4.218549) | 0.134919 / 6.500664 (-6.365745) | 0.061649 / 0.075469 (-0.013820) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.286228 / 1.841788 (-0.555560) | 19.409674 / 8.074308 (11.335366) | 14.290463 / 10.191392 (4.099071) | 0.165766 / 0.680424 (-0.514658) | 0.018200 / 0.534201 (-0.516001) | 0.390526 / 0.579283 (-0.188757) | 0.410953 / 0.434364 (-0.023411) | 0.455921 / 0.540337 (-0.084416) | 0.642271 / 1.386936 (-0.744665) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007288 / 0.011353 (-0.004064) | 0.004348 / 0.011008 (-0.006660) | 0.065935 / 0.038508 (0.027427) | 0.087327 / 0.023109 (0.064218) | 0.413461 / 0.275898 (0.137563) | 0.458904 / 0.323480 (0.135424) | 0.005996 / 0.007986 (-0.001990) | 0.003648 / 0.004328 (-0.000680) | 0.066578 / 0.004250 (0.062328) | 0.062072 / 0.037052 (0.025020) | 0.418469 / 0.258489 (0.159980) | 0.468960 / 0.293841 (0.175119) | 0.032616 / 0.128546 (-0.095930) | 0.008961 / 0.075646 (-0.066686) | 0.072537 / 0.419271 (-0.346734) | 0.048302 / 0.043533 (0.004769) | 0.411845 / 0.255139 (0.156706) | 0.441730 / 0.283200 (0.158530) | 0.025038 / 0.141683 (-0.116645) | 1.519402 / 1.452155 (0.067248) | 1.601791 / 1.492716 (0.109074) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.322494 / 0.018006 (0.304488) | 0.570210 / 0.000490 (0.569720) | 0.025815 / 0.000200 (0.025615) | 0.000166 / 0.000054 (0.000111) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034657 / 0.037411 (-0.002754) | 0.096024 / 0.014526 (0.081498) | 0.109134 / 0.176557 (-0.067422) | 0.162170 / 0.737135 (-0.574965) | 0.110472 / 0.296338 (-0.185866) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.439032 / 0.215209 (0.223823) | 4.385768 / 2.077655 (2.308113) | 2.343261 / 1.504120 (0.839142) | 2.157926 / 1.541195 (0.616731) | 2.299193 / 1.468490 (0.830703) | 0.498961 / 4.584777 (-4.085816) | 3.651909 / 3.745712 (-0.093803) | 3.387587 / 5.269862 (-1.882275) | 2.144553 / 4.565676 (-2.421123) | 0.058242 / 0.424275 (-0.366033) | 0.007416 / 0.007607 (-0.000191) | 0.512714 / 0.226044 (0.286670) | 5.138569 / 2.268929 (2.869641) | 2.778683 / 55.444624 (-52.665941) | 2.532990 / 6.876477 (-4.343487) | 2.782211 / 2.142072 (0.640139) | 0.591881 / 4.805227 (-4.213346) | 0.135005 / 6.500664 (-6.365660) | 0.060965 / 0.075469 (-0.014504) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.356311 / 1.841788 (-0.485477) | 20.029994 / 8.074308 (11.955686) | 14.666570 / 10.191392 (4.475178) | 0.164363 / 0.680424 (-0.516061) | 0.020685 / 0.534201 (-0.513516) | 0.396020 / 0.579283 (-0.183263) | 0.429407 / 0.434364 (-0.004957) | 0.476924 / 0.540337 (-0.063413) | 0.693389 / 1.386936 (-0.693547) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#292d627e398e30a538a616395f3b5ce4e89bb1e8 \"CML watermark\")\n" ]

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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) | 0.006761 / 0.011353 (-0.004592) | 0.004307 / 0.011008 (-0.006701) | 0.084682 / 0.038508 (0.046174) | 0.083994 / 0.023109 (0.060885) | 0.316612 / 0.275898 (0.040714) | 0.346157 / 0.323480 (0.022678) | 0.004490 / 0.007986 (-0.003495) | 0.003699 / 0.004328 (-0.000629) | 0.066144 / 0.004250 (0.061894) | 0.057958 / 0.037052 (0.020906) | 0.319018 / 0.258489 (0.060529) | 0.367597 / 0.293841 (0.073756) | 0.031146 / 0.128546 (-0.097401) | 0.008814 / 0.075646 (-0.066832) | 0.290971 / 0.419271 (-0.128301) | 0.052769 / 0.043533 (0.009236) | 0.313125 / 0.255139 (0.057986) | 0.330473 / 0.283200 (0.047273) | 0.025922 / 0.141683 (-0.115760) | 1.494989 / 1.452155 (0.042834) | 1.556140 / 1.492716 (0.063423) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.310580 / 0.018006 (0.292574) | 0.563600 / 0.000490 (0.563110) | 0.012344 / 0.000200 (0.012144) | 0.000382 / 0.000054 (0.000328) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031468 / 0.037411 (-0.005943) | 0.084856 / 0.014526 (0.070331) | 0.101371 / 0.176557 (-0.075186) | 0.158735 / 0.737135 (-0.578400) | 0.102451 / 0.296338 (-0.193888) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.402288 / 0.215209 (0.187079) | 4.001351 / 2.077655 (1.923696) | 2.022710 / 1.504120 (0.518590) | 1.850236 / 1.541195 (0.309041) | 1.946779 / 1.468490 (0.478289) | 0.485828 / 4.584777 (-4.098949) | 3.584925 / 3.745712 (-0.160787) | 3.400815 / 5.269862 (-1.869046) | 2.123187 / 4.565676 (-2.442490) | 0.057373 / 0.424275 (-0.366902) | 0.007383 / 0.007607 (-0.000224) | 0.479773 / 0.226044 (0.253729) | 4.805342 / 2.268929 (2.536414) | 2.530151 / 55.444624 (-52.914473) | 2.190136 / 6.876477 (-4.686341) | 2.463666 / 2.142072 (0.321593) | 0.583512 / 4.805227 (-4.221715) | 0.134205 / 6.500664 (-6.366459) | 0.062021 / 0.075469 (-0.013448) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.239532 / 1.841788 (-0.602255) | 20.252941 / 8.074308 (12.178633) | 14.265697 / 10.191392 (4.074305) | 0.158745 / 0.680424 (-0.521679) | 0.018605 / 0.534201 (-0.515596) | 0.394246 / 0.579283 (-0.185037) | 0.415260 / 0.434364 (-0.019104) | 0.462636 / 0.540337 (-0.077701) | 0.645318 / 1.386936 (-0.741618) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007063 / 0.011353 (-0.004290) | 0.004388 / 0.011008 (-0.006621) | 0.064997 / 0.038508 (0.026489) | 0.085135 / 0.023109 (0.062026) | 0.424349 / 0.275898 (0.148451) | 0.456033 / 0.323480 (0.132553) | 0.005745 / 0.007986 (-0.002241) | 0.003705 / 0.004328 (-0.000624) | 0.065835 / 0.004250 (0.061585) | 0.058366 / 0.037052 (0.021314) | 0.421654 / 0.258489 (0.163165) | 0.460334 / 0.293841 (0.166493) | 0.032828 / 0.128546 (-0.095718) | 0.008974 / 0.075646 (-0.066673) | 0.072524 / 0.419271 (-0.346747) | 0.048558 / 0.043533 (0.005025) | 0.413546 / 0.255139 (0.158407) | 0.435765 / 0.283200 (0.152565) | 0.023754 / 0.141683 (-0.117929) | 1.476884 / 1.452155 (0.024730) | 1.560011 / 1.492716 (0.067294) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.318279 / 0.018006 (0.300272) | 0.544990 / 0.000490 (0.544501) | 0.007118 / 0.000200 (0.006918) | 0.000097 / 0.000054 (0.000043) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033352 / 0.037411 (-0.004059) | 0.092921 / 0.014526 (0.078395) | 0.109028 / 0.176557 (-0.067528) | 0.161433 / 0.737135 (-0.575703) | 0.108445 / 0.296338 (-0.187893) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.438925 / 0.215209 (0.223716) | 4.400714 / 2.077655 (2.323059) | 2.403727 / 1.504120 (0.899607) | 2.236472 / 1.541195 (0.695277) | 2.319219 / 1.468490 (0.850729) | 0.490159 / 4.584777 (-4.094618) | 3.647474 / 3.745712 (-0.098238) | 3.433144 / 5.269862 (-1.836718) | 2.145367 / 4.565676 (-2.420310) | 0.057994 / 0.424275 (-0.366281) | 0.007452 / 0.007607 (-0.000155) | 0.513808 / 0.226044 (0.287763) | 5.130792 / 2.268929 (2.861863) | 2.861691 / 55.444624 (-52.582934) | 2.473292 / 6.876477 (-4.403185) | 2.756445 / 2.142072 (0.614372) | 0.586783 / 4.805227 (-4.218444) | 0.134170 / 6.500664 (-6.366494) | 0.061149 / 0.075469 (-0.014320) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.350144 / 1.841788 (-0.491644) | 21.003528 / 8.074308 (12.929220) | 15.174314 / 10.191392 (4.982922) | 0.186535 / 0.680424 (-0.493888) | 0.020821 / 0.534201 (-0.513380) | 0.399210 / 0.579283 (-0.180073) | 0.431942 / 0.434364 (-0.002422) | 0.475395 / 0.540337 (-0.064942) | 0.677457 / 1.386936 (-0.709479) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6aa5fc278324a253eab43ad1bc048e822e4ae5c7 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007062 / 0.011353 (-0.004291) | 0.004299 / 0.011008 (-0.006710) | 0.086019 / 0.038508 (0.047511) | 0.085166 / 0.023109 (0.062057) | 0.355804 / 0.275898 (0.079906) | 0.381056 / 0.323480 (0.057577) | 0.005500 / 0.007986 (-0.002486) | 0.003496 / 0.004328 (-0.000833) | 0.064866 / 0.004250 (0.060615) | 0.057399 / 0.037052 (0.020346) | 0.357914 / 0.258489 (0.099425) | 0.395387 / 0.293841 (0.101546) | 0.031763 / 0.128546 (-0.096784) | 0.008665 / 0.075646 (-0.066981) | 0.290097 / 0.419271 (-0.129175) | 0.053297 / 0.043533 (0.009765) | 0.355659 / 0.255139 (0.100520) | 0.378232 / 0.283200 (0.095032) | 0.026015 / 0.141683 (-0.115668) | 1.437121 / 1.452155 (-0.015034) | 1.538798 / 1.492716 (0.046082) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.243518 / 0.018006 (0.225511) | 0.461361 / 0.000490 (0.460871) | 0.009529 / 0.000200 (0.009329) | 0.000473 / 0.000054 (0.000419) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030379 / 0.037411 (-0.007032) | 0.089851 / 0.014526 (0.075325) | 0.098278 / 0.176557 (-0.078278) | 0.157077 / 0.737135 (-0.580058) | 0.098997 / 0.296338 (-0.197341) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.382415 / 0.215209 (0.167206) | 3.801964 / 2.077655 (1.724309) | 1.887680 / 1.504120 (0.383560) | 1.775903 / 1.541195 (0.234709) | 1.851338 / 1.468490 (0.382848) | 0.483616 / 4.584777 (-4.101161) | 3.612977 / 3.745712 (-0.132736) | 3.397700 / 5.269862 (-1.872162) | 2.114572 / 4.565676 (-2.451105) | 0.057250 / 0.424275 (-0.367025) | 0.007362 / 0.007607 (-0.000245) | 0.456873 / 0.226044 (0.230829) | 4.567319 / 2.268929 (2.298391) | 2.399476 / 55.444624 (-53.045148) | 2.054542 / 6.876477 (-4.821935) | 2.343432 / 2.142072 (0.201359) | 0.582319 / 4.805227 (-4.222908) | 0.134045 / 6.500664 (-6.366619) | 0.062726 / 0.075469 (-0.012743) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.283390 / 1.841788 (-0.558398) | 20.358511 / 8.074308 (12.284202) | 14.933989 / 10.191392 (4.742597) | 0.164960 / 0.680424 (-0.515464) | 0.018625 / 0.534201 (-0.515576) | 0.394087 / 0.579283 (-0.185196) | 0.416761 / 0.434364 (-0.017603) | 0.466669 / 0.540337 (-0.073669) | 0.643161 / 1.386936 (-0.743775) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007141 / 0.011353 (-0.004212) | 0.004185 / 0.011008 (-0.006824) | 0.066097 / 0.038508 (0.027588) | 0.088436 / 0.023109 (0.065327) | 0.401189 / 0.275898 (0.125291) | 0.440402 / 0.323480 (0.116922) | 0.005729 / 0.007986 (-0.002257) | 0.003527 / 0.004328 (-0.000801) | 0.065278 / 0.004250 (0.061027) | 0.060866 / 0.037052 (0.023813) | 0.407035 / 0.258489 (0.148546) | 0.443923 / 0.293841 (0.150083) | 0.032922 / 0.128546 (-0.095625) | 0.008739 / 0.075646 (-0.066907) | 0.071800 / 0.419271 (-0.347472) | 0.048994 / 0.043533 (0.005461) | 0.403736 / 0.255139 (0.148597) | 0.419566 / 0.283200 (0.136366) | 0.025369 / 0.141683 (-0.116314) | 1.474980 / 1.452155 (0.022825) | 1.553500 / 1.492716 (0.060784) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.225224 / 0.018006 (0.207218) | 0.462891 / 0.000490 (0.462401) | 0.006958 / 0.000200 (0.006758) | 0.000163 / 0.000054 (0.000108) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034431 / 0.037411 (-0.002980) | 0.100021 / 0.014526 (0.085495) | 0.108339 / 0.176557 (-0.068217) | 0.162762 / 0.737135 (-0.574374) | 0.108951 / 0.296338 (-0.187388) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.435966 / 0.215209 (0.220757) | 4.351744 / 2.077655 (2.274089) | 2.372307 / 1.504120 (0.868187) | 2.192146 / 1.541195 (0.650951) | 2.326839 / 1.468490 (0.858349) | 0.488292 / 4.584777 (-4.096485) | 3.745227 / 3.745712 (-0.000485) | 3.456306 / 5.269862 (-1.813556) | 2.159771 / 4.565676 (-2.405906) | 0.057953 / 0.424275 (-0.366322) | 0.007469 / 0.007607 (-0.000138) | 0.515116 / 0.226044 (0.289071) | 5.162871 / 2.268929 (2.893942) | 2.850336 / 55.444624 (-52.594288) | 2.514700 / 6.876477 (-4.361777) | 2.748843 / 2.142072 (0.606770) | 0.587687 / 4.805227 (-4.217540) | 0.134333 / 6.500664 (-6.366331) | 0.062097 / 0.075469 (-0.013372) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.377082 / 1.841788 (-0.464705) | 21.103127 / 8.074308 (13.028819) | 15.325275 / 10.191392 (5.133883) | 0.166225 / 0.680424 (-0.514199) | 0.020472 / 0.534201 (-0.513729) | 0.395866 / 0.579283 (-0.183417) | 0.444964 / 0.434364 (0.010600) | 0.475367 / 0.540337 (-0.064970) | 0.693325 / 1.386936 (-0.693611) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#79b5bbbd52ffd90dd958c05b333d7c90a03756cc \"CML watermark\")\n" ]

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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) | 0.006920 / 0.011353 (-0.004433) | 0.004306 / 0.011008 (-0.006703) | 0.085961 / 0.038508 (0.047453) | 0.087008 / 0.023109 (0.063899) | 0.308953 / 0.275898 (0.033055) | 0.349919 / 0.323480 (0.026440) | 0.005705 / 0.007986 (-0.002281) | 0.003565 / 0.004328 (-0.000763) | 0.066272 / 0.004250 (0.062022) | 0.056438 / 0.037052 (0.019385) | 0.312927 / 0.258489 (0.054437) | 0.363081 / 0.293841 (0.069240) | 0.031947 / 0.128546 (-0.096600) | 0.008801 / 0.075646 (-0.066845) | 0.288657 / 0.419271 (-0.130615) | 0.053746 / 0.043533 (0.010213) | 0.305815 / 0.255139 (0.050676) | 0.327174 / 0.283200 (0.043975) | 0.024863 / 0.141683 (-0.116820) | 1.489718 / 1.452155 (0.037563) | 1.566726 / 1.492716 (0.074009) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.289273 / 0.018006 (0.271266) | 0.555519 / 0.000490 (0.555029) | 0.006522 / 0.000200 (0.006322) | 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) | 0.031968 / 0.037411 (-0.005443) | 0.085113 / 0.014526 (0.070587) | 0.103931 / 0.176557 (-0.072625) | 0.158471 / 0.737135 (-0.578665) | 0.102633 / 0.296338 (-0.193705) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.399592 / 0.215209 (0.184383) | 4.004453 / 2.077655 (1.926798) | 2.047224 / 1.504120 (0.543104) | 1.896203 / 1.541195 (0.355008) | 1.974056 / 1.468490 (0.505566) | 0.485964 / 4.584777 (-4.098813) | 3.650648 / 3.745712 (-0.095064) | 3.475953 / 5.269862 (-1.793908) | 2.168105 / 4.565676 (-2.397571) | 0.058167 / 0.424275 (-0.366108) | 0.007517 / 0.007607 (-0.000090) | 0.475386 / 0.226044 (0.249342) | 4.758300 / 2.268929 (2.489372) | 2.527540 / 55.444624 (-52.917085) | 2.180544 / 6.876477 (-4.695933) | 2.460148 / 2.142072 (0.318076) | 0.589944 / 4.805227 (-4.215284) | 0.136474 / 6.500664 (-6.364190) | 0.061462 / 0.075469 (-0.014007) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.245816 / 1.841788 (-0.595972) | 20.376958 / 8.074308 (12.302650) | 14.764579 / 10.191392 (4.573187) | 0.152436 / 0.680424 (-0.527988) | 0.018580 / 0.534201 (-0.515621) | 0.394680 / 0.579283 (-0.184603) | 0.424162 / 0.434364 (-0.010202) | 0.465604 / 0.540337 (-0.074733) | 0.658531 / 1.386936 (-0.728405) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007105 / 0.011353 (-0.004248) | 0.004441 / 0.011008 (-0.006567) | 0.068792 / 0.038508 (0.030284) | 0.080371 / 0.023109 (0.057262) | 0.430263 / 0.275898 (0.154365) | 0.451743 / 0.323480 (0.128263) | 0.005987 / 0.007986 (-0.001999) | 0.003639 / 0.004328 (-0.000690) | 0.065462 / 0.004250 (0.061212) | 0.059852 / 0.037052 (0.022800) | 0.438390 / 0.258489 (0.179901) | 0.458679 / 0.293841 (0.164838) | 0.033044 / 0.128546 (-0.095502) | 0.008845 / 0.075646 (-0.066802) | 0.071772 / 0.419271 (-0.347500) | 0.048840 / 0.043533 (0.005307) | 0.415707 / 0.255139 (0.160568) | 0.431216 / 0.283200 (0.148017) | 0.024422 / 0.141683 (-0.117260) | 1.502249 / 1.452155 (0.050094) | 1.566767 / 1.492716 (0.074050) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.311352 / 0.018006 (0.293346) | 0.550395 / 0.000490 (0.549906) | 0.005190 / 0.000200 (0.004990) | 0.000116 / 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) | 0.034264 / 0.037411 (-0.003147) | 0.098712 / 0.014526 (0.084186) | 0.110906 / 0.176557 (-0.065651) | 0.161670 / 0.737135 (-0.575465) | 0.111023 / 0.296338 (-0.185316) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.435296 / 0.215209 (0.220087) | 4.331231 / 2.077655 (2.253576) | 2.305009 / 1.504120 (0.800889) | 2.154492 / 1.541195 (0.613297) | 2.344017 / 1.468490 (0.875527) | 0.496924 / 4.584777 (-4.087853) | 3.750782 / 3.745712 (0.005070) | 3.380193 / 5.269862 (-1.889669) | 2.161239 / 4.565676 (-2.404438) | 0.058456 / 0.424275 (-0.365819) | 0.007395 / 0.007607 (-0.000212) | 0.507824 / 0.226044 (0.281780) | 5.081564 / 2.268929 (2.812635) | 2.824080 / 55.444624 (-52.620544) | 2.458835 / 6.876477 (-4.417642) | 2.747897 / 2.142072 (0.605824) | 0.600727 / 4.805227 (-4.204500) | 0.135085 / 6.500664 (-6.365579) | 0.060506 / 0.075469 (-0.014963) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.376873 / 1.841788 (-0.464915) | 21.211922 / 8.074308 (13.137614) | 15.022845 / 10.191392 (4.831453) | 0.195388 / 0.680424 (-0.485036) | 0.020268 / 0.534201 (-0.513933) | 0.398971 / 0.579283 (-0.180312) | 0.427588 / 0.434364 (-0.006776) | 0.478044 / 0.540337 (-0.062293) | 0.687904 / 1.386936 (-0.699033) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7fb5fae8f79b3db4a94013aa2af7c63796ef2d64 \"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) | 0.006134 / 0.011353 (-0.005219) | 0.003655 / 0.011008 (-0.007354) | 0.081295 / 0.038508 (0.042787) | 0.060202 / 0.023109 (0.037093) | 0.330005 / 0.275898 (0.054107) | 0.361219 / 0.323480 (0.037739) | 0.004766 / 0.007986 (-0.003220) | 0.002942 / 0.004328 (-0.001386) | 0.063322 / 0.004250 (0.059072) | 0.047844 / 0.037052 (0.010791) | 0.340375 / 0.258489 (0.081886) | 0.406301 / 0.293841 (0.112460) | 0.027474 / 0.128546 (-0.101072) | 0.007991 / 0.075646 (-0.067655) | 0.262746 / 0.419271 (-0.156526) | 0.045575 / 0.043533 (0.002042) | 0.324123 / 0.255139 (0.068984) | 0.344399 / 0.283200 (0.061199) | 0.021806 / 0.141683 (-0.119877) | 1.425390 / 1.452155 (-0.026765) | 1.487920 / 1.492716 (-0.004796) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.217504 / 0.018006 (0.199498) | 0.420878 / 0.000490 (0.420388) | 0.007312 / 0.000200 (0.007112) | 0.000218 / 0.000054 (0.000163) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023507 / 0.037411 (-0.013905) | 0.073493 / 0.014526 (0.058967) | 0.084857 / 0.176557 (-0.091700) | 0.145130 / 0.737135 (-0.592005) | 0.085204 / 0.296338 (-0.211135) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.388767 / 0.215209 (0.173557) | 3.877998 / 2.077655 (1.800344) | 1.881447 / 1.504120 (0.377327) | 1.714555 / 1.541195 (0.173360) | 1.772551 / 1.468490 (0.304061) | 0.505146 / 4.584777 (-4.079631) | 3.045471 / 3.745712 (-0.700241) | 2.834436 / 5.269862 (-2.435426) | 1.859896 / 4.565676 (-2.705780) | 0.057806 / 0.424275 (-0.366469) | 0.006378 / 0.007607 (-0.001229) | 0.458339 / 0.226044 (0.232294) | 4.588125 / 2.268929 (2.319196) | 2.302215 / 55.444624 (-53.142409) | 1.981297 / 6.876477 (-4.895180) | 2.152967 / 2.142072 (0.010895) | 0.590166 / 4.805227 (-4.215061) | 0.125753 / 6.500664 (-6.374911) | 0.061583 / 0.075469 (-0.013887) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.232195 / 1.841788 (-0.609593) | 17.761159 / 8.074308 (9.686851) | 13.829498 / 10.191392 (3.638106) | 0.131936 / 0.680424 (-0.548488) | 0.016909 / 0.534201 (-0.517292) | 0.332615 / 0.579283 (-0.246668) | 0.358149 / 0.434364 (-0.076215) | 0.384251 / 0.540337 (-0.156087) | 0.536453 / 1.386936 (-0.850483) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006253 / 0.011353 (-0.005100) | 0.003639 / 0.011008 (-0.007370) | 0.062810 / 0.038508 (0.024302) | 0.063761 / 0.023109 (0.040652) | 0.450538 / 0.275898 (0.174640) | 0.483793 / 0.323480 (0.160313) | 0.004973 / 0.007986 (-0.003013) | 0.002918 / 0.004328 (-0.001411) | 0.062140 / 0.004250 (0.057889) | 0.050328 / 0.037052 (0.013275) | 0.455860 / 0.258489 (0.197371) | 0.492399 / 0.293841 (0.198558) | 0.028928 / 0.128546 (-0.099618) | 0.008166 / 0.075646 (-0.067481) | 0.067860 / 0.419271 (-0.351411) | 0.040990 / 0.043533 (-0.002542) | 0.451343 / 0.255139 (0.196204) | 0.473769 / 0.283200 (0.190569) | 0.021585 / 0.141683 (-0.120097) | 1.451040 / 1.452155 (-0.001115) | 1.516065 / 1.492716 (0.023349) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.230994 / 0.018006 (0.212988) | 0.428404 / 0.000490 (0.427915) | 0.003777 / 0.000200 (0.003577) | 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) | 0.027394 / 0.037411 (-0.010018) | 0.081692 / 0.014526 (0.067166) | 0.091568 / 0.176557 (-0.084988) | 0.146149 / 0.737135 (-0.590987) | 0.092200 / 0.296338 (-0.204139) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.467086 / 0.215209 (0.251877) | 4.664862 / 2.077655 (2.587207) | 2.575703 / 1.504120 (1.071583) | 2.396587 / 1.541195 (0.855392) | 2.506064 / 1.468490 (1.037574) | 0.511942 / 4.584777 (-4.072834) | 3.196320 / 3.745712 (-0.549392) | 2.916627 / 5.269862 (-2.353235) | 1.919372 / 4.565676 (-2.646305) | 0.058769 / 0.424275 (-0.365506) | 0.006487 / 0.007607 (-0.001120) | 0.539095 / 0.226044 (0.313051) | 5.404675 / 2.268929 (3.135746) | 2.988962 / 55.444624 (-52.455662) | 2.670134 / 6.876477 (-4.206343) | 2.837414 / 2.142072 (0.695342) | 0.614776 / 4.805227 (-4.190451) | 0.125806 / 6.500664 (-6.374858) | 0.061593 / 0.075469 (-0.013876) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.346171 / 1.841788 (-0.495617) | 18.374626 / 8.074308 (10.300318) | 14.508723 / 10.191392 (4.317331) | 0.146771 / 0.680424 (-0.533652) | 0.018438 / 0.534201 (-0.515763) | 0.336944 / 0.579283 (-0.242339) | 0.385631 / 0.434364 (-0.048733) | 0.391922 / 0.540337 (-0.148416) | 0.568904 / 1.386936 (-0.818032) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3e8d420808718c9a1453a2e7ee3484ca12c9c70d \"CML watermark\")\n" ]

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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) | 0.006695 / 0.011353 (-0.004658) | 0.004321 / 0.011008 (-0.006687) | 0.084558 / 0.038508 (0.046050) | 0.076290 / 0.023109 (0.053181) | 0.312331 / 0.275898 (0.036433) | 0.349854 / 0.323480 (0.026374) | 0.004267 / 0.007986 (-0.003719) | 0.003595 / 0.004328 (-0.000733) | 0.065077 / 0.004250 (0.060826) | 0.057461 / 0.037052 (0.020409) | 0.314989 / 0.258489 (0.056500) | 0.364767 / 0.293841 (0.070926) | 0.031726 / 0.128546 (-0.096820) | 0.008674 / 0.075646 (-0.066972) | 0.288282 / 0.419271 (-0.130990) | 0.052845 / 0.043533 (0.009312) | 0.317501 / 0.255139 (0.062362) | 0.333241 / 0.283200 (0.050041) | 0.026412 / 0.141683 (-0.115271) | 1.475648 / 1.452155 (0.023493) | 1.551656 / 1.492716 (0.058939) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.276512 / 0.018006 (0.258506) | 0.576350 / 0.000490 (0.575861) | 0.009518 / 0.000200 (0.009318) | 0.000280 / 0.000054 (0.000226) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029332 / 0.037411 (-0.008079) | 0.082904 / 0.014526 (0.068379) | 0.102516 / 0.176557 (-0.074041) | 0.159355 / 0.737135 (-0.577780) | 0.104112 / 0.296338 (-0.192226) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.379144 / 0.215209 (0.163935) | 3.785283 / 2.077655 (1.707629) | 1.833753 / 1.504120 (0.329633) | 1.667906 / 1.541195 (0.126711) | 1.751551 / 1.468490 (0.283061) | 0.480998 / 4.584777 (-4.103779) | 3.533433 / 3.745712 (-0.212279) | 3.343363 / 5.269862 (-1.926498) | 2.094169 / 4.565676 (-2.471508) | 0.056613 / 0.424275 (-0.367662) | 0.007410 / 0.007607 (-0.000197) | 0.455077 / 0.226044 (0.229033) | 4.541380 / 2.268929 (2.272452) | 2.269151 / 55.444624 (-53.175473) | 1.955663 / 6.876477 (-4.920814) | 2.227663 / 2.142072 (0.085591) | 0.580597 / 4.805227 (-4.224630) | 0.135034 / 6.500664 (-6.365630) | 0.062091 / 0.075469 (-0.013378) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.276295 / 1.841788 (-0.565492) | 20.072827 / 8.074308 (11.998519) | 14.296462 / 10.191392 (4.105070) | 0.164936 / 0.680424 (-0.515488) | 0.018415 / 0.534201 (-0.515786) | 0.390894 / 0.579283 (-0.188389) | 0.415515 / 0.434364 (-0.018849) | 0.462798 / 0.540337 (-0.077540) | 0.650099 / 1.386936 (-0.736837) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007218 / 0.011353 (-0.004135) | 0.004246 / 0.011008 (-0.006763) | 0.065818 / 0.038508 (0.027310) | 0.087315 / 0.023109 (0.064206) | 0.406449 / 0.275898 (0.130551) | 0.442008 / 0.323480 (0.118528) | 0.005752 / 0.007986 (-0.002233) | 0.003624 / 0.004328 (-0.000704) | 0.065349 / 0.004250 (0.061099) | 0.062423 / 0.037052 (0.025371) | 0.410099 / 0.258489 (0.151610) | 0.448929 / 0.293841 (0.155088) | 0.032498 / 0.128546 (-0.096048) | 0.008877 / 0.075646 (-0.066770) | 0.071611 / 0.419271 (-0.347661) | 0.048038 / 0.043533 (0.004506) | 0.407957 / 0.255139 (0.152818) | 0.424045 / 0.283200 (0.140846) | 0.025222 / 0.141683 (-0.116461) | 1.496191 / 1.452155 (0.044037) | 1.580765 / 1.492716 (0.088048) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.274798 / 0.018006 (0.256792) | 0.581410 / 0.000490 (0.580920) | 0.007302 / 0.000200 (0.007102) | 0.000160 / 0.000054 (0.000106) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034068 / 0.037411 (-0.003343) | 0.096116 / 0.014526 (0.081590) | 0.110234 / 0.176557 (-0.066323) | 0.163246 / 0.737135 (-0.573889) | 0.110250 / 0.296338 (-0.186089) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.442381 / 0.215209 (0.227172) | 4.427061 / 2.077655 (2.349406) | 2.361013 / 1.504120 (0.856893) | 2.185048 / 1.541195 (0.643853) | 2.312544 / 1.468490 (0.844054) | 0.498347 / 4.584777 (-4.086430) | 3.640839 / 3.745712 (-0.104873) | 3.353405 / 5.269862 (-1.916457) | 2.082038 / 4.565676 (-2.483638) | 0.058786 / 0.424275 (-0.365489) | 0.007403 / 0.007607 (-0.000205) | 0.517894 / 0.226044 (0.291850) | 5.184257 / 2.268929 (2.915329) | 2.838467 / 55.444624 (-52.606157) | 2.511116 / 6.876477 (-4.365361) | 2.757816 / 2.142072 (0.615743) | 0.644050 / 4.805227 (-4.161177) | 0.136446 / 6.500664 (-6.364218) | 0.062219 / 0.075469 (-0.013250) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.350916 / 1.841788 (-0.490872) | 20.549280 / 8.074308 (12.474972) | 14.697569 / 10.191392 (4.506177) | 0.149818 / 0.680424 (-0.530606) | 0.020187 / 0.534201 (-0.514014) | 0.396008 / 0.579283 (-0.183275) | 0.427535 / 0.434364 (-0.006829) | 0.484544 / 0.540337 (-0.055794) | 0.687076 / 1.386936 (-0.699860) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#02a0d7cc9bdbc745c355c0bf8a210d8bf0b90327 \"CML watermark\")\n", "I'd rather be consistent with `huggingface_hub` and have this module in `utils/` with the exceptions exposed in `utils/__init__.py` ...", "Ok, I'll close this PR.\r\n\r\n> Maybe we could ask huggingface_hub to align with the rest of open-source libraries and expose the errors/exceptions at the root of the library...\r\n\r\ncc @Wauplin \r\n\r\nIt would be nice to have an HF style guide to ensure consistency across our libraries 🙂. ", "I can expose exceptions at root level yes.\r\n\r\nAbout having guidelines and consistency, let's try to do our best but it's not really in the essence of HF to formalize stuff in libraries :unamused: " ]

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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) | 0.008112 / 0.011353 (-0.003241) | 0.004762 / 0.011008 (-0.006247) | 0.101349 / 0.038508 (0.062841) | 0.092361 / 0.023109 (0.069252) | 0.418429 / 0.275898 (0.142531) | 0.427332 / 0.323480 (0.103852) | 0.006112 / 0.007986 (-0.001874) | 0.003920 / 0.004328 (-0.000408) | 0.076813 / 0.004250 (0.072563) | 0.064361 / 0.037052 (0.027309) | 0.420526 / 0.258489 (0.162037) | 0.441576 / 0.293841 (0.147735) | 0.044760 / 0.128546 (-0.083787) | 0.010054 / 0.075646 (-0.065592) | 0.346063 / 0.419271 (-0.073209) | 0.077453 / 0.043533 (0.033920) | 0.412871 / 0.255139 (0.157732) | 0.408307 / 0.283200 (0.125107) | 0.033398 / 0.141683 (-0.108285) | 1.755825 / 1.452155 (0.303671) | 1.852347 / 1.492716 (0.359630) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.274201 / 0.018006 (0.256194) | 0.536375 / 0.000490 (0.535885) | 0.008076 / 0.000200 (0.007876) | 0.000159 / 0.000054 (0.000105) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033567 / 0.037411 (-0.003845) | 0.102378 / 0.014526 (0.087852) | 0.114176 / 0.176557 (-0.062381) | 0.180576 / 0.737135 (-0.556560) | 0.114801 / 0.296338 (-0.181538) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.450300 / 0.215209 (0.235091) | 4.490940 / 2.077655 (2.413285) | 2.172412 / 1.504120 (0.668292) | 1.978746 / 1.541195 (0.437551) | 2.065602 / 1.468490 (0.597112) | 0.571260 / 4.584777 (-4.013517) | 4.185485 / 3.745712 (0.439773) | 3.885594 / 5.269862 (-1.384268) | 2.532942 / 4.565676 (-2.032735) | 0.067612 / 0.424275 (-0.356663) | 0.008694 / 0.007607 (0.001087) | 0.533375 / 0.226044 (0.307331) | 5.321261 / 2.268929 (3.052333) | 2.697788 / 55.444624 (-52.746836) | 2.331328 / 6.876477 (-4.545149) | 2.585168 / 2.142072 (0.443096) | 0.681760 / 4.805227 (-4.123467) | 0.157687 / 6.500664 (-6.342977) | 0.071014 / 0.075469 (-0.004455) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.525689 / 1.841788 (-0.316098) | 23.162280 / 8.074308 (15.087972) | 16.644941 / 10.191392 (6.453548) | 0.182588 / 0.680424 (-0.497836) | 0.021653 / 0.534201 (-0.512548) | 0.466556 / 0.579283 (-0.112727) | 0.511902 / 0.434364 (0.077538) | 0.553707 / 0.540337 (0.013370) | 0.777830 / 1.386936 (-0.609106) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007954 / 0.011353 (-0.003399) | 0.004645 / 0.011008 (-0.006363) | 0.079096 / 0.038508 (0.040587) | 0.088200 / 0.023109 (0.065090) | 0.508882 / 0.275898 (0.232984) | 0.545986 / 0.323480 (0.222506) | 0.006233 / 0.007986 (-0.001752) | 0.004016 / 0.004328 (-0.000312) | 0.078103 / 0.004250 (0.073853) | 0.066354 / 0.037052 (0.029302) | 0.504132 / 0.258489 (0.245643) | 0.543714 / 0.293841 (0.249873) | 0.038140 / 0.128546 (-0.090407) | 0.011201 / 0.075646 (-0.064446) | 0.085713 / 0.419271 (-0.333559) | 0.057169 / 0.043533 (0.013637) | 0.488161 / 0.255139 (0.233022) | 0.516231 / 0.283200 (0.233031) | 0.027868 / 0.141683 (-0.113814) | 1.794084 / 1.452155 (0.341930) | 1.884993 / 1.492716 (0.392276) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.263108 / 0.018006 (0.245102) | 0.495761 / 0.000490 (0.495272) | 0.007056 / 0.000200 (0.006856) | 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) | 0.039089 / 0.037411 (0.001678) | 0.113332 / 0.014526 (0.098806) | 0.130137 / 0.176557 (-0.046419) | 0.189330 / 0.737135 (-0.547805) | 0.125860 / 0.296338 (-0.170479) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.530496 / 0.215209 (0.315287) | 5.349235 / 2.077655 (3.271581) | 2.975886 / 1.504120 (1.471766) | 2.786368 / 1.541195 (1.245173) | 2.920448 / 1.468490 (1.451958) | 0.575677 / 4.584777 (-4.009100) | 4.215535 / 3.745712 (0.469823) | 3.879984 / 5.269862 (-1.389878) | 2.420193 / 4.565676 (-2.145484) | 0.068506 / 0.424275 (-0.355769) | 0.008785 / 0.007607 (0.001178) | 0.611471 / 0.226044 (0.385427) | 6.118399 / 2.268929 (3.849471) | 3.509376 / 55.444624 (-51.935248) | 3.149219 / 6.876477 (-3.727257) | 3.413861 / 2.142072 (1.271788) | 0.697586 / 4.805227 (-4.107641) | 0.157767 / 6.500664 (-6.342897) | 0.071539 / 0.075469 (-0.003930) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.625196 / 1.841788 (-0.216591) | 24.347319 / 8.074308 (16.273011) | 17.365789 / 10.191392 (7.174397) | 0.217590 / 0.680424 (-0.462834) | 0.023885 / 0.534201 (-0.510316) | 0.477226 / 0.579283 (-0.102057) | 0.529319 / 0.434364 (0.094955) | 0.622299 / 0.540337 (0.081962) | 0.835295 / 1.386936 (-0.551641) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3de42c8fae86c602fc71ac6d166e5c77f4149446 \"CML watermark\")\n", "CI errors are unrelated or due to flaky tests", "<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) | 0.006288 / 0.011353 (-0.005065) | 0.003836 / 0.011008 (-0.007172) | 0.080958 / 0.038508 (0.042450) | 0.065934 / 0.023109 (0.042825) | 0.312597 / 0.275898 (0.036699) | 0.351216 / 0.323480 (0.027736) | 0.004864 / 0.007986 (-0.003121) | 0.002961 / 0.004328 (-0.001368) | 0.063142 / 0.004250 (0.058892) | 0.049822 / 0.037052 (0.012770) | 0.320305 / 0.258489 (0.061816) | 0.363151 / 0.293841 (0.069310) | 0.027561 / 0.128546 (-0.100985) | 0.008176 / 0.075646 (-0.067470) | 0.261290 / 0.419271 (-0.157982) | 0.045517 / 0.043533 (0.001984) | 0.309218 / 0.255139 (0.054079) | 0.340140 / 0.283200 (0.056940) | 0.021000 / 0.141683 (-0.120683) | 1.448699 / 1.452155 (-0.003456) | 1.523904 / 1.492716 (0.031188) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.224294 / 0.018006 (0.206288) | 0.434928 / 0.000490 (0.434439) | 0.007541 / 0.000200 (0.007341) | 0.000286 / 0.000054 (0.000232) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025257 / 0.037411 (-0.012154) | 0.077364 / 0.014526 (0.062838) | 0.085825 / 0.176557 (-0.090732) | 0.148121 / 0.737135 (-0.589014) | 0.086838 / 0.296338 (-0.209500) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.396900 / 0.215209 (0.181691) | 3.953381 / 2.077655 (1.875727) | 1.933561 / 1.504120 (0.429441) | 1.760549 / 1.541195 (0.219354) | 1.824014 / 1.468490 (0.355523) | 0.495385 / 4.584777 (-4.089392) | 3.005558 / 3.745712 (-0.740154) | 2.931022 / 5.269862 (-2.338840) | 1.905113 / 4.565676 (-2.660563) | 0.057232 / 0.424275 (-0.367043) | 0.006472 / 0.007607 (-0.001135) | 0.464261 / 0.226044 (0.238216) | 4.629388 / 2.268929 (2.360459) | 2.342004 / 55.444624 (-53.102620) | 1.977295 / 6.876477 (-4.899181) | 2.167151 / 2.142072 (0.025079) | 0.582483 / 4.805227 (-4.222744) | 0.129444 / 6.500664 (-6.371220) | 0.061057 / 0.075469 (-0.014412) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.259444 / 1.841788 (-0.582344) | 18.189338 / 8.074308 (10.115030) | 14.313174 / 10.191392 (4.121782) | 0.146209 / 0.680424 (-0.534215) | 0.017115 / 0.534201 (-0.517086) | 0.336643 / 0.579283 (-0.242640) | 0.370824 / 0.434364 (-0.063540) | 0.387032 / 0.540337 (-0.153306) | 0.546688 / 1.386936 (-0.840248) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006371 / 0.011353 (-0.004982) | 0.003693 / 0.011008 (-0.007315) | 0.062499 / 0.038508 (0.023991) | 0.066367 / 0.023109 (0.043257) | 0.451481 / 0.275898 (0.175583) | 0.482495 / 0.323480 (0.159015) | 0.005676 / 0.007986 (-0.002310) | 0.002940 / 0.004328 (-0.001389) | 0.063011 / 0.004250 (0.058760) | 0.051500 / 0.037052 (0.014447) | 0.455482 / 0.258489 (0.196993) | 0.488888 / 0.293841 (0.195047) | 0.028714 / 0.128546 (-0.099832) | 0.008178 / 0.075646 (-0.067468) | 0.067218 / 0.419271 (-0.352053) | 0.041323 / 0.043533 (-0.002210) | 0.454007 / 0.255139 (0.198868) | 0.476241 / 0.283200 (0.193041) | 0.021530 / 0.141683 (-0.120153) | 1.457859 / 1.452155 (0.005705) | 1.506437 / 1.492716 (0.013721) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.228280 / 0.018006 (0.210274) | 0.427574 / 0.000490 (0.427084) | 0.003793 / 0.000200 (0.003593) | 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) | 0.028420 / 0.037411 (-0.008992) | 0.087935 / 0.014526 (0.073409) | 0.092761 / 0.176557 (-0.083796) | 0.148084 / 0.737135 (-0.589051) | 0.095301 / 0.296338 (-0.201037) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.462457 / 0.215209 (0.247248) | 4.618016 / 2.077655 (2.540361) | 2.540531 / 1.504120 (1.036412) | 2.384696 / 1.541195 (0.843501) | 2.493108 / 1.468490 (1.024618) | 0.511689 / 4.584777 (-4.073088) | 3.173701 / 3.745712 (-0.572011) | 2.917046 / 5.269862 (-2.352816) | 1.916294 / 4.565676 (-2.649382) | 0.058969 / 0.424275 (-0.365306) | 0.006461 / 0.007607 (-0.001147) | 0.540997 / 0.226044 (0.314952) | 5.406596 / 2.268929 (3.137667) | 3.071189 / 55.444624 (-52.373435) | 2.701982 / 6.876477 (-4.174494) | 2.860194 / 2.142072 (0.718121) | 0.602684 / 4.805227 (-4.202543) | 0.127384 / 6.500664 (-6.373280) | 0.061718 / 0.075469 (-0.013751) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.340587 / 1.841788 (-0.501201) | 18.543831 / 8.074308 (10.469523) | 14.847319 / 10.191392 (4.655927) | 0.146523 / 0.680424 (-0.533901) | 0.018172 / 0.534201 (-0.516029) | 0.333276 / 0.579283 (-0.246007) | 0.375874 / 0.434364 (-0.058490) | 0.396766 / 0.540337 (-0.143572) | 0.572562 / 1.386936 (-0.814374) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f2d9fcc0840f9d94f63635e9b40a1a7f11b34ea2 \"CML watermark\")\n" ]

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[ "It looks to be the same issue as the one reported in https://discuss.huggingface.co/t/indexerror-invalid-key-16-is-out-of-bounds-for-size-0.\r\n\r\nCan you check the length of `train_dataset` before the `train_sampler = self._get_train_sampler()` (and after `_remove_unused_columns`) line?" ]

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[ "Hi! Can you provide a code that reproduces the issue?\r\n\r\nAlso, which version of `datasets` are you using? You can check this by running `python -c \"import datasets; print(datasets.__version__)\"` inside the env. We added support for \"float images\" in `datasets 2.9`." ]

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[ "Thanks for reporting! I've opened a PR with a fix" ]

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[ "Yea I think waiting for #6269 would be best, or branching from it. For reference, this [PR](https://github.com/LAION-AI/Discord-Scrapers/pull/2) is progressing pretty well which will do similar using the hf hub for our LAION dataset bot https://github.com/LAION-AI/Discord-Scrapers/pull/2. " ]

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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) | 0.006424 / 0.011353 (-0.004929) | 0.003960 / 0.011008 (-0.007048) | 0.084022 / 0.038508 (0.045514) | 0.070770 / 0.023109 (0.047661) | 0.320525 / 0.275898 (0.044627) | 0.354507 / 0.323480 (0.031027) | 0.003939 / 0.007986 (-0.004047) | 0.004161 / 0.004328 (-0.000168) | 0.064754 / 0.004250 (0.060503) | 0.053630 / 0.037052 (0.016578) | 0.323948 / 0.258489 (0.065459) | 0.376908 / 0.293841 (0.083067) | 0.031063 / 0.128546 (-0.097483) | 0.008470 / 0.075646 (-0.067177) | 0.288110 / 0.419271 (-0.131161) | 0.053062 / 0.043533 (0.009529) | 0.328176 / 0.255139 (0.073037) | 0.345203 / 0.283200 (0.062003) | 0.024579 / 0.141683 (-0.117104) | 1.471649 / 1.452155 (0.019495) | 1.561458 / 1.492716 (0.068742) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.223591 / 0.018006 (0.205585) | 0.450758 / 0.000490 (0.450269) | 0.003751 / 0.000200 (0.003552) | 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) | 0.027859 / 0.037411 (-0.009552) | 0.080607 / 0.014526 (0.066081) | 0.093835 / 0.176557 (-0.082722) | 0.150466 / 0.737135 (-0.586669) | 0.094381 / 0.296338 (-0.201957) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.394011 / 0.215209 (0.178802) | 3.918318 / 2.077655 (1.840664) | 1.928684 / 1.504120 (0.424564) | 1.765944 / 1.541195 (0.224749) | 1.784716 / 1.468490 (0.316226) | 0.487189 / 4.584777 (-4.097588) | 3.537705 / 3.745712 (-0.208008) | 3.312162 / 5.269862 (-1.957699) | 2.024520 / 4.565676 (-2.541156) | 0.057571 / 0.424275 (-0.366704) | 0.007203 / 0.007607 (-0.000404) | 0.467253 / 0.226044 (0.241208) | 4.659934 / 2.268929 (2.391005) | 2.377764 / 55.444624 (-53.066860) | 2.021984 / 6.876477 (-4.854492) | 2.197468 / 2.142072 (0.055395) | 0.586415 / 4.805227 (-4.218812) | 0.136636 / 6.500664 (-6.364028) | 0.060885 / 0.075469 (-0.014584) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.241879 / 1.841788 (-0.599908) | 18.719327 / 8.074308 (10.645019) | 14.408689 / 10.191392 (4.217297) | 0.155778 / 0.680424 (-0.524646) | 0.018475 / 0.534201 (-0.515726) | 0.392316 / 0.579283 (-0.186967) | 0.409803 / 0.434364 (-0.024561) | 0.458701 / 0.540337 (-0.081637) | 0.630561 / 1.386936 (-0.756375) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006541 / 0.011353 (-0.004812) | 0.003915 / 0.011008 (-0.007094) | 0.064292 / 0.038508 (0.025784) | 0.069174 / 0.023109 (0.046065) | 0.402048 / 0.275898 (0.126150) | 0.423960 / 0.323480 (0.100480) | 0.005355 / 0.007986 (-0.002631) | 0.003295 / 0.004328 (-0.001033) | 0.065212 / 0.004250 (0.060962) | 0.054292 / 0.037052 (0.017240) | 0.402930 / 0.258489 (0.144441) | 0.441840 / 0.293841 (0.147999) | 0.032732 / 0.128546 (-0.095814) | 0.008565 / 0.075646 (-0.067081) | 0.070705 / 0.419271 (-0.348567) | 0.047908 / 0.043533 (0.004375) | 0.401400 / 0.255139 (0.146261) | 0.422682 / 0.283200 (0.139483) | 0.022244 / 0.141683 (-0.119439) | 1.532018 / 1.452155 (0.079864) | 1.597955 / 1.492716 (0.105239) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.226277 / 0.018006 (0.208271) | 0.475578 / 0.000490 (0.475088) | 0.005456 / 0.000200 (0.005256) | 0.000140 / 0.000054 (0.000085) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033111 / 0.037411 (-0.004300) | 0.093138 / 0.014526 (0.078613) | 0.104619 / 0.176557 (-0.071937) | 0.157972 / 0.737135 (-0.579164) | 0.105017 / 0.296338 (-0.191321) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.441771 / 0.215209 (0.226562) | 4.396981 / 2.077655 (2.319326) | 2.410745 / 1.504120 (0.906625) | 2.258359 / 1.541195 (0.717164) | 2.372628 / 1.468490 (0.904138) | 0.491411 / 4.584777 (-4.093366) | 3.650084 / 3.745712 (-0.095628) | 3.279557 / 5.269862 (-1.990304) | 2.011377 / 4.565676 (-2.554300) | 0.058283 / 0.424275 (-0.365992) | 0.007435 / 0.007607 (-0.000172) | 0.507212 / 0.226044 (0.281167) | 5.080104 / 2.268929 (2.811176) | 2.822680 / 55.444624 (-52.621945) | 2.507608 / 6.876477 (-4.368869) | 2.719349 / 2.142072 (0.577277) | 0.586157 / 4.805227 (-4.219071) | 0.132851 / 6.500664 (-6.367813) | 0.059944 / 0.075469 (-0.015525) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.374801 / 1.841788 (-0.466987) | 19.089359 / 8.074308 (11.015051) | 14.525861 / 10.191392 (4.334469) | 0.184758 / 0.680424 (-0.495666) | 0.020206 / 0.534201 (-0.513995) | 0.397309 / 0.579283 (-0.181975) | 0.418120 / 0.434364 (-0.016244) | 0.471817 / 0.540337 (-0.068520) | 0.681691 / 1.386936 (-0.705245) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2076cb857e90cf7a6050bba230f586993c5e034a \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._" ]

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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')`" ]

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[ "There is no \"text\" column in the `amazon_reviews_multi`, hence the `KeyError`. You can get the column names by running `dataset.column_names`." ]

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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) | 0.009157 / 0.011353 (-0.002195) | 0.004275 / 0.011008 (-0.006734) | 0.099341 / 0.038508 (0.060833) | 0.080634 / 0.023109 (0.057525) | 0.373598 / 0.275898 (0.097700) | 0.445048 / 0.323480 (0.121568) | 0.006541 / 0.007986 (-0.001444) | 0.003550 / 0.004328 (-0.000779) | 0.071034 / 0.004250 (0.066784) | 0.062637 / 0.037052 (0.025585) | 0.379110 / 0.258489 (0.120621) | 0.447896 / 0.293841 (0.154055) | 0.047739 / 0.128546 (-0.080807) | 0.012575 / 0.075646 (-0.063071) | 0.332314 / 0.419271 (-0.086957) | 0.065500 / 0.043533 (0.021967) | 0.365919 / 0.255139 (0.110780) | 0.438611 / 0.283200 (0.155412) | 0.034243 / 0.141683 (-0.107440) | 1.628034 / 1.452155 (0.175880) | 1.802970 / 1.492716 (0.310253) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.224528 / 0.018006 (0.206522) | 0.482094 / 0.000490 (0.481604) | 0.012752 / 0.000200 (0.012552) | 0.000570 / 0.000054 (0.000515) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025456 / 0.037411 (-0.011956) | 0.082281 / 0.014526 (0.067756) | 0.100050 / 0.176557 (-0.076506) | 0.156931 / 0.737135 (-0.580204) | 0.108229 / 0.296338 (-0.188110) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.560688 / 0.215209 (0.345479) | 5.171711 / 2.077655 (3.094056) | 2.273178 / 1.504120 (0.769058) | 1.948158 / 1.541195 (0.406963) | 1.879744 / 1.468490 (0.411254) | 0.789216 / 4.584777 (-3.795561) | 4.529370 / 3.745712 (0.783658) | 4.008743 / 5.269862 (-1.261118) | 2.633555 / 4.565676 (-1.932121) | 0.085411 / 0.424275 (-0.338864) | 0.007256 / 0.007607 (-0.000351) | 0.623254 / 0.226044 (0.397209) | 6.327256 / 2.268929 (4.058327) | 2.911787 / 55.444624 (-52.532837) | 2.240610 / 6.876477 (-4.635867) | 2.352811 / 2.142072 (0.210738) | 0.930114 / 4.805227 (-3.875114) | 0.185028 / 6.500664 (-6.315636) | 0.062115 / 0.075469 (-0.013354) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.394261 / 1.841788 (-0.447527) | 19.689376 / 8.074308 (11.615067) | 17.242289 / 10.191392 (7.050897) | 0.209122 / 0.680424 (-0.471302) | 0.027205 / 0.534201 (-0.506996) | 0.408613 / 0.579283 (-0.170670) | 0.503836 / 0.434364 (0.069472) | 0.485179 / 0.540337 (-0.055158) | 0.674333 / 1.386936 (-0.712603) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007506 / 0.011353 (-0.003847) | 0.004683 / 0.011008 (-0.006325) | 0.067584 / 0.038508 (0.029076) | 0.065635 / 0.023109 (0.042525) | 0.458814 / 0.275898 (0.182916) | 0.477549 / 0.323480 (0.154069) | 0.005212 / 0.007986 (-0.002774) | 0.003393 / 0.004328 (-0.000936) | 0.075307 / 0.004250 (0.071057) | 0.051989 / 0.037052 (0.014937) | 0.484229 / 0.258489 (0.225740) | 0.470889 / 0.293841 (0.177048) | 0.043528 / 0.128546 (-0.085018) | 0.014685 / 0.075646 (-0.060962) | 0.084199 / 0.419271 (-0.335073) | 0.053970 / 0.043533 (0.010437) | 0.432362 / 0.255139 (0.177223) | 0.467472 / 0.283200 (0.184272) | 0.031109 / 0.141683 (-0.110574) | 1.525938 / 1.452155 (0.073784) | 1.631993 / 1.492716 (0.139276) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.200196 / 0.018006 (0.182190) | 0.479316 / 0.000490 (0.478827) | 0.010146 / 0.000200 (0.009947) | 0.000118 / 0.000054 (0.000063) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027911 / 0.037411 (-0.009500) | 0.089720 / 0.014526 (0.075194) | 0.097000 / 0.176557 (-0.079557) | 0.157549 / 0.737135 (-0.579587) | 0.098247 / 0.296338 (-0.198092) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.581401 / 0.215209 (0.366192) | 5.703829 / 2.077655 (3.626174) | 2.688272 / 1.504120 (1.184152) | 2.321691 / 1.541195 (0.780496) | 2.355987 / 1.468490 (0.887497) | 0.759109 / 4.584777 (-3.825668) | 4.711288 / 3.745712 (0.965576) | 4.093019 / 5.269862 (-1.176843) | 2.648240 / 4.565676 (-1.917437) | 0.087839 / 0.424275 (-0.336436) | 0.007060 / 0.007607 (-0.000547) | 0.702783 / 0.226044 (0.476739) | 6.986924 / 2.268929 (4.717996) | 3.365970 / 55.444624 (-52.078654) | 2.670876 / 6.876477 (-4.205600) | 2.776431 / 2.142072 (0.634358) | 0.920005 / 4.805227 (-3.885222) | 0.197521 / 6.500664 (-6.303143) | 0.069974 / 0.075469 (-0.005495) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.596947 / 1.841788 (-0.244841) | 20.606007 / 8.074308 (12.531699) | 18.437425 / 10.191392 (8.246033) | 0.222445 / 0.680424 (-0.457978) | 0.028610 / 0.534201 (-0.505591) | 0.419748 / 0.579283 (-0.159535) | 0.513409 / 0.434364 (0.079045) | 0.487517 / 0.540337 (-0.052820) | 0.706637 / 1.386936 (-0.680299) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d664439eb82d62889c21c5236a5869dae75ae779 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007744 / 0.011353 (-0.003609) | 0.004678 / 0.011008 (-0.006330) | 0.101243 / 0.038508 (0.062735) | 0.085653 / 0.023109 (0.062543) | 0.383772 / 0.275898 (0.107874) | 0.422151 / 0.323480 (0.098671) | 0.004566 / 0.007986 (-0.003419) | 0.003900 / 0.004328 (-0.000429) | 0.077778 / 0.004250 (0.073528) | 0.063761 / 0.037052 (0.026709) | 0.385505 / 0.258489 (0.127016) | 0.436186 / 0.293841 (0.142345) | 0.036172 / 0.128546 (-0.092374) | 0.009935 / 0.075646 (-0.065711) | 0.341434 / 0.419271 (-0.077837) | 0.061866 / 0.043533 (0.018333) | 0.385020 / 0.255139 (0.129881) | 0.399455 / 0.283200 (0.116256) | 0.029324 / 0.141683 (-0.112358) | 1.784749 / 1.452155 (0.332594) | 1.845926 / 1.492716 (0.353209) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.266322 / 0.018006 (0.248316) | 0.508708 / 0.000490 (0.508218) | 0.013680 / 0.000200 (0.013480) | 0.000868 / 0.000054 (0.000814) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033887 / 0.037411 (-0.003525) | 0.096709 / 0.014526 (0.082183) | 0.109472 / 0.176557 (-0.067084) | 0.174422 / 0.737135 (-0.562713) | 0.110830 / 0.296338 (-0.185509) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.457533 / 0.215209 (0.242324) | 4.615229 / 2.077655 (2.537575) | 2.418820 / 1.504120 (0.914700) | 2.181079 / 1.541195 (0.639884) | 2.229164 / 1.468490 (0.760674) | 0.554861 / 4.584777 (-4.029916) | 4.323787 / 3.745712 (0.578075) | 3.769396 / 5.269862 (-1.500466) | 2.376850 / 4.565676 (-2.188826) | 0.065030 / 0.424275 (-0.359245) | 0.008397 / 0.007607 (0.000790) | 0.541109 / 0.226044 (0.315065) | 5.477540 / 2.268929 (3.208612) | 2.957049 / 55.444624 (-52.487576) | 2.511732 / 6.876477 (-4.364744) | 2.703953 / 2.142072 (0.561881) | 0.660822 / 4.805227 (-4.144405) | 0.147035 / 6.500664 (-6.353630) | 0.066045 / 0.075469 (-0.009424) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.526481 / 1.841788 (-0.315307) | 22.020256 / 8.074308 (13.945948) | 16.854566 / 10.191392 (6.663174) | 0.192958 / 0.680424 (-0.487466) | 0.021505 / 0.534201 (-0.512696) | 0.462867 / 0.579283 (-0.116416) | 0.514813 / 0.434364 (0.080449) | 0.546147 / 0.540337 (0.005809) | 0.767853 / 1.386936 (-0.619083) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007770 / 0.011353 (-0.003583) | 0.004671 / 0.011008 (-0.006337) | 0.080862 / 0.038508 (0.042354) | 0.087049 / 0.023109 (0.063940) | 0.479497 / 0.275898 (0.203599) | 0.559787 / 0.323480 (0.236307) | 0.007168 / 0.007986 (-0.000818) | 0.003829 / 0.004328 (-0.000500) | 0.079018 / 0.004250 (0.074768) | 0.067359 / 0.037052 (0.030307) | 0.516140 / 0.258489 (0.257651) | 0.547000 / 0.293841 (0.253159) | 0.037955 / 0.128546 (-0.090591) | 0.010007 / 0.075646 (-0.065639) | 0.087673 / 0.419271 (-0.331598) | 0.059309 / 0.043533 (0.015777) | 0.473920 / 0.255139 (0.218781) | 0.529216 / 0.283200 (0.246017) | 0.028236 / 0.141683 (-0.113447) | 1.771127 / 1.452155 (0.318972) | 1.918878 / 1.492716 (0.426162) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.242010 / 0.018006 (0.224004) | 0.494944 / 0.000490 (0.494454) | 0.006319 / 0.000200 (0.006119) | 0.000111 / 0.000054 (0.000056) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.039220 / 0.037411 (0.001809) | 0.113805 / 0.014526 (0.099279) | 0.125704 / 0.176557 (-0.050853) | 0.189198 / 0.737135 (-0.547937) | 0.126334 / 0.296338 (-0.170004) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.502226 / 0.215209 (0.287017) | 5.039133 / 2.077655 (2.961478) | 2.782352 / 1.504120 (1.278232) | 2.587654 / 1.541195 (1.046460) | 2.692588 / 1.468490 (1.224098) | 0.585672 / 4.584777 (-3.999105) | 4.553078 / 3.745712 (0.807366) | 3.864739 / 5.269862 (-1.405123) | 2.536109 / 4.565676 (-2.029567) | 0.069567 / 0.424275 (-0.354708) | 0.008749 / 0.007607 (0.001142) | 0.620645 / 0.226044 (0.394601) | 6.247286 / 2.268929 (3.978357) | 3.345293 / 55.444624 (-52.099332) | 2.873970 / 6.876477 (-4.002507) | 3.123190 / 2.142072 (0.981118) | 0.687391 / 4.805227 (-4.117837) | 0.159046 / 6.500664 (-6.341618) | 0.071019 / 0.075469 (-0.004450) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.728724 / 1.841788 (-0.113064) | 22.828390 / 8.074308 (14.754082) | 17.305225 / 10.191392 (7.113833) | 0.176571 / 0.680424 (-0.503853) | 0.023837 / 0.534201 (-0.510364) | 0.467935 / 0.579283 (-0.111348) | 0.503701 / 0.434364 (0.069337) | 0.558140 / 0.540337 (0.017803) | 0.789326 / 1.386936 (-0.597610) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7d357eb4b499cd530c3f4e626f2825a50ee6c8aa \"CML watermark\")\n" ]

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[ "You should be able to load the images by modifying the `load_dataset` call like this:\r\n```python\r\ndataset = load_dataset(\"imagefolder\", data_dir=\"/content/datasets/PotholeDetectionYOLOv8-1\")\r\n```\r\n\r\nThe `imagefolder` builder expects the image files to be in `path/label/image_file` (e.g. .`.../train/dog/image_1.jpg`), so the solution for the labels in your case is to create metadata files (one for each split; as explained [here](https://huggingface.co/docs/datasets/image_dataset#imagefolder)) that map the images to their labels.", "> You should be able to load the images by modifying the `load_dataset` call like this:\r\n> \r\n> ```python\r\n> dataset = load_dataset(\"imagefolder\", data_dir=\"/content/datasets/PotholeDetectionYOLOv8-1\")\r\n> ```\r\n> \r\n> The `imagefolder` builder expects the image files to be in `path/label/image_file` (e.g. .`.../train/dog/image_1.jpg`), so the solution for the labels in your case is to create metadata files (one for each split; as explained [here](https://huggingface.co/docs/datasets/image_dataset#imagefolder)) that map the images to their labels.\r\n\r\nI tried like this but only uploads images and not labels, Andyrasika/potholes-dataset", "As explained in my previous comment, you need to define metadata files to load the labels or update the paths to be in the format `train/label/image` (`train- image /n -labels` is not supported by the loader).", "I downloaded my file after annotating using roboflow . It gives train-\r\nimages, labels , test- images, labels , valid- images, labels . I hope it\r\ngives you an idea of the dataset . Please advise on this dataset\r\n\r\nOn Tue, Oct 10, 2023 at 18:12 Mario Šaško ***@***.***> wrote:\r\n\r\n> As explained in my previous comment, you need to define metadata files to\r\n> load the labels or update the paths to be in the format train/label/image\r\n> (train- image /n -labels is not supported by the loader).\r\n>\r\n> —\r\n> Reply to this email directly, view it on GitHub\r\n> <https://github.com/huggingface/datasets/issues/6285#issuecomment-1755335215>,\r\n> or unsubscribe\r\n> <https://github.com/notifications/unsubscribe-auth/AE4LJNN56FWWTSBYTSTUWHLX6U7CVAVCNFSM6AAAAAA5YHCSTGVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMYTONJVGMZTKMRRGU>\r\n> .\r\n> You are receiving this because you authored the thread.Message ID:\r\n> ***@***.***>\r\n>\r\n" ]

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[ "This dataset is already available on the Hub: https://huggingface.co/datasets/facebook/belebele.\r\n" ]

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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) | 0.006278 / 0.011353 (-0.005075) | 0.003692 / 0.011008 (-0.007316) | 0.080464 / 0.038508 (0.041956) | 0.064751 / 0.023109 (0.041642) | 0.318586 / 0.275898 (0.042688) | 0.351435 / 0.323480 (0.027955) | 0.005044 / 0.007986 (-0.002942) | 0.003034 / 0.004328 (-0.001295) | 0.063710 / 0.004250 (0.059460) | 0.050607 / 0.037052 (0.013555) | 0.318491 / 0.258489 (0.060001) | 0.365688 / 0.293841 (0.071847) | 0.027818 / 0.128546 (-0.100729) | 0.008119 / 0.075646 (-0.067527) | 0.262141 / 0.419271 (-0.157131) | 0.044710 / 0.043533 (0.001177) | 0.318875 / 0.255139 (0.063736) | 0.344559 / 0.283200 (0.061360) | 0.022861 / 0.141683 (-0.118822) | 1.452402 / 1.452155 (0.000247) | 1.502340 / 1.492716 (0.009624) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.219355 / 0.018006 (0.201349) | 0.433311 / 0.000490 (0.432822) | 0.006545 / 0.000200 (0.006345) | 0.000078 / 0.000054 (0.000024) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024538 / 0.037411 (-0.012874) | 0.073346 / 0.014526 (0.058821) | 0.083824 / 0.176557 (-0.092733) | 0.145176 / 0.737135 (-0.591959) | 0.085941 / 0.296338 (-0.210397) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.395153 / 0.215209 (0.179944) | 3.944734 / 2.077655 (1.867080) | 1.883910 / 1.504120 (0.379790) | 1.690560 / 1.541195 (0.149365) | 1.775180 / 1.468490 (0.306690) | 0.506873 / 4.584777 (-4.077904) | 3.111095 / 3.745712 (-0.634617) | 2.915358 / 5.269862 (-2.354504) | 1.892886 / 4.565676 (-2.672791) | 0.058690 / 0.424275 (-0.365585) | 0.006550 / 0.007607 (-0.001057) | 0.463372 / 0.226044 (0.237328) | 4.640511 / 2.268929 (2.371583) | 2.321051 / 55.444624 (-53.123573) | 1.986330 / 6.876477 (-4.890147) | 2.160046 / 2.142072 (0.017973) | 0.597833 / 4.805227 (-4.207394) | 0.127946 / 6.500664 (-6.372718) | 0.059709 / 0.075469 (-0.015760) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.278966 / 1.841788 (-0.562822) | 17.863102 / 8.074308 (9.788794) | 13.896057 / 10.191392 (3.704665) | 0.147512 / 0.680424 (-0.532912) | 0.016771 / 0.534201 (-0.517430) | 0.335260 / 0.579283 (-0.244024) | 0.383019 / 0.434364 (-0.051345) | 0.384821 / 0.540337 (-0.155516) | 0.550143 / 1.386936 (-0.836793) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006234 / 0.011353 (-0.005118) | 0.003695 / 0.011008 (-0.007313) | 0.062654 / 0.038508 (0.024146) | 0.059397 / 0.023109 (0.036287) | 0.458375 / 0.275898 (0.182477) | 0.488951 / 0.323480 (0.165471) | 0.004971 / 0.007986 (-0.003014) | 0.002914 / 0.004328 (-0.001415) | 0.061184 / 0.004250 (0.056934) | 0.051246 / 0.037052 (0.014194) | 0.458035 / 0.258489 (0.199546) | 0.490838 / 0.293841 (0.196997) | 0.028746 / 0.128546 (-0.099800) | 0.008167 / 0.075646 (-0.067480) | 0.068006 / 0.419271 (-0.351265) | 0.041809 / 0.043533 (-0.001724) | 0.453896 / 0.255139 (0.198757) | 0.477583 / 0.283200 (0.194383) | 0.020906 / 0.141683 (-0.120777) | 1.443275 / 1.452155 (-0.008879) | 1.493431 / 1.492716 (0.000714) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.219903 / 0.018006 (0.201896) | 0.410275 / 0.000490 (0.409785) | 0.003919 / 0.000200 (0.003719) | 0.000078 / 0.000054 (0.000024) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027850 / 0.037411 (-0.009561) | 0.080444 / 0.014526 (0.065918) | 0.089943 / 0.176557 (-0.086614) | 0.145810 / 0.737135 (-0.591326) | 0.090908 / 0.296338 (-0.205430) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.464386 / 0.215209 (0.249177) | 4.633787 / 2.077655 (2.556133) | 2.581658 / 1.504120 (1.077538) | 2.408486 / 1.541195 (0.867291) | 2.460491 / 1.468490 (0.992001) | 0.507512 / 4.584777 (-4.077265) | 3.190363 / 3.745712 (-0.555349) | 2.895581 / 5.269862 (-2.374280) | 1.871506 / 4.565676 (-2.694171) | 0.058469 / 0.424275 (-0.365806) | 0.006526 / 0.007607 (-0.001082) | 0.537641 / 0.226044 (0.311596) | 5.396660 / 2.268929 (3.127731) | 3.027028 / 55.444624 (-52.417596) | 2.703771 / 6.876477 (-4.172705) | 2.865576 / 2.142072 (0.723503) | 0.600103 / 4.805227 (-4.205124) | 0.127109 / 6.500664 (-6.373555) | 0.060985 / 0.075469 (-0.014484) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.365030 / 1.841788 (-0.476758) | 17.988218 / 8.074308 (9.913909) | 14.900796 / 10.191392 (4.709404) | 0.158211 / 0.680424 (-0.522213) | 0.018291 / 0.534201 (-0.515910) | 0.337437 / 0.579283 (-0.241846) | 0.383710 / 0.434364 (-0.050654) | 0.392341 / 0.540337 (-0.147997) | 0.561584 / 1.386936 (-0.825352) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b7571ab4b0d9b67b767c55db400b4ffac0f752f1 \"CML watermark\")\n", "CI failures are unrelated", "I also plan to address https://github.com/huggingface/datasets/issues/6280#issuecomment-1749310065 in this PR :).", "Oh ok, ping me again whenever you want another review :)", "Have you had a chance to continue this ? I can also take a look if you want", "Yes, I'll finish it next week :).", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6283). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "@lhoestq Feel free to review this again. I've bumped PyArrow to 12.0.0 to simplify the implementation (no need for custom `array_concat` and less `pa.Array.from_buffers`). However, this makes `apache-beam` complain as it only supports `<12.0.0`. The next `apache-beam` release will set this boundary to `<15.0.0.`, so I think the only solution is to wait for it to be published.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005188 / 0.011353 (-0.006165) | 0.003997 / 0.011008 (-0.007011) | 0.062642 / 0.038508 (0.024134) | 0.028913 / 0.023109 (0.005804) | 0.248289 / 0.275898 (-0.027609) | 0.268084 / 0.323480 (-0.055396) | 0.004093 / 0.007986 (-0.003893) | 0.002822 / 0.004328 (-0.001506) | 0.048263 / 0.004250 (0.044012) | 0.041520 / 0.037052 (0.004468) | 0.263277 / 0.258489 (0.004788) | 0.289835 / 0.293841 (-0.004006) | 0.027621 / 0.128546 (-0.100925) | 0.010793 / 0.075646 (-0.064853) | 0.207624 / 0.419271 (-0.211648) | 0.035597 / 0.043533 (-0.007936) | 0.245706 / 0.255139 (-0.009433) | 0.268157 / 0.283200 (-0.015043) | 0.017310 / 0.141683 (-0.124373) | 1.130656 / 1.452155 (-0.321499) | 1.162134 / 1.492716 (-0.330583) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094081 / 0.018006 (0.076075) | 0.302298 / 0.000490 (0.301809) | 0.000220 / 0.000200 (0.000020) | 0.000048 / 0.000054 (-0.000006) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019072 / 0.037411 (-0.018339) | 0.061162 / 0.014526 (0.046636) | 0.072820 / 0.176557 (-0.103737) | 0.122628 / 0.737135 (-0.614507) | 0.074962 / 0.296338 (-0.221377) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.277858 / 0.215209 (0.062649) | 2.688478 / 2.077655 (0.610823) | 1.397366 / 1.504120 (-0.106754) | 1.285078 / 1.541195 (-0.256117) | 1.291559 / 1.468490 (-0.176931) | 0.553646 / 4.584777 (-4.031131) | 2.355737 / 3.745712 (-1.389975) | 2.773025 / 5.269862 (-2.496836) | 1.731195 / 4.565676 (-2.834481) | 0.061372 / 0.424275 (-0.362903) | 0.004928 / 0.007607 (-0.002679) | 0.321703 / 0.226044 (0.095659) | 3.212927 / 2.268929 (0.943999) | 1.727104 / 55.444624 (-53.717521) | 1.479430 / 6.876477 (-5.397047) | 1.513436 / 2.142072 (-0.628637) | 0.629913 / 4.805227 (-4.175315) | 0.114607 / 6.500664 (-6.386057) | 0.041707 / 0.075469 (-0.033762) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.976060 / 1.841788 (-0.865727) | 11.575163 / 8.074308 (3.500855) | 9.521390 / 10.191392 (-0.670003) | 0.138725 / 0.680424 (-0.541699) | 0.013752 / 0.534201 (-0.520449) | 0.286252 / 0.579283 (-0.293031) | 0.263420 / 0.434364 (-0.170944) | 0.325531 / 0.540337 (-0.214806) | 0.419466 / 1.386936 (-0.967470) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005615 / 0.011353 (-0.005738) | 0.003884 / 0.011008 (-0.007124) | 0.049563 / 0.038508 (0.011055) | 0.032573 / 0.023109 (0.009464) | 0.276917 / 0.275898 (0.001019) | 0.298403 / 0.323480 (-0.025077) | 0.004367 / 0.007986 (-0.003618) | 0.002794 / 0.004328 (-0.001534) | 0.049105 / 0.004250 (0.044855) | 0.045597 / 0.037052 (0.008545) | 0.289762 / 0.258489 (0.031273) | 0.318440 / 0.293841 (0.024599) | 0.051883 / 0.128546 (-0.076664) | 0.010644 / 0.075646 (-0.065003) | 0.057455 / 0.419271 (-0.361816) | 0.033667 / 0.043533 (-0.009866) | 0.274424 / 0.255139 (0.019285) | 0.295890 / 0.283200 (0.012690) | 0.017029 / 0.141683 (-0.124654) | 1.130123 / 1.452155 (-0.322031) | 1.214827 / 1.492716 (-0.277889) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094882 / 0.018006 (0.076876) | 0.302505 / 0.000490 (0.302015) | 0.000228 / 0.000200 (0.000028) | 0.000052 / 0.000054 (-0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021695 / 0.037411 (-0.015716) | 0.075196 / 0.014526 (0.060670) | 0.086641 / 0.176557 (-0.089915) | 0.124893 / 0.737135 (-0.612243) | 0.088765 / 0.296338 (-0.207574) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.303388 / 0.215209 (0.088179) | 2.934506 / 2.077655 (0.856852) | 1.608607 / 1.504120 (0.104487) | 1.494632 / 1.541195 (-0.046563) | 1.512801 / 1.468490 (0.044310) | 0.558563 / 4.584777 (-4.026214) | 2.383212 / 3.745712 (-1.362500) | 2.634629 / 5.269862 (-2.635233) | 1.729319 / 4.565676 (-2.836357) | 0.062345 / 0.424275 (-0.361930) | 0.004981 / 0.007607 (-0.002626) | 0.358333 / 0.226044 (0.132289) | 3.484229 / 2.268929 (1.215301) | 2.010043 / 55.444624 (-53.434581) | 1.693733 / 6.876477 (-5.182744) | 1.824150 / 2.142072 (-0.317922) | 0.650835 / 4.805227 (-4.154392) | 0.115933 / 6.500664 (-6.384732) | 0.041270 / 0.075469 (-0.034199) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.007949 / 1.841788 (-0.833838) | 12.000085 / 8.074308 (3.925776) | 10.453119 / 10.191392 (0.261727) | 0.143583 / 0.680424 (-0.536840) | 0.015937 / 0.534201 (-0.518264) | 0.286653 / 0.579283 (-0.292631) | 0.272359 / 0.434364 (-0.162005) | 0.330520 / 0.540337 (-0.209818) | 0.417015 / 1.386936 (-0.969921) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ac05bac20fcc8e0e22a852707162e15a7e2ae357 \"CML watermark\")\n" ]

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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) | 0.006934 / 0.011353 (-0.004419) | 0.004097 / 0.011008 (-0.006911) | 0.084662 / 0.038508 (0.046154) | 0.077106 / 0.023109 (0.053996) | 0.355035 / 0.275898 (0.079137) | 0.381466 / 0.323480 (0.057986) | 0.004182 / 0.007986 (-0.003803) | 0.003411 / 0.004328 (-0.000917) | 0.065279 / 0.004250 (0.061029) | 0.058192 / 0.037052 (0.021140) | 0.372363 / 0.258489 (0.113874) | 0.401621 / 0.293841 (0.107780) | 0.031719 / 0.128546 (-0.096827) | 0.008753 / 0.075646 (-0.066893) | 0.287125 / 0.419271 (-0.132146) | 0.052943 / 0.043533 (0.009410) | 0.349680 / 0.255139 (0.094541) | 0.364004 / 0.283200 (0.080805) | 0.026705 / 0.141683 (-0.114977) | 1.472708 / 1.452155 (0.020553) | 1.556559 / 1.492716 (0.063842) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.224868 / 0.018006 (0.206862) | 0.458793 / 0.000490 (0.458304) | 0.009434 / 0.000200 (0.009234) | 0.000356 / 0.000054 (0.000301) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029670 / 0.037411 (-0.007741) | 0.086517 / 0.014526 (0.071991) | 0.097342 / 0.176557 (-0.079215) | 0.153722 / 0.737135 (-0.583413) | 0.098465 / 0.296338 (-0.197874) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.400739 / 0.215209 (0.185530) | 3.998087 / 2.077655 (1.920432) | 2.025772 / 1.504120 (0.521652) | 1.858679 / 1.541195 (0.317485) | 1.951573 / 1.468490 (0.483083) | 0.483028 / 4.584777 (-4.101749) | 3.554085 / 3.745712 (-0.191627) | 3.306983 / 5.269862 (-1.962879) | 2.087043 / 4.565676 (-2.478633) | 0.057127 / 0.424275 (-0.367148) | 0.007252 / 0.007607 (-0.000355) | 0.480180 / 0.226044 (0.254136) | 4.787183 / 2.268929 (2.518255) | 2.489667 / 55.444624 (-52.954957) | 2.150774 / 6.876477 (-4.725703) | 2.403197 / 2.142072 (0.261124) | 0.581843 / 4.805227 (-4.223384) | 0.134915 / 6.500664 (-6.365749) | 0.061283 / 0.075469 (-0.014186) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.285700 / 1.841788 (-0.556088) | 19.474093 / 8.074308 (11.399785) | 14.336349 / 10.191392 (4.144957) | 0.170932 / 0.680424 (-0.509492) | 0.018348 / 0.534201 (-0.515853) | 0.391909 / 0.579283 (-0.187374) | 0.414706 / 0.434364 (-0.019658) | 0.458156 / 0.540337 (-0.082182) | 0.656303 / 1.386936 (-0.730633) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006738 / 0.011353 (-0.004615) | 0.004029 / 0.011008 (-0.006979) | 0.064411 / 0.038508 (0.025903) | 0.078225 / 0.023109 (0.055116) | 0.408468 / 0.275898 (0.132569) | 0.445585 / 0.323480 (0.122105) | 0.005490 / 0.007986 (-0.002495) | 0.003419 / 0.004328 (-0.000910) | 0.063966 / 0.004250 (0.059715) | 0.056779 / 0.037052 (0.019727) | 0.415258 / 0.258489 (0.156769) | 0.461258 / 0.293841 (0.167418) | 0.032051 / 0.128546 (-0.096495) | 0.008471 / 0.075646 (-0.067176) | 0.071004 / 0.419271 (-0.348267) | 0.049068 / 0.043533 (0.005536) | 0.409575 / 0.255139 (0.154436) | 0.430748 / 0.283200 (0.147548) | 0.023784 / 0.141683 (-0.117899) | 1.507894 / 1.452155 (0.055739) | 1.586575 / 1.492716 (0.093859) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.228574 / 0.018006 (0.210568) | 0.451389 / 0.000490 (0.450900) | 0.006312 / 0.000200 (0.006112) | 0.000100 / 0.000054 (0.000045) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033391 / 0.037411 (-0.004020) | 0.096816 / 0.014526 (0.082290) | 0.107269 / 0.176557 (-0.069288) | 0.159749 / 0.737135 (-0.577387) | 0.108240 / 0.296338 (-0.188098) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.437643 / 0.215209 (0.222434) | 4.378173 / 2.077655 (2.300518) | 2.367218 / 1.504120 (0.863098) | 2.229493 / 1.541195 (0.688298) | 2.329849 / 1.468490 (0.861359) | 0.494985 / 4.584777 (-4.089792) | 3.578540 / 3.745712 (-0.167172) | 3.338220 / 5.269862 (-1.931642) | 2.092482 / 4.565676 (-2.473194) | 0.058495 / 0.424275 (-0.365780) | 0.007396 / 0.007607 (-0.000211) | 0.511001 / 0.226044 (0.284957) | 5.113497 / 2.268929 (2.844568) | 2.806215 / 55.444624 (-52.638409) | 2.485428 / 6.876477 (-4.391048) | 2.764907 / 2.142072 (0.622835) | 0.598824 / 4.805227 (-4.206404) | 0.134988 / 6.500664 (-6.365676) | 0.061752 / 0.075469 (-0.013717) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.365583 / 1.841788 (-0.476205) | 20.270297 / 8.074308 (12.195989) | 15.331673 / 10.191392 (5.140281) | 0.166152 / 0.680424 (-0.514272) | 0.020678 / 0.534201 (-0.513523) | 0.394821 / 0.579283 (-0.184462) | 0.420493 / 0.434364 (-0.013871) | 0.468551 / 0.540337 (-0.071787) | 0.654903 / 1.386936 (-0.732033) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5f268dd4ad4fb6dada15937d57fb367cb2810162 \"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) | 0.007803 / 0.011353 (-0.003550) | 0.004664 / 0.011008 (-0.006344) | 0.099908 / 0.038508 (0.061400) | 0.090674 / 0.023109 (0.067565) | 0.406009 / 0.275898 (0.130111) | 0.465098 / 0.323480 (0.141618) | 0.004667 / 0.007986 (-0.003319) | 0.003880 / 0.004328 (-0.000449) | 0.076552 / 0.004250 (0.072301) | 0.066345 / 0.037052 (0.029292) | 0.419195 / 0.258489 (0.160706) | 0.478581 / 0.293841 (0.184741) | 0.036967 / 0.128546 (-0.091579) | 0.010000 / 0.075646 (-0.065647) | 0.347126 / 0.419271 (-0.072145) | 0.062265 / 0.043533 (0.018733) | 0.406653 / 0.255139 (0.151514) | 0.439044 / 0.283200 (0.155845) | 0.031289 / 0.141683 (-0.110394) | 1.797674 / 1.452155 (0.345520) | 1.835183 / 1.492716 (0.342467) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.268194 / 0.018006 (0.250187) | 0.493614 / 0.000490 (0.493124) | 0.015636 / 0.000200 (0.015436) | 0.000417 / 0.000054 (0.000362) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034188 / 0.037411 (-0.003223) | 0.099127 / 0.014526 (0.084601) | 0.113949 / 0.176557 (-0.062607) | 0.181209 / 0.737135 (-0.555926) | 0.114943 / 0.296338 (-0.181395) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.455767 / 0.215209 (0.240558) | 4.542947 / 2.077655 (2.465293) | 2.214605 / 1.504120 (0.710485) | 2.015163 / 1.541195 (0.473969) | 2.084945 / 1.468490 (0.616455) | 0.583827 / 4.584777 (-4.000950) | 4.187009 / 3.745712 (0.441297) | 3.920841 / 5.269862 (-1.349020) | 2.447260 / 4.565676 (-2.118417) | 0.069139 / 0.424275 (-0.355137) | 0.008734 / 0.007607 (0.001127) | 0.544673 / 0.226044 (0.318629) | 5.445094 / 2.268929 (3.176165) | 2.788284 / 55.444624 (-52.656340) | 2.395863 / 6.876477 (-4.480614) | 2.622632 / 2.142072 (0.480560) | 0.703931 / 4.805227 (-4.101297) | 0.160502 / 6.500664 (-6.340162) | 0.073734 / 0.075469 (-0.001735) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.498992 / 1.841788 (-0.342795) | 22.761476 / 8.074308 (14.687168) | 17.123919 / 10.191392 (6.932527) | 0.170272 / 0.680424 (-0.510151) | 0.021307 / 0.534201 (-0.512894) | 0.467548 / 0.579283 (-0.111735) | 0.480777 / 0.434364 (0.046413) | 0.542168 / 0.540337 (0.001830) | 0.771092 / 1.386936 (-0.615844) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007923 / 0.011353 (-0.003430) | 0.004664 / 0.011008 (-0.006344) | 0.077795 / 0.038508 (0.039286) | 0.090293 / 0.023109 (0.067184) | 0.494682 / 0.275898 (0.218784) | 0.539973 / 0.323480 (0.216494) | 0.006302 / 0.007986 (-0.001684) | 0.003794 / 0.004328 (-0.000535) | 0.076567 / 0.004250 (0.072317) | 0.067141 / 0.037052 (0.030089) | 0.501279 / 0.258489 (0.242790) | 0.555670 / 0.293841 (0.261829) | 0.037773 / 0.128546 (-0.090773) | 0.009930 / 0.075646 (-0.065716) | 0.084839 / 0.419271 (-0.334433) | 0.056876 / 0.043533 (0.013344) | 0.499329 / 0.255139 (0.244190) | 0.518449 / 0.283200 (0.235249) | 0.026041 / 0.141683 (-0.115642) | 1.787259 / 1.452155 (0.335105) | 1.853505 / 1.492716 (0.360788) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.238413 / 0.018006 (0.220407) | 0.488889 / 0.000490 (0.488399) | 0.007476 / 0.000200 (0.007277) | 0.000141 / 0.000054 (0.000087) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.038701 / 0.037411 (0.001290) | 0.115391 / 0.014526 (0.100865) | 0.125553 / 0.176557 (-0.051004) | 0.190267 / 0.737135 (-0.546868) | 0.126401 / 0.296338 (-0.169937) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.509270 / 0.215209 (0.294061) | 5.087631 / 2.077655 (3.009976) | 2.745863 / 1.504120 (1.241743) | 2.560259 / 1.541195 (1.019064) | 2.653124 / 1.468490 (1.184634) | 0.582118 / 4.584777 (-4.002659) | 4.181144 / 3.745712 (0.435431) | 3.871179 / 5.269862 (-1.398683) | 2.459849 / 4.565676 (-2.105827) | 0.068844 / 0.424275 (-0.355431) | 0.008672 / 0.007607 (0.001065) | 0.604898 / 0.226044 (0.378854) | 6.073263 / 2.268929 (3.804334) | 3.366638 / 55.444624 (-52.077986) | 2.937261 / 6.876477 (-3.939215) | 3.181173 / 2.142072 (1.039100) | 0.700478 / 4.805227 (-4.104750) | 0.158361 / 6.500664 (-6.342303) | 0.072860 / 0.075469 (-0.002609) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.621363 / 1.841788 (-0.220425) | 23.614315 / 8.074308 (15.540007) | 17.607213 / 10.191392 (7.415821) | 0.198031 / 0.680424 (-0.482393) | 0.023859 / 0.534201 (-0.510342) | 0.474674 / 0.579283 (-0.104609) | 0.491173 / 0.434364 (0.056809) | 0.581995 / 0.540337 (0.041658) | 0.792168 / 1.386936 (-0.594768) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#56fa9645fd24e083adee3cfd0f7d972fce391f0e \"CML watermark\")\n", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6282). 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) | 0.004779 / 0.011353 (-0.006574) | 0.002916 / 0.011008 (-0.008092) | 0.061962 / 0.038508 (0.023454) | 0.029537 / 0.023109 (0.006428) | 0.242574 / 0.275898 (-0.033324) | 0.268585 / 0.323480 (-0.054894) | 0.004006 / 0.007986 (-0.003979) | 0.002434 / 0.004328 (-0.001895) | 0.048289 / 0.004250 (0.044039) | 0.045534 / 0.037052 (0.008481) | 0.248251 / 0.258489 (-0.010239) | 0.277037 / 0.293841 (-0.016804) | 0.023728 / 0.128546 (-0.104818) | 0.007295 / 0.075646 (-0.068351) | 0.205813 / 0.419271 (-0.213459) | 0.059093 / 0.043533 (0.015560) | 0.244336 / 0.255139 (-0.010803) | 0.262865 / 0.283200 (-0.020335) | 0.017232 / 0.141683 (-0.124451) | 1.126729 / 1.452155 (-0.325426) | 1.198987 / 1.492716 (-0.293729) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091246 / 0.018006 (0.073240) | 0.300747 / 0.000490 (0.300258) | 0.000202 / 0.000200 (0.000003) | 0.000041 / 0.000054 (-0.000013) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018681 / 0.037411 (-0.018731) | 0.063567 / 0.014526 (0.049041) | 0.074019 / 0.176557 (-0.102538) | 0.120856 / 0.737135 (-0.616279) | 0.076525 / 0.296338 (-0.219814) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.282230 / 0.215209 (0.067021) | 2.731502 / 2.077655 (0.653847) | 1.473901 / 1.504120 (-0.030219) | 1.351165 / 1.541195 (-0.190030) | 1.390582 / 1.468490 (-0.077908) | 0.398443 / 4.584777 (-4.186334) | 2.360497 / 3.745712 (-1.385215) | 2.548158 / 5.269862 (-2.721703) | 1.552416 / 4.565676 (-3.013260) | 0.045659 / 0.424275 (-0.378616) | 0.004778 / 0.007607 (-0.002829) | 0.330191 / 0.226044 (0.104146) | 3.262510 / 2.268929 (0.993582) | 1.823076 / 55.444624 (-53.621549) | 1.541206 / 6.876477 (-5.335271) | 1.589069 / 2.142072 (-0.553004) | 0.472265 / 4.805227 (-4.332963) | 0.099712 / 6.500664 (-6.400952) | 0.042803 / 0.075469 (-0.032666) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.963022 / 1.841788 (-0.878766) | 11.998807 / 8.074308 (3.924499) | 10.526006 / 10.191392 (0.334614) | 0.140965 / 0.680424 (-0.539459) | 0.014197 / 0.534201 (-0.520004) | 0.271668 / 0.579283 (-0.307615) | 0.263993 / 0.434364 (-0.170371) | 0.307213 / 0.540337 (-0.233124) | 0.427411 / 1.386936 (-0.959525) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004761 / 0.011353 (-0.006592) | 0.002652 / 0.011008 (-0.008357) | 0.047949 / 0.038508 (0.009441) | 0.049714 / 0.023109 (0.026604) | 0.274021 / 0.275898 (-0.001877) | 0.292413 / 0.323480 (-0.031067) | 0.003912 / 0.007986 (-0.004074) | 0.002290 / 0.004328 (-0.002038) | 0.047320 / 0.004250 (0.043069) | 0.038061 / 0.037052 (0.001009) | 0.279318 / 0.258489 (0.020829) | 0.305167 / 0.293841 (0.011326) | 0.024595 / 0.128546 (-0.103952) | 0.006976 / 0.075646 (-0.068671) | 0.052987 / 0.419271 (-0.366285) | 0.032454 / 0.043533 (-0.011079) | 0.273986 / 0.255139 (0.018847) | 0.297641 / 0.283200 (0.014442) | 0.017680 / 0.141683 (-0.124003) | 1.141218 / 1.452155 (-0.310937) | 1.222543 / 1.492716 (-0.270173) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092880 / 0.018006 (0.074873) | 0.305080 / 0.000490 (0.304590) | 0.000215 / 0.000200 (0.000016) | 0.000051 / 0.000054 (-0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021050 / 0.037411 (-0.016362) | 0.069676 / 0.014526 (0.055150) | 0.081082 / 0.176557 (-0.095475) | 0.119234 / 0.737135 (-0.617902) | 0.081242 / 0.296338 (-0.215096) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.295916 / 0.215209 (0.080707) | 2.909769 / 2.077655 (0.832115) | 1.623118 / 1.504120 (0.118998) | 1.502297 / 1.541195 (-0.038898) | 1.540290 / 1.468490 (0.071800) | 0.401176 / 4.584777 (-4.183601) | 2.427764 / 3.745712 (-1.317948) | 2.568610 / 5.269862 (-2.701252) | 1.550486 / 4.565676 (-3.015190) | 0.046895 / 0.424275 (-0.377380) | 0.004800 / 0.007607 (-0.002807) | 0.344524 / 0.226044 (0.118479) | 3.429189 / 2.268929 (1.160261) | 1.949738 / 55.444624 (-53.494887) | 1.681440 / 6.876477 (-5.195037) | 1.675304 / 2.142072 (-0.466769) | 0.469663 / 4.805227 (-4.335564) | 0.097470 / 6.500664 (-6.403194) | 0.040121 / 0.075469 (-0.035348) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.957947 / 1.841788 (-0.883841) | 11.968455 / 8.074308 (3.894147) | 10.809763 / 10.191392 (0.618371) | 0.140603 / 0.680424 (-0.539820) | 0.015562 / 0.534201 (-0.518638) | 0.276406 / 0.579283 (-0.302877) | 0.295267 / 0.434364 (-0.139097) | 0.315744 / 0.540337 (-0.224593) | 0.417985 / 1.386936 (-0.968951) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#12e01642a6978cbb9d5778c8b7f1c6b20a9887d5 \"CML watermark\")\n" ]

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[ "I have looked at the doc failures, and I do not think that my change caused the doc build failure, but I'm not 100% sure about that.\r\nI have high confidence that the integration test failures are not something I introduced:-)", "<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) | 0.008557 / 0.011353 (-0.002796) | 0.005224 / 0.011008 (-0.005784) | 0.109402 / 0.038508 (0.070893) | 0.075008 / 0.023109 (0.051899) | 0.388910 / 0.275898 (0.113012) | 0.425481 / 0.323480 (0.102002) | 0.005046 / 0.007986 (-0.002939) | 0.004166 / 0.004328 (-0.000162) | 0.079890 / 0.004250 (0.075639) | 0.061992 / 0.037052 (0.024940) | 0.409933 / 0.258489 (0.151444) | 0.444096 / 0.293841 (0.150255) | 0.043958 / 0.128546 (-0.084588) | 0.013655 / 0.075646 (-0.061991) | 0.402620 / 0.419271 (-0.016651) | 0.062784 / 0.043533 (0.019251) | 0.399653 / 0.255139 (0.144514) | 0.432926 / 0.283200 (0.149727) | 0.034631 / 0.141683 (-0.107052) | 1.801450 / 1.452155 (0.349296) | 1.965007 / 1.492716 (0.472290) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.305744 / 0.018006 (0.287738) | 0.590825 / 0.000490 (0.590335) | 0.014561 / 0.000200 (0.014361) | 0.000430 / 0.000054 (0.000375) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030449 / 0.037411 (-0.006962) | 0.091753 / 0.014526 (0.077227) | 0.106259 / 0.176557 (-0.070298) | 0.174599 / 0.737135 (-0.562537) | 0.107069 / 0.296338 (-0.189269) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.607544 / 0.215209 (0.392335) | 6.182592 / 2.077655 (4.104937) | 2.699782 / 1.504120 (1.195663) | 2.386915 / 1.541195 (0.845720) | 2.441763 / 1.468490 (0.973273) | 0.811360 / 4.584777 (-3.773417) | 5.253799 / 3.745712 (1.508087) | 4.762054 / 5.269862 (-0.507807) | 3.045161 / 4.565676 (-1.520515) | 0.095983 / 0.424275 (-0.328292) | 0.008653 / 0.007607 (0.001046) | 0.714218 / 0.226044 (0.488174) | 7.279325 / 2.268929 (5.010397) | 3.356107 / 55.444624 (-52.088517) | 2.765867 / 6.876477 (-4.110610) | 2.997756 / 2.142072 (0.855684) | 1.008740 / 4.805227 (-3.796487) | 0.201462 / 6.500664 (-6.299202) | 0.075780 / 0.075469 (0.000311) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.677034 / 1.841788 (-0.164754) | 23.546919 / 8.074308 (15.472610) | 21.576985 / 10.191392 (11.385593) | 0.239253 / 0.680424 (-0.441171) | 0.028740 / 0.534201 (-0.505460) | 0.468519 / 0.579283 (-0.110765) | 0.593935 / 0.434364 (0.159571) | 0.536830 / 0.540337 (-0.003507) | 0.779925 / 1.386936 (-0.607011) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009582 / 0.011353 (-0.001771) | 0.004971 / 0.011008 (-0.006037) | 0.081304 / 0.038508 (0.042796) | 0.077588 / 0.023109 (0.054478) | 0.486610 / 0.275898 (0.210712) | 0.580228 / 0.323480 (0.256748) | 0.006707 / 0.007986 (-0.001279) | 0.004325 / 0.004328 (-0.000004) | 0.086170 / 0.004250 (0.081920) | 0.060591 / 0.037052 (0.023539) | 0.501723 / 0.258489 (0.243234) | 0.548633 / 0.293841 (0.254793) | 0.050306 / 0.128546 (-0.078240) | 0.017458 / 0.075646 (-0.058188) | 0.093295 / 0.419271 (-0.325977) | 0.064588 / 0.043533 (0.021056) | 0.519395 / 0.255139 (0.264256) | 0.526021 / 0.283200 (0.242821) | 0.035795 / 0.141683 (-0.105888) | 1.792927 / 1.452155 (0.340772) | 1.956499 / 1.492716 (0.463783) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.296249 / 0.018006 (0.278243) | 0.594482 / 0.000490 (0.593992) | 0.007318 / 0.000200 (0.007118) | 0.000182 / 0.000054 (0.000128) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.036110 / 0.037411 (-0.001301) | 0.107924 / 0.014526 (0.093399) | 0.119975 / 0.176557 (-0.056582) | 0.177499 / 0.737135 (-0.559636) | 0.123299 / 0.296338 (-0.173039) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.632994 / 0.215209 (0.417785) | 6.481663 / 2.077655 (4.404008) | 3.231259 / 1.504120 (1.727139) | 2.768298 / 1.541195 (1.227103) | 2.694543 / 1.468490 (1.226053) | 0.837384 / 4.584777 (-3.747393) | 5.405278 / 3.745712 (1.659566) | 4.639424 / 5.269862 (-0.630437) | 2.944251 / 4.565676 (-1.621426) | 0.094978 / 0.424275 (-0.329297) | 0.008716 / 0.007607 (0.001108) | 0.795820 / 0.226044 (0.569776) | 8.514233 / 2.268929 (6.245304) | 3.800463 / 55.444624 (-51.644161) | 3.000005 / 6.876477 (-3.876472) | 3.298853 / 2.142072 (1.156781) | 0.994112 / 4.805227 (-3.811115) | 0.209435 / 6.500664 (-6.291229) | 0.075610 / 0.075469 (0.000141) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.681127 / 1.841788 (-0.160661) | 23.874465 / 8.074308 (15.800156) | 21.638567 / 10.191392 (11.447175) | 0.233303 / 0.680424 (-0.447121) | 0.032504 / 0.534201 (-0.501697) | 0.460462 / 0.579283 (-0.118821) | 0.560043 / 0.434364 (0.125679) | 0.555059 / 0.540337 (0.014721) | 0.831444 / 1.386936 (-0.555492) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#faada1742e1f25fce9cc5691ec11d3f91d4aa120 \"CML watermark\")\n" ]

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[ "Thanks for reporting! I've opened a PR with a fix.", "Thanks for the quick response @mariosasko! I just installed your branch via `poetry add 'git+https://github.com/huggingface/datasets#fix-array_values'` and I can confirm it works on the example provided.\r\n\r\nFollow up question for you, should `None`s be supported in these types of features as they are in others?\r\n\r\nFor example, the following script:\r\n\r\n```\r\nfrom datasets import Features, Value, Sequence, ClassLabel, Dataset\r\n\r\ndataset_features = Features({\r\n 'text': Value('string'),\r\n 'embedding': Sequence(Value('double'), length=2),\r\n 'categories': Sequence(ClassLabel(names=sorted([\r\n 'one',\r\n 'two',\r\n 'three'\r\n ]))),\r\n})\r\n\r\ndataset = Dataset.from_dict(\r\n {\r\n 'text': ['A'] * 10000,\r\n \"embedding\": [None] * 10000, # THIS LINE CHANGED\r\n 'categories': [[0]] * 10000,\r\n },\r\n features=dataset_features\r\n)\r\n\r\ndef test_mapper(r):\r\n r['text'] = list(map(lambda t: t + ' b', r['text']))\r\n return r\r\n\r\n\r\ndataset = dataset.map(test_mapper, batched=True, batch_size=10, features=dataset_features, num_proc=2)\r\n```\r\n\r\nfails with\r\n\r\n```\r\nTraceback (most recent call last):\r\n File \"/home/jmif/.virtualenvs/llm-training/lib/python3.10/site-packages/multiprocess/pool.py\", line 125, in worker\r\n result = (True, func(*args, **kwds))\r\n File \"/home/jmif/.virtualenvs/llm-training/lib/python3.10/site-packages/datasets/utils/py_utils.py\", line 1354, in _write_generator_to_queue\r\n for i, result in enumerate(func(**kwargs)):\r\n File \"/home/jmif/.virtualenvs/llm-training/lib/python3.10/site-packages/datasets/arrow_dataset.py\", line 3493, in _map_single\r\n writer.write_batch(batch)\r\n File \"/home/jmif/.virtualenvs/llm-training/lib/python3.10/site-packages/datasets/arrow_writer.py\", line 549, in write_batch\r\n array = cast_array_to_feature(col_values, col_type) if col_type is not None else col_values\r\n File \"/home/jmif/.virtualenvs/llm-training/lib/python3.10/site-packages/datasets/table.py\", line 1831, in wrapper\r\n return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])\r\n File \"/home/jmif/.virtualenvs/llm-training/lib/python3.10/site-packages/datasets/table.py\", line 1831, in <listcomp>\r\n return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])\r\n File \"/home/jmif/.virtualenvs/llm-training/lib/python3.10/site-packages/datasets/table.py\", line 2160, in cast_array_to_feature\r\n raise TypeError(f\"Couldn't cast array of type\\n{array.type}\\nto\\n{feature}\")\r\nTypeError: Couldn't cast array of type\r\nfixed_size_list<item: double>[2]\r\nto\r\nSequence(feature=Value(dtype='float64', id=None), length=2, id=None)\r\n```\r\n\r\nIdeally we can have empty embedding columns as well!", "This part of PyArrow is buggy and inconsistent regarding features implemented across the types, so the only option is to operate on the Arrow buffer level to fix issues such as the above one.", "Ok - can you take the POC I did [here](https://github.com/huggingface/datasets/commit/15443098e9ce053943172f7ec6fce3769d7dff6e)? Happy to turn this into an actual PR but would appreciate feedback on the implementation before I take another pass!" ]

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[ "This is exactly what I was looking for. It would also be very useful for me :-)" ]

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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) | 0.009624 / 0.011353 (-0.001729) | 0.005121 / 0.011008 (-0.005887) | 0.105560 / 0.038508 (0.067052) | 0.090749 / 0.023109 (0.067640) | 0.430274 / 0.275898 (0.154376) | 0.443399 / 0.323480 (0.119919) | 0.006575 / 0.007986 (-0.001411) | 0.004396 / 0.004328 (0.000068) | 0.080900 / 0.004250 (0.076649) | 0.064921 / 0.037052 (0.027868) | 0.410092 / 0.258489 (0.151603) | 0.470058 / 0.293841 (0.176217) | 0.054160 / 0.128546 (-0.074386) | 0.014367 / 0.075646 (-0.061279) | 0.384844 / 0.419271 (-0.034428) | 0.072818 / 0.043533 (0.029285) | 0.429341 / 0.255139 (0.174202) | 0.430968 / 0.283200 (0.147769) | 0.038437 / 0.141683 (-0.103246) | 1.814456 / 1.452155 (0.362301) | 1.832122 / 1.492716 (0.339406) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.329266 / 0.018006 (0.311260) | 0.596848 / 0.000490 (0.596358) | 0.018291 / 0.000200 (0.018091) | 0.000113 / 0.000054 (0.000058) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030505 / 0.037411 (-0.006907) | 0.097394 / 0.014526 (0.082869) | 0.127144 / 0.176557 (-0.049412) | 0.190251 / 0.737135 (-0.546884) | 0.116543 / 0.296338 (-0.179795) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.592124 / 0.215209 (0.376915) | 5.979801 / 2.077655 (3.902146) | 2.837753 / 1.504120 (1.333633) | 2.492942 / 1.541195 (0.951747) | 2.548083 / 1.468490 (1.079593) | 0.870446 / 4.584777 (-3.714330) | 5.493718 / 3.745712 (1.748006) | 4.945135 / 5.269862 (-0.324727) | 3.133994 / 4.565676 (-1.431683) | 0.097742 / 0.424275 (-0.326533) | 0.008750 / 0.007607 (0.001143) | 0.723304 / 0.226044 (0.497260) | 7.353766 / 2.268929 (5.084838) | 3.504808 / 55.444624 (-51.939816) | 2.872490 / 6.876477 (-4.003987) | 3.186628 / 2.142072 (1.044556) | 1.035470 / 4.805227 (-3.769758) | 0.211980 / 6.500664 (-6.288684) | 0.080356 / 0.075469 (0.004887) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.623389 / 1.841788 (-0.218399) | 23.492350 / 8.074308 (15.418042) | 21.053525 / 10.191392 (10.862133) | 0.225668 / 0.680424 (-0.454756) | 0.028311 / 0.534201 (-0.505890) | 0.472672 / 0.579283 (-0.106611) | 0.581536 / 0.434364 (0.147172) | 0.525180 / 0.540337 (-0.015158) | 0.790420 / 1.386936 (-0.596516) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009091 / 0.011353 (-0.002262) | 0.004978 / 0.011008 (-0.006030) | 0.077633 / 0.038508 (0.039125) | 0.103189 / 0.023109 (0.080080) | 0.500194 / 0.275898 (0.224296) | 0.524310 / 0.323480 (0.200831) | 0.006656 / 0.007986 (-0.001329) | 0.004586 / 0.004328 (0.000257) | 0.075535 / 0.004250 (0.071284) | 0.065100 / 0.037052 (0.028048) | 0.513776 / 0.258489 (0.255287) | 0.528483 / 0.293841 (0.234642) | 0.049877 / 0.128546 (-0.078669) | 0.012494 / 0.075646 (-0.063152) | 0.090225 / 0.419271 (-0.329046) | 0.054648 / 0.043533 (0.011116) | 0.510369 / 0.255139 (0.255230) | 0.540042 / 0.283200 (0.256842) | 0.035966 / 0.141683 (-0.105717) | 1.825965 / 1.452155 (0.373810) | 1.965647 / 1.492716 (0.472931) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.295921 / 0.018006 (0.277914) | 0.605751 / 0.000490 (0.605262) | 0.007243 / 0.000200 (0.007043) | 0.000134 / 0.000054 (0.000079) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032954 / 0.037411 (-0.004457) | 0.093613 / 0.014526 (0.079087) | 0.120010 / 0.176557 (-0.056546) | 0.176168 / 0.737135 (-0.560967) | 0.113978 / 0.296338 (-0.182360) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.682904 / 0.215209 (0.467695) | 6.674640 / 2.077655 (4.596986) | 3.360660 / 1.504120 (1.856540) | 3.227246 / 1.541195 (1.686051) | 3.188852 / 1.468490 (1.720362) | 0.862293 / 4.584777 (-3.722484) | 5.518455 / 3.745712 (1.772743) | 4.881904 / 5.269862 (-0.387957) | 3.066964 / 4.565676 (-1.498712) | 0.099284 / 0.424275 (-0.324991) | 0.008644 / 0.007607 (0.001037) | 0.789231 / 0.226044 (0.563186) | 7.872017 / 2.268929 (5.603089) | 4.037105 / 55.444624 (-51.407519) | 3.318921 / 6.876477 (-3.557555) | 3.621953 / 2.142072 (1.479881) | 1.012049 / 4.805227 (-3.793178) | 0.204541 / 6.500664 (-6.296123) | 0.074509 / 0.075469 (-0.000960) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.748215 / 1.841788 (-0.093573) | 24.274974 / 8.074308 (16.200665) | 20.582389 / 10.191392 (10.390997) | 0.251001 / 0.680424 (-0.429423) | 0.032390 / 0.534201 (-0.501811) | 0.479211 / 0.579283 (-0.100072) | 0.607482 / 0.434364 (0.173118) | 0.587867 / 0.540337 (0.047530) | 0.822399 / 1.386936 (-0.564537) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f2b6b2fd90ba47f19e9ab125f6f7656903dd065f \"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) | 0.009715 / 0.011353 (-0.001638) | 0.005449 / 0.011008 (-0.005559) | 0.108556 / 0.038508 (0.070048) | 0.080512 / 0.023109 (0.057403) | 0.450736 / 0.275898 (0.174838) | 0.487771 / 0.323480 (0.164291) | 0.005155 / 0.007986 (-0.002830) | 0.004213 / 0.004328 (-0.000115) | 0.087247 / 0.004250 (0.082997) | 0.063962 / 0.037052 (0.026909) | 0.454153 / 0.258489 (0.195664) | 0.499917 / 0.293841 (0.206076) | 0.052605 / 0.128546 (-0.075942) | 0.013019 / 0.075646 (-0.062627) | 0.379716 / 0.419271 (-0.039555) | 0.073241 / 0.043533 (0.029708) | 0.473488 / 0.255139 (0.218349) | 0.482944 / 0.283200 (0.199745) | 0.041541 / 0.141683 (-0.100142) | 1.829415 / 1.452155 (0.377261) | 1.953280 / 1.492716 (0.460564) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.313725 / 0.018006 (0.295719) | 0.591336 / 0.000490 (0.590847) | 0.021224 / 0.000200 (0.021025) | 0.000969 / 0.000054 (0.000914) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031874 / 0.037411 (-0.005537) | 0.099786 / 0.014526 (0.085260) | 0.116987 / 0.176557 (-0.059569) | 0.205538 / 0.737135 (-0.531597) | 0.118716 / 0.296338 (-0.177622) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.617145 / 0.215209 (0.401936) | 6.079144 / 2.077655 (4.001489) | 2.567233 / 1.504120 (1.063113) | 2.265301 / 1.541195 (0.724107) | 2.314001 / 1.468490 (0.845511) | 0.871561 / 4.584777 (-3.713216) | 5.477049 / 3.745712 (1.731337) | 4.720552 / 5.269862 (-0.549309) | 3.107515 / 4.565676 (-1.458162) | 0.100438 / 0.424275 (-0.323838) | 0.008586 / 0.007607 (0.000979) | 0.716913 / 0.226044 (0.490869) | 7.108417 / 2.268929 (4.839489) | 3.391336 / 55.444624 (-52.053288) | 2.734052 / 6.876477 (-4.142425) | 2.857226 / 2.142072 (0.715153) | 1.024121 / 4.805227 (-3.781106) | 0.216735 / 6.500664 (-6.283929) | 0.081605 / 0.075469 (0.006136) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.678176 / 1.841788 (-0.163611) | 23.606037 / 8.074308 (15.531729) | 21.485331 / 10.191392 (11.293939) | 0.218312 / 0.680424 (-0.462112) | 0.027061 / 0.534201 (-0.507140) | 0.481188 / 0.579283 (-0.098096) | 0.620592 / 0.434364 (0.186228) | 0.574778 / 0.540337 (0.034441) | 0.831529 / 1.386936 (-0.555407) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.011666 / 0.011353 (0.000313) | 0.005187 / 0.011008 (-0.005821) | 0.080692 / 0.038508 (0.042184) | 0.079159 / 0.023109 (0.056049) | 0.530823 / 0.275898 (0.254925) | 0.577807 / 0.323480 (0.254327) | 0.006246 / 0.007986 (-0.001740) | 0.004355 / 0.004328 (0.000026) | 0.080702 / 0.004250 (0.076452) | 0.062279 / 0.037052 (0.025226) | 0.553712 / 0.258489 (0.295223) | 0.579112 / 0.293841 (0.285271) | 0.056374 / 0.128546 (-0.072172) | 0.014681 / 0.075646 (-0.060966) | 0.097110 / 0.419271 (-0.322161) | 0.061040 / 0.043533 (0.017507) | 0.524718 / 0.255139 (0.269579) | 0.568586 / 0.283200 (0.285386) | 0.035774 / 0.141683 (-0.105909) | 1.864590 / 1.452155 (0.412435) | 1.953715 / 1.492716 (0.460998) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.271315 / 0.018006 (0.253309) | 0.571343 / 0.000490 (0.570854) | 0.015812 / 0.000200 (0.015612) | 0.000115 / 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) | 0.038582 / 0.037411 (0.001170) | 0.117523 / 0.014526 (0.102997) | 0.128864 / 0.176557 (-0.047693) | 0.191164 / 0.737135 (-0.545971) | 0.133161 / 0.296338 (-0.163178) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.679305 / 0.215209 (0.464096) | 6.814451 / 2.077655 (4.736796) | 3.377431 / 1.504120 (1.873311) | 3.011008 / 1.541195 (1.469813) | 3.093200 / 1.468490 (1.624710) | 0.905827 / 4.584777 (-3.678950) | 5.456094 / 3.745712 (1.710382) | 4.848511 / 5.269862 (-0.421351) | 3.064230 / 4.565676 (-1.501447) | 0.107478 / 0.424275 (-0.316798) | 0.009234 / 0.007607 (0.001627) | 0.833944 / 0.226044 (0.607899) | 8.286100 / 2.268929 (6.017171) | 4.241455 / 55.444624 (-51.203169) | 3.405460 / 6.876477 (-3.471017) | 3.660618 / 2.142072 (1.518546) | 1.046310 / 4.805227 (-3.758917) | 0.210891 / 6.500664 (-6.289773) | 0.079413 / 0.075469 (0.003944) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.825448 / 1.841788 (-0.016340) | 24.639059 / 8.074308 (16.564750) | 21.970417 / 10.191392 (11.779025) | 0.247708 / 0.680424 (-0.432715) | 0.033810 / 0.534201 (-0.500391) | 0.495517 / 0.579283 (-0.083766) | 0.601820 / 0.434364 (0.167456) | 0.585618 / 0.540337 (0.045280) | 0.858722 / 1.386936 (-0.528214) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0477e20dccb77b68f0add77fd5c9b4cb05473235 \"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) | 0.006137 / 0.011353 (-0.005216) | 0.003685 / 0.011008 (-0.007324) | 0.079985 / 0.038508 (0.041476) | 0.060937 / 0.023109 (0.037828) | 0.390583 / 0.275898 (0.114685) | 0.425307 / 0.323480 (0.101827) | 0.003433 / 0.007986 (-0.004552) | 0.002868 / 0.004328 (-0.001461) | 0.062572 / 0.004250 (0.058322) | 0.048642 / 0.037052 (0.011590) | 0.401096 / 0.258489 (0.142607) | 0.436988 / 0.293841 (0.143147) | 0.027645 / 0.128546 (-0.100901) | 0.007973 / 0.075646 (-0.067673) | 0.261997 / 0.419271 (-0.157275) | 0.045393 / 0.043533 (0.001860) | 0.394266 / 0.255139 (0.139127) | 0.414448 / 0.283200 (0.131248) | 0.022551 / 0.141683 (-0.119131) | 1.438458 / 1.452155 (-0.013697) | 1.501568 / 1.492716 (0.008852) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.224335 / 0.018006 (0.206329) | 0.421918 / 0.000490 (0.421428) | 0.006883 / 0.000200 (0.006683) | 0.000210 / 0.000054 (0.000155) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023505 / 0.037411 (-0.013906) | 0.072438 / 0.014526 (0.057912) | 0.083576 / 0.176557 (-0.092981) | 0.142906 / 0.737135 (-0.594229) | 0.083910 / 0.296338 (-0.212428) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.396004 / 0.215209 (0.180795) | 3.969852 / 2.077655 (1.892197) | 1.966000 / 1.504120 (0.461880) | 1.786453 / 1.541195 (0.245258) | 1.866082 / 1.468490 (0.397592) | 0.502633 / 4.584777 (-4.082144) | 3.114331 / 3.745712 (-0.631382) | 2.940003 / 5.269862 (-2.329859) | 1.901844 / 4.565676 (-2.663832) | 0.058109 / 0.424275 (-0.366166) | 0.006502 / 0.007607 (-0.001105) | 0.463465 / 0.226044 (0.237420) | 4.641531 / 2.268929 (2.372603) | 2.315759 / 55.444624 (-53.128865) | 2.253088 / 6.876477 (-4.623389) | 2.151399 / 2.142072 (0.009326) | 0.592225 / 4.805227 (-4.213002) | 0.125072 / 6.500664 (-6.375592) | 0.059966 / 0.075469 (-0.015503) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.231392 / 1.841788 (-0.610396) | 17.533893 / 8.074308 (9.459585) | 13.710478 / 10.191392 (3.519086) | 0.147389 / 0.680424 (-0.533035) | 0.017932 / 0.534201 (-0.516269) | 0.334144 / 0.579283 (-0.245139) | 0.368817 / 0.434364 (-0.065547) | 0.383790 / 0.540337 (-0.156547) | 0.540262 / 1.386936 (-0.846674) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006066 / 0.011353 (-0.005287) | 0.003804 / 0.011008 (-0.007205) | 0.062474 / 0.038508 (0.023966) | 0.060547 / 0.023109 (0.037437) | 0.448643 / 0.275898 (0.172745) | 0.487005 / 0.323480 (0.163525) | 0.004884 / 0.007986 (-0.003102) | 0.002911 / 0.004328 (-0.001418) | 0.062950 / 0.004250 (0.058700) | 0.049672 / 0.037052 (0.012620) | 0.477491 / 0.258489 (0.219002) | 0.488234 / 0.293841 (0.194393) | 0.028711 / 0.128546 (-0.099835) | 0.008101 / 0.075646 (-0.067545) | 0.068333 / 0.419271 (-0.350939) | 0.040959 / 0.043533 (-0.002574) | 0.450716 / 0.255139 (0.195577) | 0.471089 / 0.283200 (0.187890) | 0.020710 / 0.141683 (-0.120973) | 1.474850 / 1.452155 (0.022695) | 1.540115 / 1.492716 (0.047399) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.229811 / 0.018006 (0.211805) | 0.419526 / 0.000490 (0.419036) | 0.003818 / 0.000200 (0.003618) | 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) | 0.026045 / 0.037411 (-0.011366) | 0.080325 / 0.014526 (0.065799) | 0.091549 / 0.176557 (-0.085007) | 0.145253 / 0.737135 (-0.591882) | 0.091849 / 0.296338 (-0.204489) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.463047 / 0.215209 (0.247838) | 4.598727 / 2.077655 (2.521072) | 2.558996 / 1.504120 (1.054877) | 2.405896 / 1.541195 (0.864701) | 2.447291 / 1.468490 (0.978801) | 0.510393 / 4.584777 (-4.074384) | 3.173344 / 3.745712 (-0.572368) | 2.901201 / 5.269862 (-2.368661) | 1.896440 / 4.565676 (-2.669236) | 0.058374 / 0.424275 (-0.365901) | 0.006449 / 0.007607 (-0.001158) | 0.539653 / 0.226044 (0.313608) | 5.408217 / 2.268929 (3.139289) | 3.042453 / 55.444624 (-52.402172) | 2.656724 / 6.876477 (-4.219753) | 2.838165 / 2.142072 (0.696092) | 0.598663 / 4.805227 (-4.206565) | 0.126211 / 6.500664 (-6.374453) | 0.062830 / 0.075469 (-0.012639) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.392412 / 1.841788 (-0.449376) | 18.195170 / 8.074308 (10.120862) | 14.788251 / 10.191392 (4.596859) | 0.132579 / 0.680424 (-0.547845) | 0.017867 / 0.534201 (-0.516334) | 0.340020 / 0.579283 (-0.239263) | 0.386719 / 0.434364 (-0.047645) | 0.398863 / 0.540337 (-0.141475) | 0.579320 / 1.386936 (-0.807617) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a2569fdfcf387f8885974a35fafa409fbc6dd059 \"CML watermark\")\n", "closing in favor of https://github.com/huggingface/datasets/pull/6282" ]

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[ "`evaluate.load(\"paws-x\", \"es\")` throws the error because there is no such metric in the `evaluate` lib.\r\n\r\nSo, this is unrelated to our lib." ]

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[ "Since you are using Windows, maybe moving the `map` call inside `if __name__ == \"__main__\"` can fix the issue:\r\n```python\r\nif __name__ == \"__main__\":\r\n common_voice = common_voice.map(prepare_dataset, remove_columns=common_voice.column_names[\"train\"], num_proc=4)\r\n```\r\n\r\nOtherwise, the only solution is to set `num_proc=1`.", "> Since you are using Windows, maybe moving the `map` call inside `if __name__ == \"__main__\"` can fix the issue:\r\n> \r\n> ```python\r\n> if __name__ == \"__main__\":\r\n> common_voice = common_voice.map(prepare_dataset, remove_columns=common_voice.column_names[\"train\"], num_proc=4)\r\n> ```\r\n> \r\n> Otherwise, the only solution is to set `num_proc=1`.\r\n\r\nThank you very much for the response, i eventually tried setting `num_proc=1` and now the jupyter notebook kernel keers dying after running the command, what do you think the issue could be, could it be that my system is not capable of running the command \"i'm using a Lenovo Thinkpad T440 with no GPU\"", "Firstly, you didn't define feature_extractor variable. Secondly, it is large nlp model. Hence you should use proper gpu, otherwise your machine's cpu will be overclock and you can do nothing." ]

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[ "Hi! The process of contributing a dataset is explained here: https://huggingface.co/docs/datasets/upload_dataset. Also, check https://huggingface.co/docs/datasets/image_dataset for a more detailed explanation of how to share an image dataset." ]

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[ "Please tell me if the above info is not enough for solving the problem. I will then make my dataset public temporarily so that you can really reproduce the bug. " ]