Datasets:

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[ "If `test.jsonl` contains more lines like:\r\n```\r\n{\"id\": 0, \"indexs\": {\"-1\": [0, 10]}}\r\n{\"id\": 1, \"indexs\": {\"-1\": [0, 10]}}\r\n{\"id\": 2, \"indexs\": {\"-2\": [0, 10]}}\r\n...\r\n{\"id\": n, \"indexs\": {\"-9999\": [0, 10]}}\r\n```\r\n\r\n`Dataset.from_json` will just raise an error:\r\n```\r\nAn error occurred while generating the dataset\r\nTypeError: Couldn't cast array of type\r\nstruct<-5942: list<item: int64>, -5943: list<item: int64>, -5944: list<item: int64>, -5945: list<item: int64>, -5946: list<item: int64>, -5947: list<item: int64>, -5948: list<item: int64>, -5949: list<item: int64>: ...\r\nto\r\n{... '-5312': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None), '-5313': Sequence(feature=Value(dtype='int64', id=None), length=-1, id=None)}\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nTraceback (most recent call last):\r\n File \"/home/scruel/mambaforge/envs/vae/lib/python3.11/runpy.py\", line 198, in _run_module_as_main\r\n return _run_code(code, main_globals, None,\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/home/scruel/mambaforge/envs/vae/lib/python3.11/runpy.py\", line 88, in _run_code\r\n exec(code, run_globals)\r\n File \"/home/scruel/.vscode-server/extensions/ms-python.debugpy-2024.0.0-linux-x64/bundled/libs/debugpy/adapter/../../debugpy/launcher/../../debugpy/__main__.py\", line 39, in <module>\r\n cli.main()\r\n File \"/home/scruel/.vscode-server/extensions/ms-python.debugpy-2024.0.0-linux-x64/bundled/libs/debugpy/adapter/../../debugpy/launcher/../../debugpy/../debugpy/server/cli.py\", line 430, in main\r\n run()\r\n File \"/home/scruel/.vscode-server/extensions/ms-python.debugpy-2024.0.0-linux-x64/bundled/libs/debugpy/adapter/../../debugpy/launcher/../../debugpy/../debugpy/server/cli.py\", line 284, in run_file\r\n runpy.run_path(target, run_name=\"__main__\")\r\n File \"/home/scruel/.vscode-server/extensions/ms-python.debugpy-2024.0.0-linux-x64/bundled/libs/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_runpy.py\", line 321, in run_path\r\n return _run_module_code(code, init_globals, run_name,\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/home/scruel/.vscode-server/extensions/ms-python.debugpy-2024.0.0-linux-x64/bundled/libs/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_runpy.py\", line 135, in _run_module_code\r\n _run_code(code, mod_globals, init_globals,\r\n File \"/home/scruel/.vscode-server/extensions/ms-python.debugpy-2024.0.0-linux-x64/bundled/libs/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_runpy.py\", line 124, in _run_code\r\n exec(code, run_globals)\r\n File \"/home/scruel/Code/Python/Working/llm-memory/data_reader.py\", line 120, in <module>\r\n reader = SnippetReader(jsonl_path, npy_path)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/home/scruel/Code/Python/Working/llm-memory/data_reader.py\", line 85, in __init__\r\n self._dataset = Dataset.from_json(jsonl_path, features=)\r\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\r\n File \"/home/scruel/mambaforge/envs/vae/lib/python3.11/site-packages/datasets/arrow_dataset.py\", line 1130, in from_json\r\n ).read()\r\n ^^^^^^\r\n File \"/home/scruel/mambaforge/envs/vae/lib/python3.11/site-packages/datasets/io/json.py\", line 59, in read\r\n self.builder.download_and_prepare(\r\n File \"/home/scruel/mambaforge/envs/vae/lib/python3.11/site-packages/datasets/builder.py\", line 1005, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"/home/scruel/mambaforge/envs/vae/lib/python3.11/site-packages/datasets/builder.py\", line 1100, in _download_and_prepare\r\n self._prepare_split(split_generator, **prepare_split_kwargs)\r\n File \"/home/scruel/mambaforge/envs/vae/lib/python3.11/site-packages/datasets/builder.py\", line 1860, in _prepare_split\r\n for job_id, done, content in self._prepare_split_single(\r\n File \"/home/scruel/mambaforge/envs/vae/lib/python3.11/site-packages/datasets/builder.py\", line 2016, in _prepare_split_single\r\n raise DatasetGenerationError(\"An error occurred while generating the dataset\") from e\r\ndatasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset\r\n```", "Hi! Our JSON parser expects all examples/rows to share the same set of columns (applies to nested columns, too), hence the error. \r\n\r\nTo read the `index` column, we would have to manually cast the input to PyArrow's `pa.map_` type, but this requires a more thorough investigation, as `pa.map_` has limited support in PyArrow." ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6698). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "CI failure is unrelated to the changes.", "<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) | 0.005499 / 0.011353 (-0.005854) | 0.003824 / 0.011008 (-0.007184) | 0.064230 / 0.038508 (0.025722) | 0.028962 / 0.023109 (0.005853) | 0.283540 / 0.275898 (0.007642) | 0.300774 / 0.323480 (-0.022706) | 0.003405 / 0.007986 (-0.004581) | 0.002796 / 0.004328 (-0.001532) | 0.049834 / 0.004250 (0.045584) | 0.045924 / 0.037052 (0.008872) | 0.274818 / 0.258489 (0.016328) | 0.306189 / 0.293841 (0.012348) | 0.028304 / 0.128546 (-0.100242) | 0.011496 / 0.075646 (-0.064150) | 0.208236 / 0.419271 (-0.211036) | 0.035720 / 0.043533 (-0.007813) | 0.261190 / 0.255139 (0.006051) | 0.281545 / 0.283200 (-0.001655) | 0.019388 / 0.141683 (-0.122295) | 1.134999 / 1.452155 (-0.317156) | 1.203053 / 1.492716 (-0.289663) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.096007 / 0.018006 (0.078000) | 0.316958 / 0.000490 (0.316469) | 0.000210 / 0.000200 (0.000010) | 0.000053 / 0.000054 (-0.000001) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018330 / 0.037411 (-0.019081) | 0.063299 / 0.014526 (0.048773) | 0.073833 / 0.176557 (-0.102723) | 0.122285 / 0.737135 (-0.614850) | 0.077352 / 0.296338 (-0.218987) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.304487 / 0.215209 (0.089278) | 3.017666 / 2.077655 (0.940012) | 1.664292 / 1.504120 (0.160172) | 1.448446 / 1.541195 (-0.092748) | 1.435612 / 1.468490 (-0.032878) | 0.569704 / 4.584777 (-4.015073) | 2.362015 / 3.745712 (-1.383698) | 2.910380 / 5.269862 (-2.359481) | 1.814560 / 4.565676 (-2.751116) | 0.063986 / 0.424275 (-0.360289) | 0.005022 / 0.007607 (-0.002585) | 0.363528 / 0.226044 (0.137483) | 3.641940 / 2.268929 (1.373011) | 1.961589 / 55.444624 (-53.483035) | 1.603683 / 6.876477 (-5.272793) | 1.663144 / 2.142072 (-0.478928) | 0.645628 / 4.805227 (-4.159599) | 0.118759 / 6.500664 (-6.381905) | 0.042631 / 0.075469 (-0.032838) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.985648 / 1.841788 (-0.856140) | 13.082558 / 8.074308 (5.008250) | 9.909811 / 10.191392 (-0.281581) | 0.131340 / 0.680424 (-0.549083) | 0.013983 / 0.534201 (-0.520218) | 0.289869 / 0.579283 (-0.289414) | 0.271775 / 0.434364 (-0.162589) | 0.334853 / 0.540337 (-0.205485) | 0.457017 / 1.386936 (-0.929919) |\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) | 0.005580 / 0.011353 (-0.005773) | 0.003788 / 0.011008 (-0.007221) | 0.049401 / 0.038508 (0.010893) | 0.030372 / 0.023109 (0.007263) | 0.278554 / 0.275898 (0.002655) | 0.302462 / 0.323480 (-0.021018) | 0.004412 / 0.007986 (-0.003573) | 0.002825 / 0.004328 (-0.001504) | 0.047826 / 0.004250 (0.043576) | 0.047903 / 0.037052 (0.010851) | 0.293098 / 0.258489 (0.034609) | 0.322777 / 0.293841 (0.028936) | 0.030010 / 0.128546 (-0.098536) | 0.011187 / 0.075646 (-0.064459) | 0.057639 / 0.419271 (-0.361632) | 0.059693 / 0.043533 (0.016160) | 0.280288 / 0.255139 (0.025149) | 0.294022 / 0.283200 (0.010823) | 0.019635 / 0.141683 (-0.122048) | 1.154733 / 1.452155 (-0.297422) | 1.200808 / 1.492716 (-0.291908) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.099682 / 0.018006 (0.081676) | 0.319521 / 0.000490 (0.319031) | 0.000224 / 0.000200 (0.000024) | 0.000053 / 0.000054 (-0.000001) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022042 / 0.037411 (-0.015370) | 0.078842 / 0.014526 (0.064317) | 0.088715 / 0.176557 (-0.087841) | 0.126832 / 0.737135 (-0.610303) | 0.089217 / 0.296338 (-0.207122) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.300099 / 0.215209 (0.084890) | 2.907746 / 2.077655 (0.830092) | 1.619418 / 1.504120 (0.115298) | 1.495693 / 1.541195 (-0.045501) | 1.544956 / 1.468490 (0.076466) | 0.556652 / 4.584777 (-4.028124) | 2.414408 / 3.745712 (-1.331304) | 2.737227 / 5.269862 (-2.532635) | 1.763187 / 4.565676 (-2.802490) | 0.062207 / 0.424275 (-0.362069) | 0.005076 / 0.007607 (-0.002531) | 0.349880 / 0.226044 (0.123836) | 3.425355 / 2.268929 (1.156427) | 1.972094 / 55.444624 (-53.472531) | 1.710650 / 6.876477 (-5.165827) | 1.902218 / 2.142072 (-0.239855) | 0.640699 / 4.805227 (-4.164529) | 0.117879 / 6.500664 (-6.382785) | 0.042412 / 0.075469 (-0.033057) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.030131 / 1.841788 (-0.811656) | 12.750637 / 8.074308 (4.676329) | 10.352636 / 10.191392 (0.161244) | 0.141139 / 0.680424 (-0.539285) | 0.015343 / 0.534201 (-0.518858) | 0.294931 / 0.579283 (-0.284352) | 0.275237 / 0.434364 (-0.159127) | 0.336669 / 0.540337 (-0.203668) | 0.429945 / 1.386936 (-0.956991) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9c424fa517a1b8517c89545f979e0c8c7d90c3e3 \"CML watermark\")\n" ]

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[ "FWIW, I run `load_dataset(\"llm-blender/mix-instruct\")` and it ran successfully.\r\nCan you clear your cache and try again?\r\n\r\n\r\n### Environment Info\r\n\r\n- `datasets` version: 2.17.0\r\n- Platform: Linux-6.2.6-76060206-generic-x86_64-with-glibc2.35\r\n- Python version: 3.9.13\r\n- `huggingface_hub` version: 0.20.3\r\n- PyArrow version: 15.0.0\r\n- Pandas version: 1.5.3\r\n- `fsspec` version: 2023.10.0", "It is working on the Kaggle GPU instance but gives this same error when running on the CPU instance. Still to run it on Kaggle you require to install the latest versions of datasets and transformers.", "This error means that `fsspec>=2023.12.0` is installed, which is incompatible with the current releases (the next `datasets` release will be the first to support it). In the meantime, downgrading `fsspec` (`pip install fsspec<=2023.12.0`) should fix the issue.", "@mariosasko Thanks I got it to work with installing that version of fsspec." ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6696). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005057 / 0.011353 (-0.006296) | 0.003665 / 0.011008 (-0.007343) | 0.063217 / 0.038508 (0.024709) | 0.028789 / 0.023109 (0.005679) | 0.233597 / 0.275898 (-0.042301) | 0.254792 / 0.323480 (-0.068687) | 0.003065 / 0.007986 (-0.004921) | 0.002686 / 0.004328 (-0.001642) | 0.050182 / 0.004250 (0.045932) | 0.042204 / 0.037052 (0.005151) | 0.254262 / 0.258489 (-0.004227) | 0.277099 / 0.293841 (-0.016742) | 0.027564 / 0.128546 (-0.100982) | 0.010768 / 0.075646 (-0.064878) | 0.207302 / 0.419271 (-0.211969) | 0.035737 / 0.043533 (-0.007796) | 0.242388 / 0.255139 (-0.012751) | 0.259833 / 0.283200 (-0.023367) | 0.019833 / 0.141683 (-0.121850) | 1.135928 / 1.452155 (-0.316227) | 1.162851 / 1.492716 (-0.329865) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.089209 / 0.018006 (0.071202) | 0.300493 / 0.000490 (0.300003) | 0.000216 / 0.000200 (0.000016) | 0.000049 / 0.000054 (-0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.017968 / 0.037411 (-0.019444) | 0.061773 / 0.014526 (0.047247) | 0.073835 / 0.176557 (-0.102722) | 0.118592 / 0.737135 (-0.618544) | 0.073606 / 0.296338 (-0.222732) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.287858 / 0.215209 (0.072649) | 2.822917 / 2.077655 (0.745262) | 1.485259 / 1.504120 (-0.018861) | 1.355922 / 1.541195 (-0.185273) | 1.364008 / 1.468490 (-0.104482) | 0.557713 / 4.584777 (-4.027064) | 2.378972 / 3.745712 (-1.366741) | 2.737218 / 5.269862 (-2.532643) | 1.718317 / 4.565676 (-2.847359) | 0.062362 / 0.424275 (-0.361913) | 0.004992 / 0.007607 (-0.002615) | 0.350765 / 0.226044 (0.124721) | 3.387579 / 2.268929 (1.118650) | 1.860408 / 55.444624 (-53.584216) | 1.569355 / 6.876477 (-5.307122) | 1.593013 / 2.142072 (-0.549059) | 0.639325 / 4.805227 (-4.165902) | 0.121769 / 6.500664 (-6.378895) | 0.042148 / 0.075469 (-0.033322) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.990594 / 1.841788 (-0.851194) | 11.460904 / 8.074308 (3.386596) | 9.438691 / 10.191392 (-0.752701) | 0.141884 / 0.680424 (-0.538540) | 0.013725 / 0.534201 (-0.520476) | 0.288847 / 0.579283 (-0.290436) | 0.278815 / 0.434364 (-0.155549) | 0.337108 / 0.540337 (-0.203229) | 0.441659 / 1.386936 (-0.945277) |\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) | 0.005265 / 0.011353 (-0.006088) | 0.003734 / 0.011008 (-0.007274) | 0.049365 / 0.038508 (0.010857) | 0.030483 / 0.023109 (0.007373) | 0.275085 / 0.275898 (-0.000813) | 0.296004 / 0.323480 (-0.027475) | 0.004964 / 0.007986 (-0.003022) | 0.002542 / 0.004328 (-0.001787) | 0.048734 / 0.004250 (0.044483) | 0.044098 / 0.037052 (0.007046) | 0.292517 / 0.258489 (0.034028) | 0.319992 / 0.293841 (0.026151) | 0.029552 / 0.128546 (-0.098994) | 0.010669 / 0.075646 (-0.064977) | 0.058887 / 0.419271 (-0.360385) | 0.051163 / 0.043533 (0.007630) | 0.277266 / 0.255139 (0.022127) | 0.295347 / 0.283200 (0.012147) | 0.018403 / 0.141683 (-0.123280) | 1.151979 / 1.452155 (-0.300176) | 1.204583 / 1.492716 (-0.288134) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091157 / 0.018006 (0.073151) | 0.300109 / 0.000490 (0.299619) | 0.000211 / 0.000200 (0.000011) | 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) | 0.021521 / 0.037411 (-0.015890) | 0.074954 / 0.014526 (0.060428) | 0.087010 / 0.176557 (-0.089546) | 0.125853 / 0.737135 (-0.611282) | 0.087877 / 0.296338 (-0.208461) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.297890 / 0.215209 (0.082681) | 2.912159 / 2.077655 (0.834504) | 1.619311 / 1.504120 (0.115192) | 1.501726 / 1.541195 (-0.039468) | 1.494143 / 1.468490 (0.025652) | 0.566744 / 4.584777 (-4.018033) | 2.497594 / 3.745712 (-1.248118) | 2.631403 / 5.269862 (-2.638459) | 1.727896 / 4.565676 (-2.837780) | 0.065937 / 0.424275 (-0.358339) | 0.005023 / 0.007607 (-0.002585) | 0.345747 / 0.226044 (0.119702) | 3.417615 / 2.268929 (1.148686) | 1.949970 / 55.444624 (-53.494654) | 1.680019 / 6.876477 (-5.196457) | 1.789879 / 2.142072 (-0.352193) | 0.648053 / 4.805227 (-4.157174) | 0.117408 / 6.500664 (-6.383256) | 0.040681 / 0.075469 (-0.034788) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.012535 / 1.841788 (-0.829252) | 11.935819 / 8.074308 (3.861511) | 10.241452 / 10.191392 (0.050060) | 0.130956 / 0.680424 (-0.549468) | 0.015396 / 0.534201 (-0.518805) | 0.289166 / 0.579283 (-0.290117) | 0.274149 / 0.434364 (-0.160215) | 0.325844 / 0.540337 (-0.214493) | 0.424919 / 1.386936 (-0.962017) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#cb834d9c63ab8cb14725ae8e4fc2da8672892a6d \"CML watermark\")\n" ]

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[ "https://huggingface.co/datasets/CausalLM/Refined-Anime-Text/discussions/1 has been fixed, but how can we check if there are other datasets with the same error, in datasets-server's database? I don't know how to get the list of erroneous cache entries, since we only copied `Error code: JobManagerCrashedError`, but not the traceback in `details`... Do you remember the error message, or the underlying exception, we had?" ]

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[ "Hi! You can find a reason why we are against this feature in https://github.com/huggingface/datasets/issues/3449. \r\n\r\n> It's too cumbersome to write this command every time we perform a dataset merging operation\r\n\r\nExplicit is better than implicit, so this isn't a good enough reason. \r\n\r\nThanks for the effort nonetheless :)!" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6693). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005069 / 0.011353 (-0.006284) | 0.003682 / 0.011008 (-0.007326) | 0.063733 / 0.038508 (0.025225) | 0.030377 / 0.023109 (0.007268) | 0.242962 / 0.275898 (-0.032936) | 0.262865 / 0.323480 (-0.060615) | 0.004760 / 0.007986 (-0.003225) | 0.002772 / 0.004328 (-0.001557) | 0.049094 / 0.004250 (0.044843) | 0.041093 / 0.037052 (0.004041) | 0.260423 / 0.258489 (0.001934) | 0.283908 / 0.293841 (-0.009933) | 0.027409 / 0.128546 (-0.101138) | 0.010548 / 0.075646 (-0.065098) | 0.208637 / 0.419271 (-0.210634) | 0.035386 / 0.043533 (-0.008147) | 0.242352 / 0.255139 (-0.012787) | 0.264201 / 0.283200 (-0.018999) | 0.017822 / 0.141683 (-0.123860) | 1.140792 / 1.452155 (-0.311363) | 1.166782 / 1.492716 (-0.325934) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094727 / 0.018006 (0.076720) | 0.308548 / 0.000490 (0.308059) | 0.000213 / 0.000200 (0.000013) | 0.000052 / 0.000054 (-0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018106 / 0.037411 (-0.019305) | 0.062057 / 0.014526 (0.047531) | 0.073821 / 0.176557 (-0.102735) | 0.121269 / 0.737135 (-0.615867) | 0.074062 / 0.296338 (-0.222277) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.282978 / 0.215209 (0.067768) | 2.788626 / 2.077655 (0.710971) | 1.479756 / 1.504120 (-0.024364) | 1.360620 / 1.541195 (-0.180575) | 1.363996 / 1.468490 (-0.104494) | 0.571646 / 4.584777 (-4.013131) | 2.430630 / 3.745712 (-1.315083) | 2.783909 / 5.269862 (-2.485953) | 1.744617 / 4.565676 (-2.821060) | 0.062771 / 0.424275 (-0.361504) | 0.004978 / 0.007607 (-0.002629) | 0.347929 / 0.226044 (0.121884) | 3.368837 / 2.268929 (1.099908) | 1.855635 / 55.444624 (-53.588990) | 1.581555 / 6.876477 (-5.294922) | 1.589888 / 2.142072 (-0.552184) | 0.655821 / 4.805227 (-4.149406) | 0.118990 / 6.500664 (-6.381674) | 0.042191 / 0.075469 (-0.033278) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.991099 / 1.841788 (-0.850688) | 11.627919 / 8.074308 (3.553611) | 9.554180 / 10.191392 (-0.637212) | 0.140541 / 0.680424 (-0.539882) | 0.014264 / 0.534201 (-0.519937) | 0.288465 / 0.579283 (-0.290818) | 0.266400 / 0.434364 (-0.167964) | 0.324400 / 0.540337 (-0.215938) | 0.423158 / 1.386936 (-0.963778) |\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) | 0.005588 / 0.011353 (-0.005765) | 0.003784 / 0.011008 (-0.007224) | 0.049961 / 0.038508 (0.011453) | 0.031215 / 0.023109 (0.008105) | 0.280859 / 0.275898 (0.004961) | 0.306416 / 0.323480 (-0.017063) | 0.004310 / 0.007986 (-0.003676) | 0.002884 / 0.004328 (-0.001445) | 0.049662 / 0.004250 (0.045412) | 0.046611 / 0.037052 (0.009559) | 0.293353 / 0.258489 (0.034864) | 0.327839 / 0.293841 (0.033998) | 0.050784 / 0.128546 (-0.077763) | 0.010890 / 0.075646 (-0.064757) | 0.059612 / 0.419271 (-0.359659) | 0.033175 / 0.043533 (-0.010358) | 0.281085 / 0.255139 (0.025946) | 0.302746 / 0.283200 (0.019547) | 0.019201 / 0.141683 (-0.122481) | 1.126722 / 1.452155 (-0.325433) | 1.225678 / 1.492716 (-0.267038) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094335 / 0.018006 (0.076329) | 0.304774 / 0.000490 (0.304285) | 0.000207 / 0.000200 (0.000007) | 0.000050 / 0.000054 (-0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021648 / 0.037411 (-0.015763) | 0.077920 / 0.014526 (0.063394) | 0.087125 / 0.176557 (-0.089432) | 0.125481 / 0.737135 (-0.611654) | 0.089415 / 0.296338 (-0.206924) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.304955 / 0.215209 (0.089746) | 2.992587 / 2.077655 (0.914932) | 1.654609 / 1.504120 (0.150490) | 1.509114 / 1.541195 (-0.032081) | 1.530906 / 1.468490 (0.062416) | 0.572092 / 4.584777 (-4.012685) | 2.477902 / 3.745712 (-1.267810) | 2.731363 / 5.269862 (-2.538498) | 1.750000 / 4.565676 (-2.815677) | 0.063662 / 0.424275 (-0.360613) | 0.005008 / 0.007607 (-0.002600) | 0.353066 / 0.226044 (0.127022) | 3.528309 / 2.268929 (1.259380) | 2.009238 / 55.444624 (-53.435387) | 1.717792 / 6.876477 (-5.158685) | 1.861699 / 2.142072 (-0.280373) | 0.667392 / 4.805227 (-4.137835) | 0.119197 / 6.500664 (-6.381467) | 0.041131 / 0.075469 (-0.034338) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.032182 / 1.841788 (-0.809605) | 12.042613 / 8.074308 (3.968305) | 10.256293 / 10.191392 (0.064901) | 0.141180 / 0.680424 (-0.539244) | 0.015005 / 0.534201 (-0.519196) | 0.290081 / 0.579283 (-0.289202) | 0.281081 / 0.434364 (-0.153283) | 0.331425 / 0.540337 (-0.208912) | 0.418674 / 1.386936 (-0.968262) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ad5b221c01a183a66cbf52a6d708f94e0cff0b53 \"CML watermark\")\n" ]

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[ "Hi @harsh1504660,\r\n\r\nThanks for your work, but this functionality already exists. See my comment in the corresponding issue: https://github.com/huggingface/datasets/issues/6691#issuecomment-1963449923\r\n\r\nNext time you would like to contribute, I would suggest you take on an issue that is previously validated by one of the maintainers. Thanks anyway." ]

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[ "#self-assign", "Hi @dipsivenkatesh,\r\n\r\nPlease note that this functionality is already implemented. Our CSV builder uses `pandas.read_csv` under the hood, and you can pass the parameter `delimiter=\"\\t\"` to read TSV files.\r\n\r\nSee the list of CSV config parameters in our docs: https://huggingface.co/docs/datasets/package_reference/loading_methods#datasets.packaged_modules.csv.CsvConfig" ]

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[ "The dataset is made of JSON files compressed using zstandard, as you can see here: https://huggingface.co/datasets/cerebras/SlimPajama-627B/tree/main/test/chunk1\r\n\r\nThat's why it asks for zstandard to be installed.\r\n\r\nThough I'm intrigued that you manage to load the dataset without zstandard installed. Maybe `pyarrow` that we use to load JSON data under the hood got support for zstandard at one point.", "> The dataset is made of JSON files compressed using zstandard, as you can see here: https://huggingface.co/datasets/cerebras/SlimPajama-627B/tree/main/test/chunk1\r\n> \r\n> That's why it asks for zstandard to be installed.\r\n> \r\n> Though I'm intrigued that you manage to load the dataset without zstandard installed. Maybe `pyarrow` that we use to load JSON data under the hood got support for zstandard at one point.\r\n\r\nQuestion, then.\r\n\r\nWhen I loaded this dataset back in October, it downloaded all the files, and then loaded into memory just fine.\r\n\r\nNOW, it has to sit there and unpack all these zstd files (3.6TB worth). Further, when they're in my harddrive, they're regular json files. It's only when looking at the LFS, or when the loading script runs, that I get asked to install zstd.\r\n\r\nMy question is, **is this normal?** As far as I can tell, there's no reason the dataset or the loading methods should have changed between then and now. Was my old behavior flawed, and the new behavior correct?\r\n\r\nI mean, I got it working eventually, but it was pulling teeth, and it still doesn't load right, as I had to unpack each chunk separately, so there's no clean mapping between the chunks and the broader dataset.", "The `ZstdExtractor` has been added 3 years ago and we haven't touched it since then. Same for the JSON loader.\r\n\r\n`zstandard` is required as soon as you try to load a file with the `.zstd` extension or if a file starts with the Zstandard magic number `b\"\\x28\\xb5\\x2f\\xfd\"` (used to recognize Zstandard files).\r\n\r\nNote that the extraction only has to happen once - if you reload the dataset it will be reloaded from your cache directly.\r\n\r\nNot sure what happened between October and now unfortunately", "Understood, thank you for clarifying that for me.\r\n\r\nI'll look into how best to collate my stack of batches w/o creating duplicate arrow tables for each one." ]

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[ "Hi, this is expected behavior since all the tensors are converted to Arrow data (the storage type behind a Dataset).\r\n\r\nTo get pytorch tensors back, you can set the dataset format to \"torch\":\r\n\r\n```python\r\nds = ds.with_format(\"torch\")\r\n```", "Thanks. Just one additional question. During the pipeline `<framework> -> arrow -> <framework>`, does `.with_format` zero-copies the tensors or is it a deep copy? And is this behavior framework-dependent?\r\n\r\nThanks again.", "We do zero-copy Arrow <-> NumPy <-> PyTorch when the output dtype matches the original dtype, but for other frameworks it depends. For example JAX doesn't allow zero-copy NumPy -> JAX at all IIRC.\r\n\r\nCurrently tokenized data are formatted using a copy though, since tokens are stored as int32 and returned as int64 torch tensors." ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6687). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "Looking into the CI failure, this PR is incompatible with `huggingface-hub>=0.20.0`. It looks like there were several changes made to HfFileSystem in 0.20.0, @lhoestq any ideas on what the issue might be in particular?\r\n\r\na bisect indicates that it's related to https://github.com/huggingface/huggingface_hub/pull/1815", "It looks like huggingface-hub's `HfFileSystem.glob` is broken for exact string matches (that don't contain glob wildcards) when combining `huggingface-hub>=0.20.0` and `fsspec>=2023.12.0`.\r\n\r\nI did a quick test with huggingface-hub `main`, and adding this test case to `tests/test_hf_filesystem::HfFileSystemTests::test_glob` (https://github.com/huggingface/huggingface_hub/blob/main/tests/test_hf_file_system.py) passes with `fsspec==2023.10.0` and fails with `fsspec==2023.12.0`\r\n```python\r\n self.assertEqual(\r\n sorted(self.hffs.glob(self.hf_path + \"/.gitattributes\")),\r\n sorted([self.hf_path + \"/.gitattributes\"]),\r\n )\r\n\r\n```\r\n\r\nthe `hffs.glob()` call with a pattern that does not contain any wildcards returns an empty list:\r\n```\r\nE AssertionError: Lists differ: [] != ['datasets/__DUMMY_TRANSFORMERS_USER__/rep[35 chars]tes']\r\nE\r\nE Second list contains 1 additional elements.\r\nE First extra element 0:\r\nE 'datasets/__DUMMY_TRANSFORMERS_USER__/repo-7d0ae9-17091013467064/.gitattributes'\r\nE\r\nE - []\r\nE + ['datasets/__DUMMY_TRANSFORMERS_USER__/repo-7d0ae9-17091013467064/.gitattributes']\r\n```\r\n(and with the compatible/passing older fsspec versions the glob call returns the single exact file match as expected)\r\n\r\nSo it looks like the CI failure here isn't directly related to this PR. The failing patterns that don't contain any `*` wildcards are generated by `datasets` with or without this PR, but now that this PR installs the incompatible fsspec version, the underlying `HfFileSystem.glob()` call ends up failing.", "I just opened https://github.com/huggingface/huggingface_hub/pull/2056 to fix this.\r\n\r\nDo you mind if I continue this PR to run the CI against `huggingface_hub@main` until the fix is released ?\r\n\r\nEDIT: the fix has been released in `huggingface_hub` 0.21.2 - I removed my commits that were using `huggingface_hub@main`", "I just added two additional patterns to cover cases like `test-data/xxx.csv` and `data-test/xxx.csv`", "<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) | 0.005461 / 0.011353 (-0.005892) | 0.003861 / 0.011008 (-0.007148) | 0.063252 / 0.038508 (0.024744) | 0.031474 / 0.023109 (0.008364) | 0.250321 / 0.275898 (-0.025577) | 0.275198 / 0.323480 (-0.048282) | 0.003275 / 0.007986 (-0.004710) | 0.002874 / 0.004328 (-0.001454) | 0.049499 / 0.004250 (0.045248) | 0.045334 / 0.037052 (0.008282) | 0.266347 / 0.258489 (0.007858) | 0.308974 / 0.293841 (0.015133) | 0.027742 / 0.128546 (-0.100804) | 0.010274 / 0.075646 (-0.065373) | 0.207516 / 0.419271 (-0.211755) | 0.036538 / 0.043533 (-0.006995) | 0.247949 / 0.255139 (-0.007190) | 0.268986 / 0.283200 (-0.014214) | 0.019842 / 0.141683 (-0.121841) | 1.117547 / 1.452155 (-0.334607) | 1.175813 / 1.492716 (-0.316903) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.103661 / 0.018006 (0.085655) | 0.331023 / 0.000490 (0.330534) | 0.000240 / 0.000200 (0.000040) | 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) | 0.019767 / 0.037411 (-0.017645) | 0.061500 / 0.014526 (0.046974) | 0.075899 / 0.176557 (-0.100658) | 0.122240 / 0.737135 (-0.614895) | 0.074621 / 0.296338 (-0.221717) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.287501 / 0.215209 (0.072292) | 2.794737 / 2.077655 (0.717082) | 1.505362 / 1.504120 (0.001242) | 1.379481 / 1.541195 (-0.161713) | 1.394836 / 1.468490 (-0.073654) | 0.545803 / 4.584777 (-4.038974) | 2.364167 / 3.745712 (-1.381545) | 2.800923 / 5.269862 (-2.468939) | 1.723910 / 4.565676 (-2.841766) | 0.061270 / 0.424275 (-0.363005) | 0.005006 / 0.007607 (-0.002601) | 0.334952 / 0.226044 (0.108908) | 3.367122 / 2.268929 (1.098194) | 1.839822 / 55.444624 (-53.604803) | 1.553774 / 6.876477 (-5.322703) | 1.583585 / 2.142072 (-0.558488) | 0.624680 / 4.805227 (-4.180547) | 0.116364 / 6.500664 (-6.384300) | 0.042412 / 0.075469 (-0.033057) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.975207 / 1.841788 (-0.866580) | 11.843126 / 8.074308 (3.768818) | 9.418537 / 10.191392 (-0.772855) | 0.130648 / 0.680424 (-0.549775) | 0.013747 / 0.534201 (-0.520454) | 0.288195 / 0.579283 (-0.291088) | 0.269861 / 0.434364 (-0.164503) | 0.326732 / 0.540337 (-0.213606) | 0.441256 / 1.386936 (-0.945680) |\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) | 0.005185 / 0.011353 (-0.006168) | 0.003836 / 0.011008 (-0.007172) | 0.050057 / 0.038508 (0.011549) | 0.030929 / 0.023109 (0.007820) | 0.263558 / 0.275898 (-0.012340) | 0.284553 / 0.323480 (-0.038927) | 0.004331 / 0.007986 (-0.003655) | 0.002815 / 0.004328 (-0.001513) | 0.050187 / 0.004250 (0.045936) | 0.048431 / 0.037052 (0.011379) | 0.271005 / 0.258489 (0.012515) | 0.304749 / 0.293841 (0.010908) | 0.029286 / 0.128546 (-0.099260) | 0.010598 / 0.075646 (-0.065048) | 0.058111 / 0.419271 (-0.361160) | 0.053665 / 0.043533 (0.010132) | 0.257574 / 0.255139 (0.002436) | 0.285802 / 0.283200 (0.002602) | 0.018917 / 0.141683 (-0.122766) | 1.206517 / 1.452155 (-0.245638) | 1.220572 / 1.492716 (-0.272144) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.122466 / 0.018006 (0.104460) | 0.567887 / 0.000490 (0.567397) | 0.000321 / 0.000200 (0.000121) | 0.000049 / 0.000054 (-0.000006) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022120 / 0.037411 (-0.015292) | 0.075456 / 0.014526 (0.060931) | 0.086385 / 0.176557 (-0.090171) | 0.126045 / 0.737135 (-0.611091) | 0.087502 / 0.296338 (-0.208837) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.304847 / 0.215209 (0.089638) | 3.008095 / 2.077655 (0.930441) | 1.726178 / 1.504120 (0.222058) | 1.592332 / 1.541195 (0.051138) | 1.603714 / 1.468490 (0.135224) | 0.576875 / 4.584777 (-4.007902) | 2.450884 / 3.745712 (-1.294828) | 2.719073 / 5.269862 (-2.550789) | 1.775261 / 4.565676 (-2.790415) | 0.063144 / 0.424275 (-0.361131) | 0.005122 / 0.007607 (-0.002485) | 0.350004 / 0.226044 (0.123960) | 3.467146 / 2.268929 (1.198218) | 2.062907 / 55.444624 (-53.381717) | 1.798793 / 6.876477 (-5.077684) | 1.921204 / 2.142072 (-0.220868) | 0.651832 / 4.805227 (-4.153396) | 0.122326 / 6.500664 (-6.378338) | 0.041396 / 0.075469 (-0.034073) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.024859 / 1.841788 (-0.816928) | 12.569744 / 8.074308 (4.495436) | 10.448487 / 10.191392 (0.257095) | 0.131529 / 0.680424 (-0.548895) | 0.014853 / 0.534201 (-0.519348) | 0.287683 / 0.579283 (-0.291600) | 0.289814 / 0.434364 (-0.144550) | 0.323935 / 0.540337 (-0.216403) | 0.425208 / 1.386936 (-0.961728) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ba71e92c59c9bd9d1ee6168691977f0c4728ed6e \"CML watermark\")\n", "> EDIT: the fix has been released in `huggingface_hub` 0.21.2 - I removed my commits that were using `huggingface_hub@main`\r\n\r\nPlease note that people using `huggingface_hub` < 0.21.2 and latest `fsspec` will have issues when using `datasets`:\r\n- https://github.com/huggingface/lighteval/actions/runs/8139147047/job/22241658122?pr=86\r\n- https://github.com/huggingface/lighteval/pull/84\r\n\r\nCC: @clefourrier \r\n" ]

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[ "```\r\nimport pandas as pd\r\nfrom datasets import Dataset, Image\r\n\r\n# Read the CSV file\r\ndata = pd.read_csv(\"XXXX.csv\")\r\n\r\n# Create a Hugging Face Dataset\r\ndataset = Dataset.from_pandas(data)\r\ndataset = dataset.cast_column(\"file_name\", Image())\r\n\r\n# Upload to Hugging Face Hub (make sure authentication is set up)\r\ndataset.push_to_hub(\"XXXXX\"\")\r\n```\r\n\r\nstuck in \"Casting the dataset\r\n\r\n\"\r\n" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6685). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005386 / 0.011353 (-0.005967) | 0.003707 / 0.011008 (-0.007301) | 0.062661 / 0.038508 (0.024153) | 0.029058 / 0.023109 (0.005949) | 0.249669 / 0.275898 (-0.026230) | 0.280996 / 0.323480 (-0.042484) | 0.004041 / 0.007986 (-0.003945) | 0.002713 / 0.004328 (-0.001616) | 0.047914 / 0.004250 (0.043664) | 0.042014 / 0.037052 (0.004961) | 0.265209 / 0.258489 (0.006720) | 0.297320 / 0.293841 (0.003479) | 0.028323 / 0.128546 (-0.100223) | 0.010844 / 0.075646 (-0.064802) | 0.205895 / 0.419271 (-0.213377) | 0.035997 / 0.043533 (-0.007536) | 0.245069 / 0.255139 (-0.010070) | 0.266159 / 0.283200 (-0.017040) | 0.017590 / 0.141683 (-0.124093) | 1.132046 / 1.452155 (-0.320109) | 1.177496 / 1.492716 (-0.315220) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.105441 / 0.018006 (0.087435) | 0.301321 / 0.000490 (0.300831) | 0.000211 / 0.000200 (0.000011) | 0.000064 / 0.000054 (0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018687 / 0.037411 (-0.018724) | 0.061221 / 0.014526 (0.046695) | 0.072556 / 0.176557 (-0.104001) | 0.119641 / 0.737135 (-0.617495) | 0.073781 / 0.296338 (-0.222557) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.284564 / 0.215209 (0.069354) | 2.795786 / 2.077655 (0.718131) | 1.437059 / 1.504120 (-0.067061) | 1.309319 / 1.541195 (-0.231876) | 1.315849 / 1.468490 (-0.152641) | 0.578571 / 4.584777 (-4.006206) | 2.350754 / 3.745712 (-1.394958) | 2.758499 / 5.269862 (-2.511362) | 1.705545 / 4.565676 (-2.860131) | 0.063660 / 0.424275 (-0.360615) | 0.005506 / 0.007607 (-0.002101) | 0.334915 / 0.226044 (0.108871) | 3.295922 / 2.268929 (1.026994) | 1.796513 / 55.444624 (-53.648111) | 1.488113 / 6.876477 (-5.388364) | 1.523042 / 2.142072 (-0.619031) | 0.648169 / 4.805227 (-4.157058) | 0.119321 / 6.500664 (-6.381343) | 0.041932 / 0.075469 (-0.033537) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.982432 / 1.841788 (-0.859356) | 11.344780 / 8.074308 (3.270472) | 9.627219 / 10.191392 (-0.564173) | 0.142590 / 0.680424 (-0.537834) | 0.013899 / 0.534201 (-0.520302) | 0.286335 / 0.579283 (-0.292948) | 0.266552 / 0.434364 (-0.167812) | 0.320361 / 0.540337 (-0.219977) | 0.420303 / 1.386936 (-0.966633) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005251 / 0.011353 (-0.006102) | 0.003515 / 0.011008 (-0.007494) | 0.049344 / 0.038508 (0.010836) | 0.032055 / 0.023109 (0.008945) | 0.280653 / 0.275898 (0.004755) | 0.303989 / 0.323480 (-0.019491) | 0.004402 / 0.007986 (-0.003584) | 0.002758 / 0.004328 (-0.001570) | 0.050947 / 0.004250 (0.046697) | 0.044405 / 0.037052 (0.007353) | 0.292856 / 0.258489 (0.034367) | 0.325307 / 0.293841 (0.031466) | 0.047720 / 0.128546 (-0.080827) | 0.010589 / 0.075646 (-0.065057) | 0.057728 / 0.419271 (-0.361543) | 0.033842 / 0.043533 (-0.009691) | 0.285443 / 0.255139 (0.030304) | 0.300013 / 0.283200 (0.016814) | 0.017444 / 0.141683 (-0.124238) | 1.152880 / 1.452155 (-0.299275) | 1.200670 / 1.492716 (-0.292046) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092355 / 0.018006 (0.074349) | 0.307907 / 0.000490 (0.307418) | 0.000226 / 0.000200 (0.000026) | 0.000053 / 0.000054 (-0.000001) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021624 / 0.037411 (-0.015787) | 0.075855 / 0.014526 (0.061329) | 0.087109 / 0.176557 (-0.089447) | 0.124859 / 0.737135 (-0.612276) | 0.088933 / 0.296338 (-0.207406) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.294213 / 0.215209 (0.079004) | 2.893146 / 2.077655 (0.815491) | 1.595061 / 1.504120 (0.090942) | 1.480959 / 1.541195 (-0.060236) | 1.528277 / 1.468490 (0.059787) | 0.570273 / 4.584777 (-4.014504) | 2.412948 / 3.745712 (-1.332764) | 2.675009 / 5.269862 (-2.594852) | 1.724005 / 4.565676 (-2.841671) | 0.063359 / 0.424275 (-0.360916) | 0.005008 / 0.007607 (-0.002599) | 0.346570 / 0.226044 (0.120526) | 3.456566 / 2.268929 (1.187637) | 1.973109 / 55.444624 (-53.471515) | 1.657562 / 6.876477 (-5.218915) | 1.790086 / 2.142072 (-0.351986) | 0.655277 / 4.805227 (-4.149950) | 0.117985 / 6.500664 (-6.382679) | 0.041128 / 0.075469 (-0.034342) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.001428 / 1.841788 (-0.840360) | 11.953458 / 8.074308 (3.879150) | 10.188439 / 10.191392 (-0.002953) | 0.140863 / 0.680424 (-0.539561) | 0.015278 / 0.534201 (-0.518923) | 0.288193 / 0.579283 (-0.291090) | 0.281732 / 0.434364 (-0.152632) | 0.328034 / 0.540337 (-0.212304) | 0.414571 / 1.386936 (-0.972365) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#531f35e688f81ec6b4c9044856a89a6b48142bd8 \"CML watermark\")\n" ]

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[ "Thank you ! Should we also add the link to the dataset page ?", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6684). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "> Thank you ! Should we also add the link to the dataset page ?\r\n\r\nGood idea! Done in https://github.com/huggingface/datasets/pull/6684/commits/4ab55210dca1815b6c2f23901598bfb29fc92a47", "Looks like a test is failing: `test_load_dataset_cached_local_script `.\r\n\r\nApparently your new message is also shown for datasets that don't exist, which is maybe not ideal", "Ah let me take a look!", "> Looks like a test is failing: `test_load_dataset_cached_local_script `.\r\n> \r\n> Apparently your new message is also shown for datasets that don't exist, which is maybe not ideal\r\n\r\nFixed by reverting the error message root + added a small clarifying 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) | 0.005634 / 0.011353 (-0.005719) | 0.003786 / 0.011008 (-0.007222) | 0.064245 / 0.038508 (0.025737) | 0.031228 / 0.023109 (0.008119) | 0.248162 / 0.275898 (-0.027736) | 0.273454 / 0.323480 (-0.050026) | 0.003176 / 0.007986 (-0.004809) | 0.002814 / 0.004328 (-0.001515) | 0.049234 / 0.004250 (0.044984) | 0.046075 / 0.037052 (0.009023) | 0.262410 / 0.258489 (0.003921) | 0.290597 / 0.293841 (-0.003244) | 0.028545 / 0.128546 (-0.100001) | 0.010881 / 0.075646 (-0.064766) | 0.212098 / 0.419271 (-0.207173) | 0.036406 / 0.043533 (-0.007127) | 0.244571 / 0.255139 (-0.010568) | 0.269537 / 0.283200 (-0.013663) | 0.019574 / 0.141683 (-0.122109) | 1.120369 / 1.452155 (-0.331785) | 1.170188 / 1.492716 (-0.322529) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.108088 / 0.018006 (0.090082) | 0.299836 / 0.000490 (0.299346) | 0.000204 / 0.000200 (0.000004) | 0.000043 / 0.000054 (-0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.020881 / 0.037411 (-0.016531) | 0.065290 / 0.014526 (0.050764) | 0.074283 / 0.176557 (-0.102274) | 0.122189 / 0.737135 (-0.614947) | 0.077772 / 0.296338 (-0.218566) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.278329 / 0.215209 (0.063120) | 2.709885 / 2.077655 (0.632230) | 1.428824 / 1.504120 (-0.075296) | 1.314338 / 1.541195 (-0.226857) | 1.349445 / 1.468490 (-0.119045) | 0.571863 / 4.584777 (-4.012914) | 2.358306 / 3.745712 (-1.387407) | 2.873498 / 5.269862 (-2.396364) | 1.779897 / 4.565676 (-2.785779) | 0.062828 / 0.424275 (-0.361447) | 0.005416 / 0.007607 (-0.002191) | 0.337645 / 0.226044 (0.111601) | 3.328868 / 2.268929 (1.059940) | 1.793387 / 55.444624 (-53.651238) | 1.539201 / 6.876477 (-5.337276) | 1.589552 / 2.142072 (-0.552520) | 0.645454 / 4.805227 (-4.159773) | 0.116966 / 6.500664 (-6.383698) | 0.043339 / 0.075469 (-0.032130) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.995743 / 1.841788 (-0.846045) | 12.096551 / 8.074308 (4.022243) | 10.214299 / 10.191392 (0.022907) | 0.133025 / 0.680424 (-0.547399) | 0.014393 / 0.534201 (-0.519808) | 0.289018 / 0.579283 (-0.290266) | 0.267879 / 0.434364 (-0.166485) | 0.324362 / 0.540337 (-0.215976) | 0.425596 / 1.386936 (-0.961340) |\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) | 0.005739 / 0.011353 (-0.005614) | 0.003992 / 0.011008 (-0.007017) | 0.051362 / 0.038508 (0.012854) | 0.031707 / 0.023109 (0.008598) | 0.274807 / 0.275898 (-0.001091) | 0.298897 / 0.323480 (-0.024583) | 0.004363 / 0.007986 (-0.003622) | 0.002862 / 0.004328 (-0.001466) | 0.050462 / 0.004250 (0.046212) | 0.048158 / 0.037052 (0.011106) | 0.282759 / 0.258489 (0.024270) | 0.317766 / 0.293841 (0.023926) | 0.060245 / 0.128546 (-0.068301) | 0.011279 / 0.075646 (-0.064367) | 0.061175 / 0.419271 (-0.358097) | 0.035876 / 0.043533 (-0.007656) | 0.273963 / 0.255139 (0.018824) | 0.288788 / 0.283200 (0.005589) | 0.019690 / 0.141683 (-0.121992) | 1.167074 / 1.452155 (-0.285080) | 1.206344 / 1.492716 (-0.286372) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091211 / 0.018006 (0.073205) | 0.299295 / 0.000490 (0.298805) | 0.000216 / 0.000200 (0.000016) | 0.000043 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022718 / 0.037411 (-0.014693) | 0.079483 / 0.014526 (0.064957) | 0.087437 / 0.176557 (-0.089120) | 0.126977 / 0.737135 (-0.610159) | 0.089678 / 0.296338 (-0.206660) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.294719 / 0.215209 (0.079510) | 2.864505 / 2.077655 (0.786851) | 1.583993 / 1.504120 (0.079873) | 1.455079 / 1.541195 (-0.086115) | 1.504080 / 1.468490 (0.035590) | 0.569040 / 4.584777 (-4.015737) | 2.423472 / 3.745712 (-1.322240) | 2.742848 / 5.269862 (-2.527014) | 1.785244 / 4.565676 (-2.780432) | 0.062655 / 0.424275 (-0.361620) | 0.005027 / 0.007607 (-0.002580) | 0.343863 / 0.226044 (0.117818) | 3.376286 / 2.268929 (1.107358) | 1.933846 / 55.444624 (-53.510779) | 1.667316 / 6.876477 (-5.209161) | 1.815621 / 2.142072 (-0.326451) | 0.639378 / 4.805227 (-4.165850) | 0.116514 / 6.500664 (-6.384150) | 0.042191 / 0.075469 (-0.033279) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.007103 / 1.841788 (-0.834685) | 12.791193 / 8.074308 (4.716885) | 10.870575 / 10.191392 (0.679183) | 0.131040 / 0.680424 (-0.549384) | 0.016510 / 0.534201 (-0.517691) | 0.288372 / 0.579283 (-0.290911) | 0.275574 / 0.434364 (-0.158790) | 0.327801 / 0.540337 (-0.212536) | 0.415942 / 1.386936 (-0.970994) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b775390900132834e5edf487f5cbbf1299af1d88 \"CML watermark\")\n" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6683). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005501 / 0.011353 (-0.005851) | 0.003907 / 0.011008 (-0.007101) | 0.063524 / 0.038508 (0.025016) | 0.031773 / 0.023109 (0.008664) | 0.244672 / 0.275898 (-0.031226) | 0.293342 / 0.323480 (-0.030138) | 0.004091 / 0.007986 (-0.003895) | 0.002837 / 0.004328 (-0.001491) | 0.049181 / 0.004250 (0.044930) | 0.044515 / 0.037052 (0.007462) | 0.263932 / 0.258489 (0.005443) | 0.288412 / 0.293841 (-0.005429) | 0.028338 / 0.128546 (-0.100208) | 0.010865 / 0.075646 (-0.064781) | 0.207979 / 0.419271 (-0.211293) | 0.036149 / 0.043533 (-0.007384) | 0.250674 / 0.255139 (-0.004465) | 0.263232 / 0.283200 (-0.019968) | 0.017919 / 0.141683 (-0.123763) | 1.127794 / 1.452155 (-0.324360) | 1.172071 / 1.492716 (-0.320645) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.090435 / 0.018006 (0.072429) | 0.300041 / 0.000490 (0.299552) | 0.000217 / 0.000200 (0.000018) | 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) | 0.018986 / 0.037411 (-0.018426) | 0.064872 / 0.014526 (0.050346) | 0.074738 / 0.176557 (-0.101818) | 0.121577 / 0.737135 (-0.615558) | 0.076416 / 0.296338 (-0.219923) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.279471 / 0.215209 (0.064262) | 2.743066 / 2.077655 (0.665411) | 1.429511 / 1.504120 (-0.074609) | 1.315391 / 1.541195 (-0.225804) | 1.371255 / 1.468490 (-0.097235) | 0.570708 / 4.584777 (-4.014069) | 2.373047 / 3.745712 (-1.372666) | 2.813198 / 5.269862 (-2.456663) | 1.768928 / 4.565676 (-2.796749) | 0.066031 / 0.424275 (-0.358244) | 0.005074 / 0.007607 (-0.002533) | 0.333484 / 0.226044 (0.107440) | 3.295002 / 2.268929 (1.026074) | 1.796089 / 55.444624 (-53.648535) | 1.521849 / 6.876477 (-5.354627) | 1.604417 / 2.142072 (-0.537655) | 0.645235 / 4.805227 (-4.159992) | 0.119226 / 6.500664 (-6.381439) | 0.043275 / 0.075469 (-0.032194) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.986350 / 1.841788 (-0.855438) | 11.921886 / 8.074308 (3.847578) | 9.878841 / 10.191392 (-0.312551) | 0.141072 / 0.680424 (-0.539352) | 0.014514 / 0.534201 (-0.519687) | 0.304060 / 0.579283 (-0.275223) | 0.267844 / 0.434364 (-0.166520) | 0.324881 / 0.540337 (-0.215457) | 0.421426 / 1.386936 (-0.965510) |\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) | 0.005322 / 0.011353 (-0.006030) | 0.003942 / 0.011008 (-0.007066) | 0.050629 / 0.038508 (0.012121) | 0.031176 / 0.023109 (0.008066) | 0.279627 / 0.275898 (0.003729) | 0.302667 / 0.323480 (-0.020813) | 0.004281 / 0.007986 (-0.003705) | 0.002900 / 0.004328 (-0.001428) | 0.048168 / 0.004250 (0.043918) | 0.046094 / 0.037052 (0.009042) | 0.290714 / 0.258489 (0.032224) | 0.321336 / 0.293841 (0.027496) | 0.047934 / 0.128546 (-0.080612) | 0.010773 / 0.075646 (-0.064873) | 0.059439 / 0.419271 (-0.359832) | 0.033644 / 0.043533 (-0.009889) | 0.273710 / 0.255139 (0.018571) | 0.295144 / 0.283200 (0.011944) | 0.018115 / 0.141683 (-0.123568) | 1.150302 / 1.452155 (-0.301853) | 1.197304 / 1.492716 (-0.295412) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.090262 / 0.018006 (0.072255) | 0.300727 / 0.000490 (0.300238) | 0.000228 / 0.000200 (0.000028) | 0.000055 / 0.000054 (0.000001) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022706 / 0.037411 (-0.014706) | 0.077420 / 0.014526 (0.062894) | 0.089119 / 0.176557 (-0.087437) | 0.126760 / 0.737135 (-0.610375) | 0.090702 / 0.296338 (-0.205637) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.296558 / 0.215209 (0.081349) | 2.865311 / 2.077655 (0.787656) | 1.587355 / 1.504120 (0.083235) | 1.491660 / 1.541195 (-0.049534) | 1.513604 / 1.468490 (0.045114) | 0.565209 / 4.584777 (-4.019568) | 2.450648 / 3.745712 (-1.295064) | 2.709941 / 5.269862 (-2.559921) | 1.775032 / 4.565676 (-2.790645) | 0.063767 / 0.424275 (-0.360508) | 0.005047 / 0.007607 (-0.002560) | 0.347406 / 0.226044 (0.121361) | 3.416671 / 2.268929 (1.147743) | 1.949653 / 55.444624 (-53.494971) | 1.669885 / 6.876477 (-5.206592) | 1.848125 / 2.142072 (-0.293947) | 0.648179 / 4.805227 (-4.157048) | 0.116374 / 6.500664 (-6.384290) | 0.041816 / 0.075469 (-0.033653) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.007009 / 1.841788 (-0.834779) | 12.749964 / 8.074308 (4.675656) | 10.765890 / 10.191392 (0.574498) | 0.141743 / 0.680424 (-0.538681) | 0.016077 / 0.534201 (-0.518124) | 0.293275 / 0.579283 (-0.286008) | 0.277064 / 0.434364 (-0.157300) | 0.327039 / 0.540337 (-0.213299) | 0.421784 / 1.386936 (-0.965152) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f807cd4c733a3616011a3f7f53a9fa56f7d5f685 \"CML watermark\")\n" ]

[]

[]

[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6682). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005292 / 0.011353 (-0.006060) | 0.003354 / 0.011008 (-0.007654) | 0.063150 / 0.038508 (0.024642) | 0.028616 / 0.023109 (0.005507) | 0.242267 / 0.275898 (-0.033631) | 0.267305 / 0.323480 (-0.056175) | 0.003041 / 0.007986 (-0.004944) | 0.003346 / 0.004328 (-0.000982) | 0.048268 / 0.004250 (0.044018) | 0.042070 / 0.037052 (0.005018) | 0.256526 / 0.258489 (-0.001963) | 0.279744 / 0.293841 (-0.014097) | 0.027862 / 0.128546 (-0.100684) | 0.010786 / 0.075646 (-0.064861) | 0.206998 / 0.419271 (-0.212273) | 0.035503 / 0.043533 (-0.008030) | 0.248454 / 0.255139 (-0.006685) | 0.265639 / 0.283200 (-0.017561) | 0.019590 / 0.141683 (-0.122093) | 1.134445 / 1.452155 (-0.317709) | 1.194956 / 1.492716 (-0.297761) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.090987 / 0.018006 (0.072981) | 0.301907 / 0.000490 (0.301418) | 0.000213 / 0.000200 (0.000013) | 0.000043 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018324 / 0.037411 (-0.019088) | 0.061492 / 0.014526 (0.046966) | 0.074166 / 0.176557 (-0.102391) | 0.119990 / 0.737135 (-0.617145) | 0.074554 / 0.296338 (-0.221785) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.279646 / 0.215209 (0.064437) | 2.773819 / 2.077655 (0.696164) | 1.436460 / 1.504120 (-0.067660) | 1.310303 / 1.541195 (-0.230892) | 1.315328 / 1.468490 (-0.153162) | 0.558328 / 4.584777 (-4.026449) | 2.383819 / 3.745712 (-1.361893) | 2.735034 / 5.269862 (-2.534827) | 1.724413 / 4.565676 (-2.841263) | 0.061476 / 0.424275 (-0.362799) | 0.004899 / 0.007607 (-0.002708) | 0.333195 / 0.226044 (0.107151) | 3.228900 / 2.268929 (0.959971) | 1.787315 / 55.444624 (-53.657309) | 1.526949 / 6.876477 (-5.349527) | 1.539816 / 2.142072 (-0.602257) | 0.636926 / 4.805227 (-4.168302) | 0.117533 / 6.500664 (-6.383131) | 0.041859 / 0.075469 (-0.033610) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.964637 / 1.841788 (-0.877151) | 11.296021 / 8.074308 (3.221713) | 9.375436 / 10.191392 (-0.815956) | 0.140330 / 0.680424 (-0.540094) | 0.013638 / 0.534201 (-0.520563) | 0.287046 / 0.579283 (-0.292237) | 0.265054 / 0.434364 (-0.169310) | 0.331548 / 0.540337 (-0.208790) | 0.438418 / 1.386936 (-0.948518) |\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) | 0.005284 / 0.011353 (-0.006069) | 0.003853 / 0.011008 (-0.007155) | 0.049301 / 0.038508 (0.010793) | 0.030477 / 0.023109 (0.007368) | 0.278507 / 0.275898 (0.002609) | 0.298245 / 0.323480 (-0.025235) | 0.004225 / 0.007986 (-0.003761) | 0.002736 / 0.004328 (-0.001593) | 0.049345 / 0.004250 (0.045094) | 0.045141 / 0.037052 (0.008088) | 0.290992 / 0.258489 (0.032503) | 0.317430 / 0.293841 (0.023589) | 0.029623 / 0.128546 (-0.098924) | 0.010351 / 0.075646 (-0.065295) | 0.058027 / 0.419271 (-0.361244) | 0.051306 / 0.043533 (0.007773) | 0.279947 / 0.255139 (0.024808) | 0.296916 / 0.283200 (0.013717) | 0.018859 / 0.141683 (-0.122823) | 1.153484 / 1.452155 (-0.298670) | 1.189141 / 1.492716 (-0.303575) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091030 / 0.018006 (0.073024) | 0.301305 / 0.000490 (0.300815) | 0.000230 / 0.000200 (0.000030) | 0.000049 / 0.000054 (-0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021801 / 0.037411 (-0.015611) | 0.075162 / 0.014526 (0.060636) | 0.086455 / 0.176557 (-0.090102) | 0.125431 / 0.737135 (-0.611705) | 0.087797 / 0.296338 (-0.208542) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.295950 / 0.215209 (0.080741) | 2.895839 / 2.077655 (0.818184) | 1.603121 / 1.504120 (0.099001) | 1.482162 / 1.541195 (-0.059033) | 1.474231 / 1.468490 (0.005741) | 0.571370 / 4.584777 (-4.013407) | 2.466864 / 3.745712 (-1.278848) | 2.607279 / 5.269862 (-2.662582) | 1.723106 / 4.565676 (-2.842571) | 0.062068 / 0.424275 (-0.362208) | 0.004958 / 0.007607 (-0.002649) | 0.345213 / 0.226044 (0.119168) | 3.403916 / 2.268929 (1.134987) | 1.935538 / 55.444624 (-53.509086) | 1.658930 / 6.876477 (-5.217547) | 1.767611 / 2.142072 (-0.374461) | 0.645780 / 4.805227 (-4.159447) | 0.116077 / 6.500664 (-6.384587) | 0.040774 / 0.075469 (-0.034695) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.025952 / 1.841788 (-0.815836) | 11.935970 / 8.074308 (3.861662) | 9.935799 / 10.191392 (-0.255593) | 0.131081 / 0.680424 (-0.549343) | 0.016010 / 0.534201 (-0.518191) | 0.285476 / 0.579283 (-0.293807) | 0.274928 / 0.434364 (-0.159435) | 0.325788 / 0.540337 (-0.214550) | 0.421666 / 1.386936 (-0.965270) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7a79064b7a2255c0d6950dc998509ecefb893689 \"CML watermark\")\n" ]

[ { "color": "d4c5f9", "default": false, "description": "Maintenance tasks", "id": 4296013012, "name": "maintenance", "node_id": "LA_kwDODunzps8AAAABAA_01A", "url": "https://api.github.com/repos/huggingface/datasets/labels/maintenance" } ]

[]

[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6681). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005410 / 0.011353 (-0.005943) | 0.003862 / 0.011008 (-0.007146) | 0.063457 / 0.038508 (0.024949) | 0.030081 / 0.023109 (0.006972) | 0.250657 / 0.275898 (-0.025241) | 0.275483 / 0.323480 (-0.047997) | 0.004048 / 0.007986 (-0.003938) | 0.002818 / 0.004328 (-0.001511) | 0.048940 / 0.004250 (0.044689) | 0.043397 / 0.037052 (0.006345) | 0.262160 / 0.258489 (0.003671) | 0.294154 / 0.293841 (0.000313) | 0.030028 / 0.128546 (-0.098519) | 0.010789 / 0.075646 (-0.064857) | 0.209665 / 0.419271 (-0.209606) | 0.035297 / 0.043533 (-0.008236) | 0.253169 / 0.255139 (-0.001970) | 0.271775 / 0.283200 (-0.011424) | 0.018332 / 0.141683 (-0.123351) | 1.152420 / 1.452155 (-0.299735) | 1.262767 / 1.492716 (-0.229949) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.089990 / 0.018006 (0.071984) | 0.298552 / 0.000490 (0.298062) | 0.000217 / 0.000200 (0.000017) | 0.000050 / 0.000054 (-0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018414 / 0.037411 (-0.018997) | 0.061566 / 0.014526 (0.047040) | 0.075360 / 0.176557 (-0.101196) | 0.123470 / 0.737135 (-0.613665) | 0.075215 / 0.296338 (-0.221124) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.279563 / 0.215209 (0.064354) | 2.725212 / 2.077655 (0.647557) | 1.446413 / 1.504120 (-0.057707) | 1.321665 / 1.541195 (-0.219529) | 1.352475 / 1.468490 (-0.116015) | 0.568440 / 4.584777 (-4.016337) | 2.393217 / 3.745712 (-1.352495) | 2.793150 / 5.269862 (-2.476711) | 1.764316 / 4.565676 (-2.801360) | 0.063157 / 0.424275 (-0.361118) | 0.005117 / 0.007607 (-0.002491) | 0.333310 / 0.226044 (0.107265) | 3.291000 / 2.268929 (1.022071) | 1.824654 / 55.444624 (-53.619971) | 1.558681 / 6.876477 (-5.317795) | 1.580558 / 2.142072 (-0.561514) | 0.649831 / 4.805227 (-4.155396) | 0.118674 / 6.500664 (-6.381990) | 0.042247 / 0.075469 (-0.033222) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.976552 / 1.841788 (-0.865236) | 11.847361 / 8.074308 (3.773053) | 9.490786 / 10.191392 (-0.700606) | 0.141643 / 0.680424 (-0.538781) | 0.013653 / 0.534201 (-0.520548) | 0.284345 / 0.579283 (-0.294938) | 0.268314 / 0.434364 (-0.166050) | 0.339586 / 0.540337 (-0.200751) | 0.445239 / 1.386936 (-0.941697) |\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) | 0.005754 / 0.011353 (-0.005599) | 0.004038 / 0.011008 (-0.006970) | 0.050027 / 0.038508 (0.011519) | 0.033598 / 0.023109 (0.010488) | 0.286514 / 0.275898 (0.010616) | 0.302493 / 0.323480 (-0.020986) | 0.004254 / 0.007986 (-0.003731) | 0.002827 / 0.004328 (-0.001502) | 0.050433 / 0.004250 (0.046182) | 0.046106 / 0.037052 (0.009054) | 0.301522 / 0.258489 (0.043033) | 0.325784 / 0.293841 (0.031943) | 0.030014 / 0.128546 (-0.098532) | 0.010891 / 0.075646 (-0.064756) | 0.059899 / 0.419271 (-0.359373) | 0.057252 / 0.043533 (0.013719) | 0.280276 / 0.255139 (0.025137) | 0.295632 / 0.283200 (0.012433) | 0.019060 / 0.141683 (-0.122622) | 1.141423 / 1.452155 (-0.310731) | 1.226960 / 1.492716 (-0.265757) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091919 / 0.018006 (0.073913) | 0.300769 / 0.000490 (0.300279) | 0.000220 / 0.000200 (0.000020) | 0.000053 / 0.000054 (-0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022467 / 0.037411 (-0.014945) | 0.075342 / 0.014526 (0.060816) | 0.087988 / 0.176557 (-0.088569) | 0.128304 / 0.737135 (-0.608831) | 0.089058 / 0.296338 (-0.207280) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.294662 / 0.215209 (0.079453) | 2.887743 / 2.077655 (0.810088) | 1.591756 / 1.504120 (0.087636) | 1.469249 / 1.541195 (-0.071945) | 1.495639 / 1.468490 (0.027149) | 0.575507 / 4.584777 (-4.009270) | 2.449674 / 3.745712 (-1.296038) | 2.737217 / 5.269862 (-2.532645) | 1.783066 / 4.565676 (-2.782610) | 0.063388 / 0.424275 (-0.360887) | 0.005044 / 0.007607 (-0.002563) | 0.344807 / 0.226044 (0.118763) | 3.410845 / 2.268929 (1.141916) | 1.967452 / 55.444624 (-53.477173) | 1.699884 / 6.876477 (-5.176593) | 1.862466 / 2.142072 (-0.279607) | 0.663714 / 4.805227 (-4.141513) | 0.118356 / 6.500664 (-6.382308) | 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) | 1.013523 / 1.841788 (-0.828264) | 12.498866 / 8.074308 (4.424558) | 10.382595 / 10.191392 (0.191203) | 0.141757 / 0.680424 (-0.538667) | 0.015992 / 0.534201 (-0.518209) | 0.295639 / 0.579283 (-0.283644) | 0.278382 / 0.434364 (-0.155982) | 0.330351 / 0.540337 (-0.209986) | 0.431293 / 1.386936 (-0.955643) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#cf1aaa32eddd73076cf6600125661df4a32cb20a \"CML watermark\")\n" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6680). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004981 / 0.011353 (-0.006372) | 0.003030 / 0.011008 (-0.007978) | 0.059862 / 0.038508 (0.021354) | 0.030595 / 0.023109 (0.007486) | 0.262638 / 0.275898 (-0.013260) | 0.276287 / 0.323480 (-0.047193) | 0.003955 / 0.007986 (-0.004030) | 0.002667 / 0.004328 (-0.001661) | 0.047827 / 0.004250 (0.043576) | 0.041170 / 0.037052 (0.004118) | 0.252494 / 0.258489 (-0.005995) | 0.277493 / 0.293841 (-0.016348) | 0.027269 / 0.128546 (-0.101277) | 0.010380 / 0.075646 (-0.065266) | 0.204404 / 0.419271 (-0.214867) | 0.035251 / 0.043533 (-0.008282) | 0.244368 / 0.255139 (-0.010771) | 0.258003 / 0.283200 (-0.025197) | 0.016751 / 0.141683 (-0.124932) | 1.134108 / 1.452155 (-0.318047) | 1.159969 / 1.492716 (-0.332748) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.087011 / 0.018006 (0.069004) | 0.295577 / 0.000490 (0.295087) | 0.000213 / 0.000200 (0.000013) | 0.000044 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.017993 / 0.037411 (-0.019419) | 0.061690 / 0.014526 (0.047164) | 0.071791 / 0.176557 (-0.104765) | 0.118282 / 0.737135 (-0.618853) | 0.073453 / 0.296338 (-0.222885) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.284764 / 0.215209 (0.069555) | 2.771791 / 2.077655 (0.694136) | 1.469614 / 1.504120 (-0.034506) | 1.334096 / 1.541195 (-0.207099) | 1.339995 / 1.468490 (-0.128495) | 0.562740 / 4.584777 (-4.022037) | 2.390219 / 3.745712 (-1.355493) | 2.679776 / 5.269862 (-2.590086) | 1.684397 / 4.565676 (-2.881279) | 0.062137 / 0.424275 (-0.362138) | 0.004934 / 0.007607 (-0.002673) | 0.336257 / 0.226044 (0.110212) | 3.256330 / 2.268929 (0.987401) | 1.801520 / 55.444624 (-53.643105) | 1.520662 / 6.876477 (-5.355815) | 1.537023 / 2.142072 (-0.605049) | 0.644360 / 4.805227 (-4.160867) | 0.115603 / 6.500664 (-6.385061) | 0.040601 / 0.075469 (-0.034868) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.982992 / 1.841788 (-0.858796) | 11.002182 / 8.074308 (2.927873) | 9.564671 / 10.191392 (-0.626721) | 0.137682 / 0.680424 (-0.542742) | 0.013936 / 0.534201 (-0.520265) | 0.285898 / 0.579283 (-0.293385) | 0.264426 / 0.434364 (-0.169938) | 0.321615 / 0.540337 (-0.218723) | 0.420216 / 1.386936 (-0.966720) |\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) | 0.005239 / 0.011353 (-0.006114) | 0.003165 / 0.011008 (-0.007844) | 0.048176 / 0.038508 (0.009668) | 0.030680 / 0.023109 (0.007571) | 0.258176 / 0.275898 (-0.017722) | 0.282342 / 0.323480 (-0.041138) | 0.004218 / 0.007986 (-0.003767) | 0.002616 / 0.004328 (-0.001713) | 0.047253 / 0.004250 (0.043003) | 0.044178 / 0.037052 (0.007126) | 0.276942 / 0.258489 (0.018453) | 0.312353 / 0.293841 (0.018512) | 0.046714 / 0.128546 (-0.081832) | 0.009892 / 0.075646 (-0.065755) | 0.056123 / 0.419271 (-0.363149) | 0.032691 / 0.043533 (-0.010842) | 0.268781 / 0.255139 (0.013642) | 0.285921 / 0.283200 (0.002722) | 0.016050 / 0.141683 (-0.125633) | 1.138058 / 1.452155 (-0.314096) | 1.193405 / 1.492716 (-0.299311) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.089280 / 0.018006 (0.071273) | 0.288425 / 0.000490 (0.287935) | 0.000201 / 0.000200 (0.000001) | 0.000049 / 0.000054 (-0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021536 / 0.037411 (-0.015875) | 0.075157 / 0.014526 (0.060631) | 0.088943 / 0.176557 (-0.087613) | 0.125191 / 0.737135 (-0.611945) | 0.087991 / 0.296338 (-0.208348) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.285103 / 0.215209 (0.069894) | 2.791798 / 2.077655 (0.714144) | 1.518104 / 1.504120 (0.013984) | 1.388690 / 1.541195 (-0.152505) | 1.409896 / 1.468490 (-0.058594) | 0.554077 / 4.584777 (-4.030700) | 2.396994 / 3.745712 (-1.348718) | 2.596801 / 5.269862 (-2.673060) | 1.683761 / 4.565676 (-2.881915) | 0.061209 / 0.424275 (-0.363066) | 0.004735 / 0.007607 (-0.002873) | 0.337566 / 0.226044 (0.111522) | 3.258183 / 2.268929 (0.989254) | 1.886185 / 55.444624 (-53.558439) | 1.599148 / 6.876477 (-5.277329) | 1.726867 / 2.142072 (-0.415206) | 0.642784 / 4.805227 (-4.162444) | 0.114947 / 6.500664 (-6.385717) | 0.040450 / 0.075469 (-0.035019) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.001316 / 1.841788 (-0.840472) | 11.695367 / 8.074308 (3.621058) | 9.854870 / 10.191392 (-0.336522) | 0.136462 / 0.680424 (-0.543961) | 0.016708 / 0.534201 (-0.517493) | 0.286421 / 0.579283 (-0.292862) | 0.270773 / 0.434364 (-0.163591) | 0.322947 / 0.540337 (-0.217390) | 0.416772 / 1.386936 (-0.970164) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6ba542847314bd349301937e59c3de04ce13aa5e \"CML watermark\")\n" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6678). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005070 / 0.011353 (-0.006283) | 0.003685 / 0.011008 (-0.007323) | 0.063191 / 0.038508 (0.024683) | 0.030506 / 0.023109 (0.007397) | 0.258033 / 0.275898 (-0.017865) | 0.269790 / 0.323480 (-0.053690) | 0.004180 / 0.007986 (-0.003805) | 0.002811 / 0.004328 (-0.001517) | 0.048718 / 0.004250 (0.044467) | 0.043473 / 0.037052 (0.006421) | 0.267306 / 0.258489 (0.008817) | 0.290315 / 0.293841 (-0.003526) | 0.027402 / 0.128546 (-0.101144) | 0.010782 / 0.075646 (-0.064864) | 0.207243 / 0.419271 (-0.212029) | 0.035637 / 0.043533 (-0.007896) | 0.264032 / 0.255139 (0.008893) | 0.270450 / 0.283200 (-0.012749) | 0.017407 / 0.141683 (-0.124276) | 1.107481 / 1.452155 (-0.344674) | 1.163187 / 1.492716 (-0.329529) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.095065 / 0.018006 (0.077059) | 0.305169 / 0.000490 (0.304680) | 0.000221 / 0.000200 (0.000021) | 0.000045 / 0.000054 (-0.000009) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.017706 / 0.037411 (-0.019706) | 0.061431 / 0.014526 (0.046905) | 0.073541 / 0.176557 (-0.103016) | 0.117326 / 0.737135 (-0.619809) | 0.074368 / 0.296338 (-0.221971) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.284533 / 0.215209 (0.069324) | 2.775230 / 2.077655 (0.697575) | 1.455196 / 1.504120 (-0.048924) | 1.357651 / 1.541195 (-0.183544) | 1.337477 / 1.468490 (-0.131013) | 0.567439 / 4.584777 (-4.017338) | 2.380612 / 3.745712 (-1.365100) | 2.792305 / 5.269862 (-2.477556) | 1.726501 / 4.565676 (-2.839176) | 0.061729 / 0.424275 (-0.362546) | 0.004928 / 0.007607 (-0.002679) | 0.331989 / 0.226044 (0.105944) | 3.301704 / 2.268929 (1.032776) | 1.805107 / 55.444624 (-53.639518) | 1.500434 / 6.876477 (-5.376043) | 1.535548 / 2.142072 (-0.606524) | 0.639490 / 4.805227 (-4.165737) | 0.115876 / 6.500664 (-6.384788) | 0.041895 / 0.075469 (-0.033574) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.993584 / 1.841788 (-0.848203) | 11.596680 / 8.074308 (3.522371) | 9.631726 / 10.191392 (-0.559666) | 0.141153 / 0.680424 (-0.539271) | 0.014077 / 0.534201 (-0.520124) | 0.288237 / 0.579283 (-0.291046) | 0.261213 / 0.434364 (-0.173151) | 0.323897 / 0.540337 (-0.216441) | 0.420350 / 1.386936 (-0.966586) |\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) | 0.005275 / 0.011353 (-0.006078) | 0.003739 / 0.011008 (-0.007269) | 0.049801 / 0.038508 (0.011293) | 0.030544 / 0.023109 (0.007435) | 0.264835 / 0.275898 (-0.011063) | 0.297738 / 0.323480 (-0.025742) | 0.004487 / 0.007986 (-0.003499) | 0.002835 / 0.004328 (-0.001493) | 0.048091 / 0.004250 (0.043841) | 0.044375 / 0.037052 (0.007322) | 0.286538 / 0.258489 (0.028049) | 0.319561 / 0.293841 (0.025720) | 0.047925 / 0.128546 (-0.080621) | 0.010816 / 0.075646 (-0.064831) | 0.057940 / 0.419271 (-0.361331) | 0.033588 / 0.043533 (-0.009945) | 0.270075 / 0.255139 (0.014936) | 0.290441 / 0.283200 (0.007242) | 0.017173 / 0.141683 (-0.124509) | 1.164686 / 1.452155 (-0.287469) | 1.213205 / 1.492716 (-0.279511) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093408 / 0.018006 (0.075402) | 0.305525 / 0.000490 (0.305036) | 0.000235 / 0.000200 (0.000035) | 0.000045 / 0.000054 (-0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021605 / 0.037411 (-0.015806) | 0.075479 / 0.014526 (0.060953) | 0.085990 / 0.176557 (-0.090567) | 0.124783 / 0.737135 (-0.612352) | 0.089108 / 0.296338 (-0.207230) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.306222 / 0.215209 (0.091013) | 2.987282 / 2.077655 (0.909627) | 1.664714 / 1.504120 (0.160594) | 1.523136 / 1.541195 (-0.018059) | 1.534112 / 1.468490 (0.065622) | 0.566347 / 4.584777 (-4.018430) | 2.438641 / 3.745712 (-1.307071) | 2.669048 / 5.269862 (-2.600814) | 1.732935 / 4.565676 (-2.832741) | 0.063460 / 0.424275 (-0.360815) | 0.004973 / 0.007607 (-0.002634) | 0.366233 / 0.226044 (0.140189) | 3.553578 / 2.268929 (1.284649) | 1.984343 / 55.444624 (-53.460281) | 1.711038 / 6.876477 (-5.165439) | 1.857346 / 2.142072 (-0.284726) | 0.651077 / 4.805227 (-4.154150) | 0.118670 / 6.500664 (-6.381994) | 0.041839 / 0.075469 (-0.033631) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.008230 / 1.841788 (-0.833558) | 12.047403 / 8.074308 (3.973095) | 10.039053 / 10.191392 (-0.152339) | 0.141640 / 0.680424 (-0.538784) | 0.014758 / 0.534201 (-0.519443) | 0.285016 / 0.579283 (-0.294267) | 0.275461 / 0.434364 (-0.158903) | 0.325535 / 0.540337 (-0.214803) | 0.415871 / 1.386936 (-0.971065) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5d2268261bf0fb3eed8faae6bc1fa20a25b4382c \"CML watermark\")\n" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6677). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007162 / 0.011353 (-0.004191) | 0.004125 / 0.011008 (-0.006883) | 0.064011 / 0.038508 (0.025503) | 0.031795 / 0.023109 (0.008686) | 0.248761 / 0.275898 (-0.027137) | 0.275130 / 0.323480 (-0.048350) | 0.003138 / 0.007986 (-0.004847) | 0.002736 / 0.004328 (-0.001592) | 0.050515 / 0.004250 (0.046264) | 0.044787 / 0.037052 (0.007735) | 0.261997 / 0.258489 (0.003507) | 0.292170 / 0.293841 (-0.001671) | 0.028122 / 0.128546 (-0.100424) | 0.010780 / 0.075646 (-0.064866) | 0.208805 / 0.419271 (-0.210467) | 0.036362 / 0.043533 (-0.007171) | 0.251599 / 0.255139 (-0.003540) | 0.271200 / 0.283200 (-0.012000) | 0.020215 / 0.141683 (-0.121468) | 1.133352 / 1.452155 (-0.318803) | 1.185240 / 1.492716 (-0.307477) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.089990 / 0.018006 (0.071984) | 0.298099 / 0.000490 (0.297609) | 0.000221 / 0.000200 (0.000021) | 0.000055 / 0.000054 (0.000001) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018432 / 0.037411 (-0.018980) | 0.062641 / 0.014526 (0.048115) | 0.075210 / 0.176557 (-0.101346) | 0.122239 / 0.737135 (-0.614897) | 0.078914 / 0.296338 (-0.217424) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.287682 / 0.215209 (0.072473) | 2.815030 / 2.077655 (0.737375) | 1.499512 / 1.504120 (-0.004607) | 1.370210 / 1.541195 (-0.170985) | 1.381944 / 1.468490 (-0.086546) | 0.571645 / 4.584777 (-4.013132) | 2.377773 / 3.745712 (-1.367939) | 2.757206 / 5.269862 (-2.512655) | 1.717159 / 4.565676 (-2.848518) | 0.063038 / 0.424275 (-0.361237) | 0.004913 / 0.007607 (-0.002694) | 0.340854 / 0.226044 (0.114810) | 3.348087 / 2.268929 (1.079159) | 1.843123 / 55.444624 (-53.601502) | 1.569714 / 6.876477 (-5.306763) | 1.593791 / 2.142072 (-0.548281) | 0.642865 / 4.805227 (-4.162362) | 0.116933 / 6.500664 (-6.383731) | 0.041891 / 0.075469 (-0.033578) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.976453 / 1.841788 (-0.865334) | 12.229986 / 8.074308 (4.155678) | 9.617912 / 10.191392 (-0.573480) | 0.141292 / 0.680424 (-0.539132) | 0.013732 / 0.534201 (-0.520469) | 0.291424 / 0.579283 (-0.287859) | 0.264748 / 0.434364 (-0.169616) | 0.345262 / 0.540337 (-0.195075) | 0.445126 / 1.386936 (-0.941810) |\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) | 0.005286 / 0.011353 (-0.006067) | 0.003749 / 0.011008 (-0.007259) | 0.049070 / 0.038508 (0.010562) | 0.031779 / 0.023109 (0.008670) | 0.275636 / 0.275898 (-0.000262) | 0.296956 / 0.323480 (-0.026524) | 0.004278 / 0.007986 (-0.003708) | 0.002702 / 0.004328 (-0.001626) | 0.049658 / 0.004250 (0.045408) | 0.046025 / 0.037052 (0.008973) | 0.293238 / 0.258489 (0.034749) | 0.316676 / 0.293841 (0.022835) | 0.029277 / 0.128546 (-0.099269) | 0.010096 / 0.075646 (-0.065550) | 0.059861 / 0.419271 (-0.359411) | 0.054310 / 0.043533 (0.010778) | 0.275025 / 0.255139 (0.019886) | 0.292995 / 0.283200 (0.009796) | 0.018448 / 0.141683 (-0.123235) | 1.150805 / 1.452155 (-0.301350) | 1.178310 / 1.492716 (-0.314406) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092644 / 0.018006 (0.074638) | 0.297979 / 0.000490 (0.297489) | 0.000207 / 0.000200 (0.000007) | 0.000042 / 0.000054 (-0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021758 / 0.037411 (-0.015654) | 0.076734 / 0.014526 (0.062208) | 0.088522 / 0.176557 (-0.088034) | 0.126190 / 0.737135 (-0.610945) | 0.090466 / 0.296338 (-0.205873) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.305355 / 0.215209 (0.090146) | 2.978927 / 2.077655 (0.901272) | 1.612312 / 1.504120 (0.108192) | 1.485829 / 1.541195 (-0.055366) | 1.513303 / 1.468490 (0.044813) | 0.592368 / 4.584777 (-3.992409) | 2.448529 / 3.745712 (-1.297183) | 2.713460 / 5.269862 (-2.556402) | 1.803859 / 4.565676 (-2.761817) | 0.065630 / 0.424275 (-0.358645) | 0.005072 / 0.007607 (-0.002535) | 0.358340 / 0.226044 (0.132295) | 3.528516 / 2.268929 (1.259588) | 1.977901 / 55.444624 (-53.466723) | 1.692526 / 6.876477 (-5.183950) | 1.858405 / 2.142072 (-0.283668) | 0.676169 / 4.805227 (-4.129059) | 0.121136 / 6.500664 (-6.379528) | 0.041384 / 0.075469 (-0.034085) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.011801 / 1.841788 (-0.829987) | 12.496459 / 8.074308 (4.422151) | 10.465659 / 10.191392 (0.274267) | 0.154121 / 0.680424 (-0.526302) | 0.016796 / 0.534201 (-0.517405) | 0.288908 / 0.579283 (-0.290376) | 0.274328 / 0.434364 (-0.160036) | 0.322366 / 0.540337 (-0.217971) | 0.423498 / 1.386936 (-0.963438) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#52b9273b5ddbcadfdb512a693bc813b21e863b1b \"CML watermark\")\n" ]

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[ "Found the issue, if there are other files in the directory, it gets caught into this `*` so essentially it should be `*.json`. Could we possibly to check for list of files to make sure the pattern matches json files and raise error if not?", "I don't think we should filter for `*.json` as this might silently remove desired files for many users. And this could be a major breaking change for many organizations.\r\n\r\nYou could do the globbing yourself which would keep the code clean.\r\n\r\n```python\r\nfrom glob import glob\r\n\r\nDataset.from_json(glob('folder/*.json'))\r\n```", "I think it should still be fine to log a warning message in case the folder contains different files? I also don't get why would this be breaking as in the end using `from_FILE_TYPE` should be able to read a specific file type only. Maybe some other use case I am not aware of but since globbing or this case not mentioned anywhere in the doc, I spent quite a bit of time trying to figure out where the issue was. Just making sure it's clear for users." ]

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

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[ "It would be a great addition indeed :)\r\n\r\nThis can be implemented the same way we have `sampling_rate` for Audio(): we just add a new parameter to the Image() type and take this parameter into account in `Image.decode_example`\r\n\r\nEDIT: adding an example of how it can be used:\r\n\r\n```python\r\nds = ds.cast_column(\"image\", Image(mode=...))\r\n```" ]

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[ "Good catch! Feel free to open a PR to fix the link." ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6672). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "I am merging this PR (so that it is included in the next patch release) to remove the deprecation warning raised by the CSV builder from pandas 2.2.0.", "<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) | 0.005374 / 0.011353 (-0.005979) | 0.003833 / 0.011008 (-0.007175) | 0.063465 / 0.038508 (0.024957) | 0.029564 / 0.023109 (0.006455) | 0.252759 / 0.275898 (-0.023139) | 0.274726 / 0.323480 (-0.048754) | 0.004014 / 0.007986 (-0.003971) | 0.002754 / 0.004328 (-0.001574) | 0.049351 / 0.004250 (0.045101) | 0.041858 / 0.037052 (0.004806) | 0.269023 / 0.258489 (0.010534) | 0.290670 / 0.293841 (-0.003171) | 0.028435 / 0.128546 (-0.100111) | 0.010988 / 0.075646 (-0.064658) | 0.207447 / 0.419271 (-0.211824) | 0.035945 / 0.043533 (-0.007588) | 0.257336 / 0.255139 (0.002197) | 0.267310 / 0.283200 (-0.015890) | 0.018575 / 0.141683 (-0.123108) | 1.144515 / 1.452155 (-0.307640) | 1.214614 / 1.492716 (-0.278102) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.103527 / 0.018006 (0.085521) | 0.310607 / 0.000490 (0.310117) | 0.000216 / 0.000200 (0.000016) | 0.000044 / 0.000054 (-0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018597 / 0.037411 (-0.018814) | 0.063176 / 0.014526 (0.048650) | 0.073553 / 0.176557 (-0.103003) | 0.120648 / 0.737135 (-0.616487) | 0.075625 / 0.296338 (-0.220713) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.289148 / 0.215209 (0.073939) | 2.798351 / 2.077655 (0.720696) | 1.487909 / 1.504120 (-0.016211) | 1.369945 / 1.541195 (-0.171250) | 1.378889 / 1.468490 (-0.089602) | 0.569825 / 4.584777 (-4.014952) | 2.413309 / 3.745712 (-1.332403) | 2.795668 / 5.269862 (-2.474193) | 1.757748 / 4.565676 (-2.807929) | 0.064686 / 0.424275 (-0.359589) | 0.005027 / 0.007607 (-0.002580) | 0.341835 / 0.226044 (0.115791) | 3.349915 / 2.268929 (1.080987) | 1.864253 / 55.444624 (-53.580371) | 1.595788 / 6.876477 (-5.280688) | 1.666127 / 2.142072 (-0.475945) | 0.665239 / 4.805227 (-4.139989) | 0.120563 / 6.500664 (-6.380101) | 0.043649 / 0.075469 (-0.031820) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.988543 / 1.841788 (-0.853244) | 11.973275 / 8.074308 (3.898967) | 9.685401 / 10.191392 (-0.505991) | 0.141416 / 0.680424 (-0.539008) | 0.014328 / 0.534201 (-0.519873) | 0.287063 / 0.579283 (-0.292220) | 0.266284 / 0.434364 (-0.168080) | 0.324643 / 0.540337 (-0.215694) | 0.423845 / 1.386936 (-0.963091) |\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) | 0.005430 / 0.011353 (-0.005923) | 0.003770 / 0.011008 (-0.007239) | 0.050879 / 0.038508 (0.012371) | 0.031929 / 0.023109 (0.008819) | 0.297739 / 0.275898 (0.021841) | 0.319380 / 0.323480 (-0.004100) | 0.004348 / 0.007986 (-0.003637) | 0.002783 / 0.004328 (-0.001545) | 0.050024 / 0.004250 (0.045774) | 0.045209 / 0.037052 (0.008157) | 0.307608 / 0.258489 (0.049119) | 0.338168 / 0.293841 (0.044327) | 0.051712 / 0.128546 (-0.076834) | 0.011092 / 0.075646 (-0.064554) | 0.059830 / 0.419271 (-0.359441) | 0.033894 / 0.043533 (-0.009638) | 0.295278 / 0.255139 (0.040139) | 0.310749 / 0.283200 (0.027550) | 0.018676 / 0.141683 (-0.123007) | 1.201086 / 1.452155 (-0.251069) | 1.258214 / 1.492716 (-0.234502) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094079 / 0.018006 (0.076073) | 0.304657 / 0.000490 (0.304168) | 0.000225 / 0.000200 (0.000026) | 0.000057 / 0.000054 (0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021969 / 0.037411 (-0.015442) | 0.075749 / 0.014526 (0.061223) | 0.087878 / 0.176557 (-0.088679) | 0.126022 / 0.737135 (-0.611114) | 0.089466 / 0.296338 (-0.206873) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.286415 / 0.215209 (0.071206) | 2.831867 / 2.077655 (0.754212) | 1.584119 / 1.504120 (0.079999) | 1.468454 / 1.541195 (-0.072740) | 1.495831 / 1.468490 (0.027341) | 0.579569 / 4.584777 (-4.005208) | 2.477248 / 3.745712 (-1.268464) | 2.830536 / 5.269862 (-2.439325) | 1.820188 / 4.565676 (-2.745488) | 0.064408 / 0.424275 (-0.359867) | 0.005156 / 0.007607 (-0.002451) | 0.342391 / 0.226044 (0.116347) | 3.424380 / 2.268929 (1.155452) | 1.993110 / 55.444624 (-53.451514) | 1.702971 / 6.876477 (-5.173506) | 1.844281 / 2.142072 (-0.297792) | 0.668208 / 4.805227 (-4.137020) | 0.120306 / 6.500664 (-6.380358) | 0.042127 / 0.075469 (-0.033342) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.019118 / 1.841788 (-0.822670) | 12.418330 / 8.074308 (4.344022) | 10.474226 / 10.191392 (0.282834) | 0.148510 / 0.680424 (-0.531914) | 0.015107 / 0.534201 (-0.519094) | 0.289488 / 0.579283 (-0.289795) | 0.278149 / 0.434364 (-0.156215) | 0.334655 / 0.540337 (-0.205682) | 0.419127 / 1.386936 (-0.967809) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#58733d2824192fc748cc8730cf77c33be5ded2ea \"CML watermark\")\n" ]

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[ "Hi @prashanth19bolukonda,\r\n\r\nYou have to restart the notebook runtime session after the installation of `datasets`.\r\n\r\nDuplicate of:\r\n- #5923", "Thank you soo much\r\n\r\nOn Fri, Feb 16, 2024 at 8:14 PM Albert Villanova del Moral <\r\n***@***.***> wrote:\r\n\r\n> Closed #6670 <https://github.com/huggingface/datasets/issues/6670> as\r\n> completed.\r\n>\r\n> —\r\n> Reply to this email directly, view it on GitHub\r\n> <https://github.com/huggingface/datasets/issues/6670#event-11829788289>,\r\n> or unsubscribe\r\n> <https://github.com/notifications/unsubscribe-auth/A2Y44YDQOBUFUWMR4C5O3QTYT5WDJAVCNFSM6AAAAABDL24S5SVHI2DSMVQWIX3LMV45UABCJFZXG5LFIV3GK3TUJZXXI2LGNFRWC5DJN5XDWMJRHAZDSNZYHAZDQOI>\r\n> .\r\n> You are receiving this because you were mentioned.Message ID:\r\n> ***@***.***>\r\n>\r\n" ]

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[ "Hi! Kaggle notebooks use an outdated version of `datasets`, so you should update the `datasets` installation (with `!pip install -U datasets`) to avoid the error.", "Thank you for your response\r\n\r\nOn Thu, Feb 29, 2024 at 10:55 PM Mario Šaško ***@***.***>\r\nwrote:\r\n\r\n> Closed #6669 <https://github.com/huggingface/datasets/issues/6669> as\r\n> completed.\r\n>\r\n> —\r\n> Reply to this email directly, view it on GitHub\r\n> <https://github.com/huggingface/datasets/issues/6669#event-11969246964>,\r\n> or unsubscribe\r\n> <https://github.com/notifications/unsubscribe-auth/A2Y44YG2RRVMYONNKPLBVE3YV5SAPAVCNFSM6AAAAABDLZ3BTSVHI2DSMVQWIX3LMV45UABCJFZXG5LFIV3GK3TUJZXXI2LGNFRWC5DJN5XDWMJRHE3DSMRUGY4TMNA>\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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[ "you can try: pip install datasets==2.16.1" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6665). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004968 / 0.011353 (-0.006385) | 0.003732 / 0.011008 (-0.007276) | 0.063672 / 0.038508 (0.025164) | 0.027066 / 0.023109 (0.003957) | 0.253306 / 0.275898 (-0.022592) | 0.283382 / 0.323480 (-0.040098) | 0.004217 / 0.007986 (-0.003768) | 0.002865 / 0.004328 (-0.001464) | 0.048672 / 0.004250 (0.044421) | 0.040740 / 0.037052 (0.003688) | 0.271848 / 0.258489 (0.013359) | 0.293162 / 0.293841 (-0.000679) | 0.027410 / 0.128546 (-0.101136) | 0.010605 / 0.075646 (-0.065042) | 0.210545 / 0.419271 (-0.208726) | 0.036085 / 0.043533 (-0.007447) | 0.259807 / 0.255139 (0.004668) | 0.274056 / 0.283200 (-0.009144) | 0.018812 / 0.141683 (-0.122871) | 1.116687 / 1.452155 (-0.335468) | 1.164276 / 1.492716 (-0.328440) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092874 / 0.018006 (0.074868) | 0.355897 / 0.000490 (0.355407) | 0.000224 / 0.000200 (0.000024) | 0.000051 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018461 / 0.037411 (-0.018950) | 0.062061 / 0.014526 (0.047535) | 0.072353 / 0.176557 (-0.104203) | 0.119162 / 0.737135 (-0.617974) | 0.082974 / 0.296338 (-0.213364) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.291631 / 0.215209 (0.076422) | 2.861495 / 2.077655 (0.783841) | 1.496753 / 1.504120 (-0.007367) | 1.371164 / 1.541195 (-0.170031) | 1.415473 / 1.468490 (-0.053018) | 0.566778 / 4.584777 (-4.017999) | 2.376209 / 3.745712 (-1.369503) | 2.812326 / 5.269862 (-2.457535) | 1.765640 / 4.565676 (-2.800037) | 0.063274 / 0.424275 (-0.361001) | 0.004933 / 0.007607 (-0.002674) | 0.342345 / 0.226044 (0.116301) | 3.407487 / 2.268929 (1.138558) | 1.856646 / 55.444624 (-53.587978) | 1.590284 / 6.876477 (-5.286193) | 1.610068 / 2.142072 (-0.532004) | 0.656007 / 4.805227 (-4.149220) | 0.118310 / 6.500664 (-6.382354) | 0.042596 / 0.075469 (-0.032873) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.991392 / 1.841788 (-0.850395) | 11.612397 / 8.074308 (3.538089) | 9.627836 / 10.191392 (-0.563556) | 0.130575 / 0.680424 (-0.549848) | 0.014152 / 0.534201 (-0.520049) | 0.289736 / 0.579283 (-0.289548) | 0.260041 / 0.434364 (-0.174323) | 0.339730 / 0.540337 (-0.200608) | 0.447529 / 1.386936 (-0.939407) |\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) | 0.005315 / 0.011353 (-0.006038) | 0.003955 / 0.011008 (-0.007053) | 0.049618 / 0.038508 (0.011110) | 0.030404 / 0.023109 (0.007295) | 0.258727 / 0.275898 (-0.017171) | 0.282020 / 0.323480 (-0.041460) | 0.004356 / 0.007986 (-0.003629) | 0.002866 / 0.004328 (-0.001462) | 0.049122 / 0.004250 (0.044872) | 0.045534 / 0.037052 (0.008482) | 0.269560 / 0.258489 (0.011071) | 0.301225 / 0.293841 (0.007384) | 0.029786 / 0.128546 (-0.098761) | 0.010433 / 0.075646 (-0.065213) | 0.058222 / 0.419271 (-0.361049) | 0.052968 / 0.043533 (0.009435) | 0.256605 / 0.255139 (0.001467) | 0.279899 / 0.283200 (-0.003300) | 0.018233 / 0.141683 (-0.123450) | 1.164060 / 1.452155 (-0.288095) | 1.218049 / 1.492716 (-0.274667) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093646 / 0.018006 (0.075639) | 0.288804 / 0.000490 (0.288314) | 0.000224 / 0.000200 (0.000024) | 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) | 0.022193 / 0.037411 (-0.015219) | 0.075507 / 0.014526 (0.060981) | 0.086091 / 0.176557 (-0.090465) | 0.127433 / 0.737135 (-0.609703) | 0.087064 / 0.296338 (-0.209274) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.292459 / 0.215209 (0.077250) | 2.842430 / 2.077655 (0.764776) | 1.505824 / 1.504120 (0.001704) | 1.377052 / 1.541195 (-0.164143) | 1.408757 / 1.468490 (-0.059733) | 0.571705 / 4.584777 (-4.013072) | 2.459798 / 3.745712 (-1.285914) | 2.714826 / 5.269862 (-2.555035) | 1.782064 / 4.565676 (-2.783613) | 0.063113 / 0.424275 (-0.361162) | 0.005099 / 0.007607 (-0.002509) | 0.343624 / 0.226044 (0.117579) | 3.415806 / 2.268929 (1.146878) | 1.853253 / 55.444624 (-53.591371) | 1.584392 / 6.876477 (-5.292084) | 1.720384 / 2.142072 (-0.421689) | 0.646637 / 4.805227 (-4.158590) | 0.118072 / 6.500664 (-6.382593) | 0.041362 / 0.075469 (-0.034107) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.020086 / 1.841788 (-0.821701) | 12.303980 / 8.074308 (4.229672) | 10.322869 / 10.191392 (0.131477) | 0.140959 / 0.680424 (-0.539465) | 0.015372 / 0.534201 (-0.518829) | 0.288552 / 0.579283 (-0.290731) | 0.278243 / 0.434364 (-0.156121) | 0.328399 / 0.540337 (-0.211939) | 0.433618 / 1.386936 (-0.953318) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9469092d88ff7bb4d3f7fe6c2de0109ca458b5da \"CML watermark\")\n" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6664). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "> Hi! We can't revert this as the \"reverted\" implementation has quadratic time complexity. Instead, let's fix it:\r\n\r\nI agree, but it's the implementation we have had so far. Why don't we:\r\n1. Release a hotfix ASAP (since would be doing a revert, we know it works as before) so people can continue using this library fine since AFAIU right now mostly writing examples for people is broken.\r\n2. Then, focus on still applying the performance improvement and release again", "The fix is straightforward, so one patch release (after this PR is merged) is enough.\r\n\r\nBtw, let's also add a test to `tests/test_arrow_writer.py` to avoid this issue in the future.", "> Btw, let's also add a test to tests/test_arrow_writer.py to avoid this issue in the future.\r\n\r\nWould you mind adding such test, as you're more familiar with the codebase?", "<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) | 0.005083 / 0.011353 (-0.006270) | 0.003697 / 0.011008 (-0.007311) | 0.063302 / 0.038508 (0.024794) | 0.028866 / 0.023109 (0.005757) | 0.249987 / 0.275898 (-0.025911) | 0.270803 / 0.323480 (-0.052677) | 0.004096 / 0.007986 (-0.003890) | 0.002752 / 0.004328 (-0.001577) | 0.049156 / 0.004250 (0.044906) | 0.042936 / 0.037052 (0.005884) | 0.266907 / 0.258489 (0.008418) | 0.291462 / 0.293841 (-0.002379) | 0.027703 / 0.128546 (-0.100844) | 0.011006 / 0.075646 (-0.064641) | 0.206238 / 0.419271 (-0.213033) | 0.035446 / 0.043533 (-0.008087) | 0.248923 / 0.255139 (-0.006216) | 0.264141 / 0.283200 (-0.019058) | 0.017545 / 0.141683 (-0.124138) | 1.157145 / 1.452155 (-0.295009) | 1.199007 / 1.492716 (-0.293710) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092741 / 0.018006 (0.074734) | 0.299057 / 0.000490 (0.298567) | 0.000211 / 0.000200 (0.000011) | 0.000049 / 0.000054 (-0.000006) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.017936 / 0.037411 (-0.019475) | 0.061552 / 0.014526 (0.047026) | 0.072938 / 0.176557 (-0.103618) | 0.118192 / 0.737135 (-0.618944) | 0.074589 / 0.296338 (-0.221750) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.287186 / 0.215209 (0.071977) | 2.795694 / 2.077655 (0.718039) | 1.474386 / 1.504120 (-0.029734) | 1.359065 / 1.541195 (-0.182130) | 1.375295 / 1.468490 (-0.093196) | 0.569448 / 4.584777 (-4.015329) | 2.374428 / 3.745712 (-1.371284) | 2.770198 / 5.269862 (-2.499663) | 1.716346 / 4.565676 (-2.849330) | 0.063173 / 0.424275 (-0.361102) | 0.005031 / 0.007607 (-0.002576) | 0.333197 / 0.226044 (0.107153) | 3.271739 / 2.268929 (1.002811) | 1.826406 / 55.444624 (-53.618218) | 1.554537 / 6.876477 (-5.321939) | 1.565927 / 2.142072 (-0.576146) | 0.649796 / 4.805227 (-4.155431) | 0.118371 / 6.500664 (-6.382293) | 0.042536 / 0.075469 (-0.032933) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.969882 / 1.841788 (-0.871906) | 11.638201 / 8.074308 (3.563893) | 9.759370 / 10.191392 (-0.432022) | 0.128069 / 0.680424 (-0.552355) | 0.013493 / 0.534201 (-0.520708) | 0.287324 / 0.579283 (-0.291959) | 0.267542 / 0.434364 (-0.166821) | 0.320072 / 0.540337 (-0.220265) | 0.421132 / 1.386936 (-0.965804) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005679 / 0.011353 (-0.005674) | 0.003746 / 0.011008 (-0.007262) | 0.050149 / 0.038508 (0.011641) | 0.034382 / 0.023109 (0.011273) | 0.289802 / 0.275898 (0.013904) | 0.314993 / 0.323480 (-0.008487) | 0.004488 / 0.007986 (-0.003498) | 0.002786 / 0.004328 (-0.001542) | 0.047987 / 0.004250 (0.043737) | 0.046589 / 0.037052 (0.009537) | 0.301420 / 0.258489 (0.042931) | 0.335384 / 0.293841 (0.041543) | 0.050701 / 0.128546 (-0.077845) | 0.010987 / 0.075646 (-0.064660) | 0.058292 / 0.419271 (-0.360979) | 0.033973 / 0.043533 (-0.009560) | 0.288923 / 0.255139 (0.033784) | 0.306263 / 0.283200 (0.023064) | 0.018856 / 0.141683 (-0.122827) | 1.160721 / 1.452155 (-0.291433) | 1.208151 / 1.492716 (-0.284565) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092633 / 0.018006 (0.074626) | 0.300353 / 0.000490 (0.299864) | 0.000219 / 0.000200 (0.000019) | 0.000045 / 0.000054 (-0.000009) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022257 / 0.037411 (-0.015154) | 0.075417 / 0.014526 (0.060892) | 0.087289 / 0.176557 (-0.089268) | 0.125416 / 0.737135 (-0.611720) | 0.088751 / 0.296338 (-0.207588) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.286477 / 0.215209 (0.071268) | 2.801931 / 2.077655 (0.724277) | 1.553034 / 1.504120 (0.048914) | 1.426152 / 1.541195 (-0.115043) | 1.443824 / 1.468490 (-0.024666) | 0.563298 / 4.584777 (-4.021479) | 2.428968 / 3.745712 (-1.316744) | 2.685964 / 5.269862 (-2.583897) | 1.752304 / 4.565676 (-2.813372) | 0.064174 / 0.424275 (-0.360101) | 0.005079 / 0.007607 (-0.002528) | 0.344899 / 0.226044 (0.118855) | 3.372528 / 2.268929 (1.103600) | 1.900723 / 55.444624 (-53.543901) | 1.623721 / 6.876477 (-5.252756) | 1.781009 / 2.142072 (-0.361064) | 0.655229 / 4.805227 (-4.149998) | 0.116050 / 6.500664 (-6.384614) | 0.040374 / 0.075469 (-0.035095) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.004714 / 1.841788 (-0.837074) | 12.108179 / 8.074308 (4.033871) | 10.233447 / 10.191392 (0.042055) | 0.141438 / 0.680424 (-0.538986) | 0.015387 / 0.534201 (-0.518814) | 0.288068 / 0.579283 (-0.291216) | 0.277025 / 0.434364 (-0.157339) | 0.331714 / 0.540337 (-0.208623) | 0.424209 / 1.386936 (-0.962727) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#bdebf1922663c30744efb8869c86b28f102b84dd \"CML watermark\")\n" ]

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[ "Thanks for reporting! I've left some comments on the PR on how to fix this recent change rather than reverting it.", "> Thanks for reporting! I've left some comments on the PR on how to fix this recent change rather than reverting it.\r\n\r\nI feel that'd be good, but it'd be great to release a hotfix ASAP (a revert is a fast thing to do) so people can continue using this library and then focus on still applying the improvement.", "Fixed by #6664 " ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6662). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005135 / 0.011353 (-0.006218) | 0.003666 / 0.011008 (-0.007342) | 0.062660 / 0.038508 (0.024152) | 0.028656 / 0.023109 (0.005546) | 0.249601 / 0.275898 (-0.026297) | 0.265745 / 0.323480 (-0.057735) | 0.002935 / 0.007986 (-0.005051) | 0.002606 / 0.004328 (-0.001723) | 0.048774 / 0.004250 (0.044523) | 0.043643 / 0.037052 (0.006591) | 0.263114 / 0.258489 (0.004625) | 0.284596 / 0.293841 (-0.009245) | 0.027818 / 0.128546 (-0.100728) | 0.010726 / 0.075646 (-0.064921) | 0.205900 / 0.419271 (-0.213371) | 0.035646 / 0.043533 (-0.007887) | 0.245599 / 0.255139 (-0.009540) | 0.267706 / 0.283200 (-0.015493) | 0.018441 / 0.141683 (-0.123242) | 1.143365 / 1.452155 (-0.308790) | 1.191823 / 1.492716 (-0.300893) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.089703 / 0.018006 (0.071696) | 0.298073 / 0.000490 (0.297583) | 0.000209 / 0.000200 (0.000009) | 0.000042 / 0.000054 (-0.000013) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018068 / 0.037411 (-0.019343) | 0.061416 / 0.014526 (0.046890) | 0.075989 / 0.176557 (-0.100567) | 0.120765 / 0.737135 (-0.616370) | 0.075476 / 0.296338 (-0.220863) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.284043 / 0.215209 (0.068834) | 2.770282 / 2.077655 (0.692627) | 1.473040 / 1.504120 (-0.031080) | 1.349064 / 1.541195 (-0.192131) | 1.362783 / 1.468490 (-0.105708) | 0.560765 / 4.584777 (-4.024012) | 2.357731 / 3.745712 (-1.387981) | 2.745771 / 5.269862 (-2.524090) | 1.726764 / 4.565676 (-2.838913) | 0.061212 / 0.424275 (-0.363063) | 0.004902 / 0.007607 (-0.002705) | 0.336963 / 0.226044 (0.110919) | 3.324519 / 2.268929 (1.055591) | 1.825826 / 55.444624 (-53.618798) | 1.548811 / 6.876477 (-5.327666) | 1.570618 / 2.142072 (-0.571454) | 0.642411 / 4.805227 (-4.162816) | 0.116068 / 6.500664 (-6.384596) | 0.042433 / 0.075469 (-0.033036) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.988402 / 1.841788 (-0.853386) | 11.509601 / 8.074308 (3.435293) | 9.555338 / 10.191392 (-0.636054) | 0.138728 / 0.680424 (-0.541696) | 0.014107 / 0.534201 (-0.520094) | 0.285465 / 0.579283 (-0.293818) | 0.263086 / 0.434364 (-0.171278) | 0.327469 / 0.540337 (-0.212869) | 0.444799 / 1.386936 (-0.942137) |\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) | 0.005359 / 0.011353 (-0.005993) | 0.003605 / 0.011008 (-0.007403) | 0.049734 / 0.038508 (0.011226) | 0.029792 / 0.023109 (0.006683) | 0.276384 / 0.275898 (0.000486) | 0.297915 / 0.323480 (-0.025564) | 0.004949 / 0.007986 (-0.003036) | 0.002713 / 0.004328 (-0.001616) | 0.049499 / 0.004250 (0.045249) | 0.044969 / 0.037052 (0.007917) | 0.284558 / 0.258489 (0.026069) | 0.315170 / 0.293841 (0.021329) | 0.029457 / 0.128546 (-0.099089) | 0.010573 / 0.075646 (-0.065073) | 0.058191 / 0.419271 (-0.361080) | 0.051461 / 0.043533 (0.007928) | 0.270744 / 0.255139 (0.015605) | 0.291664 / 0.283200 (0.008465) | 0.018607 / 0.141683 (-0.123076) | 1.158799 / 1.452155 (-0.293355) | 1.210509 / 1.492716 (-0.282208) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.090277 / 0.018006 (0.072270) | 0.298748 / 0.000490 (0.298258) | 0.000228 / 0.000200 (0.000028) | 0.000060 / 0.000054 (0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021850 / 0.037411 (-0.015561) | 0.075433 / 0.014526 (0.060907) | 0.087171 / 0.176557 (-0.089386) | 0.125828 / 0.737135 (-0.611308) | 0.090343 / 0.296338 (-0.205996) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.297267 / 0.215209 (0.082058) | 2.865234 / 2.077655 (0.787579) | 1.595024 / 1.504120 (0.090904) | 1.476100 / 1.541195 (-0.065094) | 1.494896 / 1.468490 (0.026406) | 0.569086 / 4.584777 (-4.015691) | 2.401976 / 3.745712 (-1.343736) | 2.676091 / 5.269862 (-2.593771) | 1.742087 / 4.565676 (-2.823590) | 0.065161 / 0.424275 (-0.359114) | 0.005006 / 0.007607 (-0.002602) | 0.342302 / 0.226044 (0.116257) | 3.450571 / 2.268929 (1.181643) | 1.928754 / 55.444624 (-53.515871) | 1.672823 / 6.876477 (-5.203653) | 1.798830 / 2.142072 (-0.343243) | 0.648730 / 4.805227 (-4.156498) | 0.116433 / 6.500664 (-6.384231) | 0.040683 / 0.075469 (-0.034786) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.006158 / 1.841788 (-0.835630) | 12.200093 / 8.074308 (4.125785) | 10.180691 / 10.191392 (-0.010701) | 0.146620 / 0.680424 (-0.533804) | 0.015621 / 0.534201 (-0.518580) | 0.287956 / 0.579283 (-0.291327) | 0.277231 / 0.434364 (-0.157133) | 0.323815 / 0.540337 (-0.216522) | 0.429655 / 1.386936 (-0.957281) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#273e16f9a21d6eaba1fd40fbdf0c05e66642c5a7 \"CML watermark\")\n" ]

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[ "Hi! This can happen if an incompatible `pyarrow` version (`pyarrow<12.0.0`) has been imported before the `datasets` installation and the Colab session hasn't been restarted afterward. To avoid the error, go to \"Runtime -> Restart session\" after `!pip install -U datasets` and before `import datasets`, or insert the `import os; os.kill(os.getpid(), 9)` cell between `!pip install -U datasets` and `import datasets` to do the same programmatically.", "One possible cause might be the one pointed out by @mariosasko above, and you get the following warning on Colab:\r\n```\r\nWARNING: The following packages were previously imported in this runtime:\r\n [pyarrow]\r\nYou must restart the runtime in order to use newly installed versions.\r\n```\r\n\r\nOn the other hand, if the old version of `pyarrow` is not previously imported (before the installation of `datasets`), the reported issue here is not reproducible: `datasets` can be installed, imported and used on Colab.", "Duplicate of:\r\n- #5923", "Google Colab now pre-installs PyArrow 14.0.2, making this issue unlikely to happen. So, I'm unpinning it." ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6660). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004995 / 0.011353 (-0.006357) | 0.003230 / 0.011008 (-0.007779) | 0.062836 / 0.038508 (0.024328) | 0.026684 / 0.023109 (0.003575) | 0.249286 / 0.275898 (-0.026612) | 0.272936 / 0.323480 (-0.050544) | 0.003952 / 0.007986 (-0.004033) | 0.002708 / 0.004328 (-0.001620) | 0.055346 / 0.004250 (0.051095) | 0.040023 / 0.037052 (0.002971) | 0.263350 / 0.258489 (0.004860) | 0.294727 / 0.293841 (0.000886) | 0.027280 / 0.128546 (-0.101266) | 0.010273 / 0.075646 (-0.065373) | 0.206035 / 0.419271 (-0.213236) | 0.035715 / 0.043533 (-0.007818) | 0.255474 / 0.255139 (0.000335) | 0.273960 / 0.283200 (-0.009240) | 0.018563 / 0.141683 (-0.123120) | 1.140013 / 1.452155 (-0.312142) | 1.188655 / 1.492716 (-0.304062) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091895 / 0.018006 (0.073888) | 0.284621 / 0.000490 (0.284131) | 0.000213 / 0.000200 (0.000013) | 0.000043 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018610 / 0.037411 (-0.018801) | 0.061554 / 0.014526 (0.047028) | 0.072454 / 0.176557 (-0.104103) | 0.120283 / 0.737135 (-0.616853) | 0.073744 / 0.296338 (-0.222595) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.288850 / 0.215209 (0.073641) | 2.836761 / 2.077655 (0.759107) | 1.533407 / 1.504120 (0.029287) | 1.409394 / 1.541195 (-0.131801) | 1.421667 / 1.468490 (-0.046823) | 0.566183 / 4.584777 (-4.018594) | 2.390670 / 3.745712 (-1.355043) | 2.732031 / 5.269862 (-2.537831) | 1.730886 / 4.565676 (-2.834791) | 0.064280 / 0.424275 (-0.359995) | 0.004959 / 0.007607 (-0.002648) | 0.342664 / 0.226044 (0.116619) | 3.398969 / 2.268929 (1.130040) | 1.887354 / 55.444624 (-53.557270) | 1.572955 / 6.876477 (-5.303522) | 1.596179 / 2.142072 (-0.545894) | 0.645844 / 4.805227 (-4.159383) | 0.118050 / 6.500664 (-6.382614) | 0.042158 / 0.075469 (-0.033311) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.959170 / 1.841788 (-0.882617) | 11.276491 / 8.074308 (3.202183) | 9.471198 / 10.191392 (-0.720194) | 0.128346 / 0.680424 (-0.552078) | 0.013851 / 0.534201 (-0.520350) | 0.286125 / 0.579283 (-0.293158) | 0.266915 / 0.434364 (-0.167449) | 0.332811 / 0.540337 (-0.207526) | 0.444780 / 1.386936 (-0.942156) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005665 / 0.011353 (-0.005687) | 0.003267 / 0.011008 (-0.007741) | 0.050238 / 0.038508 (0.011730) | 0.032882 / 0.023109 (0.009773) | 0.269320 / 0.275898 (-0.006578) | 0.293140 / 0.323480 (-0.030340) | 0.004127 / 0.007986 (-0.003858) | 0.002728 / 0.004328 (-0.001601) | 0.049360 / 0.004250 (0.045109) | 0.043764 / 0.037052 (0.006712) | 0.291211 / 0.258489 (0.032722) | 0.319745 / 0.293841 (0.025904) | 0.029371 / 0.128546 (-0.099175) | 0.010212 / 0.075646 (-0.065434) | 0.059064 / 0.419271 (-0.360207) | 0.051148 / 0.043533 (0.007615) | 0.276698 / 0.255139 (0.021559) | 0.292329 / 0.283200 (0.009129) | 0.018349 / 0.141683 (-0.123334) | 1.150816 / 1.452155 (-0.301338) | 1.184292 / 1.492716 (-0.308425) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091646 / 0.018006 (0.073640) | 0.301737 / 0.000490 (0.301247) | 0.000214 / 0.000200 (0.000014) | 0.000051 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021529 / 0.037411 (-0.015883) | 0.075596 / 0.014526 (0.061070) | 0.087912 / 0.176557 (-0.088645) | 0.125240 / 0.737135 (-0.611895) | 0.088035 / 0.296338 (-0.208303) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.305097 / 0.215209 (0.089888) | 2.979612 / 2.077655 (0.901957) | 1.647009 / 1.504120 (0.142889) | 1.520251 / 1.541195 (-0.020944) | 1.513361 / 1.468490 (0.044870) | 0.571733 / 4.584777 (-4.013044) | 2.415587 / 3.745712 (-1.330125) | 2.615983 / 5.269862 (-2.653879) | 1.732637 / 4.565676 (-2.833039) | 0.062830 / 0.424275 (-0.361445) | 0.004972 / 0.007607 (-0.002635) | 0.348559 / 0.226044 (0.122515) | 3.450567 / 2.268929 (1.181639) | 1.970743 / 55.444624 (-53.473882) | 1.702232 / 6.876477 (-5.174245) | 1.799592 / 2.142072 (-0.342480) | 0.649477 / 4.805227 (-4.155751) | 0.115940 / 6.500664 (-6.384724) | 0.040364 / 0.075469 (-0.035105) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.000014 / 1.841788 (-0.841773) | 11.937886 / 8.074308 (3.863578) | 10.169478 / 10.191392 (-0.021914) | 0.153359 / 0.680424 (-0.527064) | 0.015205 / 0.534201 (-0.518996) | 0.287812 / 0.579283 (-0.291471) | 0.278688 / 0.434364 (-0.155676) | 0.322831 / 0.540337 (-0.217507) | 0.425631 / 1.386936 (-0.961305) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6e176efbed29374e7c2cd33da64aeeae3c11ca0f \"CML watermark\")\n" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6659). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "Can someone check this out?", "<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) | 0.005008 / 0.011353 (-0.006345) | 0.003267 / 0.011008 (-0.007741) | 0.064140 / 0.038508 (0.025632) | 0.027419 / 0.023109 (0.004309) | 0.246692 / 0.275898 (-0.029206) | 0.271303 / 0.323480 (-0.052177) | 0.004127 / 0.007986 (-0.003859) | 0.002698 / 0.004328 (-0.001631) | 0.050415 / 0.004250 (0.046165) | 0.040323 / 0.037052 (0.003271) | 0.265738 / 0.258489 (0.007249) | 0.291556 / 0.293841 (-0.002285) | 0.027924 / 0.128546 (-0.100622) | 0.010206 / 0.075646 (-0.065441) | 0.207106 / 0.419271 (-0.212165) | 0.036087 / 0.043533 (-0.007446) | 0.250412 / 0.255139 (-0.004727) | 0.269014 / 0.283200 (-0.014186) | 0.018102 / 0.141683 (-0.123581) | 1.135137 / 1.452155 (-0.317018) | 1.177718 / 1.492716 (-0.314998) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.095557 / 0.018006 (0.077550) | 0.306235 / 0.000490 (0.305745) | 0.000214 / 0.000200 (0.000014) | 0.000044 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018217 / 0.037411 (-0.019194) | 0.060993 / 0.014526 (0.046467) | 0.072748 / 0.176557 (-0.103808) | 0.119357 / 0.737135 (-0.617778) | 0.073719 / 0.296338 (-0.222619) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.295924 / 0.215209 (0.080715) | 2.901071 / 2.077655 (0.823417) | 1.497316 / 1.504120 (-0.006804) | 1.371232 / 1.541195 (-0.169962) | 1.395643 / 1.468490 (-0.072847) | 0.577548 / 4.584777 (-4.007229) | 2.383813 / 3.745712 (-1.361899) | 2.764451 / 5.269862 (-2.505411) | 1.733074 / 4.565676 (-2.832602) | 0.063730 / 0.424275 (-0.360545) | 0.004933 / 0.007607 (-0.002674) | 0.347135 / 0.226044 (0.121090) | 3.390814 / 2.268929 (1.121885) | 1.849454 / 55.444624 (-53.595170) | 1.561801 / 6.876477 (-5.314675) | 1.587818 / 2.142072 (-0.554254) | 0.652061 / 4.805227 (-4.153166) | 0.117195 / 6.500664 (-6.383469) | 0.041922 / 0.075469 (-0.033548) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.949050 / 1.841788 (-0.892738) | 11.353664 / 8.074308 (3.279355) | 9.261581 / 10.191392 (-0.929811) | 0.140374 / 0.680424 (-0.540050) | 0.014254 / 0.534201 (-0.519946) | 0.288124 / 0.579283 (-0.291159) | 0.262888 / 0.434364 (-0.171476) | 0.330774 / 0.540337 (-0.209564) | 0.444777 / 1.386936 (-0.942159) |\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) | 0.005162 / 0.011353 (-0.006191) | 0.003418 / 0.011008 (-0.007591) | 0.049764 / 0.038508 (0.011256) | 0.029336 / 0.023109 (0.006226) | 0.278570 / 0.275898 (0.002672) | 0.300676 / 0.323480 (-0.022804) | 0.004292 / 0.007986 (-0.003694) | 0.002745 / 0.004328 (-0.001584) | 0.049194 / 0.004250 (0.044943) | 0.044036 / 0.037052 (0.006984) | 0.299258 / 0.258489 (0.040769) | 0.324451 / 0.293841 (0.030610) | 0.029777 / 0.128546 (-0.098769) | 0.010426 / 0.075646 (-0.065221) | 0.057267 / 0.419271 (-0.362004) | 0.051276 / 0.043533 (0.007743) | 0.278012 / 0.255139 (0.022873) | 0.297099 / 0.283200 (0.013899) | 0.018340 / 0.141683 (-0.123343) | 1.179255 / 1.452155 (-0.272899) | 1.231536 / 1.492716 (-0.261180) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092546 / 0.018006 (0.074540) | 0.299959 / 0.000490 (0.299469) | 0.000220 / 0.000200 (0.000020) | 0.000043 / 0.000054 (-0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021657 / 0.037411 (-0.015755) | 0.075440 / 0.014526 (0.060914) | 0.086246 / 0.176557 (-0.090310) | 0.126511 / 0.737135 (-0.610624) | 0.091303 / 0.296338 (-0.205036) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.294775 / 0.215209 (0.079566) | 2.868973 / 2.077655 (0.791319) | 1.666971 / 1.504120 (0.162851) | 1.545680 / 1.541195 (0.004486) | 1.559983 / 1.468490 (0.091493) | 0.572191 / 4.584777 (-4.012586) | 2.429317 / 3.745712 (-1.316395) | 2.673334 / 5.269862 (-2.596527) | 1.758114 / 4.565676 (-2.807563) | 0.063766 / 0.424275 (-0.360509) | 0.005070 / 0.007607 (-0.002537) | 0.345488 / 0.226044 (0.119443) | 3.464525 / 2.268929 (1.195596) | 1.975717 / 55.444624 (-53.468908) | 1.686671 / 6.876477 (-5.189806) | 1.825434 / 2.142072 (-0.316638) | 0.655853 / 4.805227 (-4.149374) | 0.116372 / 6.500664 (-6.384292) | 0.040647 / 0.075469 (-0.034822) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.014080 / 1.841788 (-0.827707) | 12.038496 / 8.074308 (3.964188) | 10.354536 / 10.191392 (0.163144) | 0.130285 / 0.680424 (-0.550139) | 0.015514 / 0.534201 (-0.518687) | 0.284743 / 0.579283 (-0.294540) | 0.280275 / 0.434364 (-0.154088) | 0.321175 / 0.540337 (-0.219162) | 0.425840 / 1.386936 (-0.961096) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4bb6c6d46a171c4fa1b65167cb81998e2f863892 \"CML watermark\")\n" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6658). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "would be nice to have this feature in the new dataset release!", "Before finalising this this I'd like to make sure this philosophy makes sense for other libs like `accelerate` for example.\r\n\r\ncc @muellerzr I'd love your feedback on this one\r\ncc @LysandreJik also if you think other people should take a look", "> One design question though: what's the logic behind self._state_dict rather than having it all be state_dict?\r\n\r\nThe `_state_dict` is the internal object that is updated in-place while you iterate on the dataset.\r\n\r\nWe need to copy it every time the user accesses it.\r\n\r\nOtherwise we would get\r\n```python\r\nstate_dict = ds.state_dict()\r\nfor x in ds:\r\n assert ds.state_dict() == state_dict # and actually `assert ds.state_dict() is state_dict`\r\n```\r\n\r\nThe state is updated in-place since it's made of dictionaries that are shared with the steps in the IterableDataset pipeline.", "What do you think of making it a full property with a docstring explicitly stating users shouldn’t call/modify it directly?\r\n\r\nI can imagine some exploratory users getting curious", "I don't think users read docstrings of properties that often. What about explaining the logic in the `.state_dict()` docstring ? This also feels aligned with the way `.state_dict()` and `.load_state_dict()` works in pytorch (you should use load_state_dict to load a modified copy of the state dict)", "Sure, I can agree with that!", "Just a small note mentioning returns a copy of the state dict should be enough imo", "looking forward as well for this PR to be merge", "> I don't think users read docstrings of properties that often. What about explaining the logic in the `.state_dict()` docstring ? This also feels aligned with the way `.state_dict()` and `.load_state_dict()` works in pytorch (you should use load_state_dict to load a modified copy of the state dict)\r\n\r\nHi, I'm experimenting with LLM pretraining using your code. I found that the time of resuming an iterable dataset can be reduced to 5% (my streaming process includes tokenization), but I'm not sure if I'm using it correctly. Could you help me check it? Thanks.\r\n\r\n```\r\nclass CustomTrainer(Trainer):\r\n def _save_rng_state(self, output_dir):\r\n super()._save_rng_state(output_dir)\r\n if self.args.should_save:\r\n with open(os.path.join(output_dir, f'iterable_data_state_dict.json'), 'w', encoding='utf-8') as fo:\r\n json.dump(self.train_dataset.state_dict(), fo, ensure_ascii=False)\r\n```\r\n\r\n```\r\n dataset = <A IterableDataset constructed by (interleave, map(tokenization))>\r\n lask_ckpt_iterable_data_state_dict_file_path = os.path.join(training_args.resume_from_checkpoint, f'iterable_data_state_dict.json')\r\n if os.path.exists(lask_ckpt_iterable_data_state_dict_file_path) and finetuning_args.load_iteratable_state_dict:\r\n if not training_args.ignore_data_skip:\r\n raise ValueError(f'Found `iterable_data_state_dict_file_path`: `{lask_ckpt_iterable_data_state_dict_file_path}`. Please set `ignore_data_skip`=True to skip tokenization.')\r\n with open(lask_ckpt_iterable_data_state_dict_file_path) as f:\r\n lask_ckpt_iterable_data_state_dict = json.load(f)\r\n dataset.load_state_dict(lask_ckpt_iterable_data_state_dict)\r\n logger.info(f'Loading `iterable_data_state_dict` from {lask_ckpt_iterable_data_state_dict_file_path}')\r\n```\r\n", "it sounds good to me :)", "@lhoestq Hi, if I set `prefetch`, does this dataset work well?", "It does work well if you prefetch and then resume from a state, but you might lose the samples that were in the prefetch buffer of the DataLoader (which could be acceptable in some circumstances).\r\n\r\nFortunately we're about to ship an integration with the new StatefulDataLoader from torchdata which can help on this matter :)", "yeah, what I meant is that prefetch might drop a few data entries. really looking forward to the new StatefulDataLoader. ：)", "<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) | 0.005788 / 0.011353 (-0.005564) | 0.004036 / 0.011008 (-0.006972) | 0.064720 / 0.038508 (0.026212) | 0.034990 / 0.023109 (0.011881) | 0.245488 / 0.275898 (-0.030410) | 0.272596 / 0.323480 (-0.050884) | 0.003170 / 0.007986 (-0.004815) | 0.002867 / 0.004328 (-0.001461) | 0.049961 / 0.004250 (0.045711) | 0.050951 / 0.037052 (0.013899) | 0.257757 / 0.258489 (-0.000732) | 0.292957 / 0.293841 (-0.000884) | 0.027739 / 0.128546 (-0.100807) | 0.010942 / 0.075646 (-0.064705) | 0.205153 / 0.419271 (-0.214118) | 0.037892 / 0.043533 (-0.005641) | 0.247536 / 0.255139 (-0.007603) | 0.267239 / 0.283200 (-0.015960) | 0.021490 / 0.141683 (-0.120193) | 1.107306 / 1.452155 (-0.344848) | 1.144675 / 1.492716 (-0.348041) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.103212 / 0.018006 (0.085205) | 0.315174 / 0.000490 (0.314684) | 0.000229 / 0.000200 (0.000029) | 0.000044 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019771 / 0.037411 (-0.017641) | 0.064033 / 0.014526 (0.049507) | 0.076751 / 0.176557 (-0.099805) | 0.122615 / 0.737135 (-0.614521) | 0.078490 / 0.296338 (-0.217848) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.286236 / 0.215209 (0.071027) | 2.841469 / 2.077655 (0.763814) | 1.514079 / 1.504120 (0.009959) | 1.393792 / 1.541195 (-0.147403) | 1.432741 / 1.468490 (-0.035749) | 0.571003 / 4.584777 (-4.013774) | 2.369031 / 3.745712 (-1.376681) | 2.825246 / 5.269862 (-2.444616) | 1.858524 / 4.565676 (-2.707153) | 0.065366 / 0.424275 (-0.358909) | 0.005107 / 0.007607 (-0.002500) | 0.341010 / 0.226044 (0.114965) | 3.443894 / 2.268929 (1.174966) | 1.879192 / 55.444624 (-53.565433) | 1.603046 / 6.876477 (-5.273431) | 1.807639 / 2.142072 (-0.334433) | 0.646726 / 4.805227 (-4.158502) | 0.119409 / 6.500664 (-6.381255) | 0.044564 / 0.075469 (-0.030905) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.971026 / 1.841788 (-0.870762) | 12.593884 / 8.074308 (4.519576) | 10.305243 / 10.191392 (0.113851) | 0.132018 / 0.680424 (-0.548406) | 0.014387 / 0.534201 (-0.519814) | 0.288597 / 0.579283 (-0.290686) | 0.267373 / 0.434364 (-0.166991) | 0.325626 / 0.540337 (-0.214711) | 0.488808 / 1.386936 (-0.898128) |\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) | 0.005991 / 0.011353 (-0.005362) | 0.004028 / 0.011008 (-0.006980) | 0.051951 / 0.038508 (0.013443) | 0.036870 / 0.023109 (0.013761) | 0.263777 / 0.275898 (-0.012122) | 0.290914 / 0.323480 (-0.032566) | 0.004594 / 0.007986 (-0.003392) | 0.002971 / 0.004328 (-0.001357) | 0.049699 / 0.004250 (0.045449) | 0.044939 / 0.037052 (0.007887) | 0.275055 / 0.258489 (0.016566) | 0.316244 / 0.293841 (0.022403) | 0.030501 / 0.128546 (-0.098045) | 0.011197 / 0.075646 (-0.064449) | 0.058718 / 0.419271 (-0.360554) | 0.034926 / 0.043533 (-0.008607) | 0.259172 / 0.255139 (0.004033) | 0.280127 / 0.283200 (-0.003072) | 0.019775 / 0.141683 (-0.121908) | 1.169468 / 1.452155 (-0.282687) | 1.178098 / 1.492716 (-0.314619) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.101633 / 0.018006 (0.083626) | 0.314684 / 0.000490 (0.314194) | 0.000224 / 0.000200 (0.000024) | 0.000055 / 0.000054 (0.000001) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024071 / 0.037411 (-0.013341) | 0.079894 / 0.014526 (0.065368) | 0.090915 / 0.176557 (-0.085642) | 0.132397 / 0.737135 (-0.604738) | 0.091919 / 0.296338 (-0.204419) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.296237 / 0.215209 (0.081028) | 2.891752 / 2.077655 (0.814097) | 1.551937 / 1.504120 (0.047817) | 1.414179 / 1.541195 (-0.127016) | 1.450192 / 1.468490 (-0.018298) | 0.556272 / 4.584777 (-4.028504) | 0.952374 / 3.745712 (-2.793339) | 2.709450 / 5.269862 (-2.560411) | 1.771251 / 4.565676 (-2.794426) | 0.061873 / 0.424275 (-0.362402) | 0.005058 / 0.007607 (-0.002549) | 0.344790 / 0.226044 (0.118746) | 3.398982 / 2.268929 (1.130053) | 1.905832 / 55.444624 (-53.538792) | 1.632357 / 6.876477 (-5.244120) | 1.822913 / 2.142072 (-0.319160) | 0.643426 / 4.805227 (-4.161802) | 0.117321 / 6.500664 (-6.383343) | 0.042107 / 0.075469 (-0.033363) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.974921 / 1.841788 (-0.866867) | 12.497801 / 8.074308 (4.423493) | 11.216174 / 10.191392 (1.024782) | 0.135288 / 0.680424 (-0.545136) | 0.016731 / 0.534201 (-0.517470) | 0.287987 / 0.579283 (-0.291296) | 0.130246 / 0.434364 (-0.304117) | 0.323282 / 0.540337 (-0.217055) | 0.414595 / 1.386936 (-0.972341) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#43fd659eacab37b50abfe7f1b4efe2564055990c \"CML watermark\")\n", "@lhoestq Hello, I'm wondering if there are any solutions to work with shuffle now. I've noticed the caveats in docs, \r\n> examples from shuffle buffers are lost when resuming and the buffers are refilled with new data ", "Hi ! I haven't experimented with implementing state_dict for the shuffle buffer. Not sure if this is a good idea to add this, given a shuffle buffer can be quite big and poses serialization challenges.\r\n\r\nIt shouldn't be difficult to experiment with a simple implementation in `BufferShuffledExamplesIterable` though", "@lhoestq thank you for your quick response! I'll try it :}", "@lhoestq Hi, just revise the `BufferShuffledExamplesIterable` and it works\r\n```py\r\n\r\nclass BufferShuffledExamplesIterable(datasets.iterable_dataset.BufferShuffledExamplesIterable):\r\n\r\n def __init__(self, *args, **kwargs):\r\n super().__init__(*args, **kwargs)\r\n\r\n def _init_state_dict(self) -> dict:\r\n self._state_dict = self.ex_iterable._init_state_dict()\r\n self._state_dict['mem_buffer'] = ([],)\r\n self._state_dict['gloabl_example_index'] = 0\r\n return self._state_dict\r\n\r\n def __iter__(self):\r\n buffer_size = self.buffer_size\r\n rng = deepcopy(self.generator)\r\n indices_iterator = self._iter_random_indices(rng, buffer_size)\r\n # this is the shuffle buffer that we keep in memory\r\n mem_buffer = self._state_dict['mem_buffer'][0]\r\n gloabl_example_index_start = self._state_dict[\"gloabl_example_index\"] if self._state_dict else 0\r\n # skip already consumed ones\r\n for i in range(gloabl_example_index_start):\r\n _ = next(indices_iterator)\r\n for x in self.ex_iterable:\r\n if len(mem_buffer) == buffer_size: # if the buffer is full, pick and example from it\r\n i = next(indices_iterator)\r\n if self._state_dict:\r\n self._state_dict['gloabl_example_index'] += 1\r\n yield mem_buffer[i]\r\n mem_buffer[i] = x # replace the picked example by a new one\r\n else: # otherwise, keep filling the buffer\r\n mem_buffer.append(x)\r\n # when we run out of examples, we shuffle the remaining examples in the buffer and yield them\r\n rng.shuffle(mem_buffer)\r\n yield from mem_buffer\r\n\r\n def shuffle_data_sources(self, generator: np.random.Generator) -> BufferShuffledExamplesIterable:\r\n \"\"\"Shuffle the wrapped examples iterable as well as the shuffling buffer.\"\"\"\r\n return BufferShuffledExamplesIterable(\r\n self.ex_iterable.shuffle_data_sources(generator), buffer_size=self.buffer_size, generator=generator\r\n )\r\n\r\n def shard_data_sources(self, worker_id: int, num_workers: int) -> BufferShuffledExamplesIterable:\r\n \"\"\"Keep only the requested shard.\"\"\"\r\n return BufferShuffledExamplesIterable(\r\n self.ex_iterable.shard_data_sources(worker_id, num_workers),\r\n buffer_size=self.buffer_size,\r\n generator=self.generator,\r\n )\r\n\r\n def load_state_dict(self, state_dict: dict) -> dict:\r\n def _inner_load_state_dict(state, new_state):\r\n if new_state is not None and isinstance(state, dict):\r\n for key in state:\r\n state[key] = _inner_load_state_dict(state[key], new_state[key])\r\n return state\r\n elif new_state is not None and isinstance(state, list):\r\n for i in range(len(state)):\r\n state[i] = _inner_load_state_dict(state[i], new_state[i])\r\n return state\r\n return new_state\r\n\r\n return _inner_load_state_dict(self._state_dict, state_dict)\r\n```\r\n\r\nI've noticed that it uses significantly more RAM than the original version and experiences a considerable decrease in GPU utilization. Could you offer some suggestions to address this issue?\r\n\r\nor **is it prohibited** to maintain sth except for simple indices that small enough for each worker 😢 \r\n\r\n", "Some ExamplesIterable copy and store old versions of the state_dict of parent ExamplesIterable. It is the case for example for batched `map()` (state_dict of beginning of the batch) or `interleave_dataset()` (state_dict of the previous step since it buffers one example to know if the iterable is exhausted).\r\n\r\nCopying a shuffle buffer takes some RAM and some time, which can slow down the data loading pipeline.\r\nMaybe the examples in the shuffle buffer shouldn't not be copied (only do a shallow copy of the list), this would surely help." ]

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[ "Thanks for reporting, @atulsaurav.\r\n\r\nWe are investigating the issue. ", "I can't fix this issue because I do not appear as a team member of the huggingface datasets project: https://anaconda.org/huggingface/datasets\r\n\r\n@lhoestq could you please add `datasets` team members to the corresponding Anaconda project?\r\n\r\nOnce this done, I could recreate and update the Anaconda token, as mentioned above it seems the current one has expired.", "I think @LysandreJik has access ?", "FYI it failed for 2.18.0 too: https://github.com/huggingface/datasets/actions/runs/8117132330/job/22188677936", "We updated the token and I re-ran the conda releases :)" ]

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[ "I get similar when dealing with a large jsonl file (6k lines), \r\n\r\n> TypeError: Couldn't cast array of type timestamp[us] to null\r\n\r\nYet when I split it into 1k lines, files, load_dataset works fine!\r\n\r\nhttps://github.com/huggingface/course/issues/692\r\n\r\n" ]

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[ "Thanks for reporting, @arame.\r\n\r\nI guess you have an old version of `transformers` (that submodule is present in `transformers` since version 3.0.1, since nearly 4 years ago). If you update it, the error should disappear:\r\n```shell\r\npip install -U transformers\r\n```\r\n\r\nOn the other hand, I am wondering: does it make sense to use `transformers` in this case, even if we don't need it to load the `go_emotions` dataset (already converted to Parquet files)?\r\n- Maybe @mariosasko can give some insight, as he included these code lines:\r\n - #6454\r\n\r\nhttps://github.com/huggingface/datasets/blob/9751fb14594d354e952f0ebdfaf31cb203b011e7/src/datasets/utils/_dill.py#L60-L63\r\n", "The linked code lazily registers a custom reducer for `transformers.PreTrainedTokenizerBase` only if `transformers` have already been imported (imports are expensive, so we check `sys.modules`).\r\n\r\nHowever, the logic does not account for `transformers<3`, so we should add a version check to fix that.", "> The linked code lazily registers a custom reducer for `transformers.PreTrainedTokenizerBase` only if `transformers` have already been imported (imports are expensive, so we check `sys.modules`).\r\n> \r\n> However, the logic does not account for `transformers<3`, so we should add a version check to fix that.\r\n\r\nThank you for that Mario. Would this fix solve the problem and do you have any idea when it will be done? \r\nI tried the pip install suggested by Albert and it made no difference.", "I tried running the code today and the problem appears to be fixed." ]

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[ "Hi ! This issue has been fixed by https://github.com/huggingface/datasets/pull/6283\r\n\r\nCan you try again with the new release 2.17.0 ?\r\n\r\n```\r\npip install -U datasets\r\n```\r\n\r\n", "Amazing! It's indeed fixed now. Thanks!" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6653). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005076 / 0.011353 (-0.006277) | 0.003424 / 0.011008 (-0.007584) | 0.064195 / 0.038508 (0.025687) | 0.031742 / 0.023109 (0.008633) | 0.244774 / 0.275898 (-0.031124) | 0.268529 / 0.323480 (-0.054951) | 0.003970 / 0.007986 (-0.004016) | 0.002657 / 0.004328 (-0.001672) | 0.048847 / 0.004250 (0.044597) | 0.042196 / 0.037052 (0.005144) | 0.266044 / 0.258489 (0.007555) | 0.282400 / 0.293841 (-0.011441) | 0.027617 / 0.128546 (-0.100929) | 0.010400 / 0.075646 (-0.065246) | 0.205910 / 0.419271 (-0.213362) | 0.035820 / 0.043533 (-0.007713) | 0.247750 / 0.255139 (-0.007389) | 0.267318 / 0.283200 (-0.015882) | 0.017980 / 0.141683 (-0.123703) | 1.107263 / 1.452155 (-0.344892) | 1.173208 / 1.492716 (-0.319509) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.095830 / 0.018006 (0.077824) | 0.293891 / 0.000490 (0.293401) | 0.000257 / 0.000200 (0.000057) | 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) | 0.018138 / 0.037411 (-0.019273) | 0.061631 / 0.014526 (0.047105) | 0.073038 / 0.176557 (-0.103519) | 0.118317 / 0.737135 (-0.618818) | 0.074190 / 0.296338 (-0.222148) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.287026 / 0.215209 (0.071817) | 2.786137 / 2.077655 (0.708482) | 1.472575 / 1.504120 (-0.031544) | 1.346919 / 1.541195 (-0.194276) | 1.388535 / 1.468490 (-0.079955) | 0.565731 / 4.584777 (-4.019046) | 2.382573 / 3.745712 (-1.363139) | 2.736926 / 5.269862 (-2.532935) | 1.716517 / 4.565676 (-2.849159) | 0.062168 / 0.424275 (-0.362108) | 0.004924 / 0.007607 (-0.002683) | 0.341897 / 0.226044 (0.115853) | 3.355715 / 2.268929 (1.086787) | 1.837014 / 55.444624 (-53.607611) | 1.532063 / 6.876477 (-5.344414) | 1.548193 / 2.142072 (-0.593880) | 0.634995 / 4.805227 (-4.170232) | 0.115622 / 6.500664 (-6.385042) | 0.042252 / 0.075469 (-0.033217) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.970713 / 1.841788 (-0.871075) | 11.727576 / 8.074308 (3.653268) | 9.806524 / 10.191392 (-0.384868) | 0.127622 / 0.680424 (-0.552802) | 0.014140 / 0.534201 (-0.520061) | 0.286832 / 0.579283 (-0.292451) | 0.266556 / 0.434364 (-0.167808) | 0.325940 / 0.540337 (-0.214398) | 0.421839 / 1.386936 (-0.965097) |\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) | 0.005495 / 0.011353 (-0.005858) | 0.003676 / 0.011008 (-0.007332) | 0.054361 / 0.038508 (0.015853) | 0.030743 / 0.023109 (0.007633) | 0.277200 / 0.275898 (0.001302) | 0.313459 / 0.323480 (-0.010021) | 0.004316 / 0.007986 (-0.003670) | 0.002750 / 0.004328 (-0.001578) | 0.049491 / 0.004250 (0.045241) | 0.044268 / 0.037052 (0.007215) | 0.292529 / 0.258489 (0.034039) | 0.326524 / 0.293841 (0.032683) | 0.048040 / 0.128546 (-0.080507) | 0.010390 / 0.075646 (-0.065256) | 0.058459 / 0.419271 (-0.360813) | 0.033765 / 0.043533 (-0.009768) | 0.276003 / 0.255139 (0.020864) | 0.297299 / 0.283200 (0.014099) | 0.018532 / 0.141683 (-0.123151) | 1.157639 / 1.452155 (-0.294515) | 1.220492 / 1.492716 (-0.272225) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093903 / 0.018006 (0.075897) | 0.303005 / 0.000490 (0.302515) | 0.000224 / 0.000200 (0.000024) | 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) | 0.021580 / 0.037411 (-0.015831) | 0.076176 / 0.014526 (0.061650) | 0.086998 / 0.176557 (-0.089558) | 0.124148 / 0.737135 (-0.612987) | 0.088613 / 0.296338 (-0.207725) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.300623 / 0.215209 (0.085414) | 2.911876 / 2.077655 (0.834221) | 1.588398 / 1.504120 (0.084278) | 1.471251 / 1.541195 (-0.069944) | 1.505528 / 1.468490 (0.037038) | 0.570635 / 4.584777 (-4.014142) | 2.485769 / 3.745712 (-1.259943) | 2.785355 / 5.269862 (-2.484507) | 1.752944 / 4.565676 (-2.812732) | 0.063146 / 0.424275 (-0.361129) | 0.004980 / 0.007607 (-0.002627) | 0.354577 / 0.226044 (0.128532) | 3.477181 / 2.268929 (1.208253) | 1.951906 / 55.444624 (-53.492718) | 1.677169 / 6.876477 (-5.199307) | 1.686338 / 2.142072 (-0.455735) | 0.637156 / 4.805227 (-4.168071) | 0.117732 / 6.500664 (-6.382932) | 0.041091 / 0.075469 (-0.034378) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.010071 / 1.841788 (-0.831717) | 12.172242 / 8.074308 (4.097934) | 10.422811 / 10.191392 (0.231419) | 0.137185 / 0.680424 (-0.543239) | 0.014643 / 0.534201 (-0.519558) | 0.287248 / 0.579283 (-0.292035) | 0.272779 / 0.434364 (-0.161585) | 0.331761 / 0.540337 (-0.208576) | 0.417266 / 1.386936 (-0.969670) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9751fb14594d354e952f0ebdfaf31cb203b011e7 \"CML watermark\")\n" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6652). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005207 / 0.011353 (-0.006145) | 0.003785 / 0.011008 (-0.007223) | 0.064221 / 0.038508 (0.025713) | 0.028981 / 0.023109 (0.005872) | 0.246215 / 0.275898 (-0.029683) | 0.268058 / 0.323480 (-0.055422) | 0.004028 / 0.007986 (-0.003958) | 0.002804 / 0.004328 (-0.001525) | 0.048878 / 0.004250 (0.044627) | 0.042641 / 0.037052 (0.005589) | 0.255590 / 0.258489 (-0.002899) | 0.287377 / 0.293841 (-0.006464) | 0.027772 / 0.128546 (-0.100774) | 0.010637 / 0.075646 (-0.065009) | 0.211526 / 0.419271 (-0.207746) | 0.035789 / 0.043533 (-0.007744) | 0.243042 / 0.255139 (-0.012097) | 0.268369 / 0.283200 (-0.014830) | 0.017907 / 0.141683 (-0.123776) | 1.138829 / 1.452155 (-0.313326) | 1.175732 / 1.492716 (-0.316984) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094205 / 0.018006 (0.076199) | 0.304317 / 0.000490 (0.303827) | 0.000206 / 0.000200 (0.000006) | 0.000049 / 0.000054 (-0.000005) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018424 / 0.037411 (-0.018987) | 0.061719 / 0.014526 (0.047193) | 0.073471 / 0.176557 (-0.103085) | 0.121577 / 0.737135 (-0.615558) | 0.075134 / 0.296338 (-0.221204) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.275178 / 0.215209 (0.059969) | 2.689222 / 2.077655 (0.611568) | 1.396680 / 1.504120 (-0.107439) | 1.278782 / 1.541195 (-0.262413) | 1.326632 / 1.468490 (-0.141858) | 0.566915 / 4.584777 (-4.017862) | 2.365928 / 3.745712 (-1.379784) | 2.785435 / 5.269862 (-2.484427) | 1.745131 / 4.565676 (-2.820546) | 0.062798 / 0.424275 (-0.361477) | 0.005107 / 0.007607 (-0.002500) | 0.330441 / 0.226044 (0.104396) | 3.266265 / 2.268929 (0.997337) | 1.792588 / 55.444624 (-53.652036) | 1.516021 / 6.876477 (-5.360455) | 1.562750 / 2.142072 (-0.579323) | 0.652964 / 4.805227 (-4.152264) | 0.117813 / 6.500664 (-6.382852) | 0.042372 / 0.075469 (-0.033097) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.010107 / 1.841788 (-0.831680) | 11.819910 / 8.074308 (3.745602) | 9.701673 / 10.191392 (-0.489719) | 0.178165 / 0.680424 (-0.502259) | 0.014438 / 0.534201 (-0.519763) | 0.297733 / 0.579283 (-0.281550) | 0.264914 / 0.434364 (-0.169450) | 0.324531 / 0.540337 (-0.215806) | 0.430207 / 1.386936 (-0.956729) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005848 / 0.011353 (-0.005505) | 0.003870 / 0.011008 (-0.007138) | 0.050379 / 0.038508 (0.011871) | 0.031238 / 0.023109 (0.008129) | 0.276839 / 0.275898 (0.000941) | 0.299488 / 0.323480 (-0.023992) | 0.005143 / 0.007986 (-0.002842) | 0.002725 / 0.004328 (-0.001604) | 0.048184 / 0.004250 (0.043934) | 0.046232 / 0.037052 (0.009180) | 0.287058 / 0.258489 (0.028569) | 0.322659 / 0.293841 (0.028818) | 0.047598 / 0.128546 (-0.080949) | 0.011116 / 0.075646 (-0.064530) | 0.058252 / 0.419271 (-0.361019) | 0.033404 / 0.043533 (-0.010128) | 0.277650 / 0.255139 (0.022511) | 0.295610 / 0.283200 (0.012410) | 0.018124 / 0.141683 (-0.123559) | 1.135052 / 1.452155 (-0.317103) | 1.194261 / 1.492716 (-0.298456) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.095595 / 0.018006 (0.077588) | 0.306408 / 0.000490 (0.305918) | 0.000216 / 0.000200 (0.000016) | 0.000044 / 0.000054 (-0.000010) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022027 / 0.037411 (-0.015385) | 0.076224 / 0.014526 (0.061698) | 0.087441 / 0.176557 (-0.089116) | 0.126636 / 0.737135 (-0.610499) | 0.089442 / 0.296338 (-0.206896) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.291315 / 0.215209 (0.076106) | 2.835304 / 2.077655 (0.757650) | 1.581102 / 1.504120 (0.076982) | 1.463046 / 1.541195 (-0.078149) | 1.481982 / 1.468490 (0.013492) | 0.559989 / 4.584777 (-4.024788) | 2.385262 / 3.745712 (-1.360450) | 2.773478 / 5.269862 (-2.496383) | 1.744427 / 4.565676 (-2.821249) | 0.062687 / 0.424275 (-0.361589) | 0.005149 / 0.007607 (-0.002458) | 0.374600 / 0.226044 (0.148555) | 3.376507 / 2.268929 (1.107579) | 1.935290 / 55.444624 (-53.509334) | 1.663227 / 6.876477 (-5.213250) | 1.678987 / 2.142072 (-0.463085) | 0.638970 / 4.805227 (-4.166258) | 0.120000 / 6.500664 (-6.380664) | 0.040862 / 0.075469 (-0.034608) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.008795 / 1.841788 (-0.832993) | 12.275084 / 8.074308 (4.200776) | 10.340088 / 10.191392 (0.148696) | 0.136454 / 0.680424 (-0.543970) | 0.014404 / 0.534201 (-0.519797) | 0.289478 / 0.579283 (-0.289805) | 0.279243 / 0.434364 (-0.155121) | 0.330992 / 0.540337 (-0.209346) | 0.422043 / 1.386936 (-0.964893) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#70633576ecf1f3f5e5cdfd8c9189246b3604f4b6 \"CML watermark\")\n" ]

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

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[ "Hi! Does running the following code also return the same error on your machine? \r\n\r\n```python\r\nimport copy\r\nimport pyarrow as pa\r\nfrom datasets.table import InMemoryTable\r\n\r\ncopy.deepcopy(InMemoryTable(pa.table({\"a\": [1, 2, 3], \"b\": [\"foo\", \"bar\", \"foobar\"]})))\r\n```", "No, it doesn't, it runs fine. But what's really strange is that the error just went away after I reran the data prep script for conversion from csv to a datasets object. I realize that's not very helpful since the problem isn't reproducible. ", "Feel free to close the issue then :)." ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6649). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005197 / 0.011353 (-0.006156) | 0.003469 / 0.011008 (-0.007539) | 0.062306 / 0.038508 (0.023798) | 0.028417 / 0.023109 (0.005308) | 0.241147 / 0.275898 (-0.034751) | 0.270910 / 0.323480 (-0.052569) | 0.003053 / 0.007986 (-0.004933) | 0.003343 / 0.004328 (-0.000985) | 0.048044 / 0.004250 (0.043794) | 0.043738 / 0.037052 (0.006686) | 0.259274 / 0.258489 (0.000785) | 0.282522 / 0.293841 (-0.011319) | 0.027807 / 0.128546 (-0.100739) | 0.010413 / 0.075646 (-0.065234) | 0.206322 / 0.419271 (-0.212950) | 0.035770 / 0.043533 (-0.007763) | 0.243465 / 0.255139 (-0.011674) | 0.261596 / 0.283200 (-0.021604) | 0.018613 / 0.141683 (-0.123070) | 1.115509 / 1.452155 (-0.336645) | 1.189403 / 1.492716 (-0.303314) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.086075 / 0.018006 (0.068069) | 0.296140 / 0.000490 (0.295650) | 0.000198 / 0.000200 (-0.000002) | 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) | 0.018238 / 0.037411 (-0.019173) | 0.061783 / 0.014526 (0.047257) | 0.072014 / 0.176557 (-0.104543) | 0.118746 / 0.737135 (-0.618389) | 0.073279 / 0.296338 (-0.223060) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.278281 / 0.215209 (0.063072) | 2.772209 / 2.077655 (0.694555) | 1.404503 / 1.504120 (-0.099617) | 1.274753 / 1.541195 (-0.266441) | 1.304394 / 1.468490 (-0.164096) | 0.556903 / 4.584777 (-4.027874) | 2.335428 / 3.745712 (-1.410284) | 2.712255 / 5.269862 (-2.557606) | 1.722252 / 4.565676 (-2.843425) | 0.061268 / 0.424275 (-0.363007) | 0.005029 / 0.007607 (-0.002578) | 0.326112 / 0.226044 (0.100067) | 3.207917 / 2.268929 (0.938988) | 1.743513 / 55.444624 (-53.701111) | 1.476418 / 6.876477 (-5.400059) | 1.489776 / 2.142072 (-0.652297) | 0.628181 / 4.805227 (-4.177046) | 0.115959 / 6.500664 (-6.384706) | 0.041854 / 0.075469 (-0.033615) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.969039 / 1.841788 (-0.872749) | 11.178646 / 8.074308 (3.104338) | 9.639716 / 10.191392 (-0.551676) | 0.139750 / 0.680424 (-0.540674) | 0.014230 / 0.534201 (-0.519971) | 0.285318 / 0.579283 (-0.293965) | 0.260788 / 0.434364 (-0.173576) | 0.324183 / 0.540337 (-0.216154) | 0.416326 / 1.386936 (-0.970610) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005149 / 0.011353 (-0.006204) | 0.003469 / 0.011008 (-0.007539) | 0.049761 / 0.038508 (0.011253) | 0.030723 / 0.023109 (0.007614) | 0.271562 / 0.275898 (-0.004336) | 0.297843 / 0.323480 (-0.025637) | 0.004296 / 0.007986 (-0.003690) | 0.002704 / 0.004328 (-0.001624) | 0.048890 / 0.004250 (0.044640) | 0.044776 / 0.037052 (0.007723) | 0.285490 / 0.258489 (0.027001) | 0.312888 / 0.293841 (0.019047) | 0.046239 / 0.128546 (-0.082307) | 0.010238 / 0.075646 (-0.065408) | 0.057968 / 0.419271 (-0.361304) | 0.033295 / 0.043533 (-0.010238) | 0.274320 / 0.255139 (0.019181) | 0.296199 / 0.283200 (0.012999) | 0.017856 / 0.141683 (-0.123827) | 1.147532 / 1.452155 (-0.304622) | 1.211647 / 1.492716 (-0.281070) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.089655 / 0.018006 (0.071649) | 0.297275 / 0.000490 (0.296785) | 0.000207 / 0.000200 (0.000007) | 0.000043 / 0.000054 (-0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021739 / 0.037411 (-0.015672) | 0.075041 / 0.014526 (0.060515) | 0.085754 / 0.176557 (-0.090802) | 0.124512 / 0.737135 (-0.612623) | 0.086926 / 0.296338 (-0.209412) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.290306 / 0.215209 (0.075097) | 2.847404 / 2.077655 (0.769749) | 1.606175 / 1.504120 (0.102055) | 1.483220 / 1.541195 (-0.057974) | 1.514551 / 1.468490 (0.046061) | 0.559332 / 4.584777 (-4.025445) | 2.403089 / 3.745712 (-1.342624) | 2.715179 / 5.269862 (-2.554683) | 1.688340 / 4.565676 (-2.877337) | 0.062057 / 0.424275 (-0.362218) | 0.004955 / 0.007607 (-0.002652) | 0.338909 / 0.226044 (0.112865) | 3.356882 / 2.268929 (1.087954) | 1.942259 / 55.444624 (-53.502366) | 1.675195 / 6.876477 (-5.201282) | 1.688158 / 2.142072 (-0.453914) | 0.637270 / 4.805227 (-4.167957) | 0.114314 / 6.500664 (-6.386350) | 0.040677 / 0.075469 (-0.034792) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.022126 / 1.841788 (-0.819661) | 11.783359 / 8.074308 (3.709051) | 10.247652 / 10.191392 (0.056260) | 0.138188 / 0.680424 (-0.542236) | 0.014850 / 0.534201 (-0.519351) | 0.287414 / 0.579283 (-0.291869) | 0.274393 / 0.434364 (-0.159971) | 0.327255 / 0.540337 (-0.213082) | 0.416355 / 1.386936 (-0.970581) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#727a952367966a98b759d54f333b1e2c28cfd4d4 \"CML watermark\")\n" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6648). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004951 / 0.011353 (-0.006402) | 0.003187 / 0.011008 (-0.007821) | 0.062959 / 0.038508 (0.024451) | 0.028037 / 0.023109 (0.004928) | 0.241374 / 0.275898 (-0.034524) | 0.262792 / 0.323480 (-0.060688) | 0.004132 / 0.007986 (-0.003854) | 0.002766 / 0.004328 (-0.001563) | 0.051416 / 0.004250 (0.047165) | 0.040957 / 0.037052 (0.003904) | 0.260760 / 0.258489 (0.002271) | 0.282018 / 0.293841 (-0.011823) | 0.027689 / 0.128546 (-0.100857) | 0.010433 / 0.075646 (-0.065214) | 0.211598 / 0.419271 (-0.207674) | 0.035447 / 0.043533 (-0.008086) | 0.244333 / 0.255139 (-0.010806) | 0.263192 / 0.283200 (-0.020008) | 0.016816 / 0.141683 (-0.124867) | 1.103188 / 1.452155 (-0.348967) | 1.179093 / 1.492716 (-0.313623) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092412 / 0.018006 (0.074406) | 0.301226 / 0.000490 (0.300736) | 0.000208 / 0.000200 (0.000008) | 0.000051 / 0.000054 (-0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018146 / 0.037411 (-0.019265) | 0.061447 / 0.014526 (0.046921) | 0.072162 / 0.176557 (-0.104394) | 0.118965 / 0.737135 (-0.618170) | 0.073756 / 0.296338 (-0.222583) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.285361 / 0.215209 (0.070152) | 2.776928 / 2.077655 (0.699273) | 1.506859 / 1.504120 (0.002739) | 1.379119 / 1.541195 (-0.162075) | 1.401798 / 1.468490 (-0.066692) | 0.572512 / 4.584777 (-4.012265) | 2.403793 / 3.745712 (-1.341919) | 2.740496 / 5.269862 (-2.529366) | 1.714611 / 4.565676 (-2.851065) | 0.063496 / 0.424275 (-0.360780) | 0.005009 / 0.007607 (-0.002598) | 0.342438 / 0.226044 (0.116393) | 3.368129 / 2.268929 (1.099200) | 1.831200 / 55.444624 (-53.613424) | 1.553611 / 6.876477 (-5.322866) | 1.578116 / 2.142072 (-0.563956) | 0.653034 / 4.805227 (-4.152193) | 0.117724 / 6.500664 (-6.382940) | 0.041188 / 0.075469 (-0.034282) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.972520 / 1.841788 (-0.869268) | 11.186297 / 8.074308 (3.111989) | 9.485829 / 10.191392 (-0.705563) | 0.139715 / 0.680424 (-0.540708) | 0.013705 / 0.534201 (-0.520496) | 0.287384 / 0.579283 (-0.291899) | 0.266784 / 0.434364 (-0.167580) | 0.320789 / 0.540337 (-0.219548) | 0.417484 / 1.386936 (-0.969452) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005570 / 0.011353 (-0.005783) | 0.003416 / 0.011008 (-0.007592) | 0.051160 / 0.038508 (0.012652) | 0.031082 / 0.023109 (0.007973) | 0.279336 / 0.275898 (0.003438) | 0.300529 / 0.323480 (-0.022951) | 0.004320 / 0.007986 (-0.003666) | 0.002781 / 0.004328 (-0.001548) | 0.049642 / 0.004250 (0.045391) | 0.044379 / 0.037052 (0.007327) | 0.293797 / 0.258489 (0.035308) | 0.317844 / 0.293841 (0.024003) | 0.049697 / 0.128546 (-0.078849) | 0.010624 / 0.075646 (-0.065023) | 0.058834 / 0.419271 (-0.360437) | 0.033869 / 0.043533 (-0.009664) | 0.280547 / 0.255139 (0.025408) | 0.300685 / 0.283200 (0.017486) | 0.017010 / 0.141683 (-0.124673) | 1.172277 / 1.452155 (-0.279878) | 1.205359 / 1.492716 (-0.287358) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092914 / 0.018006 (0.074907) | 0.303561 / 0.000490 (0.303071) | 0.000219 / 0.000200 (0.000019) | 0.000054 / 0.000054 (-0.000001) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022379 / 0.037411 (-0.015032) | 0.075460 / 0.014526 (0.060934) | 0.085795 / 0.176557 (-0.090762) | 0.124776 / 0.737135 (-0.612360) | 0.088260 / 0.296338 (-0.208079) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.302873 / 0.215209 (0.087664) | 2.936173 / 2.077655 (0.858519) | 1.589251 / 1.504120 (0.085131) | 1.477552 / 1.541195 (-0.063643) | 1.479322 / 1.468490 (0.010832) | 0.570481 / 4.584777 (-4.014296) | 2.434137 / 3.745712 (-1.311575) | 2.774012 / 5.269862 (-2.495849) | 1.718103 / 4.565676 (-2.847574) | 0.061951 / 0.424275 (-0.362324) | 0.004992 / 0.007607 (-0.002615) | 0.352250 / 0.226044 (0.126205) | 3.457417 / 2.268929 (1.188488) | 1.934587 / 55.444624 (-53.510037) | 1.646904 / 6.876477 (-5.229573) | 1.669429 / 2.142072 (-0.472643) | 0.649665 / 4.805227 (-4.155562) | 0.116630 / 6.500664 (-6.384034) | 0.040669 / 0.075469 (-0.034800) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.011488 / 1.841788 (-0.830300) | 11.866394 / 8.074308 (3.792086) | 10.144588 / 10.191392 (-0.046804) | 0.129931 / 0.680424 (-0.550493) | 0.014885 / 0.534201 (-0.519316) | 0.287463 / 0.579283 (-0.291821) | 0.280754 / 0.434364 (-0.153610) | 0.330139 / 0.540337 (-0.210199) | 0.414653 / 1.386936 (-0.972283) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#585275b8deaebd1bdcbd3725fa63172395791c73 \"CML watermark\")\n" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6647). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "> Thanks for adding the explicit loading command.\r\n> \r\n> However, I would move it just below, where we present the JSON-Lines example.\r\n> \r\n> * Maybe adding that this format is called JSON-Lines\r\n> * Add the example after the JSON-Lines data example\r\n> \r\n> https://github.com/huggingface/datasets/blob/14d9afbb7ae1b787c450261ca0ff374551993031/docs/source/loading.mdx#L135-L138\r\n\r\nThank you @albertvillanova for the feedback! I moved the jsonl file loading example to a more appropriate location. " ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6646). All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005598 / 0.011353 (-0.005755) | 0.003640 / 0.011008 (-0.007369) | 0.064557 / 0.038508 (0.026049) | 0.029645 / 0.023109 (0.006536) | 0.243695 / 0.275898 (-0.032203) | 0.261252 / 0.323480 (-0.062228) | 0.004067 / 0.007986 (-0.003919) | 0.002883 / 0.004328 (-0.001446) | 0.049192 / 0.004250 (0.044942) | 0.045299 / 0.037052 (0.008246) | 0.273207 / 0.258489 (0.014718) | 0.288668 / 0.293841 (-0.005173) | 0.028114 / 0.128546 (-0.100432) | 0.010597 / 0.075646 (-0.065049) | 0.215345 / 0.419271 (-0.203927) | 0.036119 / 0.043533 (-0.007414) | 0.243718 / 0.255139 (-0.011421) | 0.266657 / 0.283200 (-0.016543) | 0.018176 / 0.141683 (-0.123507) | 1.127926 / 1.452155 (-0.324229) | 1.168066 / 1.492716 (-0.324650) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.096001 / 0.018006 (0.077994) | 0.304317 / 0.000490 (0.303828) | 0.000209 / 0.000200 (0.000009) | 0.000051 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018241 / 0.037411 (-0.019170) | 0.061505 / 0.014526 (0.046979) | 0.072456 / 0.176557 (-0.104101) | 0.118315 / 0.737135 (-0.618821) | 0.075154 / 0.296338 (-0.221184) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.278748 / 0.215209 (0.063538) | 2.729923 / 2.077655 (0.652268) | 1.416835 / 1.504120 (-0.087285) | 1.294016 / 1.541195 (-0.247179) | 1.323249 / 1.468490 (-0.145241) | 0.575389 / 4.584777 (-4.009388) | 2.404923 / 3.745712 (-1.340789) | 2.769233 / 5.269862 (-2.500629) | 1.742340 / 4.565676 (-2.823336) | 0.062664 / 0.424275 (-0.361611) | 0.004951 / 0.007607 (-0.002656) | 0.335024 / 0.226044 (0.108979) | 3.291446 / 2.268929 (1.022518) | 1.797095 / 55.444624 (-53.647530) | 1.532963 / 6.876477 (-5.343513) | 1.529315 / 2.142072 (-0.612758) | 0.654922 / 4.805227 (-4.150305) | 0.118772 / 6.500664 (-6.381892) | 0.042034 / 0.075469 (-0.033435) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.983646 / 1.841788 (-0.858141) | 11.518625 / 8.074308 (3.444317) | 9.538781 / 10.191392 (-0.652611) | 0.140300 / 0.680424 (-0.540124) | 0.013966 / 0.534201 (-0.520235) | 0.287071 / 0.579283 (-0.292212) | 0.270201 / 0.434364 (-0.164163) | 0.323294 / 0.540337 (-0.217044) | 0.418130 / 1.386936 (-0.968806) |\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) | 0.005508 / 0.011353 (-0.005844) | 0.003714 / 0.011008 (-0.007294) | 0.050031 / 0.038508 (0.011523) | 0.031866 / 0.023109 (0.008756) | 0.272248 / 0.275898 (-0.003650) | 0.295105 / 0.323480 (-0.028375) | 0.005179 / 0.007986 (-0.002807) | 0.002820 / 0.004328 (-0.001508) | 0.048896 / 0.004250 (0.044646) | 0.045975 / 0.037052 (0.008922) | 0.287662 / 0.258489 (0.029173) | 0.321139 / 0.293841 (0.027298) | 0.049242 / 0.128546 (-0.079304) | 0.010732 / 0.075646 (-0.064914) | 0.057943 / 0.419271 (-0.361328) | 0.033527 / 0.043533 (-0.010006) | 0.271746 / 0.255139 (0.016607) | 0.291404 / 0.283200 (0.008204) | 0.019351 / 0.141683 (-0.122332) | 1.157221 / 1.452155 (-0.294934) | 1.215757 / 1.492716 (-0.276959) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.096950 / 0.018006 (0.078944) | 0.312002 / 0.000490 (0.311512) | 0.000223 / 0.000200 (0.000023) | 0.000055 / 0.000054 (0.000001) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022288 / 0.037411 (-0.015123) | 0.075282 / 0.014526 (0.060756) | 0.087445 / 0.176557 (-0.089112) | 0.125617 / 0.737135 (-0.611519) | 0.088878 / 0.296338 (-0.207460) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.291961 / 0.215209 (0.076752) | 2.881445 / 2.077655 (0.803790) | 1.586128 / 1.504120 (0.082008) | 1.458636 / 1.541195 (-0.082558) | 1.487001 / 1.468490 (0.018511) | 0.575466 / 4.584777 (-4.009311) | 2.454941 / 3.745712 (-1.290771) | 2.878077 / 5.269862 (-2.391785) | 1.787215 / 4.565676 (-2.778462) | 0.064010 / 0.424275 (-0.360265) | 0.005092 / 0.007607 (-0.002516) | 0.360500 / 0.226044 (0.134455) | 3.465574 / 2.268929 (1.196646) | 1.957516 / 55.444624 (-53.487108) | 1.666282 / 6.876477 (-5.210195) | 1.690070 / 2.142072 (-0.452002) | 0.661323 / 4.805227 (-4.143905) | 0.117824 / 6.500664 (-6.382840) | 0.042286 / 0.075469 (-0.033183) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.026517 / 1.841788 (-0.815270) | 12.083347 / 8.074308 (4.009039) | 10.269319 / 10.191392 (0.077927) | 0.139253 / 0.680424 (-0.541171) | 0.016258 / 0.534201 (-0.517943) | 0.290583 / 0.579283 (-0.288700) | 0.284338 / 0.434364 (-0.150026) | 0.335865 / 0.540337 (-0.204473) | 0.416600 / 1.386936 (-0.970336) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ba3cfad91e9366cda0ba203700fc745d8bcd1f17 \"CML watermark\")\n", "Thanks, I was needing this example today <3 " ]

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

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[ "I'd be very grateful. This upper bound banished me straight into dependency hell today. :(" ]

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

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[ "The pinned fsspec version range dependency conflict has been affecting several of our users in https://github.com/iterative/dvc. I've opened an initial PR that I think should resolve the glob behavior changes with using datasets + the latest fsspec release.\r\n\r\nPlease let us know if there's any other fsspec related behavior in datasets that needs to be updated to get 2024.2 supported, we'd like to get this conflict resolved as quickly as possible and we're willing to contribute any additional work that's required here.\r\n\r\ncc @dberenbaum" ]

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[ "Hi ! make sure your query embeddings are numpy arrays, not torch tensors ;)", "Hi Quentin, not sure how that solves the problem number 1. I am trying to pass on a dataset with a faiss gpu for training to the standard trainer but getting this serialisation error. What is a workaround this? I do not want to remove the faiss index, as I would want to use it to create batches of retrieved samples from the dataset. \r\nThanks in advance for your help!", "Issue number one seems to be an issue with FAISS indexes not being compatible with copy.deepcopy.\r\n\r\nMaybe you try to not remove the columns, e.g. by passing `remove_unused_columns=False`" ]

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[ "I see now, that I have to use `load_from_disk`, in order to load dataset properly, not `load_dataset`. Why is this behavior split? Why do we need both, `load_dataset` and `load_from_disk`?\r\n\r\nUnless answered, I believe this might be helpful for other hf datasets newbies.\r\n\r\nAnyway, made a `load_dataset` compatible dataset in a following way. I created a directory, and just copied jsonl there as `train.jsonl/test.jsonl`.\r\n```python\r\noutput_folder = os.path.join(args.output_folder, f\"{task_meta_type}_{task_type}\")\r\nos.makedirs(output_folder, exist_ok=True)\r\nfile = f\"{task_meta_type}_{task_type}_train.jsonl\"\r\nshutil.copy(os.path.join(input_folder, file),\r\n os.path.join(output_folder, \"train.jsonl\"))\r\n# now test\r\nfile = f\"{task_meta_type}_{task_type}_test.jsonl\"\r\nshutil.copy(os.path.join(input_folder, file),\r\n os.path.join(output_folder, \"test.jsonl\"))\r\n```\r\n", "Hi @MFajcik, \r\n\r\nYou can find information about save_to_disk/load_from_disk in our docs:\r\n- https://huggingface.co/docs/datasets/v2.16.1/en/process#save\r\n- https://huggingface.co/docs/datasets/v2.16.1/en/package_reference/main_classes#datasets.Dataset.save_to_disk\r\n- https://huggingface.co/docs/datasets/v2.16.1/en/package_reference/main_classes#datasets.Dataset.load_from_disk" ]

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