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[ "Sorry, I just found https://github.com/apache/beam/issues/24458. It seems this issue is being worked on. ", "Reopening, since I think the docs should inform the user of this problem. For example, [this page](https://huggingface.co/docs/datasets/installation) says \r\n> Datasets is tested on Python 3.7+.\r\n\r\nbut it should probably say that Beam Datasets do not work with Python 3.10 (or link to a known issues page). ", "Same problem on Colab using a vanilla setup running :\r\nPython 3.10.11 \r\napache-beam 2.47.0\r\ndatasets 2.12.0", "Same problem, \r\npy 3.10.11\r\napache-beam==2.47.0\r\ndatasets==2.12.0", "I have made a workaround by forcing an install of the version of `multiprocess` version `0.70.15` (after installing `datasets` and `apache-beam`). I can confirm that (on Python 3.10 in [this colab notebook](https://colab.research.google.com/drive/1PTeGlshamFcJZix_GiS3vMXX_YzAhGv0?usp=sharing)) `datasets` can download pre-processed Wikipedia dumps and can download non-pre-processed dumps using `beam_runner=\"DirectRunner\"`. I don't know if/how other `beam_runner`s can be made compatible." ]

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[ "I'm attaching a minimal reproducible example:\r\n```python\r\nfrom datasets import load_dataset\r\nimport pyarrow as pa\r\nimport pyarrow.parquet as pq\r\n\r\ntable_with_map = pa.Table.from_pydict(\r\n {\"a\": [1, 2], \"b\": [[(\"a\", 2)], [(\"b\", 4)]]},\r\n schema=pa.schema({\"a\": pa.int32(), \"b\": pa.map_(pa.string(), pa.int32())})\r\n)\r\npq.write_table(table_with_map, \"parquet_with_map.parquet\")\r\ndset = load_dataset(\"parquet\", data_files=\"parquet_with_map.parquet\", split=\"train\") # error unless streaming=True\r\n``` \r\n\r\nFor a dataset generated with the packaged loaders (CSV, JSON, Parquet), `streaming=True` sets the dataset's features to `None` (unless explicitly provided in `load_dataset`), hence no error will be thrown as long as the features stay \"unresolved\" (resolving the features with `_resolve_features` will lead to an error)." ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "Hi, thanks for working on this! `Table.to_pylist` requires PyArrow 7.0+, and our minimal version requirement is 6.0, so we need to bump the version requirement to avoid CI failure. I'll do this in a separate PR.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006857 / 0.011353 (-0.004496) | 0.004711 / 0.011008 (-0.006297) | 0.098332 / 0.038508 (0.059824) | 0.028547 / 0.023109 (0.005438) | 0.307647 / 0.275898 (0.031749) | 0.334891 / 0.323480 (0.011411) | 0.005252 / 0.007986 (-0.002734) | 0.003495 / 0.004328 (-0.000833) | 0.075529 / 0.004250 (0.071279) | 0.042167 / 0.037052 (0.005114) | 0.308509 / 0.258489 (0.050020) | 0.348294 / 0.293841 (0.054453) | 0.032042 / 0.128546 (-0.096504) | 0.011684 / 0.075646 (-0.063962) | 0.321740 / 0.419271 (-0.097531) | 0.057725 / 0.043533 (0.014193) | 0.309431 / 0.255139 (0.054292) | 0.326818 / 0.283200 (0.043618) | 0.093261 / 0.141683 (-0.048422) | 1.475344 / 1.452155 (0.023190) | 1.563952 / 1.492716 (0.071236) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.205056 / 0.018006 (0.187050) | 0.421656 / 0.000490 (0.421166) | 0.004167 / 0.000200 (0.003967) | 0.000075 / 0.000054 (0.000020) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023935 / 0.037411 (-0.013476) | 0.097220 / 0.014526 (0.082695) | 0.104942 / 0.176557 (-0.071615) | 0.170339 / 0.737135 (-0.566796) | 0.107556 / 0.296338 (-0.188782) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.424509 / 0.215209 (0.209300) | 4.223637 / 2.077655 (2.145982) | 2.090700 / 1.504120 (0.586580) | 1.902537 / 1.541195 (0.361343) | 1.981192 / 1.468490 (0.512701) | 0.695272 / 4.584777 (-3.889505) | 3.570169 / 3.745712 (-0.175544) | 1.885007 / 5.269862 (-3.384854) | 1.162828 / 4.565676 (-3.402848) | 0.084956 / 0.424275 (-0.339319) | 0.012818 / 0.007607 (0.005210) | 0.534395 / 0.226044 (0.308351) | 5.354318 / 2.268929 (3.085389) | 2.436875 / 55.444624 (-53.007749) | 2.111365 / 6.876477 (-4.765112) | 2.232874 / 2.142072 (0.090802) | 0.804703 / 4.805227 (-4.000524) | 0.152406 / 6.500664 (-6.348258) | 0.066926 / 0.075469 (-0.008543) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.198621 / 1.841788 (-0.643166) | 13.907491 / 8.074308 (5.833183) | 14.356286 / 10.191392 (4.164894) | 0.140714 / 0.680424 (-0.539710) | 0.016440 / 0.534201 (-0.517761) | 0.380868 / 0.579283 (-0.198415) | 0.396004 / 0.434364 (-0.038360) | 0.448275 / 0.540337 (-0.092062) | 0.537818 / 1.386936 (-0.849118) |\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) | 0.006789 / 0.011353 (-0.004564) | 0.004652 / 0.011008 (-0.006356) | 0.076449 / 0.038508 (0.037941) | 0.028389 / 0.023109 (0.005280) | 0.378644 / 0.275898 (0.102746) | 0.423870 / 0.323480 (0.100391) | 0.005824 / 0.007986 (-0.002162) | 0.003398 / 0.004328 (-0.000931) | 0.075575 / 0.004250 (0.071324) | 0.039656 / 0.037052 (0.002604) | 0.370072 / 0.258489 (0.111583) | 0.441812 / 0.293841 (0.147971) | 0.031817 / 0.128546 (-0.096729) | 0.011701 / 0.075646 (-0.063946) | 0.085759 / 0.419271 (-0.333513) | 0.042328 / 0.043533 (-0.001205) | 0.364103 / 0.255139 (0.108964) | 0.413910 / 0.283200 (0.130711) | 0.090871 / 0.141683 (-0.050812) | 1.505749 / 1.452155 (0.053594) | 1.608555 / 1.492716 (0.115839) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.212533 / 0.018006 (0.194527) | 0.404519 / 0.000490 (0.404030) | 0.000373 / 0.000200 (0.000174) | 0.000059 / 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) | 0.024849 / 0.037411 (-0.012562) | 0.100769 / 0.014526 (0.086243) | 0.110450 / 0.176557 (-0.066107) | 0.161715 / 0.737135 (-0.575420) | 0.113599 / 0.296338 (-0.182739) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.436780 / 0.215209 (0.221571) | 4.387103 / 2.077655 (2.309448) | 2.081942 / 1.504120 (0.577822) | 1.873661 / 1.541195 (0.332466) | 1.947718 / 1.468490 (0.479228) | 0.696434 / 4.584777 (-3.888343) | 3.405300 / 3.745712 (-0.340412) | 1.897388 / 5.269862 (-3.372474) | 1.169969 / 4.565676 (-3.395707) | 0.083085 / 0.424275 (-0.341190) | 0.012480 / 0.007607 (0.004873) | 0.535635 / 0.226044 (0.309591) | 5.364462 / 2.268929 (3.095533) | 2.531168 / 55.444624 (-52.913457) | 2.184324 / 6.876477 (-4.692153) | 2.228613 / 2.142072 (0.086541) | 0.807127 / 4.805227 (-3.998100) | 0.151971 / 6.500664 (-6.348693) | 0.068430 / 0.075469 (-0.007039) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.306401 / 1.841788 (-0.535387) | 14.479552 / 8.074308 (6.405244) | 14.428398 / 10.191392 (4.237006) | 0.159505 / 0.680424 (-0.520919) | 0.016856 / 0.534201 (-0.517344) | 0.375197 / 0.579283 (-0.204086) | 0.384328 / 0.434364 (-0.050036) | 0.440688 / 0.540337 (-0.099650) | 0.524998 / 1.386936 (-0.861938) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#50b887b840cf3cab86b0394b41050b579c4b79ba \"CML watermark\")\n" ]

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[ "Same problem, \r\ntransformers 4.28.1\r\ndatasets 2.12.0\r\n\r\nleak around 100Mb per 10 seconds when use dataloader_num_werker > 0 in training argumennts for transformer train, possile bug in transformers repo, but still not found solution :(\r\n", "found an article described a problem, may be helpful for somebody:\r\nhttps://ppwwyyxx.com/blog/2022/Demystify-RAM-Usage-in-Multiprocess-DataLoader/\r\nI confirm, it`s not memory leak, after some time memory growing has stopped" ]

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[ "Hi! We've recently made some improvements to `save_to_disk`/`list_to_disk` (100x faster in some scenarios), so it would help if you could install `datasets` directly from `main` (`pip install git+https://github.com/huggingface/datasets.git`) and re-run the \"benchmark\".", "Great to hear! I'll give it a try when I've got a moment.", "@mariosasko is that fix released to pip in the meantime? Asking cause im facing still the same issue (regarding loading images from local paths):\r\n```\r\ndataset = load_dataset(\"csv\", cache_dir=\"cache\", data_files=[\"/STORAGE/DATA/mijam/vit/code/list_filtered.csv\"], num_proc=16, split=\"train\").cast_column(\"image\", Image())\r\ndataset = dataset.class_encode_column(\"label\")\r\n```\r\nquite fast. \r\n\r\nThen I do `save_to_disk()` and some time later:\r\n```\r\ndataset = load_from_disk('/STORAGE/DATA/mijam/accel/saved_arrow_big')\r\n```\r\nreally slow. In theory it should be quicked since it only loads arrow files, no conversions and so on.\r\n", "@mjamroz I assume your CSV file stores image file paths. This means `save_to_disk` needs to embed the image bytes resulting in a much bigger Arrow file (than the initial one). Maybe specifying `num_shards` to make the Arrow files smaller can help (large Arrow files on some systems take a long time to load)." ]

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[ "Hi!\r\n\r\n> naming convention of mp3 files\r\n\r\nYes, this could be the problem. MP3 files should end with `.mp3`/`.MP3` to be recognized as audio files.\r\n\r\nIf the file names are not the culprit, can you paste the audio folder's directory structure to help us reproduce the error (e.g., by running the `tree \"x\"` command)?", "Hi! I'm sorry, I don't want to reveal my entire dataset, but here's a snippet (all of the mp3 files below are some of the ones not being recognized by audiofolder. Also, for another dataset, audiofolder loaded zero mp3 files because \"train\" was in the name of one of the mp3 files. \r\nmy_dataset\r\n├── data\r\n│   ├── VHA_Innovation_Stories_-_Day_2-123.mp3\r\n│   ├── VHA_Innovation_Stories_-_Day_2-124.mp3\r\n│   ├── ASSOCIATION_OF_GENERAL_PRACTITIONERS_OF_JAMAICA_NEPHROLOGY_CONFERENCE_-_JULY_3,_2022-93.mp3\r\n│   ├── ASSOCIATION_OF_GENERAL_PRACTITIONERS_OF_JAMAICA_NEPHROLOGY_CONFERENCE_-_JULY_3,_2022-94.mp3\r\n│   ├── ASSOCIATION_OF_GENERAL_PRACTITIONERS_OF_JAMAICA_NEPHROLOGY_CONFERENCE_-_JULY_3,_2022-95.mp3\r\n│   ├── Your_Impact\\357\\274\\232_Neurosurgery_equipment-5.mp3\r\n│   └── Your_Impact\\357\\274\\232_Neurosurgery_equipment-6.mp3\r\n└── metadata.csv\r\n\r\nHere's a few of the 13 files recognized by the dataset:\r\nBritish_Heart_Foundation_-_Your_guide_to_a_Coronary_Angiogram,_a_test_for_heart_disease-1.mp3\r\nBritish_Heart_Foundation_-_Your_guide_to_a_Coronary_Angiogram,_a_test_for_heart_disease-2.mp3\r\nBritish_Heart_Foundation_-_Your_guide_to_a_Coronary_Angiogram,_a_test_for_heart_disease-3.mp3\r\nIVP_⧸_IVU_test_Procedure_for_Kidneys_intravenous_pyelogram_-_medical_radiology_X-ray_ivp-1.mp3\r\nIVP_⧸_IVU_test_Procedure_for_Kidneys_intravenous_pyelogram_-_medical_radiology_X-ray_ivp-2.mp3" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006142 / 0.011353 (-0.005211) | 0.004506 / 0.011008 (-0.006502) | 0.100224 / 0.038508 (0.061715) | 0.026988 / 0.023109 (0.003879) | 0.301625 / 0.275898 (0.025727) | 0.346337 / 0.323480 (0.022857) | 0.004642 / 0.007986 (-0.003343) | 0.003481 / 0.004328 (-0.000847) | 0.075847 / 0.004250 (0.071597) | 0.036959 / 0.037052 (-0.000094) | 0.302697 / 0.258489 (0.044208) | 0.351917 / 0.293841 (0.058076) | 0.030719 / 0.128546 (-0.097828) | 0.011591 / 0.075646 (-0.064056) | 0.319709 / 0.419271 (-0.099563) | 0.042000 / 0.043533 (-0.001532) | 0.306854 / 0.255139 (0.051715) | 0.326903 / 0.283200 (0.043703) | 0.082711 / 0.141683 (-0.058972) | 1.486616 / 1.452155 (0.034461) | 1.603229 / 1.492716 (0.110513) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.198990 / 0.018006 (0.180983) | 0.427733 / 0.000490 (0.427243) | 0.003612 / 0.000200 (0.003412) | 0.000071 / 0.000054 (0.000016) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022932 / 0.037411 (-0.014480) | 0.096969 / 0.014526 (0.082443) | 0.105749 / 0.176557 (-0.070807) | 0.166101 / 0.737135 (-0.571034) | 0.108646 / 0.296338 (-0.187692) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.428174 / 0.215209 (0.212965) | 4.271452 / 2.077655 (2.193797) | 1.907588 / 1.504120 (0.403468) | 1.680870 / 1.541195 (0.139675) | 1.761336 / 1.468490 (0.292846) | 0.700380 / 4.584777 (-3.884396) | 3.415168 / 3.745712 (-0.330544) | 1.886122 / 5.269862 (-3.383740) | 1.276814 / 4.565676 (-3.288863) | 0.083429 / 0.424275 (-0.340846) | 0.012988 / 0.007607 (0.005381) | 0.518821 / 0.226044 (0.292776) | 5.188284 / 2.268929 (2.919356) | 2.433084 / 55.444624 (-53.011540) | 1.988034 / 6.876477 (-4.888443) | 2.100275 / 2.142072 (-0.041797) | 0.808252 / 4.805227 (-3.996976) | 0.158102 / 6.500664 (-6.342562) | 0.067686 / 0.075469 (-0.007783) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.204171 / 1.841788 (-0.637616) | 13.548756 / 8.074308 (5.474448) | 14.339805 / 10.191392 (4.148413) | 0.142853 / 0.680424 (-0.537571) | 0.016529 / 0.534201 (-0.517672) | 0.383800 / 0.579283 (-0.195483) | 0.380362 / 0.434364 (-0.054002) | 0.437716 / 0.540337 (-0.102621) | 0.524306 / 1.386936 (-0.862630) |\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) | 0.006730 / 0.011353 (-0.004623) | 0.004652 / 0.011008 (-0.006356) | 0.077476 / 0.038508 (0.038968) | 0.027584 / 0.023109 (0.004475) | 0.340907 / 0.275898 (0.065009) | 0.377950 / 0.323480 (0.054470) | 0.005946 / 0.007986 (-0.002040) | 0.003548 / 0.004328 (-0.000780) | 0.076270 / 0.004250 (0.072019) | 0.037483 / 0.037052 (0.000431) | 0.346390 / 0.258489 (0.087901) | 0.384739 / 0.293841 (0.090898) | 0.031744 / 0.128546 (-0.096802) | 0.011598 / 0.075646 (-0.064049) | 0.085651 / 0.419271 (-0.333620) | 0.047308 / 0.043533 (0.003775) | 0.344704 / 0.255139 (0.089565) | 0.363410 / 0.283200 (0.080211) | 0.095009 / 0.141683 (-0.046674) | 1.478307 / 1.452155 (0.026152) | 1.576808 / 1.492716 (0.084092) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.197545 / 0.018006 (0.179539) | 0.431984 / 0.000490 (0.431494) | 0.001529 / 0.000200 (0.001329) | 0.000079 / 0.000054 (0.000025) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025452 / 0.037411 (-0.011959) | 0.100176 / 0.014526 (0.085651) | 0.108222 / 0.176557 (-0.068335) | 0.160556 / 0.737135 (-0.576580) | 0.112748 / 0.296338 (-0.183591) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.436326 / 0.215209 (0.221117) | 4.378443 / 2.077655 (2.300788) | 2.056001 / 1.504120 (0.551881) | 1.853406 / 1.541195 (0.312211) | 1.931645 / 1.468490 (0.463155) | 0.698340 / 4.584777 (-3.886437) | 3.368961 / 3.745712 (-0.376751) | 2.583622 / 5.269862 (-2.686239) | 1.501274 / 4.565676 (-3.064402) | 0.083034 / 0.424275 (-0.341241) | 0.012725 / 0.007607 (0.005117) | 0.539991 / 0.226044 (0.313947) | 5.418413 / 2.268929 (3.149485) | 2.517205 / 55.444624 (-52.927420) | 2.179332 / 6.876477 (-4.697144) | 2.215376 / 2.142072 (0.073304) | 0.806133 / 4.805227 (-3.999094) | 0.151499 / 6.500664 (-6.349165) | 0.067270 / 0.075469 (-0.008199) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.308324 / 1.841788 (-0.533464) | 14.357361 / 8.074308 (6.283053) | 14.684768 / 10.191392 (4.493376) | 0.139575 / 0.680424 (-0.540849) | 0.016409 / 0.534201 (-0.517792) | 0.374087 / 0.579283 (-0.205196) | 0.390628 / 0.434364 (-0.043735) | 0.443102 / 0.540337 (-0.097235) | 0.536089 / 1.386936 (-0.850847) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#778d4e1c13ece980e706f8c7cb06e8473fd61315 \"CML watermark\")\n" ]

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[ "Hello, I have an interest in this issue.\r\nIs the `Dataset.to_dict` you are describing correct in the code here?\r\n\r\nhttps://github.com/huggingface/datasets/blob/35b789e8f6826b6b5a6b48fcc2416c890a1f326a/src/datasets/arrow_dataset.py#L4633-L4667", "Yes, this is where `Dataset.to_dict` is defined.", "#self-assign" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "Are you sure it's safe to remove? https://github.com/huggingface/datasets/pull/3866", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009520 / 0.011353 (-0.001833) | 0.005319 / 0.011008 (-0.005690) | 0.099372 / 0.038508 (0.060863) | 0.036173 / 0.023109 (0.013064) | 0.295752 / 0.275898 (0.019853) | 0.362882 / 0.323480 (0.039402) | 0.008442 / 0.007986 (0.000456) | 0.004225 / 0.004328 (-0.000103) | 0.076645 / 0.004250 (0.072394) | 0.044198 / 0.037052 (0.007146) | 0.311948 / 0.258489 (0.053459) | 0.342963 / 0.293841 (0.049122) | 0.038613 / 0.128546 (-0.089933) | 0.012127 / 0.075646 (-0.063519) | 0.334427 / 0.419271 (-0.084844) | 0.048309 / 0.043533 (0.004776) | 0.297046 / 0.255139 (0.041907) | 0.314562 / 0.283200 (0.031363) | 0.105797 / 0.141683 (-0.035886) | 1.460967 / 1.452155 (0.008812) | 1.500907 / 1.492716 (0.008190) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.216185 / 0.018006 (0.198179) | 0.438924 / 0.000490 (0.438435) | 0.001210 / 0.000200 (0.001011) | 0.000081 / 0.000054 (0.000027) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026193 / 0.037411 (-0.011219) | 0.105888 / 0.014526 (0.091363) | 0.115812 / 0.176557 (-0.060744) | 0.158748 / 0.737135 (-0.578387) | 0.121514 / 0.296338 (-0.174824) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.399837 / 0.215209 (0.184628) | 3.996992 / 2.077655 (1.919338) | 1.784964 / 1.504120 (0.280844) | 1.591078 / 1.541195 (0.049883) | 1.666424 / 1.468490 (0.197934) | 0.711450 / 4.584777 (-3.873327) | 3.787814 / 3.745712 (0.042102) | 2.056776 / 5.269862 (-3.213085) | 1.332163 / 4.565676 (-3.233514) | 0.085755 / 0.424275 (-0.338520) | 0.012033 / 0.007607 (0.004426) | 0.511500 / 0.226044 (0.285455) | 5.098999 / 2.268929 (2.830071) | 2.288261 / 55.444624 (-53.156364) | 1.947483 / 6.876477 (-4.928994) | 1.987838 / 2.142072 (-0.154234) | 0.852241 / 4.805227 (-3.952986) | 0.164781 / 6.500664 (-6.335883) | 0.061825 / 0.075469 (-0.013644) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.202253 / 1.841788 (-0.639534) | 14.632608 / 8.074308 (6.558300) | 13.331320 / 10.191392 (3.139928) | 0.157944 / 0.680424 (-0.522480) | 0.029284 / 0.534201 (-0.504917) | 0.446636 / 0.579283 (-0.132647) | 0.437009 / 0.434364 (0.002645) | 0.521883 / 0.540337 (-0.018455) | 0.606687 / 1.386936 (-0.780249) |\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) | 0.007528 / 0.011353 (-0.003825) | 0.005274 / 0.011008 (-0.005734) | 0.073524 / 0.038508 (0.035016) | 0.033893 / 0.023109 (0.010784) | 0.335432 / 0.275898 (0.059534) | 0.379981 / 0.323480 (0.056501) | 0.005954 / 0.007986 (-0.002031) | 0.004126 / 0.004328 (-0.000203) | 0.072891 / 0.004250 (0.068641) | 0.046517 / 0.037052 (0.009465) | 0.337241 / 0.258489 (0.078752) | 0.385562 / 0.293841 (0.091721) | 0.036410 / 0.128546 (-0.092136) | 0.012246 / 0.075646 (-0.063401) | 0.085974 / 0.419271 (-0.333298) | 0.049665 / 0.043533 (0.006133) | 0.330919 / 0.255139 (0.075780) | 0.352041 / 0.283200 (0.068841) | 0.103751 / 0.141683 (-0.037931) | 1.468851 / 1.452155 (0.016696) | 1.565380 / 1.492716 (0.072663) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.260431 / 0.018006 (0.242425) | 0.444554 / 0.000490 (0.444064) | 0.016055 / 0.000200 (0.015855) | 0.000283 / 0.000054 (0.000228) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029130 / 0.037411 (-0.008281) | 0.112002 / 0.014526 (0.097476) | 0.120769 / 0.176557 (-0.055788) | 0.169345 / 0.737135 (-0.567790) | 0.129609 / 0.296338 (-0.166730) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.432211 / 0.215209 (0.217002) | 4.293008 / 2.077655 (2.215353) | 2.071291 / 1.504120 (0.567171) | 1.859322 / 1.541195 (0.318127) | 1.971434 / 1.468490 (0.502943) | 0.704042 / 4.584777 (-3.880735) | 3.791696 / 3.745712 (0.045983) | 3.142632 / 5.269862 (-2.127230) | 1.735151 / 4.565676 (-2.830525) | 0.086203 / 0.424275 (-0.338072) | 0.012542 / 0.007607 (0.004935) | 0.534870 / 0.226044 (0.308826) | 5.326042 / 2.268929 (3.057113) | 2.547960 / 55.444624 (-52.896664) | 2.212730 / 6.876477 (-4.663747) | 2.296177 / 2.142072 (0.154105) | 0.840311 / 4.805227 (-3.964917) | 0.168353 / 6.500664 (-6.332311) | 0.065949 / 0.075469 (-0.009520) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.255589 / 1.841788 (-0.586199) | 14.947344 / 8.074308 (6.873036) | 13.253721 / 10.191392 (3.062329) | 0.162349 / 0.680424 (-0.518075) | 0.017579 / 0.534201 (-0.516622) | 0.420758 / 0.579283 (-0.158525) | 0.430030 / 0.434364 (-0.004334) | 0.524669 / 0.540337 (-0.015669) | 0.623920 / 1.386936 (-0.763016) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#35b789e8f6826b6b5a6b48fcc2416c890a1f326a \"CML watermark\")\n" ]

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[ "Hi! \r\n\r\n\r\nYou can specify `download_config=DownloadConfig(resume_download=True))` in `load_dataset` to resume the download when re-running the code after the timeout error:\r\n```python\r\nfrom datasets import load_dataset, DownloadConfig\r\ndataset = load_dataset('the_pile', split='train', cache_dir='F:\\datasets', download_config=DownloadConfig(resume_download=True))\r\n```\r\n\r\n", "@mariosasko , I used your suggestion but its not saving anything , just stops and runs from the same point .\r\nbelow is the script to download and save on disk .\r\n\r\n```\r\nfrom datasets import load_dataset, DownloadConfig\r\n\r\n\r\n#load the Pile dataset from Hugging Face Datasets\r\n#dataset = load_dataset('the_pile')\r\ndataset = load_dataset('the_pile', split='train', cache_dir='datasets', download_config=DownloadConfig(resume_download=True))\r\n\r\n\r\n# save each file in the dataset to disk\r\nfor i, example in enumerate(dataset['train']):\r\n filename = f'pile_file_{i}.json'\r\n with open(filename, 'w') as f:\r\n f.write(str(example))\r\n\r\nprint(\"Finished saving Pile dataset files to disk.\")\r\n```\r\n", "@mariosasko , it shows nothing in dataset folder\r\n\r\n```\r\n du -sh /mnt/nlp/hugging_face/*\r\n20K /mnt/nlp/hugging_face/datasets\r\n4.0K /mnt/nlp/hugging_face/download_pile.py\r\n```\r\n", "@mariosasko \r\n\r\n```\r\nroot@d20f0ab8f4f8:/mnt/hugging_face# python3 download_pile.py\r\nNo config specified, defaulting to: the_pile/all\r\nDownloading and preparing dataset the_pile/all to /mnt/hugging_face/datasets/the_pile/all/0.0.0/6fadc480ecb32470826cbf5900a9558b791ce55d5e9a0fdc8ad653e7b64bb349...\r\nDownloading data files: 0%| | 0/3 [00:00<?, ?it/s]\r\n\r\n\r\n\r\n\r\n\r\nDownloading data: 70%|████████████████████████████████████████████████████████████████████▊ | 10.7G/15.2G [12:09<11:53, 6.36MB/s]\r\nDownloading data: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████| 15.2G/15.2G [22:15<00:00, 7.25MB/s]\r\nDownloading data: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████| 15.2G/15.2G [46:17<00:00, 5.48MB/s]\r\nDownloading data: 40%|██████████████████████████████████████▏ | 6.07G/15.3G [50:49<1:17:02, 1.99MB/s]\r\nTraceback (most recent call last):██████████████████████████▊ | 6.07G/15.3G [50:49<25:35:23, 99.9kB/s]\r\n File \"/usr/local/lib/python3.8/dist-packages/urllib3/response.py\", line 444, in _error_catcher\r\n yield\r\n File \"/usr/local/lib/python3.8/dist-packages/urllib3/response.py\", line 567, in read\r\n data = self._fp_read(amt) if not fp_closed else b\"\"\r\n File \"/usr/local/lib/python3.8/dist-packages/urllib3/response.py\", line 525, in _fp_read\r\n data = self._fp.read(chunk_amt)\r\n File \"/usr/lib/python3.8/http/client.py\", line 459, in read\r\n n = self.readinto(b)\r\n File \"/usr/lib/python3.8/http/client.py\", line 503, in readinto\r\n n = self.fp.readinto(b)\r\n File \"/usr/lib/python3.8/socket.py\", line 669, in readinto\r\n return self._sock.recv_into(b)\r\n File \"/usr/lib/python3.8/ssl.py\", line 1241, in recv_into\r\n return self.read(nbytes, buffer)\r\n File \"/usr/lib/python3.8/ssl.py\", line 1099, in read\r\n return self._sslobj.read(len, buffer)\r\nConnectionResetError: [Errno 104] Connection reset by peer\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"/usr/local/lib/python3.8/dist-packages/requests/models.py\", line 816, in generate\r\n yield from self.raw.stream(chunk_size, decode_content=True)\r\n File \"/usr/local/lib/python3.8/dist-packages/urllib3/response.py\", line 628, in stream\r\n data = self.read(amt=amt, decode_content=decode_content)\r\n File \"/usr/local/lib/python3.8/dist-packages/urllib3/response.py\", line 593, in read\r\n raise IncompleteRead(self._fp_bytes_read, self.length_remaining)\r\n File \"/usr/lib/python3.8/contextlib.py\", line 131, in __exit__\r\n self.gen.throw(type, value, traceback)\r\n File \"/usr/local/lib/python3.8/dist-packages/urllib3/response.py\", line 461, in _error_catcher\r\n raise ProtocolError(\"Connection broken: %r\" % e, e)\r\nurllib3.exceptions.ProtocolError: (\"Connection broken: ConnectionResetError(104, 'Connection reset by peer')\", ConnectionResetError(104, 'Connection reset by peer'))\r\n\r\nDuring handling of the above exception, another exception occurred:\r\n\r\nTraceback (most recent call last):\r\n File \"download_pile.py\", line 6, in <module>\r\n dataset = load_dataset('the_pile', split='train', cache_dir='datasets', download_config=DownloadConfig(resume_download=True))\r\n File \"/usr/local/lib/python3.8/dist-packages/datasets/load.py\", line 1782, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"/usr/local/lib/python3.8/dist-packages/datasets/builder.py\", line 872, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"/usr/local/lib/python3.8/dist-packages/datasets/builder.py\", line 1649, in _download_and_prepare\r\n super()._download_and_prepare(\r\n File \"/usr/local/lib/python3.8/dist-packages/datasets/builder.py\", line 945, in _download_and_prepare\r\n split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\r\n File \"/root/.cache/huggingface/modules/datasets_modules/datasets/the_pile/6fadc480ecb32470826cbf5900a9558b791ce55d5e9a0fdc8ad653e7b64bb349/the_pile.py\", line 192, in _split_generators\r\n data_dir = dl_manager.download(_DATA_URLS[self.config.name])\r\n File \"/usr/local/lib/python3.8/dist-packages/datasets/download/download_manager.py\", line 427, in download\r\n downloaded_path_or_paths = map_nested(\r\n File \"/usr/local/lib/python3.8/dist-packages/datasets/utils/py_utils.py\", line 443, in map_nested\r\n mapped = [\r\n File \"/usr/local/lib/python3.8/dist-packages/datasets/utils/py_utils.py\", line 444, in <listcomp>\r\n _single_map_nested((function, obj, types, None, True, None))\r\n File \"/usr/local/lib/python3.8/dist-packages/datasets/utils/py_utils.py\", line 363, in _single_map_nested\r\n mapped = [_single_map_nested((function, v, types, None, True, None)) for v in pbar]\r\n File \"/usr/local/lib/python3.8/dist-packages/datasets/utils/py_utils.py\", line 363, in <listcomp>\r\n mapped = [_single_map_nested((function, v, types, None, True, None)) for v in pbar]\r\n File \"/usr/local/lib/python3.8/dist-packages/datasets/utils/py_utils.py\", line 346, in _single_map_nested\r\n return function(data_struct)\r\n File \"/usr/local/lib/python3.8/dist-packages/datasets/download/download_manager.py\", line 453, in _download\r\n return cached_path(url_or_filename, download_config=download_config)\r\n File \"/usr/local/lib/python3.8/dist-packages/datasets/utils/file_utils.py\", line 182, in cached_path\r\n output_path = get_from_cache(\r\n File \"/usr/local/lib/python3.8/dist-packages/datasets/utils/file_utils.py\", line 575, in get_from_cache\r\n http_get(\r\n File \"/usr/local/lib/python3.8/dist-packages/datasets/utils/file_utils.py\", line 379, in http_get\r\n for chunk in response.iter_content(chunk_size=1024):\r\n File \"/usr/local/lib/python3.8/dist-packages/requests/models.py\", line 818, in generate\r\n raise ChunkedEncodingError(e)\r\nrequests.exceptions.ChunkedEncodingError: (\"Connection broken: ConnectionResetError(104, 'Connection reset by peer')\", ConnectionResetError(104, 'Connection reset by peer'))\r\n```\r\n", "Users with slow internet speed are doomed (4MB/s). The dataset downloads fine at minimum speed 10MB/s.\n\nAlso, when the train splits were generated and then I removed the downloads folder to save up disk space, it started redownloading the whole dataset. Is there any way to use the already generated splits instead?", "@sentialx @mariosasko , anytime on my above script , am I downloading and saving dataset correctly . Please suggest :)" ]

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[]

[]

[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008550 / 0.011353 (-0.002803) | 0.004476 / 0.011008 (-0.006532) | 0.100902 / 0.038508 (0.062394) | 0.029684 / 0.023109 (0.006575) | 0.308081 / 0.275898 (0.032183) | 0.363435 / 0.323480 (0.039955) | 0.006987 / 0.007986 (-0.000999) | 0.003401 / 0.004328 (-0.000927) | 0.078218 / 0.004250 (0.073967) | 0.036657 / 0.037052 (-0.000395) | 0.319670 / 0.258489 (0.061181) | 0.349952 / 0.293841 (0.056111) | 0.033416 / 0.128546 (-0.095130) | 0.011511 / 0.075646 (-0.064135) | 0.323888 / 0.419271 (-0.095384) | 0.042429 / 0.043533 (-0.001104) | 0.307310 / 0.255139 (0.052171) | 0.329459 / 0.283200 (0.046259) | 0.085209 / 0.141683 (-0.056474) | 1.475893 / 1.452155 (0.023739) | 1.502782 / 1.492716 (0.010065) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.200137 / 0.018006 (0.182131) | 0.411269 / 0.000490 (0.410780) | 0.000415 / 0.000200 (0.000215) | 0.000061 / 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) | 0.022626 / 0.037411 (-0.014785) | 0.097045 / 0.014526 (0.082519) | 0.102955 / 0.176557 (-0.073602) | 0.148411 / 0.737135 (-0.588725) | 0.107238 / 0.296338 (-0.189100) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.421683 / 0.215209 (0.206474) | 4.203031 / 2.077655 (2.125376) | 1.908232 / 1.504120 (0.404112) | 1.698867 / 1.541195 (0.157672) | 1.743561 / 1.468490 (0.275071) | 0.693199 / 4.584777 (-3.891578) | 3.361022 / 3.745712 (-0.384690) | 2.989610 / 5.269862 (-2.280251) | 1.533036 / 4.565676 (-3.032641) | 0.082675 / 0.424275 (-0.341601) | 0.012419 / 0.007607 (0.004812) | 0.531543 / 0.226044 (0.305499) | 5.330595 / 2.268929 (3.061666) | 2.347519 / 55.444624 (-53.097105) | 1.975672 / 6.876477 (-4.900804) | 2.039541 / 2.142072 (-0.102532) | 0.810281 / 4.805227 (-3.994946) | 0.148917 / 6.500664 (-6.351747) | 0.065441 / 0.075469 (-0.010028) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.266213 / 1.841788 (-0.575574) | 13.628106 / 8.074308 (5.553798) | 13.852191 / 10.191392 (3.660799) | 0.149004 / 0.680424 (-0.531420) | 0.028549 / 0.534201 (-0.505652) | 0.399824 / 0.579283 (-0.179459) | 0.401231 / 0.434364 (-0.033133) | 0.473251 / 0.540337 (-0.067086) | 0.561094 / 1.386936 (-0.825842) |\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) | 0.006669 / 0.011353 (-0.004684) | 0.004477 / 0.011008 (-0.006532) | 0.077514 / 0.038508 (0.039006) | 0.027489 / 0.023109 (0.004380) | 0.341935 / 0.275898 (0.066037) | 0.377392 / 0.323480 (0.053912) | 0.004947 / 0.007986 (-0.003039) | 0.004600 / 0.004328 (0.000271) | 0.075938 / 0.004250 (0.071687) | 0.039586 / 0.037052 (0.002534) | 0.344966 / 0.258489 (0.086477) | 0.392181 / 0.293841 (0.098340) | 0.031838 / 0.128546 (-0.096708) | 0.011572 / 0.075646 (-0.064075) | 0.085811 / 0.419271 (-0.333461) | 0.042250 / 0.043533 (-0.001283) | 0.345605 / 0.255139 (0.090466) | 0.367814 / 0.283200 (0.084615) | 0.090683 / 0.141683 (-0.051000) | 1.483168 / 1.452155 (0.031014) | 1.559724 / 1.492716 (0.067008) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.235655 / 0.018006 (0.217649) | 0.399016 / 0.000490 (0.398527) | 0.003096 / 0.000200 (0.002896) | 0.000077 / 0.000054 (0.000022) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024454 / 0.037411 (-0.012957) | 0.100710 / 0.014526 (0.086185) | 0.107950 / 0.176557 (-0.068606) | 0.161560 / 0.737135 (-0.575576) | 0.111840 / 0.296338 (-0.184498) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.441362 / 0.215209 (0.226153) | 4.428105 / 2.077655 (2.350450) | 2.074501 / 1.504120 (0.570381) | 1.866672 / 1.541195 (0.325477) | 1.928266 / 1.468490 (0.459776) | 0.703561 / 4.584777 (-3.881216) | 3.396537 / 3.745712 (-0.349175) | 3.047369 / 5.269862 (-2.222492) | 1.595133 / 4.565676 (-2.970543) | 0.084028 / 0.424275 (-0.340247) | 0.012349 / 0.007607 (0.004741) | 0.539354 / 0.226044 (0.313310) | 5.401535 / 2.268929 (3.132606) | 2.499874 / 55.444624 (-52.944750) | 2.161406 / 6.876477 (-4.715071) | 2.197385 / 2.142072 (0.055313) | 0.810864 / 4.805227 (-3.994363) | 0.152277 / 6.500664 (-6.348387) | 0.067266 / 0.075469 (-0.008203) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.280900 / 1.841788 (-0.560887) | 13.815731 / 8.074308 (5.741423) | 13.007438 / 10.191392 (2.816046) | 0.129711 / 0.680424 (-0.550713) | 0.016852 / 0.534201 (-0.517349) | 0.380775 / 0.579283 (-0.198508) | 0.384143 / 0.434364 (-0.050221) | 0.459954 / 0.540337 (-0.080383) | 0.549335 / 1.386936 (-0.837601) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8805d67bd81ce48f481d5c1e56b84e6ebcaa2b2b \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009570 / 0.011353 (-0.001783) | 0.005219 / 0.011008 (-0.005789) | 0.098472 / 0.038508 (0.059964) | 0.035429 / 0.023109 (0.012320) | 0.303086 / 0.275898 (0.027188) | 0.365926 / 0.323480 (0.042446) | 0.008797 / 0.007986 (0.000811) | 0.004220 / 0.004328 (-0.000108) | 0.076670 / 0.004250 (0.072419) | 0.045596 / 0.037052 (0.008543) | 0.309476 / 0.258489 (0.050987) | 0.343958 / 0.293841 (0.050117) | 0.038741 / 0.128546 (-0.089805) | 0.011990 / 0.075646 (-0.063657) | 0.332326 / 0.419271 (-0.086945) | 0.048897 / 0.043533 (0.005364) | 0.296002 / 0.255139 (0.040863) | 0.322048 / 0.283200 (0.038849) | 0.104403 / 0.141683 (-0.037280) | 1.461777 / 1.452155 (0.009622) | 1.516362 / 1.492716 (0.023645) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.201565 / 0.018006 (0.183559) | 0.435781 / 0.000490 (0.435291) | 0.004215 / 0.000200 (0.004015) | 0.000282 / 0.000054 (0.000227) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027272 / 0.037411 (-0.010139) | 0.106157 / 0.014526 (0.091631) | 0.116948 / 0.176557 (-0.059609) | 0.160404 / 0.737135 (-0.576731) | 0.122518 / 0.296338 (-0.173820) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.397721 / 0.215209 (0.182512) | 3.966433 / 2.077655 (1.888778) | 1.755410 / 1.504120 (0.251290) | 1.566480 / 1.541195 (0.025285) | 1.623684 / 1.468490 (0.155194) | 0.696820 / 4.584777 (-3.887957) | 3.750437 / 3.745712 (0.004725) | 2.105875 / 5.269862 (-3.163986) | 1.442026 / 4.565676 (-3.123650) | 0.085026 / 0.424275 (-0.339249) | 0.012239 / 0.007607 (0.004632) | 0.502613 / 0.226044 (0.276569) | 5.049016 / 2.268929 (2.780087) | 2.314499 / 55.444624 (-53.130126) | 1.967943 / 6.876477 (-4.908534) | 2.033507 / 2.142072 (-0.108565) | 0.861908 / 4.805227 (-3.943319) | 0.167784 / 6.500664 (-6.332880) | 0.063022 / 0.075469 (-0.012447) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.210434 / 1.841788 (-0.631353) | 14.979319 / 8.074308 (6.905011) | 14.095263 / 10.191392 (3.903871) | 0.174203 / 0.680424 (-0.506221) | 0.028547 / 0.534201 (-0.505654) | 0.442509 / 0.579283 (-0.136774) | 0.445811 / 0.434364 (0.011447) | 0.531313 / 0.540337 (-0.009024) | 0.636541 / 1.386936 (-0.750395) |\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) | 0.007341 / 0.011353 (-0.004012) | 0.005197 / 0.011008 (-0.005811) | 0.075413 / 0.038508 (0.036905) | 0.033261 / 0.023109 (0.010152) | 0.339596 / 0.275898 (0.063698) | 0.376051 / 0.323480 (0.052571) | 0.005827 / 0.007986 (-0.002159) | 0.005473 / 0.004328 (0.001144) | 0.074851 / 0.004250 (0.070600) | 0.049059 / 0.037052 (0.012007) | 0.357182 / 0.258489 (0.098693) | 0.384589 / 0.293841 (0.090748) | 0.037122 / 0.128546 (-0.091424) | 0.012298 / 0.075646 (-0.063348) | 0.088191 / 0.419271 (-0.331081) | 0.052002 / 0.043533 (0.008469) | 0.343216 / 0.255139 (0.088077) | 0.364534 / 0.283200 (0.081334) | 0.105462 / 0.141683 (-0.036221) | 1.486717 / 1.452155 (0.034562) | 1.584725 / 1.492716 (0.092009) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.199210 / 0.018006 (0.181203) | 0.439069 / 0.000490 (0.438580) | 0.000436 / 0.000200 (0.000236) | 0.000059 / 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) | 0.029931 / 0.037411 (-0.007480) | 0.109564 / 0.014526 (0.095038) | 0.122284 / 0.176557 (-0.054273) | 0.170819 / 0.737135 (-0.566317) | 0.125886 / 0.296338 (-0.170452) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.422724 / 0.215209 (0.207515) | 4.210304 / 2.077655 (2.132650) | 2.001481 / 1.504120 (0.497361) | 1.810818 / 1.541195 (0.269623) | 1.901367 / 1.468490 (0.432877) | 0.686004 / 4.584777 (-3.898773) | 3.768850 / 3.745712 (0.023138) | 2.079501 / 5.269862 (-3.190360) | 1.326970 / 4.565676 (-3.238706) | 0.085991 / 0.424275 (-0.338284) | 0.012298 / 0.007607 (0.004690) | 0.526878 / 0.226044 (0.300833) | 5.267241 / 2.268929 (2.998312) | 2.451781 / 55.444624 (-52.992843) | 2.109143 / 6.876477 (-4.767333) | 2.185426 / 2.142072 (0.043353) | 0.830165 / 4.805227 (-3.975063) | 0.166167 / 6.500664 (-6.334497) | 0.064077 / 0.075469 (-0.011392) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.270430 / 1.841788 (-0.571358) | 14.844852 / 8.074308 (6.770544) | 13.196672 / 10.191392 (3.005280) | 0.162853 / 0.680424 (-0.517571) | 0.017727 / 0.534201 (-0.516474) | 0.424803 / 0.579283 (-0.154480) | 0.439970 / 0.434364 (0.005606) | 0.530691 / 0.540337 (-0.009647) | 0.630474 / 1.386936 (-0.756462) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#24fb01b720ef4203d4ae6225f43cba912b1f6d55 \"CML watermark\")\n" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5602). All of your documentation changes will be reflected on that endpoint.", "This is a great PR! Thinking about the UX though, maybe we could do it without the extra argument? Before this PR, the logic in `to_tf_dataset` was that if the user passed a single column name in either `columns` or `label_cols`, we converted it to a length-1 list. Then, later in the code, we convert output dicts with only one key to naked Tensors.\r\n\r\nWould it be easier if we removed the argument, but instead treated the cases differently? Passing a column name as a string could yield a single naked Tensor in the output as before, but passing a list of length 1 would yield a full dict? That way if you wanted dict output with a single key you could just say `columns=[col_name]`.\r\n\r\n(I'm not totally convinced this is a good idea yet, it just seems like it might be more intuitive)", "@Rocketknight1 Happy to implement it that way - it's certainly cleaner to not have another arg. In this case, am I right in saying we'd effectively set `return_dict` [here](https://github.com/huggingface/datasets/blob/6569014a9948eab7d031a3587405e64ba92d6c59/src/datasets/arrow_dataset.py#L410) - where columns are made into a list if they were a string? \r\n\r\nThere only concern I have is this changes the default behaviour, which might break things for people who were happily using `columns=[\"my_col_str\"]` before. \r\n\r\n\r\n", "@amyeroberts That's correct! Probably the simplest way to implement it would be to just add the flag there.\r\n\r\nAnd yeah, I'm aware this might be a slightly breaking change, but we've mostly tried to move users to `prepare_tf_dataset` in `transformers` at this point, so hopefully as long as that method doesn't break then most users won't be negatively affected by the change.", "@lhoestq @Rocketknight1 - I've remove the `return_dict` argument and implemented @Rocketknight1 's suggestion. LMK what you think :) ", "@lhoestq Of course :) I've opened a draft PR here for the updates needed in transformers examples and docs to keep the returned data structure consistent: https://github.com/huggingface/transformers/pull/21935. Note: even with the different structure, `model.fit` can still successfully be called. \r\n\r\nFor the [link you shared](https://github.com/huggingface/datasets/pull/url) - for me it returns a 404 error. Is there another link I could follow to see how to run the transformers CI with this branch? \r\n\r\nCurrently looking into the failing tests 😭 ", "Oh sorry - I fixed the URL: https://github.com/huggingface/transformers/commit/4eb55bbd593adf2e49362613ee32a11ddc4a854d", "The error shows `There appear to be 80 leaked shared_memory objects to clean up at shutdown`. IIRC to_tf_dataset does some shared memory stuff for multiprocessing - maybe @Rocketknight1 you know what's going on ?", "@lhoestq That warning appears anytime you interrupt a process using Python `SharedMemory` objects - it's only a problem if you still get the error when the process finishes normally! Our implementation of `to_tf_dataset` should clean things up properly.", "Ok, not sure why it fails then :/", "Hmm, will investigate! Sorry, I misread - I thought that warning was coming up in the context of another error", "IMO outputing different types based on nuances in the input could confuse users.\r\n\r\nAlso, in the ideal scenario,`to_tf_function` should return a `tf.data.Dataset` that iterates over the underlying Arrow data and yields (unprocessed) dicts of TF tensors, and all the model-specific code should live in Transformers (e.g., in `prepare_tf_dataset`). So the goal would be to make `to_tf_dataset` more user-friendly, not more complex :).", "I think we agree @mariosasko :) \r\n\r\n> Also, in the ideal scenario,to_tf_function should return a tf.data.Dataset that iterates over the underlying Arrow data and yields (unprocessed) dicts of TF tensors\r\n\r\nThis I'll leave for another PR as it's outside the scope of this one and @Rocketknight1 will have far more knowledge and ideas about what is possible\r\n\r\n> all the model-specific code should live in Transformers (e.g., in prepare_tf_dataset\r\n\r\nAgreed! This PR isn't really a model specific change - although it was highlighted when trying to train a model. We definitely want to move model specific things out of datasets as much as possible. \r\n\r\n> IMO outputing different types based on nuances in the input could confuse users.\r\n> So the goal would be to make to_tf_dataset more user-friendly, not more complex :).\r\n\r\nThe aim was to move more towards being able to return the dict of TF tensors you suggest, whilst maintaining backwards compatibility. Personally, I found it surprising to be returned a tuple structure when I was using `to_tf_dataset`. The aim was to make `to_tf_dataset` more user friendly, but I agree that it has the potential to be confusing. \r\n\r\nFor context, the thought process behind this design was to: \r\n* Not add even more arguments to `to_tf_dataset`. \r\n* Have a feature selection -> return type logic in keeping with `datasets` e.g. `dataset['train'][:10]['feat1']` returns a list of values, whereas `dataset['train'][:10]['feat1', 'feat2']` returns a dictionary. \r\n\r\nVery happy to add any suggestions or changes you might have about how to make this design better! :) \r\n", "Hi ! Anything blocking here ? I'b be happy to help", "Hi @lhoestq - sorry this hasn't been very active for the past ~1.5 weeks. There's nothing specific blocking, other than not being able to replicate without running on CI, and still need to test a bit more to narrow down the issue. I should have time tomorrow to pick it up again :) ", "@lhoestq @Rocketknight1 Friendly ping for a review :) ", "Awesome ! What about showing a warning that this change is about to happen in the next version of `datasets`, and then apply this change in a subsequent major release ? This way folks at twitter won't hate us: https://github.com/twitter/the-algorithm/blob/138bb519975407d4ea0dc1478d897d451ef05dab/trust_and_safety_models/toxicity/data/mb_generator.py#L142-L148", "@lhoestq Sounds good! How would you like this warning to happen? I could open a PR to add a warning message within `to_tf_dataset`?", "Yup sounds good :)" ]

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[ "Hi! \r\n\r\nIt's better to report this kind of issue in the `huggingface_hub` repo, so if you still haven't resolved it, I suggest you open an issue there.", "Yeah, I solved it. Problem was in osxkeychain. When I do `hugginface-cli login` it's add token with default account (username)`hg_user` but my repo contain other username. When I changed username in keychain - it works now." ]

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[ "Hi! \r\n\r\n> (see example of DreamboothDatasets)\r\n\r\n\r\nCould you please provide a link to it? If you are referring to the example in the `diffusers` repo, your issue is unrelated to `datasets` as that example uses `Dataset` from PyTorch to load data." ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008823 / 0.011353 (-0.002529) | 0.004738 / 0.011008 (-0.006270) | 0.102338 / 0.038508 (0.063830) | 0.030603 / 0.023109 (0.007494) | 0.302995 / 0.275898 (0.027097) | 0.362080 / 0.323480 (0.038600) | 0.007096 / 0.007986 (-0.000889) | 0.003493 / 0.004328 (-0.000835) | 0.079129 / 0.004250 (0.074878) | 0.037966 / 0.037052 (0.000914) | 0.310412 / 0.258489 (0.051923) | 0.346740 / 0.293841 (0.052899) | 0.033795 / 0.128546 (-0.094751) | 0.011595 / 0.075646 (-0.064051) | 0.325189 / 0.419271 (-0.094083) | 0.041679 / 0.043533 (-0.001854) | 0.302339 / 0.255139 (0.047200) | 0.322519 / 0.283200 (0.039319) | 0.089058 / 0.141683 (-0.052625) | 1.496223 / 1.452155 (0.044068) | 1.512562 / 1.492716 (0.019845) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.009298 / 0.018006 (-0.008709) | 0.406726 / 0.000490 (0.406236) | 0.003753 / 0.000200 (0.003553) | 0.000082 / 0.000054 (0.000028) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023327 / 0.037411 (-0.014084) | 0.098175 / 0.014526 (0.083649) | 0.106040 / 0.176557 (-0.070516) | 0.151934 / 0.737135 (-0.585201) | 0.108465 / 0.296338 (-0.187873) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.419073 / 0.215209 (0.203864) | 4.188012 / 2.077655 (2.110358) | 1.857667 / 1.504120 (0.353547) | 1.664124 / 1.541195 (0.122929) | 1.704341 / 1.468490 (0.235851) | 0.699671 / 4.584777 (-3.885106) | 3.391110 / 3.745712 (-0.354602) | 1.871136 / 5.269862 (-3.398725) | 1.176794 / 4.565676 (-3.388882) | 0.083322 / 0.424275 (-0.340953) | 0.012450 / 0.007607 (0.004843) | 0.525058 / 0.226044 (0.299014) | 5.265425 / 2.268929 (2.996497) | 2.320672 / 55.444624 (-53.123952) | 1.964806 / 6.876477 (-4.911671) | 2.027055 / 2.142072 (-0.115017) | 0.819768 / 4.805227 (-3.985459) | 0.149638 / 6.500664 (-6.351026) | 0.064774 / 0.075469 (-0.010695) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.204575 / 1.841788 (-0.637212) | 13.651878 / 8.074308 (5.577570) | 13.751973 / 10.191392 (3.560581) | 0.154781 / 0.680424 (-0.525643) | 0.028887 / 0.534201 (-0.505314) | 0.404905 / 0.579283 (-0.174379) | 0.411320 / 0.434364 (-0.023043) | 0.485026 / 0.540337 (-0.055311) | 0.579690 / 1.386936 (-0.807246) |\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) | 0.006615 / 0.011353 (-0.004737) | 0.004606 / 0.011008 (-0.006402) | 0.076099 / 0.038508 (0.037591) | 0.027247 / 0.023109 (0.004137) | 0.360731 / 0.275898 (0.084833) | 0.393688 / 0.323480 (0.070208) | 0.005079 / 0.007986 (-0.002906) | 0.003345 / 0.004328 (-0.000984) | 0.077184 / 0.004250 (0.072934) | 0.037850 / 0.037052 (0.000797) | 0.379738 / 0.258489 (0.121249) | 0.400474 / 0.293841 (0.106633) | 0.031581 / 0.128546 (-0.096966) | 0.011508 / 0.075646 (-0.064138) | 0.084966 / 0.419271 (-0.334306) | 0.041740 / 0.043533 (-0.001793) | 0.349887 / 0.255139 (0.094748) | 0.384405 / 0.283200 (0.101205) | 0.089022 / 0.141683 (-0.052661) | 1.503448 / 1.452155 (0.051293) | 1.564870 / 1.492716 (0.072154) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.233581 / 0.018006 (0.215574) | 0.413819 / 0.000490 (0.413330) | 0.000398 / 0.000200 (0.000198) | 0.000060 / 0.000054 (0.000006) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024805 / 0.037411 (-0.012607) | 0.101348 / 0.014526 (0.086822) | 0.108701 / 0.176557 (-0.067856) | 0.160011 / 0.737135 (-0.577124) | 0.111696 / 0.296338 (-0.184642) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.436303 / 0.215209 (0.221094) | 4.368684 / 2.077655 (2.291029) | 2.082366 / 1.504120 (0.578247) | 1.888108 / 1.541195 (0.346913) | 1.958295 / 1.468490 (0.489804) | 0.700858 / 4.584777 (-3.883919) | 3.408321 / 3.745712 (-0.337391) | 1.872960 / 5.269862 (-3.396902) | 1.165116 / 4.565676 (-3.400560) | 0.083556 / 0.424275 (-0.340719) | 0.012348 / 0.007607 (0.004741) | 0.536551 / 0.226044 (0.310506) | 5.359974 / 2.268929 (3.091045) | 2.539043 / 55.444624 (-52.905581) | 2.200314 / 6.876477 (-4.676162) | 2.222051 / 2.142072 (0.079979) | 0.808567 / 4.805227 (-3.996661) | 0.151222 / 6.500664 (-6.349442) | 0.066351 / 0.075469 (-0.009118) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.265502 / 1.841788 (-0.576286) | 13.692066 / 8.074308 (5.617758) | 13.124507 / 10.191392 (2.933115) | 0.129545 / 0.680424 (-0.550879) | 0.016827 / 0.534201 (-0.517374) | 0.380326 / 0.579283 (-0.198957) | 0.387268 / 0.434364 (-0.047096) | 0.463722 / 0.540337 (-0.076616) | 0.553681 / 1.386936 (-0.833255) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6569014a9948eab7d031a3587405e64ba92d6c59 \"CML watermark\")\n" ]

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[ "We won't support in-place modifications since `datasets` is based on the Apache Arrow format which doesn't support in-place modifications.\r\n\r\nIn your case the old dataset is garbage collected pretty quickly so you won't have memory issues.\r\n\r\nNote that datasets loaded from disk (memory mapped) are not loaded in memory, and therefore the new dataset actually use the same buffers as the old one.", "Thank you for your detailed reply.\r\n\r\n> In your case the old dataset is garbage collected pretty quickly so you won't have memory issues.\r\n\r\nI understand this, but it still copies the old dataset to create the new one, is this correct? So maybe it is not memory-consuming, but time-consuming?", "Indeed, and because of that it is more efficient to add multiple rows at once instead of one by one, using `concatenate_datasets` for example." ]

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[ "Apparently some JSON objects have a `\"labels\"` field. Since this field is not present in every object, you must specify all the fields types in the README.md\r\n\r\nEDIT: actually specifying the feature types doesn’t solve the issue, it raises an error because “labels” is missing in the data", "We've updated the dataset to remove the extra `labels` field from some files, closing this issue. Thanks!", "A similar error occurs in the Pile dataset (EleutherAI/the_pile)\r\n\r\nLoading the dataset produces the following error.\r\n\r\n```\r\nTypeError: Couldn't cast array of type\r\nstruct<file: string, id: string>\r\nto\r\n{'id': Value(dtype='string', id=None)}\r\n```\r\n", "I think this was fixed in https://huggingface.co/datasets/EleutherAI/the_pile/discussions/11" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5595). All of your documentation changes will be reflected on that endpoint.", "It looks like this issue hasn't been fixed yet, so let's wait a bit more.", "@lazarust thanks for your work, but unfortunately we cannot merge it.\r\n\r\nSee my comment in: https://github.com/huggingface/datasets/issues/5477#issuecomment-1495512688\r\n\r\nThe fix was released yesterday (2023-04-03) only in `pandas-2.0.0`:\r\n- https://github.com/pandas-dev/pandas/releases/tag/v2.0.0\r\n\r\nbut it will not be back-ported to `pandas-1`:\r\n- https://github.com/pandas-dev/pandas/pull/48576#issuecomment-1466467159\r\n\r\nAlso note that `pandas-2.0.0` dropped support for Python 3.7:\r\n- https://github.com/pandas-dev/pandas/issues/41678\r\n- https://github.com/pandas-dev/pandas/pull/41989\r\n\r\nTherefore, we cannot unpin `sqlalchemy` until we drop support for Python 3.7 (these Python users cannot use `pandas-2`). See our latest CI checks below:\r\n- \"CI / test\" fails because it runs on Python 3.7\r\n- \"CI / test_py310\" succeeds because it runs on Python 3.10 " ]

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[ "Hi! I cannot reproduce this error on my machine.\r\n\r\nThe raised error could mean that one of the downloaded files is corrupted. To verify this is not the case, you can run `load_dataset` as follows:\r\n```python\r\ntrain_dataset = load_dataset('xtreme', 'udpos.English', split=\"train\", cache_dir=args.cache_dir, download_mode=\"force_redownload\", verification_mode=\"all_checks\")\r\n```", "Hi! Apologies for the delayed response! I tried the above and it doesn't solve the issue. Actually, the dataset gets downloaded most times, but sometimes this error occurs (at random afaik). Is it possible that there is a server issue for this particular dataset? I am able to download other datasets using the same code on the same machine with no issues :( I get this error now : \r\n```\r\nDownloading data: 16%|███████████████▌ | 55.9M/355M [04:45<25:25, 196kB/s]\r\nTraceback (most recent call last):\r\n File \"/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py\", line 1107, in <module>\r\n main()\r\n File \"/home/skhanuja/Optimal-Resource-Allocation-for-Multilingual-Finetuning/src/train_al.py\", line 439, in main\r\n en_dataset = load_dataset(\"xtreme\", \"udpos.English\", split=\"train\", download_mode=\"force_redownload\", verification_mode=\"all_checks\")\r\n File \"/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/load.py\", line 1782, in load_dataset\r\n builder_instance.download_and_prepare(\r\n File \"/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py\", line 872, in download_and_prepare\r\n self._download_and_prepare(\r\n File \"/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py\", line 1649, in _download_and_prepare\r\n super()._download_and_prepare(\r\n File \"/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/builder.py\", line 949, in _download_and_prepare\r\n verify_checksums(\r\n File \"/home/skhanuja/miniconda3/envs/multilingual_ft/lib/python3.10/site-packages/datasets/utils/info_utils.py\", line 62, in verify_checksums\r\n raise NonMatchingChecksumError(\r\ndatasets.utils.info_utils.NonMatchingChecksumError: Checksums didn't match for dataset source files:\r\n['https://lindat.mff.cuni.cz/repository/xmlui/bitstream/handle/11234/1-3105/ud-treebanks-v2.5.tgz']\r\nSet `verification_mode='no_checks'` to skip checksums verification and ignore this error\r\n```", "If this happens randomly, then this means the data file from the error message is not always downloaded correctly. \r\n\r\nThe only solution in this scenario is to download the dataset again by passing `download_mode=\"force_redownload\"` to the `load_dataset` call." ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009526 / 0.011353 (-0.001827) | 0.005132 / 0.011008 (-0.005876) | 0.101312 / 0.038508 (0.062804) | 0.035703 / 0.023109 (0.012594) | 0.301788 / 0.275898 (0.025890) | 0.368411 / 0.323480 (0.044932) | 0.008163 / 0.007986 (0.000177) | 0.005462 / 0.004328 (0.001134) | 0.077282 / 0.004250 (0.073031) | 0.044139 / 0.037052 (0.007086) | 0.312280 / 0.258489 (0.053791) | 0.351870 / 0.293841 (0.058029) | 0.038266 / 0.128546 (-0.090281) | 0.012051 / 0.075646 (-0.063595) | 0.335109 / 0.419271 (-0.084163) | 0.047596 / 0.043533 (0.004064) | 0.300931 / 0.255139 (0.045792) | 0.325705 / 0.283200 (0.042505) | 0.100472 / 0.141683 (-0.041211) | 1.475037 / 1.452155 (0.022882) | 1.520059 / 1.492716 (0.027343) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.211096 / 0.018006 (0.193089) | 0.442988 / 0.000490 (0.442498) | 0.003644 / 0.000200 (0.003444) | 0.000090 / 0.000054 (0.000036) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027492 / 0.037411 (-0.009919) | 0.108981 / 0.014526 (0.094455) | 0.117836 / 0.176557 (-0.058720) | 0.161220 / 0.737135 (-0.575915) | 0.124765 / 0.296338 (-0.171574) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.413480 / 0.215209 (0.198271) | 4.111355 / 2.077655 (2.033700) | 1.933024 / 1.504120 (0.428904) | 1.727467 / 1.541195 (0.186272) | 1.827106 / 1.468490 (0.358616) | 0.688209 / 4.584777 (-3.896568) | 3.759672 / 3.745712 (0.013960) | 2.163806 / 5.269862 (-3.106056) | 1.473521 / 4.565676 (-3.092155) | 0.082859 / 0.424275 (-0.341416) | 0.012320 / 0.007607 (0.004713) | 0.515321 / 0.226044 (0.289277) | 5.158651 / 2.268929 (2.889722) | 2.489123 / 55.444624 (-52.955501) | 2.218910 / 6.876477 (-4.657566) | 2.257306 / 2.142072 (0.115233) | 0.861477 / 4.805227 (-3.943750) | 0.165857 / 6.500664 (-6.334807) | 0.063723 / 0.075469 (-0.011746) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.195163 / 1.841788 (-0.646625) | 14.954518 / 8.074308 (6.880210) | 14.272289 / 10.191392 (4.080897) | 0.167420 / 0.680424 (-0.513004) | 0.028907 / 0.534201 (-0.505294) | 0.450117 / 0.579283 (-0.129166) | 0.448532 / 0.434364 (0.014168) | 0.534406 / 0.540337 (-0.005931) | 0.633468 / 1.386936 (-0.753468) |\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) | 0.007658 / 0.011353 (-0.003694) | 0.005266 / 0.011008 (-0.005742) | 0.075293 / 0.038508 (0.036785) | 0.034442 / 0.023109 (0.011333) | 0.346558 / 0.275898 (0.070660) | 0.391496 / 0.323480 (0.068017) | 0.005852 / 0.007986 (-0.002133) | 0.004121 / 0.004328 (-0.000207) | 0.074254 / 0.004250 (0.070004) | 0.048361 / 0.037052 (0.011309) | 0.344613 / 0.258489 (0.086124) | 0.401497 / 0.293841 (0.107656) | 0.037243 / 0.128546 (-0.091303) | 0.012505 / 0.075646 (-0.063142) | 0.087188 / 0.419271 (-0.332084) | 0.050114 / 0.043533 (0.006581) | 0.340454 / 0.255139 (0.085315) | 0.361087 / 0.283200 (0.077887) | 0.104692 / 0.141683 (-0.036991) | 1.419432 / 1.452155 (-0.032722) | 1.524709 / 1.492716 (0.031993) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.231820 / 0.018006 (0.213814) | 0.445791 / 0.000490 (0.445301) | 0.000442 / 0.000200 (0.000242) | 0.000061 / 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) | 0.030445 / 0.037411 (-0.006967) | 0.111183 / 0.014526 (0.096657) | 0.123494 / 0.176557 (-0.053063) | 0.173121 / 0.737135 (-0.564014) | 0.124968 / 0.296338 (-0.171371) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.428854 / 0.215209 (0.213645) | 4.270262 / 2.077655 (2.192608) | 2.012075 / 1.504120 (0.507955) | 1.826564 / 1.541195 (0.285370) | 1.931699 / 1.468490 (0.463209) | 0.728762 / 4.584777 (-3.856015) | 3.879640 / 3.745712 (0.133928) | 3.325715 / 5.269862 (-1.944147) | 1.818573 / 4.565676 (-2.747104) | 0.087879 / 0.424275 (-0.336396) | 0.012530 / 0.007607 (0.004923) | 0.530249 / 0.226044 (0.304204) | 5.286110 / 2.268929 (3.017181) | 2.566649 / 55.444624 (-52.877975) | 2.210162 / 6.876477 (-4.666315) | 2.297562 / 2.142072 (0.155490) | 0.906161 / 4.805227 (-3.899066) | 0.171914 / 6.500664 (-6.328750) | 0.064182 / 0.075469 (-0.011287) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.285781 / 1.841788 (-0.556006) | 16.159072 / 8.074308 (8.084763) | 14.087492 / 10.191392 (3.896100) | 0.148789 / 0.680424 (-0.531635) | 0.018078 / 0.534201 (-0.516123) | 0.427748 / 0.579283 (-0.151535) | 0.447079 / 0.434364 (0.012715) | 0.535917 / 0.540337 (-0.004421) | 0.627491 / 1.386936 (-0.759445) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#88fa043d08c12923709c0492e037130c99c029fb \"CML watermark\")\n" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5591). All of your documentation changes will be reflected on that endpoint.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008826 / 0.011353 (-0.002527) | 0.004595 / 0.011008 (-0.006413) | 0.103387 / 0.038508 (0.064879) | 0.030241 / 0.023109 (0.007132) | 0.351202 / 0.275898 (0.075303) | 0.417601 / 0.323480 (0.094121) | 0.007121 / 0.007986 (-0.000865) | 0.003497 / 0.004328 (-0.000831) | 0.079256 / 0.004250 (0.075006) | 0.037617 / 0.037052 (0.000564) | 0.380542 / 0.258489 (0.122053) | 0.397863 / 0.293841 (0.104022) | 0.034291 / 0.128546 (-0.094255) | 0.011767 / 0.075646 (-0.063879) | 0.323737 / 0.419271 (-0.095534) | 0.041502 / 0.043533 (-0.002031) | 0.352982 / 0.255139 (0.097843) | 0.378618 / 0.283200 (0.095418) | 0.091671 / 0.141683 (-0.050012) | 1.499278 / 1.452155 (0.047123) | 1.517489 / 1.492716 (0.024773) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.190108 / 0.018006 (0.172102) | 0.414404 / 0.000490 (0.413915) | 0.001064 / 0.000200 (0.000864) | 0.000066 / 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) | 0.023214 / 0.037411 (-0.014198) | 0.099351 / 0.014526 (0.084825) | 0.105227 / 0.176557 (-0.071330) | 0.150620 / 0.737135 (-0.586516) | 0.109323 / 0.296338 (-0.187015) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.412463 / 0.215209 (0.197254) | 4.138123 / 2.077655 (2.060469) | 1.845163 / 1.504120 (0.341043) | 1.641108 / 1.541195 (0.099913) | 1.715471 / 1.468490 (0.246981) | 0.697397 / 4.584777 (-3.887380) | 3.449829 / 3.745712 (-0.295883) | 1.959309 / 5.269862 (-3.310553) | 1.285754 / 4.565676 (-3.279923) | 0.082746 / 0.424275 (-0.341529) | 0.012523 / 0.007607 (0.004916) | 0.524745 / 0.226044 (0.298700) | 5.257085 / 2.268929 (2.988156) | 2.293163 / 55.444624 (-53.151461) | 1.958309 / 6.876477 (-4.918168) | 2.016106 / 2.142072 (-0.125966) | 0.814359 / 4.805227 (-3.990869) | 0.149443 / 6.500664 (-6.351221) | 0.066013 / 0.075469 (-0.009456) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.248495 / 1.841788 (-0.593292) | 14.303301 / 8.074308 (6.228993) | 14.238533 / 10.191392 (4.047141) | 0.161421 / 0.680424 (-0.519003) | 0.028779 / 0.534201 (-0.505422) | 0.396511 / 0.579283 (-0.182772) | 0.412784 / 0.434364 (-0.021580) | 0.473984 / 0.540337 (-0.066353) | 0.569610 / 1.386936 (-0.817327) |\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) | 0.007003 / 0.011353 (-0.004350) | 0.004621 / 0.011008 (-0.006387) | 0.079418 / 0.038508 (0.040910) | 0.028659 / 0.023109 (0.005550) | 0.340594 / 0.275898 (0.064696) | 0.377972 / 0.323480 (0.054492) | 0.005421 / 0.007986 (-0.002565) | 0.004852 / 0.004328 (0.000523) | 0.077579 / 0.004250 (0.073329) | 0.042662 / 0.037052 (0.005610) | 0.342264 / 0.258489 (0.083775) | 0.387255 / 0.293841 (0.093414) | 0.032574 / 0.128546 (-0.095972) | 0.011820 / 0.075646 (-0.063826) | 0.087960 / 0.419271 (-0.331312) | 0.045199 / 0.043533 (0.001667) | 0.341785 / 0.255139 (0.086646) | 0.365014 / 0.283200 (0.081814) | 0.096129 / 0.141683 (-0.045554) | 1.498962 / 1.452155 (0.046807) | 1.557331 / 1.492716 (0.064615) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.236216 / 0.018006 (0.218210) | 0.440189 / 0.000490 (0.439699) | 0.000399 / 0.000200 (0.000199) | 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) | 0.026357 / 0.037411 (-0.011055) | 0.104485 / 0.014526 (0.089959) | 0.109616 / 0.176557 (-0.066941) | 0.163005 / 0.737135 (-0.574130) | 0.113859 / 0.296338 (-0.182479) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.437452 / 0.215209 (0.222243) | 4.371854 / 2.077655 (2.294199) | 2.056845 / 1.504120 (0.552725) | 1.856071 / 1.541195 (0.314876) | 1.957978 / 1.468490 (0.489488) | 0.703171 / 4.584777 (-3.881606) | 3.433889 / 3.745712 (-0.311823) | 1.968321 / 5.269862 (-3.301541) | 1.204947 / 4.565676 (-3.360729) | 0.084499 / 0.424275 (-0.339777) | 0.012729 / 0.007607 (0.005122) | 0.537534 / 0.226044 (0.311490) | 5.383346 / 2.268929 (3.114417) | 2.522136 / 55.444624 (-52.922488) | 2.192715 / 6.876477 (-4.683762) | 2.243579 / 2.142072 (0.101507) | 0.811136 / 4.805227 (-3.994091) | 0.154015 / 6.500664 (-6.346649) | 0.069324 / 0.075469 (-0.006145) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.294232 / 1.841788 (-0.547556) | 14.809448 / 8.074308 (6.735140) | 13.510074 / 10.191392 (3.318682) | 0.158033 / 0.680424 (-0.522391) | 0.016703 / 0.534201 (-0.517498) | 0.393976 / 0.579283 (-0.185307) | 0.385983 / 0.434364 (-0.048381) | 0.476691 / 0.540337 (-0.063646) | 0.565694 / 1.386936 (-0.821242) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b0dd3126196e8fcd9ba81a6602b46623b4e77e6e \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009155 / 0.011353 (-0.002198) | 0.005227 / 0.011008 (-0.005781) | 0.099767 / 0.038508 (0.061259) | 0.035338 / 0.023109 (0.012229) | 0.293913 / 0.275898 (0.018015) | 0.366976 / 0.323480 (0.043496) | 0.007802 / 0.007986 (-0.000184) | 0.005286 / 0.004328 (0.000958) | 0.075117 / 0.004250 (0.070867) | 0.042336 / 0.037052 (0.005284) | 0.304690 / 0.258489 (0.046201) | 0.343496 / 0.293841 (0.049655) | 0.038745 / 0.128546 (-0.089802) | 0.012275 / 0.075646 (-0.063371) | 0.334455 / 0.419271 (-0.084817) | 0.052611 / 0.043533 (0.009078) | 0.293229 / 0.255139 (0.038090) | 0.314340 / 0.283200 (0.031140) | 0.108676 / 0.141683 (-0.033007) | 1.444495 / 1.452155 (-0.007659) | 1.492244 / 1.492716 (-0.000472) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.204852 / 0.018006 (0.186846) | 0.438202 / 0.000490 (0.437712) | 0.005043 / 0.000200 (0.004843) | 0.000282 / 0.000054 (0.000228) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027268 / 0.037411 (-0.010143) | 0.109497 / 0.014526 (0.094972) | 0.117187 / 0.176557 (-0.059369) | 0.162551 / 0.737135 (-0.574584) | 0.124175 / 0.296338 (-0.172164) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.401667 / 0.215209 (0.186458) | 4.010274 / 2.077655 (1.932619) | 1.882617 / 1.504120 (0.378497) | 1.721960 / 1.541195 (0.180765) | 1.806874 / 1.468490 (0.338384) | 0.711253 / 4.584777 (-3.873524) | 3.806585 / 3.745712 (0.060873) | 3.713011 / 5.269862 (-1.556851) | 1.896558 / 4.565676 (-2.669119) | 0.086092 / 0.424275 (-0.338184) | 0.012129 / 0.007607 (0.004522) | 0.504905 / 0.226044 (0.278861) | 5.050794 / 2.268929 (2.781865) | 2.324331 / 55.444624 (-53.120293) | 2.020170 / 6.876477 (-4.856307) | 2.079685 / 2.142072 (-0.062388) | 0.854782 / 4.805227 (-3.950445) | 0.166754 / 6.500664 (-6.333910) | 0.062434 / 0.075469 (-0.013035) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.187897 / 1.841788 (-0.653891) | 14.618517 / 8.074308 (6.544209) | 13.205760 / 10.191392 (3.014368) | 0.154322 / 0.680424 (-0.526102) | 0.029243 / 0.534201 (-0.504958) | 0.442390 / 0.579283 (-0.136893) | 0.434651 / 0.434364 (0.000287) | 0.523082 / 0.540337 (-0.017256) | 0.602675 / 1.386936 (-0.784261) |\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) | 0.007214 / 0.011353 (-0.004139) | 0.005225 / 0.011008 (-0.005783) | 0.076497 / 0.038508 (0.037989) | 0.032761 / 0.023109 (0.009652) | 0.336005 / 0.275898 (0.060107) | 0.373547 / 0.323480 (0.050067) | 0.005460 / 0.007986 (-0.002526) | 0.003933 / 0.004328 (-0.000395) | 0.074540 / 0.004250 (0.070289) | 0.047785 / 0.037052 (0.010733) | 0.341917 / 0.258489 (0.083428) | 0.396978 / 0.293841 (0.103137) | 0.036763 / 0.128546 (-0.091783) | 0.012043 / 0.075646 (-0.063603) | 0.087632 / 0.419271 (-0.331640) | 0.049376 / 0.043533 (0.005843) | 0.335169 / 0.255139 (0.080030) | 0.354852 / 0.283200 (0.071652) | 0.100180 / 0.141683 (-0.041503) | 1.443422 / 1.452155 (-0.008733) | 1.518618 / 1.492716 (0.025901) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.209593 / 0.018006 (0.191587) | 0.444028 / 0.000490 (0.443538) | 0.004545 / 0.000200 (0.004345) | 0.000100 / 0.000054 (0.000046) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029676 / 0.037411 (-0.007735) | 0.115444 / 0.014526 (0.100918) | 0.121765 / 0.176557 (-0.054791) | 0.171037 / 0.737135 (-0.566098) | 0.128592 / 0.296338 (-0.167746) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.428556 / 0.215209 (0.213347) | 4.228531 / 2.077655 (2.150877) | 2.039190 / 1.504120 (0.535070) | 1.836518 / 1.541195 (0.295324) | 1.897040 / 1.468490 (0.428550) | 0.698893 / 4.584777 (-3.885884) | 3.753998 / 3.745712 (0.008286) | 2.097731 / 5.269862 (-3.172131) | 1.338315 / 4.565676 (-3.227361) | 0.087119 / 0.424275 (-0.337156) | 0.012149 / 0.007607 (0.004542) | 0.520774 / 0.226044 (0.294730) | 5.227420 / 2.268929 (2.958492) | 2.522235 / 55.444624 (-52.922389) | 2.194213 / 6.876477 (-4.682264) | 2.241406 / 2.142072 (0.099333) | 0.843119 / 4.805227 (-3.962109) | 0.169128 / 6.500664 (-6.331536) | 0.065071 / 0.075469 (-0.010398) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.254490 / 1.841788 (-0.587298) | 15.037137 / 8.074308 (6.962829) | 13.115333 / 10.191392 (2.923941) | 0.181743 / 0.680424 (-0.498681) | 0.017748 / 0.534201 (-0.516453) | 0.425758 / 0.579283 (-0.153525) | 0.429926 / 0.434364 (-0.004438) | 0.524386 / 0.540337 (-0.015951) | 0.643044 / 1.386936 (-0.743892) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#09e820e79a3b879855b514e2a62d84b738013940 \"CML watermark\")\n" ]

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[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008717 / 0.011353 (-0.002636) | 0.004570 / 0.011008 (-0.006439) | 0.100228 / 0.038508 (0.061720) | 0.030076 / 0.023109 (0.006967) | 0.317919 / 0.275898 (0.042021) | 0.366360 / 0.323480 (0.042880) | 0.007008 / 0.007986 (-0.000978) | 0.003498 / 0.004328 (-0.000831) | 0.077607 / 0.004250 (0.073356) | 0.036106 / 0.037052 (-0.000946) | 0.314128 / 0.258489 (0.055639) | 0.351450 / 0.293841 (0.057609) | 0.033697 / 0.128546 (-0.094849) | 0.011424 / 0.075646 (-0.064222) | 0.323867 / 0.419271 (-0.095404) | 0.042073 / 0.043533 (-0.001460) | 0.304564 / 0.255139 (0.049425) | 0.334865 / 0.283200 (0.051665) | 0.087791 / 0.141683 (-0.053892) | 1.488075 / 1.452155 (0.035920) | 1.513676 / 1.492716 (0.020959) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.010936 / 0.018006 (-0.007070) | 0.409610 / 0.000490 (0.409121) | 0.004820 / 0.000200 (0.004620) | 0.000079 / 0.000054 (0.000025) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023931 / 0.037411 (-0.013481) | 0.096826 / 0.014526 (0.082300) | 0.105764 / 0.176557 (-0.070792) | 0.153241 / 0.737135 (-0.583895) | 0.108976 / 0.296338 (-0.187363) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.412833 / 0.215209 (0.197624) | 4.129735 / 2.077655 (2.052081) | 1.819049 / 1.504120 (0.314929) | 1.617411 / 1.541195 (0.076216) | 1.682353 / 1.468490 (0.213863) | 0.688987 / 4.584777 (-3.895790) | 3.388276 / 3.745712 (-0.357436) | 1.857452 / 5.269862 (-3.412410) | 1.158020 / 4.565676 (-3.407657) | 0.082161 / 0.424275 (-0.342114) | 0.012319 / 0.007607 (0.004712) | 0.523052 / 0.226044 (0.297008) | 5.237726 / 2.268929 (2.968797) | 2.275605 / 55.444624 (-53.169020) | 1.931664 / 6.876477 (-4.944813) | 1.970026 / 2.142072 (-0.172046) | 0.805240 / 4.805227 (-3.999988) | 0.148431 / 6.500664 (-6.352233) | 0.064707 / 0.075469 (-0.010762) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.196456 / 1.841788 (-0.645332) | 13.750113 / 8.074308 (5.675805) | 13.853543 / 10.191392 (3.662151) | 0.137892 / 0.680424 (-0.542532) | 0.028304 / 0.534201 (-0.505897) | 0.400128 / 0.579283 (-0.179155) | 0.410409 / 0.434364 (-0.023955) | 0.479165 / 0.540337 (-0.061172) | 0.575002 / 1.386936 (-0.811934) |\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) | 0.006587 / 0.011353 (-0.004766) | 0.004526 / 0.011008 (-0.006482) | 0.075673 / 0.038508 (0.037165) | 0.027429 / 0.023109 (0.004320) | 0.341808 / 0.275898 (0.065910) | 0.379520 / 0.323480 (0.056040) | 0.004972 / 0.007986 (-0.003014) | 0.003354 / 0.004328 (-0.000975) | 0.075373 / 0.004250 (0.071123) | 0.038347 / 0.037052 (0.001294) | 0.343671 / 0.258489 (0.085181) | 0.389632 / 0.293841 (0.095791) | 0.031694 / 0.128546 (-0.096853) | 0.011458 / 0.075646 (-0.064188) | 0.084210 / 0.419271 (-0.335062) | 0.042662 / 0.043533 (-0.000871) | 0.339436 / 0.255139 (0.084297) | 0.367493 / 0.283200 (0.084294) | 0.091604 / 0.141683 (-0.050079) | 1.526762 / 1.452155 (0.074607) | 1.569110 / 1.492716 (0.076394) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.211496 / 0.018006 (0.193489) | 0.404868 / 0.000490 (0.404379) | 0.004267 / 0.000200 (0.004067) | 0.000083 / 0.000054 (0.000029) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025189 / 0.037411 (-0.012222) | 0.099139 / 0.014526 (0.084613) | 0.105898 / 0.176557 (-0.070659) | 0.160997 / 0.737135 (-0.576138) | 0.110158 / 0.296338 (-0.186180) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.444286 / 0.215209 (0.229077) | 4.445479 / 2.077655 (2.367824) | 2.118920 / 1.504120 (0.614800) | 1.908296 / 1.541195 (0.367102) | 1.947211 / 1.468490 (0.478721) | 0.704850 / 4.584777 (-3.879927) | 3.395990 / 3.745712 (-0.349723) | 1.892529 / 5.269862 (-3.377332) | 1.172190 / 4.565676 (-3.393486) | 0.084235 / 0.424275 (-0.340040) | 0.012588 / 0.007607 (0.004981) | 0.546962 / 0.226044 (0.320918) | 5.475842 / 2.268929 (3.206913) | 2.575280 / 55.444624 (-52.869344) | 2.245658 / 6.876477 (-4.630818) | 2.274767 / 2.142072 (0.132695) | 0.813755 / 4.805227 (-3.991473) | 0.151927 / 6.500664 (-6.348737) | 0.067167 / 0.075469 (-0.008302) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.267666 / 1.841788 (-0.574122) | 13.658905 / 8.074308 (5.584597) | 13.207249 / 10.191392 (3.015857) | 0.128590 / 0.680424 (-0.551833) | 0.016531 / 0.534201 (-0.517670) | 0.385050 / 0.579283 (-0.194233) | 0.388945 / 0.434364 (-0.045419) | 0.472378 / 0.540337 (-0.067959) | 0.568929 / 1.386936 (-0.818007) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#87cd5f7f7fda60d0f91f50424bcc3f327fe0d059 \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009339 / 0.011353 (-0.002014) | 0.005197 / 0.011008 (-0.005811) | 0.100698 / 0.038508 (0.062190) | 0.035484 / 0.023109 (0.012375) | 0.299030 / 0.275898 (0.023132) | 0.366603 / 0.323480 (0.043124) | 0.007909 / 0.007986 (-0.000077) | 0.005683 / 0.004328 (0.001355) | 0.077719 / 0.004250 (0.073469) | 0.042147 / 0.037052 (0.005094) | 0.310174 / 0.258489 (0.051685) | 0.342720 / 0.293841 (0.048879) | 0.039679 / 0.128546 (-0.088867) | 0.012042 / 0.075646 (-0.063605) | 0.335663 / 0.419271 (-0.083609) | 0.051137 / 0.043533 (0.007604) | 0.298218 / 0.255139 (0.043079) | 0.316398 / 0.283200 (0.033198) | 0.108906 / 0.141683 (-0.032776) | 1.422823 / 1.452155 (-0.029331) | 1.472955 / 1.492716 (-0.019761) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.205845 / 0.018006 (0.187839) | 0.445942 / 0.000490 (0.445453) | 0.003553 / 0.000200 (0.003353) | 0.000083 / 0.000054 (0.000028) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025506 / 0.037411 (-0.011906) | 0.107494 / 0.014526 (0.092969) | 0.116226 / 0.176557 (-0.060331) | 0.157313 / 0.737135 (-0.579822) | 0.123822 / 0.296338 (-0.172516) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.400908 / 0.215209 (0.185699) | 3.980232 / 2.077655 (1.902578) | 1.805410 / 1.504120 (0.301290) | 1.615698 / 1.541195 (0.074503) | 1.677213 / 1.468490 (0.208723) | 0.697882 / 4.584777 (-3.886895) | 3.752781 / 3.745712 (0.007069) | 2.076062 / 5.269862 (-3.193800) | 1.446909 / 4.565676 (-3.118768) | 0.084572 / 0.424275 (-0.339703) | 0.011917 / 0.007607 (0.004310) | 0.511815 / 0.226044 (0.285771) | 5.121487 / 2.268929 (2.852558) | 2.277642 / 55.444624 (-53.166982) | 1.930393 / 6.876477 (-4.946084) | 1.965855 / 2.142072 (-0.176218) | 0.843391 / 4.805227 (-3.961837) | 0.163581 / 6.500664 (-6.337083) | 0.062547 / 0.075469 (-0.012922) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.223930 / 1.841788 (-0.617858) | 14.354466 / 8.074308 (6.280158) | 14.015159 / 10.191392 (3.823767) | 0.148658 / 0.680424 (-0.531766) | 0.028469 / 0.534201 (-0.505732) | 0.437614 / 0.579283 (-0.141669) | 0.435452 / 0.434364 (0.001089) | 0.523623 / 0.540337 (-0.016715) | 0.625109 / 1.386936 (-0.761827) |\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) | 0.006917 / 0.011353 (-0.004436) | 0.005080 / 0.011008 (-0.005928) | 0.075806 / 0.038508 (0.037298) | 0.032402 / 0.023109 (0.009293) | 0.331105 / 0.275898 (0.055207) | 0.361226 / 0.323480 (0.037746) | 0.005694 / 0.007986 (-0.002292) | 0.003810 / 0.004328 (-0.000518) | 0.076886 / 0.004250 (0.072635) | 0.046158 / 0.037052 (0.009106) | 0.338791 / 0.258489 (0.080302) | 0.385733 / 0.293841 (0.091892) | 0.035590 / 0.128546 (-0.092956) | 0.011997 / 0.075646 (-0.063649) | 0.087854 / 0.419271 (-0.331417) | 0.048985 / 0.043533 (0.005452) | 0.331248 / 0.255139 (0.076109) | 0.354633 / 0.283200 (0.071434) | 0.101609 / 0.141683 (-0.040074) | 1.496899 / 1.452155 (0.044745) | 1.570469 / 1.492716 (0.077753) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.180871 / 0.018006 (0.162865) | 0.449417 / 0.000490 (0.448928) | 0.004300 / 0.000200 (0.004100) | 0.000102 / 0.000054 (0.000048) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029054 / 0.037411 (-0.008358) | 0.110888 / 0.014526 (0.096362) | 0.121736 / 0.176557 (-0.054821) | 0.172563 / 0.737135 (-0.564572) | 0.126565 / 0.296338 (-0.169773) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.419545 / 0.215209 (0.204336) | 4.193685 / 2.077655 (2.116031) | 2.049967 / 1.504120 (0.545847) | 1.855038 / 1.541195 (0.313843) | 1.899822 / 1.468490 (0.431332) | 0.709123 / 4.584777 (-3.875654) | 3.795939 / 3.745712 (0.050227) | 2.076055 / 5.269862 (-3.193807) | 1.335864 / 4.565676 (-3.229812) | 0.085555 / 0.424275 (-0.338720) | 0.012197 / 0.007607 (0.004590) | 0.516164 / 0.226044 (0.290119) | 5.158983 / 2.268929 (2.890054) | 2.445581 / 55.444624 (-52.999044) | 2.122256 / 6.876477 (-4.754221) | 2.160011 / 2.142072 (0.017939) | 0.840251 / 4.805227 (-3.964976) | 0.165924 / 6.500664 (-6.334740) | 0.064080 / 0.075469 (-0.011389) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.285292 / 1.841788 (-0.556495) | 14.561084 / 8.074308 (6.486776) | 12.899269 / 10.191392 (2.707877) | 0.185657 / 0.680424 (-0.494767) | 0.017866 / 0.534201 (-0.516335) | 0.425365 / 0.579283 (-0.153918) | 0.427183 / 0.434364 (-0.007181) | 0.529773 / 0.540337 (-0.010564) | 0.642061 / 1.386936 (-0.744875) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0628013d009dd5150e8a1c1a4ac9d93887b88a76 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008995 / 0.011353 (-0.002357) | 0.004540 / 0.011008 (-0.006469) | 0.099675 / 0.038508 (0.061167) | 0.030338 / 0.023109 (0.007229) | 0.307167 / 0.275898 (0.031269) | 0.338789 / 0.323480 (0.015309) | 0.007293 / 0.007986 (-0.000692) | 0.004681 / 0.004328 (0.000352) | 0.077475 / 0.004250 (0.073225) | 0.036399 / 0.037052 (-0.000654) | 0.304615 / 0.258489 (0.046126) | 0.351611 / 0.293841 (0.057770) | 0.034449 / 0.128546 (-0.094097) | 0.011565 / 0.075646 (-0.064082) | 0.322765 / 0.419271 (-0.096506) | 0.041971 / 0.043533 (-0.001562) | 0.307492 / 0.255139 (0.052354) | 0.327240 / 0.283200 (0.044040) | 0.087110 / 0.141683 (-0.054573) | 1.484600 / 1.452155 (0.032445) | 1.536651 / 1.492716 (0.043934) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.185876 / 0.018006 (0.167869) | 0.404276 / 0.000490 (0.403787) | 0.001592 / 0.000200 (0.001392) | 0.000072 / 0.000054 (0.000018) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023272 / 0.037411 (-0.014139) | 0.096273 / 0.014526 (0.081747) | 0.105400 / 0.176557 (-0.071157) | 0.149720 / 0.737135 (-0.587416) | 0.107807 / 0.296338 (-0.188532) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.420072 / 0.215209 (0.204863) | 4.184108 / 2.077655 (2.106454) | 1.880690 / 1.504120 (0.376570) | 1.673103 / 1.541195 (0.131909) | 1.715792 / 1.468490 (0.247302) | 0.695771 / 4.584777 (-3.889006) | 3.450224 / 3.745712 (-0.295488) | 2.999218 / 5.269862 (-2.270644) | 1.585571 / 4.565676 (-2.980106) | 0.082105 / 0.424275 (-0.342170) | 0.012453 / 0.007607 (0.004846) | 0.528538 / 0.226044 (0.302494) | 5.287951 / 2.268929 (3.019023) | 2.289127 / 55.444624 (-53.155497) | 1.956503 / 6.876477 (-4.919974) | 2.004498 / 2.142072 (-0.137575) | 0.813547 / 4.805227 (-3.991681) | 0.151574 / 6.500664 (-6.349090) | 0.063763 / 0.075469 (-0.011706) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.239125 / 1.841788 (-0.602662) | 13.627676 / 8.074308 (5.553368) | 13.747815 / 10.191392 (3.556423) | 0.157745 / 0.680424 (-0.522679) | 0.028590 / 0.534201 (-0.505611) | 0.397472 / 0.579283 (-0.181811) | 0.405925 / 0.434364 (-0.028439) | 0.477942 / 0.540337 (-0.062396) | 0.572379 / 1.386936 (-0.814557) |\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) | 0.006637 / 0.011353 (-0.004716) | 0.004657 / 0.011008 (-0.006351) | 0.082056 / 0.038508 (0.043548) | 0.027974 / 0.023109 (0.004865) | 0.342887 / 0.275898 (0.066989) | 0.375938 / 0.323480 (0.052458) | 0.004958 / 0.007986 (-0.003028) | 0.004738 / 0.004328 (0.000409) | 0.080449 / 0.004250 (0.076198) | 0.038138 / 0.037052 (0.001085) | 0.345636 / 0.258489 (0.087147) | 0.385992 / 0.293841 (0.092151) | 0.033265 / 0.128546 (-0.095281) | 0.011965 / 0.075646 (-0.063681) | 0.091441 / 0.419271 (-0.327830) | 0.051407 / 0.043533 (0.007874) | 0.353758 / 0.255139 (0.098619) | 0.372118 / 0.283200 (0.088919) | 0.093947 / 0.141683 (-0.047735) | 1.468197 / 1.452155 (0.016042) | 1.554677 / 1.492716 (0.061960) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.222034 / 0.018006 (0.204027) | 0.403658 / 0.000490 (0.403169) | 0.003242 / 0.000200 (0.003042) | 0.000082 / 0.000054 (0.000027) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025335 / 0.037411 (-0.012076) | 0.100404 / 0.014526 (0.085878) | 0.107858 / 0.176557 (-0.068698) | 0.156115 / 0.737135 (-0.581021) | 0.113967 / 0.296338 (-0.182372) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.437567 / 0.215209 (0.222358) | 4.362486 / 2.077655 (2.284832) | 2.067315 / 1.504120 (0.563195) | 1.857669 / 1.541195 (0.316475) | 1.926380 / 1.468490 (0.457890) | 0.703905 / 4.584777 (-3.880872) | 3.437139 / 3.745712 (-0.308573) | 3.051931 / 5.269862 (-2.217930) | 1.356494 / 4.565676 (-3.209182) | 0.083679 / 0.424275 (-0.340596) | 0.012507 / 0.007607 (0.004900) | 0.539572 / 0.226044 (0.313528) | 5.405790 / 2.268929 (3.136861) | 2.532769 / 55.444624 (-52.911855) | 2.181950 / 6.876477 (-4.694527) | 2.212627 / 2.142072 (0.070554) | 0.807468 / 4.805227 (-3.997759) | 0.152146 / 6.500664 (-6.348518) | 0.068891 / 0.075469 (-0.006578) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.286972 / 1.841788 (-0.554816) | 13.987186 / 8.074308 (5.912878) | 13.115065 / 10.191392 (2.923673) | 0.162143 / 0.680424 (-0.518281) | 0.016767 / 0.534201 (-0.517434) | 0.384766 / 0.579283 (-0.194517) | 0.397438 / 0.434364 (-0.036926) | 0.470850 / 0.540337 (-0.069487) | 0.562216 / 1.386936 (-0.824720) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2843fceabc428932754ba497f643d6e94173b91e \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.010877 / 0.011353 (-0.000476) | 0.005739 / 0.011008 (-0.005269) | 0.118542 / 0.038508 (0.080034) | 0.042266 / 0.023109 (0.019157) | 0.359317 / 0.275898 (0.083419) | 0.412995 / 0.323480 (0.089515) | 0.009158 / 0.007986 (0.001173) | 0.006343 / 0.004328 (0.002014) | 0.089587 / 0.004250 (0.085336) | 0.047899 / 0.037052 (0.010847) | 0.358745 / 0.258489 (0.100256) | 0.421316 / 0.293841 (0.127476) | 0.044540 / 0.128546 (-0.084006) | 0.013872 / 0.075646 (-0.061774) | 0.399856 / 0.419271 (-0.019415) | 0.056484 / 0.043533 (0.012951) | 0.356922 / 0.255139 (0.101783) | 0.385598 / 0.283200 (0.102398) | 0.116039 / 0.141683 (-0.025644) | 1.726095 / 1.452155 (0.273940) | 1.888643 / 1.492716 (0.395927) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.269517 / 0.018006 (0.251511) | 0.511204 / 0.000490 (0.510714) | 0.001906 / 0.000200 (0.001706) | 0.000103 / 0.000054 (0.000048) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031133 / 0.037411 (-0.006278) | 0.128513 / 0.014526 (0.113987) | 0.139639 / 0.176557 (-0.036918) | 0.189778 / 0.737135 (-0.547358) | 0.145219 / 0.296338 (-0.151120) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.486693 / 0.215209 (0.271484) | 4.851999 / 2.077655 (2.774344) | 2.255334 / 1.504120 (0.751214) | 2.052271 / 1.541195 (0.511077) | 2.143262 / 1.468490 (0.674772) | 0.835765 / 4.584777 (-3.749012) | 4.451280 / 3.745712 (0.705568) | 2.534392 / 5.269862 (-2.735469) | 1.747817 / 4.565676 (-2.817859) | 0.101186 / 0.424275 (-0.323089) | 0.014281 / 0.007607 (0.006674) | 0.616164 / 0.226044 (0.390120) | 6.161789 / 2.268929 (3.892860) | 2.815347 / 55.444624 (-52.629277) | 2.408305 / 6.876477 (-4.468172) | 2.508240 / 2.142072 (0.366167) | 1.017709 / 4.805227 (-3.787519) | 0.198272 / 6.500664 (-6.302392) | 0.075663 / 0.075469 (0.000194) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.435501 / 1.841788 (-0.406287) | 18.149581 / 8.074308 (10.075273) | 16.619011 / 10.191392 (6.427619) | 0.205080 / 0.680424 (-0.475344) | 0.033780 / 0.534201 (-0.500421) | 0.515768 / 0.579283 (-0.063515) | 0.542628 / 0.434364 (0.108264) | 0.634067 / 0.540337 (0.093730) | 0.757841 / 1.386936 (-0.629095) |\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) | 0.008541 / 0.011353 (-0.002812) | 0.005733 / 0.011008 (-0.005275) | 0.089859 / 0.038508 (0.051351) | 0.039379 / 0.023109 (0.016270) | 0.402037 / 0.275898 (0.126139) | 0.454046 / 0.323480 (0.130566) | 0.006652 / 0.007986 (-0.001334) | 0.004555 / 0.004328 (0.000227) | 0.087651 / 0.004250 (0.083401) | 0.054934 / 0.037052 (0.017881) | 0.404468 / 0.258489 (0.145979) | 0.467127 / 0.293841 (0.173286) | 0.042034 / 0.128546 (-0.086512) | 0.014225 / 0.075646 (-0.061421) | 0.103281 / 0.419271 (-0.315990) | 0.057767 / 0.043533 (0.014234) | 0.396391 / 0.255139 (0.141252) | 0.429364 / 0.283200 (0.146165) | 0.120193 / 0.141683 (-0.021489) | 1.794029 / 1.452155 (0.341875) | 1.875431 / 1.492716 (0.382714) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.325707 / 0.018006 (0.307701) | 0.503841 / 0.000490 (0.503351) | 0.010224 / 0.000200 (0.010024) | 0.000137 / 0.000054 (0.000082) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035289 / 0.037411 (-0.002123) | 0.139018 / 0.014526 (0.124492) | 0.145112 / 0.176557 (-0.031445) | 0.202616 / 0.737135 (-0.534519) | 0.152975 / 0.296338 (-0.143363) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.493110 / 0.215209 (0.277901) | 4.885713 / 2.077655 (2.808058) | 2.344417 / 1.504120 (0.840297) | 2.135734 / 1.541195 (0.594540) | 2.254118 / 1.468490 (0.785628) | 0.811516 / 4.584777 (-3.773261) | 4.484454 / 3.745712 (0.738742) | 2.459913 / 5.269862 (-2.809948) | 1.553106 / 4.565676 (-3.012570) | 0.100943 / 0.424275 (-0.323332) | 0.014848 / 0.007607 (0.007241) | 0.626214 / 0.226044 (0.400170) | 6.206925 / 2.268929 (3.937997) | 2.986549 / 55.444624 (-52.458076) | 2.521895 / 6.876477 (-4.354582) | 2.610917 / 2.142072 (0.468845) | 0.998496 / 4.805227 (-3.806731) | 0.199405 / 6.500664 (-6.301260) | 0.077355 / 0.075469 (0.001886) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.525135 / 1.841788 (-0.316653) | 18.708407 / 8.074308 (10.634099) | 16.049482 / 10.191392 (5.858090) | 0.170986 / 0.680424 (-0.509437) | 0.021090 / 0.534201 (-0.513111) | 0.511734 / 0.579283 (-0.067549) | 0.495507 / 0.434364 (0.061143) | 0.628578 / 0.540337 (0.088241) | 0.749546 / 1.386936 (-0.637390) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2843fceabc428932754ba497f643d6e94173b91e \"CML watermark\")\n" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008442 / 0.011353 (-0.002911) | 0.004567 / 0.011008 (-0.006441) | 0.100688 / 0.038508 (0.062180) | 0.029568 / 0.023109 (0.006459) | 0.306993 / 0.275898 (0.031095) | 0.362626 / 0.323480 (0.039146) | 0.006983 / 0.007986 (-0.001002) | 0.003424 / 0.004328 (-0.000905) | 0.079050 / 0.004250 (0.074799) | 0.036087 / 0.037052 (-0.000966) | 0.318205 / 0.258489 (0.059716) | 0.353882 / 0.293841 (0.060041) | 0.033091 / 0.128546 (-0.095455) | 0.011468 / 0.075646 (-0.064178) | 0.321125 / 0.419271 (-0.098146) | 0.040645 / 0.043533 (-0.002888) | 0.309827 / 0.255139 (0.054688) | 0.344848 / 0.283200 (0.061648) | 0.087100 / 0.141683 (-0.054583) | 1.465123 / 1.452155 (0.012968) | 1.499457 / 1.492716 (0.006741) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.171619 / 0.018006 (0.153613) | 0.410198 / 0.000490 (0.409709) | 0.002391 / 0.000200 (0.002191) | 0.000075 / 0.000054 (0.000021) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022913 / 0.037411 (-0.014499) | 0.097275 / 0.014526 (0.082749) | 0.103902 / 0.176557 (-0.072655) | 0.148855 / 0.737135 (-0.588281) | 0.107247 / 0.296338 (-0.189092) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.413139 / 0.215209 (0.197930) | 4.131760 / 2.077655 (2.054105) | 1.854491 / 1.504120 (0.350371) | 1.625524 / 1.541195 (0.084329) | 1.666665 / 1.468490 (0.198175) | 0.687105 / 4.584777 (-3.897672) | 3.327124 / 3.745712 (-0.418588) | 1.830820 / 5.269862 (-3.439042) | 1.147930 / 4.565676 (-3.417746) | 0.081586 / 0.424275 (-0.342689) | 0.012422 / 0.007607 (0.004815) | 0.523723 / 0.226044 (0.297678) | 5.246977 / 2.268929 (2.978049) | 2.288350 / 55.444624 (-53.156275) | 1.933740 / 6.876477 (-4.942737) | 1.954356 / 2.142072 (-0.187716) | 0.804434 / 4.805227 (-4.000793) | 0.147621 / 6.500664 (-6.353043) | 0.064835 / 0.075469 (-0.010634) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.244841 / 1.841788 (-0.596947) | 13.758465 / 8.074308 (5.684157) | 13.984576 / 10.191392 (3.793184) | 0.144860 / 0.680424 (-0.535564) | 0.028616 / 0.534201 (-0.505584) | 0.401928 / 0.579283 (-0.177355) | 0.415294 / 0.434364 (-0.019069) | 0.476483 / 0.540337 (-0.063854) | 0.569257 / 1.386936 (-0.817679) |\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) | 0.006556 / 0.011353 (-0.004797) | 0.004502 / 0.011008 (-0.006507) | 0.074828 / 0.038508 (0.036319) | 0.027537 / 0.023109 (0.004427) | 0.339961 / 0.275898 (0.064063) | 0.372491 / 0.323480 (0.049011) | 0.005010 / 0.007986 (-0.002976) | 0.004624 / 0.004328 (0.000295) | 0.074459 / 0.004250 (0.070208) | 0.037539 / 0.037052 (0.000486) | 0.341031 / 0.258489 (0.082542) | 0.383397 / 0.293841 (0.089556) | 0.031706 / 0.128546 (-0.096840) | 0.011542 / 0.075646 (-0.064104) | 0.084882 / 0.419271 (-0.334389) | 0.041860 / 0.043533 (-0.001673) | 0.338699 / 0.255139 (0.083560) | 0.365666 / 0.283200 (0.082467) | 0.088966 / 0.141683 (-0.052717) | 1.502493 / 1.452155 (0.050339) | 1.570746 / 1.492716 (0.078030) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.217547 / 0.018006 (0.199541) | 0.392407 / 0.000490 (0.391918) | 0.000388 / 0.000200 (0.000188) | 0.000058 / 0.000054 (0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024571 / 0.037411 (-0.012840) | 0.099259 / 0.014526 (0.084734) | 0.107850 / 0.176557 (-0.068707) | 0.157686 / 0.737135 (-0.579449) | 0.109761 / 0.296338 (-0.186578) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.434791 / 0.215209 (0.219582) | 4.323099 / 2.077655 (2.245444) | 2.063610 / 1.504120 (0.559490) | 1.866136 / 1.541195 (0.324941) | 1.910185 / 1.468490 (0.441695) | 0.696584 / 4.584777 (-3.888193) | 3.398017 / 3.745712 (-0.347695) | 1.848473 / 5.269862 (-3.421388) | 1.168238 / 4.565676 (-3.397438) | 0.083222 / 0.424275 (-0.341053) | 0.012332 / 0.007607 (0.004725) | 0.538953 / 0.226044 (0.312909) | 5.421273 / 2.268929 (3.152344) | 2.499877 / 55.444624 (-52.944747) | 2.161853 / 6.876477 (-4.714624) | 2.183941 / 2.142072 (0.041868) | 0.803916 / 4.805227 (-4.001311) | 0.150266 / 6.500664 (-6.350398) | 0.067399 / 0.075469 (-0.008070) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.280479 / 1.841788 (-0.561309) | 13.728074 / 8.074308 (5.653766) | 12.946098 / 10.191392 (2.754706) | 0.128459 / 0.680424 (-0.551965) | 0.016567 / 0.534201 (-0.517634) | 0.374461 / 0.579283 (-0.204822) | 0.386973 / 0.434364 (-0.047391) | 0.459754 / 0.540337 (-0.080583) | 0.543870 / 1.386936 (-0.843066) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#595b3d47e1fc579f5db1cbc376f756edf32904dd \"CML watermark\")\n", "Instead of reverting the change, maybe we can use the same conversion in `to_iterable_dataset` as in `ArrowBasedBuilder._as_streaming_dataset` to avoid decoding images twice?", "True, let me take a look", "Closing in favor of https://github.com/huggingface/datasets/pull/5655" ]

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[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009866 / 0.011353 (-0.001487) | 0.005334 / 0.011008 (-0.005675) | 0.101771 / 0.038508 (0.063263) | 0.037722 / 0.023109 (0.014613) | 0.301026 / 0.275898 (0.025128) | 0.336618 / 0.323480 (0.013138) | 0.008679 / 0.007986 (0.000693) | 0.005640 / 0.004328 (0.001312) | 0.077076 / 0.004250 (0.072825) | 0.045068 / 0.037052 (0.008016) | 0.302570 / 0.258489 (0.044081) | 0.359093 / 0.293841 (0.065252) | 0.038865 / 0.128546 (-0.089681) | 0.012318 / 0.075646 (-0.063328) | 0.334819 / 0.419271 (-0.084452) | 0.047980 / 0.043533 (0.004447) | 0.296999 / 0.255139 (0.041860) | 0.318855 / 0.283200 (0.035656) | 0.110633 / 0.141683 (-0.031050) | 1.464326 / 1.452155 (0.012172) | 1.537386 / 1.492716 (0.044670) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.282906 / 0.018006 (0.264900) | 0.498418 / 0.000490 (0.497928) | 0.001507 / 0.000200 (0.001307) | 0.000087 / 0.000054 (0.000032) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029948 / 0.037411 (-0.007463) | 0.114385 / 0.014526 (0.099859) | 0.125783 / 0.176557 (-0.050774) | 0.193458 / 0.737135 (-0.543678) | 0.129725 / 0.296338 (-0.166614) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.403822 / 0.215209 (0.188613) | 4.034180 / 2.077655 (1.956525) | 1.768206 / 1.504120 (0.264086) | 1.579267 / 1.541195 (0.038072) | 1.725077 / 1.468490 (0.256587) | 0.698743 / 4.584777 (-3.886034) | 3.723481 / 3.745712 (-0.022231) | 2.302374 / 5.269862 (-2.967488) | 1.497954 / 4.565676 (-3.067723) | 0.087360 / 0.424275 (-0.336915) | 0.012453 / 0.007607 (0.004846) | 0.523374 / 0.226044 (0.297329) | 5.244962 / 2.268929 (2.976033) | 2.272874 / 55.444624 (-53.171750) | 1.935570 / 6.876477 (-4.940907) | 2.043151 / 2.142072 (-0.098921) | 0.866298 / 4.805227 (-3.938929) | 0.169376 / 6.500664 (-6.331288) | 0.064578 / 0.075469 (-0.010892) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.217372 / 1.841788 (-0.624416) | 15.896050 / 8.074308 (7.821742) | 15.165190 / 10.191392 (4.973798) | 0.171168 / 0.680424 (-0.509256) | 0.029770 / 0.534201 (-0.504431) | 0.449030 / 0.579283 (-0.130253) | 0.454704 / 0.434364 (0.020340) | 0.550689 / 0.540337 (0.010351) | 0.651182 / 1.386936 (-0.735754) |\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) | 0.008072 / 0.011353 (-0.003281) | 0.005533 / 0.011008 (-0.005475) | 0.076343 / 0.038508 (0.037835) | 0.037997 / 0.023109 (0.014888) | 0.350465 / 0.275898 (0.074567) | 0.391168 / 0.323480 (0.067688) | 0.006475 / 0.007986 (-0.001511) | 0.004299 / 0.004328 (-0.000029) | 0.074867 / 0.004250 (0.070617) | 0.055256 / 0.037052 (0.018204) | 0.363919 / 0.258489 (0.105430) | 0.396521 / 0.293841 (0.102680) | 0.037746 / 0.128546 (-0.090801) | 0.012556 / 0.075646 (-0.063091) | 0.087974 / 0.419271 (-0.331297) | 0.050850 / 0.043533 (0.007317) | 0.345857 / 0.255139 (0.090718) | 0.361019 / 0.283200 (0.077820) | 0.111007 / 0.141683 (-0.030676) | 1.444014 / 1.452155 (-0.008140) | 1.533154 / 1.492716 (0.040438) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.332114 / 0.018006 (0.314108) | 0.517232 / 0.000490 (0.516742) | 0.004459 / 0.000200 (0.004259) | 0.000102 / 0.000054 (0.000048) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033147 / 0.037411 (-0.004264) | 0.119983 / 0.014526 (0.105457) | 0.125970 / 0.176557 (-0.050586) | 0.196375 / 0.737135 (-0.540760) | 0.133849 / 0.296338 (-0.162489) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.429477 / 0.215209 (0.214267) | 4.263750 / 2.077655 (2.186096) | 2.079409 / 1.504120 (0.575289) | 1.899831 / 1.541195 (0.358636) | 2.048472 / 1.468490 (0.579982) | 0.720945 / 4.584777 (-3.863832) | 3.813195 / 3.745712 (0.067483) | 2.250353 / 5.269862 (-3.019508) | 1.401496 / 4.565676 (-3.164181) | 0.090052 / 0.424275 (-0.334223) | 0.012552 / 0.007607 (0.004945) | 0.536839 / 0.226044 (0.310794) | 5.361089 / 2.268929 (3.092161) | 2.559710 / 55.444624 (-52.884914) | 2.226963 / 6.876477 (-4.649513) | 2.341898 / 2.142072 (0.199825) | 0.872115 / 4.805227 (-3.933112) | 0.173776 / 6.500664 (-6.326888) | 0.068567 / 0.075469 (-0.006902) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.294583 / 1.841788 (-0.547205) | 16.624099 / 8.074308 (8.549791) | 13.698509 / 10.191392 (3.507117) | 0.161917 / 0.680424 (-0.518506) | 0.017744 / 0.534201 (-0.516457) | 0.428547 / 0.579283 (-0.150736) | 0.424687 / 0.434364 (-0.009677) | 0.525812 / 0.540337 (-0.014525) | 0.629075 / 1.386936 (-0.757861) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#33e4d6af919db17bf9a1eac544a0501b5972393b \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008667 / 0.011353 (-0.002686) | 0.004921 / 0.011008 (-0.006087) | 0.098352 / 0.038508 (0.059844) | 0.033983 / 0.023109 (0.010873) | 0.291640 / 0.275898 (0.015742) | 0.323388 / 0.323480 (-0.000092) | 0.007943 / 0.007986 (-0.000043) | 0.003922 / 0.004328 (-0.000407) | 0.075861 / 0.004250 (0.071610) | 0.042606 / 0.037052 (0.005554) | 0.298571 / 0.258489 (0.040081) | 0.345496 / 0.293841 (0.051655) | 0.037443 / 0.128546 (-0.091103) | 0.012114 / 0.075646 (-0.063532) | 0.333269 / 0.419271 (-0.086003) | 0.047762 / 0.043533 (0.004229) | 0.295452 / 0.255139 (0.040313) | 0.319641 / 0.283200 (0.036441) | 0.101083 / 0.141683 (-0.040600) | 1.432179 / 1.452155 (-0.019976) | 1.523976 / 1.492716 (0.031260) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.241327 / 0.018006 (0.223321) | 0.538315 / 0.000490 (0.537825) | 0.003479 / 0.000200 (0.003279) | 0.000082 / 0.000054 (0.000028) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025857 / 0.037411 (-0.011554) | 0.104833 / 0.014526 (0.090307) | 0.116826 / 0.176557 (-0.059730) | 0.183460 / 0.737135 (-0.553675) | 0.119595 / 0.296338 (-0.176743) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.397533 / 0.215209 (0.182324) | 3.968664 / 2.077655 (1.891010) | 1.774025 / 1.504120 (0.269905) | 1.577424 / 1.541195 (0.036229) | 1.623049 / 1.468490 (0.154559) | 0.701008 / 4.584777 (-3.883769) | 3.753278 / 3.745712 (0.007565) | 2.078313 / 5.269862 (-3.191549) | 1.335639 / 4.565676 (-3.230037) | 0.085216 / 0.424275 (-0.339059) | 0.012087 / 0.007607 (0.004480) | 0.513219 / 0.226044 (0.287174) | 5.097693 / 2.268929 (2.828765) | 2.275030 / 55.444624 (-53.169594) | 1.928037 / 6.876477 (-4.948439) | 1.941216 / 2.142072 (-0.200856) | 0.856720 / 4.805227 (-3.948507) | 0.166723 / 6.500664 (-6.333941) | 0.062263 / 0.075469 (-0.013206) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.196054 / 1.841788 (-0.645734) | 14.190526 / 8.074308 (6.116218) | 14.053768 / 10.191392 (3.862376) | 0.179982 / 0.680424 (-0.500442) | 0.029024 / 0.534201 (-0.505177) | 0.440391 / 0.579283 (-0.138892) | 0.445627 / 0.434364 (0.011264) | 0.543098 / 0.540337 (0.002761) | 0.640577 / 1.386936 (-0.746359) |\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) | 0.007008 / 0.011353 (-0.004345) | 0.005015 / 0.011008 (-0.005993) | 0.073783 / 0.038508 (0.035274) | 0.032401 / 0.023109 (0.009292) | 0.343382 / 0.275898 (0.067484) | 0.358317 / 0.323480 (0.034837) | 0.005548 / 0.007986 (-0.002437) | 0.005188 / 0.004328 (0.000859) | 0.072867 / 0.004250 (0.068617) | 0.048555 / 0.037052 (0.011502) | 0.334516 / 0.258489 (0.076027) | 0.390263 / 0.293841 (0.096422) | 0.036343 / 0.128546 (-0.092203) | 0.012243 / 0.075646 (-0.063404) | 0.087067 / 0.419271 (-0.332205) | 0.049025 / 0.043533 (0.005492) | 0.333977 / 0.255139 (0.078838) | 0.354427 / 0.283200 (0.071227) | 0.104771 / 0.141683 (-0.036912) | 1.434588 / 1.452155 (-0.017567) | 1.519788 / 1.492716 (0.027072) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.264002 / 0.018006 (0.245996) | 0.547902 / 0.000490 (0.547412) | 0.000461 / 0.000200 (0.000261) | 0.000062 / 0.000054 (0.000008) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028916 / 0.037411 (-0.008496) | 0.110267 / 0.014526 (0.095741) | 0.119190 / 0.176557 (-0.057367) | 0.188599 / 0.737135 (-0.548537) | 0.126948 / 0.296338 (-0.169391) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.422777 / 0.215209 (0.207568) | 4.209813 / 2.077655 (2.132158) | 2.001360 / 1.504120 (0.497240) | 1.802651 / 1.541195 (0.261456) | 1.860357 / 1.468490 (0.391867) | 0.695006 / 4.584777 (-3.889771) | 3.741917 / 3.745712 (-0.003795) | 3.313071 / 5.269862 (-1.956791) | 1.726366 / 4.565676 (-2.839311) | 0.086185 / 0.424275 (-0.338090) | 0.012256 / 0.007607 (0.004649) | 0.536874 / 0.226044 (0.310830) | 5.253008 / 2.268929 (2.984079) | 2.457189 / 55.444624 (-52.987436) | 2.112199 / 6.876477 (-4.764278) | 2.117867 / 2.142072 (-0.024205) | 0.831914 / 4.805227 (-3.973314) | 0.168238 / 6.500664 (-6.332426) | 0.065075 / 0.075469 (-0.010394) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.280795 / 1.841788 (-0.560993) | 14.606608 / 8.074308 (6.532299) | 13.317597 / 10.191392 (3.126205) | 0.166590 / 0.680424 (-0.513834) | 0.017520 / 0.534201 (-0.516681) | 0.420978 / 0.579283 (-0.158305) | 0.415708 / 0.434364 (-0.018656) | 0.523619 / 0.540337 (-0.016718) | 0.625299 / 1.386936 (-0.761637) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a2a83a8ea4b3a87a925ef44b787e87b59bf68225 \"CML watermark\")\n" ]

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[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008740 / 0.011353 (-0.002613) | 0.004501 / 0.011008 (-0.006507) | 0.100045 / 0.038508 (0.061537) | 0.029999 / 0.023109 (0.006890) | 0.303556 / 0.275898 (0.027658) | 0.335342 / 0.323480 (0.011863) | 0.006996 / 0.007986 (-0.000989) | 0.004183 / 0.004328 (-0.000145) | 0.076434 / 0.004250 (0.072183) | 0.033899 / 0.037052 (-0.003153) | 0.301312 / 0.258489 (0.042823) | 0.343136 / 0.293841 (0.049295) | 0.034062 / 0.128546 (-0.094484) | 0.011465 / 0.075646 (-0.064181) | 0.323134 / 0.419271 (-0.096137) | 0.040820 / 0.043533 (-0.002713) | 0.301708 / 0.255139 (0.046569) | 0.329528 / 0.283200 (0.046328) | 0.088393 / 0.141683 (-0.053290) | 1.460996 / 1.452155 (0.008842) | 1.531145 / 1.492716 (0.038429) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.191918 / 0.018006 (0.173912) | 0.414099 / 0.000490 (0.413610) | 0.000411 / 0.000200 (0.000211) | 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) | 0.022707 / 0.037411 (-0.014704) | 0.096991 / 0.014526 (0.082465) | 0.106070 / 0.176557 (-0.070487) | 0.151275 / 0.737135 (-0.585860) | 0.108909 / 0.296338 (-0.187430) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.422499 / 0.215209 (0.207289) | 4.205551 / 2.077655 (2.127896) | 1.918960 / 1.504120 (0.414841) | 1.715421 / 1.541195 (0.174227) | 1.768969 / 1.468490 (0.300479) | 0.692243 / 4.584777 (-3.892534) | 3.382452 / 3.745712 (-0.363260) | 1.943695 / 5.269862 (-3.326166) | 1.250482 / 4.565676 (-3.315195) | 0.082084 / 0.424275 (-0.342191) | 0.012446 / 0.007607 (0.004839) | 0.525584 / 0.226044 (0.299539) | 5.275530 / 2.268929 (3.006602) | 2.386207 / 55.444624 (-53.058418) | 2.043920 / 6.876477 (-4.832557) | 2.030932 / 2.142072 (-0.111140) | 0.810233 / 4.805227 (-3.994994) | 0.148139 / 6.500664 (-6.352525) | 0.064617 / 0.075469 (-0.010852) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.227352 / 1.841788 (-0.614436) | 13.527623 / 8.074308 (5.453315) | 14.018551 / 10.191392 (3.827159) | 0.140333 / 0.680424 (-0.540091) | 0.028349 / 0.534201 (-0.505852) | 0.394904 / 0.579283 (-0.184379) | 0.406532 / 0.434364 (-0.027831) | 0.471714 / 0.540337 (-0.068624) | 0.568517 / 1.386936 (-0.818419) |\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) | 0.006623 / 0.011353 (-0.004730) | 0.004464 / 0.011008 (-0.006544) | 0.076342 / 0.038508 (0.037834) | 0.027451 / 0.023109 (0.004341) | 0.343851 / 0.275898 (0.067953) | 0.385723 / 0.323480 (0.062243) | 0.005624 / 0.007986 (-0.002362) | 0.004685 / 0.004328 (0.000356) | 0.075669 / 0.004250 (0.071419) | 0.037297 / 0.037052 (0.000244) | 0.343363 / 0.258489 (0.084874) | 0.396115 / 0.293841 (0.102274) | 0.031577 / 0.128546 (-0.096970) | 0.011557 / 0.075646 (-0.064090) | 0.085626 / 0.419271 (-0.333645) | 0.041699 / 0.043533 (-0.001834) | 0.340826 / 0.255139 (0.085687) | 0.377167 / 0.283200 (0.093967) | 0.088632 / 0.141683 (-0.053051) | 1.464500 / 1.452155 (0.012345) | 1.556686 / 1.492716 (0.063969) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.231136 / 0.018006 (0.213130) | 0.402687 / 0.000490 (0.402197) | 0.000590 / 0.000200 (0.000390) | 0.000059 / 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) | 0.024926 / 0.037411 (-0.012485) | 0.101062 / 0.014526 (0.086536) | 0.106481 / 0.176557 (-0.070075) | 0.159167 / 0.737135 (-0.577968) | 0.110948 / 0.296338 (-0.185390) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.441813 / 0.215209 (0.226603) | 4.416332 / 2.077655 (2.338677) | 2.080621 / 1.504120 (0.576501) | 1.877832 / 1.541195 (0.336637) | 1.944778 / 1.468490 (0.476288) | 0.704634 / 4.584777 (-3.880143) | 3.433955 / 3.745712 (-0.311758) | 1.863493 / 5.269862 (-3.406368) | 1.168869 / 4.565676 (-3.396807) | 0.084095 / 0.424275 (-0.340180) | 0.012440 / 0.007607 (0.004833) | 0.545122 / 0.226044 (0.319077) | 5.472214 / 2.268929 (3.203285) | 2.514580 / 55.444624 (-52.930044) | 2.164570 / 6.876477 (-4.711907) | 2.193467 / 2.142072 (0.051395) | 0.809056 / 4.805227 (-3.996171) | 0.152343 / 6.500664 (-6.348321) | 0.067610 / 0.075469 (-0.007859) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.280968 / 1.841788 (-0.560820) | 13.887674 / 8.074308 (5.813366) | 13.160405 / 10.191392 (2.969013) | 0.128601 / 0.680424 (-0.551823) | 0.016420 / 0.534201 (-0.517780) | 0.382810 / 0.579283 (-0.196473) | 0.394386 / 0.434364 (-0.039978) | 0.470254 / 0.540337 (-0.070083) | 0.566907 / 1.386936 (-0.820029) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8cc6950322337ea8873939541c53858b10c0f3b9 \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008673 / 0.011353 (-0.002679) | 0.004475 / 0.011008 (-0.006533) | 0.102060 / 0.038508 (0.063552) | 0.029438 / 0.023109 (0.006329) | 0.351785 / 0.275898 (0.075887) | 0.388199 / 0.323480 (0.064719) | 0.007011 / 0.007986 (-0.000974) | 0.003317 / 0.004328 (-0.001012) | 0.080931 / 0.004250 (0.076681) | 0.033449 / 0.037052 (-0.003603) | 0.360329 / 0.258489 (0.101840) | 0.400069 / 0.293841 (0.106228) | 0.033628 / 0.128546 (-0.094918) | 0.011462 / 0.075646 (-0.064184) | 0.323781 / 0.419271 (-0.095490) | 0.040686 / 0.043533 (-0.002847) | 0.332715 / 0.255139 (0.077576) | 0.370339 / 0.283200 (0.087139) | 0.084633 / 0.141683 (-0.057050) | 1.459452 / 1.452155 (0.007297) | 1.547719 / 1.492716 (0.055003) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.187051 / 0.018006 (0.169045) | 0.402625 / 0.000490 (0.402135) | 0.002218 / 0.000200 (0.002018) | 0.000070 / 0.000054 (0.000016) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025240 / 0.037411 (-0.012171) | 0.102201 / 0.014526 (0.087675) | 0.108629 / 0.176557 (-0.067927) | 0.156686 / 0.737135 (-0.580449) | 0.111383 / 0.296338 (-0.184955) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.418099 / 0.215209 (0.202890) | 4.163345 / 2.077655 (2.085690) | 1.868419 / 1.504120 (0.364300) | 1.662066 / 1.541195 (0.120871) | 1.705912 / 1.468490 (0.237422) | 0.696391 / 4.584777 (-3.888386) | 3.338307 / 3.745712 (-0.407405) | 1.923255 / 5.269862 (-3.346607) | 1.249220 / 4.565676 (-3.316457) | 0.082037 / 0.424275 (-0.342238) | 0.012232 / 0.007607 (0.004624) | 0.523913 / 0.226044 (0.297869) | 5.290036 / 2.268929 (3.021107) | 2.319729 / 55.444624 (-53.124896) | 1.987345 / 6.876477 (-4.889132) | 2.044516 / 2.142072 (-0.097556) | 0.812098 / 4.805227 (-3.993129) | 0.147327 / 6.500664 (-6.353337) | 0.063838 / 0.075469 (-0.011631) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.219652 / 1.841788 (-0.622136) | 13.271513 / 8.074308 (5.197205) | 13.799982 / 10.191392 (3.608590) | 0.150055 / 0.680424 (-0.530369) | 0.028804 / 0.534201 (-0.505397) | 0.395452 / 0.579283 (-0.183831) | 0.398758 / 0.434364 (-0.035606) | 0.468575 / 0.540337 (-0.071763) | 0.553324 / 1.386936 (-0.833612) |\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) | 0.006498 / 0.011353 (-0.004855) | 0.004439 / 0.011008 (-0.006569) | 0.076525 / 0.038508 (0.038017) | 0.027184 / 0.023109 (0.004074) | 0.364705 / 0.275898 (0.088807) | 0.409481 / 0.323480 (0.086001) | 0.004831 / 0.007986 (-0.003154) | 0.004524 / 0.004328 (0.000196) | 0.075403 / 0.004250 (0.071153) | 0.039013 / 0.037052 (0.001960) | 0.364042 / 0.258489 (0.105553) | 0.413090 / 0.293841 (0.119249) | 0.032052 / 0.128546 (-0.096495) | 0.011514 / 0.075646 (-0.064132) | 0.085219 / 0.419271 (-0.334053) | 0.041448 / 0.043533 (-0.002085) | 0.350371 / 0.255139 (0.095232) | 0.386670 / 0.283200 (0.103470) | 0.089824 / 0.141683 (-0.051859) | 1.487392 / 1.452155 (0.035238) | 1.537201 / 1.492716 (0.044485) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.231555 / 0.018006 (0.213549) | 0.407505 / 0.000490 (0.407016) | 0.000382 / 0.000200 (0.000182) | 0.000060 / 0.000054 (0.000006) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026665 / 0.037411 (-0.010747) | 0.105852 / 0.014526 (0.091326) | 0.108228 / 0.176557 (-0.068328) | 0.164164 / 0.737135 (-0.572972) | 0.114284 / 0.296338 (-0.182054) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.448957 / 0.215209 (0.233748) | 4.500058 / 2.077655 (2.422403) | 2.331660 / 1.504120 (0.827541) | 2.119904 / 1.541195 (0.578710) | 2.101489 / 1.468490 (0.632999) | 0.696580 / 4.584777 (-3.888197) | 3.364206 / 3.745712 (-0.381506) | 2.550157 / 5.269862 (-2.719704) | 1.496455 / 4.565676 (-3.069222) | 0.083289 / 0.424275 (-0.340986) | 0.012283 / 0.007607 (0.004676) | 0.555581 / 0.226044 (0.329537) | 5.556284 / 2.268929 (3.287355) | 2.595261 / 55.444624 (-52.849363) | 2.234793 / 6.876477 (-4.641683) | 2.280150 / 2.142072 (0.138078) | 0.817885 / 4.805227 (-3.987343) | 0.151481 / 6.500664 (-6.349183) | 0.066764 / 0.075469 (-0.008705) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.318875 / 1.841788 (-0.522913) | 14.220380 / 8.074308 (6.146072) | 13.922773 / 10.191392 (3.731381) | 0.154608 / 0.680424 (-0.525816) | 0.016343 / 0.534201 (-0.517858) | 0.380758 / 0.579283 (-0.198525) | 0.392595 / 0.434364 (-0.041769) | 0.468844 / 0.540337 (-0.071493) | 0.561047 / 1.386936 (-0.825889) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d57fdcf2c8110b4b599289695fa065d1fc4936d4 \"CML watermark\")\n" ]

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[ "Thanks for reporting and thanks @mariosasko for fixing ! We just did a patch release `2.10.1` with the fix" ]

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[ "Hi ! No the cache is not transportable in general. It will work on a shared filesystem if you use the same python environment, but not across machines/os/environments.\r\n\r\nIn particular, reloading cached datasets does work, but reloading cached processed datasets (e.g. from `map`) may not work. This is because some hashes used by caching are based on pickle dumps of the function you pass to `map`.\r\n\r\nFinally you may copy the cache to another machine, but all the `cached-*.arrow` files are unlikely to be reloaded.", "OK good to know. Thanks @lhoestq !" ]

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[ "Hi @manuaero \r\n\r\nThank you for your interest in the COYO dataset.\r\n\r\nOur dataset provides the img-url and alt-text in the form of a parquet, so to utilize the coyo dataset you will need to download it directly.\r\n\r\nWe provide a [guide](https://github.com/kakaobrain/coyo-dataset/blob/main/download/README.md) to download, so check it out.\r\n\r\nThank you." ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009044 / 0.011353 (-0.002309) | 0.004244 / 0.011008 (-0.006765) | 0.106705 / 0.038508 (0.068197) | 0.029779 / 0.023109 (0.006670) | 0.289684 / 0.275898 (0.013786) | 0.347100 / 0.323480 (0.023620) | 0.007071 / 0.007986 (-0.000915) | 0.003734 / 0.004328 (-0.000595) | 0.077971 / 0.004250 (0.073720) | 0.035323 / 0.037052 (-0.001730) | 0.334520 / 0.258489 (0.076031) | 0.375804 / 0.293841 (0.081964) | 0.049211 / 0.128546 (-0.079335) | 0.016992 / 0.075646 (-0.058654) | 0.337208 / 0.419271 (-0.082064) | 0.053700 / 0.043533 (0.010167) | 0.295750 / 0.255139 (0.040611) | 0.330157 / 0.283200 (0.046958) | 0.097017 / 0.141683 (-0.044666) | 1.379353 / 1.452155 (-0.072802) | 1.402670 / 1.492716 (-0.090047) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.012685 / 0.018006 (-0.005321) | 0.474541 / 0.000490 (0.474051) | 0.006752 / 0.000200 (0.006552) | 0.000097 / 0.000054 (0.000042) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025735 / 0.037411 (-0.011676) | 0.092507 / 0.014526 (0.077982) | 0.100275 / 0.176557 (-0.076281) | 0.180359 / 0.737135 (-0.556777) | 0.104312 / 0.296338 (-0.192026) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.456558 / 0.215209 (0.241349) | 4.786667 / 2.077655 (2.709012) | 1.873169 / 1.504120 (0.369050) | 1.640935 / 1.541195 (0.099741) | 1.614543 / 1.468490 (0.146053) | 0.936144 / 4.584777 (-3.648633) | 4.699886 / 3.745712 (0.954174) | 2.398545 / 5.269862 (-2.871317) | 1.642808 / 4.565676 (-2.922868) | 0.124803 / 0.424275 (-0.299472) | 0.011848 / 0.007607 (0.004241) | 0.631684 / 0.226044 (0.405639) | 6.096052 / 2.268929 (3.827124) | 2.463052 / 55.444624 (-52.981572) | 1.928551 / 6.876477 (-4.947926) | 1.927790 / 2.142072 (-0.214283) | 1.098912 / 4.805227 (-3.706315) | 0.196343 / 6.500664 (-6.304321) | 0.063296 / 0.075469 (-0.012173) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.255032 / 1.841788 (-0.586755) | 13.853623 / 8.074308 (5.779315) | 16.303280 / 10.191392 (6.111888) | 0.227287 / 0.680424 (-0.453137) | 0.037527 / 0.534201 (-0.496674) | 0.449345 / 0.579283 (-0.129938) | 0.522054 / 0.434364 (0.087690) | 0.552848 / 0.540337 (0.012511) | 0.642994 / 1.386936 (-0.743942) |\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) | 0.008470 / 0.011353 (-0.002883) | 0.005167 / 0.011008 (-0.005841) | 0.077794 / 0.038508 (0.039286) | 0.029228 / 0.023109 (0.006119) | 0.340828 / 0.275898 (0.064930) | 0.400170 / 0.323480 (0.076691) | 0.005485 / 0.007986 (-0.002500) | 0.003854 / 0.004328 (-0.000475) | 0.077597 / 0.004250 (0.073346) | 0.036519 / 0.037052 (-0.000533) | 0.335522 / 0.258489 (0.077033) | 0.412622 / 0.293841 (0.118781) | 0.044587 / 0.128546 (-0.083959) | 0.016024 / 0.075646 (-0.059623) | 0.092312 / 0.419271 (-0.326960) | 0.055660 / 0.043533 (0.012127) | 0.343140 / 0.255139 (0.088001) | 0.386403 / 0.283200 (0.103203) | 0.098634 / 0.141683 (-0.043049) | 1.326126 / 1.452155 (-0.126029) | 1.430316 / 1.492716 (-0.062400) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.222807 / 0.018006 (0.204801) | 0.473622 / 0.000490 (0.473132) | 0.000376 / 0.000200 (0.000176) | 0.000066 / 0.000054 (0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024599 / 0.037411 (-0.012813) | 0.100743 / 0.014526 (0.086217) | 0.112086 / 0.176557 (-0.064471) | 0.198294 / 0.737135 (-0.538842) | 0.111210 / 0.296338 (-0.185129) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.494120 / 0.215209 (0.278911) | 5.117958 / 2.077655 (3.040303) | 2.305131 / 1.504120 (0.801011) | 2.015591 / 1.541195 (0.474396) | 2.027284 / 1.468490 (0.558794) | 1.014241 / 4.584777 (-3.570536) | 4.738836 / 3.745712 (0.993124) | 2.519718 / 5.269862 (-2.750143) | 1.706379 / 4.565676 (-2.859298) | 0.122452 / 0.424275 (-0.301824) | 0.011500 / 0.007607 (0.003893) | 0.632864 / 0.226044 (0.406820) | 6.295457 / 2.268929 (4.026529) | 2.824897 / 55.444624 (-52.619727) | 2.324359 / 6.876477 (-4.552117) | 2.281046 / 2.142072 (0.138974) | 1.173570 / 4.805227 (-3.631657) | 0.197195 / 6.500664 (-6.303469) | 0.064845 / 0.075469 (-0.010624) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.273224 / 1.841788 (-0.568563) | 14.531155 / 8.074308 (6.456847) | 15.892176 / 10.191392 (5.700784) | 0.208051 / 0.680424 (-0.472373) | 0.023119 / 0.534201 (-0.511082) | 0.422317 / 0.579283 (-0.156966) | 0.519946 / 0.434364 (0.085582) | 0.544517 / 0.540337 (0.004179) | 0.605955 / 1.386936 (-0.780981) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#337a4a91d0268c68f26760321c9b45bb4a98832a \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.010806 / 0.011353 (-0.000547) | 0.005631 / 0.011008 (-0.005378) | 0.113166 / 0.038508 (0.074657) | 0.042980 / 0.023109 (0.019871) | 0.344856 / 0.275898 (0.068958) | 0.404417 / 0.323480 (0.080938) | 0.012222 / 0.007986 (0.004236) | 0.004470 / 0.004328 (0.000141) | 0.088072 / 0.004250 (0.083822) | 0.049815 / 0.037052 (0.012763) | 0.366532 / 0.258489 (0.108043) | 0.392558 / 0.293841 (0.098717) | 0.045411 / 0.128546 (-0.083135) | 0.014118 / 0.075646 (-0.061529) | 0.392894 / 0.419271 (-0.026378) | 0.067713 / 0.043533 (0.024181) | 0.353013 / 0.255139 (0.097874) | 0.378375 / 0.283200 (0.095175) | 0.123686 / 0.141683 (-0.017996) | 1.665272 / 1.452155 (0.213118) | 1.748383 / 1.492716 (0.255667) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.011672 / 0.018006 (-0.006335) | 0.481667 / 0.000490 (0.481178) | 0.003644 / 0.000200 (0.003444) | 0.000092 / 0.000054 (0.000037) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030436 / 0.037411 (-0.006976) | 0.122577 / 0.014526 (0.108052) | 0.135409 / 0.176557 (-0.041148) | 0.220385 / 0.737135 (-0.516750) | 0.143140 / 0.296338 (-0.153199) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.471146 / 0.215209 (0.255937) | 4.645023 / 2.077655 (2.567368) | 2.126783 / 1.504120 (0.622663) | 1.907905 / 1.541195 (0.366710) | 1.969561 / 1.468490 (0.501071) | 0.798670 / 4.584777 (-3.786107) | 4.394787 / 3.745712 (0.649075) | 2.353535 / 5.269862 (-2.916327) | 1.501013 / 4.565676 (-3.064664) | 0.097472 / 0.424275 (-0.326803) | 0.014015 / 0.007607 (0.006408) | 0.589365 / 0.226044 (0.363320) | 5.897331 / 2.268929 (3.628402) | 2.656198 / 55.444624 (-52.788427) | 2.256082 / 6.876477 (-4.620395) | 2.271122 / 2.142072 (0.129050) | 0.961566 / 4.805227 (-3.843661) | 0.188303 / 6.500664 (-6.312361) | 0.073258 / 0.075469 (-0.002211) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.445266 / 1.841788 (-0.396522) | 16.876710 / 8.074308 (8.802402) | 16.004287 / 10.191392 (5.812895) | 0.212252 / 0.680424 (-0.468172) | 0.033186 / 0.534201 (-0.501015) | 0.520564 / 0.579283 (-0.058719) | 0.516865 / 0.434364 (0.082501) | 0.638482 / 0.540337 (0.098144) | 0.761959 / 1.386936 (-0.624977) |\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) | 0.008101 / 0.011353 (-0.003252) | 0.005512 / 0.011008 (-0.005497) | 0.086138 / 0.038508 (0.047630) | 0.038605 / 0.023109 (0.015496) | 0.413082 / 0.275898 (0.137184) | 0.444016 / 0.323480 (0.120536) | 0.006196 / 0.007986 (-0.001790) | 0.005736 / 0.004328 (0.001408) | 0.086938 / 0.004250 (0.082688) | 0.052307 / 0.037052 (0.015255) | 0.415206 / 0.258489 (0.156717) | 0.481510 / 0.293841 (0.187669) | 0.041469 / 0.128546 (-0.087077) | 0.013481 / 0.075646 (-0.062165) | 0.101528 / 0.419271 (-0.317744) | 0.056507 / 0.043533 (0.012974) | 0.418166 / 0.255139 (0.163027) | 0.443834 / 0.283200 (0.160634) | 0.116434 / 0.141683 (-0.025249) | 1.651223 / 1.452155 (0.199068) | 1.746429 / 1.492716 (0.253713) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.242381 / 0.018006 (0.224375) | 0.478826 / 0.000490 (0.478337) | 0.000463 / 0.000200 (0.000264) | 0.000067 / 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) | 0.031743 / 0.037411 (-0.005668) | 0.126141 / 0.014526 (0.111616) | 0.134539 / 0.176557 (-0.042018) | 0.216546 / 0.737135 (-0.520590) | 0.143513 / 0.296338 (-0.152825) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.486915 / 0.215209 (0.271706) | 4.833812 / 2.077655 (2.756158) | 2.317785 / 1.504120 (0.813666) | 2.114181 / 1.541195 (0.572986) | 2.153896 / 1.468490 (0.685406) | 0.797490 / 4.584777 (-3.787287) | 4.369950 / 3.745712 (0.624238) | 2.305492 / 5.269862 (-2.964370) | 1.488860 / 4.565676 (-3.076816) | 0.098071 / 0.424275 (-0.326204) | 0.014129 / 0.007607 (0.006522) | 0.611311 / 0.226044 (0.385266) | 6.087482 / 2.268929 (3.818554) | 2.837676 / 55.444624 (-52.606948) | 2.451819 / 6.876477 (-4.424657) | 2.456763 / 2.142072 (0.314690) | 0.957637 / 4.805227 (-3.847590) | 0.190974 / 6.500664 (-6.309690) | 0.074497 / 0.075469 (-0.000972) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.466214 / 1.841788 (-0.375574) | 17.063925 / 8.074308 (8.989617) | 14.630326 / 10.191392 (4.438934) | 0.170570 / 0.680424 (-0.509854) | 0.023794 / 0.534201 (-0.510407) | 0.509175 / 0.579283 (-0.070108) | 0.506485 / 0.434364 (0.072121) | 0.616965 / 0.540337 (0.076628) | 0.718176 / 1.386936 (-0.668760) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c4f14de325e26910d026f377756dd8a231150398 \"CML watermark\")\n" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006362 / 0.011353 (-0.004991) | 0.004546 / 0.011008 (-0.006462) | 0.097003 / 0.038508 (0.058495) | 0.028007 / 0.023109 (0.004898) | 0.315097 / 0.275898 (0.039199) | 0.365128 / 0.323480 (0.041649) | 0.004819 / 0.007986 (-0.003167) | 0.003335 / 0.004328 (-0.000994) | 0.076665 / 0.004250 (0.072415) | 0.038285 / 0.037052 (0.001233) | 0.322100 / 0.258489 (0.063611) | 0.407466 / 0.293841 (0.113625) | 0.031580 / 0.128546 (-0.096966) | 0.011645 / 0.075646 (-0.064001) | 0.321789 / 0.419271 (-0.097483) | 0.051015 / 0.043533 (0.007483) | 0.331762 / 0.255139 (0.076623) | 0.369727 / 0.283200 (0.086527) | 0.090144 / 0.141683 (-0.051539) | 1.485480 / 1.452155 (0.033326) | 1.562032 / 1.492716 (0.069316) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.201192 / 0.018006 (0.183186) | 0.409760 / 0.000490 (0.409270) | 0.002220 / 0.000200 (0.002020) | 0.000070 / 0.000054 (0.000016) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022361 / 0.037411 (-0.015050) | 0.096375 / 0.014526 (0.081849) | 0.101369 / 0.176557 (-0.075188) | 0.161568 / 0.737135 (-0.575568) | 0.105094 / 0.296338 (-0.191245) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.426251 / 0.215209 (0.211042) | 4.261374 / 2.077655 (2.183720) | 2.015688 / 1.504120 (0.511569) | 1.833708 / 1.541195 (0.292513) | 1.908994 / 1.468490 (0.440504) | 0.703108 / 4.584777 (-3.881669) | 3.420767 / 3.745712 (-0.324945) | 1.844776 / 5.269862 (-3.425086) | 1.158470 / 4.565676 (-3.407207) | 0.083324 / 0.424275 (-0.340951) | 0.013054 / 0.007607 (0.005447) | 0.521473 / 0.226044 (0.295429) | 5.245505 / 2.268929 (2.976576) | 2.349110 / 55.444624 (-53.095515) | 2.011119 / 6.876477 (-4.865358) | 2.217807 / 2.142072 (0.075734) | 0.808584 / 4.805227 (-3.996643) | 0.151337 / 6.500664 (-6.349327) | 0.065815 / 0.075469 (-0.009654) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.221839 / 1.841788 (-0.619949) | 13.634161 / 8.074308 (5.559853) | 13.915360 / 10.191392 (3.723968) | 0.126448 / 0.680424 (-0.553976) | 0.016614 / 0.534201 (-0.517587) | 0.379150 / 0.579283 (-0.200133) | 0.382134 / 0.434364 (-0.052230) | 0.442845 / 0.540337 (-0.097493) | 0.519578 / 1.386936 (-0.867358) |\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) | 0.006238 / 0.011353 (-0.005115) | 0.004591 / 0.011008 (-0.006418) | 0.076652 / 0.038508 (0.038144) | 0.026882 / 0.023109 (0.003773) | 0.341948 / 0.275898 (0.066050) | 0.375244 / 0.323480 (0.051764) | 0.004770 / 0.007986 (-0.003215) | 0.004703 / 0.004328 (0.000374) | 0.075797 / 0.004250 (0.071547) | 0.035001 / 0.037052 (-0.002051) | 0.341670 / 0.258489 (0.083181) | 0.383028 / 0.293841 (0.089187) | 0.031756 / 0.128546 (-0.096791) | 0.011714 / 0.075646 (-0.063933) | 0.085552 / 0.419271 (-0.333720) | 0.047697 / 0.043533 (0.004164) | 0.340805 / 0.255139 (0.085666) | 0.365478 / 0.283200 (0.082278) | 0.093146 / 0.141683 (-0.048537) | 1.465100 / 1.452155 (0.012945) | 1.552708 / 1.492716 (0.059992) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.209117 / 0.018006 (0.191111) | 0.402622 / 0.000490 (0.402132) | 0.003940 / 0.000200 (0.003740) | 0.000078 / 0.000054 (0.000023) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026027 / 0.037411 (-0.011385) | 0.098346 / 0.014526 (0.083820) | 0.107349 / 0.176557 (-0.069207) | 0.157846 / 0.737135 (-0.579289) | 0.109566 / 0.296338 (-0.186772) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.445088 / 0.215209 (0.229879) | 4.450727 / 2.077655 (2.373072) | 2.237798 / 1.504120 (0.733678) | 2.026060 / 1.541195 (0.484866) | 2.020464 / 1.468490 (0.551974) | 0.700155 / 4.584777 (-3.884622) | 3.435497 / 3.745712 (-0.310215) | 2.851970 / 5.269862 (-2.417891) | 1.512689 / 4.565676 (-3.052988) | 0.083717 / 0.424275 (-0.340558) | 0.012466 / 0.007607 (0.004859) | 0.545130 / 0.226044 (0.319085) | 5.478228 / 2.268929 (3.209300) | 2.554169 / 55.444624 (-52.890456) | 2.214703 / 6.876477 (-4.661774) | 2.229997 / 2.142072 (0.087925) | 0.809851 / 4.805227 (-3.995376) | 0.151019 / 6.500664 (-6.349645) | 0.066354 / 0.075469 (-0.009115) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.281016 / 1.841788 (-0.560772) | 14.071312 / 8.074308 (5.997004) | 14.682465 / 10.191392 (4.491073) | 0.144197 / 0.680424 (-0.536227) | 0.017088 / 0.534201 (-0.517113) | 0.379049 / 0.579283 (-0.200234) | 0.390713 / 0.434364 (-0.043650) | 0.435804 / 0.540337 (-0.104534) | 0.518895 / 1.386936 (-0.868041) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#fc5c84f36684343bff3e424cb0fd1ac5ecdd66da \"CML watermark\")\n" ]

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[ "Thanks for reporting!" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "> Regarding the tests I think it should be possible to use the mockfs fixture, it allows to play with a dummy fsspec FileSystem with the \"mock://\" protocol.\r\n\r\n> However it requires some storage_options to be passed. Maybe it can be added to DownloadConfig which is passed to cached_path, so that fsspec_get and fsspec_head can use the user's storage_options ?\r\n\r\n@lhoestq I went ahead and tested this with a patch so that I could assign the mockfs as a return value. Let me know if I'm missing something though and we need to pass storage_options down", "> Instead of patching think it would be better to have a new filesystem TmpDirFileSystem (tmpfs) that doesn't need storage_options for the tests, and that is based on a temporary directory created just for the fixture. Maybe something like this ?\r\n\r\nThanks for the recommendation, this works great.", "Feel free to merge `main` into your PR to fix the CI :)", "Should be good to go. Thanks!", "<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) | 0.006183 / 0.011353 (-0.005170) | 0.004180 / 0.011008 (-0.006829) | 0.095965 / 0.038508 (0.057457) | 0.026754 / 0.023109 (0.003645) | 0.339724 / 0.275898 (0.063826) | 0.381628 / 0.323480 (0.058149) | 0.004615 / 0.007986 (-0.003371) | 0.004469 / 0.004328 (0.000140) | 0.074035 / 0.004250 (0.069784) | 0.035089 / 0.037052 (-0.001963) | 0.352253 / 0.258489 (0.093764) | 0.389598 / 0.293841 (0.095757) | 0.032262 / 0.128546 (-0.096285) | 0.011392 / 0.075646 (-0.064254) | 0.323884 / 0.419271 (-0.095388) | 0.042658 / 0.043533 (-0.000874) | 0.331533 / 0.255139 (0.076394) | 0.364723 / 0.283200 (0.081523) | 0.086349 / 0.141683 (-0.055334) | 1.465687 / 1.452155 (0.013533) | 1.559782 / 1.492716 (0.067066) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.198562 / 0.018006 (0.180556) | 0.457170 / 0.000490 (0.456680) | 0.000409 / 0.000200 (0.000209) | 0.000061 / 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) | 0.022439 / 0.037411 (-0.014973) | 0.096551 / 0.014526 (0.082025) | 0.102230 / 0.176557 (-0.074326) | 0.160878 / 0.737135 (-0.576257) | 0.109348 / 0.296338 (-0.186990) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.456635 / 0.215209 (0.241426) | 4.563571 / 2.077655 (2.485916) | 2.313048 / 1.504120 (0.808928) | 2.117433 / 1.541195 (0.576239) | 2.127478 / 1.468490 (0.658988) | 0.699478 / 4.584777 (-3.885299) | 3.358955 / 3.745712 (-0.386757) | 1.821437 / 5.269862 (-3.448424) | 1.158239 / 4.565676 (-3.407438) | 0.083207 / 0.424275 (-0.341068) | 0.012925 / 0.007607 (0.005318) | 0.556526 / 0.226044 (0.330482) | 5.552364 / 2.268929 (3.283435) | 2.744696 / 55.444624 (-52.699928) | 2.374455 / 6.876477 (-4.502022) | 2.442021 / 2.142072 (0.299949) | 0.809393 / 4.805227 (-3.995834) | 0.152305 / 6.500664 (-6.348359) | 0.066164 / 0.075469 (-0.009305) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.258268 / 1.841788 (-0.583520) | 13.402391 / 8.074308 (5.328083) | 13.816927 / 10.191392 (3.625535) | 0.148466 / 0.680424 (-0.531958) | 0.016487 / 0.534201 (-0.517714) | 0.385888 / 0.579283 (-0.193395) | 0.378840 / 0.434364 (-0.055524) | 0.444527 / 0.540337 (-0.095810) | 0.531011 / 1.386936 (-0.855925) |\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) | 0.006230 / 0.011353 (-0.005123) | 0.004488 / 0.011008 (-0.006520) | 0.077539 / 0.038508 (0.039031) | 0.026611 / 0.023109 (0.003502) | 0.342093 / 0.275898 (0.066195) | 0.371555 / 0.323480 (0.048075) | 0.004665 / 0.007986 (-0.003321) | 0.003289 / 0.004328 (-0.001039) | 0.078378 / 0.004250 (0.074128) | 0.035223 / 0.037052 (-0.001829) | 0.339972 / 0.258489 (0.081483) | 0.378755 / 0.293841 (0.084914) | 0.031331 / 0.128546 (-0.097215) | 0.011406 / 0.075646 (-0.064241) | 0.086891 / 0.419271 (-0.332381) | 0.047713 / 0.043533 (0.004180) | 0.342678 / 0.255139 (0.087539) | 0.364536 / 0.283200 (0.081337) | 0.092132 / 0.141683 (-0.049551) | 1.537050 / 1.452155 (0.084895) | 1.639927 / 1.492716 (0.147211) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.219933 / 0.018006 (0.201927) | 0.391627 / 0.000490 (0.391137) | 0.002238 / 0.000200 (0.002038) | 0.000072 / 0.000054 (0.000018) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024890 / 0.037411 (-0.012521) | 0.098989 / 0.014526 (0.084464) | 0.104505 / 0.176557 (-0.072052) | 0.156252 / 0.737135 (-0.580884) | 0.108027 / 0.296338 (-0.188312) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.443957 / 0.215209 (0.228748) | 4.450850 / 2.077655 (2.373196) | 2.076043 / 1.504120 (0.571923) | 1.866396 / 1.541195 (0.325202) | 1.902692 / 1.468490 (0.434202) | 0.703160 / 4.584777 (-3.881617) | 3.373761 / 3.745712 (-0.371951) | 2.615649 / 5.269862 (-2.654213) | 1.340612 / 4.565676 (-3.225065) | 0.083836 / 0.424275 (-0.340439) | 0.012619 / 0.007607 (0.005012) | 0.553410 / 0.226044 (0.327365) | 5.526500 / 2.268929 (3.257571) | 2.513213 / 55.444624 (-52.931411) | 2.152701 / 6.876477 (-4.723776) | 2.165092 / 2.142072 (0.023019) | 0.818381 / 4.805227 (-3.986846) | 0.152118 / 6.500664 (-6.348546) | 0.066950 / 0.075469 (-0.008519) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.291468 / 1.841788 (-0.550320) | 13.694828 / 8.074308 (5.620520) | 13.821019 / 10.191392 (3.629627) | 0.126077 / 0.680424 (-0.554347) | 0.016543 / 0.534201 (-0.517658) | 0.381399 / 0.579283 (-0.197884) | 0.377326 / 0.434364 (-0.057038) | 0.439275 / 0.540337 (-0.101063) | 0.524021 / 1.386936 (-0.862915) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3e6269979fc80ae8939294d26298897f0db5b84d \"CML watermark\")\n" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5579). All of your documentation changes will be reflected on that endpoint.", "I'm not sure we need this part as we provide a link to the notebook that shows how to train an object detection model, and this notebook instantiates a `DataLoader` before training the model. I'd like to hear what @stevhliu thinks.\r\n\r\nPS: Your `collate_fn` calls `torch.stack` on the `bbox` tensors, which don't have the same shape, so this will fail.", "I agree with @mariosasko; we also have a [Use with PyTorch](https://huggingface.co/docs/datasets/use_with_pytorch) guide that shows how you can create a `DataLoader`. " ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008124 / 0.011353 (-0.003229) | 0.004594 / 0.011008 (-0.006414) | 0.101575 / 0.038508 (0.063066) | 0.029074 / 0.023109 (0.005965) | 0.314641 / 0.275898 (0.038743) | 0.372006 / 0.323480 (0.048526) | 0.006882 / 0.007986 (-0.001103) | 0.003371 / 0.004328 (-0.000958) | 0.078800 / 0.004250 (0.074550) | 0.034030 / 0.037052 (-0.003023) | 0.326917 / 0.258489 (0.068428) | 0.357628 / 0.293841 (0.063788) | 0.033076 / 0.128546 (-0.095470) | 0.011552 / 0.075646 (-0.064094) | 0.321715 / 0.419271 (-0.097557) | 0.040426 / 0.043533 (-0.003107) | 0.315091 / 0.255139 (0.059952) | 0.339291 / 0.283200 (0.056091) | 0.087280 / 0.141683 (-0.054403) | 1.443445 / 1.452155 (-0.008710) | 1.489233 / 1.492716 (-0.003483) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.182643 / 0.018006 (0.164637) | 0.390205 / 0.000490 (0.389716) | 0.001361 / 0.000200 (0.001161) | 0.000072 / 0.000054 (0.000018) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022767 / 0.037411 (-0.014644) | 0.095744 / 0.014526 (0.081219) | 0.102763 / 0.176557 (-0.073794) | 0.166760 / 0.737135 (-0.570375) | 0.106393 / 0.296338 (-0.189945) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.424649 / 0.215209 (0.209440) | 4.257982 / 2.077655 (2.180327) | 2.135847 / 1.504120 (0.631727) | 1.924810 / 1.541195 (0.383615) | 1.813797 / 1.468490 (0.345307) | 0.695467 / 4.584777 (-3.889310) | 3.330164 / 3.745712 (-0.415548) | 2.665606 / 5.269862 (-2.604255) | 1.458619 / 4.565676 (-3.107058) | 0.082408 / 0.424275 (-0.341867) | 0.012259 / 0.007607 (0.004652) | 0.527737 / 0.226044 (0.301693) | 5.271119 / 2.268929 (3.002191) | 2.618655 / 55.444624 (-52.825970) | 2.312321 / 6.876477 (-4.564155) | 2.270096 / 2.142072 (0.128023) | 0.811563 / 4.805227 (-3.993664) | 0.148512 / 6.500664 (-6.352152) | 0.064562 / 0.075469 (-0.010907) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.212483 / 1.841788 (-0.629304) | 13.471679 / 8.074308 (5.397371) | 13.691054 / 10.191392 (3.499662) | 0.137399 / 0.680424 (-0.543025) | 0.028489 / 0.534201 (-0.505711) | 0.398879 / 0.579283 (-0.180404) | 0.396712 / 0.434364 (-0.037652) | 0.458879 / 0.540337 (-0.081458) | 0.537143 / 1.386936 (-0.849793) |\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) | 0.006911 / 0.011353 (-0.004442) | 0.004941 / 0.011008 (-0.006067) | 0.078606 / 0.038508 (0.040098) | 0.028411 / 0.023109 (0.005302) | 0.352172 / 0.275898 (0.076274) | 0.401155 / 0.323480 (0.077675) | 0.005433 / 0.007986 (-0.002552) | 0.003704 / 0.004328 (-0.000625) | 0.076615 / 0.004250 (0.072365) | 0.043814 / 0.037052 (0.006761) | 0.346928 / 0.258489 (0.088439) | 0.405587 / 0.293841 (0.111746) | 0.032176 / 0.128546 (-0.096370) | 0.011863 / 0.075646 (-0.063783) | 0.087209 / 0.419271 (-0.332063) | 0.042977 / 0.043533 (-0.000556) | 0.345366 / 0.255139 (0.090227) | 0.419664 / 0.283200 (0.136464) | 0.093862 / 0.141683 (-0.047821) | 1.490968 / 1.452155 (0.038813) | 1.566644 / 1.492716 (0.073927) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.216703 / 0.018006 (0.198697) | 0.472411 / 0.000490 (0.471921) | 0.002234 / 0.000200 (0.002034) | 0.000085 / 0.000054 (0.000031) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027672 / 0.037411 (-0.009740) | 0.109793 / 0.014526 (0.095267) | 0.110720 / 0.176557 (-0.065837) | 0.182342 / 0.737135 (-0.554793) | 0.116150 / 0.296338 (-0.180188) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.438165 / 0.215209 (0.222956) | 4.366213 / 2.077655 (2.288558) | 2.065036 / 1.504120 (0.560917) | 1.860105 / 1.541195 (0.318911) | 1.966885 / 1.468490 (0.498395) | 0.705194 / 4.584777 (-3.879583) | 3.389408 / 3.745712 (-0.356304) | 2.632155 / 5.269862 (-2.637707) | 1.471090 / 4.565676 (-3.094587) | 0.083579 / 0.424275 (-0.340697) | 0.012643 / 0.007607 (0.005036) | 0.542230 / 0.226044 (0.316186) | 5.416293 / 2.268929 (3.147365) | 2.517391 / 55.444624 (-52.927233) | 2.160159 / 6.876477 (-4.716317) | 2.167104 / 2.142072 (0.025031) | 0.807142 / 4.805227 (-3.998085) | 0.152249 / 6.500664 (-6.348415) | 0.067559 / 0.075469 (-0.007910) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.399516 / 1.841788 (-0.442272) | 15.289898 / 8.074308 (7.215590) | 14.188758 / 10.191392 (3.997366) | 0.161277 / 0.680424 (-0.519147) | 0.016854 / 0.534201 (-0.517347) | 0.382091 / 0.579283 (-0.197192) | 0.396639 / 0.434364 (-0.037725) | 0.467932 / 0.540337 (-0.072405) | 0.552017 / 1.386936 (-0.834919) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2e050273ec3d2a7e53d817544318b23fb51430d0 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.011038 / 0.011353 (-0.000315) | 0.005878 / 0.011008 (-0.005130) | 0.118247 / 0.038508 (0.079739) | 0.043988 / 0.023109 (0.020879) | 0.350823 / 0.275898 (0.074925) | 0.430350 / 0.323480 (0.106870) | 0.009259 / 0.007986 (0.001274) | 0.004614 / 0.004328 (0.000286) | 0.089366 / 0.004250 (0.085116) | 0.049993 / 0.037052 (0.012941) | 0.367620 / 0.258489 (0.109131) | 0.404809 / 0.293841 (0.110968) | 0.044078 / 0.128546 (-0.084468) | 0.014226 / 0.075646 (-0.061421) | 0.397707 / 0.419271 (-0.021565) | 0.056631 / 0.043533 (0.013098) | 0.355942 / 0.255139 (0.100803) | 0.375537 / 0.283200 (0.092338) | 0.121956 / 0.141683 (-0.019727) | 1.757958 / 1.452155 (0.305803) | 1.822183 / 1.492716 (0.329466) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.024505 / 0.018006 (0.006499) | 0.488754 / 0.000490 (0.488265) | 0.011032 / 0.000200 (0.010832) | 0.000540 / 0.000054 (0.000486) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032895 / 0.037411 (-0.004516) | 0.132496 / 0.014526 (0.117970) | 0.140620 / 0.176557 (-0.035937) | 0.220628 / 0.737135 (-0.516507) | 0.147622 / 0.296338 (-0.148717) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.471335 / 0.215209 (0.256126) | 4.699792 / 2.077655 (2.622137) | 2.119782 / 1.504120 (0.615662) | 1.894784 / 1.541195 (0.353590) | 2.002694 / 1.468490 (0.534204) | 0.822610 / 4.584777 (-3.762167) | 4.511510 / 3.745712 (0.765797) | 2.467017 / 5.269862 (-2.802845) | 1.568500 / 4.565676 (-2.997177) | 0.101488 / 0.424275 (-0.322787) | 0.014567 / 0.007607 (0.006960) | 0.603033 / 0.226044 (0.376989) | 6.041397 / 2.268929 (3.772468) | 2.759140 / 55.444624 (-52.685484) | 2.397192 / 6.876477 (-4.479285) | 2.491986 / 2.142072 (0.349914) | 1.021198 / 4.805227 (-3.784029) | 0.196415 / 6.500664 (-6.304249) | 0.076409 / 0.075469 (0.000939) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.406816 / 1.841788 (-0.434972) | 17.740263 / 8.074308 (9.665954) | 16.926489 / 10.191392 (6.735097) | 0.235302 / 0.680424 (-0.445122) | 0.036829 / 0.534201 (-0.497372) | 0.525326 / 0.579283 (-0.053957) | 0.530905 / 0.434364 (0.096541) | 0.650357 / 0.540337 (0.110019) | 0.770641 / 1.386936 (-0.616295) |\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) | 0.008728 / 0.011353 (-0.002625) | 0.006023 / 0.011008 (-0.004985) | 0.088694 / 0.038508 (0.050186) | 0.040345 / 0.023109 (0.017236) | 0.408126 / 0.275898 (0.132228) | 0.461178 / 0.323480 (0.137698) | 0.007456 / 0.007986 (-0.000529) | 0.004722 / 0.004328 (0.000394) | 0.087340 / 0.004250 (0.083090) | 0.055826 / 0.037052 (0.018774) | 0.422432 / 0.258489 (0.163942) | 0.466308 / 0.293841 (0.172467) | 0.043637 / 0.128546 (-0.084909) | 0.014602 / 0.075646 (-0.061044) | 0.103610 / 0.419271 (-0.315662) | 0.069999 / 0.043533 (0.026466) | 0.410676 / 0.255139 (0.155537) | 0.434551 / 0.283200 (0.151351) | 0.127699 / 0.141683 (-0.013984) | 1.699858 / 1.452155 (0.247703) | 1.830331 / 1.492716 (0.337615) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.235217 / 0.018006 (0.217211) | 0.494814 / 0.000490 (0.494325) | 0.004942 / 0.000200 (0.004742) | 0.000099 / 0.000054 (0.000045) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035996 / 0.037411 (-0.001416) | 0.139419 / 0.014526 (0.124893) | 0.146859 / 0.176557 (-0.029698) | 0.234793 / 0.737135 (-0.502343) | 0.152495 / 0.296338 (-0.143843) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.509812 / 0.215209 (0.294603) | 5.067227 / 2.077655 (2.989572) | 2.455505 / 1.504120 (0.951385) | 2.223516 / 1.541195 (0.682321) | 2.367783 / 1.468490 (0.899293) | 0.852550 / 4.584777 (-3.732227) | 4.517284 / 3.745712 (0.771572) | 4.860399 / 5.269862 (-0.409462) | 2.175290 / 4.565676 (-2.390386) | 0.106155 / 0.424275 (-0.318120) | 0.015023 / 0.007607 (0.007416) | 0.633753 / 0.226044 (0.407708) | 6.316214 / 2.268929 (4.047285) | 3.021118 / 55.444624 (-52.423506) | 2.601317 / 6.876477 (-4.275160) | 2.807988 / 2.142072 (0.665916) | 1.028695 / 4.805227 (-3.776532) | 0.204387 / 6.500664 (-6.296277) | 0.077368 / 0.075469 (0.001899) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.540299 / 1.841788 (-0.301489) | 18.311957 / 8.074308 (10.237649) | 16.139892 / 10.191392 (5.948500) | 0.217231 / 0.680424 (-0.463193) | 0.020544 / 0.534201 (-0.513657) | 0.505589 / 0.579283 (-0.073694) | 0.506694 / 0.434364 (0.072330) | 0.622162 / 0.540337 (0.081824) | 0.739537 / 1.386936 (-0.647399) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0f595fc2aa4786720f7a21da56069a1c46b4552a \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009465 / 0.011353 (-0.001887) | 0.005307 / 0.011008 (-0.005701) | 0.104111 / 0.038508 (0.065603) | 0.036083 / 0.023109 (0.012974) | 0.296608 / 0.275898 (0.020710) | 0.351365 / 0.323480 (0.027885) | 0.008309 / 0.007986 (0.000323) | 0.004383 / 0.004328 (0.000055) | 0.078297 / 0.004250 (0.074047) | 0.044062 / 0.037052 (0.007009) | 0.295592 / 0.258489 (0.037103) | 0.354442 / 0.293841 (0.060602) | 0.038651 / 0.128546 (-0.089896) | 0.012311 / 0.075646 (-0.063335) | 0.337933 / 0.419271 (-0.081338) | 0.048179 / 0.043533 (0.004646) | 0.308320 / 0.255139 (0.053181) | 0.335028 / 0.283200 (0.051829) | 0.105394 / 0.141683 (-0.036289) | 1.444104 / 1.452155 (-0.008050) | 1.573953 / 1.492716 (0.081237) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.236548 / 0.018006 (0.218542) | 0.552862 / 0.000490 (0.552372) | 0.003925 / 0.000200 (0.003726) | 0.000107 / 0.000054 (0.000053) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026386 / 0.037411 (-0.011025) | 0.108002 / 0.014526 (0.093476) | 0.118327 / 0.176557 (-0.058230) | 0.182861 / 0.737135 (-0.554274) | 0.126032 / 0.296338 (-0.170307) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.397037 / 0.215209 (0.181827) | 3.960978 / 2.077655 (1.883323) | 1.771955 / 1.504120 (0.267835) | 1.575033 / 1.541195 (0.033839) | 1.696552 / 1.468490 (0.228062) | 0.679013 / 4.584777 (-3.905764) | 3.770136 / 3.745712 (0.024424) | 2.068323 / 5.269862 (-3.201539) | 1.310823 / 4.565676 (-3.254853) | 0.083752 / 0.424275 (-0.340523) | 0.012366 / 0.007607 (0.004759) | 0.512679 / 0.226044 (0.286635) | 5.127036 / 2.268929 (2.858108) | 2.313200 / 55.444624 (-53.131424) | 1.931007 / 6.876477 (-4.945470) | 2.018336 / 2.142072 (-0.123737) | 0.833033 / 4.805227 (-3.972194) | 0.163778 / 6.500664 (-6.336886) | 0.064053 / 0.075469 (-0.011417) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.234102 / 1.841788 (-0.607685) | 15.227921 / 8.074308 (7.153613) | 14.587146 / 10.191392 (4.395754) | 0.176236 / 0.680424 (-0.504187) | 0.028905 / 0.534201 (-0.505295) | 0.439758 / 0.579283 (-0.139525) | 0.439211 / 0.434364 (0.004848) | 0.544325 / 0.540337 (0.003988) | 0.633804 / 1.386936 (-0.753132) |\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) | 0.007933 / 0.011353 (-0.003420) | 0.005446 / 0.011008 (-0.005563) | 0.077846 / 0.038508 (0.039338) | 0.036017 / 0.023109 (0.012907) | 0.358925 / 0.275898 (0.083027) | 0.402757 / 0.323480 (0.079277) | 0.006478 / 0.007986 (-0.001508) | 0.005708 / 0.004328 (0.001380) | 0.074833 / 0.004250 (0.070583) | 0.053412 / 0.037052 (0.016360) | 0.358587 / 0.258489 (0.100098) | 0.430904 / 0.293841 (0.137063) | 0.037778 / 0.128546 (-0.090768) | 0.012698 / 0.075646 (-0.062948) | 0.087615 / 0.419271 (-0.331657) | 0.050236 / 0.043533 (0.006703) | 0.344160 / 0.255139 (0.089021) | 0.390870 / 0.283200 (0.107670) | 0.111035 / 0.141683 (-0.030648) | 1.446963 / 1.452155 (-0.005192) | 1.566158 / 1.492716 (0.073442) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.302380 / 0.018006 (0.284373) | 0.554005 / 0.000490 (0.553515) | 0.007244 / 0.000200 (0.007044) | 0.000115 / 0.000054 (0.000061) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032291 / 0.037411 (-0.005120) | 0.117117 / 0.014526 (0.102591) | 0.127513 / 0.176557 (-0.049044) | 0.204208 / 0.737135 (-0.532927) | 0.133730 / 0.296338 (-0.162608) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.424597 / 0.215209 (0.209388) | 4.233852 / 2.077655 (2.156198) | 2.029731 / 1.504120 (0.525611) | 1.830075 / 1.541195 (0.288880) | 1.966198 / 1.468490 (0.497707) | 0.697881 / 4.584777 (-3.886896) | 3.758012 / 3.745712 (0.012299) | 3.405319 / 5.269862 (-1.864542) | 1.870816 / 4.565676 (-2.694860) | 0.086892 / 0.424275 (-0.337383) | 0.012438 / 0.007607 (0.004831) | 0.524252 / 0.226044 (0.298207) | 5.209534 / 2.268929 (2.940606) | 2.478608 / 55.444624 (-52.966017) | 2.151535 / 6.876477 (-4.724942) | 2.249260 / 2.142072 (0.107187) | 0.831955 / 4.805227 (-3.973273) | 0.165955 / 6.500664 (-6.334710) | 0.064663 / 0.075469 (-0.010806) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.253327 / 1.841788 (-0.588460) | 15.904393 / 8.074308 (7.830085) | 13.253464 / 10.191392 (3.062072) | 0.162148 / 0.680424 (-0.518276) | 0.017643 / 0.534201 (-0.516558) | 0.425028 / 0.579283 (-0.154255) | 0.425615 / 0.434364 (-0.008749) | 0.521503 / 0.540337 (-0.018835) | 0.629473 / 1.386936 (-0.757463) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#939b2332115c7ec3dd56f58169800ed81cc4a982 \"CML watermark\")\n" ]

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[ "Hi! I've merged a PR to use `int32` instead of `int8` for `reddit_scores`, so it should work now.\r\n\r\n" ]

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[ "Duplicated issue." ]

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[ "Hi! Indeed it would be useful to support this. PyArrow natively supports schema-level and column-level metadata, so implementing this should be straightforward. The API I have in mind would work as follows:\r\n```python\r\ncol_feature = Value(\"string\", metadata=\"Some column-level metadata\")\r\n\r\nfeatures = Features({\"col\": col_feature}, metadata=\"Some schema-level metadata\")\r\n```\r\n\r\nWDYT?", "Sorry for the late reply, \r\nYes, I think this is the most straight-forward approach with the things that we already have.\r\n\r\n", "@mariosasko Let me know how I can help." ]

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[ "Also encountering this issue for every dataset I try to stream! Installed datasets from main:\r\n```\r\n- `datasets` version: 2.10.1.dev0\r\n- Platform: macOS-13.1-arm64-arm-64bit\r\n- Python version: 3.9.13\r\n- PyArrow version: 10.0.1\r\n- Pandas version: 1.5.2\r\n```\r\n\r\nRepro:\r\n```python\r\nfrom datasets import load_dataset\r\n\r\nspigi = load_dataset(\"kensho/spgispeech\", \"dev\", split=\"validation\", streaming=True, use_auth_token=True)\r\nsample = next(iter(spigi))\r\n```\r\n\r\n<details>\r\n<summary> Traceback </summary>\r\n\r\n```python\r\n---------------------------------------------------------------------------\r\nClientResponseError Traceback (most recent call last)\r\nFile ~/venv/lib/python3.9/site-packages/fsspec/implementations/http.py:407, in HTTPFileSystem._info(self, url, **kwargs)\r\n 405 try:\r\n 406 info.update(\r\n--> 407 await _file_info(\r\n 408 self.encode_url(url),\r\n 409 size_policy=policy,\r\n 410 session=session,\r\n 411 **self.kwargs,\r\n 412 **kwargs,\r\n 413 )\r\n 414 )\r\n 415 if info.get(\"size\") is not None:\r\n\r\nFile ~/venv/lib/python3.9/site-packages/fsspec/implementations/http.py:792, in _file_info(url, session, size_policy, **kwargs)\r\n 791 async with r:\r\n--> 792 r.raise_for_status()\r\n 794 # TODO:\r\n 795 # recognise lack of 'Accept-Ranges',\r\n 796 # or 'Accept-Ranges': 'none' (not 'bytes')\r\n 797 # to mean streaming only, no random access => return None\r\n\r\nFile ~/venv/lib/python3.9/site-packages/aiohttp/client_reqrep.py:1005, in ClientResponse.raise_for_status(self)\r\n 1004 self.release()\r\n-> 1005 raise ClientResponseError(\r\n 1006 self.request_info,\r\n 1007 self.history,\r\n 1008 status=self.status,\r\n 1009 message=self.reason,\r\n 1010 headers=self.headers,\r\n 1011 )\r\n\r\nClientResponseError: 403, message='Forbidden', url=URL('[https://cdn-lfs.huggingface.co/repos/e2/89/e28905247d6f48bb4edad5baf9b1bb4158e897a13fdf18bf3b8ee89ff8387ab8/46eca7431a7b6bad344bf451800e5b10cea1dd168f26d1027a6d9eb374b7fac3?response-content-disposition=attachment%3B+filename*%3DUTF-8''dev.csv%3B+filename%3D%22dev.csv%22%3B&response-content-type=text/csv&Expires=1677494732&Policy=eyJTdGF0ZW1lbnQiOlt7IlJlc291cmNlIjoiaHR0cHM6Ly9jZG4tbGZzLmh1Z2dpbmdmYWNlLmNvL3JlcG9zL2UyLzg5L2UyODkwNTI0N2Q2ZjQ4YmI0ZWRhZDViYWY5YjFiYjQxNThlODk3YTEzZmRmMThiZjNiOGVlODlmZjgzODdhYjgvNDZlY2E3NDMxYTdiNmJhZDM0NGJmNDUxODAwZTViMTBjZWExZGQxNjhmMjZkMTAyN2E2ZDllYjM3NGI3ZmFjMz9yZXNwb25zZS1jb250ZW50LWRpc3Bvc2l0aW9uPSomcmVzcG9uc2UtY29udGVudC10eXBlPXRleHQlMkZjc3YiLCJDb25kaXRpb24iOnsiRGF0ZUxlc3NUaGFuIjp7IkFXUzpFcG9jaFRpbWUiOjE2Nzc0OTQ3MzJ9fX1dfQ__&Signature=EzQB9f7xPckvqfFB6LzcyR-wzTnQCqtPDdWtQUzZ3QJ-gY-IHG5mxQITJgMr1nVTbJZrPmGAaDngMcPFUfSQa8RmCqYH~dZl-UGE8CO4neKNUT1DvA2WEvLDS4WaAJ3SN-9rX0uFb03~c1QS78cIgIRboYvf6ugKiJz86Bd7Vs~tcp201JFR0A6jIMseqApOnkb9d8dHMP3Ny~F6gO3Qf2QpEWM-QsDIyw2Kz2QV55nq8TsDpRYZCZo50~WwD~73Hej0PoDhEA1K37d19pa0CQhkaN-gjCrbT9xLabbvhJWa~ZkWcMdD0teCgjYqv1wKyvFXDAxukxLGEc7OBXVbYw__&Key-Pair-Id=KVTP0A1DKRTAX](https://cdn-lfs.huggingface.co/repos/e2/89/e28905247d6f48bb4edad5baf9b1bb4158e897a13fdf18bf3b8ee89ff8387ab8/46eca7431a7b6bad344bf451800e5b10cea1dd168f26d1027a6d9eb374b7fac3?response-content-disposition=attachment%3B+filename*%3DUTF-8%27%27dev.csv%3B+filename%3D%22dev.csv%22%3B&response-content-type=text/csv&Expires=1677494732&Policy=eyJTdGF0ZW1lbnQiOlt7IlJlc291cmNlIjoiaHR0cHM6Ly9jZG4tbGZzLmh1Z2dpbmdmYWNlLmNvL3JlcG9zL2UyLzg5L2UyODkwNTI0N2Q2ZjQ4YmI0ZWRhZDViYWY5YjFiYjQxNThlODk3YTEzZmRmMThiZjNiOGVlODlmZjgzODdhYjgvNDZlY2E3NDMxYTdiNmJhZDM0NGJmNDUxODAwZTViMTBjZWExZGQxNjhmMjZkMTAyN2E2ZDllYjM3NGI3ZmFjMz9yZXNwb25zZS1jb250ZW50LWRpc3Bvc2l0aW9uPSomcmVzcG9uc2UtY29udGVudC10eXBlPXRleHQlMkZjc3YiLCJDb25kaXRpb24iOnsiRGF0ZUxlc3NUaGFuIjp7IkFXUzpFcG9jaFRpbWUiOjE2Nzc0OTQ3MzJ9fX1dfQ__&Signature=EzQB9f7xPckvqfFB6LzcyR-wzTnQCqtPDdWtQUzZ3QJ-gY-IHG5mxQITJgMr1nVTbJZrPmGAaDngMcPFUfSQa8RmCqYH~dZl-UGE8CO4neKNUT1DvA2WEvLDS4WaAJ3SN-9rX0uFb03~c1QS78cIgIRboYvf6ugKiJz86Bd7Vs~tcp201JFR0A6jIMseqApOnkb9d8dHMP3Ny~F6gO3Qf2QpEWM-QsDIyw2Kz2QV55nq8TsDpRYZCZo50~WwD~73Hej0PoDhEA1K37d19pa0CQhkaN-gjCrbT9xLabbvhJWa~ZkWcMdD0teCgjYqv1wKyvFXDAxukxLGEc7OBXVbYw__&Key-Pair-Id=KVTP0A1DKRTAX)')\r\n\r\nThe above exception was the direct cause of the following exception:\r\n\r\nFileNotFoundError Traceback (most recent call last)\r\nCell In[5], line 4\r\n 1 from datasets import load_dataset\r\n 3 spigi = load_dataset(\"kensho/spgispeech\", \"dev\", split=\"validation\", streaming=True)\r\n----> 4 sample = next(iter(spigi))\r\n\r\nFile ~/datasets/src/datasets/iterable_dataset.py:937, in IterableDataset.__iter__(self)\r\n 934 yield from self._iter_pytorch(ex_iterable)\r\n 935 return\r\n--> 937 for key, example in ex_iterable:\r\n 938 if self.features:\r\n 939 # `IterableDataset` automatically fills missing columns with None.\r\n 940 # This is done with `_apply_feature_types_on_example`.\r\n 941 yield _apply_feature_types_on_example(\r\n 942 example, self.features, token_per_repo_id=self._token_per_repo_id\r\n 943 )\r\n\r\nFile ~/datasets/src/datasets/iterable_dataset.py:113, in ExamplesIterable.__iter__(self)\r\n 112 def __iter__(self):\r\n--> 113 yield from self.generate_examples_fn(**self.kwargs)\r\n\r\nFile ~/.cache/huggingface/modules/datasets_modules/datasets/kensho--spgispeech/5fbf75dd9ef795a9b5a673457d2cbaf0b8fa0de8fb62acbd1da338d83a41e2f0/spgispeech.py:186, in Spgispeech._generate_examples(self, local_extracted_archive_paths, archives, meta_path)\r\n 183 dict_keys = [\"wav_filename\", \"wav_filesize\", \"transcript\"]\r\n 185 logging.info(\"Reading metadata...\")\r\n--> 186 with open(meta_path, encoding=\"utf-8\") as f:\r\n 187 csvreader = csv.DictReader(f, delimiter=\"|\")\r\n 188 metadata = {x[\"wav_filename\"]: dict((k, x[k]) for k in dict_keys) for x in csvreader}\r\n\r\nFile ~/datasets/src/datasets/streaming.py:70, in extend_module_for_streaming.<locals>.wrap_auth.<locals>.wrapper(*args, **kwargs)\r\n 68 @wraps(function)\r\n 69 def wrapper(*args, **kwargs):\r\n---> 70 return function(*args, use_auth_token=use_auth_token, **kwargs)\r\n\r\nFile ~/datasets/src/datasets/download/streaming_download_manager.py:495, in xopen(file, mode, use_auth_token, *args, **kwargs)\r\n 493 kwargs = {**kwargs, **new_kwargs}\r\n 494 try:\r\n--> 495 file_obj = fsspec.open(file, mode=mode, *args, **kwargs).open()\r\n 496 except ValueError as e:\r\n 497 if str(e) == \"Cannot seek streaming HTTP file\":\r\n\r\nFile ~/venv/lib/python3.9/site-packages/fsspec/core.py:135, in OpenFile.open(self)\r\n 128 def open(self):\r\n 129 \"\"\"Materialise this as a real open file without context\r\n 130 \r\n 131 The OpenFile object should be explicitly closed to avoid enclosed file\r\n 132 instances persisting. You must, therefore, keep a reference to the OpenFile\r\n 133 during the life of the file-like it generates.\r\n 134 \"\"\"\r\n--> 135 return self.__enter__()\r\n\r\nFile ~/venv/lib/python3.9/site-packages/fsspec/core.py:103, in OpenFile.__enter__(self)\r\n 100 def __enter__(self):\r\n 101 mode = self.mode.replace(\"t\", \"\").replace(\"b\", \"\") + \"b\"\r\n--> 103 f = self.fs.open(self.path, mode=mode)\r\n 105 self.fobjects = [f]\r\n 107 if self.compression is not None:\r\n\r\nFile ~/venv/lib/python3.9/site-packages/fsspec/spec.py:1106, in AbstractFileSystem.open(self, path, mode, block_size, cache_options, compression, **kwargs)\r\n 1104 else:\r\n 1105 ac = kwargs.pop(\"autocommit\", not self._intrans)\r\n-> 1106 f = self._open(\r\n 1107 path,\r\n 1108 mode=mode,\r\n 1109 block_size=block_size,\r\n 1110 autocommit=ac,\r\n 1111 cache_options=cache_options,\r\n 1112 **kwargs,\r\n 1113 )\r\n 1114 if compression is not None:\r\n 1115 from fsspec.compression import compr\r\n\r\nFile ~/venv/lib/python3.9/site-packages/fsspec/implementations/http.py:346, in HTTPFileSystem._open(self, path, mode, block_size, autocommit, cache_type, cache_options, size, **kwargs)\r\n 344 kw[\"asynchronous\"] = self.asynchronous\r\n 345 kw.update(kwargs)\r\n--> 346 size = size or self.info(path, **kwargs)[\"size\"]\r\n 347 session = sync(self.loop, self.set_session)\r\n 348 if block_size and size:\r\n\r\nFile ~/venv/lib/python3.9/site-packages/fsspec/asyn.py:113, in sync_wrapper.<locals>.wrapper(*args, **kwargs)\r\n 110 @functools.wraps(func)\r\n 111 def wrapper(*args, **kwargs):\r\n 112 self = obj or args[0]\r\n--> 113 return sync(self.loop, func, *args, **kwargs)\r\n\r\nFile ~/venv/lib/python3.9/site-packages/fsspec/asyn.py:98, in sync(loop, func, timeout, *args, **kwargs)\r\n 96 raise FSTimeoutError from return_result\r\n 97 elif isinstance(return_result, BaseException):\r\n---> 98 raise return_result\r\n 99 else:\r\n 100 return return_result\r\n\r\nFile ~/venv/lib/python3.9/site-packages/fsspec/asyn.py:53, in _runner(event, coro, result, timeout)\r\n 51 coro = asyncio.wait_for(coro, timeout=timeout)\r\n 52 try:\r\n---> 53 result[0] = await coro\r\n 54 except Exception as ex:\r\n 55 result[0] = ex\r\n\r\nFile ~/venv/lib/python3.9/site-packages/fsspec/implementations/http.py:420, in HTTPFileSystem._info(self, url, **kwargs)\r\n 417 except Exception as exc:\r\n 418 if policy == \"get\":\r\n 419 # If get failed, then raise a FileNotFoundError\r\n--> 420 raise FileNotFoundError(url) from exc\r\n 421 logger.debug(str(exc))\r\n 423 return {\"name\": url, \"size\": None, **info, \"type\": \"file\"}\r\n\r\nFileNotFoundError: https://huggingface.co/datasets/kensho/spgispeech/resolve/main/data/meta/dev.csv\r\n```\r\n</details>", "Hi ! We're investigating this issue, sorry for the inconvenience", "This has been resolved ! Thanks for reporting", "Wow, thanks for the very quick fix!", "This problem now appears again, this time with an underlying HTTP 502 status code:\r\n\r\n```\r\naiohttp.client_exceptions.ClientResponseError: 502, message='Bad Gateway', url=URL('https://huggingface.co/datasets/allenai/c4/resolve/1ddc917116b730e1859edef32896ec5c16be51d0/en/c4-validation.00002-of-00008.json.gz')\r\n```", "Re-executing a minute later, the underlying cause is an HTTP 403 status code, as reported yesterday:\r\n\r\n```\r\naiohttp.client_exceptions.ClientResponseError: 403, message='Forbidden', url=URL('https://cdn-lfs.huggingface.co/datasets/allenai/c4/4bf6b248b0f910dcde2cdf2118d6369d8208c8f9515ec29ab73e531f380b18e2?response-content-disposition=attachment%3B+filename*%3DUTF-8''c4-validation.00002-of-00008.json.gz%3B+filename%3D%22c4-validation.00002-of-00008.json.gz%22%3B&response-content-type=application/gzip&Expires=1677571273&Policy=eyJTdGF0ZW1lbnQiOlt7IlJlc291cmNlIjoiaHR0cHM6Ly9jZG4tbGZzLmh1Z2dpbmdmYWNlLmNvL2RhdGFzZXRzL2FsbGVuYWkvYzQvNGJmNmIyNDhiMGY5MTBkY2RlMmNkZjIxMThkNjM2OWQ4MjA4YzhmOTUxNWVjMjlhYjczZTUzMWYzODBiMThlMj9yZXNwb25zZS1jb250ZW50LWRpc3Bvc2l0aW9uPSomcmVzcG9uc2UtY29udGVudC10eXBlPWFwcGxpY2F0aW9uJTJGZ3ppcCIsIkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTY3NzU3MTI3M319fV19&Signature=WW42NOKkLuX~xVB1QfbkqzdvGo2AOXpgbF3PjTXy6iKd~ffilr1N9ScPXfvTXqy5yvdhJg1G0xJy1zYtUjGAL8GEx3Av-0vIhpWMGYTM8XKEU5gYA9qt30oVtNph6TkTYSABrsYTaj-hzQL9WCgyapmjvG69ETMh4wj44r2rcbk4T3j0l6l4u76Gh~lyRSll3aK4qycdUwcyL7FECDu~0W1mJIJwKkCrWHhSpHJSshb-0ElwG71pq4eyQ5g2uxHdK6JbRF7loxUpRQQJ1vlk0EHXdw0wTMaQ9tqHy6xcrQd8Ep0Yvx3tUD8MR0vWOcbQKnL6LwPQByc8tkChlpjnig__&Key-Pair-Id=KVTP0A1DKRTAX')\r\n```", "I'm facing the same problem. Interestingly using `wget` I can download the file. ", "It's been resolved again ;)", "> It's been resolved again ;)\r\n\r\nI'm experiencing the same issue when trying to load this dataset, `FileNotFoundError: https://huggingface.co/datasets/allenai/c4/resolve/1ddc917116b730e1859edef32896ec5c16be51d0/realnewslike/c4-train.00000-of-00512.json.gz`" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "@mariosasko thank you for the review! do you have any idea why `test_hash_torch_tensor` fails on \"ubuntu-latest deps-minimum\"? I removed the `torchaudio<0.12.0` test dependency so it uses the latest `torch` now, might it be connected?", "@polinaeterna The failure is due to `torch.from_numpy` not being picklable in newer versions of PyTorch. You can replace the current definition of `_save_tensor` in `utils/py_utils.py` with the following one to fix it: \r\n\r\n```python\r\n@pklregister(obj_type)\r\ndef _save_tensor(pickler, obj):\r\n # `torch.from_numpy` is not picklable in `torch>=1.11.0`\r\n def _create_tensor(np_array):\r\n return torch.from_numpy(np_array)\r\n\r\n dill_log(pickler, f\"To: {obj}\")\r\n args = (obj.detach().cpu().numpy(),)\r\n pickler.save_reduce(_create_tensor, args, obj=obj)\r\n dill_log(pickler, \"# To\")\r\n return\r\n```", "(doing a patch release now - please wait before merging ^^)", "@mariosasko génial, merci!! i've integrated all your changes, can you pls take a look one more time?", "Patch release is done (I did it from another branch than `main` anyway)", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.010927 / 0.011353 (-0.000426) | 0.006232 / 0.011008 (-0.004776) | 0.119815 / 0.038508 (0.081307) | 0.034138 / 0.023109 (0.011029) | 0.349945 / 0.275898 (0.074047) | 0.404967 / 0.323480 (0.081487) | 0.008672 / 0.007986 (0.000687) | 0.005010 / 0.004328 (0.000681) | 0.091931 / 0.004250 (0.087680) | 0.042534 / 0.037052 (0.005482) | 0.374701 / 0.258489 (0.116212) | 0.401027 / 0.293841 (0.107186) | 0.053523 / 0.128546 (-0.075024) | 0.019704 / 0.075646 (-0.055942) | 0.384207 / 0.419271 (-0.035064) | 0.065350 / 0.043533 (0.021817) | 0.375074 / 0.255139 (0.119935) | 0.390458 / 0.283200 (0.107259) | 0.110549 / 0.141683 (-0.031134) | 1.719812 / 1.452155 (0.267657) | 1.748906 / 1.492716 (0.256190) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.210051 / 0.018006 (0.192045) | 0.546503 / 0.000490 (0.546013) | 0.004078 / 0.000200 (0.003878) | 0.000111 / 0.000054 (0.000056) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030212 / 0.037411 (-0.007199) | 0.121845 / 0.014526 (0.107319) | 0.136309 / 0.176557 (-0.040247) | 0.204667 / 0.737135 (-0.532468) | 0.157327 / 0.296338 (-0.139012) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.672548 / 0.215209 (0.457339) | 6.239409 / 2.077655 (4.161754) | 2.462441 / 1.504120 (0.958322) | 2.063985 / 1.541195 (0.522791) | 2.098858 / 1.468490 (0.630368) | 1.262600 / 4.584777 (-3.322177) | 5.478462 / 3.745712 (1.732750) | 5.454672 / 5.269862 (0.184810) | 2.991866 / 4.565676 (-1.573810) | 0.153415 / 0.424275 (-0.270861) | 0.015061 / 0.007607 (0.007454) | 0.796115 / 0.226044 (0.570071) | 8.206858 / 2.268929 (5.937930) | 3.226395 / 55.444624 (-52.218229) | 2.503522 / 6.876477 (-4.372955) | 2.547489 / 2.142072 (0.405417) | 1.504776 / 4.805227 (-3.300451) | 0.256536 / 6.500664 (-6.244128) | 0.078543 / 0.075469 (0.003073) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.591109 / 1.841788 (-0.250678) | 18.153317 / 8.074308 (10.079008) | 20.465684 / 10.191392 (10.274292) | 0.229808 / 0.680424 (-0.450616) | 0.045263 / 0.534201 (-0.488938) | 0.556760 / 0.579283 (-0.022524) | 0.614985 / 0.434364 (0.180622) | 0.635675 / 0.540337 (0.095337) | 0.729817 / 1.386936 (-0.657119) |\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) | 0.011247 / 0.011353 (-0.000106) | 0.006823 / 0.011008 (-0.004185) | 0.101989 / 0.038508 (0.063481) | 0.036077 / 0.023109 (0.012968) | 0.413469 / 0.275898 (0.137571) | 0.505560 / 0.323480 (0.182080) | 0.007506 / 0.007986 (-0.000480) | 0.006369 / 0.004328 (0.002040) | 0.099597 / 0.004250 (0.095346) | 0.058115 / 0.037052 (0.021063) | 0.414735 / 0.258489 (0.156246) | 0.466801 / 0.293841 (0.172960) | 0.064771 / 0.128546 (-0.063775) | 0.021100 / 0.075646 (-0.054546) | 0.135407 / 0.419271 (-0.283864) | 0.068784 / 0.043533 (0.025251) | 0.410467 / 0.255139 (0.155328) | 0.465993 / 0.283200 (0.182794) | 0.119404 / 0.141683 (-0.022279) | 1.767107 / 1.452155 (0.314952) | 1.938342 / 1.492716 (0.445626) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.227038 / 0.018006 (0.209032) | 0.511389 / 0.000490 (0.510899) | 0.006723 / 0.000200 (0.006523) | 0.000118 / 0.000054 (0.000064) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033078 / 0.037411 (-0.004333) | 0.133159 / 0.014526 (0.118633) | 0.147928 / 0.176557 (-0.028629) | 0.214005 / 0.737135 (-0.523130) | 0.151655 / 0.296338 (-0.144683) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.634829 / 0.215209 (0.419620) | 6.578640 / 2.077655 (4.500985) | 2.673598 / 1.504120 (1.169478) | 2.338671 / 1.541195 (0.797476) | 2.389104 / 1.468490 (0.920614) | 1.274938 / 4.584777 (-3.309839) | 5.746524 / 3.745712 (2.000812) | 5.992084 / 5.269862 (0.722222) | 3.092090 / 4.565676 (-1.473587) | 0.150375 / 0.424275 (-0.273900) | 0.015470 / 0.007607 (0.007863) | 0.792962 / 0.226044 (0.566918) | 8.057491 / 2.268929 (5.788563) | 3.483966 / 55.444624 (-51.960659) | 2.715038 / 6.876477 (-4.161438) | 2.747186 / 2.142072 (0.605114) | 1.532951 / 4.805227 (-3.272276) | 0.262214 / 6.500664 (-6.238450) | 0.081308 / 0.075469 (0.005839) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.698448 / 1.841788 (-0.143340) | 18.590002 / 8.074308 (10.515694) | 20.584508 / 10.191392 (10.393116) | 0.227237 / 0.680424 (-0.453187) | 0.028445 / 0.534201 (-0.505756) | 0.527874 / 0.579283 (-0.051409) | 0.602844 / 0.434364 (0.168480) | 0.672948 / 0.540337 (0.132611) | 0.788103 / 1.386936 (-0.598833) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f96547708a889c09ca8a02ed7aadd8c5690503c5 \"CML watermark\")\n" ]

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[ "Hi! \r\n\r\nYou need to pass an input json file explicitly as `data_files` to `load_dataset` to avoid this error:\r\n```python\r\n ds = load_dataset(\"json\", data_files=args.input_json)\r\n```\r\n\r\n", "Thanks it worked!" ]

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[ "Hi, thanks for the feedback! Would it help to add a tip or note saying the dataset is gated and you need to accept the license before downloading it?", "The error is now more informative:\r\n```\r\nFileNotFoundError: Couldn't find a dataset script at /content/imagenet-1k/imagenet-1k.py or any data file in the same directory. Couldn't find 'imagenet-1k' on the Hugging Face Hub either: FileNotFoundError: Dataset 'imagenet-1k' doesn't exist on the Hub. If the repo is private or gated, make sure to log in with `huggingface-cli login`.\r\n```\r\n\r\n" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008753 / 0.011353 (-0.002600) | 0.004877 / 0.011008 (-0.006131) | 0.098320 / 0.038508 (0.059812) | 0.034123 / 0.023109 (0.011014) | 0.289539 / 0.275898 (0.013641) | 0.323584 / 0.323480 (0.000104) | 0.007455 / 0.007986 (-0.000531) | 0.004763 / 0.004328 (0.000434) | 0.074350 / 0.004250 (0.070100) | 0.039018 / 0.037052 (0.001966) | 0.294319 / 0.258489 (0.035830) | 0.348686 / 0.293841 (0.054845) | 0.037814 / 0.128546 (-0.090732) | 0.011808 / 0.075646 (-0.063838) | 0.333808 / 0.419271 (-0.085464) | 0.047758 / 0.043533 (0.004225) | 0.298533 / 0.255139 (0.043394) | 0.320790 / 0.283200 (0.037590) | 0.095909 / 0.141683 (-0.045774) | 1.434422 / 1.452155 (-0.017732) | 1.509703 / 1.492716 (0.016987) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.201728 / 0.018006 (0.183722) | 0.432243 / 0.000490 (0.431753) | 0.002760 / 0.000200 (0.002560) | 0.000080 / 0.000054 (0.000026) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026090 / 0.037411 (-0.011321) | 0.105914 / 0.014526 (0.091388) | 0.115869 / 0.176557 (-0.060688) | 0.178291 / 0.737135 (-0.558844) | 0.121435 / 0.296338 (-0.174904) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.402304 / 0.215209 (0.187095) | 3.995183 / 2.077655 (1.917529) | 1.794548 / 1.504120 (0.290428) | 1.603034 / 1.541195 (0.061839) | 1.643836 / 1.468490 (0.175346) | 0.694934 / 4.584777 (-3.889843) | 3.695128 / 3.745712 (-0.050584) | 2.018582 / 5.269862 (-3.251279) | 1.294315 / 4.565676 (-3.271362) | 0.085346 / 0.424275 (-0.338929) | 0.012201 / 0.007607 (0.004594) | 0.510057 / 0.226044 (0.284012) | 5.123404 / 2.268929 (2.854476) | 2.319089 / 55.444624 (-53.125535) | 1.930935 / 6.876477 (-4.945542) | 1.939700 / 2.142072 (-0.202372) | 0.848282 / 4.805227 (-3.956945) | 0.165561 / 6.500664 (-6.335103) | 0.062028 / 0.075469 (-0.013441) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.220576 / 1.841788 (-0.621212) | 14.413853 / 8.074308 (6.339544) | 14.027156 / 10.191392 (3.835764) | 0.170109 / 0.680424 (-0.510315) | 0.029412 / 0.534201 (-0.504789) | 0.443898 / 0.579283 (-0.135386) | 0.433059 / 0.434364 (-0.001305) | 0.533465 / 0.540337 (-0.006872) | 0.626562 / 1.386936 (-0.760374) |\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) | 0.007148 / 0.011353 (-0.004205) | 0.005019 / 0.011008 (-0.005989) | 0.073132 / 0.038508 (0.034624) | 0.032763 / 0.023109 (0.009654) | 0.329309 / 0.275898 (0.053411) | 0.361658 / 0.323480 (0.038178) | 0.005683 / 0.007986 (-0.002302) | 0.003793 / 0.004328 (-0.000536) | 0.071858 / 0.004250 (0.067608) | 0.045160 / 0.037052 (0.008107) | 0.335852 / 0.258489 (0.077363) | 0.384274 / 0.293841 (0.090433) | 0.036647 / 0.128546 (-0.091899) | 0.012217 / 0.075646 (-0.063430) | 0.086265 / 0.419271 (-0.333007) | 0.049223 / 0.043533 (0.005690) | 0.331460 / 0.255139 (0.076321) | 0.353175 / 0.283200 (0.069975) | 0.102214 / 0.141683 (-0.039469) | 1.491451 / 1.452155 (0.039296) | 1.553702 / 1.492716 (0.060985) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.222972 / 0.018006 (0.204966) | 0.432862 / 0.000490 (0.432372) | 0.000421 / 0.000200 (0.000221) | 0.000059 / 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) | 0.028401 / 0.037411 (-0.009010) | 0.109331 / 0.014526 (0.094805) | 0.119246 / 0.176557 (-0.057311) | 0.187997 / 0.737135 (-0.549138) | 0.124212 / 0.296338 (-0.172127) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.427240 / 0.215209 (0.212031) | 4.271619 / 2.077655 (2.193964) | 2.104948 / 1.504120 (0.600828) | 1.910624 / 1.541195 (0.369430) | 1.943812 / 1.468490 (0.475322) | 0.711466 / 4.584777 (-3.873311) | 3.778987 / 3.745712 (0.033275) | 2.976258 / 5.269862 (-2.293604) | 1.807591 / 4.565676 (-2.758086) | 0.088286 / 0.424275 (-0.335989) | 0.012461 / 0.007607 (0.004854) | 0.527554 / 0.226044 (0.301509) | 5.279461 / 2.268929 (3.010532) | 2.517911 / 55.444624 (-52.926713) | 2.176557 / 6.876477 (-4.699920) | 2.205322 / 2.142072 (0.063249) | 0.855012 / 4.805227 (-3.950215) | 0.170007 / 6.500664 (-6.330658) | 0.065273 / 0.075469 (-0.010196) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.282785 / 1.841788 (-0.559003) | 14.819500 / 8.074308 (6.745192) | 13.282211 / 10.191392 (3.090819) | 0.161804 / 0.680424 (-0.518620) | 0.017615 / 0.534201 (-0.516586) | 0.420159 / 0.579283 (-0.159124) | 0.441304 / 0.434364 (0.006940) | 0.531704 / 0.540337 (-0.008634) | 0.627477 / 1.386936 (-0.759459) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b91070b9c09673e2e148eec458036ab6a62ac042 \"CML watermark\")\n", "Hmm I think we need to add more tests. Not sure what would happen with :\r\n- decodable features that may end up decoded twice \r\n- formatted datasets \r\n\r\nI'd be in favor of reverting this until we checked those" ]

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[ "Hi ! Oh good catch. I think the features should be passed to `IterableDataset.from_generator()` in `to_iterable_dataset()` indeed.\r\n\r\nSetting this as a good first issue if someone would like to contribute, otherwise we can take care of it :)", "#self-assign", "seems like the feature parameter is missing from `return IterableDataset.from_generator(Dataset._iter_shards, gen_kwargs={\"shards\": shards})` hence it defaults to None." ]

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[]

[ "Hi ! I think is in the scope of this other issue: to https://github.com/huggingface/datasets/issues/5281 " ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008745 / 0.011353 (-0.002608) | 0.004651 / 0.011008 (-0.006357) | 0.099678 / 0.038508 (0.061170) | 0.029441 / 0.023109 (0.006332) | 0.300314 / 0.275898 (0.024416) | 0.342022 / 0.323480 (0.018542) | 0.006965 / 0.007986 (-0.001021) | 0.003382 / 0.004328 (-0.000946) | 0.078195 / 0.004250 (0.073945) | 0.033308 / 0.037052 (-0.003744) | 0.300857 / 0.258489 (0.042368) | 0.356763 / 0.293841 (0.062922) | 0.033919 / 0.128546 (-0.094627) | 0.011436 / 0.075646 (-0.064210) | 0.319581 / 0.419271 (-0.099691) | 0.041303 / 0.043533 (-0.002229) | 0.299387 / 0.255139 (0.044248) | 0.327783 / 0.283200 (0.044583) | 0.087210 / 0.141683 (-0.054473) | 1.498757 / 1.452155 (0.046603) | 1.560417 / 1.492716 (0.067701) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.191806 / 0.018006 (0.173800) | 0.407044 / 0.000490 (0.406554) | 0.005116 / 0.000200 (0.004916) | 0.000073 / 0.000054 (0.000019) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023760 / 0.037411 (-0.013652) | 0.096844 / 0.014526 (0.082318) | 0.104710 / 0.176557 (-0.071847) | 0.168161 / 0.737135 (-0.568974) | 0.107808 / 0.296338 (-0.188531) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.417707 / 0.215209 (0.202498) | 4.155952 / 2.077655 (2.078297) | 1.864934 / 1.504120 (0.360814) | 1.654925 / 1.541195 (0.113730) | 1.731341 / 1.468490 (0.262851) | 0.692014 / 4.584777 (-3.892763) | 3.407318 / 3.745712 (-0.338394) | 3.394791 / 5.269862 (-1.875071) | 1.650429 / 4.565676 (-2.915247) | 0.082177 / 0.424275 (-0.342098) | 0.012463 / 0.007607 (0.004856) | 0.523681 / 0.226044 (0.297637) | 5.249426 / 2.268929 (2.980498) | 2.327443 / 55.444624 (-53.117181) | 1.982160 / 6.876477 (-4.894317) | 2.019822 / 2.142072 (-0.122250) | 0.804820 / 4.805227 (-4.000408) | 0.148423 / 6.500664 (-6.352241) | 0.064938 / 0.075469 (-0.010531) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.225722 / 1.841788 (-0.616066) | 13.774257 / 8.074308 (5.699949) | 14.090298 / 10.191392 (3.898906) | 0.152489 / 0.680424 (-0.527935) | 0.028595 / 0.534201 (-0.505606) | 0.399011 / 0.579283 (-0.180272) | 0.399546 / 0.434364 (-0.034818) | 0.485513 / 0.540337 (-0.054824) | 0.564055 / 1.386936 (-0.822881) |\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) | 0.006891 / 0.011353 (-0.004462) | 0.004557 / 0.011008 (-0.006451) | 0.077868 / 0.038508 (0.039360) | 0.028767 / 0.023109 (0.005657) | 0.344127 / 0.275898 (0.068229) | 0.377097 / 0.323480 (0.053617) | 0.005119 / 0.007986 (-0.002866) | 0.003547 / 0.004328 (-0.000782) | 0.077047 / 0.004250 (0.072796) | 0.043037 / 0.037052 (0.005984) | 0.341900 / 0.258489 (0.083410) | 0.384570 / 0.293841 (0.090729) | 0.032606 / 0.128546 (-0.095940) | 0.011752 / 0.075646 (-0.063894) | 0.086731 / 0.419271 (-0.332540) | 0.045459 / 0.043533 (0.001926) | 0.339308 / 0.255139 (0.084169) | 0.370498 / 0.283200 (0.087298) | 0.096237 / 0.141683 (-0.045446) | 1.499253 / 1.452155 (0.047098) | 1.583871 / 1.492716 (0.091154) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.245471 / 0.018006 (0.227465) | 0.408750 / 0.000490 (0.408260) | 0.008992 / 0.000200 (0.008792) | 0.000249 / 0.000054 (0.000194) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025508 / 0.037411 (-0.011903) | 0.102103 / 0.014526 (0.087578) | 0.109247 / 0.176557 (-0.067310) | 0.176369 / 0.737135 (-0.560766) | 0.111241 / 0.296338 (-0.185097) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.437209 / 0.215209 (0.222000) | 4.354386 / 2.077655 (2.276731) | 2.064008 / 1.504120 (0.559888) | 1.855518 / 1.541195 (0.314323) | 1.931647 / 1.468490 (0.463157) | 0.704913 / 4.584777 (-3.879864) | 3.397913 / 3.745712 (-0.347800) | 1.871524 / 5.269862 (-3.398338) | 1.176492 / 4.565676 (-3.389185) | 0.083976 / 0.424275 (-0.340299) | 0.012806 / 0.007607 (0.005199) | 0.539138 / 0.226044 (0.313094) | 5.401493 / 2.268929 (3.132564) | 2.539185 / 55.444624 (-52.905440) | 2.186445 / 6.876477 (-4.690031) | 2.222170 / 2.142072 (0.080097) | 0.815641 / 4.805227 (-3.989586) | 0.153033 / 6.500664 (-6.347631) | 0.069168 / 0.075469 (-0.006301) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.283530 / 1.841788 (-0.558258) | 14.075831 / 8.074308 (6.001523) | 13.649137 / 10.191392 (3.457745) | 0.127517 / 0.680424 (-0.552907) | 0.016619 / 0.534201 (-0.517582) | 0.377400 / 0.579283 (-0.201883) | 0.410796 / 0.434364 (-0.023568) | 0.463996 / 0.540337 (-0.076342) | 0.551867 / 1.386936 (-0.835069) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1135285d80ff9cd65fc51905f08343b4d7c2fa9c \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009161 / 0.011353 (-0.002192) | 0.004987 / 0.011008 (-0.006022) | 0.098553 / 0.038508 (0.060045) | 0.034326 / 0.023109 (0.011216) | 0.295325 / 0.275898 (0.019427) | 0.326361 / 0.323480 (0.002881) | 0.007827 / 0.007986 (-0.000159) | 0.004933 / 0.004328 (0.000604) | 0.074236 / 0.004250 (0.069986) | 0.040410 / 0.037052 (0.003357) | 0.295644 / 0.258489 (0.037155) | 0.355050 / 0.293841 (0.061209) | 0.038384 / 0.128546 (-0.090162) | 0.011845 / 0.075646 (-0.063801) | 0.340678 / 0.419271 (-0.078594) | 0.047615 / 0.043533 (0.004082) | 0.292429 / 0.255139 (0.037290) | 0.312610 / 0.283200 (0.029410) | 0.100106 / 0.141683 (-0.041577) | 1.446186 / 1.452155 (-0.005969) | 1.534763 / 1.492716 (0.042046) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.213667 / 0.018006 (0.195661) | 0.447310 / 0.000490 (0.446820) | 0.000402 / 0.000200 (0.000202) | 0.000056 / 0.000054 (0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027604 / 0.037411 (-0.009807) | 0.112785 / 0.014526 (0.098259) | 0.119450 / 0.176557 (-0.057106) | 0.185728 / 0.737135 (-0.551407) | 0.122860 / 0.296338 (-0.173478) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.399162 / 0.215209 (0.183953) | 3.992701 / 2.077655 (1.915046) | 1.773881 / 1.504120 (0.269761) | 1.589842 / 1.541195 (0.048647) | 1.670065 / 1.468490 (0.201575) | 0.707669 / 4.584777 (-3.877107) | 3.719657 / 3.745712 (-0.026055) | 2.139629 / 5.269862 (-3.130232) | 1.467224 / 4.565676 (-3.098453) | 0.086033 / 0.424275 (-0.338242) | 0.012151 / 0.007607 (0.004544) | 0.519700 / 0.226044 (0.293656) | 5.150254 / 2.268929 (2.881325) | 2.305076 / 55.444624 (-53.139548) | 1.927914 / 6.876477 (-4.948563) | 1.999461 / 2.142072 (-0.142612) | 0.851819 / 4.805227 (-3.953408) | 0.165513 / 6.500664 (-6.335151) | 0.061898 / 0.075469 (-0.013571) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.226251 / 1.841788 (-0.615536) | 14.990253 / 8.074308 (6.915945) | 14.658720 / 10.191392 (4.467328) | 0.191665 / 0.680424 (-0.488759) | 0.028768 / 0.534201 (-0.505433) | 0.443907 / 0.579283 (-0.135376) | 0.455183 / 0.434364 (0.020819) | 0.552760 / 0.540337 (0.012422) | 0.653927 / 1.386936 (-0.733009) |\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) | 0.007677 / 0.011353 (-0.003675) | 0.005340 / 0.011008 (-0.005668) | 0.075644 / 0.038508 (0.037136) | 0.035046 / 0.023109 (0.011937) | 0.341437 / 0.275898 (0.065538) | 0.377782 / 0.323480 (0.054302) | 0.006091 / 0.007986 (-0.001895) | 0.004170 / 0.004328 (-0.000158) | 0.074294 / 0.004250 (0.070044) | 0.049851 / 0.037052 (0.012798) | 0.351691 / 0.258489 (0.093202) | 0.386020 / 0.293841 (0.092179) | 0.036884 / 0.128546 (-0.091662) | 0.012475 / 0.075646 (-0.063172) | 0.087267 / 0.419271 (-0.332005) | 0.058623 / 0.043533 (0.015090) | 0.347186 / 0.255139 (0.092047) | 0.355869 / 0.283200 (0.072669) | 0.112022 / 0.141683 (-0.029661) | 1.451798 / 1.452155 (-0.000357) | 1.553262 / 1.492716 (0.060546) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.233451 / 0.018006 (0.215445) | 0.444384 / 0.000490 (0.443895) | 0.003695 / 0.000200 (0.003495) | 0.000088 / 0.000054 (0.000034) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029686 / 0.037411 (-0.007725) | 0.113736 / 0.014526 (0.099210) | 0.123998 / 0.176557 (-0.052559) | 0.197847 / 0.737135 (-0.539288) | 0.129936 / 0.296338 (-0.166403) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.421904 / 0.215209 (0.206695) | 4.203533 / 2.077655 (2.125878) | 2.038199 / 1.504120 (0.534079) | 1.832402 / 1.541195 (0.291208) | 1.930765 / 1.468490 (0.462274) | 0.709775 / 4.584777 (-3.875002) | 3.760893 / 3.745712 (0.015181) | 2.091185 / 5.269862 (-3.178677) | 1.342248 / 4.565676 (-3.223428) | 0.087770 / 0.424275 (-0.336505) | 0.012357 / 0.007607 (0.004750) | 0.519605 / 0.226044 (0.293560) | 5.215883 / 2.268929 (2.946954) | 2.510200 / 55.444624 (-52.934425) | 2.192482 / 6.876477 (-4.683995) | 2.290214 / 2.142072 (0.148141) | 0.872067 / 4.805227 (-3.933160) | 0.168491 / 6.500664 (-6.332173) | 0.064707 / 0.075469 (-0.010762) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.291956 / 1.841788 (-0.549832) | 15.244530 / 8.074308 (7.170222) | 13.594895 / 10.191392 (3.403503) | 0.172669 / 0.680424 (-0.507755) | 0.017765 / 0.534201 (-0.516436) | 0.426946 / 0.579283 (-0.152337) | 0.442843 / 0.434364 (0.008479) | 0.549683 / 0.540337 (0.009346) | 0.653433 / 1.386936 (-0.733503) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b54a6d21795cf6cc50a13ff870648241a60fd2e0 \"CML watermark\")\n", "Can you review this @mariosasko ? since Albert is off", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008396 / 0.011353 (-0.002957) | 0.004556 / 0.011008 (-0.006452) | 0.101343 / 0.038508 (0.062835) | 0.029137 / 0.023109 (0.006027) | 0.298553 / 0.275898 (0.022655) | 0.334050 / 0.323480 (0.010570) | 0.006746 / 0.007986 (-0.001239) | 0.005050 / 0.004328 (0.000721) | 0.076055 / 0.004250 (0.071804) | 0.031988 / 0.037052 (-0.005064) | 0.301324 / 0.258489 (0.042835) | 0.340121 / 0.293841 (0.046280) | 0.033827 / 0.128546 (-0.094720) | 0.011447 / 0.075646 (-0.064200) | 0.321827 / 0.419271 (-0.097445) | 0.040846 / 0.043533 (-0.002687) | 0.296957 / 0.255139 (0.041818) | 0.324178 / 0.283200 (0.040979) | 0.083852 / 0.141683 (-0.057831) | 1.456123 / 1.452155 (0.003968) | 1.538311 / 1.492716 (0.045595) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.208897 / 0.018006 (0.190891) | 0.430560 / 0.000490 (0.430070) | 0.002917 / 0.000200 (0.002717) | 0.000077 / 0.000054 (0.000022) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024332 / 0.037411 (-0.013080) | 0.101659 / 0.014526 (0.087133) | 0.107636 / 0.176557 (-0.068920) | 0.168805 / 0.737135 (-0.568330) | 0.111404 / 0.296338 (-0.184934) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.412704 / 0.215209 (0.197495) | 4.124852 / 2.077655 (2.047197) | 1.843555 / 1.504120 (0.339435) | 1.641636 / 1.541195 (0.100441) | 1.755783 / 1.468490 (0.287293) | 0.693212 / 4.584777 (-3.891565) | 3.391803 / 3.745712 (-0.353909) | 1.954473 / 5.269862 (-3.315389) | 1.274395 / 4.565676 (-3.291282) | 0.082536 / 0.424275 (-0.341739) | 0.012335 / 0.007607 (0.004728) | 0.523720 / 0.226044 (0.297676) | 5.268339 / 2.268929 (2.999411) | 2.318163 / 55.444624 (-53.126461) | 1.978503 / 6.876477 (-4.897974) | 2.046689 / 2.142072 (-0.095384) | 0.806735 / 4.805227 (-3.998492) | 0.148010 / 6.500664 (-6.352654) | 0.065305 / 0.075469 (-0.010164) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.266950 / 1.841788 (-0.574838) | 13.870803 / 8.074308 (5.796495) | 14.272556 / 10.191392 (4.081164) | 0.151703 / 0.680424 (-0.528720) | 0.028991 / 0.534201 (-0.505210) | 0.400831 / 0.579283 (-0.178452) | 0.400891 / 0.434364 (-0.033473) | 0.476225 / 0.540337 (-0.064113) | 0.564925 / 1.386936 (-0.822011) |\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) | 0.006810 / 0.011353 (-0.004543) | 0.004544 / 0.011008 (-0.006464) | 0.076516 / 0.038508 (0.038008) | 0.027705 / 0.023109 (0.004596) | 0.343215 / 0.275898 (0.067317) | 0.379136 / 0.323480 (0.055656) | 0.005227 / 0.007986 (-0.002758) | 0.003527 / 0.004328 (-0.000801) | 0.074775 / 0.004250 (0.070524) | 0.041700 / 0.037052 (0.004648) | 0.343612 / 0.258489 (0.085123) | 0.385657 / 0.293841 (0.091817) | 0.032082 / 0.128546 (-0.096464) | 0.011567 / 0.075646 (-0.064079) | 0.083814 / 0.419271 (-0.335458) | 0.042173 / 0.043533 (-0.001360) | 0.340261 / 0.255139 (0.085122) | 0.364778 / 0.283200 (0.081578) | 0.093401 / 0.141683 (-0.048282) | 1.513475 / 1.452155 (0.061320) | 1.599393 / 1.492716 (0.106677) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.237117 / 0.018006 (0.219111) | 0.424241 / 0.000490 (0.423751) | 0.002900 / 0.000200 (0.002700) | 0.000076 / 0.000054 (0.000021) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031122 / 0.037411 (-0.006289) | 0.107530 / 0.014526 (0.093004) | 0.117777 / 0.176557 (-0.058780) | 0.188300 / 0.737135 (-0.548836) | 0.119989 / 0.296338 (-0.176349) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.438563 / 0.215209 (0.223354) | 4.404969 / 2.077655 (2.327315) | 2.260182 / 1.504120 (0.756062) | 2.035472 / 1.541195 (0.494277) | 2.045685 / 1.468490 (0.577195) | 0.706758 / 4.584777 (-3.878019) | 3.434843 / 3.745712 (-0.310869) | 1.909533 / 5.269862 (-3.360328) | 1.175374 / 4.565676 (-3.390303) | 0.084831 / 0.424275 (-0.339444) | 0.012441 / 0.007607 (0.004833) | 0.551818 / 0.226044 (0.325774) | 5.509005 / 2.268929 (3.240077) | 2.576545 / 55.444624 (-52.868080) | 2.226204 / 6.876477 (-4.650272) | 2.276544 / 2.142072 (0.134471) | 0.818069 / 4.805227 (-3.987158) | 0.152797 / 6.500664 (-6.347867) | 0.067896 / 0.075469 (-0.007573) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.276859 / 1.841788 (-0.564929) | 14.312914 / 8.074308 (6.238606) | 13.406602 / 10.191392 (3.215210) | 0.157466 / 0.680424 (-0.522958) | 0.016709 / 0.534201 (-0.517492) | 0.390951 / 0.579283 (-0.188333) | 0.395525 / 0.434364 (-0.038839) | 0.484486 / 0.540337 (-0.055852) | 0.576125 / 1.386936 (-0.810811) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b951e1b6cdd927604599f1aa5dadfb8ee8e62e05 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007316 / 0.011353 (-0.004037) | 0.005041 / 0.011008 (-0.005968) | 0.100477 / 0.038508 (0.061969) | 0.034068 / 0.023109 (0.010959) | 0.351156 / 0.275898 (0.075258) | 0.373892 / 0.323480 (0.050412) | 0.005748 / 0.007986 (-0.002237) | 0.003959 / 0.004328 (-0.000370) | 0.075540 / 0.004250 (0.071290) | 0.045282 / 0.037052 (0.008230) | 0.362364 / 0.258489 (0.103874) | 0.376461 / 0.293841 (0.082620) | 0.036724 / 0.128546 (-0.091822) | 0.012008 / 0.075646 (-0.063638) | 0.333802 / 0.419271 (-0.085470) | 0.050107 / 0.043533 (0.006574) | 0.348003 / 0.255139 (0.092864) | 0.367187 / 0.283200 (0.083988) | 0.103171 / 0.141683 (-0.038511) | 1.448281 / 1.452155 (-0.003874) | 1.516231 / 1.492716 (0.023514) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.203651 / 0.018006 (0.185645) | 0.438103 / 0.000490 (0.437613) | 0.004165 / 0.000200 (0.003966) | 0.000085 / 0.000054 (0.000030) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027068 / 0.037411 (-0.010343) | 0.111728 / 0.014526 (0.097202) | 0.116963 / 0.176557 (-0.059594) | 0.172652 / 0.737135 (-0.564483) | 0.124257 / 0.296338 (-0.172082) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.407937 / 0.215209 (0.192728) | 4.066008 / 2.077655 (1.988353) | 1.895000 / 1.504120 (0.390880) | 1.698422 / 1.541195 (0.157227) | 1.872446 / 1.468490 (0.403956) | 0.688888 / 4.584777 (-3.895889) | 3.743635 / 3.745712 (-0.002077) | 2.161507 / 5.269862 (-3.108354) | 1.485218 / 4.565676 (-3.080458) | 0.085959 / 0.424275 (-0.338316) | 0.012554 / 0.007607 (0.004947) | 0.510953 / 0.226044 (0.284909) | 5.103241 / 2.268929 (2.834312) | 2.439670 / 55.444624 (-53.004955) | 2.057089 / 6.876477 (-4.819387) | 2.240137 / 2.142072 (0.098065) | 0.847750 / 4.805227 (-3.957477) | 0.172952 / 6.500664 (-6.327712) | 0.066023 / 0.075469 (-0.009446) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.190677 / 1.841788 (-0.651110) | 14.593162 / 8.074308 (6.518854) | 14.254983 / 10.191392 (4.063591) | 0.155811 / 0.680424 (-0.524613) | 0.017698 / 0.534201 (-0.516503) | 0.420455 / 0.579283 (-0.158828) | 0.412146 / 0.434364 (-0.022218) | 0.493113 / 0.540337 (-0.047225) | 0.582097 / 1.386936 (-0.804839) |\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) | 0.007319 / 0.011353 (-0.004033) | 0.005102 / 0.011008 (-0.005906) | 0.073760 / 0.038508 (0.035252) | 0.033496 / 0.023109 (0.010387) | 0.338778 / 0.275898 (0.062880) | 0.371870 / 0.323480 (0.048391) | 0.005804 / 0.007986 (-0.002182) | 0.004142 / 0.004328 (-0.000186) | 0.073203 / 0.004250 (0.068953) | 0.046568 / 0.037052 (0.009516) | 0.343544 / 0.258489 (0.085055) | 0.381188 / 0.293841 (0.087347) | 0.036391 / 0.128546 (-0.092155) | 0.012046 / 0.075646 (-0.063600) | 0.086007 / 0.419271 (-0.333265) | 0.048706 / 0.043533 (0.005173) | 0.330836 / 0.255139 (0.075697) | 0.355328 / 0.283200 (0.072128) | 0.100104 / 0.141683 (-0.041579) | 1.434237 / 1.452155 (-0.017917) | 1.549380 / 1.492716 (0.056663) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.231099 / 0.018006 (0.213093) | 0.450650 / 0.000490 (0.450160) | 0.000404 / 0.000200 (0.000204) | 0.000059 / 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) | 0.030534 / 0.037411 (-0.006877) | 0.119005 / 0.014526 (0.104479) | 0.125362 / 0.176557 (-0.051195) | 0.176823 / 0.737135 (-0.560313) | 0.132044 / 0.296338 (-0.164295) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.431004 / 0.215209 (0.215795) | 4.318969 / 2.077655 (2.241315) | 1.994941 / 1.504120 (0.490821) | 1.791870 / 1.541195 (0.250675) | 1.904134 / 1.468490 (0.435644) | 0.723493 / 4.584777 (-3.861284) | 3.823670 / 3.745712 (0.077958) | 2.118892 / 5.269862 (-3.150969) | 1.375088 / 4.565676 (-3.190588) | 0.088875 / 0.424275 (-0.335400) | 0.013137 / 0.007607 (0.005530) | 0.530523 / 0.226044 (0.304479) | 5.341438 / 2.268929 (3.072509) | 2.459044 / 55.444624 (-52.985580) | 2.150119 / 6.876477 (-4.726357) | 2.228567 / 2.142072 (0.086494) | 0.877549 / 4.805227 (-3.927678) | 0.175040 / 6.500664 (-6.325625) | 0.068188 / 0.075469 (-0.007281) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.273780 / 1.841788 (-0.568008) | 15.206331 / 8.074308 (7.132023) | 14.963058 / 10.191392 (4.771666) | 0.184543 / 0.680424 (-0.495881) | 0.017612 / 0.534201 (-0.516589) | 0.426248 / 0.579283 (-0.153035) | 0.437889 / 0.434364 (0.003525) | 0.508979 / 0.540337 (-0.031359) | 0.602040 / 1.386936 (-0.784896) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c5ca1d86949ec3a5fdaec03b80500fb822bcfab4 \"CML watermark\")\n" ]

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[]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5564). All of your documentation changes will be reflected on that endpoint.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008810 / 0.011353 (-0.002543) | 0.004583 / 0.011008 (-0.006425) | 0.100787 / 0.038508 (0.062279) | 0.030170 / 0.023109 (0.007061) | 0.301749 / 0.275898 (0.025851) | 0.386958 / 0.323480 (0.063478) | 0.007211 / 0.007986 (-0.000775) | 0.004939 / 0.004328 (0.000611) | 0.078046 / 0.004250 (0.073796) | 0.035672 / 0.037052 (-0.001380) | 0.314403 / 0.258489 (0.055914) | 0.348547 / 0.293841 (0.054706) | 0.034242 / 0.128546 (-0.094304) | 0.011599 / 0.075646 (-0.064047) | 0.321936 / 0.419271 (-0.097336) | 0.043214 / 0.043533 (-0.000319) | 0.298782 / 0.255139 (0.043643) | 0.334513 / 0.283200 (0.051313) | 0.091630 / 0.141683 (-0.050053) | 1.518194 / 1.452155 (0.066039) | 1.553665 / 1.492716 (0.060949) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.196322 / 0.018006 (0.178316) | 0.427280 / 0.000490 (0.426790) | 0.001933 / 0.000200 (0.001733) | 0.000069 / 0.000054 (0.000015) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023190 / 0.037411 (-0.014221) | 0.097387 / 0.014526 (0.082862) | 0.104532 / 0.176557 (-0.072024) | 0.166670 / 0.737135 (-0.570465) | 0.108787 / 0.296338 (-0.187552) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.415776 / 0.215209 (0.200567) | 4.135899 / 2.077655 (2.058244) | 1.857600 / 1.504120 (0.353480) | 1.654099 / 1.541195 (0.112904) | 1.729102 / 1.468490 (0.260612) | 0.695946 / 4.584777 (-3.888831) | 3.352776 / 3.745712 (-0.392936) | 2.754443 / 5.269862 (-2.515418) | 1.517181 / 4.565676 (-3.048495) | 0.082782 / 0.424275 (-0.341493) | 0.012431 / 0.007607 (0.004824) | 0.526593 / 0.226044 (0.300548) | 5.263051 / 2.268929 (2.994123) | 2.290713 / 55.444624 (-53.153911) | 1.953017 / 6.876477 (-4.923460) | 1.998419 / 2.142072 (-0.143653) | 0.817055 / 4.805227 (-3.988173) | 0.148213 / 6.500664 (-6.352451) | 0.065527 / 0.075469 (-0.009942) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.254275 / 1.841788 (-0.587513) | 13.618962 / 8.074308 (5.544654) | 14.057134 / 10.191392 (3.865742) | 0.137180 / 0.680424 (-0.543244) | 0.028460 / 0.534201 (-0.505741) | 0.393836 / 0.579283 (-0.185447) | 0.406665 / 0.434364 (-0.027699) | 0.476812 / 0.540337 (-0.063526) | 0.561047 / 1.386936 (-0.825889) |\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) | 0.006483 / 0.011353 (-0.004870) | 0.004525 / 0.011008 (-0.006483) | 0.075696 / 0.038508 (0.037188) | 0.027306 / 0.023109 (0.004197) | 0.359141 / 0.275898 (0.083243) | 0.394595 / 0.323480 (0.071115) | 0.004907 / 0.007986 (-0.003079) | 0.003403 / 0.004328 (-0.000925) | 0.074473 / 0.004250 (0.070223) | 0.037801 / 0.037052 (0.000749) | 0.359350 / 0.258489 (0.100861) | 0.411902 / 0.293841 (0.118061) | 0.032280 / 0.128546 (-0.096267) | 0.011728 / 0.075646 (-0.063918) | 0.085692 / 0.419271 (-0.333580) | 0.047779 / 0.043533 (0.004246) | 0.348820 / 0.255139 (0.093681) | 0.389396 / 0.283200 (0.106197) | 0.094923 / 0.141683 (-0.046760) | 1.507137 / 1.452155 (0.054982) | 1.556873 / 1.492716 (0.064157) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.197510 / 0.018006 (0.179504) | 0.413885 / 0.000490 (0.413395) | 0.002527 / 0.000200 (0.002327) | 0.000073 / 0.000054 (0.000019) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024571 / 0.037411 (-0.012840) | 0.099845 / 0.014526 (0.085319) | 0.108130 / 0.176557 (-0.068426) | 0.176153 / 0.737135 (-0.560982) | 0.111907 / 0.296338 (-0.184432) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.436393 / 0.215209 (0.221184) | 4.343296 / 2.077655 (2.265642) | 2.056062 / 1.504120 (0.551942) | 1.855372 / 1.541195 (0.314177) | 1.946429 / 1.468490 (0.477939) | 0.701862 / 4.584777 (-3.882915) | 3.337115 / 3.745712 (-0.408597) | 2.755416 / 5.269862 (-2.514446) | 1.335596 / 4.565676 (-3.230081) | 0.083938 / 0.424275 (-0.340337) | 0.012914 / 0.007607 (0.005307) | 0.530272 / 0.226044 (0.304228) | 5.307739 / 2.268929 (3.038810) | 2.506435 / 55.444624 (-52.938189) | 2.170830 / 6.876477 (-4.705646) | 2.224641 / 2.142072 (0.082568) | 0.804416 / 4.805227 (-4.000811) | 0.151594 / 6.500664 (-6.349070) | 0.067221 / 0.075469 (-0.008248) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.257063 / 1.841788 (-0.584725) | 14.054346 / 8.074308 (5.980038) | 13.490649 / 10.191392 (3.299257) | 0.139320 / 0.680424 (-0.541104) | 0.016501 / 0.534201 (-0.517700) | 0.382655 / 0.579283 (-0.196629) | 0.383305 / 0.434364 (-0.051059) | 0.465091 / 0.540337 (-0.075247) | 0.552552 / 1.386936 (-0.834384) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c480083958126c40bb7bdba8e1eeb3945a8fe6ea \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.011278 / 0.011353 (-0.000075) | 0.007351 / 0.011008 (-0.003657) | 0.131145 / 0.038508 (0.092637) | 0.041585 / 0.023109 (0.018476) | 0.410230 / 0.275898 (0.134332) | 0.464069 / 0.323480 (0.140589) | 0.010228 / 0.007986 (0.002242) | 0.005324 / 0.004328 (0.000996) | 0.102680 / 0.004250 (0.098430) | 0.041644 / 0.037052 (0.004592) | 0.439127 / 0.258489 (0.180638) | 0.467828 / 0.293841 (0.173987) | 0.054373 / 0.128546 (-0.074173) | 0.019495 / 0.075646 (-0.056152) | 0.432425 / 0.419271 (0.013153) | 0.056863 / 0.043533 (0.013331) | 0.405883 / 0.255139 (0.150744) | 0.452786 / 0.283200 (0.169586) | 0.109888 / 0.141683 (-0.031795) | 1.797015 / 1.452155 (0.344860) | 1.985937 / 1.492716 (0.493221) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.275121 / 0.018006 (0.257115) | 0.587585 / 0.000490 (0.587095) | 0.005557 / 0.000200 (0.005357) | 0.000118 / 0.000054 (0.000064) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032968 / 0.037411 (-0.004443) | 0.135886 / 0.014526 (0.121360) | 0.154000 / 0.176557 (-0.022557) | 0.233345 / 0.737135 (-0.503790) | 0.144125 / 0.296338 (-0.152214) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.613056 / 0.215209 (0.397847) | 6.206135 / 2.077655 (4.128480) | 2.686989 / 1.504120 (1.182869) | 2.389946 / 1.541195 (0.848751) | 2.437506 / 1.468490 (0.969016) | 1.255900 / 4.584777 (-3.328877) | 5.654803 / 3.745712 (1.909091) | 5.467693 / 5.269862 (0.197832) | 2.872397 / 4.565676 (-1.693279) | 0.145658 / 0.424275 (-0.278617) | 0.016883 / 0.007607 (0.009276) | 0.793820 / 0.226044 (0.567775) | 7.961881 / 2.268929 (5.692952) | 3.617422 / 55.444624 (-51.827203) | 2.795185 / 6.876477 (-4.081292) | 2.881726 / 2.142072 (0.739653) | 1.434543 / 4.805227 (-3.370685) | 0.252206 / 6.500664 (-6.248458) | 0.094694 / 0.075469 (0.019225) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.552401 / 1.841788 (-0.289386) | 18.436068 / 8.074308 (10.361760) | 22.539049 / 10.191392 (12.347657) | 0.269471 / 0.680424 (-0.410953) | 0.053242 / 0.534201 (-0.480959) | 0.568325 / 0.579283 (-0.010958) | 0.660339 / 0.434364 (0.225975) | 0.689507 / 0.540337 (0.149169) | 0.836785 / 1.386936 (-0.550151) |\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) | 0.009853 / 0.011353 (-0.001500) | 0.009752 / 0.011008 (-0.001256) | 0.095422 / 0.038508 (0.056914) | 0.037760 / 0.023109 (0.014651) | 0.450898 / 0.275898 (0.175000) | 0.501671 / 0.323480 (0.178191) | 0.006748 / 0.007986 (-0.001237) | 0.005054 / 0.004328 (0.000725) | 0.099382 / 0.004250 (0.095131) | 0.058078 / 0.037052 (0.021026) | 0.447606 / 0.258489 (0.189116) | 0.503887 / 0.293841 (0.210046) | 0.054579 / 0.128546 (-0.073967) | 0.026150 / 0.075646 (-0.049496) | 0.113042 / 0.419271 (-0.306230) | 0.061049 / 0.043533 (0.017516) | 0.437831 / 0.255139 (0.182692) | 0.480830 / 0.283200 (0.197630) | 0.121199 / 0.141683 (-0.020484) | 1.795409 / 1.452155 (0.343254) | 1.911207 / 1.492716 (0.418491) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.311774 / 0.018006 (0.293768) | 0.602027 / 0.000490 (0.601537) | 0.000651 / 0.000200 (0.000451) | 0.000136 / 0.000054 (0.000081) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035185 / 0.037411 (-0.002227) | 0.149574 / 0.014526 (0.135048) | 0.153672 / 0.176557 (-0.022884) | 0.241720 / 0.737135 (-0.495416) | 0.153543 / 0.296338 (-0.142795) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.678508 / 0.215209 (0.463299) | 6.535313 / 2.077655 (4.457658) | 2.840175 / 1.504120 (1.336055) | 2.458141 / 1.541195 (0.916947) | 2.551369 / 1.468490 (1.082879) | 1.339117 / 4.584777 (-3.245660) | 5.844429 / 3.745712 (2.098717) | 3.221100 / 5.269862 (-2.048762) | 2.114844 / 4.565676 (-2.450833) | 0.149263 / 0.424275 (-0.275012) | 0.016101 / 0.007607 (0.008494) | 0.830650 / 0.226044 (0.604605) | 8.096655 / 2.268929 (5.827727) | 3.445947 / 55.444624 (-51.998677) | 2.826874 / 6.876477 (-4.049603) | 2.812765 / 2.142072 (0.670693) | 1.453789 / 4.805227 (-3.351438) | 0.263911 / 6.500664 (-6.236753) | 0.082609 / 0.075469 (0.007139) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.651624 / 1.841788 (-0.190163) | 18.703020 / 8.074308 (10.628712) | 21.360445 / 10.191392 (11.169053) | 0.249718 / 0.680424 (-0.430706) | 0.028373 / 0.534201 (-0.505828) | 0.576237 / 0.579283 (-0.003046) | 0.620574 / 0.434364 (0.186210) | 0.684155 / 0.540337 (0.143817) | 0.758950 / 1.386936 (-0.627986) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f51ef325602bb297a18a75680575cbe9b940b1d9 \"CML watermark\")\n" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009437 / 0.011353 (-0.001916) | 0.004999 / 0.011008 (-0.006010) | 0.098839 / 0.038508 (0.060331) | 0.035496 / 0.023109 (0.012386) | 0.300726 / 0.275898 (0.024828) | 0.359793 / 0.323480 (0.036313) | 0.007694 / 0.007986 (-0.000292) | 0.003980 / 0.004328 (-0.000348) | 0.075240 / 0.004250 (0.070989) | 0.041149 / 0.037052 (0.004097) | 0.313185 / 0.258489 (0.054696) | 0.344111 / 0.293841 (0.050270) | 0.037775 / 0.128546 (-0.090772) | 0.011901 / 0.075646 (-0.063745) | 0.332631 / 0.419271 (-0.086641) | 0.047194 / 0.043533 (0.003661) | 0.306902 / 0.255139 (0.051763) | 0.321725 / 0.283200 (0.038525) | 0.101031 / 0.141683 (-0.040652) | 1.458778 / 1.452155 (0.006623) | 1.530196 / 1.492716 (0.037480) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.203241 / 0.018006 (0.185235) | 0.447147 / 0.000490 (0.446657) | 0.004159 / 0.000200 (0.003959) | 0.000131 / 0.000054 (0.000076) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025845 / 0.037411 (-0.011566) | 0.106966 / 0.014526 (0.092440) | 0.115876 / 0.176557 (-0.060681) | 0.179052 / 0.737135 (-0.558084) | 0.123012 / 0.296338 (-0.173327) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.408766 / 0.215209 (0.193557) | 4.080400 / 2.077655 (2.002745) | 1.893747 / 1.504120 (0.389627) | 1.709389 / 1.541195 (0.168194) | 1.768071 / 1.468490 (0.299581) | 0.689717 / 4.584777 (-3.895059) | 3.760897 / 3.745712 (0.015185) | 2.017050 / 5.269862 (-3.252811) | 1.333027 / 4.565676 (-3.232650) | 0.083559 / 0.424275 (-0.340716) | 0.011951 / 0.007607 (0.004344) | 0.512313 / 0.226044 (0.286268) | 5.162696 / 2.268929 (2.893767) | 2.418559 / 55.444624 (-53.026065) | 2.110178 / 6.876477 (-4.766299) | 2.113635 / 2.142072 (-0.028437) | 0.835171 / 4.805227 (-3.970056) | 0.164222 / 6.500664 (-6.336442) | 0.061955 / 0.075469 (-0.013515) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.198336 / 1.841788 (-0.643452) | 14.531468 / 8.074308 (6.457160) | 13.882133 / 10.191392 (3.690741) | 0.154524 / 0.680424 (-0.525900) | 0.028782 / 0.534201 (-0.505419) | 0.441808 / 0.579283 (-0.137475) | 0.433096 / 0.434364 (-0.001268) | 0.518229 / 0.540337 (-0.022108) | 0.603201 / 1.386936 (-0.783735) |\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) | 0.007385 / 0.011353 (-0.003967) | 0.005193 / 0.011008 (-0.005815) | 0.075517 / 0.038508 (0.037009) | 0.033192 / 0.023109 (0.010083) | 0.332299 / 0.275898 (0.056401) | 0.363043 / 0.323480 (0.039563) | 0.006368 / 0.007986 (-0.001617) | 0.004003 / 0.004328 (-0.000326) | 0.073710 / 0.004250 (0.069460) | 0.046916 / 0.037052 (0.009863) | 0.336307 / 0.258489 (0.077818) | 0.384910 / 0.293841 (0.091069) | 0.038132 / 0.128546 (-0.090414) | 0.012283 / 0.075646 (-0.063364) | 0.088036 / 0.419271 (-0.331235) | 0.049699 / 0.043533 (0.006166) | 0.333953 / 0.255139 (0.078814) | 0.352961 / 0.283200 (0.069762) | 0.101905 / 0.141683 (-0.039778) | 1.470480 / 1.452155 (0.018325) | 1.498212 / 1.492716 (0.005496) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.275067 / 0.018006 (0.257061) | 0.452589 / 0.000490 (0.452099) | 0.047067 / 0.000200 (0.046867) | 0.000983 / 0.000054 (0.000929) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028649 / 0.037411 (-0.008762) | 0.108385 / 0.014526 (0.093859) | 0.121213 / 0.176557 (-0.055343) | 0.192236 / 0.737135 (-0.544899) | 0.124620 / 0.296338 (-0.171719) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.428742 / 0.215209 (0.213533) | 4.264893 / 2.077655 (2.187238) | 2.061650 / 1.504120 (0.557530) | 1.873267 / 1.541195 (0.332072) | 1.961012 / 1.468490 (0.492522) | 0.708904 / 4.584777 (-3.875873) | 3.821289 / 3.745712 (0.075577) | 3.287231 / 5.269862 (-1.982631) | 1.903539 / 4.565676 (-2.662137) | 0.086474 / 0.424275 (-0.337801) | 0.012101 / 0.007607 (0.004494) | 0.531411 / 0.226044 (0.305367) | 5.216785 / 2.268929 (2.947857) | 2.575209 / 55.444624 (-52.869416) | 2.264902 / 6.876477 (-4.611574) | 2.291225 / 2.142072 (0.149153) | 0.853486 / 4.805227 (-3.951741) | 0.168550 / 6.500664 (-6.332114) | 0.064158 / 0.075469 (-0.011311) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.295830 / 1.841788 (-0.545958) | 14.419524 / 8.074308 (6.345216) | 13.397985 / 10.191392 (3.206593) | 0.181367 / 0.680424 (-0.499057) | 0.017666 / 0.534201 (-0.516535) | 0.420645 / 0.579283 (-0.158638) | 0.421025 / 0.434364 (-0.013339) | 0.527369 / 0.540337 (-0.012969) | 0.627175 / 1.386936 (-0.759761) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#589b49dfaffa729bc9997a38d4cedafb107ea2e4 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008717 / 0.011353 (-0.002635) | 0.004573 / 0.011008 (-0.006435) | 0.103660 / 0.038508 (0.065151) | 0.035274 / 0.023109 (0.012165) | 0.298563 / 0.275898 (0.022665) | 0.384397 / 0.323480 (0.060917) | 0.006932 / 0.007986 (-0.001053) | 0.003422 / 0.004328 (-0.000907) | 0.080193 / 0.004250 (0.075943) | 0.039767 / 0.037052 (0.002714) | 0.310296 / 0.258489 (0.051807) | 0.351361 / 0.293841 (0.057520) | 0.033532 / 0.128546 (-0.095014) | 0.011543 / 0.075646 (-0.064104) | 0.374816 / 0.419271 (-0.044456) | 0.046046 / 0.043533 (0.002513) | 0.306918 / 0.255139 (0.051779) | 0.382242 / 0.283200 (0.099042) | 0.098945 / 0.141683 (-0.042738) | 1.456929 / 1.452155 (0.004775) | 1.535763 / 1.492716 (0.043046) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.011759 / 0.018006 (-0.006247) | 0.405345 / 0.000490 (0.404855) | 0.002667 / 0.000200 (0.002467) | 0.000075 / 0.000054 (0.000020) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023924 / 0.037411 (-0.013487) | 0.095537 / 0.014526 (0.081011) | 0.106959 / 0.176557 (-0.069598) | 0.170782 / 0.737135 (-0.566353) | 0.109169 / 0.296338 (-0.187170) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.437521 / 0.215209 (0.222312) | 4.383556 / 2.077655 (2.305902) | 2.092055 / 1.504120 (0.587935) | 1.889316 / 1.541195 (0.348121) | 1.937436 / 1.468490 (0.468946) | 0.700175 / 4.584777 (-3.884602) | 3.358107 / 3.745712 (-0.387605) | 3.243226 / 5.269862 (-2.026636) | 1.620497 / 4.565676 (-2.945180) | 0.083063 / 0.424275 (-0.341212) | 0.012970 / 0.007607 (0.005363) | 0.544226 / 0.226044 (0.318181) | 5.483315 / 2.268929 (3.214386) | 2.555183 / 55.444624 (-52.889441) | 2.204230 / 6.876477 (-4.672247) | 2.230551 / 2.142072 (0.088478) | 0.816121 / 4.805227 (-3.989106) | 0.151356 / 6.500664 (-6.349308) | 0.068564 / 0.075469 (-0.006905) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.208420 / 1.841788 (-0.633367) | 13.652597 / 8.074308 (5.578289) | 14.096318 / 10.191392 (3.904926) | 0.154473 / 0.680424 (-0.525951) | 0.028436 / 0.534201 (-0.505765) | 0.399949 / 0.579283 (-0.179334) | 0.398961 / 0.434364 (-0.035403) | 0.488703 / 0.540337 (-0.051634) | 0.572640 / 1.386936 (-0.814296) |\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) | 0.006373 / 0.011353 (-0.004979) | 0.004368 / 0.011008 (-0.006640) | 0.076410 / 0.038508 (0.037902) | 0.027055 / 0.023109 (0.003945) | 0.336969 / 0.275898 (0.061071) | 0.374533 / 0.323480 (0.051053) | 0.004781 / 0.007986 (-0.003204) | 0.003317 / 0.004328 (-0.001011) | 0.076099 / 0.004250 (0.071849) | 0.038414 / 0.037052 (0.001361) | 0.339578 / 0.258489 (0.081089) | 0.384138 / 0.293841 (0.090297) | 0.031581 / 0.128546 (-0.096965) | 0.011666 / 0.075646 (-0.063981) | 0.085690 / 0.419271 (-0.333582) | 0.042277 / 0.043533 (-0.001256) | 0.337931 / 0.255139 (0.082792) | 0.365827 / 0.283200 (0.082628) | 0.088713 / 0.141683 (-0.052970) | 1.519789 / 1.452155 (0.067635) | 1.583097 / 1.492716 (0.090381) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.223472 / 0.018006 (0.205466) | 0.392474 / 0.000490 (0.391984) | 0.002739 / 0.000200 (0.002539) | 0.000076 / 0.000054 (0.000021) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024373 / 0.037411 (-0.013038) | 0.099822 / 0.014526 (0.085296) | 0.106128 / 0.176557 (-0.070428) | 0.174688 / 0.737135 (-0.562447) | 0.112660 / 0.296338 (-0.183678) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.436317 / 0.215209 (0.221108) | 4.358277 / 2.077655 (2.280622) | 2.089746 / 1.504120 (0.585626) | 1.881040 / 1.541195 (0.339845) | 1.923653 / 1.468490 (0.455163) | 0.698176 / 4.584777 (-3.886601) | 3.346460 / 3.745712 (-0.399252) | 3.301429 / 5.269862 (-1.968433) | 1.391042 / 4.565676 (-3.174634) | 0.083025 / 0.424275 (-0.341250) | 0.012459 / 0.007607 (0.004851) | 0.533011 / 0.226044 (0.306967) | 5.334984 / 2.268929 (3.066056) | 2.534105 / 55.444624 (-52.910520) | 2.206295 / 6.876477 (-4.670181) | 2.231752 / 2.142072 (0.089680) | 0.798650 / 4.805227 (-4.006577) | 0.150070 / 6.500664 (-6.350594) | 0.066898 / 0.075469 (-0.008571) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.310527 / 1.841788 (-0.531261) | 13.920492 / 8.074308 (5.846184) | 13.359382 / 10.191392 (3.167990) | 0.154561 / 0.680424 (-0.525863) | 0.016387 / 0.534201 (-0.517814) | 0.379892 / 0.579283 (-0.199391) | 0.376746 / 0.434364 (-0.057618) | 0.462606 / 0.540337 (-0.077732) | 0.550895 / 1.386936 (-0.836041) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#cac733fdaef84cfee92856bd259ce024ec157c91 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009373 / 0.011353 (-0.001980) | 0.005212 / 0.011008 (-0.005797) | 0.099287 / 0.038508 (0.060779) | 0.035175 / 0.023109 (0.012066) | 0.307012 / 0.275898 (0.031114) | 0.335105 / 0.323480 (0.011625) | 0.008006 / 0.007986 (0.000020) | 0.004017 / 0.004328 (-0.000311) | 0.075519 / 0.004250 (0.071269) | 0.040276 / 0.037052 (0.003223) | 0.302615 / 0.258489 (0.044126) | 0.361742 / 0.293841 (0.067901) | 0.038773 / 0.128546 (-0.089773) | 0.011892 / 0.075646 (-0.063754) | 0.334199 / 0.419271 (-0.085073) | 0.048035 / 0.043533 (0.004503) | 0.301361 / 0.255139 (0.046222) | 0.321996 / 0.283200 (0.038796) | 0.101818 / 0.141683 (-0.039865) | 1.442601 / 1.452155 (-0.009554) | 1.530669 / 1.492716 (0.037953) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.201470 / 0.018006 (0.183464) | 0.496305 / 0.000490 (0.495815) | 0.003794 / 0.000200 (0.003594) | 0.000149 / 0.000054 (0.000094) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028401 / 0.037411 (-0.009010) | 0.107924 / 0.014526 (0.093398) | 0.121716 / 0.176557 (-0.054840) | 0.187407 / 0.737135 (-0.549728) | 0.124755 / 0.296338 (-0.171583) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.395667 / 0.215209 (0.180457) | 3.939079 / 2.077655 (1.861424) | 1.776308 / 1.504120 (0.272188) | 1.583487 / 1.541195 (0.042292) | 1.682957 / 1.468490 (0.214467) | 0.677322 / 4.584777 (-3.907455) | 3.796987 / 3.745712 (0.051275) | 3.406199 / 5.269862 (-1.863663) | 1.905467 / 4.565676 (-2.660210) | 0.083189 / 0.424275 (-0.341086) | 0.012156 / 0.007607 (0.004549) | 0.507078 / 0.226044 (0.281033) | 5.031293 / 2.268929 (2.762365) | 2.228403 / 55.444624 (-53.216221) | 1.885760 / 6.876477 (-4.990717) | 1.962340 / 2.142072 (-0.179732) | 0.824979 / 4.805227 (-3.980248) | 0.162107 / 6.500664 (-6.338557) | 0.062324 / 0.075469 (-0.013145) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.205104 / 1.841788 (-0.636683) | 15.368896 / 8.074308 (7.294588) | 14.757540 / 10.191392 (4.566148) | 0.177544 / 0.680424 (-0.502880) | 0.029097 / 0.534201 (-0.505104) | 0.445252 / 0.579283 (-0.134031) | 0.456521 / 0.434364 (0.022157) | 0.544166 / 0.540337 (0.003829) | 0.640675 / 1.386936 (-0.746261) |\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) | 0.007438 / 0.011353 (-0.003914) | 0.005236 / 0.011008 (-0.005772) | 0.075379 / 0.038508 (0.036871) | 0.033274 / 0.023109 (0.010165) | 0.344584 / 0.275898 (0.068686) | 0.372161 / 0.323480 (0.048681) | 0.005914 / 0.007986 (-0.002071) | 0.004176 / 0.004328 (-0.000152) | 0.073311 / 0.004250 (0.069061) | 0.050845 / 0.037052 (0.013793) | 0.338978 / 0.258489 (0.080489) | 0.391563 / 0.293841 (0.097722) | 0.037559 / 0.128546 (-0.090987) | 0.012455 / 0.075646 (-0.063192) | 0.086224 / 0.419271 (-0.333047) | 0.052956 / 0.043533 (0.009423) | 0.338529 / 0.255139 (0.083390) | 0.356752 / 0.283200 (0.073553) | 0.105864 / 0.141683 (-0.035819) | 1.467727 / 1.452155 (0.015572) | 1.588727 / 1.492716 (0.096010) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.215959 / 0.018006 (0.197953) | 0.440619 / 0.000490 (0.440129) | 0.000397 / 0.000200 (0.000197) | 0.000058 / 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) | 0.028855 / 0.037411 (-0.008556) | 0.114239 / 0.014526 (0.099713) | 0.121726 / 0.176557 (-0.054830) | 0.190377 / 0.737135 (-0.546759) | 0.127858 / 0.296338 (-0.168480) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.415399 / 0.215209 (0.200190) | 4.159012 / 2.077655 (2.081357) | 1.987593 / 1.504120 (0.483474) | 1.794785 / 1.541195 (0.253591) | 1.924819 / 1.468490 (0.456329) | 0.696082 / 4.584777 (-3.888694) | 3.820461 / 3.745712 (0.074749) | 2.139236 / 5.269862 (-3.130626) | 1.348593 / 4.565676 (-3.217084) | 0.086536 / 0.424275 (-0.337739) | 0.012510 / 0.007607 (0.004902) | 0.518804 / 0.226044 (0.292760) | 5.188659 / 2.268929 (2.919730) | 2.501303 / 55.444624 (-52.943322) | 2.138831 / 6.876477 (-4.737646) | 2.220451 / 2.142072 (0.078378) | 0.836277 / 4.805227 (-3.968950) | 0.170940 / 6.500664 (-6.329724) | 0.067326 / 0.075469 (-0.008143) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.307848 / 1.841788 (-0.533940) | 15.995785 / 8.074308 (7.921477) | 13.646285 / 10.191392 (3.454893) | 0.181120 / 0.680424 (-0.499304) | 0.017500 / 0.534201 (-0.516701) | 0.426697 / 0.579283 (-0.152586) | 0.436702 / 0.434364 (0.002338) | 0.518060 / 0.540337 (-0.022278) | 0.632577 / 1.386936 (-0.754359) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#cac733fdaef84cfee92856bd259ce024ec157c91 \"CML watermark\")\n" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "Removed it :)", "Changed it :)", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008358 / 0.011353 (-0.002995) | 0.004555 / 0.011008 (-0.006453) | 0.100935 / 0.038508 (0.062427) | 0.029473 / 0.023109 (0.006364) | 0.336165 / 0.275898 (0.060266) | 0.420397 / 0.323480 (0.096917) | 0.006609 / 0.007986 (-0.001376) | 0.003338 / 0.004328 (-0.000991) | 0.078639 / 0.004250 (0.074388) | 0.034051 / 0.037052 (-0.003001) | 0.342820 / 0.258489 (0.084331) | 0.399392 / 0.293841 (0.105551) | 0.033935 / 0.128546 (-0.094611) | 0.011555 / 0.075646 (-0.064092) | 0.323467 / 0.419271 (-0.095804) | 0.040675 / 0.043533 (-0.002858) | 0.321247 / 0.255139 (0.066108) | 0.370967 / 0.283200 (0.087767) | 0.085766 / 0.141683 (-0.055917) | 1.461158 / 1.452155 (0.009003) | 1.504641 / 1.492716 (0.011925) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.180060 / 0.018006 (0.162053) | 0.403623 / 0.000490 (0.403134) | 0.002253 / 0.000200 (0.002053) | 0.000072 / 0.000054 (0.000017) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022793 / 0.037411 (-0.014618) | 0.098869 / 0.014526 (0.084343) | 0.104512 / 0.176557 (-0.072045) | 0.167721 / 0.737135 (-0.569414) | 0.107969 / 0.296338 (-0.188370) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.411179 / 0.215209 (0.195969) | 4.095345 / 2.077655 (2.017690) | 1.825992 / 1.504120 (0.321872) | 1.624386 / 1.541195 (0.083192) | 1.654903 / 1.468490 (0.186413) | 0.695041 / 4.584777 (-3.889736) | 3.319087 / 3.745712 (-0.426625) | 1.881945 / 5.269862 (-3.387917) | 1.250360 / 4.565676 (-3.315316) | 0.082405 / 0.424275 (-0.341870) | 0.012499 / 0.007607 (0.004892) | 0.522846 / 0.226044 (0.296801) | 5.241103 / 2.268929 (2.972175) | 2.293100 / 55.444624 (-53.151524) | 1.942937 / 6.876477 (-4.933540) | 1.957434 / 2.142072 (-0.184638) | 0.809782 / 4.805227 (-3.995445) | 0.148290 / 6.500664 (-6.352374) | 0.064157 / 0.075469 (-0.011312) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.185616 / 1.841788 (-0.656172) | 13.616791 / 8.074308 (5.542483) | 13.741806 / 10.191392 (3.550414) | 0.137396 / 0.680424 (-0.543028) | 0.028751 / 0.534201 (-0.505450) | 0.397636 / 0.579283 (-0.181647) | 0.403594 / 0.434364 (-0.030770) | 0.484039 / 0.540337 (-0.056299) | 0.568398 / 1.386936 (-0.818538) |\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) | 0.006712 / 0.011353 (-0.004640) | 0.004511 / 0.011008 (-0.006497) | 0.076946 / 0.038508 (0.038438) | 0.027219 / 0.023109 (0.004110) | 0.350769 / 0.275898 (0.074871) | 0.408539 / 0.323480 (0.085059) | 0.005014 / 0.007986 (-0.002971) | 0.003361 / 0.004328 (-0.000968) | 0.077106 / 0.004250 (0.072856) | 0.040105 / 0.037052 (0.003053) | 0.342041 / 0.258489 (0.083552) | 0.426355 / 0.293841 (0.132514) | 0.031684 / 0.128546 (-0.096863) | 0.011575 / 0.075646 (-0.064072) | 0.085797 / 0.419271 (-0.333474) | 0.041575 / 0.043533 (-0.001958) | 0.340837 / 0.255139 (0.085698) | 0.390461 / 0.283200 (0.107262) | 0.089531 / 0.141683 (-0.052152) | 1.504600 / 1.452155 (0.052445) | 1.538712 / 1.492716 (0.045996) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.236679 / 0.018006 (0.218673) | 0.396258 / 0.000490 (0.395768) | 0.006479 / 0.000200 (0.006279) | 0.000081 / 0.000054 (0.000026) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024682 / 0.037411 (-0.012729) | 0.100167 / 0.014526 (0.085641) | 0.106627 / 0.176557 (-0.069929) | 0.174592 / 0.737135 (-0.562543) | 0.109499 / 0.296338 (-0.186839) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.444702 / 0.215209 (0.229493) | 4.462779 / 2.077655 (2.385125) | 2.087711 / 1.504120 (0.583591) | 1.874900 / 1.541195 (0.333705) | 1.918609 / 1.468490 (0.450119) | 0.705867 / 4.584777 (-3.878910) | 3.355483 / 3.745712 (-0.390229) | 2.808348 / 5.269862 (-2.461514) | 1.253319 / 4.565676 (-3.312358) | 0.083747 / 0.424275 (-0.340528) | 0.012491 / 0.007607 (0.004884) | 0.542885 / 0.226044 (0.316841) | 5.453921 / 2.268929 (3.184993) | 2.545688 / 55.444624 (-52.898937) | 2.185022 / 6.876477 (-4.691455) | 2.215351 / 2.142072 (0.073279) | 0.808201 / 4.805227 (-3.997027) | 0.151754 / 6.500664 (-6.348910) | 0.066886 / 0.075469 (-0.008583) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.298583 / 1.841788 (-0.543205) | 14.014276 / 8.074308 (5.939968) | 13.505338 / 10.191392 (3.313946) | 0.142033 / 0.680424 (-0.538391) | 0.016863 / 0.534201 (-0.517338) | 0.381195 / 0.579283 (-0.198088) | 0.384455 / 0.434364 (-0.049909) | 0.465765 / 0.540337 (-0.074572) | 0.552571 / 1.386936 (-0.834366) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a29cca79ce64a5c64ad7047e57845b22154d7b8d \"CML watermark\")\n" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "Better yet have someone enable pre-commit CI https://pre-commit.ci/ and it will apply the pre-commit fixes to the PR automatically as an additional commit.", "@Skylion007 hi! I agree with @nateraw here, I'd better not force to use pre-commit so I'm not setting it up in the CI for now. And regarding end-of-file - currently it's being done by `black`. \r\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008704 / 0.011353 (-0.002649) | 0.004448 / 0.011008 (-0.006560) | 0.099530 / 0.038508 (0.061022) | 0.029739 / 0.023109 (0.006629) | 0.329267 / 0.275898 (0.053369) | 0.368805 / 0.323480 (0.045325) | 0.006852 / 0.007986 (-0.001133) | 0.004575 / 0.004328 (0.000246) | 0.076838 / 0.004250 (0.072588) | 0.033885 / 0.037052 (-0.003167) | 0.336340 / 0.258489 (0.077851) | 0.384880 / 0.293841 (0.091039) | 0.034051 / 0.128546 (-0.094495) | 0.011638 / 0.075646 (-0.064009) | 0.321650 / 0.419271 (-0.097622) | 0.041202 / 0.043533 (-0.002330) | 0.330841 / 0.255139 (0.075702) | 0.361329 / 0.283200 (0.078130) | 0.084864 / 0.141683 (-0.056819) | 1.454005 / 1.452155 (0.001850) | 1.542167 / 1.492716 (0.049451) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.196207 / 0.018006 (0.178200) | 0.400675 / 0.000490 (0.400185) | 0.000403 / 0.000200 (0.000203) | 0.000059 / 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) | 0.022694 / 0.037411 (-0.014717) | 0.095139 / 0.014526 (0.080613) | 0.104129 / 0.176557 (-0.072427) | 0.168688 / 0.737135 (-0.568447) | 0.109243 / 0.296338 (-0.187096) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.427520 / 0.215209 (0.212311) | 4.237726 / 2.077655 (2.160071) | 2.191887 / 1.504120 (0.687767) | 1.987750 / 1.541195 (0.446555) | 1.996540 / 1.468490 (0.528050) | 0.696416 / 4.584777 (-3.888361) | 3.454536 / 3.745712 (-0.291176) | 2.023600 / 5.269862 (-3.246261) | 1.336394 / 4.565676 (-3.229282) | 0.082933 / 0.424275 (-0.341342) | 0.012572 / 0.007607 (0.004965) | 0.534330 / 0.226044 (0.308285) | 5.347588 / 2.268929 (3.078659) | 2.640397 / 55.444624 (-52.804228) | 2.338266 / 6.876477 (-4.538211) | 2.431969 / 2.142072 (0.289897) | 0.821335 / 4.805227 (-3.983893) | 0.151905 / 6.500664 (-6.348759) | 0.067983 / 0.075469 (-0.007486) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.228841 / 1.841788 (-0.612947) | 13.660437 / 8.074308 (5.586128) | 13.729442 / 10.191392 (3.538050) | 0.165835 / 0.680424 (-0.514589) | 0.028753 / 0.534201 (-0.505448) | 0.400143 / 0.579283 (-0.179140) | 0.403714 / 0.434364 (-0.030650) | 0.492168 / 0.540337 (-0.048170) | 0.581151 / 1.386936 (-0.805785) |\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) | 0.006289 / 0.011353 (-0.005064) | 0.004419 / 0.011008 (-0.006589) | 0.077220 / 0.038508 (0.038712) | 0.027170 / 0.023109 (0.004060) | 0.344988 / 0.275898 (0.069090) | 0.374150 / 0.323480 (0.050670) | 0.004842 / 0.007986 (-0.003144) | 0.003289 / 0.004328 (-0.001039) | 0.076200 / 0.004250 (0.071950) | 0.036287 / 0.037052 (-0.000766) | 0.345764 / 0.258489 (0.087275) | 0.387439 / 0.293841 (0.093599) | 0.031547 / 0.128546 (-0.096999) | 0.011586 / 0.075646 (-0.064060) | 0.086599 / 0.419271 (-0.332672) | 0.042338 / 0.043533 (-0.001195) | 0.355384 / 0.255139 (0.100246) | 0.369474 / 0.283200 (0.086275) | 0.090945 / 0.141683 (-0.050738) | 1.488632 / 1.452155 (0.036477) | 1.554606 / 1.492716 (0.061890) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.212962 / 0.018006 (0.194956) | 0.399647 / 0.000490 (0.399157) | 0.003055 / 0.000200 (0.002856) | 0.000083 / 0.000054 (0.000029) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024349 / 0.037411 (-0.013062) | 0.100342 / 0.014526 (0.085817) | 0.105657 / 0.176557 (-0.070899) | 0.175139 / 0.737135 (-0.561997) | 0.110014 / 0.296338 (-0.186324) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.434785 / 0.215209 (0.219575) | 4.346950 / 2.077655 (2.269295) | 2.045411 / 1.504120 (0.541291) | 1.844258 / 1.541195 (0.303064) | 1.889503 / 1.468490 (0.421013) | 0.704530 / 4.584777 (-3.880247) | 3.362435 / 3.745712 (-0.383277) | 2.797205 / 5.269862 (-2.472656) | 1.504431 / 4.565676 (-3.061245) | 0.083331 / 0.424275 (-0.340945) | 0.012274 / 0.007607 (0.004666) | 0.531123 / 0.226044 (0.305078) | 5.322588 / 2.268929 (3.053660) | 2.483875 / 55.444624 (-52.960750) | 2.147218 / 6.876477 (-4.729258) | 2.164024 / 2.142072 (0.021952) | 0.807191 / 4.805227 (-3.998036) | 0.151189 / 6.500664 (-6.349475) | 0.068027 / 0.075469 (-0.007442) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.316001 / 1.841788 (-0.525787) | 13.892785 / 8.074308 (5.818477) | 13.485982 / 10.191392 (3.294590) | 0.138904 / 0.680424 (-0.541520) | 0.016748 / 0.534201 (-0.517453) | 0.379840 / 0.579283 (-0.199443) | 0.384854 / 0.434364 (-0.049510) | 0.464275 / 0.540337 (-0.076063) | 0.553622 / 1.386936 (-0.833314) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a940972a9a38543b2066129dc6e7987e08dca082 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009179 / 0.011353 (-0.002174) | 0.005080 / 0.011008 (-0.005929) | 0.099061 / 0.038508 (0.060553) | 0.035252 / 0.023109 (0.012143) | 0.293496 / 0.275898 (0.017598) | 0.360365 / 0.323480 (0.036886) | 0.007757 / 0.007986 (-0.000229) | 0.003985 / 0.004328 (-0.000343) | 0.076021 / 0.004250 (0.071771) | 0.042286 / 0.037052 (0.005233) | 0.316542 / 0.258489 (0.058053) | 0.341711 / 0.293841 (0.047870) | 0.037970 / 0.128546 (-0.090576) | 0.011977 / 0.075646 (-0.063670) | 0.333341 / 0.419271 (-0.085931) | 0.049211 / 0.043533 (0.005678) | 0.297401 / 0.255139 (0.042262) | 0.313424 / 0.283200 (0.030224) | 0.105719 / 0.141683 (-0.035964) | 1.487879 / 1.452155 (0.035724) | 1.529785 / 1.492716 (0.037068) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.201062 / 0.018006 (0.183056) | 0.438024 / 0.000490 (0.437534) | 0.002129 / 0.000200 (0.001929) | 0.000083 / 0.000054 (0.000028) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026422 / 0.037411 (-0.010989) | 0.104863 / 0.014526 (0.090337) | 0.114934 / 0.176557 (-0.061623) | 0.179173 / 0.737135 (-0.557962) | 0.119734 / 0.296338 (-0.176604) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.397195 / 0.215209 (0.181986) | 3.959945 / 2.077655 (1.882290) | 1.794059 / 1.504120 (0.289939) | 1.606814 / 1.541195 (0.065619) | 1.674681 / 1.468490 (0.206191) | 0.680130 / 4.584777 (-3.904646) | 3.742730 / 3.745712 (-0.002982) | 2.021793 / 5.269862 (-3.248069) | 1.322726 / 4.565676 (-3.242950) | 0.084519 / 0.424275 (-0.339756) | 0.012012 / 0.007607 (0.004405) | 0.510076 / 0.226044 (0.284032) | 5.084163 / 2.268929 (2.815234) | 2.241032 / 55.444624 (-53.203592) | 1.911936 / 6.876477 (-4.964540) | 1.947992 / 2.142072 (-0.194080) | 0.838779 / 4.805227 (-3.966448) | 0.165103 / 6.500664 (-6.335561) | 0.060722 / 0.075469 (-0.014747) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.180274 / 1.841788 (-0.661514) | 14.285364 / 8.074308 (6.211056) | 12.941205 / 10.191392 (2.749813) | 0.153815 / 0.680424 (-0.526609) | 0.028554 / 0.534201 (-0.505647) | 0.441551 / 0.579283 (-0.137732) | 0.434906 / 0.434364 (0.000542) | 0.516120 / 0.540337 (-0.024217) | 0.603062 / 1.386936 (-0.783874) |\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) | 0.007287 / 0.011353 (-0.004066) | 0.004998 / 0.011008 (-0.006010) | 0.074997 / 0.038508 (0.036489) | 0.033209 / 0.023109 (0.010100) | 0.336836 / 0.275898 (0.060938) | 0.365562 / 0.323480 (0.042082) | 0.005739 / 0.007986 (-0.002246) | 0.003942 / 0.004328 (-0.000387) | 0.074681 / 0.004250 (0.070430) | 0.049530 / 0.037052 (0.012478) | 0.335642 / 0.258489 (0.077153) | 0.388874 / 0.293841 (0.095033) | 0.037198 / 0.128546 (-0.091349) | 0.011983 / 0.075646 (-0.063664) | 0.087601 / 0.419271 (-0.331671) | 0.053761 / 0.043533 (0.010228) | 0.334142 / 0.255139 (0.079003) | 0.351348 / 0.283200 (0.068148) | 0.107462 / 0.141683 (-0.034221) | 1.497015 / 1.452155 (0.044860) | 1.608287 / 1.492716 (0.115571) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.255395 / 0.018006 (0.237389) | 0.439141 / 0.000490 (0.438651) | 0.021391 / 0.000200 (0.021191) | 0.000230 / 0.000054 (0.000176) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028331 / 0.037411 (-0.009080) | 0.108744 / 0.014526 (0.094218) | 0.118201 / 0.176557 (-0.058355) | 0.189556 / 0.737135 (-0.547579) | 0.123112 / 0.296338 (-0.173226) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.431394 / 0.215209 (0.216185) | 4.296121 / 2.077655 (2.218466) | 2.126371 / 1.504120 (0.622251) | 1.978178 / 1.541195 (0.436983) | 2.082674 / 1.468490 (0.614184) | 0.701789 / 4.584777 (-3.882988) | 3.791495 / 3.745712 (0.045783) | 2.115267 / 5.269862 (-3.154594) | 1.342159 / 4.565676 (-3.223517) | 0.088132 / 0.424275 (-0.336143) | 0.011903 / 0.007607 (0.004295) | 0.528398 / 0.226044 (0.302354) | 5.270077 / 2.268929 (3.001148) | 2.498860 / 55.444624 (-52.945765) | 2.155515 / 6.876477 (-4.720962) | 2.192866 / 2.142072 (0.050793) | 0.859596 / 4.805227 (-3.945631) | 0.170544 / 6.500664 (-6.330120) | 0.063883 / 0.075469 (-0.011587) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.240679 / 1.841788 (-0.601109) | 14.497379 / 8.074308 (6.423071) | 12.881417 / 10.191392 (2.690025) | 0.147295 / 0.680424 (-0.533129) | 0.017465 / 0.534201 (-0.516736) | 0.424695 / 0.579283 (-0.154588) | 0.414929 / 0.434364 (-0.019435) | 0.536079 / 0.540337 (-0.004259) | 0.638245 / 1.386936 (-0.748691) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a940972a9a38543b2066129dc6e7987e08dca082 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008806 / 0.011353 (-0.002547) | 0.004712 / 0.011008 (-0.006297) | 0.102383 / 0.038508 (0.063875) | 0.030260 / 0.023109 (0.007151) | 0.330175 / 0.275898 (0.054277) | 0.376816 / 0.323480 (0.053337) | 0.008065 / 0.007986 (0.000079) | 0.003534 / 0.004328 (-0.000794) | 0.078824 / 0.004250 (0.074573) | 0.036704 / 0.037052 (-0.000349) | 0.331848 / 0.258489 (0.073359) | 0.351031 / 0.293841 (0.057190) | 0.033406 / 0.128546 (-0.095140) | 0.011543 / 0.075646 (-0.064103) | 0.322114 / 0.419271 (-0.097157) | 0.041249 / 0.043533 (-0.002284) | 0.309413 / 0.255139 (0.054274) | 0.329156 / 0.283200 (0.045956) | 0.088636 / 0.141683 (-0.053047) | 1.508226 / 1.452155 (0.056071) | 1.557203 / 1.492716 (0.064487) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.196696 / 0.018006 (0.178690) | 0.426360 / 0.000490 (0.425870) | 0.001263 / 0.000200 (0.001064) | 0.000079 / 0.000054 (0.000024) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023747 / 0.037411 (-0.013664) | 0.100756 / 0.014526 (0.086230) | 0.105817 / 0.176557 (-0.070739) | 0.172573 / 0.737135 (-0.564562) | 0.110705 / 0.296338 (-0.185634) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.436913 / 0.215209 (0.221704) | 4.365753 / 2.077655 (2.288099) | 2.201346 / 1.504120 (0.697226) | 1.978800 / 1.541195 (0.437605) | 1.951585 / 1.468490 (0.483094) | 0.699208 / 4.584777 (-3.885569) | 3.381492 / 3.745712 (-0.364220) | 2.966174 / 5.269862 (-2.303687) | 1.487521 / 4.565676 (-3.078156) | 0.082673 / 0.424275 (-0.341602) | 0.012436 / 0.007607 (0.004829) | 0.553276 / 0.226044 (0.327232) | 5.554081 / 2.268929 (3.285153) | 2.653286 / 55.444624 (-52.791339) | 2.404788 / 6.876477 (-4.471689) | 2.484610 / 2.142072 (0.342537) | 0.817073 / 4.805227 (-3.988154) | 0.151619 / 6.500664 (-6.349045) | 0.068259 / 0.075469 (-0.007210) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.273481 / 1.841788 (-0.568306) | 13.908825 / 8.074308 (5.834517) | 13.106695 / 10.191392 (2.915303) | 0.139609 / 0.680424 (-0.540815) | 0.028425 / 0.534201 (-0.505776) | 0.395626 / 0.579283 (-0.183657) | 0.405526 / 0.434364 (-0.028838) | 0.465628 / 0.540337 (-0.074709) | 0.542824 / 1.386936 (-0.844112) |\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) | 0.006821 / 0.011353 (-0.004532) | 0.004570 / 0.011008 (-0.006438) | 0.076568 / 0.038508 (0.038060) | 0.028109 / 0.023109 (0.004999) | 0.342768 / 0.275898 (0.066870) | 0.390680 / 0.323480 (0.067200) | 0.005056 / 0.007986 (-0.002930) | 0.003359 / 0.004328 (-0.000970) | 0.075835 / 0.004250 (0.071584) | 0.038888 / 0.037052 (0.001836) | 0.343489 / 0.258489 (0.085000) | 0.400766 / 0.293841 (0.106925) | 0.031816 / 0.128546 (-0.096730) | 0.011637 / 0.075646 (-0.064009) | 0.085474 / 0.419271 (-0.333797) | 0.041740 / 0.043533 (-0.001793) | 0.342501 / 0.255139 (0.087362) | 0.377467 / 0.283200 (0.094267) | 0.091532 / 0.141683 (-0.050151) | 1.457368 / 1.452155 (0.005213) | 1.537187 / 1.492716 (0.044471) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.187507 / 0.018006 (0.169501) | 0.415706 / 0.000490 (0.415217) | 0.001816 / 0.000200 (0.001616) | 0.000072 / 0.000054 (0.000018) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026251 / 0.037411 (-0.011161) | 0.106609 / 0.014526 (0.092083) | 0.109822 / 0.176557 (-0.066735) | 0.180462 / 0.737135 (-0.556674) | 0.114647 / 0.296338 (-0.181691) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.438804 / 0.215209 (0.223595) | 4.387960 / 2.077655 (2.310306) | 2.056804 / 1.504120 (0.552684) | 1.848584 / 1.541195 (0.307389) | 1.939470 / 1.468490 (0.470980) | 0.702539 / 4.584777 (-3.882238) | 3.419535 / 3.745712 (-0.326177) | 1.933889 / 5.269862 (-3.335973) | 1.189631 / 4.565676 (-3.376045) | 0.084105 / 0.424275 (-0.340170) | 0.012520 / 0.007607 (0.004913) | 0.538125 / 0.226044 (0.312081) | 5.370000 / 2.268929 (3.101072) | 2.497487 / 55.444624 (-52.947137) | 2.156054 / 6.876477 (-4.720423) | 2.225909 / 2.142072 (0.083837) | 0.811456 / 4.805227 (-3.993771) | 0.151461 / 6.500664 (-6.349203) | 0.066940 / 0.075469 (-0.008530) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.301246 / 1.841788 (-0.540542) | 14.459755 / 8.074308 (6.385447) | 13.147151 / 10.191392 (2.955759) | 0.129236 / 0.680424 (-0.551188) | 0.016427 / 0.534201 (-0.517774) | 0.380047 / 0.579283 (-0.199236) | 0.392217 / 0.434364 (-0.042147) | 0.470338 / 0.540337 (-0.069999) | 0.559800 / 1.386936 (-0.827136) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a940972a9a38543b2066129dc6e7987e08dca082 \"CML watermark\")\n" ]

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[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.011060 / 0.011353 (-0.000293) | 0.005752 / 0.011008 (-0.005256) | 0.120349 / 0.038508 (0.081841) | 0.045303 / 0.023109 (0.022194) | 0.359196 / 0.275898 (0.083298) | 0.406351 / 0.323480 (0.082871) | 0.009474 / 0.007986 (0.001489) | 0.004524 / 0.004328 (0.000195) | 0.091990 / 0.004250 (0.087739) | 0.050034 / 0.037052 (0.012982) | 0.372479 / 0.258489 (0.113990) | 0.418907 / 0.293841 (0.125067) | 0.044300 / 0.128546 (-0.084247) | 0.013989 / 0.075646 (-0.061657) | 0.397406 / 0.419271 (-0.021866) | 0.056070 / 0.043533 (0.012537) | 0.357597 / 0.255139 (0.102458) | 0.382938 / 0.283200 (0.099738) | 0.117060 / 0.141683 (-0.024623) | 1.670869 / 1.452155 (0.218714) | 1.780944 / 1.492716 (0.288227) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.229578 / 0.018006 (0.211572) | 0.493711 / 0.000490 (0.493222) | 0.008413 / 0.000200 (0.008213) | 0.000118 / 0.000054 (0.000063) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033364 / 0.037411 (-0.004047) | 0.135953 / 0.014526 (0.121427) | 0.141942 / 0.176557 (-0.034614) | 0.225891 / 0.737135 (-0.511244) | 0.151010 / 0.296338 (-0.145328) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.470937 / 0.215209 (0.255728) | 4.710258 / 2.077655 (2.632603) | 2.132025 / 1.504120 (0.627905) | 1.913134 / 1.541195 (0.371939) | 2.025993 / 1.468490 (0.557503) | 0.835993 / 4.584777 (-3.748784) | 4.446678 / 3.745712 (0.700965) | 4.260014 / 5.269862 (-1.009847) | 2.193078 / 4.565676 (-2.372598) | 0.100132 / 0.424275 (-0.324143) | 0.014163 / 0.007607 (0.006556) | 0.599252 / 0.226044 (0.373208) | 5.976377 / 2.268929 (3.707448) | 2.678116 / 55.444624 (-52.766508) | 2.309311 / 6.876477 (-4.567166) | 2.410284 / 2.142072 (0.268212) | 1.002415 / 4.805227 (-3.802813) | 0.194588 / 6.500664 (-6.306076) | 0.074921 / 0.075469 (-0.000548) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.432389 / 1.841788 (-0.409399) | 17.915288 / 8.074308 (9.840980) | 17.190906 / 10.191392 (6.999514) | 0.238469 / 0.680424 (-0.441955) | 0.036270 / 0.534201 (-0.497931) | 0.537320 / 0.579283 (-0.041963) | 0.512876 / 0.434364 (0.078512) | 0.629022 / 0.540337 (0.088685) | 0.750109 / 1.386936 (-0.636827) |\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) | 0.008544 / 0.011353 (-0.002809) | 0.005933 / 0.011008 (-0.005075) | 0.088879 / 0.038508 (0.050371) | 0.040387 / 0.023109 (0.017278) | 0.406392 / 0.275898 (0.130494) | 0.449572 / 0.323480 (0.126092) | 0.006623 / 0.007986 (-0.001362) | 0.004727 / 0.004328 (0.000398) | 0.086745 / 0.004250 (0.082495) | 0.054335 / 0.037052 (0.017283) | 0.405652 / 0.258489 (0.147163) | 0.473934 / 0.293841 (0.180093) | 0.042157 / 0.128546 (-0.086390) | 0.014249 / 0.075646 (-0.061397) | 0.102130 / 0.419271 (-0.317141) | 0.056815 / 0.043533 (0.013282) | 0.407945 / 0.255139 (0.152806) | 0.431720 / 0.283200 (0.148521) | 0.119901 / 0.141683 (-0.021781) | 1.738381 / 1.452155 (0.286227) | 1.838981 / 1.492716 (0.346265) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.251926 / 0.018006 (0.233919) | 0.498117 / 0.000490 (0.497627) | 0.000439 / 0.000200 (0.000239) | 0.000065 / 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) | 0.034526 / 0.037411 (-0.002886) | 0.133038 / 0.014526 (0.118512) | 0.147494 / 0.176557 (-0.029063) | 0.234392 / 0.737135 (-0.502743) | 0.152361 / 0.296338 (-0.143978) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.495144 / 0.215209 (0.279935) | 4.936646 / 2.077655 (2.858991) | 2.385549 / 1.504120 (0.881429) | 2.173817 / 1.541195 (0.632622) | 2.327508 / 1.468490 (0.859018) | 0.851899 / 4.584777 (-3.732878) | 4.820388 / 3.745712 (1.074676) | 2.500304 / 5.269862 (-2.769558) | 1.621246 / 4.565676 (-2.944430) | 0.102858 / 0.424275 (-0.321417) | 0.014719 / 0.007607 (0.007112) | 0.611880 / 0.226044 (0.385836) | 6.100737 / 2.268929 (3.831808) | 2.955681 / 55.444624 (-52.488943) | 2.563533 / 6.876477 (-4.312943) | 2.659030 / 2.142072 (0.516958) | 1.004737 / 4.805227 (-3.800490) | 0.198379 / 6.500664 (-6.302285) | 0.078705 / 0.075469 (0.003236) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.501155 / 1.841788 (-0.340633) | 18.381513 / 8.074308 (10.307205) | 16.173893 / 10.191392 (5.982501) | 0.209497 / 0.680424 (-0.470927) | 0.021640 / 0.534201 (-0.512561) | 0.505905 / 0.579283 (-0.073378) | 0.513446 / 0.434364 (0.079082) | 0.652704 / 0.540337 (0.112366) | 0.761038 / 1.386936 (-0.625898) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b8235c92b46b6a63286fcee1a56adae4c0a751d3 \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009085 / 0.011353 (-0.002268) | 0.004589 / 0.011008 (-0.006419) | 0.100820 / 0.038508 (0.062312) | 0.030677 / 0.023109 (0.007568) | 0.306702 / 0.275898 (0.030804) | 0.360623 / 0.323480 (0.037144) | 0.007377 / 0.007986 (-0.000608) | 0.003480 / 0.004328 (-0.000848) | 0.077813 / 0.004250 (0.073562) | 0.037293 / 0.037052 (0.000241) | 0.314137 / 0.258489 (0.055648) | 0.343394 / 0.293841 (0.049554) | 0.034202 / 0.128546 (-0.094344) | 0.011417 / 0.075646 (-0.064230) | 0.322584 / 0.419271 (-0.096687) | 0.041524 / 0.043533 (-0.002009) | 0.308116 / 0.255139 (0.052977) | 0.324527 / 0.283200 (0.041327) | 0.090973 / 0.141683 (-0.050710) | 1.515941 / 1.452155 (0.063787) | 1.548975 / 1.492716 (0.056259) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.185901 / 0.018006 (0.167895) | 0.420742 / 0.000490 (0.420252) | 0.002958 / 0.000200 (0.002758) | 0.000086 / 0.000054 (0.000032) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024242 / 0.037411 (-0.013170) | 0.098827 / 0.014526 (0.084302) | 0.107609 / 0.176557 (-0.068947) | 0.172228 / 0.737135 (-0.564908) | 0.110042 / 0.296338 (-0.186296) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.429647 / 0.215209 (0.214438) | 4.265406 / 2.077655 (2.187751) | 1.924514 / 1.504120 (0.420394) | 1.709881 / 1.541195 (0.168686) | 1.764872 / 1.468490 (0.296382) | 0.698089 / 4.584777 (-3.886688) | 3.439154 / 3.745712 (-0.306558) | 1.925058 / 5.269862 (-3.344804) | 1.267506 / 4.565676 (-3.298171) | 0.082167 / 0.424275 (-0.342108) | 0.012450 / 0.007607 (0.004843) | 0.523077 / 0.226044 (0.297033) | 5.240422 / 2.268929 (2.971494) | 2.363666 / 55.444624 (-53.080959) | 2.021903 / 6.876477 (-4.854574) | 2.136430 / 2.142072 (-0.005643) | 0.816377 / 4.805227 (-3.988850) | 0.151516 / 6.500664 (-6.349148) | 0.066590 / 0.075469 (-0.008879) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.216477 / 1.841788 (-0.625310) | 13.685044 / 8.074308 (5.610736) | 14.082620 / 10.191392 (3.891228) | 0.148399 / 0.680424 (-0.532025) | 0.028337 / 0.534201 (-0.505864) | 0.405379 / 0.579283 (-0.173904) | 0.405650 / 0.434364 (-0.028714) | 0.492658 / 0.540337 (-0.047679) | 0.578836 / 1.386936 (-0.808100) |\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) | 0.006863 / 0.011353 (-0.004490) | 0.004746 / 0.011008 (-0.006262) | 0.075802 / 0.038508 (0.037294) | 0.027950 / 0.023109 (0.004840) | 0.347613 / 0.275898 (0.071715) | 0.401201 / 0.323480 (0.077721) | 0.005765 / 0.007986 (-0.002221) | 0.003567 / 0.004328 (-0.000762) | 0.074188 / 0.004250 (0.069937) | 0.041209 / 0.037052 (0.004157) | 0.346541 / 0.258489 (0.088052) | 0.425729 / 0.293841 (0.131888) | 0.032430 / 0.128546 (-0.096116) | 0.011708 / 0.075646 (-0.063938) | 0.084667 / 0.419271 (-0.334604) | 0.042155 / 0.043533 (-0.001378) | 0.341210 / 0.255139 (0.086071) | 0.389759 / 0.283200 (0.106559) | 0.092640 / 0.141683 (-0.049042) | 1.526093 / 1.452155 (0.073938) | 1.556277 / 1.492716 (0.063561) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.232383 / 0.018006 (0.214377) | 0.412353 / 0.000490 (0.411863) | 0.004009 / 0.000200 (0.003809) | 0.000071 / 0.000054 (0.000017) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025854 / 0.037411 (-0.011557) | 0.102660 / 0.014526 (0.088134) | 0.108420 / 0.176557 (-0.068137) | 0.175834 / 0.737135 (-0.561301) | 0.113472 / 0.296338 (-0.182867) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.443595 / 0.215209 (0.228386) | 4.420959 / 2.077655 (2.343305) | 2.112790 / 1.504120 (0.608670) | 1.908836 / 1.541195 (0.367641) | 1.998340 / 1.468490 (0.529850) | 0.706096 / 4.584777 (-3.878681) | 3.400871 / 3.745712 (-0.344841) | 2.803315 / 5.269862 (-2.466547) | 1.539392 / 4.565676 (-3.026284) | 0.083523 / 0.424275 (-0.340752) | 0.012541 / 0.007607 (0.004934) | 0.543428 / 0.226044 (0.317383) | 5.467416 / 2.268929 (3.198488) | 2.551970 / 55.444624 (-52.892654) | 2.212708 / 6.876477 (-4.663768) | 2.266169 / 2.142072 (0.124096) | 0.809943 / 4.805227 (-3.995284) | 0.152300 / 6.500664 (-6.348364) | 0.068591 / 0.075469 (-0.006878) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.330141 / 1.841788 (-0.511646) | 14.292734 / 8.074308 (6.218426) | 13.556157 / 10.191392 (3.364765) | 0.155949 / 0.680424 (-0.524475) | 0.016464 / 0.534201 (-0.517737) | 0.377906 / 0.579283 (-0.201377) | 0.390385 / 0.434364 (-0.043979) | 0.471867 / 0.540337 (-0.068471) | 0.557794 / 1.386936 (-0.829142) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ba50512b76ef315f73bf821b0487296cdb373850 \"CML watermark\")\n", "I just tried on colab and it didn't finish the progress bar for some reason.\r\n\r\nMaybe we need to call `pbar.close()` before `return`\r\n\r\n<img width=\"729\" alt=\"image\" src=\"https://user-images.githubusercontent.com/42851186/220417517-919438a4-5462-4e87-8f84-e9399a9be27c.png\">\r\n", "(just added .close() - let me try quickly if it works now)", "it worked ! :)\r\n\r\n<img width=\"575\" alt=\"image\" src=\"https://user-images.githubusercontent.com/42851186/220419220-8108f225-13cb-4968-acff-fe4543d5a324.png\">\r\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008465 / 0.011353 (-0.002888) | 0.004622 / 0.011008 (-0.006387) | 0.100365 / 0.038508 (0.061857) | 0.029453 / 0.023109 (0.006344) | 0.358041 / 0.275898 (0.082143) | 0.424777 / 0.323480 (0.101298) | 0.006930 / 0.007986 (-0.001055) | 0.004756 / 0.004328 (0.000428) | 0.077128 / 0.004250 (0.072878) | 0.036338 / 0.037052 (-0.000715) | 0.367613 / 0.258489 (0.109124) | 0.397798 / 0.293841 (0.103957) | 0.033500 / 0.128546 (-0.095047) | 0.011427 / 0.075646 (-0.064219) | 0.321617 / 0.419271 (-0.097654) | 0.040937 / 0.043533 (-0.002596) | 0.345358 / 0.255139 (0.090219) | 0.366932 / 0.283200 (0.083733) | 0.086506 / 0.141683 (-0.055177) | 1.482434 / 1.452155 (0.030280) | 1.522773 / 1.492716 (0.030057) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.188815 / 0.018006 (0.170809) | 0.404689 / 0.000490 (0.404200) | 0.000390 / 0.000200 (0.000190) | 0.000059 / 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) | 0.023165 / 0.037411 (-0.014246) | 0.095934 / 0.014526 (0.081408) | 0.105788 / 0.176557 (-0.070769) | 0.169908 / 0.737135 (-0.567227) | 0.107871 / 0.296338 (-0.188467) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.457543 / 0.215209 (0.242334) | 4.563209 / 2.077655 (2.485554) | 2.172272 / 1.504120 (0.668152) | 1.965064 / 1.541195 (0.423870) | 2.020811 / 1.468490 (0.552321) | 0.705138 / 4.584777 (-3.879638) | 3.353430 / 3.745712 (-0.392283) | 1.861970 / 5.269862 (-3.407892) | 1.159201 / 4.565676 (-3.406476) | 0.083187 / 0.424275 (-0.341088) | 0.012750 / 0.007607 (0.005143) | 0.566377 / 0.226044 (0.340333) | 5.662645 / 2.268929 (3.393717) | 2.609565 / 55.444624 (-52.835059) | 2.244519 / 6.876477 (-4.631957) | 2.284111 / 2.142072 (0.142038) | 0.821974 / 4.805227 (-3.983253) | 0.151080 / 6.500664 (-6.349584) | 0.065373 / 0.075469 (-0.010096) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.230960 / 1.841788 (-0.610828) | 13.930408 / 8.074308 (5.856100) | 13.989082 / 10.191392 (3.797690) | 0.151961 / 0.680424 (-0.528462) | 0.028770 / 0.534201 (-0.505431) | 0.392269 / 0.579283 (-0.187015) | 0.400490 / 0.434364 (-0.033874) | 0.459770 / 0.540337 (-0.080568) | 0.534174 / 1.386936 (-0.852762) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006740 / 0.011353 (-0.004613) | 0.004496 / 0.011008 (-0.006512) | 0.076886 / 0.038508 (0.038377) | 0.027593 / 0.023109 (0.004484) | 0.339570 / 0.275898 (0.063672) | 0.379915 / 0.323480 (0.056435) | 0.004999 / 0.007986 (-0.002987) | 0.004253 / 0.004328 (-0.000076) | 0.074973 / 0.004250 (0.070722) | 0.037321 / 0.037052 (0.000269) | 0.344720 / 0.258489 (0.086230) | 0.398919 / 0.293841 (0.105078) | 0.032146 / 0.128546 (-0.096400) | 0.011694 / 0.075646 (-0.063952) | 0.085134 / 0.419271 (-0.334138) | 0.042328 / 0.043533 (-0.001205) | 0.339384 / 0.255139 (0.084245) | 0.368031 / 0.283200 (0.084831) | 0.092088 / 0.141683 (-0.049595) | 1.492313 / 1.452155 (0.040158) | 1.538406 / 1.492716 (0.045690) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.265619 / 0.018006 (0.247613) | 0.415478 / 0.000490 (0.414988) | 0.030221 / 0.000200 (0.030021) | 0.000277 / 0.000054 (0.000223) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024489 / 0.037411 (-0.012922) | 0.099920 / 0.014526 (0.085395) | 0.108301 / 0.176557 (-0.068256) | 0.179525 / 0.737135 (-0.557610) | 0.111492 / 0.296338 (-0.184847) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.440759 / 0.215209 (0.225550) | 4.382754 / 2.077655 (2.305100) | 2.088686 / 1.504120 (0.584566) | 1.890557 / 1.541195 (0.349363) | 1.947461 / 1.468490 (0.478971) | 0.701751 / 4.584777 (-3.883025) | 3.368896 / 3.745712 (-0.376816) | 1.867238 / 5.269862 (-3.402624) | 1.166787 / 4.565676 (-3.398890) | 0.083427 / 0.424275 (-0.340848) | 0.012406 / 0.007607 (0.004799) | 0.539467 / 0.226044 (0.313423) | 5.376083 / 2.268929 (3.107154) | 2.516566 / 55.444624 (-52.928058) | 2.177991 / 6.876477 (-4.698486) | 2.207438 / 2.142072 (0.065366) | 0.803316 / 4.805227 (-4.001911) | 0.150900 / 6.500664 (-6.349764) | 0.066328 / 0.075469 (-0.009141) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.295308 / 1.841788 (-0.546480) | 14.081343 / 8.074308 (6.007035) | 13.516853 / 10.191392 (3.325461) | 0.160530 / 0.680424 (-0.519894) | 0.016516 / 0.534201 (-0.517685) | 0.380160 / 0.579283 (-0.199123) | 0.443484 / 0.434364 (0.009120) | 0.466645 / 0.540337 (-0.073692) | 0.555339 / 1.386936 (-0.831597) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e8a12313cd728e37b4dc4ce67864621ffc79fedb \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.011321 / 0.011353 (-0.000031) | 0.006365 / 0.011008 (-0.004643) | 0.125613 / 0.038508 (0.087105) | 0.035327 / 0.023109 (0.012218) | 0.391998 / 0.275898 (0.116100) | 0.475402 / 0.323480 (0.151923) | 0.009579 / 0.007986 (0.001593) | 0.005621 / 0.004328 (0.001293) | 0.106097 / 0.004250 (0.101846) | 0.042774 / 0.037052 (0.005722) | 0.420850 / 0.258489 (0.162361) | 0.454501 / 0.293841 (0.160660) | 0.056885 / 0.128546 (-0.071661) | 0.021718 / 0.075646 (-0.053928) | 0.419422 / 0.419271 (0.000150) | 0.056690 / 0.043533 (0.013157) | 0.405375 / 0.255139 (0.150236) | 0.444404 / 0.283200 (0.161204) | 0.136912 / 0.141683 (-0.004771) | 1.846363 / 1.452155 (0.394208) | 1.747433 / 1.492716 (0.254717) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.282260 / 0.018006 (0.264254) | 0.615813 / 0.000490 (0.615323) | 0.000515 / 0.000200 (0.000315) | 0.000106 / 0.000054 (0.000052) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029913 / 0.037411 (-0.007499) | 0.135568 / 0.014526 (0.121042) | 0.134476 / 0.176557 (-0.042081) | 0.206974 / 0.737135 (-0.530161) | 0.136976 / 0.296338 (-0.159362) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.605241 / 0.215209 (0.390032) | 6.125097 / 2.077655 (4.047442) | 2.390102 / 1.504120 (0.885982) | 2.082196 / 1.541195 (0.541001) | 2.226527 / 1.468490 (0.758037) | 1.244807 / 4.584777 (-3.339970) | 5.476437 / 3.745712 (1.730725) | 3.014970 / 5.269862 (-2.254891) | 1.963428 / 4.565676 (-2.602249) | 0.137813 / 0.424275 (-0.286462) | 0.013794 / 0.007607 (0.006187) | 0.766149 / 0.226044 (0.540104) | 7.566103 / 2.268929 (5.297175) | 3.048958 / 55.444624 (-52.395666) | 2.394819 / 6.876477 (-4.481658) | 2.416021 / 2.142072 (0.273949) | 1.369896 / 4.805227 (-3.435331) | 0.245159 / 6.500664 (-6.255506) | 0.076848 / 0.075469 (0.001379) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.530448 / 1.841788 (-0.311340) | 18.580227 / 8.074308 (10.505919) | 20.108470 / 10.191392 (9.917078) | 0.227124 / 0.680424 (-0.453300) | 0.052050 / 0.534201 (-0.482151) | 0.604565 / 0.579283 (0.025282) | 0.686475 / 0.434364 (0.252111) | 0.672298 / 0.540337 (0.131960) | 0.770552 / 1.386936 (-0.616384) |\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) | 0.010043 / 0.011353 (-0.001310) | 0.006445 / 0.011008 (-0.004563) | 0.099486 / 0.038508 (0.060978) | 0.037720 / 0.023109 (0.014610) | 0.425571 / 0.275898 (0.149673) | 0.467031 / 0.323480 (0.143551) | 0.007394 / 0.007986 (-0.000591) | 0.005008 / 0.004328 (0.000679) | 0.096176 / 0.004250 (0.091926) | 0.053694 / 0.037052 (0.016641) | 0.418653 / 0.258489 (0.160164) | 0.492441 / 0.293841 (0.198600) | 0.054593 / 0.128546 (-0.073953) | 0.023410 / 0.075646 (-0.052236) | 0.113825 / 0.419271 (-0.305446) | 0.066000 / 0.043533 (0.022467) | 0.418127 / 0.255139 (0.162988) | 0.457416 / 0.283200 (0.174217) | 0.119911 / 0.141683 (-0.021771) | 1.733805 / 1.452155 (0.281651) | 1.961252 / 1.492716 (0.468536) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.296126 / 0.018006 (0.278120) | 0.602169 / 0.000490 (0.601680) | 0.000454 / 0.000200 (0.000254) | 0.000075 / 0.000054 (0.000021) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032970 / 0.037411 (-0.004442) | 0.124071 / 0.014526 (0.109545) | 0.143800 / 0.176557 (-0.032757) | 0.227168 / 0.737135 (-0.509967) | 0.142817 / 0.296338 (-0.153521) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.626239 / 0.215209 (0.411030) | 6.438629 / 2.077655 (4.360974) | 2.760747 / 1.504120 (1.256627) | 2.355419 / 1.541195 (0.814224) | 2.384924 / 1.468490 (0.916434) | 1.210543 / 4.584777 (-3.374234) | 5.440389 / 3.745712 (1.694677) | 5.047939 / 5.269862 (-0.221922) | 2.759618 / 4.565676 (-1.806059) | 0.132757 / 0.424275 (-0.291518) | 0.013163 / 0.007607 (0.005556) | 0.745721 / 0.226044 (0.519677) | 7.660327 / 2.268929 (5.391398) | 3.559385 / 55.444624 (-51.885240) | 2.764344 / 6.876477 (-4.112133) | 2.975274 / 2.142072 (0.833202) | 1.460346 / 4.805227 (-3.344881) | 0.257222 / 6.500664 (-6.243443) | 0.081106 / 0.075469 (0.005637) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.698245 / 1.841788 (-0.143543) | 18.754129 / 8.074308 (10.679821) | 19.065596 / 10.191392 (8.874204) | 0.228237 / 0.680424 (-0.452187) | 0.030688 / 0.534201 (-0.503513) | 0.532561 / 0.579283 (-0.046722) | 0.601133 / 0.434364 (0.166769) | 0.620218 / 0.540337 (0.079881) | 0.751392 / 1.386936 (-0.635545) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5f293ff23853fea210388bbef11d1621e54f22e7 \"CML watermark\")\n", "(the BadZipFile error is unrelated to the changes)", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009368 / 0.011353 (-0.001984) | 0.005143 / 0.011008 (-0.005865) | 0.100675 / 0.038508 (0.062167) | 0.036033 / 0.023109 (0.012924) | 0.297391 / 0.275898 (0.021493) | 0.362230 / 0.323480 (0.038750) | 0.008041 / 0.007986 (0.000055) | 0.004041 / 0.004328 (-0.000287) | 0.075395 / 0.004250 (0.071144) | 0.043020 / 0.037052 (0.005968) | 0.308936 / 0.258489 (0.050447) | 0.343723 / 0.293841 (0.049883) | 0.038416 / 0.128546 (-0.090131) | 0.012086 / 0.075646 (-0.063560) | 0.335102 / 0.419271 (-0.084170) | 0.047718 / 0.043533 (0.004185) | 0.297856 / 0.255139 (0.042717) | 0.317326 / 0.283200 (0.034126) | 0.101462 / 0.141683 (-0.040221) | 1.459965 / 1.452155 (0.007810) | 1.491194 / 1.492716 (-0.001522) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.211311 / 0.018006 (0.193305) | 0.443663 / 0.000490 (0.443174) | 0.003654 / 0.000200 (0.003454) | 0.000076 / 0.000054 (0.000022) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027316 / 0.037411 (-0.010095) | 0.109929 / 0.014526 (0.095403) | 0.117170 / 0.176557 (-0.059387) | 0.182494 / 0.737135 (-0.554641) | 0.124693 / 0.296338 (-0.171646) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.395904 / 0.215209 (0.180695) | 3.950906 / 2.077655 (1.873251) | 1.768807 / 1.504120 (0.264687) | 1.578979 / 1.541195 (0.037784) | 1.689976 / 1.468490 (0.221486) | 0.696458 / 4.584777 (-3.888319) | 3.750491 / 3.745712 (0.004778) | 2.117863 / 5.269862 (-3.151998) | 1.340403 / 4.565676 (-3.225274) | 0.085752 / 0.424275 (-0.338523) | 0.012206 / 0.007607 (0.004599) | 0.505561 / 0.226044 (0.279517) | 5.048721 / 2.268929 (2.779792) | 2.256623 / 55.444624 (-53.188001) | 1.905912 / 6.876477 (-4.970565) | 1.988400 / 2.142072 (-0.153672) | 0.843066 / 4.805227 (-3.962161) | 0.165717 / 6.500664 (-6.334947) | 0.062910 / 0.075469 (-0.012559) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.225668 / 1.841788 (-0.616120) | 14.660082 / 8.074308 (6.585773) | 14.295369 / 10.191392 (4.103977) | 0.171075 / 0.680424 (-0.509348) | 0.029279 / 0.534201 (-0.504922) | 0.441559 / 0.579283 (-0.137724) | 0.445382 / 0.434364 (0.011018) | 0.525350 / 0.540337 (-0.014987) | 0.608493 / 1.386936 (-0.778443) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007288 / 0.011353 (-0.004065) | 0.004999 / 0.011008 (-0.006009) | 0.074656 / 0.038508 (0.036147) | 0.033897 / 0.023109 (0.010788) | 0.345826 / 0.275898 (0.069928) | 0.390891 / 0.323480 (0.067411) | 0.005811 / 0.007986 (-0.002174) | 0.003976 / 0.004328 (-0.000353) | 0.073546 / 0.004250 (0.069295) | 0.047245 / 0.037052 (0.010193) | 0.351851 / 0.258489 (0.093362) | 0.403217 / 0.293841 (0.109376) | 0.036771 / 0.128546 (-0.091775) | 0.012240 / 0.075646 (-0.063407) | 0.086720 / 0.419271 (-0.332552) | 0.049440 / 0.043533 (0.005907) | 0.339520 / 0.255139 (0.084381) | 0.372160 / 0.283200 (0.088961) | 0.100813 / 0.141683 (-0.040870) | 1.436436 / 1.452155 (-0.015718) | 1.514723 / 1.492716 (0.022007) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.231394 / 0.018006 (0.213388) | 0.440825 / 0.000490 (0.440336) | 0.000994 / 0.000200 (0.000794) | 0.000077 / 0.000054 (0.000022) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028999 / 0.037411 (-0.008412) | 0.111391 / 0.014526 (0.096865) | 0.123058 / 0.176557 (-0.053498) | 0.194348 / 0.737135 (-0.542787) | 0.125730 / 0.296338 (-0.170609) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.431950 / 0.215209 (0.216741) | 4.298724 / 2.077655 (2.221069) | 2.064116 / 1.504120 (0.559996) | 1.892062 / 1.541195 (0.350867) | 1.985441 / 1.468490 (0.516951) | 0.707028 / 4.584777 (-3.877749) | 3.812976 / 3.745712 (0.067264) | 3.078704 / 5.269862 (-2.191158) | 1.832737 / 4.565676 (-2.732939) | 0.086182 / 0.424275 (-0.338093) | 0.012289 / 0.007607 (0.004681) | 0.530265 / 0.226044 (0.304220) | 5.283122 / 2.268929 (3.014194) | 2.558491 / 55.444624 (-52.886134) | 2.237046 / 6.876477 (-4.639431) | 2.354548 / 2.142072 (0.212475) | 0.848947 / 4.805227 (-3.956280) | 0.167907 / 6.500664 (-6.332757) | 0.064998 / 0.075469 (-0.010471) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.248287 / 1.841788 (-0.593500) | 14.976327 / 8.074308 (6.902019) | 13.596143 / 10.191392 (3.404751) | 0.145730 / 0.680424 (-0.534694) | 0.017340 / 0.534201 (-0.516861) | 0.430111 / 0.579283 (-0.149172) | 0.433462 / 0.434364 (-0.000902) | 0.540365 / 0.540337 (0.000028) | 0.650586 / 1.386936 (-0.736350) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1875c8a4c928aeaccc826f13ffdbf7543112024d \"CML watermark\")\n" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.011596 / 0.011353 (0.000244) | 0.005845 / 0.011008 (-0.005164) | 0.121302 / 0.038508 (0.082794) | 0.034306 / 0.023109 (0.011196) | 0.355973 / 0.275898 (0.080075) | 0.419903 / 0.323480 (0.096423) | 0.009049 / 0.007986 (0.001064) | 0.004245 / 0.004328 (-0.000084) | 0.092004 / 0.004250 (0.087753) | 0.042782 / 0.037052 (0.005730) | 0.355805 / 0.258489 (0.097316) | 0.407298 / 0.293841 (0.113457) | 0.052481 / 0.128546 (-0.076066) | 0.020880 / 0.075646 (-0.054766) | 0.379948 / 0.419271 (-0.039324) | 0.061337 / 0.043533 (0.017804) | 0.359829 / 0.255139 (0.104690) | 0.379244 / 0.283200 (0.096044) | 0.116692 / 0.141683 (-0.024990) | 1.733717 / 1.452155 (0.281562) | 1.700246 / 1.492716 (0.207530) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.014622 / 0.018006 (-0.003384) | 0.518777 / 0.000490 (0.518288) | 0.004086 / 0.000200 (0.003886) | 0.000136 / 0.000054 (0.000082) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031208 / 0.037411 (-0.006204) | 0.143003 / 0.014526 (0.128477) | 0.132625 / 0.176557 (-0.043932) | 0.187681 / 0.737135 (-0.549455) | 0.136576 / 0.296338 (-0.159763) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.626516 / 0.215209 (0.411307) | 6.282558 / 2.077655 (4.204904) | 2.702686 / 1.504120 (1.198566) | 2.287445 / 1.541195 (0.746250) | 2.333014 / 1.468490 (0.864524) | 1.227815 / 4.584777 (-3.356962) | 5.545640 / 3.745712 (1.799928) | 4.953226 / 5.269862 (-0.316635) | 2.774549 / 4.565676 (-1.791128) | 0.145257 / 0.424275 (-0.279018) | 0.014887 / 0.007607 (0.007280) | 0.812226 / 0.226044 (0.586182) | 8.002727 / 2.268929 (5.733798) | 3.314852 / 55.444624 (-52.129773) | 2.602348 / 6.876477 (-4.274128) | 2.593511 / 2.142072 (0.451438) | 1.440498 / 4.805227 (-3.364730) | 0.254849 / 6.500664 (-6.245815) | 0.077020 / 0.075469 (0.001551) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.487633 / 1.841788 (-0.354155) | 17.385773 / 8.074308 (9.311465) | 21.775511 / 10.191392 (11.584118) | 0.273514 / 0.680424 (-0.406910) | 0.059644 / 0.534201 (-0.474557) | 0.578710 / 0.579283 (-0.000573) | 0.630221 / 0.434364 (0.195857) | 0.632089 / 0.540337 (0.091752) | 0.762367 / 1.386936 (-0.624569) |\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) | 0.009513 / 0.011353 (-0.001840) | 0.006009 / 0.011008 (-0.004999) | 0.087589 / 0.038508 (0.049081) | 0.037487 / 0.023109 (0.014378) | 0.397660 / 0.275898 (0.121762) | 0.474438 / 0.323480 (0.150958) | 0.007373 / 0.007986 (-0.000613) | 0.005839 / 0.004328 (0.001511) | 0.092759 / 0.004250 (0.088509) | 0.052128 / 0.037052 (0.015075) | 0.382378 / 0.258489 (0.123889) | 0.458244 / 0.293841 (0.164403) | 0.057232 / 0.128546 (-0.071314) | 0.020662 / 0.075646 (-0.054984) | 0.110314 / 0.419271 (-0.308957) | 0.063014 / 0.043533 (0.019481) | 0.386020 / 0.255139 (0.130881) | 0.476169 / 0.283200 (0.192970) | 0.118081 / 0.141683 (-0.023602) | 1.724158 / 1.452155 (0.272003) | 1.862257 / 1.492716 (0.369541) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.224288 / 0.018006 (0.206281) | 0.523631 / 0.000490 (0.523141) | 0.004420 / 0.000200 (0.004220) | 0.000127 / 0.000054 (0.000073) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032359 / 0.037411 (-0.005052) | 0.140045 / 0.014526 (0.125519) | 0.138164 / 0.176557 (-0.038393) | 0.181068 / 0.737135 (-0.556067) | 0.143965 / 0.296338 (-0.152374) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.573809 / 0.215209 (0.358600) | 6.083247 / 2.077655 (4.005592) | 2.671258 / 1.504120 (1.167138) | 2.277062 / 1.541195 (0.735868) | 2.299544 / 1.468490 (0.831054) | 1.267351 / 4.584777 (-3.317425) | 5.494461 / 3.745712 (1.748749) | 5.083169 / 5.269862 (-0.186692) | 2.531738 / 4.565676 (-2.033938) | 0.151834 / 0.424275 (-0.272441) | 0.014123 / 0.007607 (0.006516) | 0.800222 / 0.226044 (0.574177) | 7.637624 / 2.268929 (5.368695) | 3.325574 / 55.444624 (-52.119050) | 2.563008 / 6.876477 (-4.313468) | 2.596259 / 2.142072 (0.454187) | 1.459206 / 4.805227 (-3.346021) | 0.237771 / 6.500664 (-6.262893) | 0.071854 / 0.075469 (-0.003615) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.605504 / 1.841788 (-0.236284) | 17.593594 / 8.074308 (9.519285) | 20.618005 / 10.191392 (10.426612) | 0.270938 / 0.680424 (-0.409486) | 0.026205 / 0.534201 (-0.507996) | 0.562223 / 0.579283 (-0.017060) | 0.617571 / 0.434364 (0.183207) | 0.616398 / 0.540337 (0.076060) | 0.715293 / 1.386936 (-0.671643) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#673dc0dd7d063b2313f7adcc9e0be53d4718f5cf \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.013213 / 0.011353 (0.001860) | 0.006253 / 0.011008 (-0.004756) | 0.125175 / 0.038508 (0.086667) | 0.037491 / 0.023109 (0.014382) | 0.401379 / 0.275898 (0.125481) | 0.395826 / 0.323480 (0.072346) | 0.009224 / 0.007986 (0.001238) | 0.005163 / 0.004328 (0.000835) | 0.096490 / 0.004250 (0.092239) | 0.042473 / 0.037052 (0.005420) | 0.383713 / 0.258489 (0.125224) | 0.429234 / 0.293841 (0.135393) | 0.063261 / 0.128546 (-0.065285) | 0.020114 / 0.075646 (-0.055532) | 0.401687 / 0.419271 (-0.017585) | 0.062831 / 0.043533 (0.019298) | 0.405211 / 0.255139 (0.150072) | 0.380810 / 0.283200 (0.097610) | 0.109166 / 0.141683 (-0.032517) | 1.869580 / 1.452155 (0.417426) | 1.949947 / 1.492716 (0.457231) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.207481 / 0.018006 (0.189475) | 0.504161 / 0.000490 (0.503671) | 0.008429 / 0.000200 (0.008229) | 0.000101 / 0.000054 (0.000047) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029182 / 0.037411 (-0.008229) | 0.126284 / 0.014526 (0.111758) | 0.140381 / 0.176557 (-0.036175) | 0.175878 / 0.737135 (-0.561257) | 0.138824 / 0.296338 (-0.157514) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.643658 / 0.215209 (0.428449) | 6.396224 / 2.077655 (4.318569) | 2.600702 / 1.504120 (1.096582) | 2.176721 / 1.541195 (0.635526) | 2.216116 / 1.468490 (0.747626) | 1.235069 / 4.584777 (-3.349708) | 5.457228 / 3.745712 (1.711516) | 3.060455 / 5.269862 (-2.209407) | 2.028123 / 4.565676 (-2.537554) | 0.141617 / 0.424275 (-0.282658) | 0.016596 / 0.007607 (0.008989) | 0.804915 / 0.226044 (0.578870) | 7.968821 / 2.268929 (5.699893) | 3.340650 / 55.444624 (-52.103974) | 2.533620 / 6.876477 (-4.342856) | 2.457388 / 2.142072 (0.315315) | 1.486527 / 4.805227 (-3.318700) | 0.253767 / 6.500664 (-6.246897) | 0.082192 / 0.075469 (0.006723) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.470896 / 1.841788 (-0.370892) | 17.566637 / 8.074308 (9.492329) | 23.144148 / 10.191392 (12.952756) | 0.235510 / 0.680424 (-0.444913) | 0.046051 / 0.534201 (-0.488150) | 0.559954 / 0.579283 (-0.019329) | 0.645390 / 0.434364 (0.211026) | 0.690983 / 0.540337 (0.150646) | 0.776252 / 1.386936 (-0.610684) |\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) | 0.010564 / 0.011353 (-0.000789) | 0.006150 / 0.011008 (-0.004858) | 0.100030 / 0.038508 (0.061522) | 0.036873 / 0.023109 (0.013764) | 0.448508 / 0.275898 (0.172610) | 0.492593 / 0.323480 (0.169113) | 0.007337 / 0.007986 (-0.000648) | 0.004804 / 0.004328 (0.000475) | 0.099218 / 0.004250 (0.094967) | 0.055513 / 0.037052 (0.018461) | 0.462147 / 0.258489 (0.203658) | 0.510229 / 0.293841 (0.216388) | 0.055307 / 0.128546 (-0.073239) | 0.021989 / 0.075646 (-0.053657) | 0.118487 / 0.419271 (-0.300785) | 0.071752 / 0.043533 (0.028219) | 0.456572 / 0.255139 (0.201433) | 0.475160 / 0.283200 (0.191961) | 0.117472 / 0.141683 (-0.024211) | 1.813212 / 1.452155 (0.361058) | 1.908413 / 1.492716 (0.415696) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.352929 / 0.018006 (0.334923) | 0.543874 / 0.000490 (0.543384) | 0.078529 / 0.000200 (0.078329) | 0.000669 / 0.000054 (0.000614) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033157 / 0.037411 (-0.004254) | 0.162503 / 0.014526 (0.147977) | 0.146424 / 0.176557 (-0.030132) | 0.201781 / 0.737135 (-0.535354) | 0.168110 / 0.296338 (-0.128229) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.644205 / 0.215209 (0.428996) | 6.327519 / 2.077655 (4.249865) | 2.728102 / 1.504120 (1.223982) | 2.306426 / 1.541195 (0.765232) | 2.373125 / 1.468490 (0.904635) | 1.350649 / 4.584777 (-3.234128) | 5.652714 / 3.745712 (1.907002) | 3.175335 / 5.269862 (-2.094526) | 2.222902 / 4.565676 (-2.342775) | 0.160609 / 0.424275 (-0.263666) | 0.015596 / 0.007607 (0.007989) | 0.790357 / 0.226044 (0.564313) | 8.289758 / 2.268929 (6.020830) | 3.479215 / 55.444624 (-51.965410) | 2.860063 / 6.876477 (-4.016413) | 2.806720 / 2.142072 (0.664648) | 1.639046 / 4.805227 (-3.166181) | 0.267017 / 6.500664 (-6.233648) | 0.083990 / 0.075469 (0.008521) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.632262 / 1.841788 (-0.209525) | 17.794357 / 8.074308 (9.720049) | 21.203547 / 10.191392 (11.012155) | 0.250899 / 0.680424 (-0.429525) | 0.024502 / 0.534201 (-0.509699) | 0.519960 / 0.579283 (-0.059323) | 0.615412 / 0.434364 (0.181048) | 0.641914 / 0.540337 (0.101577) | 0.772355 / 1.386936 (-0.614581) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#32cc4d10243b0feb69650f007d010971fd861dc1 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009501 / 0.011353 (-0.001852) | 0.005262 / 0.011008 (-0.005747) | 0.100809 / 0.038508 (0.062301) | 0.036601 / 0.023109 (0.013492) | 0.299612 / 0.275898 (0.023714) | 0.366970 / 0.323480 (0.043490) | 0.007879 / 0.007986 (-0.000107) | 0.004216 / 0.004328 (-0.000113) | 0.076749 / 0.004250 (0.072498) | 0.042081 / 0.037052 (0.005029) | 0.299572 / 0.258489 (0.041083) | 0.339687 / 0.293841 (0.045846) | 0.038706 / 0.128546 (-0.089840) | 0.012295 / 0.075646 (-0.063352) | 0.336172 / 0.419271 (-0.083100) | 0.047524 / 0.043533 (0.003992) | 0.296800 / 0.255139 (0.041661) | 0.331592 / 0.283200 (0.048393) | 0.101191 / 0.141683 (-0.040491) | 1.486200 / 1.452155 (0.034046) | 1.509955 / 1.492716 (0.017239) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.204735 / 0.018006 (0.186728) | 0.446381 / 0.000490 (0.445891) | 0.005177 / 0.000200 (0.004977) | 0.000099 / 0.000054 (0.000045) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028655 / 0.037411 (-0.008756) | 0.116559 / 0.014526 (0.102033) | 0.122551 / 0.176557 (-0.054006) | 0.189764 / 0.737135 (-0.547372) | 0.126446 / 0.296338 (-0.169892) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.400104 / 0.215209 (0.184895) | 4.001524 / 2.077655 (1.923869) | 1.779267 / 1.504120 (0.275147) | 1.580168 / 1.541195 (0.038974) | 1.684100 / 1.468490 (0.215610) | 0.703354 / 4.584777 (-3.881423) | 3.828131 / 3.745712 (0.082419) | 2.098500 / 5.269862 (-3.171362) | 1.331161 / 4.565676 (-3.234516) | 0.085417 / 0.424275 (-0.338858) | 0.012380 / 0.007607 (0.004772) | 0.504189 / 0.226044 (0.278144) | 5.094672 / 2.268929 (2.825743) | 2.264352 / 55.444624 (-53.180272) | 1.909573 / 6.876477 (-4.966904) | 2.005425 / 2.142072 (-0.136648) | 0.840893 / 4.805227 (-3.964335) | 0.164689 / 6.500664 (-6.335975) | 0.062754 / 0.075469 (-0.012715) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.250001 / 1.841788 (-0.591786) | 14.993313 / 8.074308 (6.919005) | 14.880601 / 10.191392 (4.689209) | 0.175141 / 0.680424 (-0.505283) | 0.028952 / 0.534201 (-0.505249) | 0.447073 / 0.579283 (-0.132210) | 0.445993 / 0.434364 (0.011629) | 0.525527 / 0.540337 (-0.014811) | 0.613156 / 1.386936 (-0.773780) |\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) | 0.007796 / 0.011353 (-0.003557) | 0.005399 / 0.011008 (-0.005609) | 0.078240 / 0.038508 (0.039732) | 0.035303 / 0.023109 (0.012193) | 0.364603 / 0.275898 (0.088705) | 0.400794 / 0.323480 (0.077314) | 0.006152 / 0.007986 (-0.001834) | 0.004324 / 0.004328 (-0.000004) | 0.074949 / 0.004250 (0.070698) | 0.051939 / 0.037052 (0.014887) | 0.377079 / 0.258489 (0.118590) | 0.413630 / 0.293841 (0.119789) | 0.037567 / 0.128546 (-0.090979) | 0.012793 / 0.075646 (-0.062854) | 0.089013 / 0.419271 (-0.330258) | 0.050748 / 0.043533 (0.007215) | 0.370100 / 0.255139 (0.114961) | 0.384838 / 0.283200 (0.101638) | 0.105840 / 0.141683 (-0.035843) | 1.476490 / 1.452155 (0.024335) | 1.544688 / 1.492716 (0.051972) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.220987 / 0.018006 (0.202981) | 0.443801 / 0.000490 (0.443311) | 0.005747 / 0.000200 (0.005547) | 0.000106 / 0.000054 (0.000051) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030187 / 0.037411 (-0.007225) | 0.118230 / 0.014526 (0.103704) | 0.126810 / 0.176557 (-0.049746) | 0.200482 / 0.737135 (-0.536654) | 0.130831 / 0.296338 (-0.165507) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.423231 / 0.215209 (0.208022) | 4.196576 / 2.077655 (2.118921) | 1.992919 / 1.504120 (0.488799) | 1.809172 / 1.541195 (0.267977) | 1.932706 / 1.468490 (0.464216) | 0.727319 / 4.584777 (-3.857458) | 3.833295 / 3.745712 (0.087583) | 3.527005 / 5.269862 (-1.742857) | 1.937348 / 4.565676 (-2.628329) | 0.088713 / 0.424275 (-0.335562) | 0.012711 / 0.007607 (0.005104) | 0.531385 / 0.226044 (0.305341) | 5.308051 / 2.268929 (3.039123) | 2.493494 / 55.444624 (-52.951131) | 2.168359 / 6.876477 (-4.708118) | 2.258160 / 2.142072 (0.116088) | 0.865629 / 4.805227 (-3.939598) | 0.171281 / 6.500664 (-6.329383) | 0.065746 / 0.075469 (-0.009723) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.290378 / 1.841788 (-0.551409) | 15.900804 / 8.074308 (7.826496) | 14.809614 / 10.191392 (4.618222) | 0.177287 / 0.680424 (-0.503137) | 0.017875 / 0.534201 (-0.516326) | 0.429646 / 0.579283 (-0.149637) | 0.451646 / 0.434364 (0.017282) | 0.545669 / 0.540337 (0.005332) | 0.633215 / 1.386936 (-0.753721) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2c67b5f4bc9cea088e977a135644d38da8c144ff \"CML watermark\")\n" ]

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[]

[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.014525 / 0.011353 (0.003172) | 0.006871 / 0.011008 (-0.004137) | 0.135577 / 0.038508 (0.097069) | 0.039620 / 0.023109 (0.016511) | 0.499829 / 0.275898 (0.223931) | 0.571000 / 0.323480 (0.247520) | 0.009726 / 0.007986 (0.001740) | 0.005654 / 0.004328 (0.001325) | 0.104732 / 0.004250 (0.100482) | 0.046849 / 0.037052 (0.009796) | 0.486667 / 0.258489 (0.228178) | 0.543611 / 0.293841 (0.249770) | 0.056414 / 0.128546 (-0.072133) | 0.019974 / 0.075646 (-0.055672) | 0.484878 / 0.419271 (0.065606) | 0.059244 / 0.043533 (0.015711) | 0.490046 / 0.255139 (0.234907) | 0.517427 / 0.283200 (0.234227) | 0.114692 / 0.141683 (-0.026991) | 1.935935 / 1.452155 (0.483780) | 1.990253 / 1.492716 (0.497537) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.271008 / 0.018006 (0.253002) | 0.610964 / 0.000490 (0.610474) | 0.013423 / 0.000200 (0.013223) | 0.000523 / 0.000054 (0.000468) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031940 / 0.037411 (-0.005472) | 0.130755 / 0.014526 (0.116229) | 0.146616 / 0.176557 (-0.029941) | 0.239386 / 0.737135 (-0.497749) | 0.146612 / 0.296338 (-0.149726) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.675383 / 0.215209 (0.460174) | 6.656828 / 2.077655 (4.579174) | 2.741231 / 1.504120 (1.237111) | 2.232921 / 1.541195 (0.691726) | 2.172116 / 1.468490 (0.703626) | 1.221623 / 4.584777 (-3.363154) | 5.683653 / 3.745712 (1.937941) | 5.344137 / 5.269862 (0.074275) | 2.969670 / 4.565676 (-1.596006) | 0.142107 / 0.424275 (-0.282168) | 0.015808 / 0.007607 (0.008201) | 0.767366 / 0.226044 (0.541321) | 8.059605 / 2.268929 (5.790676) | 3.333535 / 55.444624 (-52.111089) | 2.669619 / 6.876477 (-4.206857) | 2.652989 / 2.142072 (0.510917) | 1.526397 / 4.805227 (-3.278830) | 0.265609 / 6.500664 (-6.235055) | 0.082759 / 0.075469 (0.007290) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.631086 / 1.841788 (-0.210701) | 18.701351 / 8.074308 (10.627043) | 22.843802 / 10.191392 (12.652410) | 0.240134 / 0.680424 (-0.440290) | 0.046683 / 0.534201 (-0.487518) | 0.576488 / 0.579283 (-0.002795) | 0.650123 / 0.434364 (0.215759) | 0.661190 / 0.540337 (0.120853) | 0.759563 / 1.386936 (-0.627373) |\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) | 0.009883 / 0.011353 (-0.001470) | 0.006692 / 0.011008 (-0.004316) | 0.098550 / 0.038508 (0.060042) | 0.035188 / 0.023109 (0.012078) | 0.463535 / 0.275898 (0.187637) | 0.472762 / 0.323480 (0.149282) | 0.007199 / 0.007986 (-0.000787) | 0.007961 / 0.004328 (0.003632) | 0.093140 / 0.004250 (0.088890) | 0.051752 / 0.037052 (0.014700) | 0.453412 / 0.258489 (0.194922) | 0.502741 / 0.293841 (0.208900) | 0.056006 / 0.128546 (-0.072540) | 0.020164 / 0.075646 (-0.055482) | 0.116828 / 0.419271 (-0.302444) | 0.067205 / 0.043533 (0.023672) | 0.442715 / 0.255139 (0.187576) | 0.472525 / 0.283200 (0.189326) | 0.122767 / 0.141683 (-0.018915) | 1.881366 / 1.452155 (0.429212) | 1.978786 / 1.492716 (0.486069) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.284180 / 0.018006 (0.266174) | 0.601556 / 0.000490 (0.601067) | 0.008455 / 0.000200 (0.008255) | 0.000112 / 0.000054 (0.000057) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033515 / 0.037411 (-0.003896) | 0.136407 / 0.014526 (0.121881) | 0.143341 / 0.176557 (-0.033215) | 0.225394 / 0.737135 (-0.511741) | 0.153343 / 0.296338 (-0.142995) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.688202 / 0.215209 (0.472993) | 6.576502 / 2.077655 (4.498847) | 2.839175 / 1.504120 (1.335055) | 2.481152 / 1.541195 (0.939957) | 2.617227 / 1.468490 (1.148736) | 1.314854 / 4.584777 (-3.269922) | 5.805950 / 3.745712 (2.060238) | 3.188930 / 5.269862 (-2.080932) | 2.141719 / 4.565676 (-2.423957) | 0.145069 / 0.424275 (-0.279206) | 0.014567 / 0.007607 (0.006960) | 0.780000 / 0.226044 (0.553955) | 7.898016 / 2.268929 (5.629088) | 3.549060 / 55.444624 (-51.895564) | 2.856569 / 6.876477 (-4.019907) | 3.117719 / 2.142072 (0.975647) | 1.512560 / 4.805227 (-3.292668) | 0.262689 / 6.500664 (-6.237975) | 0.085979 / 0.075469 (0.010509) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.623550 / 1.841788 (-0.218238) | 19.597063 / 8.074308 (11.522755) | 21.293369 / 10.191392 (11.101977) | 0.263780 / 0.680424 (-0.416643) | 0.027289 / 0.534201 (-0.506912) | 0.560361 / 0.579283 (-0.018922) | 0.646288 / 0.434364 (0.211924) | 0.712699 / 0.540337 (0.172361) | 0.818332 / 1.386936 (-0.568604) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b304de5dde30c945ec1397d3b4fe86f3b323ca8b \"CML watermark\")\n" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008477 / 0.011353 (-0.002875) | 0.004565 / 0.011008 (-0.006443) | 0.101640 / 0.038508 (0.063132) | 0.029581 / 0.023109 (0.006472) | 0.296524 / 0.275898 (0.020625) | 0.363175 / 0.323480 (0.039695) | 0.006961 / 0.007986 (-0.001024) | 0.003365 / 0.004328 (-0.000963) | 0.079689 / 0.004250 (0.075439) | 0.034881 / 0.037052 (-0.002171) | 0.310979 / 0.258489 (0.052489) | 0.348663 / 0.293841 (0.054822) | 0.034549 / 0.128546 (-0.093997) | 0.011463 / 0.075646 (-0.064184) | 0.326218 / 0.419271 (-0.093053) | 0.041393 / 0.043533 (-0.002140) | 0.297604 / 0.255139 (0.042465) | 0.335751 / 0.283200 (0.052551) | 0.086521 / 0.141683 (-0.055162) | 1.478906 / 1.452155 (0.026752) | 1.512777 / 1.492716 (0.020060) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.008767 / 0.018006 (-0.009239) | 0.397386 / 0.000490 (0.396897) | 0.003136 / 0.000200 (0.002936) | 0.000096 / 0.000054 (0.000042) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022804 / 0.037411 (-0.014608) | 0.097591 / 0.014526 (0.083066) | 0.103189 / 0.176557 (-0.073368) | 0.138165 / 0.737135 (-0.598970) | 0.107464 / 0.296338 (-0.188874) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.428956 / 0.215209 (0.213747) | 4.269656 / 2.077655 (2.192001) | 2.154418 / 1.504120 (0.650298) | 1.914176 / 1.541195 (0.372982) | 1.818452 / 1.468490 (0.349962) | 0.701381 / 4.584777 (-3.883396) | 3.425190 / 3.745712 (-0.320522) | 1.862545 / 5.269862 (-3.407316) | 1.166271 / 4.565676 (-3.399405) | 0.083678 / 0.424275 (-0.340597) | 0.012254 / 0.007607 (0.004647) | 0.535710 / 0.226044 (0.309665) | 5.342528 / 2.268929 (3.073600) | 2.627135 / 55.444624 (-52.817489) | 2.308313 / 6.876477 (-4.568164) | 2.325568 / 2.142072 (0.183496) | 0.818318 / 4.805227 (-3.986909) | 0.149812 / 6.500664 (-6.350853) | 0.064559 / 0.075469 (-0.010910) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.253611 / 1.841788 (-0.588176) | 13.646763 / 8.074308 (5.572455) | 14.387630 / 10.191392 (4.196238) | 0.159937 / 0.680424 (-0.520487) | 0.029123 / 0.534201 (-0.505078) | 0.400909 / 0.579283 (-0.178374) | 0.422830 / 0.434364 (-0.011534) | 0.488205 / 0.540337 (-0.052133) | 0.577982 / 1.386936 (-0.808954) |\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) | 0.006430 / 0.011353 (-0.004923) | 0.004433 / 0.011008 (-0.006576) | 0.077459 / 0.038508 (0.038951) | 0.026949 / 0.023109 (0.003840) | 0.350276 / 0.275898 (0.074378) | 0.376189 / 0.323480 (0.052709) | 0.004945 / 0.007986 (-0.003041) | 0.003280 / 0.004328 (-0.001048) | 0.076465 / 0.004250 (0.072215) | 0.037510 / 0.037052 (0.000457) | 0.350410 / 0.258489 (0.091921) | 0.386778 / 0.293841 (0.092937) | 0.031933 / 0.128546 (-0.096613) | 0.011691 / 0.075646 (-0.063956) | 0.086519 / 0.419271 (-0.332753) | 0.042490 / 0.043533 (-0.001043) | 0.355930 / 0.255139 (0.100791) | 0.366500 / 0.283200 (0.083301) | 0.089542 / 0.141683 (-0.052141) | 1.492859 / 1.452155 (0.040704) | 1.548626 / 1.492716 (0.055910) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.220123 / 0.018006 (0.202117) | 0.396970 / 0.000490 (0.396480) | 0.000398 / 0.000200 (0.000198) | 0.000059 / 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) | 0.024831 / 0.037411 (-0.012580) | 0.099681 / 0.014526 (0.085156) | 0.108922 / 0.176557 (-0.067635) | 0.143004 / 0.737135 (-0.594131) | 0.109671 / 0.296338 (-0.186667) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.444237 / 0.215209 (0.229028) | 4.430330 / 2.077655 (2.352675) | 2.235003 / 1.504120 (0.730883) | 2.010499 / 1.541195 (0.469305) | 2.030585 / 1.468490 (0.562095) | 0.701938 / 4.584777 (-3.882839) | 3.334569 / 3.745712 (-0.411144) | 1.861680 / 5.269862 (-3.408181) | 1.166072 / 4.565676 (-3.399604) | 0.083870 / 0.424275 (-0.340405) | 0.012615 / 0.007607 (0.005008) | 0.548789 / 0.226044 (0.322744) | 5.488064 / 2.268929 (3.219136) | 2.614926 / 55.444624 (-52.829698) | 2.246455 / 6.876477 (-4.630022) | 2.277439 / 2.142072 (0.135367) | 0.808449 / 4.805227 (-3.996778) | 0.152434 / 6.500664 (-6.348230) | 0.066709 / 0.075469 (-0.008760) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.316880 / 1.841788 (-0.524908) | 13.965269 / 8.074308 (5.890961) | 13.660187 / 10.191392 (3.468795) | 0.157801 / 0.680424 (-0.522623) | 0.016580 / 0.534201 (-0.517621) | 0.382834 / 0.579283 (-0.196449) | 0.394717 / 0.434364 (-0.039647) | 0.465138 / 0.540337 (-0.075200) | 0.552399 / 1.386936 (-0.834537) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#fa06927a62e2983e2f0e8b7ba8262070c1543d78 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009341 / 0.011353 (-0.002012) | 0.005303 / 0.011008 (-0.005705) | 0.099287 / 0.038508 (0.060779) | 0.035587 / 0.023109 (0.012478) | 0.295146 / 0.275898 (0.019248) | 0.370470 / 0.323480 (0.046990) | 0.008910 / 0.007986 (0.000925) | 0.004358 / 0.004328 (0.000029) | 0.076298 / 0.004250 (0.072047) | 0.047187 / 0.037052 (0.010135) | 0.309025 / 0.258489 (0.050536) | 0.346659 / 0.293841 (0.052818) | 0.038378 / 0.128546 (-0.090168) | 0.012475 / 0.075646 (-0.063172) | 0.334370 / 0.419271 (-0.084901) | 0.048391 / 0.043533 (0.004858) | 0.298613 / 0.255139 (0.043474) | 0.317329 / 0.283200 (0.034130) | 0.108748 / 0.141683 (-0.032934) | 1.450454 / 1.452155 (-0.001701) | 1.519883 / 1.492716 (0.027167) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.011513 / 0.018006 (-0.006494) | 0.498941 / 0.000490 (0.498451) | 0.005098 / 0.000200 (0.004898) | 0.000096 / 0.000054 (0.000042) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030523 / 0.037411 (-0.006888) | 0.105478 / 0.014526 (0.090952) | 0.121101 / 0.176557 (-0.055456) | 0.159951 / 0.737135 (-0.577184) | 0.126766 / 0.296338 (-0.169572) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.399101 / 0.215209 (0.183892) | 3.997069 / 2.077655 (1.919414) | 1.851592 / 1.504120 (0.347472) | 1.695708 / 1.541195 (0.154513) | 1.759504 / 1.468490 (0.291014) | 0.708241 / 4.584777 (-3.876536) | 3.786724 / 3.745712 (0.041012) | 3.523731 / 5.269862 (-1.746131) | 1.899474 / 4.565676 (-2.666203) | 0.086680 / 0.424275 (-0.337595) | 0.012232 / 0.007607 (0.004625) | 0.508507 / 0.226044 (0.282462) | 5.086320 / 2.268929 (2.817391) | 2.234906 / 55.444624 (-53.209718) | 1.911090 / 6.876477 (-4.965386) | 1.989232 / 2.142072 (-0.152841) | 0.863660 / 4.805227 (-3.941567) | 0.169334 / 6.500664 (-6.331330) | 0.063273 / 0.075469 (-0.012196) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.237590 / 1.841788 (-0.604198) | 15.417631 / 8.074308 (7.343323) | 15.235308 / 10.191392 (5.043916) | 0.209431 / 0.680424 (-0.470993) | 0.029214 / 0.534201 (-0.504987) | 0.444767 / 0.579283 (-0.134516) | 0.447776 / 0.434364 (0.013413) | 0.538440 / 0.540337 (-0.001897) | 0.635760 / 1.386936 (-0.751176) |\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) | 0.007758 / 0.011353 (-0.003594) | 0.005539 / 0.011008 (-0.005469) | 0.077011 / 0.038508 (0.038503) | 0.034305 / 0.023109 (0.011196) | 0.363352 / 0.275898 (0.087454) | 0.411882 / 0.323480 (0.088403) | 0.006286 / 0.007986 (-0.001700) | 0.004378 / 0.004328 (0.000050) | 0.075504 / 0.004250 (0.071253) | 0.052728 / 0.037052 (0.015675) | 0.370122 / 0.258489 (0.111633) | 0.421910 / 0.293841 (0.128069) | 0.038444 / 0.128546 (-0.090102) | 0.012602 / 0.075646 (-0.063045) | 0.088540 / 0.419271 (-0.330731) | 0.060321 / 0.043533 (0.016788) | 0.350502 / 0.255139 (0.095363) | 0.393211 / 0.283200 (0.110011) | 0.113057 / 0.141683 (-0.028626) | 1.453275 / 1.452155 (0.001120) | 1.541033 / 1.492716 (0.048317) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.333603 / 0.018006 (0.315597) | 0.510548 / 0.000490 (0.510058) | 0.003573 / 0.000200 (0.003373) | 0.000116 / 0.000054 (0.000061) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032783 / 0.037411 (-0.004628) | 0.111943 / 0.014526 (0.097418) | 0.127154 / 0.176557 (-0.049403) | 0.171716 / 0.737135 (-0.565420) | 0.132441 / 0.296338 (-0.163898) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.439110 / 0.215209 (0.223901) | 4.440874 / 2.077655 (2.363220) | 2.145850 / 1.504120 (0.641730) | 1.909566 / 1.541195 (0.368371) | 2.032199 / 1.468490 (0.563709) | 0.711295 / 4.584777 (-3.873482) | 3.845729 / 3.745712 (0.100017) | 3.583555 / 5.269862 (-1.686307) | 1.836856 / 4.565676 (-2.728820) | 0.085966 / 0.424275 (-0.338309) | 0.012479 / 0.007607 (0.004872) | 0.545379 / 0.226044 (0.319334) | 5.425724 / 2.268929 (3.156796) | 2.648304 / 55.444624 (-52.796321) | 2.286369 / 6.876477 (-4.590108) | 2.367714 / 2.142072 (0.225642) | 0.831035 / 4.805227 (-3.974192) | 0.167603 / 6.500664 (-6.333061) | 0.064721 / 0.075469 (-0.010748) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.244495 / 1.841788 (-0.597292) | 15.304267 / 8.074308 (7.229958) | 13.912185 / 10.191392 (3.720793) | 0.156459 / 0.680424 (-0.523965) | 0.019181 / 0.534201 (-0.515019) | 0.425940 / 0.579283 (-0.153343) | 0.427956 / 0.434364 (-0.006408) | 0.529126 / 0.540337 (-0.011212) | 0.628360 / 1.386936 (-0.758576) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#da31f6ee02af29d92ee5541e4a3fc388c3d9abfc \"CML watermark\")\n" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008730 / 0.011353 (-0.002623) | 0.004551 / 0.011008 (-0.006457) | 0.100206 / 0.038508 (0.061698) | 0.030264 / 0.023109 (0.007154) | 0.303310 / 0.275898 (0.027412) | 0.339040 / 0.323480 (0.015560) | 0.006923 / 0.007986 (-0.001063) | 0.004707 / 0.004328 (0.000379) | 0.077822 / 0.004250 (0.073571) | 0.034368 / 0.037052 (-0.002684) | 0.303125 / 0.258489 (0.044636) | 0.348322 / 0.293841 (0.054481) | 0.033831 / 0.128546 (-0.094715) | 0.011459 / 0.075646 (-0.064187) | 0.322092 / 0.419271 (-0.097180) | 0.047720 / 0.043533 (0.004187) | 0.304849 / 0.255139 (0.049710) | 0.330767 / 0.283200 (0.047567) | 0.087362 / 0.141683 (-0.054321) | 1.536095 / 1.452155 (0.083941) | 1.599979 / 1.492716 (0.107263) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.188985 / 0.018006 (0.170979) | 0.410775 / 0.000490 (0.410286) | 0.004215 / 0.000200 (0.004015) | 0.000086 / 0.000054 (0.000032) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023124 / 0.037411 (-0.014287) | 0.096962 / 0.014526 (0.082436) | 0.104070 / 0.176557 (-0.072486) | 0.141248 / 0.737135 (-0.595887) | 0.108534 / 0.296338 (-0.187804) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.417118 / 0.215209 (0.201909) | 4.167808 / 2.077655 (2.090154) | 2.016540 / 1.504120 (0.512420) | 1.847812 / 1.541195 (0.306617) | 1.967023 / 1.468490 (0.498532) | 0.689262 / 4.584777 (-3.895515) | 3.378747 / 3.745712 (-0.366965) | 1.854126 / 5.269862 (-3.415735) | 1.152102 / 4.565676 (-3.413575) | 0.081839 / 0.424275 (-0.342437) | 0.012426 / 0.007607 (0.004819) | 0.521334 / 0.226044 (0.295289) | 5.230593 / 2.268929 (2.961664) | 2.269386 / 55.444624 (-53.175238) | 1.965631 / 6.876477 (-4.910846) | 2.028994 / 2.142072 (-0.113079) | 0.802142 / 4.805227 (-4.003085) | 0.147954 / 6.500664 (-6.352710) | 0.065031 / 0.075469 (-0.010438) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.235289 / 1.841788 (-0.606499) | 13.723507 / 8.074308 (5.649199) | 14.197923 / 10.191392 (4.006531) | 0.147950 / 0.680424 (-0.532473) | 0.028332 / 0.534201 (-0.505869) | 0.400180 / 0.579283 (-0.179103) | 0.418970 / 0.434364 (-0.015393) | 0.478381 / 0.540337 (-0.061957) | 0.576138 / 1.386936 (-0.810798) |\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) | 0.006548 / 0.011353 (-0.004805) | 0.004567 / 0.011008 (-0.006441) | 0.075658 / 0.038508 (0.037150) | 0.027190 / 0.023109 (0.004080) | 0.363417 / 0.275898 (0.087518) | 0.399575 / 0.323480 (0.076095) | 0.004982 / 0.007986 (-0.003004) | 0.003364 / 0.004328 (-0.000964) | 0.074392 / 0.004250 (0.070142) | 0.038839 / 0.037052 (0.001787) | 0.361133 / 0.258489 (0.102644) | 0.408557 / 0.293841 (0.114717) | 0.031468 / 0.128546 (-0.097078) | 0.011645 / 0.075646 (-0.064001) | 0.085145 / 0.419271 (-0.334126) | 0.041775 / 0.043533 (-0.001758) | 0.348624 / 0.255139 (0.093485) | 0.389610 / 0.283200 (0.106410) | 0.088576 / 0.141683 (-0.053107) | 1.511208 / 1.452155 (0.059054) | 1.560568 / 1.492716 (0.067852) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.185017 / 0.018006 (0.167011) | 0.407543 / 0.000490 (0.407053) | 0.002486 / 0.000200 (0.002286) | 0.000076 / 0.000054 (0.000021) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025181 / 0.037411 (-0.012231) | 0.099056 / 0.014526 (0.084530) | 0.108597 / 0.176557 (-0.067959) | 0.144664 / 0.737135 (-0.592471) | 0.110417 / 0.296338 (-0.185922) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.434302 / 0.215209 (0.219093) | 4.327840 / 2.077655 (2.250185) | 2.059939 / 1.504120 (0.555819) | 1.853267 / 1.541195 (0.312072) | 1.906616 / 1.468490 (0.438126) | 0.700165 / 4.584777 (-3.884611) | 3.439216 / 3.745712 (-0.306496) | 2.792034 / 5.269862 (-2.477827) | 1.424852 / 4.565676 (-3.140824) | 0.083926 / 0.424275 (-0.340349) | 0.013943 / 0.007607 (0.006336) | 0.535964 / 0.226044 (0.309920) | 5.368671 / 2.268929 (3.099743) | 2.497027 / 55.444624 (-52.947597) | 2.166222 / 6.876477 (-4.710254) | 2.183766 / 2.142072 (0.041693) | 0.805886 / 4.805227 (-3.999341) | 0.152474 / 6.500664 (-6.348190) | 0.067354 / 0.075469 (-0.008115) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.284052 / 1.841788 (-0.557736) | 13.714066 / 8.074308 (5.639758) | 14.195212 / 10.191392 (4.003820) | 0.151815 / 0.680424 (-0.528609) | 0.016847 / 0.534201 (-0.517354) | 0.391174 / 0.579283 (-0.188109) | 0.409784 / 0.434364 (-0.024580) | 0.473880 / 0.540337 (-0.066458) | 0.561016 / 1.386936 (-0.825920) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#47ab08d9f06abd5bc23bddaa4875b93e926dd3b1 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.010284 / 0.011353 (-0.001068) | 0.005654 / 0.011008 (-0.005355) | 0.100522 / 0.038508 (0.062014) | 0.039201 / 0.023109 (0.016092) | 0.320831 / 0.275898 (0.044933) | 0.365351 / 0.323480 (0.041871) | 0.009066 / 0.007986 (0.001080) | 0.005805 / 0.004328 (0.001476) | 0.076969 / 0.004250 (0.072719) | 0.045813 / 0.037052 (0.008760) | 0.327115 / 0.258489 (0.068626) | 0.362823 / 0.293841 (0.068982) | 0.040521 / 0.128546 (-0.088025) | 0.013166 / 0.075646 (-0.062481) | 0.358579 / 0.419271 (-0.060692) | 0.051753 / 0.043533 (0.008220) | 0.323741 / 0.255139 (0.068602) | 0.360211 / 0.283200 (0.077011) | 0.111534 / 0.141683 (-0.030149) | 1.594887 / 1.452155 (0.142732) | 1.651516 / 1.492716 (0.158799) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.012051 / 0.018006 (-0.005956) | 0.475316 / 0.000490 (0.474826) | 0.004804 / 0.000200 (0.004604) | 0.000100 / 0.000054 (0.000046) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027480 / 0.037411 (-0.009931) | 0.112022 / 0.014526 (0.097496) | 0.121539 / 0.176557 (-0.055017) | 0.166327 / 0.737135 (-0.570809) | 0.132575 / 0.296338 (-0.163763) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.418322 / 0.215209 (0.203113) | 4.149463 / 2.077655 (2.071808) | 1.890901 / 1.504120 (0.386781) | 1.682521 / 1.541195 (0.141327) | 1.716331 / 1.468490 (0.247841) | 0.729176 / 4.584777 (-3.855601) | 4.173303 / 3.745712 (0.427591) | 2.166249 / 5.269862 (-3.103612) | 1.384623 / 4.565676 (-3.181053) | 0.095486 / 0.424275 (-0.328789) | 0.013800 / 0.007607 (0.006193) | 0.573917 / 0.226044 (0.347872) | 5.348843 / 2.268929 (3.079914) | 2.421716 / 55.444624 (-53.022909) | 2.002048 / 6.876477 (-4.874428) | 2.079493 / 2.142072 (-0.062579) | 0.882818 / 4.805227 (-3.922409) | 0.172936 / 6.500664 (-6.327728) | 0.068384 / 0.075469 (-0.007085) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.285704 / 1.841788 (-0.556084) | 16.036346 / 8.074308 (7.962038) | 15.181557 / 10.191392 (4.990165) | 0.194044 / 0.680424 (-0.486380) | 0.033128 / 0.534201 (-0.501073) | 0.480290 / 0.579283 (-0.098993) | 0.497525 / 0.434364 (0.063161) | 0.602304 / 0.540337 (0.061966) | 0.754273 / 1.386936 (-0.632663) |\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) | 0.007263 / 0.011353 (-0.004090) | 0.005164 / 0.011008 (-0.005845) | 0.079833 / 0.038508 (0.041325) | 0.033974 / 0.023109 (0.010865) | 0.382057 / 0.275898 (0.106159) | 0.402924 / 0.323480 (0.079444) | 0.007273 / 0.007986 (-0.000712) | 0.004378 / 0.004328 (0.000049) | 0.080556 / 0.004250 (0.076305) | 0.047376 / 0.037052 (0.010324) | 0.379044 / 0.258489 (0.120555) | 0.422135 / 0.293841 (0.128294) | 0.038294 / 0.128546 (-0.090252) | 0.013974 / 0.075646 (-0.061672) | 0.094936 / 0.419271 (-0.324335) | 0.051033 / 0.043533 (0.007501) | 0.368197 / 0.255139 (0.113058) | 0.409627 / 0.283200 (0.126427) | 0.107365 / 0.141683 (-0.034318) | 1.537501 / 1.452155 (0.085346) | 1.618021 / 1.492716 (0.125305) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.227187 / 0.018006 (0.209181) | 0.473226 / 0.000490 (0.472736) | 0.006532 / 0.000200 (0.006332) | 0.000121 / 0.000054 (0.000066) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029814 / 0.037411 (-0.007597) | 0.121113 / 0.014526 (0.106587) | 0.125107 / 0.176557 (-0.051450) | 0.167008 / 0.737135 (-0.570127) | 0.128720 / 0.296338 (-0.167619) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.452158 / 0.215209 (0.236949) | 4.507087 / 2.077655 (2.429433) | 2.193910 / 1.504120 (0.689790) | 1.991234 / 1.541195 (0.450039) | 2.120256 / 1.468490 (0.651766) | 0.726664 / 4.584777 (-3.858113) | 4.213148 / 3.745712 (0.467436) | 4.082857 / 5.269862 (-1.187005) | 1.741018 / 4.565676 (-2.824658) | 0.090176 / 0.424275 (-0.334099) | 0.013221 / 0.007607 (0.005614) | 0.558868 / 0.226044 (0.332824) | 5.617242 / 2.268929 (3.348313) | 2.985430 / 55.444624 (-52.459194) | 2.623136 / 6.876477 (-4.253341) | 2.383177 / 2.142072 (0.241105) | 0.917237 / 4.805227 (-3.887990) | 0.178774 / 6.500664 (-6.321890) | 0.064707 / 0.075469 (-0.010762) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.365402 / 1.841788 (-0.476385) | 16.035773 / 8.074308 (7.961465) | 13.917612 / 10.191392 (3.726220) | 0.152191 / 0.680424 (-0.528233) | 0.020734 / 0.534201 (-0.513467) | 0.442055 / 0.579283 (-0.137228) | 0.470588 / 0.434364 (0.036224) | 0.563433 / 0.540337 (0.023096) | 0.651161 / 1.386936 (-0.735775) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6ab909a44b723fe0a8a586beafc8c5cbf9c91c21 \"CML watermark\")\n", "If it's good for you @polinaeterna I'd like to merge it and then run the `transformers` CI to see if it changes anything", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008829 / 0.011353 (-0.002524) | 0.004652 / 0.011008 (-0.006356) | 0.102505 / 0.038508 (0.063997) | 0.030164 / 0.023109 (0.007054) | 0.306551 / 0.275898 (0.030653) | 0.368920 / 0.323480 (0.045440) | 0.007084 / 0.007986 (-0.000902) | 0.003545 / 0.004328 (-0.000783) | 0.079402 / 0.004250 (0.075152) | 0.035296 / 0.037052 (-0.001756) | 0.312010 / 0.258489 (0.053520) | 0.348773 / 0.293841 (0.054932) | 0.034622 / 0.128546 (-0.093924) | 0.011727 / 0.075646 (-0.063920) | 0.326911 / 0.419271 (-0.092361) | 0.043832 / 0.043533 (0.000300) | 0.306357 / 0.255139 (0.051218) | 0.328744 / 0.283200 (0.045544) | 0.091954 / 0.141683 (-0.049729) | 1.563989 / 1.452155 (0.111834) | 1.591901 / 1.492716 (0.099185) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.194955 / 0.018006 (0.176948) | 0.412864 / 0.000490 (0.412374) | 0.003710 / 0.000200 (0.003510) | 0.000081 / 0.000054 (0.000026) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023132 / 0.037411 (-0.014279) | 0.099586 / 0.014526 (0.085060) | 0.105031 / 0.176557 (-0.071525) | 0.141206 / 0.737135 (-0.595929) | 0.111978 / 0.296338 (-0.184360) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.413729 / 0.215209 (0.198520) | 4.161713 / 2.077655 (2.084058) | 1.887442 / 1.504120 (0.383322) | 1.711847 / 1.541195 (0.170653) | 1.756833 / 1.468490 (0.288343) | 0.699239 / 4.584777 (-3.885538) | 3.346213 / 3.745712 (-0.399499) | 2.822289 / 5.269862 (-2.447573) | 1.475650 / 4.565676 (-3.090027) | 0.082800 / 0.424275 (-0.341475) | 0.012302 / 0.007607 (0.004695) | 0.523068 / 0.226044 (0.297024) | 5.242833 / 2.268929 (2.973904) | 2.310768 / 55.444624 (-53.133856) | 1.954629 / 6.876477 (-4.921847) | 2.015563 / 2.142072 (-0.126510) | 0.812613 / 4.805227 (-3.992614) | 0.149512 / 6.500664 (-6.351152) | 0.065162 / 0.075469 (-0.010307) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.270177 / 1.841788 (-0.571610) | 13.664765 / 8.074308 (5.590457) | 14.317968 / 10.191392 (4.126576) | 0.138135 / 0.680424 (-0.542289) | 0.028503 / 0.534201 (-0.505698) | 0.402921 / 0.579283 (-0.176362) | 0.400999 / 0.434364 (-0.033365) | 0.470983 / 0.540337 (-0.069355) | 0.544319 / 1.386936 (-0.842617) |\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) | 0.006841 / 0.011353 (-0.004512) | 0.004570 / 0.011008 (-0.006439) | 0.076398 / 0.038508 (0.037890) | 0.028136 / 0.023109 (0.005027) | 0.339864 / 0.275898 (0.063966) | 0.375496 / 0.323480 (0.052016) | 0.004967 / 0.007986 (-0.003019) | 0.003411 / 0.004328 (-0.000917) | 0.075727 / 0.004250 (0.071476) | 0.040025 / 0.037052 (0.002973) | 0.340473 / 0.258489 (0.081984) | 0.384396 / 0.293841 (0.090555) | 0.031683 / 0.128546 (-0.096863) | 0.011752 / 0.075646 (-0.063894) | 0.085635 / 0.419271 (-0.333636) | 0.042764 / 0.043533 (-0.000769) | 0.339417 / 0.255139 (0.084278) | 0.364190 / 0.283200 (0.080991) | 0.093842 / 0.141683 (-0.047841) | 1.480999 / 1.452155 (0.028844) | 1.549752 / 1.492716 (0.057036) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.174146 / 0.018006 (0.156140) | 0.415459 / 0.000490 (0.414970) | 0.002854 / 0.000200 (0.002654) | 0.000077 / 0.000054 (0.000023) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024671 / 0.037411 (-0.012740) | 0.101229 / 0.014526 (0.086703) | 0.108841 / 0.176557 (-0.067716) | 0.144530 / 0.737135 (-0.592606) | 0.112509 / 0.296338 (-0.183829) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.460561 / 0.215209 (0.245352) | 4.591139 / 2.077655 (2.513484) | 2.275535 / 1.504120 (0.771415) | 2.070976 / 1.541195 (0.529781) | 2.028766 / 1.468490 (0.560276) | 0.706166 / 4.584777 (-3.878611) | 3.408498 / 3.745712 (-0.337215) | 3.034665 / 5.269862 (-2.235197) | 1.586805 / 4.565676 (-2.978872) | 0.083355 / 0.424275 (-0.340920) | 0.012460 / 0.007607 (0.004853) | 0.565256 / 0.226044 (0.339212) | 5.662643 / 2.268929 (3.393715) | 2.697019 / 55.444624 (-52.747605) | 2.302044 / 6.876477 (-4.574433) | 2.373081 / 2.142072 (0.231009) | 0.809804 / 4.805227 (-3.995423) | 0.151481 / 6.500664 (-6.349183) | 0.066870 / 0.075469 (-0.008599) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.257293 / 1.841788 (-0.584495) | 14.059454 / 8.074308 (5.985146) | 13.783251 / 10.191392 (3.591859) | 0.140007 / 0.680424 (-0.540417) | 0.016624 / 0.534201 (-0.517577) | 0.381703 / 0.579283 (-0.197580) | 0.389032 / 0.434364 (-0.045332) | 0.466127 / 0.540337 (-0.074211) | 0.551052 / 1.386936 (-0.835884) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4a767f7a3dffdf45886690b81c6e624146ae14da \"CML watermark\")\n" ]

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[ "Hi ! The indices mapping is written in the same cachedirectory as your dataset.\r\n\r\nCan you run this to show your current cache directory ?\r\n```python\r\nprint(train_dataset.cache_files)\r\n```", "```\r\n[{'filename': '.../train/dataset.arrow'}, {'filename': '.../train/dataset.arrow'}]\r\n```\r\n\r\nThese are the actual paths where `.hf` files are stored. ", "I'm not aware of any `.hf` file ? What are you referring to ?\r\n\r\nAlso the error says \"Protocol unknown: parent\". Is there a chance you may have ended up with a path that contains this string `parent://` ?", "I figured out why the issue was occuring but don't know the long-term fix.\r\nThe dataset I was trying to shuffle was loaded from a saved file which had `::` delimiter in filename. When I try with the exact same file without `::` in filename, it works as expected.\r\nQuick fix is to not use colons in filename. But if this is expected behaviour, this should be clearly stated in the documentation.\r\nThanks for help @lhoestq " ]

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[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008735 / 0.011353 (-0.002618) | 0.004514 / 0.011008 (-0.006494) | 0.099348 / 0.038508 (0.060840) | 0.030060 / 0.023109 (0.006951) | 0.302189 / 0.275898 (0.026291) | 0.339535 / 0.323480 (0.016055) | 0.007053 / 0.007986 (-0.000933) | 0.003420 / 0.004328 (-0.000909) | 0.076967 / 0.004250 (0.072717) | 0.034484 / 0.037052 (-0.002568) | 0.304349 / 0.258489 (0.045860) | 0.354032 / 0.293841 (0.060191) | 0.033552 / 0.128546 (-0.094995) | 0.011405 / 0.075646 (-0.064241) | 0.324773 / 0.419271 (-0.094498) | 0.041103 / 0.043533 (-0.002429) | 0.313559 / 0.255139 (0.058420) | 0.333251 / 0.283200 (0.050052) | 0.087580 / 0.141683 (-0.054103) | 1.460324 / 1.452155 (0.008169) | 1.552239 / 1.492716 (0.059523) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.183759 / 0.018006 (0.165753) | 0.413274 / 0.000490 (0.412784) | 0.001684 / 0.000200 (0.001484) | 0.000076 / 0.000054 (0.000022) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023341 / 0.037411 (-0.014071) | 0.098368 / 0.014526 (0.083842) | 0.105522 / 0.176557 (-0.071034) | 0.151581 / 0.737135 (-0.585554) | 0.108980 / 0.296338 (-0.187358) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.417856 / 0.215209 (0.202647) | 4.167570 / 2.077655 (2.089915) | 1.843669 / 1.504120 (0.339549) | 1.643130 / 1.541195 (0.101936) | 1.717587 / 1.468490 (0.249097) | 0.696392 / 4.584777 (-3.888384) | 3.427617 / 3.745712 (-0.318096) | 2.816486 / 5.269862 (-2.453376) | 1.539519 / 4.565676 (-3.026157) | 0.082112 / 0.424275 (-0.342163) | 0.012425 / 0.007607 (0.004818) | 0.525325 / 0.226044 (0.299281) | 5.251710 / 2.268929 (2.982781) | 2.273641 / 55.444624 (-53.170983) | 1.931002 / 6.876477 (-4.945474) | 1.977253 / 2.142072 (-0.164819) | 0.804794 / 4.805227 (-4.000434) | 0.147324 / 6.500664 (-6.353340) | 0.064966 / 0.075469 (-0.010503) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.193173 / 1.841788 (-0.648615) | 13.705127 / 8.074308 (5.630819) | 14.348408 / 10.191392 (4.157016) | 0.165374 / 0.680424 (-0.515050) | 0.028288 / 0.534201 (-0.505913) | 0.402546 / 0.579283 (-0.176737) | 0.413503 / 0.434364 (-0.020861) | 0.473298 / 0.540337 (-0.067039) | 0.567571 / 1.386936 (-0.819365) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006735 / 0.011353 (-0.004618) | 0.004601 / 0.011008 (-0.006407) | 0.077414 / 0.038508 (0.038906) | 0.027402 / 0.023109 (0.004293) | 0.353469 / 0.275898 (0.077571) | 0.381697 / 0.323480 (0.058218) | 0.005076 / 0.007986 (-0.002910) | 0.004665 / 0.004328 (0.000336) | 0.076210 / 0.004250 (0.071960) | 0.039114 / 0.037052 (0.002061) | 0.354980 / 0.258489 (0.096491) | 0.389648 / 0.293841 (0.095807) | 0.031674 / 0.128546 (-0.096872) | 0.011752 / 0.075646 (-0.063894) | 0.086330 / 0.419271 (-0.332942) | 0.041530 / 0.043533 (-0.002003) | 0.343002 / 0.255139 (0.087863) | 0.365959 / 0.283200 (0.082760) | 0.091848 / 0.141683 (-0.049835) | 1.519427 / 1.452155 (0.067272) | 1.591529 / 1.492716 (0.098813) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.216458 / 0.018006 (0.198452) | 0.403326 / 0.000490 (0.402836) | 0.000432 / 0.000200 (0.000232) | 0.000059 / 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) | 0.025106 / 0.037411 (-0.012305) | 0.101113 / 0.014526 (0.086588) | 0.108104 / 0.176557 (-0.068453) | 0.142342 / 0.737135 (-0.594794) | 0.112012 / 0.296338 (-0.184326) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.443128 / 0.215209 (0.227919) | 4.434707 / 2.077655 (2.357052) | 2.115434 / 1.504120 (0.611315) | 1.902865 / 1.541195 (0.361670) | 1.996981 / 1.468490 (0.528491) | 0.702485 / 4.584777 (-3.882292) | 3.419151 / 3.745712 (-0.326561) | 1.911977 / 5.269862 (-3.357884) | 1.178195 / 4.565676 (-3.387481) | 0.082985 / 0.424275 (-0.341290) | 0.012415 / 0.007607 (0.004808) | 0.546188 / 0.226044 (0.320144) | 5.463592 / 2.268929 (3.194664) | 2.574911 / 55.444624 (-52.869713) | 2.232883 / 6.876477 (-4.643594) | 2.284391 / 2.142072 (0.142319) | 0.807389 / 4.805227 (-3.997839) | 0.151461 / 6.500664 (-6.349203) | 0.067831 / 0.075469 (-0.007638) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.286605 / 1.841788 (-0.555183) | 14.230328 / 8.074308 (6.156020) | 13.944645 / 10.191392 (3.753253) | 0.153725 / 0.680424 (-0.526699) | 0.016876 / 0.534201 (-0.517325) | 0.386109 / 0.579283 (-0.193174) | 0.401798 / 0.434364 (-0.032566) | 0.467883 / 0.540337 (-0.072454) | 0.557788 / 1.386936 (-0.829148) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c07f5c9268ce55d0e2022b018d5f44cfcedf1e43 \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009305 / 0.011353 (-0.002048) | 0.004978 / 0.011008 (-0.006031) | 0.101687 / 0.038508 (0.063179) | 0.035339 / 0.023109 (0.012230) | 0.294770 / 0.275898 (0.018872) | 0.355491 / 0.323480 (0.032011) | 0.008183 / 0.007986 (0.000197) | 0.004076 / 0.004328 (-0.000253) | 0.077552 / 0.004250 (0.073302) | 0.042891 / 0.037052 (0.005838) | 0.305727 / 0.258489 (0.047238) | 0.336508 / 0.293841 (0.042667) | 0.038525 / 0.128546 (-0.090022) | 0.011878 / 0.075646 (-0.063768) | 0.334136 / 0.419271 (-0.085136) | 0.047548 / 0.043533 (0.004015) | 0.301749 / 0.255139 (0.046610) | 0.318221 / 0.283200 (0.035022) | 0.099172 / 0.141683 (-0.042511) | 1.440638 / 1.452155 (-0.011516) | 1.503505 / 1.492716 (0.010789) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.202748 / 0.018006 (0.184742) | 0.433670 / 0.000490 (0.433181) | 0.003139 / 0.000200 (0.002939) | 0.000083 / 0.000054 (0.000028) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025555 / 0.037411 (-0.011856) | 0.107156 / 0.014526 (0.092631) | 0.116706 / 0.176557 (-0.059851) | 0.153165 / 0.737135 (-0.583970) | 0.122614 / 0.296338 (-0.173724) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.398912 / 0.215209 (0.183703) | 3.965048 / 2.077655 (1.887394) | 1.894678 / 1.504120 (0.390558) | 1.706925 / 1.541195 (0.165730) | 1.745264 / 1.468490 (0.276774) | 0.691174 / 4.584777 (-3.893603) | 3.824583 / 3.745712 (0.078871) | 3.876806 / 5.269862 (-1.393055) | 1.898991 / 4.565676 (-2.666685) | 0.083687 / 0.424275 (-0.340588) | 0.012122 / 0.007607 (0.004514) | 0.510870 / 0.226044 (0.284825) | 5.094523 / 2.268929 (2.825594) | 2.265557 / 55.444624 (-53.179067) | 1.930882 / 6.876477 (-4.945594) | 2.016090 / 2.142072 (-0.125983) | 0.833108 / 4.805227 (-3.972119) | 0.164804 / 6.500664 (-6.335860) | 0.062864 / 0.075469 (-0.012605) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.192673 / 1.841788 (-0.649115) | 14.730393 / 8.074308 (6.656085) | 14.550736 / 10.191392 (4.359344) | 0.154451 / 0.680424 (-0.525973) | 0.029222 / 0.534201 (-0.504979) | 0.440939 / 0.579283 (-0.138345) | 0.442772 / 0.434364 (0.008409) | 0.543948 / 0.540337 (0.003610) | 0.638113 / 1.386936 (-0.748824) |\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) | 0.007589 / 0.011353 (-0.003764) | 0.005208 / 0.011008 (-0.005800) | 0.073797 / 0.038508 (0.035289) | 0.034021 / 0.023109 (0.010912) | 0.366120 / 0.275898 (0.090222) | 0.397105 / 0.323480 (0.073625) | 0.005837 / 0.007986 (-0.002148) | 0.004028 / 0.004328 (-0.000301) | 0.073502 / 0.004250 (0.069252) | 0.051233 / 0.037052 (0.014181) | 0.359849 / 0.258489 (0.101360) | 0.397476 / 0.293841 (0.103635) | 0.036727 / 0.128546 (-0.091819) | 0.012249 / 0.075646 (-0.063397) | 0.086600 / 0.419271 (-0.332671) | 0.051156 / 0.043533 (0.007623) | 0.343441 / 0.255139 (0.088302) | 0.389672 / 0.283200 (0.106472) | 0.105180 / 0.141683 (-0.036503) | 1.439719 / 1.452155 (-0.012435) | 1.537779 / 1.492716 (0.045062) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.199429 / 0.018006 (0.181422) | 0.440837 / 0.000490 (0.440347) | 0.005333 / 0.000200 (0.005133) | 0.000099 / 0.000054 (0.000044) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029581 / 0.037411 (-0.007830) | 0.113789 / 0.014526 (0.099263) | 0.123799 / 0.176557 (-0.052758) | 0.163772 / 0.737135 (-0.573363) | 0.127156 / 0.296338 (-0.169183) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.422803 / 0.215209 (0.207594) | 4.192400 / 2.077655 (2.114745) | 1.994561 / 1.504120 (0.490441) | 1.807085 / 1.541195 (0.265890) | 1.927539 / 1.468490 (0.459049) | 0.708804 / 4.584777 (-3.875973) | 3.790662 / 3.745712 (0.044950) | 3.667207 / 5.269862 (-1.602655) | 1.985107 / 4.565676 (-2.580570) | 0.086609 / 0.424275 (-0.337666) | 0.012613 / 0.007607 (0.005006) | 0.520167 / 0.226044 (0.294122) | 5.208657 / 2.268929 (2.939729) | 2.500383 / 55.444624 (-52.944241) | 2.129817 / 6.876477 (-4.746660) | 2.181205 / 2.142072 (0.039133) | 0.847925 / 4.805227 (-3.957303) | 0.168293 / 6.500664 (-6.332372) | 0.065066 / 0.075469 (-0.010403) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.261053 / 1.841788 (-0.580735) | 15.091644 / 8.074308 (7.017336) | 14.126139 / 10.191392 (3.934747) | 0.184956 / 0.680424 (-0.495468) | 0.017909 / 0.534201 (-0.516292) | 0.428918 / 0.579283 (-0.150365) | 0.429637 / 0.434364 (-0.004727) | 0.530900 / 0.540337 (-0.009437) | 0.627966 / 1.386936 (-0.758970) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a72fd153d3499a5c5eda783673073c9f557f11e0 \"CML watermark\")\n", "I think we should also suggest installing `resampy` in the error message thrown by the Audio feature when `librosa` is not installed.", "exploring a better solution at https://github.com/huggingface/datasets/pull/5556" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.014953 / 0.011353 (0.003600) | 0.006936 / 0.011008 (-0.004072) | 0.144039 / 0.038508 (0.105531) | 0.046719 / 0.023109 (0.023610) | 0.408832 / 0.275898 (0.132934) | 0.501419 / 0.323480 (0.177939) | 0.010190 / 0.007986 (0.002204) | 0.007618 / 0.004328 (0.003290) | 0.108553 / 0.004250 (0.104303) | 0.048484 / 0.037052 (0.011432) | 0.451586 / 0.258489 (0.193097) | 0.469864 / 0.293841 (0.176023) | 0.062159 / 0.128546 (-0.066387) | 0.019937 / 0.075646 (-0.055710) | 0.473718 / 0.419271 (0.054446) | 0.064777 / 0.043533 (0.021244) | 0.428675 / 0.255139 (0.173536) | 0.467665 / 0.283200 (0.184465) | 0.133528 / 0.141683 (-0.008155) | 1.978084 / 1.452155 (0.525930) | 1.965878 / 1.492716 (0.473162) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.290112 / 0.018006 (0.272106) | 0.629481 / 0.000490 (0.628992) | 0.003600 / 0.000200 (0.003400) | 0.000144 / 0.000054 (0.000089) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030806 / 0.037411 (-0.006605) | 0.142376 / 0.014526 (0.127850) | 0.150020 / 0.176557 (-0.026537) | 0.193679 / 0.737135 (-0.543457) | 0.151329 / 0.296338 (-0.145009) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.629725 / 0.215209 (0.414516) | 6.656313 / 2.077655 (4.578659) | 2.712160 / 1.504120 (1.208041) | 2.328461 / 1.541195 (0.787266) | 2.452502 / 1.468490 (0.984012) | 1.353183 / 4.584777 (-3.231594) | 5.981521 / 3.745712 (2.235809) | 3.707186 / 5.269862 (-1.562676) | 2.460583 / 4.565676 (-2.105094) | 0.178300 / 0.424275 (-0.245975) | 0.020357 / 0.007607 (0.012750) | 0.813564 / 0.226044 (0.587520) | 8.465600 / 2.268929 (6.196671) | 3.491507 / 55.444624 (-51.953117) | 2.810781 / 6.876477 (-4.065695) | 3.100182 / 2.142072 (0.958110) | 1.539321 / 4.805227 (-3.265906) | 0.257735 / 6.500664 (-6.242929) | 0.082785 / 0.075469 (0.007316) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.766586 / 1.841788 (-0.075201) | 20.534638 / 8.074308 (12.460330) | 24.066176 / 10.191392 (13.874784) | 0.272419 / 0.680424 (-0.408005) | 0.048940 / 0.534201 (-0.485261) | 0.606004 / 0.579283 (0.026721) | 0.669684 / 0.434364 (0.235320) | 0.716858 / 0.540337 (0.176521) | 0.949394 / 1.386936 (-0.437542) |\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) | 0.010865 / 0.011353 (-0.000488) | 0.009855 / 0.011008 (-0.001153) | 0.105973 / 0.038508 (0.067465) | 0.039818 / 0.023109 (0.016709) | 0.544505 / 0.275898 (0.268607) | 0.511253 / 0.323480 (0.187773) | 0.007350 / 0.007986 (-0.000635) | 0.006950 / 0.004328 (0.002622) | 0.106548 / 0.004250 (0.102298) | 0.062740 / 0.037052 (0.025688) | 0.465881 / 0.258489 (0.207392) | 0.524426 / 0.293841 (0.230585) | 0.056052 / 0.128546 (-0.072495) | 0.020906 / 0.075646 (-0.054741) | 0.125337 / 0.419271 (-0.293935) | 0.064689 / 0.043533 (0.021156) | 0.483055 / 0.255139 (0.227916) | 0.518878 / 0.283200 (0.235678) | 0.127288 / 0.141683 (-0.014394) | 1.936246 / 1.452155 (0.484092) | 2.162532 / 1.492716 (0.669816) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.253691 / 0.018006 (0.235685) | 0.606244 / 0.000490 (0.605754) | 0.004251 / 0.000200 (0.004051) | 0.000126 / 0.000054 (0.000071) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.038356 / 0.037411 (0.000944) | 0.146690 / 0.014526 (0.132164) | 0.146545 / 0.176557 (-0.030012) | 0.218452 / 0.737135 (-0.518684) | 0.165314 / 0.296338 (-0.131025) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.645768 / 0.215209 (0.430559) | 7.229186 / 2.077655 (5.151531) | 3.484778 / 1.504120 (1.980658) | 2.585310 / 1.541195 (1.044116) | 2.727670 / 1.468490 (1.259180) | 1.393416 / 4.584777 (-3.191361) | 6.448707 / 3.745712 (2.702995) | 3.433652 / 5.269862 (-1.836209) | 2.106450 / 4.565676 (-2.459226) | 0.143899 / 0.424275 (-0.280376) | 0.015097 / 0.007607 (0.007490) | 0.860960 / 0.226044 (0.634916) | 9.509725 / 2.268929 (7.240797) | 3.881601 / 55.444624 (-51.563024) | 3.156018 / 6.876477 (-3.720459) | 3.556330 / 2.142072 (1.414257) | 1.525940 / 4.805227 (-3.279287) | 0.264588 / 6.500664 (-6.236076) | 0.090327 / 0.075469 (0.014858) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.829761 / 1.841788 (-0.012027) | 21.037774 / 8.074308 (12.963466) | 24.464737 / 10.191392 (14.273345) | 0.394165 / 0.680424 (-0.286259) | 0.039286 / 0.534201 (-0.494915) | 0.546412 / 0.579283 (-0.032871) | 0.741760 / 0.434364 (0.307396) | 0.683969 / 0.540337 (0.143632) | 0.831392 / 1.386936 (-0.555544) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e453eeac5239d0ff3e98adcba59a6724ee68b46b \"CML watermark\")\n" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.011635 / 0.011353 (0.000282) | 0.005446 / 0.011008 (-0.005562) | 0.111044 / 0.038508 (0.072536) | 0.034243 / 0.023109 (0.011134) | 0.357560 / 0.275898 (0.081662) | 0.403940 / 0.323480 (0.080460) | 0.008532 / 0.007986 (0.000546) | 0.004327 / 0.004328 (-0.000002) | 0.084659 / 0.004250 (0.080408) | 0.040914 / 0.037052 (0.003861) | 0.367142 / 0.258489 (0.108653) | 0.381651 / 0.293841 (0.087810) | 0.053865 / 0.128546 (-0.074681) | 0.019060 / 0.075646 (-0.056587) | 0.371994 / 0.419271 (-0.047277) | 0.058417 / 0.043533 (0.014884) | 0.357740 / 0.255139 (0.102601) | 0.367423 / 0.283200 (0.084224) | 0.104336 / 0.141683 (-0.037347) | 1.632128 / 1.452155 (0.179974) | 1.676216 / 1.492716 (0.183499) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.199649 / 0.018006 (0.181642) | 0.490945 / 0.000490 (0.490455) | 0.001598 / 0.000200 (0.001398) | 0.000094 / 0.000054 (0.000039) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024541 / 0.037411 (-0.012871) | 0.104713 / 0.014526 (0.090187) | 0.119438 / 0.176557 (-0.057118) | 0.160854 / 0.737135 (-0.576281) | 0.127323 / 0.296338 (-0.169016) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.586483 / 0.215209 (0.371274) | 5.771689 / 2.077655 (3.694034) | 2.378962 / 1.504120 (0.874842) | 1.998787 / 1.541195 (0.457592) | 1.993016 / 1.468490 (0.524526) | 1.199169 / 4.584777 (-3.385608) | 5.281648 / 3.745712 (1.535936) | 5.589235 / 5.269862 (0.319373) | 2.715162 / 4.565676 (-1.850514) | 0.153312 / 0.424275 (-0.270963) | 0.014302 / 0.007607 (0.006695) | 0.761185 / 0.226044 (0.535140) | 7.602517 / 2.268929 (5.333589) | 3.095271 / 55.444624 (-52.349354) | 2.407394 / 6.876477 (-4.469083) | 2.519074 / 2.142072 (0.377002) | 1.459270 / 4.805227 (-3.345957) | 0.259578 / 6.500664 (-6.241086) | 0.077356 / 0.075469 (0.001887) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.502123 / 1.841788 (-0.339665) | 16.254010 / 8.074308 (8.179702) | 19.971713 / 10.191392 (9.780321) | 0.221491 / 0.680424 (-0.458933) | 0.043959 / 0.534201 (-0.490242) | 0.512566 / 0.579283 (-0.066717) | 0.594724 / 0.434364 (0.160360) | 0.573855 / 0.540337 (0.033518) | 0.680503 / 1.386936 (-0.706433) |\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) | 0.008543 / 0.011353 (-0.002810) | 0.005828 / 0.011008 (-0.005180) | 0.083696 / 0.038508 (0.045188) | 0.036186 / 0.023109 (0.013077) | 0.379777 / 0.275898 (0.103879) | 0.437361 / 0.323480 (0.113881) | 0.006788 / 0.007986 (-0.001197) | 0.005110 / 0.004328 (0.000782) | 0.106075 / 0.004250 (0.101824) | 0.048770 / 0.037052 (0.011718) | 0.390770 / 0.258489 (0.132281) | 0.420813 / 0.293841 (0.126972) | 0.050622 / 0.128546 (-0.077924) | 0.019939 / 0.075646 (-0.055707) | 0.106890 / 0.419271 (-0.312382) | 0.070800 / 0.043533 (0.027267) | 0.406094 / 0.255139 (0.150955) | 0.419796 / 0.283200 (0.136597) | 0.107237 / 0.141683 (-0.034446) | 1.687894 / 1.452155 (0.235739) | 1.735680 / 1.492716 (0.242963) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.216403 / 0.018006 (0.198397) | 0.495002 / 0.000490 (0.494512) | 0.004841 / 0.000200 (0.004641) | 0.000117 / 0.000054 (0.000063) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.043774 / 0.037411 (0.006363) | 0.119144 / 0.014526 (0.104618) | 0.143694 / 0.176557 (-0.032862) | 0.195548 / 0.737135 (-0.541587) | 0.151426 / 0.296338 (-0.144912) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.617694 / 0.215209 (0.402485) | 6.216237 / 2.077655 (4.138582) | 2.578341 / 1.504120 (1.074221) | 2.184868 / 1.541195 (0.643673) | 2.244954 / 1.468490 (0.776464) | 1.236072 / 4.584777 (-3.348705) | 5.257919 / 3.745712 (1.512207) | 4.634682 / 5.269862 (-0.635180) | 2.722579 / 4.565676 (-1.843097) | 0.131433 / 0.424275 (-0.292843) | 0.012928 / 0.007607 (0.005321) | 0.768315 / 0.226044 (0.542270) | 7.625277 / 2.268929 (5.356349) | 3.146364 / 55.444624 (-52.298260) | 2.577886 / 6.876477 (-4.298590) | 2.572626 / 2.142072 (0.430554) | 1.468160 / 4.805227 (-3.337067) | 0.252524 / 6.500664 (-6.248140) | 0.083264 / 0.075469 (0.007794) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.452614 / 1.841788 (-0.389174) | 15.906162 / 8.074308 (7.831853) | 17.803630 / 10.191392 (7.612238) | 0.210769 / 0.680424 (-0.469655) | 0.024672 / 0.534201 (-0.509529) | 0.486486 / 0.579283 (-0.092797) | 0.545256 / 0.434364 (0.110892) | 0.598736 / 0.540337 (0.058399) | 0.689083 / 1.386936 (-0.697853) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#189a870b4f0964d77b43c2f4e79c4ca7b799f690 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008806 / 0.011353 (-0.002547) | 0.004947 / 0.011008 (-0.006061) | 0.098559 / 0.038508 (0.060051) | 0.034293 / 0.023109 (0.011183) | 0.311924 / 0.275898 (0.036026) | 0.377501 / 0.323480 (0.054021) | 0.007916 / 0.007986 (-0.000069) | 0.004131 / 0.004328 (-0.000197) | 0.074934 / 0.004250 (0.070684) | 0.043396 / 0.037052 (0.006344) | 0.344788 / 0.258489 (0.086299) | 0.369943 / 0.293841 (0.076102) | 0.036846 / 0.128546 (-0.091700) | 0.011803 / 0.075646 (-0.063843) | 0.331306 / 0.419271 (-0.087965) | 0.047015 / 0.043533 (0.003483) | 0.305890 / 0.255139 (0.050751) | 0.332658 / 0.283200 (0.049459) | 0.101134 / 0.141683 (-0.040549) | 1.485615 / 1.452155 (0.033461) | 1.510230 / 1.492716 (0.017514) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.274272 / 0.018006 (0.256266) | 0.514739 / 0.000490 (0.514250) | 0.003433 / 0.000200 (0.003234) | 0.000078 / 0.000054 (0.000023) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027054 / 0.037411 (-0.010357) | 0.106416 / 0.014526 (0.091890) | 0.118761 / 0.176557 (-0.057796) | 0.156115 / 0.737135 (-0.581021) | 0.123801 / 0.296338 (-0.172537) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.403121 / 0.215209 (0.187912) | 4.008806 / 2.077655 (1.931151) | 1.891253 / 1.504120 (0.387133) | 1.698523 / 1.541195 (0.157328) | 1.778533 / 1.468490 (0.310043) | 0.688207 / 4.584777 (-3.896570) | 3.674350 / 3.745712 (-0.071362) | 1.848438 / 5.269862 (-3.421423) | 1.202380 / 4.565676 (-3.363297) | 0.073490 / 0.424275 (-0.350785) | 0.010655 / 0.007607 (0.003048) | 0.446939 / 0.226044 (0.220894) | 4.478489 / 2.268929 (2.209560) | 1.992281 / 55.444624 (-53.452343) | 1.684077 / 6.876477 (-5.192400) | 1.715435 / 2.142072 (-0.426638) | 0.731454 / 4.805227 (-4.073773) | 0.143679 / 6.500664 (-6.356985) | 0.053415 / 0.075469 (-0.022054) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.060583 / 1.841788 (-0.781205) | 13.730462 / 8.074308 (5.656153) | 13.038976 / 10.191392 (2.847583) | 0.144168 / 0.680424 (-0.536256) | 0.025788 / 0.534201 (-0.508413) | 0.393332 / 0.579283 (-0.185951) | 0.409495 / 0.434364 (-0.024869) | 0.523745 / 0.540337 (-0.016592) | 0.601595 / 1.386936 (-0.785341) |\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) | 0.006369 / 0.011353 (-0.004983) | 0.005019 / 0.011008 (-0.005990) | 0.065226 / 0.038508 (0.026718) | 0.029634 / 0.023109 (0.006524) | 0.302871 / 0.275898 (0.026972) | 0.331055 / 0.323480 (0.007575) | 0.005470 / 0.007986 (-0.002516) | 0.005372 / 0.004328 (0.001043) | 0.064930 / 0.004250 (0.060680) | 0.046979 / 0.037052 (0.009927) | 0.305633 / 0.258489 (0.047144) | 0.345305 / 0.293841 (0.051464) | 0.032951 / 0.128546 (-0.095596) | 0.011447 / 0.075646 (-0.064199) | 0.077054 / 0.419271 (-0.342218) | 0.045744 / 0.043533 (0.002211) | 0.303446 / 0.255139 (0.048307) | 0.319837 / 0.283200 (0.036637) | 0.098631 / 0.141683 (-0.043052) | 1.266593 / 1.452155 (-0.185562) | 1.355388 / 1.492716 (-0.137328) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.291301 / 0.018006 (0.273295) | 0.537848 / 0.000490 (0.537359) | 0.006697 / 0.000200 (0.006497) | 0.000110 / 0.000054 (0.000055) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027677 / 0.037411 (-0.009734) | 0.099633 / 0.014526 (0.085107) | 0.110626 / 0.176557 (-0.065931) | 0.144724 / 0.737135 (-0.592412) | 0.114955 / 0.296338 (-0.181383) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.375344 / 0.215209 (0.160135) | 3.717490 / 2.077655 (1.639835) | 1.845886 / 1.504120 (0.341766) | 1.713274 / 1.541195 (0.172079) | 1.761286 / 1.468490 (0.292796) | 0.627924 / 4.584777 (-3.956853) | 3.628154 / 3.745712 (-0.117558) | 3.261851 / 5.269862 (-2.008011) | 1.701008 / 4.565676 (-2.864669) | 0.076703 / 0.424275 (-0.347572) | 0.010839 / 0.007607 (0.003231) | 0.459193 / 0.226044 (0.233148) | 4.589066 / 2.268929 (2.320137) | 2.193972 / 55.444624 (-53.250653) | 1.892115 / 6.876477 (-4.984362) | 1.892453 / 2.142072 (-0.249619) | 0.745727 / 4.805227 (-4.059500) | 0.150232 / 6.500664 (-6.350432) | 0.057245 / 0.075469 (-0.018224) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.114657 / 1.841788 (-0.727131) | 13.595215 / 8.074308 (5.520907) | 12.267177 / 10.191392 (2.075785) | 0.151362 / 0.680424 (-0.529061) | 0.015609 / 0.534201 (-0.518591) | 0.379151 / 0.579283 (-0.200132) | 0.386125 / 0.434364 (-0.048238) | 0.470037 / 0.540337 (-0.070301) | 0.562340 / 1.386936 (-0.824596) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#526578cd473a266fa86643d15905181bf346ecac \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009847 / 0.011353 (-0.001505) | 0.005609 / 0.011008 (-0.005399) | 0.101951 / 0.038508 (0.063443) | 0.038082 / 0.023109 (0.014972) | 0.299933 / 0.275898 (0.024035) | 0.377081 / 0.323480 (0.053601) | 0.008900 / 0.007986 (0.000915) | 0.004608 / 0.004328 (0.000279) | 0.077723 / 0.004250 (0.073473) | 0.048592 / 0.037052 (0.011540) | 0.310789 / 0.258489 (0.052300) | 0.345627 / 0.293841 (0.051787) | 0.038716 / 0.128546 (-0.089830) | 0.012653 / 0.075646 (-0.062993) | 0.336885 / 0.419271 (-0.082387) | 0.048715 / 0.043533 (0.005182) | 0.295336 / 0.255139 (0.040197) | 0.316735 / 0.283200 (0.033536) | 0.115142 / 0.141683 (-0.026541) | 1.480332 / 1.452155 (0.028177) | 1.604972 / 1.492716 (0.112256) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.299516 / 0.018006 (0.281510) | 0.525892 / 0.000490 (0.525402) | 0.002246 / 0.000200 (0.002046) | 0.000095 / 0.000054 (0.000040) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031547 / 0.037411 (-0.005864) | 0.120611 / 0.014526 (0.106085) | 0.124516 / 0.176557 (-0.052041) | 0.166036 / 0.737135 (-0.571100) | 0.131689 / 0.296338 (-0.164650) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.400728 / 0.215209 (0.185519) | 4.007027 / 2.077655 (1.929372) | 1.793922 / 1.504120 (0.289803) | 1.596709 / 1.541195 (0.055514) | 1.752130 / 1.468490 (0.283640) | 0.717464 / 4.584777 (-3.867313) | 3.798844 / 3.745712 (0.053132) | 3.685088 / 5.269862 (-1.584774) | 1.914041 / 4.565676 (-2.651636) | 0.086181 / 0.424275 (-0.338094) | 0.012753 / 0.007607 (0.005146) | 0.507984 / 0.226044 (0.281940) | 5.086255 / 2.268929 (2.817326) | 2.280650 / 55.444624 (-53.163974) | 1.929294 / 6.876477 (-4.947183) | 2.057884 / 2.142072 (-0.084188) | 0.852863 / 4.805227 (-3.952364) | 0.165497 / 6.500664 (-6.335168) | 0.063356 / 0.075469 (-0.012113) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.212593 / 1.841788 (-0.629194) | 16.270507 / 8.074308 (8.196199) | 15.708406 / 10.191392 (5.517014) | 0.162346 / 0.680424 (-0.518078) | 0.029702 / 0.534201 (-0.504499) | 0.447685 / 0.579283 (-0.131598) | 0.449361 / 0.434364 (0.014997) | 0.530536 / 0.540337 (-0.009801) | 0.613439 / 1.386936 (-0.773497) |\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) | 0.007741 / 0.011353 (-0.003612) | 0.005752 / 0.011008 (-0.005256) | 0.076600 / 0.038508 (0.038092) | 0.034841 / 0.023109 (0.011732) | 0.345106 / 0.275898 (0.069208) | 0.385685 / 0.323480 (0.062205) | 0.006466 / 0.007986 (-0.001519) | 0.005806 / 0.004328 (0.001478) | 0.075110 / 0.004250 (0.070860) | 0.052936 / 0.037052 (0.015883) | 0.343576 / 0.258489 (0.085087) | 0.408749 / 0.293841 (0.114908) | 0.037345 / 0.128546 (-0.091201) | 0.012807 / 0.075646 (-0.062839) | 0.087732 / 0.419271 (-0.331540) | 0.050218 / 0.043533 (0.006685) | 0.338963 / 0.255139 (0.083824) | 0.361629 / 0.283200 (0.078429) | 0.107488 / 0.141683 (-0.034195) | 1.465284 / 1.452155 (0.013130) | 1.562218 / 1.492716 (0.069502) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.322496 / 0.018006 (0.304489) | 0.522782 / 0.000490 (0.522292) | 0.006680 / 0.000200 (0.006480) | 0.000144 / 0.000054 (0.000090) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031801 / 0.037411 (-0.005611) | 0.116839 / 0.014526 (0.102313) | 0.127552 / 0.176557 (-0.049005) | 0.167670 / 0.737135 (-0.569465) | 0.134170 / 0.296338 (-0.162168) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.425449 / 0.215209 (0.210240) | 4.229367 / 2.077655 (2.151713) | 2.014663 / 1.504120 (0.510543) | 1.812981 / 1.541195 (0.271787) | 1.964039 / 1.468490 (0.495549) | 0.703454 / 4.584777 (-3.881323) | 3.786985 / 3.745712 (0.041273) | 2.262377 / 5.269862 (-3.007485) | 1.404868 / 4.565676 (-3.160808) | 0.086234 / 0.424275 (-0.338041) | 0.012616 / 0.007607 (0.005009) | 0.525784 / 0.226044 (0.299739) | 5.268295 / 2.268929 (2.999366) | 2.496674 / 55.444624 (-52.947950) | 2.177773 / 6.876477 (-4.698704) | 2.313677 / 2.142072 (0.171605) | 0.846202 / 4.805227 (-3.959026) | 0.170152 / 6.500664 (-6.330513) | 0.066772 / 0.075469 (-0.008698) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.254719 / 1.841788 (-0.587069) | 16.017627 / 8.074308 (7.943319) | 14.560583 / 10.191392 (4.369191) | 0.168275 / 0.680424 (-0.512149) | 0.017935 / 0.534201 (-0.516266) | 0.430806 / 0.579283 (-0.148477) | 0.428737 / 0.434364 (-0.005626) | 0.532001 / 0.540337 (-0.008336) | 0.633680 / 1.386936 (-0.753256) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c2c75dff81c3f060cc4731be3416fd962cc6383e \"CML watermark\")\n" ]

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[ "good catch!", "_The documentation is not available anymore as the PR was closed or merged._", "The test fail is unrelated to this PR and fixed on `main` - merging :)", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008942 / 0.011353 (-0.002411) | 0.004617 / 0.011008 (-0.006391) | 0.101310 / 0.038508 (0.062802) | 0.030997 / 0.023109 (0.007888) | 0.306292 / 0.275898 (0.030394) | 0.370533 / 0.323480 (0.047053) | 0.007318 / 0.007986 (-0.000667) | 0.003473 / 0.004328 (-0.000856) | 0.078557 / 0.004250 (0.074307) | 0.036312 / 0.037052 (-0.000740) | 0.308993 / 0.258489 (0.050504) | 0.344411 / 0.293841 (0.050570) | 0.034384 / 0.128546 (-0.094162) | 0.011631 / 0.075646 (-0.064016) | 0.323948 / 0.419271 (-0.095324) | 0.041176 / 0.043533 (-0.002357) | 0.302512 / 0.255139 (0.047373) | 0.322439 / 0.283200 (0.039239) | 0.088955 / 0.141683 (-0.052728) | 1.534918 / 1.452155 (0.082763) | 1.555803 / 1.492716 (0.063087) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.195639 / 0.018006 (0.177633) | 0.423068 / 0.000490 (0.422579) | 0.004101 / 0.000200 (0.003901) | 0.000079 / 0.000054 (0.000025) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023691 / 0.037411 (-0.013721) | 0.100536 / 0.014526 (0.086011) | 0.108399 / 0.176557 (-0.068157) | 0.143515 / 0.737135 (-0.593620) | 0.111886 / 0.296338 (-0.184452) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.417519 / 0.215209 (0.202310) | 4.180463 / 2.077655 (2.102808) | 1.862511 / 1.504120 (0.358391) | 1.658724 / 1.541195 (0.117529) | 1.735847 / 1.468490 (0.267357) | 0.688257 / 4.584777 (-3.896520) | 3.447976 / 3.745712 (-0.297737) | 1.877939 / 5.269862 (-3.391922) | 1.157385 / 4.565676 (-3.408292) | 0.081418 / 0.424275 (-0.342857) | 0.012395 / 0.007607 (0.004788) | 0.518935 / 0.226044 (0.292891) | 5.220355 / 2.268929 (2.951427) | 2.308355 / 55.444624 (-53.136269) | 1.960026 / 6.876477 (-4.916450) | 2.013179 / 2.142072 (-0.128893) | 0.802850 / 4.805227 (-4.002377) | 0.146941 / 6.500664 (-6.353723) | 0.064080 / 0.075469 (-0.011389) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.284443 / 1.841788 (-0.557344) | 13.903755 / 8.074308 (5.829447) | 14.467101 / 10.191392 (4.275709) | 0.156813 / 0.680424 (-0.523611) | 0.028583 / 0.534201 (-0.505618) | 0.406349 / 0.579283 (-0.172934) | 0.413178 / 0.434364 (-0.021186) | 0.491283 / 0.540337 (-0.049055) | 0.571171 / 1.386936 (-0.815765) |\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) | 0.006868 / 0.011353 (-0.004484) | 0.004593 / 0.011008 (-0.006416) | 0.077574 / 0.038508 (0.039066) | 0.027703 / 0.023109 (0.004593) | 0.342096 / 0.275898 (0.066198) | 0.378500 / 0.323480 (0.055020) | 0.005785 / 0.007986 (-0.002201) | 0.003342 / 0.004328 (-0.000986) | 0.076105 / 0.004250 (0.071855) | 0.040369 / 0.037052 (0.003317) | 0.343611 / 0.258489 (0.085122) | 0.391859 / 0.293841 (0.098018) | 0.032675 / 0.128546 (-0.095871) | 0.011623 / 0.075646 (-0.064023) | 0.086623 / 0.419271 (-0.332648) | 0.051955 / 0.043533 (0.008423) | 0.343425 / 0.255139 (0.088286) | 0.368887 / 0.283200 (0.085688) | 0.097117 / 0.141683 (-0.044566) | 1.499546 / 1.452155 (0.047391) | 1.593100 / 1.492716 (0.100383) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.193568 / 0.018006 (0.175562) | 0.409211 / 0.000490 (0.408722) | 0.003797 / 0.000200 (0.003597) | 0.000083 / 0.000054 (0.000029) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024982 / 0.037411 (-0.012430) | 0.101367 / 0.014526 (0.086841) | 0.108546 / 0.176557 (-0.068010) | 0.144402 / 0.737135 (-0.592733) | 0.112233 / 0.296338 (-0.184105) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.432820 / 0.215209 (0.217611) | 4.341045 / 2.077655 (2.263391) | 2.058326 / 1.504120 (0.554207) | 1.853913 / 1.541195 (0.312718) | 1.942436 / 1.468490 (0.473946) | 0.699130 / 4.584777 (-3.885647) | 3.392879 / 3.745712 (-0.352833) | 1.908277 / 5.269862 (-3.361585) | 1.177998 / 4.565676 (-3.387678) | 0.082700 / 0.424275 (-0.341576) | 0.012505 / 0.007607 (0.004898) | 0.526286 / 0.226044 (0.300242) | 5.279599 / 2.268929 (3.010670) | 2.505771 / 55.444624 (-52.938854) | 2.158460 / 6.876477 (-4.718016) | 2.211437 / 2.142072 (0.069365) | 0.802065 / 4.805227 (-4.003163) | 0.150766 / 6.500664 (-6.349898) | 0.067639 / 0.075469 (-0.007830) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.286595 / 1.841788 (-0.555192) | 13.961894 / 8.074308 (5.887586) | 14.021865 / 10.191392 (3.830473) | 0.164590 / 0.680424 (-0.515834) | 0.016909 / 0.534201 (-0.517292) | 0.392215 / 0.579283 (-0.187069) | 0.408080 / 0.434364 (-0.026284) | 0.488247 / 0.540337 (-0.052090) | 0.575524 / 1.386936 (-0.811412) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#699b0293876015457bfce40f7245d346c34c7717 \"CML watermark\")\n" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "See the resolved changes [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5550/en/package_reference/main_classes#datasets.Dataset.class_encode_column), [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5550/en/package_reference/main_classes#datasets.Dataset.unique) and [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5550/en/package_reference/main_classes#datasets.DatasetDict.class_encode_column), respectively", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008256 / 0.011353 (-0.003097) | 0.004400 / 0.011008 (-0.006608) | 0.098676 / 0.038508 (0.060168) | 0.028937 / 0.023109 (0.005828) | 0.302578 / 0.275898 (0.026680) | 0.334170 / 0.323480 (0.010690) | 0.006657 / 0.007986 (-0.001329) | 0.004581 / 0.004328 (0.000253) | 0.076874 / 0.004250 (0.072624) | 0.034401 / 0.037052 (-0.002652) | 0.303928 / 0.258489 (0.045439) | 0.348421 / 0.293841 (0.054580) | 0.033303 / 0.128546 (-0.095243) | 0.011445 / 0.075646 (-0.064202) | 0.322137 / 0.419271 (-0.097135) | 0.041072 / 0.043533 (-0.002461) | 0.306007 / 0.255139 (0.050868) | 0.325945 / 0.283200 (0.042745) | 0.086685 / 0.141683 (-0.054998) | 1.454956 / 1.452155 (0.002801) | 1.545525 / 1.492716 (0.052809) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.175536 / 0.018006 (0.157530) | 0.400203 / 0.000490 (0.399713) | 0.002103 / 0.000200 (0.001903) | 0.000072 / 0.000054 (0.000018) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022750 / 0.037411 (-0.014661) | 0.095163 / 0.014526 (0.080637) | 0.103995 / 0.176557 (-0.072561) | 0.138806 / 0.737135 (-0.598330) | 0.105711 / 0.296338 (-0.190628) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.427860 / 0.215209 (0.212651) | 4.259594 / 2.077655 (2.181940) | 2.157986 / 1.504120 (0.653866) | 1.913814 / 1.541195 (0.372619) | 1.793455 / 1.468490 (0.324965) | 0.702341 / 4.584777 (-3.882436) | 3.353086 / 3.745712 (-0.392626) | 1.856952 / 5.269862 (-3.412909) | 1.149963 / 4.565676 (-3.415713) | 0.082926 / 0.424275 (-0.341349) | 0.012307 / 0.007607 (0.004700) | 0.524531 / 0.226044 (0.298487) | 5.254766 / 2.268929 (2.985838) | 2.590157 / 55.444624 (-52.854468) | 2.272613 / 6.876477 (-4.603864) | 2.304367 / 2.142072 (0.162294) | 0.819298 / 4.805227 (-3.985929) | 0.152170 / 6.500664 (-6.348494) | 0.066563 / 0.075469 (-0.008906) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.205054 / 1.841788 (-0.636733) | 13.729073 / 8.074308 (5.654765) | 14.061037 / 10.191392 (3.869645) | 0.138020 / 0.680424 (-0.542404) | 0.028042 / 0.534201 (-0.506159) | 0.392260 / 0.579283 (-0.187024) | 0.405632 / 0.434364 (-0.028732) | 0.469583 / 0.540337 (-0.070755) | 0.563110 / 1.386936 (-0.823826) |\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) | 0.006513 / 0.011353 (-0.004839) | 0.004402 / 0.011008 (-0.006606) | 0.076339 / 0.038508 (0.037831) | 0.027222 / 0.023109 (0.004112) | 0.338968 / 0.275898 (0.063070) | 0.378475 / 0.323480 (0.054995) | 0.005443 / 0.007986 (-0.002542) | 0.003312 / 0.004328 (-0.001016) | 0.075352 / 0.004250 (0.071102) | 0.034951 / 0.037052 (-0.002102) | 0.342268 / 0.258489 (0.083779) | 0.381024 / 0.293841 (0.087183) | 0.031568 / 0.128546 (-0.096979) | 0.011558 / 0.075646 (-0.064088) | 0.085267 / 0.419271 (-0.334005) | 0.041248 / 0.043533 (-0.002284) | 0.340422 / 0.255139 (0.085283) | 0.365497 / 0.283200 (0.082297) | 0.088278 / 0.141683 (-0.053405) | 1.479838 / 1.452155 (0.027683) | 1.554440 / 1.492716 (0.061724) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.223240 / 0.018006 (0.205234) | 0.394771 / 0.000490 (0.394282) | 0.003022 / 0.000200 (0.002822) | 0.000071 / 0.000054 (0.000016) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024842 / 0.037411 (-0.012570) | 0.099167 / 0.014526 (0.084641) | 0.106376 / 0.176557 (-0.070180) | 0.141397 / 0.737135 (-0.595738) | 0.110355 / 0.296338 (-0.185983) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.437598 / 0.215209 (0.222389) | 4.394964 / 2.077655 (2.317310) | 2.082660 / 1.504120 (0.578540) | 1.868690 / 1.541195 (0.327496) | 1.915190 / 1.468490 (0.446700) | 0.701035 / 4.584777 (-3.883742) | 3.306594 / 3.745712 (-0.439118) | 1.842681 / 5.269862 (-3.427181) | 1.155022 / 4.565676 (-3.410654) | 0.083310 / 0.424275 (-0.340965) | 0.012413 / 0.007607 (0.004806) | 0.543179 / 0.226044 (0.317135) | 5.445605 / 2.268929 (3.176676) | 2.545080 / 55.444624 (-52.899544) | 2.188741 / 6.876477 (-4.687736) | 2.205561 / 2.142072 (0.063489) | 0.804967 / 4.805227 (-4.000261) | 0.151024 / 6.500664 (-6.349640) | 0.066448 / 0.075469 (-0.009021) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.304671 / 1.841788 (-0.537117) | 13.996631 / 8.074308 (5.922323) | 13.617626 / 10.191392 (3.426234) | 0.141512 / 0.680424 (-0.538912) | 0.016527 / 0.534201 (-0.517674) | 0.384981 / 0.579283 (-0.194302) | 0.385198 / 0.434364 (-0.049166) | 0.469033 / 0.540337 (-0.071305) | 0.554738 / 1.386936 (-0.832198) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d09dc897e153fed7c7f459a122fb03faa46688ed \"CML watermark\")\n" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009598 / 0.011353 (-0.001755) | 0.005115 / 0.011008 (-0.005893) | 0.100100 / 0.038508 (0.061592) | 0.036193 / 0.023109 (0.013083) | 0.296478 / 0.275898 (0.020580) | 0.355997 / 0.323480 (0.032517) | 0.007846 / 0.007986 (-0.000140) | 0.004082 / 0.004328 (-0.000247) | 0.076949 / 0.004250 (0.072699) | 0.044304 / 0.037052 (0.007252) | 0.310775 / 0.258489 (0.052286) | 0.333914 / 0.293841 (0.040073) | 0.037783 / 0.128546 (-0.090763) | 0.012023 / 0.075646 (-0.063623) | 0.333311 / 0.419271 (-0.085961) | 0.047568 / 0.043533 (0.004035) | 0.295567 / 0.255139 (0.040428) | 0.315707 / 0.283200 (0.032507) | 0.102675 / 0.141683 (-0.039008) | 1.471546 / 1.452155 (0.019391) | 1.507991 / 1.492716 (0.015274) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.208658 / 0.018006 (0.190651) | 0.445026 / 0.000490 (0.444536) | 0.002593 / 0.000200 (0.002393) | 0.000084 / 0.000054 (0.000029) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026968 / 0.037411 (-0.010444) | 0.108188 / 0.014526 (0.093662) | 0.117965 / 0.176557 (-0.058592) | 0.182769 / 0.737135 (-0.554366) | 0.121671 / 0.296338 (-0.174667) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.400677 / 0.215209 (0.185468) | 4.012577 / 2.077655 (1.934922) | 1.821324 / 1.504120 (0.317204) | 1.624438 / 1.541195 (0.083244) | 1.731886 / 1.468490 (0.263396) | 0.698089 / 4.584777 (-3.886688) | 3.786165 / 3.745712 (0.040453) | 2.079742 / 5.269862 (-3.190119) | 1.325032 / 4.565676 (-3.240644) | 0.085229 / 0.424275 (-0.339046) | 0.012017 / 0.007607 (0.004410) | 0.511779 / 0.226044 (0.285734) | 5.114358 / 2.268929 (2.845430) | 2.324763 / 55.444624 (-53.119861) | 2.011864 / 6.876477 (-4.864612) | 2.075875 / 2.142072 (-0.066198) | 0.853475 / 4.805227 (-3.951752) | 0.166949 / 6.500664 (-6.333715) | 0.064669 / 0.075469 (-0.010800) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.230212 / 1.841788 (-0.611576) | 14.942371 / 8.074308 (6.868063) | 14.075795 / 10.191392 (3.884403) | 0.156920 / 0.680424 (-0.523504) | 0.029002 / 0.534201 (-0.505199) | 0.442213 / 0.579283 (-0.137070) | 0.436888 / 0.434364 (0.002524) | 0.519725 / 0.540337 (-0.020613) | 0.604634 / 1.386936 (-0.782303) |\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) | 0.007649 / 0.011353 (-0.003704) | 0.005298 / 0.011008 (-0.005710) | 0.076559 / 0.038508 (0.038050) | 0.033723 / 0.023109 (0.010614) | 0.334946 / 0.275898 (0.059048) | 0.372785 / 0.323480 (0.049305) | 0.006032 / 0.007986 (-0.001953) | 0.004125 / 0.004328 (-0.000204) | 0.075366 / 0.004250 (0.071116) | 0.049061 / 0.037052 (0.012009) | 0.338188 / 0.258489 (0.079699) | 0.389693 / 0.293841 (0.095852) | 0.037246 / 0.128546 (-0.091301) | 0.012530 / 0.075646 (-0.063116) | 0.088053 / 0.419271 (-0.331219) | 0.049844 / 0.043533 (0.006311) | 0.338476 / 0.255139 (0.083337) | 0.361672 / 0.283200 (0.078473) | 0.101982 / 0.141683 (-0.039701) | 1.479550 / 1.452155 (0.027396) | 1.541031 / 1.492716 (0.048315) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.226162 / 0.018006 (0.208156) | 0.439108 / 0.000490 (0.438618) | 0.001102 / 0.000200 (0.000902) | 0.000076 / 0.000054 (0.000021) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030240 / 0.037411 (-0.007171) | 0.113754 / 0.014526 (0.099229) | 0.122839 / 0.176557 (-0.053717) | 0.192531 / 0.737135 (-0.544604) | 0.129455 / 0.296338 (-0.166884) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.424701 / 0.215209 (0.209492) | 4.208161 / 2.077655 (2.130507) | 2.045733 / 1.504120 (0.541613) | 1.892369 / 1.541195 (0.351174) | 1.997024 / 1.468490 (0.528534) | 0.739883 / 4.584777 (-3.844894) | 3.760939 / 3.745712 (0.015227) | 3.195748 / 5.269862 (-2.074113) | 1.731480 / 4.565676 (-2.834197) | 0.087013 / 0.424275 (-0.337262) | 0.012550 / 0.007607 (0.004943) | 0.540829 / 0.226044 (0.314785) | 5.329933 / 2.268929 (3.061005) | 2.507572 / 55.444624 (-52.937052) | 2.167761 / 6.876477 (-4.708716) | 2.250298 / 2.142072 (0.108226) | 0.868718 / 4.805227 (-3.936510) | 0.181643 / 6.500664 (-6.319021) | 0.064817 / 0.075469 (-0.010653) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.295001 / 1.841788 (-0.546787) | 15.236413 / 8.074308 (7.162105) | 13.692212 / 10.191392 (3.500820) | 0.186330 / 0.680424 (-0.494094) | 0.017492 / 0.534201 (-0.516709) | 0.427365 / 0.579283 (-0.151919) | 0.427781 / 0.434364 (-0.006583) | 0.533763 / 0.540337 (-0.006575) | 0.636011 / 1.386936 (-0.750925) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#94b16b674111ca5e1a03ddcb71dc0b53acc2f934 \"CML watermark\")\n" ]

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[ "The code below was throwing a warning:\r\n\r\n```python\r\nclass JaxFormatter(Formatter[Mapping, \"jax.Array\", Mapping]):\r\n def __init__(self, features=None, device=None, **jnp_array_kwargs):\r\n super().__init__(features=features)\r\n import jax\r\n from jaxlib.xla_extension import Device\r\n \r\n self.device = (\r\n device if isinstance(device, Device) else jax.devices()[0]\r\n )\r\n self.jnp_array_kwargs = jnp_array_kwargs\r\n\r\n ...\r\n\r\n def _tensorize(self, value):\r\n ...\r\n\r\n with jax.default_device(self.device):\r\n # calling jnp.array on a np.ndarray does copy the data\r\n # see https://github.com/google/jax/issues/4486\r\n return jnp.array(value, **{**default_dtype, **self.jnp_array_kwargs})\r\n```\r\n\r\nWhen providing `device` via param:\r\n\r\n```python\r\nfrom datasets import Dataset\r\nimport jax\r\n\r\nds = Dataset.from_dict({\"a\": [1, 2, 3], \"b\": [4, 5, 6]})\r\nds = ds.with_format(\"jax\", device=jax.devices()[0])\r\nprint(ds[0])\r\n```\r\n\r\nProducing the following warning:\r\n\r\n```\r\nWARNING:datasets.fingerprint:Parameter 'device'=TFRT_CPU_0 of the transform datasets.arrow_dataset.Dataset.set_format couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.\r\n```\r\n\r\nThat's why I decided to map all the available devices, and assign their string representation e.g. `TFRT_CPU_0` to `self.device` instead of `jaxlib.xla_extension.Device`, so that the value of the param `device` is washable. So on, the code that remains at the end is:\r\n\r\n```python\r\nclass JaxFormatter(Formatter[Mapping, \"jax.Array\", Mapping]):\r\n def __init__(self, features=None, device=None, **jnp_array_kwargs):\r\n super().__init__(features=features)\r\n import jax\r\n from jaxlib.xla_client import Device\r\n\r\n self.device_mapping = self._map_devices_to_str()\r\n self.device = (\r\n device if isinstance(device, str) else str(device) if isinstance(device, Device) else str(jax.devices()[0])\r\n )\r\n self.jnp_array_kwargs = jnp_array_kwargs\r\n\r\n def _map_devices_to_str(self) -> Mapping[str, \"jaxlib.xla_extension.Device\"]:\r\n import jax\r\n\r\n return {str(device): device for device in jax.devices()}\r\n\r\n ...\r\n\r\n def _tensorize(self, value):\r\n ...\r\n\r\n with jax.default_device(self.device_mapping[self.device]):\r\n # calling jnp.array on a np.ndarray does copy the data\r\n # see https://github.com/google/jax/issues/4486\r\n return jnp.array(value, **{**default_dtype, **self.jnp_array_kwargs})\r\n```\r\n\r\nBut note that the latter also throws a warning if the provided `device` is not a string but a `jaxlib.xla_extension.Device`, so that's why it needs to be converted to string.", "_The documentation is not available anymore as the PR was closed or merged._", "After some investigation, it seems that when using `device=jaxlib.xla_extension.Device` instead of `device=string` it shows the warning so that later formats fail as that cannot be unpickled.\r\n\r\nSo I think we can either add that specifically in `use_with_jax.mdx` documentation entry I'm creating at #5535 so that the users know that they need to surroung the `jaxlib.xla_extension.Device` with `str()`, or find a workaround to override default `deepcopy` behavior with `def __deepcopy__(self)` so that the device param is converted to string if provided as a `jaxlib.xla_extension.Device`, but not sure if the latter works 😕 \r\n\r\nDo you think there's any other possible solution to this issue? Thanks, @lhoestq ", "Cool ! Specifying the device is indeed super important.\r\n\r\n\r\nI think we can just require `device` to always be a string for now, and add an example in the doc on how to get the string that corresponds to a `jaxlib.xla_extension.Device` ? This way we never deal with objects that are not picklable", "> Cool ! Specifying the device is indeed super important.\r\n> \r\n> I think we can just require `device` to always be a string for now, and add an example in the doc on how to get the string that corresponds to a `jaxlib.xla_extension.Device` ? This way we never deal with objects that are not picklable\r\n\r\nSure, then I'll restrict it to string for now! Also regarding the documentation update, should we wait until #5535 is merged so that I add this on top of that?", "CI is failing due to missing `resampy` in `librosa` already being fixed by @lhoestq in https://github.com/huggingface/datasets/pull/5554", "@lhoestq already moved to a global variable, I can confirm that the following now works:\r\n\r\n```python\r\nimport copy\r\nimport pickle\r\n\r\nimport jax\r\nimport pyarrow as pa\r\n\r\nfrom datasets.formatting import JaxFormatter\r\n\r\n\r\n_COL_A = [0, 1, 2]\r\n_COL_B = [\"foo\", \"bar\", \"foobar\"]\r\n_COL_C = [[[1.0, 0.0, 0.0]] * 2, [[0.0, 1.0, 0.0]] * 2, [[0.0, 0.0, 1.0]] * 2]\r\npa_table = pa.Table.from_pydict({\"a\": _COL_A, \"b\": _COL_B, \"c\": _COL_C})\r\n\r\ndevice = jax.devices()[0]\r\nformatter = JaxFormatter(device=str(device))\r\n\r\npickle.dumps(formatter)\r\ncopy.deepcopy(formatter)\r\n```", "> Looks all good now thank you !\r\n> \r\n> Is there anything else you wanted to add ? Otherwise I think it's ready for merge\r\n\r\nNothing else to add, I've already applied your suggestions, so ready to merge! Thanks for your input/feedback @lhoestq :hugs:", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009815 / 0.011353 (-0.001538) | 0.005443 / 0.011008 (-0.005565) | 0.101244 / 0.038508 (0.062736) | 0.036573 / 0.023109 (0.013464) | 0.304761 / 0.275898 (0.028863) | 0.365527 / 0.323480 (0.042047) | 0.008244 / 0.007986 (0.000258) | 0.004200 / 0.004328 (-0.000128) | 0.077471 / 0.004250 (0.073221) | 0.045266 / 0.037052 (0.008214) | 0.310213 / 0.258489 (0.051724) | 0.344247 / 0.293841 (0.050406) | 0.039530 / 0.128546 (-0.089016) | 0.012254 / 0.075646 (-0.063393) | 0.335039 / 0.419271 (-0.084233) | 0.049525 / 0.043533 (0.005992) | 0.298350 / 0.255139 (0.043211) | 0.312031 / 0.283200 (0.028832) | 0.108581 / 0.141683 (-0.033102) | 1.481178 / 1.452155 (0.029023) | 1.497662 / 1.492716 (0.004946) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.014762 / 0.018006 (-0.003244) | 0.447099 / 0.000490 (0.446609) | 0.009074 / 0.000200 (0.008874) | 0.000688 / 0.000054 (0.000633) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027466 / 0.037411 (-0.009945) | 0.109715 / 0.014526 (0.095189) | 0.119062 / 0.176557 (-0.057495) | 0.188964 / 0.737135 (-0.548171) | 0.127057 / 0.296338 (-0.169282) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.395092 / 0.215209 (0.179883) | 3.948091 / 2.077655 (1.870436) | 1.795160 / 1.504120 (0.291040) | 1.603704 / 1.541195 (0.062509) | 1.714491 / 1.468490 (0.246001) | 0.700489 / 4.584777 (-3.884288) | 3.767493 / 3.745712 (0.021781) | 3.288374 / 5.269862 (-1.981488) | 1.783711 / 4.565676 (-2.781965) | 0.085119 / 0.424275 (-0.339156) | 0.012349 / 0.007607 (0.004742) | 0.502135 / 0.226044 (0.276091) | 5.019321 / 2.268929 (2.750392) | 2.236469 / 55.444624 (-53.208155) | 1.914376 / 6.876477 (-4.962101) | 1.998579 / 2.142072 (-0.143494) | 0.847841 / 4.805227 (-3.957386) | 0.166035 / 6.500664 (-6.334629) | 0.062469 / 0.075469 (-0.013000) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.245380 / 1.841788 (-0.596408) | 14.757872 / 8.074308 (6.683564) | 14.460373 / 10.191392 (4.268981) | 0.152981 / 0.680424 (-0.527443) | 0.029001 / 0.534201 (-0.505200) | 0.439597 / 0.579283 (-0.139686) | 0.437232 / 0.434364 (0.002868) | 0.532464 / 0.540337 (-0.007873) | 0.629225 / 1.386936 (-0.757711) |\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) | 0.007165 / 0.011353 (-0.004188) | 0.005220 / 0.011008 (-0.005789) | 0.075849 / 0.038508 (0.037341) | 0.032717 / 0.023109 (0.009608) | 0.331205 / 0.275898 (0.055307) | 0.364955 / 0.323480 (0.041475) | 0.005518 / 0.007986 (-0.002468) | 0.004069 / 0.004328 (-0.000259) | 0.073900 / 0.004250 (0.069650) | 0.046346 / 0.037052 (0.009294) | 0.337473 / 0.258489 (0.078984) | 0.393062 / 0.293841 (0.099222) | 0.037533 / 0.128546 (-0.091013) | 0.012577 / 0.075646 (-0.063070) | 0.087975 / 0.419271 (-0.331297) | 0.049508 / 0.043533 (0.005975) | 0.333423 / 0.255139 (0.078284) | 0.354345 / 0.283200 (0.071145) | 0.099879 / 0.141683 (-0.041804) | 1.413304 / 1.452155 (-0.038851) | 1.494222 / 1.492716 (0.001506) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.206835 / 0.018006 (0.188828) | 0.438246 / 0.000490 (0.437757) | 0.000410 / 0.000200 (0.000210) | 0.000059 / 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) | 0.028186 / 0.037411 (-0.009225) | 0.109322 / 0.014526 (0.094797) | 0.119581 / 0.176557 (-0.056975) | 0.191784 / 0.737135 (-0.545351) | 0.125100 / 0.296338 (-0.171238) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.419418 / 0.215209 (0.204209) | 4.167374 / 2.077655 (2.089720) | 1.995812 / 1.504120 (0.491693) | 1.804602 / 1.541195 (0.263407) | 1.869131 / 1.468490 (0.400641) | 0.709486 / 4.584777 (-3.875291) | 3.838019 / 3.745712 (0.092307) | 2.086206 / 5.269862 (-3.183656) | 1.323970 / 4.565676 (-3.241707) | 0.089477 / 0.424275 (-0.334798) | 0.012402 / 0.007607 (0.004795) | 0.519291 / 0.226044 (0.293246) | 5.194091 / 2.268929 (2.925162) | 2.487055 / 55.444624 (-52.957570) | 2.122495 / 6.876477 (-4.753982) | 2.194910 / 2.142072 (0.052837) | 0.842837 / 4.805227 (-3.962390) | 0.167229 / 6.500664 (-6.333435) | 0.064690 / 0.075469 (-0.010779) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.275931 / 1.841788 (-0.565857) | 14.577000 / 8.074308 (6.502692) | 13.633235 / 10.191392 (3.441843) | 0.184511 / 0.680424 (-0.495913) | 0.017439 / 0.534201 (-0.516762) | 0.424374 / 0.579283 (-0.154909) | 0.427803 / 0.434364 (-0.006561) | 0.527790 / 0.540337 (-0.012548) | 0.627301 / 1.386936 (-0.759635) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#21c86d570faad32c3abbed4305bfd3698daa7fd0 \"CML watermark\")\n" ]

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[ "Hi ! Can you make sure you set `HF_HOME` before importing `datasets` ?\r\n\r\nThen you can print\r\n```python\r\nprint(datasets.config.HF_CACHE_HOME)\r\nprint(datasets.config.HF_DATASETS_CACHE)\r\n```" ]

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[ "Hi ! Maybe we'd need to align with `transformers` and other libraries that implement `push_to_hub` to agree on what it should return.\r\n\r\ne.g. in `transformers` the typing says it returns a string, but in practice it returns a `CommitInfo`.\r\n\r\nTherefore I'd not add an output to `push_to_hub` here unless we had a chance to discuss more broadly.\r\n\r\nAnyway in my opinion it should no just return the URL of the repository, but ideally the URL at the revision where the data were pushed", "Perhaps a mixin or something similar could be defined on the `hfh` side to ensure the `push_to_hub` API is aligned across our projects. \r\n\r\nPS: this would also mean that the PRs such as https://github.com/huggingface/datasets/pull/5528 would no longer be our responsibility\r\n\r\ncc @Wauplin ", "I agree, with universability and the idea is more about returning at least something that references where to find the uploaded file/model or otherwise. \r\n\r\nIdeally, the referenced PR would work.", "imo this would be a good use case to just use `huggingface_hub` and align to what we do there :)", "@mariosasko, can you give me some pointers to where I might help implementing this for the `huggingface-hub`?" ]

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[ "Thanks for reporting, @wjfwzzc.\r\n\r\nI am transferring this issue to the corresponding dataset on the Hub: https://huggingface.co/datasets/bookcorpusopen/discussions/1", "Thank you. All fixes are done:\r\n- [x] https://huggingface.co/datasets/bookcorpusopen/discussions/2\r\n- [x] https://huggingface.co/datasets/the_pile/discussions/1\r\n- [x] https://huggingface.co/datasets/the_pile_books3/discussions/1\r\n- [x] https://huggingface.co/datasets/the_pile_openwebtext2/discussions/2\r\n- [x] https://huggingface.co/datasets/the_pile_stack_exchange/discussions/2" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008452 / 0.011353 (-0.002901) | 0.004500 / 0.011008 (-0.006508) | 0.100103 / 0.038508 (0.061595) | 0.029395 / 0.023109 (0.006286) | 0.297740 / 0.275898 (0.021842) | 0.359132 / 0.323480 (0.035652) | 0.007045 / 0.007986 (-0.000941) | 0.003415 / 0.004328 (-0.000913) | 0.076389 / 0.004250 (0.072138) | 0.036612 / 0.037052 (-0.000440) | 0.308773 / 0.258489 (0.050284) | 0.345701 / 0.293841 (0.051860) | 0.033230 / 0.128546 (-0.095317) | 0.011463 / 0.075646 (-0.064183) | 0.322382 / 0.419271 (-0.096890) | 0.041194 / 0.043533 (-0.002339) | 0.300685 / 0.255139 (0.045546) | 0.323076 / 0.283200 (0.039876) | 0.087330 / 0.141683 (-0.054353) | 1.508661 / 1.452155 (0.056506) | 1.531776 / 1.492716 (0.039059) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.188391 / 0.018006 (0.170385) | 0.400102 / 0.000490 (0.399612) | 0.002006 / 0.000200 (0.001806) | 0.000075 / 0.000054 (0.000021) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023232 / 0.037411 (-0.014179) | 0.097313 / 0.014526 (0.082787) | 0.106244 / 0.176557 (-0.070313) | 0.141180 / 0.737135 (-0.595955) | 0.107871 / 0.296338 (-0.188468) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.418610 / 0.215209 (0.203400) | 4.162243 / 2.077655 (2.084588) | 1.884300 / 1.504120 (0.380180) | 1.694197 / 1.541195 (0.153002) | 1.727740 / 1.468490 (0.259250) | 0.692129 / 4.584777 (-3.892648) | 3.364230 / 3.745712 (-0.381482) | 1.871507 / 5.269862 (-3.398355) | 1.261520 / 4.565676 (-3.304156) | 0.083258 / 0.424275 (-0.341017) | 0.012479 / 0.007607 (0.004872) | 0.528802 / 0.226044 (0.302757) | 5.281029 / 2.268929 (3.012100) | 2.402222 / 55.444624 (-53.042403) | 2.064954 / 6.876477 (-4.811522) | 2.027044 / 2.142072 (-0.115029) | 0.813124 / 4.805227 (-3.992103) | 0.149397 / 6.500664 (-6.351267) | 0.065032 / 0.075469 (-0.010437) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.239192 / 1.841788 (-0.602595) | 13.529913 / 8.074308 (5.455605) | 14.253251 / 10.191392 (4.061859) | 0.165145 / 0.680424 (-0.515278) | 0.028367 / 0.534201 (-0.505834) | 0.395121 / 0.579283 (-0.184162) | 0.405372 / 0.434364 (-0.028992) | 0.472201 / 0.540337 (-0.068137) | 0.560620 / 1.386936 (-0.826316) |\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) | 0.006368 / 0.011353 (-0.004985) | 0.004542 / 0.011008 (-0.006466) | 0.076361 / 0.038508 (0.037853) | 0.026893 / 0.023109 (0.003784) | 0.341210 / 0.275898 (0.065312) | 0.378377 / 0.323480 (0.054898) | 0.004833 / 0.007986 (-0.003153) | 0.003358 / 0.004328 (-0.000970) | 0.075516 / 0.004250 (0.071265) | 0.038841 / 0.037052 (0.001788) | 0.342230 / 0.258489 (0.083741) | 0.384317 / 0.293841 (0.090476) | 0.031874 / 0.128546 (-0.096672) | 0.011651 / 0.075646 (-0.063995) | 0.085816 / 0.419271 (-0.333455) | 0.042389 / 0.043533 (-0.001144) | 0.340678 / 0.255139 (0.085539) | 0.367441 / 0.283200 (0.084241) | 0.089748 / 0.141683 (-0.051935) | 1.487358 / 1.452155 (0.035203) | 1.615049 / 1.492716 (0.122333) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.220933 / 0.018006 (0.202926) | 0.397162 / 0.000490 (0.396673) | 0.002336 / 0.000200 (0.002136) | 0.000069 / 0.000054 (0.000015) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025004 / 0.037411 (-0.012407) | 0.100877 / 0.014526 (0.086351) | 0.110624 / 0.176557 (-0.065932) | 0.152042 / 0.737135 (-0.585094) | 0.112951 / 0.296338 (-0.183388) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.441071 / 0.215209 (0.225862) | 4.419471 / 2.077655 (2.341817) | 2.082976 / 1.504120 (0.578856) | 1.884023 / 1.541195 (0.342828) | 1.950590 / 1.468490 (0.482100) | 0.706104 / 4.584777 (-3.878673) | 3.329825 / 3.745712 (-0.415887) | 1.868850 / 5.269862 (-3.401011) | 1.178785 / 4.565676 (-3.386892) | 0.083910 / 0.424275 (-0.340365) | 0.012296 / 0.007607 (0.004689) | 0.542998 / 0.226044 (0.316953) | 5.429944 / 2.268929 (3.161015) | 2.502285 / 55.444624 (-52.942339) | 2.150507 / 6.876477 (-4.725970) | 2.170492 / 2.142072 (0.028420) | 0.813410 / 4.805227 (-3.991817) | 0.152310 / 6.500664 (-6.348354) | 0.066999 / 0.075469 (-0.008470) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.290839 / 1.841788 (-0.550949) | 14.089491 / 8.074308 (6.015183) | 13.704922 / 10.191392 (3.513530) | 0.130089 / 0.680424 (-0.550335) | 0.017000 / 0.534201 (-0.517201) | 0.381173 / 0.579283 (-0.198110) | 0.389271 / 0.434364 (-0.045093) | 0.461700 / 0.540337 (-0.078637) | 0.556428 / 1.386936 (-0.830508) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2cfa9be08f17519ff3deeae63cb998f4be7616e0 \"CML watermark\")\n" ]

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[ "Running the script above on the branch https://github.com/huggingface/datasets/pull/5542 results in the expected behaviour:\r\n```\r\nNum chunks for original ds: 1\r\nOriginal ds save/load\r\nsave_to_disk -- RAM memory used: 0.671875 MB -- Total time: 0.255265 s\r\nload_from_disk -- RAM memory used: 42.796875 MB -- Total time: 0.014899 s\r\nNum chunks for original ds after reloading: 5000\r\n\r\nNum chunks for selected ds: 1\r\nflatten_indices -- RAM memory used: 42.546875 MB -- Total time: 23.735089 s\r\nNum chunks for selected ds after flattening: 5000\r\n\r\nSelected ds save/load\r\nsave_to_disk -- RAM memory used: 0.0 MB -- Total time: 0.287112 s\r\nload_from_disk -- RAM memory used: 38.84375 MB -- Total time: 0.014772 s\r\nNum chunks for selected ds after reloading: 5000\r\n```", "Wouahouh super cool @marioga thanks a lot!", "We just released `datasets==2.10.0` with this big improvement, thanks again @marioga " ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.012018 / 0.011353 (0.000665) | 0.006204 / 0.011008 (-0.004804) | 0.134119 / 0.038508 (0.095611) | 0.038436 / 0.023109 (0.015327) | 0.381397 / 0.275898 (0.105499) | 0.456362 / 0.323480 (0.132882) | 0.009826 / 0.007986 (0.001840) | 0.004746 / 0.004328 (0.000417) | 0.103755 / 0.004250 (0.099505) | 0.043867 / 0.037052 (0.006815) | 0.395322 / 0.258489 (0.136833) | 0.475812 / 0.293841 (0.181971) | 0.057865 / 0.128546 (-0.070682) | 0.019919 / 0.075646 (-0.055727) | 0.465343 / 0.419271 (0.046072) | 0.061574 / 0.043533 (0.018041) | 0.371668 / 0.255139 (0.116529) | 0.400375 / 0.283200 (0.117176) | 0.106539 / 0.141683 (-0.035144) | 1.822931 / 1.452155 (0.370776) | 1.875535 / 1.492716 (0.382819) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.013583 / 0.018006 (-0.004423) | 0.535515 / 0.000490 (0.535025) | 0.007920 / 0.000200 (0.007720) | 0.000305 / 0.000054 (0.000250) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030204 / 0.037411 (-0.007207) | 0.131671 / 0.014526 (0.117145) | 0.143977 / 0.176557 (-0.032579) | 0.175498 / 0.737135 (-0.561637) | 0.166134 / 0.296338 (-0.130204) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.630995 / 0.215209 (0.415786) | 6.152275 / 2.077655 (4.074620) | 2.519887 / 1.504120 (1.015767) | 2.110926 / 1.541195 (0.569732) | 2.207555 / 1.468490 (0.739064) | 1.296197 / 4.584777 (-3.288580) | 5.510619 / 3.745712 (1.764906) | 3.167468 / 5.269862 (-2.102394) | 2.043924 / 4.565676 (-2.521753) | 0.144772 / 0.424275 (-0.279503) | 0.014456 / 0.007607 (0.006848) | 0.783629 / 0.226044 (0.557585) | 7.836962 / 2.268929 (5.568033) | 3.248593 / 55.444624 (-52.196032) | 2.577092 / 6.876477 (-4.299385) | 2.671918 / 2.142072 (0.529846) | 1.471586 / 4.805227 (-3.333641) | 0.251391 / 6.500664 (-6.249273) | 0.091947 / 0.075469 (0.016478) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.594839 / 1.841788 (-0.246949) | 18.250630 / 8.074308 (10.176322) | 23.948781 / 10.191392 (13.757389) | 0.275505 / 0.680424 (-0.404919) | 0.045202 / 0.534201 (-0.488999) | 0.545552 / 0.579283 (-0.033731) | 0.639352 / 0.434364 (0.204989) | 0.666345 / 0.540337 (0.126008) | 0.795614 / 1.386936 (-0.591322) |\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) | 0.011234 / 0.011353 (-0.000119) | 0.005983 / 0.011008 (-0.005025) | 0.109144 / 0.038508 (0.070636) | 0.036070 / 0.023109 (0.012961) | 0.429313 / 0.275898 (0.153415) | 0.490615 / 0.323480 (0.167135) | 0.007448 / 0.007986 (-0.000538) | 0.004424 / 0.004328 (0.000095) | 0.097100 / 0.004250 (0.092850) | 0.049719 / 0.037052 (0.012667) | 0.412719 / 0.258489 (0.154230) | 0.485717 / 0.293841 (0.191876) | 0.061168 / 0.128546 (-0.067378) | 0.021510 / 0.075646 (-0.054136) | 0.116598 / 0.419271 (-0.302673) | 0.066116 / 0.043533 (0.022583) | 0.426212 / 0.255139 (0.171073) | 0.448368 / 0.283200 (0.165168) | 0.116003 / 0.141683 (-0.025680) | 1.799329 / 1.452155 (0.347175) | 1.967256 / 1.492716 (0.474540) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.214893 / 0.018006 (0.196887) | 0.497843 / 0.000490 (0.497354) | 0.000464 / 0.000200 (0.000264) | 0.000094 / 0.000054 (0.000039) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031758 / 0.037411 (-0.005653) | 0.131182 / 0.014526 (0.116656) | 0.141251 / 0.176557 (-0.035305) | 0.186526 / 0.737135 (-0.550609) | 0.142975 / 0.296338 (-0.153363) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.662094 / 0.215209 (0.446885) | 6.664841 / 2.077655 (4.587186) | 2.690613 / 1.504120 (1.186493) | 2.305399 / 1.541195 (0.764205) | 2.383697 / 1.468490 (0.915207) | 1.280692 / 4.584777 (-3.304085) | 5.629215 / 3.745712 (1.883503) | 5.007083 / 5.269862 (-0.262778) | 2.482163 / 4.565676 (-2.083513) | 0.147662 / 0.424275 (-0.276613) | 0.017770 / 0.007607 (0.010163) | 0.818380 / 0.226044 (0.592335) | 8.006521 / 2.268929 (5.737592) | 3.472262 / 55.444624 (-51.972363) | 2.709550 / 6.876477 (-4.166926) | 2.775138 / 2.142072 (0.633066) | 1.570545 / 4.805227 (-3.234683) | 0.266323 / 6.500664 (-6.234341) | 0.090591 / 0.075469 (0.015122) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.657927 / 1.841788 (-0.183861) | 18.448981 / 8.074308 (10.374673) | 20.336909 / 10.191392 (10.145517) | 0.230322 / 0.680424 (-0.450102) | 0.025972 / 0.534201 (-0.508229) | 0.561361 / 0.579283 (-0.017922) | 0.623758 / 0.434364 (0.189394) | 0.664120 / 0.540337 (0.123783) | 0.763144 / 1.386936 (-0.623792) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#29de6179766418c937fb33b0cc8803ec24a39e9e \"CML watermark\")\n" ]

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[ "Hi! The `set_transform` does not apply a custom formatting transform on a single example but the entire batch, so the fixed version of your transform would look as follows:\r\n```python\r\nfrom datasets import load_dataset\r\nimport torch\r\n\r\ndataset = load_dataset(\"lambdalabs/pokemon-blip-captions\", split='train')\r\ndef t(batch):\r\n return {\"test\": torch.tensor([1] * len(batch[next(iter(batch))]))}\r\n \r\ndataset.set_transform(t)\r\nd_0 = dataset[0]\r\n```\r\n\r\nStill, the formatter's error message should mention that a dict of **sequences** is expected as the returned value (not just a dict) to make debugging easier.", "I can take this", "Fixed in #5553 ", "> Hi! The `set_transform` does not apply a custom formatting transform on a single example but the entire batch, so the fixed version of your transform would look as follows:\r\n> \r\n> ```python\r\n> from datasets import load_dataset\r\n> import torch\r\n> \r\n> dataset = load_dataset(\"lambdalabs/pokemon-blip-captions\", split='train')\r\n> def t(batch):\r\n> return {\"test\": torch.tensor([1] * len(batch[next(iter(batch))]))}\r\n> \r\n> dataset.set_transform(t)\r\n> d_0 = dataset[0]\r\n> ```\r\n> \r\n> Still, the formatter's error message should mention that a dict of **sequences** is expected as the returned value (not just a dict) to make debugging easier.\r\n\r\nok, will change it according to suggestion. Thanks for the reply!" ]

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[ "Hi! `seaborn`'s `load_dataset` pulls datasets from [here](https://github.com/mwaskom/seaborn-data) and not from our Hub, so this issue is not related to our library in any way and should be reported in their repo instead." ]

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[ "#self-assign", "You were right, if `self.dir_cache` is not None in glob, it is exactly the same as what is returned by find, at least for all the tests we have, and some extended evaluation I did across a random sample of about 1000 datasets. \r\n\r\nThanks for the nice hints, and let me know if this is not exactly what we want here!\r\n\r\nsee PR: https://github.com/huggingface/datasets/pull/5704\r\n\r\n", "I think we can make the data files resolution (significantly) faster in 2 steps:\r\n\r\n1. `glob` calls `find` (which in turn calls `ls`), so we need `find` to be fast, and this can be achieved by fetching all the entries in a single API call and avoiding calls to `ls`. Implementing this for `HfFileSystem.find` (the one in `huggingface_hub`) is on my TO-DO list.\r\n2. caching the repeated `find` calls in `_get_data_files_patterns` when the `data_files` patterns are not provided in `load_dataset`. To address this, we can introduce a `_resolve_single_pattern` function that would accept a filesystem object and a list of regex patterns to resolve. Then we can wrap this filesystem object in `_get_data_files_patterns` with an object that would cache the find calls before resolving the patterns with `_resolve_single_pattern`. (Feel free to suggest a cleaner implementation)\r\n\r\nWDYT?", "Good idea :) \r\n\r\nFor 2:\r\n\r\nThat would work ! It's also possible to have a FileSystem with a cache on `.find` and use it inside the resolver passed to `_get_data_files_patterns`. Right now they're pretty simple:\r\n\r\n```python\r\n# for remote repositories\r\nresolver = partial(_resolve_single_pattern_in_dataset_repository, dataset_info, base_path=base_path)\r\n# for local\r\nresolver = partial(_resolve_single_pattern_locally, base_path)\r\n```", "something like this maybe (with Quentin's reimplementation of `HfFilesystem.find`)?\r\n\r\n ```\r\n @lru_cache(max_size=None)\r\n def _find(self, path, maxdepth=None, withdirs=False, detail=False, **kwargs):\r\n```\r\n\r\nIn any case please let me know if I can help in any way!" ]

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[ "Hi ! `enc` is not hashable:\r\n```python\r\nimport tiktoken\r\nfrom datasets.fingerprint import Hasher\r\n\r\nenc = tiktoken.get_encoding(\"gpt2\")\r\nHasher.hash(enc)\r\n# raises TypeError: cannot pickle 'builtins.CoreBPE' object\r\n```\r\nIt happens because it's not picklable, and because of that it's not possible to cache the result of `map`, hence the warning message.\r\n\r\nYou can find more details about caching here: https://huggingface.co/docs/datasets/about_cache\r\n\r\nYou can also provide your own unique hash in `map` if you want, with the `new_fingerprint` argument.\r\nOr disable caching using\r\n```python\r\nimport datasets\r\ndatasets.disable_caching()\r\n```", "@lhoestq Thank you for the explanation and advice. Will relay all of this to the repo where this (non)issue arose. \r\n\r\nGreat job with huggingface! ", "We made tiktoken tokenizers hashable in #5552, which is included in today's release `datasets==2.10.0`", "Just a heads up that when I'm trying to use TikToken along with the a given Dataset `.map()` method, I am still met with the following error :\r\n\r\n```\r\n File \"/opt/conda/lib/python3.8/site-packages/dill/_dill.py\", line 388, in save\r\n StockPickler.save(self, obj, save_persistent_id)\r\n File \"/opt/conda/lib/python3.8/pickle.py\", line 578, in save\r\n rv = reduce(self.proto)\r\nTypeError: cannot pickle 'builtins.CoreBPE' object\r\n```\r\n\r\nMy current environment is running datasets v2.10.0.", "cc @mariosasko ", "@lhoestq @edhenry I am also seeing this, do you have any suggested solution?", "With which `datasets` version ? Can you try to udpate ?", "@lhoestq @edhenry I am on datasets version `'2.12.0'. I see the same `TypeError: cannot pickle 'builtins.CoreBPE' object` that others are seeing.", "I am able to reproduce this on datasets 2.14.2. The `datasets.disable_caching()` doesn't work around it.\r\n\r\n@lhoestq - you might want to reopen this issue. Because of this issue folks won't be able run Karpathy's NanoGPT :(.", "update： temporarily solved the problem by setting\r\n```\r\n--preprocess_num_workers 1\r\n```\r\n\r\n-------------\r\nI have met the same problem, here is my env:\r\n```\r\ndatasets 2.14.4\r\ntransformers 4.31.0\r\ntiktoken 0.4.0\r\ntorch 1.13.1\r\n```", "@mengban I cannot reproduce the issue even with these versions installed. It would help if you could provide info about your system and the `pip list` output.", "@mariosasko Please take a look at this\r\n```python\r\nfrom typing import Any\r\nfrom datasets import Dataset\r\nimport tiktoken\r\n\r\ndataset = Dataset.from_list([{\"n\": str(i)} for i in range(20)])\r\nenc = tiktoken.get_encoding(\"gpt2\")\r\n\r\n\r\nclass A:\r\n tokenizer = enc #tiktoken.get_encoding(\"gpt2\")\r\n\r\n def __call__(self, example) -> Any:\r\n ids = self.tokenizer.encode(example[\"n\"])\r\n example[\"len\"] = len(ids)\r\n return example\r\n\r\na = A()\r\n\r\ndef process(example):\r\n ids = a.tokenizer.encode(example[\"n\"])\r\n example[\"len\"] = len(ids)\r\n return example\r\n\r\n# success\r\ntokenized = dataset.map(process, desc=\"tiktoken\", num_proc=2)\r\n\r\n# raise TypeError: cannot pickle 'builtins.CoreBPE' object\r\ntokenized = dataset.map(a, desc=\"tiktoken\", num_proc=2)\r\n```\r\n\r\npip list\r\n```\r\ndatasets 2.14.4\r\ntiktoken 0.4.0\r\n```", "Thanks @maxwellzh! Our `Hasher` works with this snippet, but the problem is running multiprocessing with a non-serializable `tiktoken.Encoding` object.\r\n\r\nInserting the following code before the `map` should fix this:\r\n```python\r\nimport copyreg\r\n\r\ndef pickle_Encoding(enc):\r\n return (functools.partial(tiktoken.core.Encoding, enc.name, pat_str=enc._pat_str, mergeable_ranks=enc._mergeable_ranks, special_tokens=enc._special_tokens), ())\r\n\r\ncopyreg.pickle(tiktoken.core.Encoding, pickle_Encoding)\r\n```\r\n\r\nBut the best fix would be implementing `__reduce__` for `tiktoken.Encoding` or `tiktoken.CoreBPE`. If I find time, I'll try to fix this in the `tiktoken` repo.", "I think the right way to fix this would be to have new tokenizer instance for each process. This applies to many other tokenizers that don't support multi-process or have bugs. To do this, first define tokenizer factory class like this:\r\n\r\n```\r\n class TikTokenFactory:\r\n def __init__(self):\r\n self._enc = None\r\n self.eot_token = None\r\n\r\n def encode_ordinary(self, text):\r\n if self._enc is None:\r\n self._enc = tiktoken.get_encoding(\"gpt2\")\r\n self.eot_token = self._enc.eot_token\r\n return self._enc.encode_ordinary(text)\r\n```\r\n\r\nNow use this in `.map()` like this:\r\n\r\n```\r\n # tokenize the dataset\r\n tokenized = dataset.map(\r\n partial(process, TikTokenFactory()),\r\n remove_columns=['text'],\r\n desc=\"tokenizing the splits\",\r\n num_proc=max(1, cpu_count()//2),\r\n )\r\n```\r\n\r\nA full working example is here: https://github.com/sytelus/nanoGPT/blob/refactor/nanogpt_common/hf_data_prepare.py" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "> Awesome thank you !\r\n> \r\n> Could you also explain how to use certain types like ClassLabel, Image or Audio with jax ? You can get a lot of inspiration from the \"Other feature types\" section in the [PyTorch page](https://huggingface.co/docs/datasets/use_with_pytorch)\r\n> \r\n> I also think it's be nice if this page had the same structure as the pytorch or tf ones, with sections named\r\n> \r\n> * Dataset format\r\n> \r\n> * N-dimensional arrays\r\n> \r\n> * Other feature types\r\n> \r\n> * Data loading\r\n\r\nSure @lhoestq I'll do that later this afternoon whenever I'm done working! Thanks for the feedback as always 🤗", "Also, @lhoestq do you want me to elaborate more on the `## Data loading` section on how to use `datasets` to train a JAX model offering alternatives e.g. `Flax`, or do I keep it pure JAX? Thanks!", "If you have a good example with `flax` it can also be helpful for users", "For now, I think that probably it's not worth adding a `Flax` example, as train loops need to be done manually as in pure JAX, so probably the JAX example is enough. Anyway, let me know if you see something missing/incomplete/misleading/etc. and I'll update that ASAP 👍🏻 ", "P.S. I see that the `benchmark` action is being triggered on every PR, is it worth it? e.g. now I'm just editing the docs, so does it make any sense to trigger still the whole CI pipeline (including `benchmark`)? Just asking because in this PR for example it could be skipped.", "> P.S. I see that the benchmark action is being triggered on every PR, is it worth it? e.g. now I'm just editing the docs, so does it make any sense to trigger still the whole CI pipeline (including benchmark)? Just asking because in this PR for example it could be skipped.\r\n\r\nWe could restrict it to PRs modifying files in src/ indeed ^^'", "> LGTM :)\n\nCool thanks! My bad I didn't update those code blocks 🙃 Thanks for doing so before merge!", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009336 / 0.011353 (-0.002017) | 0.005037 / 0.011008 (-0.005971) | 0.102168 / 0.038508 (0.063659) | 0.035351 / 0.023109 (0.012242) | 0.299616 / 0.275898 (0.023718) | 0.333269 / 0.323480 (0.009789) | 0.008215 / 0.007986 (0.000229) | 0.005047 / 0.004328 (0.000718) | 0.074257 / 0.004250 (0.070007) | 0.045080 / 0.037052 (0.008028) | 0.300657 / 0.258489 (0.042168) | 0.357569 / 0.293841 (0.063728) | 0.038614 / 0.128546 (-0.089932) | 0.011995 / 0.075646 (-0.063651) | 0.369141 / 0.419271 (-0.050130) | 0.047603 / 0.043533 (0.004070) | 0.297694 / 0.255139 (0.042555) | 0.315380 / 0.283200 (0.032180) | 0.105009 / 0.141683 (-0.036674) | 1.421077 / 1.452155 (-0.031078) | 1.550024 / 1.492716 (0.057308) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.239026 / 0.018006 (0.221020) | 0.550010 / 0.000490 (0.549520) | 0.003294 / 0.000200 (0.003094) | 0.000093 / 0.000054 (0.000038) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027180 / 0.037411 (-0.010231) | 0.107942 / 0.014526 (0.093416) | 0.121092 / 0.176557 (-0.055464) | 0.161028 / 0.737135 (-0.576108) | 0.124615 / 0.296338 (-0.171723) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.399492 / 0.215209 (0.184283) | 3.984685 / 2.077655 (1.907030) | 1.794784 / 1.504120 (0.290664) | 1.604849 / 1.541195 (0.063654) | 1.682994 / 1.468490 (0.214504) | 0.691197 / 4.584777 (-3.893580) | 3.741816 / 3.745712 (-0.003897) | 2.092151 / 5.269862 (-3.177711) | 1.319106 / 4.565676 (-3.246570) | 0.083875 / 0.424275 (-0.340400) | 0.012473 / 0.007607 (0.004866) | 0.514057 / 0.226044 (0.288012) | 5.110217 / 2.268929 (2.841288) | 2.259105 / 55.444624 (-53.185519) | 1.914021 / 6.876477 (-4.962455) | 1.958371 / 2.142072 (-0.183701) | 0.819800 / 4.805227 (-3.985428) | 0.161153 / 6.500664 (-6.339511) | 0.061967 / 0.075469 (-0.013502) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.198553 / 1.841788 (-0.643234) | 14.793201 / 8.074308 (6.718893) | 14.646807 / 10.191392 (4.455415) | 0.152805 / 0.680424 (-0.527619) | 0.029206 / 0.534201 (-0.504995) | 0.440875 / 0.579283 (-0.138408) | 0.434925 / 0.434364 (0.000561) | 0.533495 / 0.540337 (-0.006842) | 0.624479 / 1.386936 (-0.762457) |\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) | 0.007346 / 0.011353 (-0.004007) | 0.005422 / 0.011008 (-0.005586) | 0.073930 / 0.038508 (0.035422) | 0.032978 / 0.023109 (0.009869) | 0.335182 / 0.275898 (0.059284) | 0.371916 / 0.323480 (0.048436) | 0.005851 / 0.007986 (-0.002135) | 0.005582 / 0.004328 (0.001254) | 0.073090 / 0.004250 (0.068839) | 0.048395 / 0.037052 (0.011342) | 0.353921 / 0.258489 (0.095432) | 0.380678 / 0.293841 (0.086837) | 0.036628 / 0.128546 (-0.091919) | 0.012392 / 0.075646 (-0.063254) | 0.086265 / 0.419271 (-0.333006) | 0.049262 / 0.043533 (0.005729) | 0.334790 / 0.255139 (0.079651) | 0.355278 / 0.283200 (0.072078) | 0.102714 / 0.141683 (-0.038969) | 1.536366 / 1.452155 (0.084211) | 1.565984 / 1.492716 (0.073268) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.216050 / 0.018006 (0.198043) | 0.554972 / 0.000490 (0.554482) | 0.002432 / 0.000200 (0.002232) | 0.000110 / 0.000054 (0.000055) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028602 / 0.037411 (-0.008809) | 0.123681 / 0.014526 (0.109155) | 0.136763 / 0.176557 (-0.039793) | 0.170083 / 0.737135 (-0.567052) | 0.138771 / 0.296338 (-0.157567) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.420036 / 0.215209 (0.204827) | 4.188734 / 2.077655 (2.111079) | 2.014758 / 1.504120 (0.510638) | 1.818423 / 1.541195 (0.277228) | 1.940790 / 1.468490 (0.472300) | 0.691420 / 4.584777 (-3.893357) | 3.782996 / 3.745712 (0.037284) | 2.131278 / 5.269862 (-3.138583) | 1.363043 / 4.565676 (-3.202633) | 0.087182 / 0.424275 (-0.337093) | 0.012448 / 0.007607 (0.004841) | 0.519296 / 0.226044 (0.293252) | 5.220397 / 2.268929 (2.951469) | 2.474243 / 55.444624 (-52.970381) | 2.139726 / 6.876477 (-4.736751) | 2.200700 / 2.142072 (0.058627) | 0.841171 / 4.805227 (-3.964056) | 0.169234 / 6.500664 (-6.331430) | 0.063879 / 0.075469 (-0.011590) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.260262 / 1.841788 (-0.581526) | 14.853209 / 8.074308 (6.778901) | 13.944085 / 10.191392 (3.752693) | 0.192014 / 0.680424 (-0.488410) | 0.017811 / 0.534201 (-0.516390) | 0.427166 / 0.579283 (-0.152117) | 0.438263 / 0.434364 (0.003899) | 0.538815 / 0.540337 (-0.001523) | 0.641398 / 1.386936 (-0.745538) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#139e9ae67a88cd79274bbf8315d861ee8bc7175f \"CML watermark\")\n" ]

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[ "Hi! This code works for me locally or in Colab. What's the output of `python -c \"import pyarrow as pa; print(pa.__version__)\"` when you run it inside your environment?", "Thanks for looking into this!\r\nThe output of `python -c \"import pyarrow as pa; print(pa.__version__)\"` is:\r\n```\r\n11.0.0\r\n```\r\n\r\nI did the following to setup the environment:\r\n```\r\nconda create -n datasets_debug python=3.9\r\nconda activate datasets_debug\r\npip install datasets==2.9.0\r\n```\r\n\r\nI just tested this on another machine (Ubuntu 18.04.6 LTS) with the same result as mentioned in the issue description.\r\n" ]

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[ "I agree that it would be a good idea to introduce a `combiner` argument in another PR.\r\n\r\nI did take quite a lot of inspiration from the implementation of `map`, but it did not seem obvious how to resuse `map` for the implementation. Do you have any suggestions, i could give a try?\r\n\r\nThose were exactly my thoughts, regarding the non-obvious initializer for batched and formatted datasets, so i agree! I'll introduce a `initializer` argument, and have it mandatory when `batched=True`.", "I added `initializer`. It is optional for `batched=False` and mandatory for `batched=True`. It has to be of the same length as `input_columns`, if `input_columns=None` it has to have the same length as `_data.column_names`. \r\n\r\nIf the initializer is not set for `batched=False` the first example is set as the `initializer`. \r\n\r\nThe initializer is used to initiliaze for each shard, so that means if that:\r\n```python\r\ndset = Dataset.from_dict({\"x\": [1, 2, 3]})\r\nsum_reduce = lambda x, y: x + y\r\nreduction = dset.reduce(sum_reduce, batched=True, initializer=1, input_columns='x', num_proc=2)\r\n# reduction is 8, i.e. reduction + num_proc * initializer\r\n```", "> I added initializer. It is optional for batched=False and mandatory for batched=True. It has to be of the same length as input_columns, if input_columns=None it has to have the same length as _data.column_names.\r\n> \r\n> If the initializer is not set for batched=False the first example is set as the initializer.\r\n\r\nSounds good to me !\r\n\r\n> The initializer is used to initiliaze for each shard, so that means if that:\r\n> \r\n> ```python\r\n> dset = Dataset.from_dict({\"x\": [1, 2, 3]})\r\n> sum_reduce = lambda x, y: x + y\r\n> reduction = dset.reduce(sum_reduce, batched=True, initializer=1, input_columns='x', num_proc=2)\r\n> # reduction is 8, i.e. reduction + num_proc * initializer\r\n> ```\r\n\r\nHmm this can be confusing for some users. Maybe we should consider making `combiner` mandatory for multiprocessing.\r\n\r\nIf we agree on this, maybe for this PR you can either:\r\n- remove multiprocessing (and we add combiner + multiprocessing in a subsequent PR)\r\n- OR add `combiner` directly\r\n\r\nMaybe we can get more feedback from @huggingface/datasets as well", "> > I added initializer. It is optional for batched=False and mandatory for batched=True. It has to be of the same length as input_columns, if input_columns=None it has to have the same length as _data.column_names.\r\n> > If the initializer is not set for batched=False the first example is set as the initializer.\r\n> \r\n> Sounds good to me !\r\n> \r\n> > The initializer is used to initiliaze for each shard, so that means if that:\r\n> > ```python\r\n> > dset = Dataset.from_dict({\"x\": [1, 2, 3]})\r\n> > sum_reduce = lambda x, y: x + y\r\n> > reduction = dset.reduce(sum_reduce, batched=True, initializer=1, input_columns='x', num_proc=2)\r\n> > # reduction is 8, i.e. reduction + num_proc * initializer\r\n> > ```\r\n> \r\n> Hmm this can be confusing for some users. Maybe we should consider making `combiner` mandatory for multiprocessing.\r\n> \r\n> If we agree on this, maybe for this PR you can either:\r\n> \r\n> * remove multiprocessing (and we add combiner + multiprocessing in a subsequent PR)\r\n> * OR add `combiner` directly\r\n> \r\n> Maybe we can get more feedback from @huggingface/datasets as well\r\n\r\nI think i prefer adding `combiner` in this PR. I think ill make `combiner` mandatory for `batched=True`, instead of assuming that `combiner=function`. Ill look at this one of the coming days. Also at some point i have to define `reduce` for `DatasetDict`, and not just `Dataset`.", "I added the `combiner` parameter as described. I added some examples in the docstring, as i felt it might still be a bit confusing what happens during multiprocessing / batching.\r\n\r\nStill need to look at `DatasetDict`.", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_5533). All of your documentation changes will be reflected on that endpoint.", "Feel free to merge `main` into your branch - we fixed some CI failures today", "The proposed API doesn't seem intuitive to me - one can already use `functools.reduce` or `Dataset.map` for this purpose ([Colab](https://colab.research.google.com/drive/1jCLv31Y4cDfqD0lhO0AnqEv3Or-LLvWe?usp=sharing) with examples), so perhaps we could have a section in the docs that uses these methods to perform reductions rather than introducing a new method (which needs to be maintained later)", "Thanks for sharing this google colab, it has nice examples !\r\n\r\nThough I still think `functools.reduce` with multiprocessing can be a pain - we offer something easier here:\r\n- no need to use a pool yourself\r\n- no need to use `map` just to iterate on the dataset (not its main purpose)\r\n- native support for lambdas (using dill)\r\n- the combiner is **mandatory** for multiprocessing to avoid ending up with an incorrect result as in your example\r\n\r\nHowever I agree that maintaining this can be challenging, especially if you think about how `map` already is, and if we also have to deal with dataset formatting.", "> native support for lambdas (using dill)\r\n\r\nReplacing `multiprocessing` with `multiprocess` in the example would allow that.\r\n\r\n> no need to use map just to iterate on the dataset (not its main purpose)\r\n\r\nNot the main purpose, but this was mentioned as a \"feature\" in the previous docs if I remember.\r\n\r\nAnd all this is related to the multi-processing case, which we can document.\r\n\r\nBesides the linked issue, I can't find requests for `Dataset.reduce`, which makes me think `functools.reduce` does the job for most users.", "> Besides the linked issue, I can't find requests for Dataset.reduce, which makes me think functools.reduce does the job for most users.\r\n\r\nI think @srush was looking for a way to do a word count but ended up using a single processed `map`. I also saw some users on the forum wanting to compute `max`\r\n\r\n> Not the main purpose, but this was mentioned as a \"feature\" in the previous docs if I remember.\r\n> \r\n> And all this is related to the multi-processing case, which we can document.\r\n\r\nYup indeed", "While counting is one example, I often find I want to compute different statistics over a dataset. This seems like a natural way to do it in a stateless manner.\n\n\nI guess you could use functools reduce, but that wouldn't allow batching, right?", "I've updated the [Colab](https://colab.research.google.com/drive/1jCLv31Y4cDfqD0lhO0AnqEv3Or-LLvWe?usp=sharing) with an example that reduces batches with `map` and then computes the final result. It would be nice to have a similar example (explained in detail) in the docs to show the full power of `map`.\r\n\r\nPlus, for simple reductions such as `max`, one can do `pc.max(ds.with_format(\"arrow\")[\"col\"])` to directly get the result (without loading the entire column in RAM).\r\n\r\n@srush \r\n\r\n> I guess you could use functools reduce, but that wouldn't allow batching, right?\r\n\r\nYou can use `.iter(batch_size)` to get batches\r\n ", "That `functools` tools example is clean. I didn't know about `iter`. That would handle my use case.\n\nThe stateful `map` with a global variable is pretty hairy. I don't think we should recommend people do that.\n\n", "Whenever I in the past wanted to calculate statistics for datasets I used `functools` similarly to how it's described in the colab, but I always felt it was a bit of a hassle to use it together with multiprocessing, which is why I picked up the issue, to do it \"once and for all\".", "Should i close this and open another PR, with descriptions of how to use `map` for reduction, or?", "Yes I think good documentation is the way to go here. @mariosasko 's examples are clear and efficient.\r\n\r\nMaybe we could have an `Aggregations` section in the `Process` page with some guides on how to:\r\n- use `.map()` to compute aggregates\r\n- use `.with_format(\"arrow\")` for max, min, etc. to save RAM and get max speed\r\n- use a multiprocessed `.map()` to get partial results in parallel and combine them (max text length example)\r\n- (advanced) use multiprocessing with an arbitrary accumulator (word count example)\r\n\r\nAnd also a new conceptual guide on `Multiprocessed mapping` to say that it helps speed up CPU intensive processing but why it may lead to incorrect results when computing aggregates.\r\n\r\ncc @stevhliu for visibility and if you have some comments", "I would create a `Reduce` - to be more exact - subsection under `Map` to demonstrate these examples since we're showing how they can be done with the `Dataset.map` function. It'd also be good to add a link to the new concept guide from this section to solidify user understanding :)", "Coolio. Ill close this PR and get going on another one adding what we've discussed during the next couple of days!" ]

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[ "Hi! You can get this behavior by specifying `stratify_by_column=\"label\"` in `train_test_split`.\r\n\r\nThis is the full example:\r\n```python\r\nimport numpy as np\r\nfrom datasets import Dataset, ClassLabel\r\n\r\ndata = [\r\n {'label': 0, 'text': \"example1\"},\r\n {'label': 1, 'text': \"example2\"},\r\n {'label': 1, 'text': \"example3\"},\r\n {'label': 1, 'text': \"example4\"},\r\n {'label': 0, 'text': \"example5\"},\r\n {'label': 1, 'text': \"example6\"},\r\n {'label': 2, 'text': \"example7\"},\r\n {'label': 2, 'text': \"example8\"}\r\n]\r\n\r\nfor _ in range(10):\r\n data_set = Dataset.from_list(data)\r\n data_set = data_set.cast_column(\"label\", ClassLabel(num_classes=3))\r\n data_set = data_set.train_test_split(test_size=0.5, stratify_by_column=\"label\")\r\n unique_labels_train = np.unique(data_set[\"train\"][:][\"label\"])\r\n unique_labels_test = np.unique(data_set[\"test\"][:][\"label\"])\r\n assert len(unique_labels_train) >= len(unique_labels_test) \r\n```\r\n" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008837 / 0.011353 (-0.002516) | 0.004608 / 0.011008 (-0.006400) | 0.101821 / 0.038508 (0.063312) | 0.030300 / 0.023109 (0.007191) | 0.301275 / 0.275898 (0.025377) | 0.365027 / 0.323480 (0.041547) | 0.007043 / 0.007986 (-0.000943) | 0.003493 / 0.004328 (-0.000835) | 0.078444 / 0.004250 (0.074194) | 0.036963 / 0.037052 (-0.000089) | 0.310510 / 0.258489 (0.052020) | 0.343769 / 0.293841 (0.049928) | 0.033560 / 0.128546 (-0.094986) | 0.011427 / 0.075646 (-0.064220) | 0.323542 / 0.419271 (-0.095730) | 0.043063 / 0.043533 (-0.000470) | 0.308869 / 0.255139 (0.053730) | 0.326436 / 0.283200 (0.043236) | 0.091775 / 0.141683 (-0.049908) | 1.471020 / 1.452155 (0.018865) | 1.494328 / 1.492716 (0.001612) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.009299 / 0.018006 (-0.008707) | 0.415705 / 0.000490 (0.415215) | 0.002406 / 0.000200 (0.002206) | 0.000066 / 0.000054 (0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.022959 / 0.037411 (-0.014452) | 0.097111 / 0.014526 (0.082585) | 0.103399 / 0.176557 (-0.073157) | 0.144385 / 0.737135 (-0.592750) | 0.109069 / 0.296338 (-0.187269) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.417796 / 0.215209 (0.202587) | 4.158198 / 2.077655 (2.080543) | 1.862036 / 1.504120 (0.357916) | 1.650130 / 1.541195 (0.108936) | 1.717150 / 1.468490 (0.248660) | 0.691704 / 4.584777 (-3.893073) | 3.328254 / 3.745712 (-0.417458) | 1.850070 / 5.269862 (-3.419792) | 1.154331 / 4.565676 (-3.411346) | 0.082199 / 0.424275 (-0.342076) | 0.012226 / 0.007607 (0.004619) | 0.522491 / 0.226044 (0.296446) | 5.244181 / 2.268929 (2.975253) | 2.286651 / 55.444624 (-53.157973) | 1.954439 / 6.876477 (-4.922038) | 1.992052 / 2.142072 (-0.150020) | 0.804779 / 4.805227 (-4.000449) | 0.147341 / 6.500664 (-6.353323) | 0.063863 / 0.075469 (-0.011606) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.270778 / 1.841788 (-0.571010) | 13.676378 / 8.074308 (5.602070) | 14.253498 / 10.191392 (4.062106) | 0.170748 / 0.680424 (-0.509676) | 0.028451 / 0.534201 (-0.505750) | 0.395034 / 0.579283 (-0.184249) | 0.407512 / 0.434364 (-0.026852) | 0.466740 / 0.540337 (-0.073598) | 0.564338 / 1.386936 (-0.822598) |\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) | 0.006733 / 0.011353 (-0.004620) | 0.004635 / 0.011008 (-0.006373) | 0.075464 / 0.038508 (0.036956) | 0.027732 / 0.023109 (0.004623) | 0.343622 / 0.275898 (0.067724) | 0.380388 / 0.323480 (0.056908) | 0.005177 / 0.007986 (-0.002808) | 0.003435 / 0.004328 (-0.000893) | 0.074546 / 0.004250 (0.070296) | 0.039115 / 0.037052 (0.002063) | 0.342207 / 0.258489 (0.083718) | 0.390324 / 0.293841 (0.096483) | 0.031665 / 0.128546 (-0.096882) | 0.011695 / 0.075646 (-0.063951) | 0.085788 / 0.419271 (-0.333484) | 0.042423 / 0.043533 (-0.001110) | 0.340748 / 0.255139 (0.085609) | 0.372813 / 0.283200 (0.089614) | 0.092395 / 0.141683 (-0.049288) | 1.502158 / 1.452155 (0.050004) | 1.618233 / 1.492716 (0.125516) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.224451 / 0.018006 (0.206444) | 0.398712 / 0.000490 (0.398222) | 0.002739 / 0.000200 (0.002539) | 0.000074 / 0.000054 (0.000020) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025393 / 0.037411 (-0.012018) | 0.100480 / 0.014526 (0.085954) | 0.106913 / 0.176557 (-0.069644) | 0.148639 / 0.737135 (-0.588496) | 0.110098 / 0.296338 (-0.186240) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.439359 / 0.215209 (0.224150) | 4.396801 / 2.077655 (2.319146) | 2.069809 / 1.504120 (0.565689) | 1.851014 / 1.541195 (0.309820) | 1.885003 / 1.468490 (0.416513) | 0.701387 / 4.584777 (-3.883390) | 3.404943 / 3.745712 (-0.340769) | 1.874506 / 5.269862 (-3.395355) | 1.174925 / 4.565676 (-3.390752) | 0.083282 / 0.424275 (-0.340993) | 0.012352 / 0.007607 (0.004745) | 0.543058 / 0.226044 (0.317013) | 5.458186 / 2.268929 (3.189258) | 2.562159 / 55.444624 (-52.882466) | 2.198810 / 6.876477 (-4.677667) | 2.238976 / 2.142072 (0.096903) | 0.810958 / 4.805227 (-3.994269) | 0.153341 / 6.500664 (-6.347323) | 0.067773 / 0.075469 (-0.007696) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.303938 / 1.841788 (-0.537850) | 14.170363 / 8.074308 (6.096055) | 13.727012 / 10.191392 (3.535620) | 0.129118 / 0.680424 (-0.551306) | 0.016746 / 0.534201 (-0.517455) | 0.382759 / 0.579283 (-0.196524) | 0.391070 / 0.434364 (-0.043294) | 0.461197 / 0.540337 (-0.079141) | 0.557641 / 1.386936 (-0.829295) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#004bba88db03fb87d57252e38a4d7abdb0a5f0a9 \"CML watermark\")\n" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "Hmm, should this also be updated in `Dataset.load_from_disk` and `DatasetDict.load_from_disk`? https://github.com/huggingface/datasets/pull/5466 As there the paths are joined using `Path(..., ...)` and it won't work on Windows OS according to that PR, right?", "Hi, @lhoestq could you review this PR? Thank you in advance and sorry for the ping 🤗 ", "Besides that, I was also thinking of adding a `skip_validation` boolean arg in both `Dataset.load_from_disk` and `DatasetDict.load_from_disk` to avoid duplicating those calls too when those functions are called from `datasets.load_from_disk`.\r\n\r\nSo that `skip_validation` is set to `False` by default, but passed as `True` if called from `datasets.load_from_disk`, and that just affects the file checking part of the code on both functions, do you agree @lhoestq?", "I think we should always verify", "> I think we should always verify\r\n\r\nBut with the current way we're also verifying twice right? First on `datasets.load_from_disk` then on `Dataset.load_from_disk`, right?\r\n\r\nMaybe a warning before calling either `Dataset.load_from_disk` or `DatasetDict.load_from_disk` is enough?\r\n\r\ne.g. **\"Consider using `Dataset.load_from_disk` instead to avoid `fsspec` from verifying the presence of `dataset_info.json` and `state.json` in the remote filesystem twice.\"** to be showed just when `fs` is remote.", "I don't think it's worth adding a new argument just for that. Usually we keep the set of arguments to the strict minimum", "> I don't think it's worth adding a new argument just for that. Usually we keep the set of arguments to the strict minimum\r\n\r\nWhat about the warning?\r\n\r\nAnyway, if you don't think that's worth it feel free to merge 👍🏻 ", "> What about the warning?\r\n\r\nWe may show warnings for suggestions, but only if the user does a very unoptimized thing. Here we're not at that level ^^'", "Thanks for the explanation and feedback @lhoestq 🤗 ", "> Thank you :) Added my last suggestions:\r\n\r\nThanks for the feedback, I agree with everything besides one nit! 👍🏻 ", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==6.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.011556 / 0.011353 (0.000203) | 0.006213 / 0.011008 (-0.004796) | 0.132390 / 0.038508 (0.093882) | 0.034609 / 0.023109 (0.011500) | 0.361156 / 0.275898 (0.085258) | 0.402524 / 0.323480 (0.079044) | 0.009138 / 0.007986 (0.001152) | 0.005728 / 0.004328 (0.001399) | 0.115406 / 0.004250 (0.111156) | 0.041440 / 0.037052 (0.004388) | 0.370232 / 0.258489 (0.111742) | 0.409944 / 0.293841 (0.116103) | 0.053803 / 0.128546 (-0.074744) | 0.022029 / 0.075646 (-0.053617) | 0.400325 / 0.419271 (-0.018946) | 0.055324 / 0.043533 (0.011791) | 0.368699 / 0.255139 (0.113560) | 0.391836 / 0.283200 (0.108636) | 0.099356 / 0.141683 (-0.042327) | 1.687881 / 1.452155 (0.235726) | 1.752202 / 1.492716 (0.259485) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.012992 / 0.018006 (-0.005014) | 0.518756 / 0.000490 (0.518267) | 0.004702 / 0.000200 (0.004502) | 0.000105 / 0.000054 (0.000050) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028371 / 0.037411 (-0.009041) | 0.127058 / 0.014526 (0.112532) | 0.136908 / 0.176557 (-0.039649) | 0.210168 / 0.737135 (-0.526968) | 0.139600 / 0.296338 (-0.156738) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.570901 / 0.215209 (0.355692) | 5.967213 / 2.077655 (3.889558) | 2.286745 / 1.504120 (0.782626) | 1.950682 / 1.541195 (0.409487) | 2.062536 / 1.468490 (0.594046) | 1.255671 / 4.584777 (-3.329106) | 5.454951 / 3.745712 (1.709238) | 3.076429 / 5.269862 (-2.193433) | 2.082871 / 4.565676 (-2.482806) | 0.150069 / 0.424275 (-0.274206) | 0.014864 / 0.007607 (0.007257) | 0.774672 / 0.226044 (0.548627) | 7.873992 / 2.268929 (5.605064) | 3.196165 / 55.444624 (-52.248459) | 2.366854 / 6.876477 (-4.509623) | 2.407381 / 2.142072 (0.265309) | 1.419130 / 4.805227 (-3.386097) | 0.249210 / 6.500664 (-6.251454) | 0.088648 / 0.075469 (0.013179) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.528368 / 1.841788 (-0.313420) | 17.554000 / 8.074308 (9.479692) | 20.773300 / 10.191392 (10.581908) | 0.216903 / 0.680424 (-0.463521) | 0.046544 / 0.534201 (-0.487657) | 0.538238 / 0.579283 (-0.041045) | 0.673926 / 0.434364 (0.239562) | 0.656108 / 0.540337 (0.115770) | 0.774026 / 1.386936 (-0.612910) |\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) | 0.010177 / 0.011353 (-0.001176) | 0.006334 / 0.011008 (-0.004675) | 0.100097 / 0.038508 (0.061589) | 0.039996 / 0.023109 (0.016887) | 0.420225 / 0.275898 (0.144327) | 0.437694 / 0.323480 (0.114214) | 0.007987 / 0.007986 (0.000002) | 0.005782 / 0.004328 (0.001454) | 0.106421 / 0.004250 (0.102171) | 0.046993 / 0.037052 (0.009941) | 0.397304 / 0.258489 (0.138815) | 0.441780 / 0.293841 (0.147939) | 0.064594 / 0.128546 (-0.063952) | 0.020823 / 0.075646 (-0.054823) | 0.108854 / 0.419271 (-0.310417) | 0.076457 / 0.043533 (0.032924) | 0.401712 / 0.255139 (0.146573) | 0.459292 / 0.283200 (0.176093) | 0.125044 / 0.141683 (-0.016639) | 1.765531 / 1.452155 (0.313377) | 1.845429 / 1.492716 (0.352713) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.225549 / 0.018006 (0.207543) | 0.524402 / 0.000490 (0.523913) | 0.006994 / 0.000200 (0.006794) | 0.000120 / 0.000054 (0.000065) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033787 / 0.037411 (-0.003624) | 0.144895 / 0.014526 (0.130369) | 0.147185 / 0.176557 (-0.029371) | 0.228227 / 0.737135 (-0.508908) | 0.164967 / 0.296338 (-0.131371) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.628242 / 0.215209 (0.413033) | 6.348176 / 2.077655 (4.270522) | 2.615832 / 1.504120 (1.111712) | 2.217481 / 1.541195 (0.676286) | 2.287058 / 1.468490 (0.818568) | 1.322854 / 4.584777 (-3.261923) | 5.547831 / 3.745712 (1.802119) | 3.199467 / 5.269862 (-2.070395) | 2.135297 / 4.565676 (-2.430380) | 0.165134 / 0.424275 (-0.259141) | 0.014753 / 0.007607 (0.007146) | 0.778579 / 0.226044 (0.552535) | 7.982329 / 2.268929 (5.713401) | 3.331712 / 55.444624 (-52.112913) | 2.642606 / 6.876477 (-4.233871) | 2.699362 / 2.142072 (0.557290) | 1.572268 / 4.805227 (-3.232959) | 0.273348 / 6.500664 (-6.227316) | 0.082975 / 0.075469 (0.007506) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.730421 / 1.841788 (-0.111367) | 18.154495 / 8.074308 (10.080187) | 20.969885 / 10.191392 (10.778493) | 0.233652 / 0.680424 (-0.446772) | 0.026609 / 0.534201 (-0.507592) | 0.546874 / 0.579283 (-0.032410) | 0.602891 / 0.434364 (0.168527) | 0.641073 / 0.540337 (0.100736) | 0.772138 / 1.386936 (-0.614798) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#20703458e3c42ee7bfc1a26e47805c0db4dda2d6 \"CML watermark\")\n" ]

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