Datasets:

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ans-imran

/

github-issues

like 0

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

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

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[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006424 / 0.011353 (-0.004929) | 0.003960 / 0.011008 (-0.007048) | 0.084022 / 0.038508 (0.045514) | 0.070770 / 0.023109 (0.047661) | 0.320525 / 0.275898 (0.044627) | 0.354507 / 0.323480 (0.031027) | 0.003939 / 0.007986 (-0.004047) | 0.004161 / 0.004328 (-0.000168) | 0.064754 / 0.004250 (0.060503) | 0.053630 / 0.037052 (0.016578) | 0.323948 / 0.258489 (0.065459) | 0.376908 / 0.293841 (0.083067) | 0.031063 / 0.128546 (-0.097483) | 0.008470 / 0.075646 (-0.067177) | 0.288110 / 0.419271 (-0.131161) | 0.053062 / 0.043533 (0.009529) | 0.328176 / 0.255139 (0.073037) | 0.345203 / 0.283200 (0.062003) | 0.024579 / 0.141683 (-0.117104) | 1.471649 / 1.452155 (0.019495) | 1.561458 / 1.492716 (0.068742) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.223591 / 0.018006 (0.205585) | 0.450758 / 0.000490 (0.450269) | 0.003751 / 0.000200 (0.003552) | 0.000079 / 0.000054 (0.000025) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027859 / 0.037411 (-0.009552) | 0.080607 / 0.014526 (0.066081) | 0.093835 / 0.176557 (-0.082722) | 0.150466 / 0.737135 (-0.586669) | 0.094381 / 0.296338 (-0.201957) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.394011 / 0.215209 (0.178802) | 3.918318 / 2.077655 (1.840664) | 1.928684 / 1.504120 (0.424564) | 1.765944 / 1.541195 (0.224749) | 1.784716 / 1.468490 (0.316226) | 0.487189 / 4.584777 (-4.097588) | 3.537705 / 3.745712 (-0.208008) | 3.312162 / 5.269862 (-1.957699) | 2.024520 / 4.565676 (-2.541156) | 0.057571 / 0.424275 (-0.366704) | 0.007203 / 0.007607 (-0.000404) | 0.467253 / 0.226044 (0.241208) | 4.659934 / 2.268929 (2.391005) | 2.377764 / 55.444624 (-53.066860) | 2.021984 / 6.876477 (-4.854492) | 2.197468 / 2.142072 (0.055395) | 0.586415 / 4.805227 (-4.218812) | 0.136636 / 6.500664 (-6.364028) | 0.060885 / 0.075469 (-0.014584) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.241879 / 1.841788 (-0.599908) | 18.719327 / 8.074308 (10.645019) | 14.408689 / 10.191392 (4.217297) | 0.155778 / 0.680424 (-0.524646) | 0.018475 / 0.534201 (-0.515726) | 0.392316 / 0.579283 (-0.186967) | 0.409803 / 0.434364 (-0.024561) | 0.458701 / 0.540337 (-0.081637) | 0.630561 / 1.386936 (-0.756375) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006541 / 0.011353 (-0.004812) | 0.003915 / 0.011008 (-0.007094) | 0.064292 / 0.038508 (0.025784) | 0.069174 / 0.023109 (0.046065) | 0.402048 / 0.275898 (0.126150) | 0.423960 / 0.323480 (0.100480) | 0.005355 / 0.007986 (-0.002631) | 0.003295 / 0.004328 (-0.001033) | 0.065212 / 0.004250 (0.060962) | 0.054292 / 0.037052 (0.017240) | 0.402930 / 0.258489 (0.144441) | 0.441840 / 0.293841 (0.147999) | 0.032732 / 0.128546 (-0.095814) | 0.008565 / 0.075646 (-0.067081) | 0.070705 / 0.419271 (-0.348567) | 0.047908 / 0.043533 (0.004375) | 0.401400 / 0.255139 (0.146261) | 0.422682 / 0.283200 (0.139483) | 0.022244 / 0.141683 (-0.119439) | 1.532018 / 1.452155 (0.079864) | 1.597955 / 1.492716 (0.105239) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.226277 / 0.018006 (0.208271) | 0.475578 / 0.000490 (0.475088) | 0.005456 / 0.000200 (0.005256) | 0.000140 / 0.000054 (0.000085) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033111 / 0.037411 (-0.004300) | 0.093138 / 0.014526 (0.078613) | 0.104619 / 0.176557 (-0.071937) | 0.157972 / 0.737135 (-0.579164) | 0.105017 / 0.296338 (-0.191321) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.441771 / 0.215209 (0.226562) | 4.396981 / 2.077655 (2.319326) | 2.410745 / 1.504120 (0.906625) | 2.258359 / 1.541195 (0.717164) | 2.372628 / 1.468490 (0.904138) | 0.491411 / 4.584777 (-4.093366) | 3.650084 / 3.745712 (-0.095628) | 3.279557 / 5.269862 (-1.990304) | 2.011377 / 4.565676 (-2.554300) | 0.058283 / 0.424275 (-0.365992) | 0.007435 / 0.007607 (-0.000172) | 0.507212 / 0.226044 (0.281167) | 5.080104 / 2.268929 (2.811176) | 2.822680 / 55.444624 (-52.621945) | 2.507608 / 6.876477 (-4.368869) | 2.719349 / 2.142072 (0.577277) | 0.586157 / 4.805227 (-4.219071) | 0.132851 / 6.500664 (-6.367813) | 0.059944 / 0.075469 (-0.015525) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.374801 / 1.841788 (-0.466987) | 19.089359 / 8.074308 (11.015051) | 14.525861 / 10.191392 (4.334469) | 0.184758 / 0.680424 (-0.495666) | 0.020206 / 0.534201 (-0.513995) | 0.397309 / 0.579283 (-0.181975) | 0.418120 / 0.434364 (-0.016244) | 0.471817 / 0.540337 (-0.068520) | 0.681691 / 1.386936 (-0.705245) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2076cb857e90cf7a6050bba230f586993c5e034a \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._" ]

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

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

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

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

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

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

[]

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

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

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[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006934 / 0.011353 (-0.004419) | 0.004097 / 0.011008 (-0.006911) | 0.084662 / 0.038508 (0.046154) | 0.077106 / 0.023109 (0.053996) | 0.355035 / 0.275898 (0.079137) | 0.381466 / 0.323480 (0.057986) | 0.004182 / 0.007986 (-0.003803) | 0.003411 / 0.004328 (-0.000917) | 0.065279 / 0.004250 (0.061029) | 0.058192 / 0.037052 (0.021140) | 0.372363 / 0.258489 (0.113874) | 0.401621 / 0.293841 (0.107780) | 0.031719 / 0.128546 (-0.096827) | 0.008753 / 0.075646 (-0.066893) | 0.287125 / 0.419271 (-0.132146) | 0.052943 / 0.043533 (0.009410) | 0.349680 / 0.255139 (0.094541) | 0.364004 / 0.283200 (0.080805) | 0.026705 / 0.141683 (-0.114977) | 1.472708 / 1.452155 (0.020553) | 1.556559 / 1.492716 (0.063842) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.224868 / 0.018006 (0.206862) | 0.458793 / 0.000490 (0.458304) | 0.009434 / 0.000200 (0.009234) | 0.000356 / 0.000054 (0.000301) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029670 / 0.037411 (-0.007741) | 0.086517 / 0.014526 (0.071991) | 0.097342 / 0.176557 (-0.079215) | 0.153722 / 0.737135 (-0.583413) | 0.098465 / 0.296338 (-0.197874) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.400739 / 0.215209 (0.185530) | 3.998087 / 2.077655 (1.920432) | 2.025772 / 1.504120 (0.521652) | 1.858679 / 1.541195 (0.317485) | 1.951573 / 1.468490 (0.483083) | 0.483028 / 4.584777 (-4.101749) | 3.554085 / 3.745712 (-0.191627) | 3.306983 / 5.269862 (-1.962879) | 2.087043 / 4.565676 (-2.478633) | 0.057127 / 0.424275 (-0.367148) | 0.007252 / 0.007607 (-0.000355) | 0.480180 / 0.226044 (0.254136) | 4.787183 / 2.268929 (2.518255) | 2.489667 / 55.444624 (-52.954957) | 2.150774 / 6.876477 (-4.725703) | 2.403197 / 2.142072 (0.261124) | 0.581843 / 4.805227 (-4.223384) | 0.134915 / 6.500664 (-6.365749) | 0.061283 / 0.075469 (-0.014186) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.285700 / 1.841788 (-0.556088) | 19.474093 / 8.074308 (11.399785) | 14.336349 / 10.191392 (4.144957) | 0.170932 / 0.680424 (-0.509492) | 0.018348 / 0.534201 (-0.515853) | 0.391909 / 0.579283 (-0.187374) | 0.414706 / 0.434364 (-0.019658) | 0.458156 / 0.540337 (-0.082182) | 0.656303 / 1.386936 (-0.730633) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006738 / 0.011353 (-0.004615) | 0.004029 / 0.011008 (-0.006979) | 0.064411 / 0.038508 (0.025903) | 0.078225 / 0.023109 (0.055116) | 0.408468 / 0.275898 (0.132569) | 0.445585 / 0.323480 (0.122105) | 0.005490 / 0.007986 (-0.002495) | 0.003419 / 0.004328 (-0.000910) | 0.063966 / 0.004250 (0.059715) | 0.056779 / 0.037052 (0.019727) | 0.415258 / 0.258489 (0.156769) | 0.461258 / 0.293841 (0.167418) | 0.032051 / 0.128546 (-0.096495) | 0.008471 / 0.075646 (-0.067176) | 0.071004 / 0.419271 (-0.348267) | 0.049068 / 0.043533 (0.005536) | 0.409575 / 0.255139 (0.154436) | 0.430748 / 0.283200 (0.147548) | 0.023784 / 0.141683 (-0.117899) | 1.507894 / 1.452155 (0.055739) | 1.586575 / 1.492716 (0.093859) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.228574 / 0.018006 (0.210568) | 0.451389 / 0.000490 (0.450900) | 0.006312 / 0.000200 (0.006112) | 0.000100 / 0.000054 (0.000045) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033391 / 0.037411 (-0.004020) | 0.096816 / 0.014526 (0.082290) | 0.107269 / 0.176557 (-0.069288) | 0.159749 / 0.737135 (-0.577387) | 0.108240 / 0.296338 (-0.188098) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.437643 / 0.215209 (0.222434) | 4.378173 / 2.077655 (2.300518) | 2.367218 / 1.504120 (0.863098) | 2.229493 / 1.541195 (0.688298) | 2.329849 / 1.468490 (0.861359) | 0.494985 / 4.584777 (-4.089792) | 3.578540 / 3.745712 (-0.167172) | 3.338220 / 5.269862 (-1.931642) | 2.092482 / 4.565676 (-2.473194) | 0.058495 / 0.424275 (-0.365780) | 0.007396 / 0.007607 (-0.000211) | 0.511001 / 0.226044 (0.284957) | 5.113497 / 2.268929 (2.844568) | 2.806215 / 55.444624 (-52.638409) | 2.485428 / 6.876477 (-4.391048) | 2.764907 / 2.142072 (0.622835) | 0.598824 / 4.805227 (-4.206404) | 0.134988 / 6.500664 (-6.365676) | 0.061752 / 0.075469 (-0.013717) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.365583 / 1.841788 (-0.476205) | 20.270297 / 8.074308 (12.195989) | 15.331673 / 10.191392 (5.140281) | 0.166152 / 0.680424 (-0.514272) | 0.020678 / 0.534201 (-0.513523) | 0.394821 / 0.579283 (-0.184462) | 0.420493 / 0.434364 (-0.013871) | 0.468551 / 0.540337 (-0.071787) | 0.654903 / 1.386936 (-0.732033) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5f268dd4ad4fb6dada15937d57fb367cb2810162 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007803 / 0.011353 (-0.003550) | 0.004664 / 0.011008 (-0.006344) | 0.099908 / 0.038508 (0.061400) | 0.090674 / 0.023109 (0.067565) | 0.406009 / 0.275898 (0.130111) | 0.465098 / 0.323480 (0.141618) | 0.004667 / 0.007986 (-0.003319) | 0.003880 / 0.004328 (-0.000449) | 0.076552 / 0.004250 (0.072301) | 0.066345 / 0.037052 (0.029292) | 0.419195 / 0.258489 (0.160706) | 0.478581 / 0.293841 (0.184741) | 0.036967 / 0.128546 (-0.091579) | 0.010000 / 0.075646 (-0.065647) | 0.347126 / 0.419271 (-0.072145) | 0.062265 / 0.043533 (0.018733) | 0.406653 / 0.255139 (0.151514) | 0.439044 / 0.283200 (0.155845) | 0.031289 / 0.141683 (-0.110394) | 1.797674 / 1.452155 (0.345520) | 1.835183 / 1.492716 (0.342467) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.268194 / 0.018006 (0.250187) | 0.493614 / 0.000490 (0.493124) | 0.015636 / 0.000200 (0.015436) | 0.000417 / 0.000054 (0.000362) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034188 / 0.037411 (-0.003223) | 0.099127 / 0.014526 (0.084601) | 0.113949 / 0.176557 (-0.062607) | 0.181209 / 0.737135 (-0.555926) | 0.114943 / 0.296338 (-0.181395) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.455767 / 0.215209 (0.240558) | 4.542947 / 2.077655 (2.465293) | 2.214605 / 1.504120 (0.710485) | 2.015163 / 1.541195 (0.473969) | 2.084945 / 1.468490 (0.616455) | 0.583827 / 4.584777 (-4.000950) | 4.187009 / 3.745712 (0.441297) | 3.920841 / 5.269862 (-1.349020) | 2.447260 / 4.565676 (-2.118417) | 0.069139 / 0.424275 (-0.355137) | 0.008734 / 0.007607 (0.001127) | 0.544673 / 0.226044 (0.318629) | 5.445094 / 2.268929 (3.176165) | 2.788284 / 55.444624 (-52.656340) | 2.395863 / 6.876477 (-4.480614) | 2.622632 / 2.142072 (0.480560) | 0.703931 / 4.805227 (-4.101297) | 0.160502 / 6.500664 (-6.340162) | 0.073734 / 0.075469 (-0.001735) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.498992 / 1.841788 (-0.342795) | 22.761476 / 8.074308 (14.687168) | 17.123919 / 10.191392 (6.932527) | 0.170272 / 0.680424 (-0.510151) | 0.021307 / 0.534201 (-0.512894) | 0.467548 / 0.579283 (-0.111735) | 0.480777 / 0.434364 (0.046413) | 0.542168 / 0.540337 (0.001830) | 0.771092 / 1.386936 (-0.615844) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007923 / 0.011353 (-0.003430) | 0.004664 / 0.011008 (-0.006344) | 0.077795 / 0.038508 (0.039286) | 0.090293 / 0.023109 (0.067184) | 0.494682 / 0.275898 (0.218784) | 0.539973 / 0.323480 (0.216494) | 0.006302 / 0.007986 (-0.001684) | 0.003794 / 0.004328 (-0.000535) | 0.076567 / 0.004250 (0.072317) | 0.067141 / 0.037052 (0.030089) | 0.501279 / 0.258489 (0.242790) | 0.555670 / 0.293841 (0.261829) | 0.037773 / 0.128546 (-0.090773) | 0.009930 / 0.075646 (-0.065716) | 0.084839 / 0.419271 (-0.334433) | 0.056876 / 0.043533 (0.013344) | 0.499329 / 0.255139 (0.244190) | 0.518449 / 0.283200 (0.235249) | 0.026041 / 0.141683 (-0.115642) | 1.787259 / 1.452155 (0.335105) | 1.853505 / 1.492716 (0.360788) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.238413 / 0.018006 (0.220407) | 0.488889 / 0.000490 (0.488399) | 0.007476 / 0.000200 (0.007277) | 0.000141 / 0.000054 (0.000087) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.038701 / 0.037411 (0.001290) | 0.115391 / 0.014526 (0.100865) | 0.125553 / 0.176557 (-0.051004) | 0.190267 / 0.737135 (-0.546868) | 0.126401 / 0.296338 (-0.169937) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.509270 / 0.215209 (0.294061) | 5.087631 / 2.077655 (3.009976) | 2.745863 / 1.504120 (1.241743) | 2.560259 / 1.541195 (1.019064) | 2.653124 / 1.468490 (1.184634) | 0.582118 / 4.584777 (-4.002659) | 4.181144 / 3.745712 (0.435431) | 3.871179 / 5.269862 (-1.398683) | 2.459849 / 4.565676 (-2.105827) | 0.068844 / 0.424275 (-0.355431) | 0.008672 / 0.007607 (0.001065) | 0.604898 / 0.226044 (0.378854) | 6.073263 / 2.268929 (3.804334) | 3.366638 / 55.444624 (-52.077986) | 2.937261 / 6.876477 (-3.939215) | 3.181173 / 2.142072 (1.039100) | 0.700478 / 4.805227 (-4.104750) | 0.158361 / 6.500664 (-6.342303) | 0.072860 / 0.075469 (-0.002609) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.621363 / 1.841788 (-0.220425) | 23.614315 / 8.074308 (15.540007) | 17.607213 / 10.191392 (7.415821) | 0.198031 / 0.680424 (-0.482393) | 0.023859 / 0.534201 (-0.510342) | 0.474674 / 0.579283 (-0.104609) | 0.491173 / 0.434364 (0.056809) | 0.581995 / 0.540337 (0.041658) | 0.792168 / 1.386936 (-0.594768) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#56fa9645fd24e083adee3cfd0f7d972fce391f0e \"CML watermark\")\n", "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6282). All of your documentation changes will be reflected on that endpoint.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004779 / 0.011353 (-0.006574) | 0.002916 / 0.011008 (-0.008092) | 0.061962 / 0.038508 (0.023454) | 0.029537 / 0.023109 (0.006428) | 0.242574 / 0.275898 (-0.033324) | 0.268585 / 0.323480 (-0.054894) | 0.004006 / 0.007986 (-0.003979) | 0.002434 / 0.004328 (-0.001895) | 0.048289 / 0.004250 (0.044039) | 0.045534 / 0.037052 (0.008481) | 0.248251 / 0.258489 (-0.010239) | 0.277037 / 0.293841 (-0.016804) | 0.023728 / 0.128546 (-0.104818) | 0.007295 / 0.075646 (-0.068351) | 0.205813 / 0.419271 (-0.213459) | 0.059093 / 0.043533 (0.015560) | 0.244336 / 0.255139 (-0.010803) | 0.262865 / 0.283200 (-0.020335) | 0.017232 / 0.141683 (-0.124451) | 1.126729 / 1.452155 (-0.325426) | 1.198987 / 1.492716 (-0.293729) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.091246 / 0.018006 (0.073240) | 0.300747 / 0.000490 (0.300258) | 0.000202 / 0.000200 (0.000003) | 0.000041 / 0.000054 (-0.000013) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018681 / 0.037411 (-0.018731) | 0.063567 / 0.014526 (0.049041) | 0.074019 / 0.176557 (-0.102538) | 0.120856 / 0.737135 (-0.616279) | 0.076525 / 0.296338 (-0.219814) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.282230 / 0.215209 (0.067021) | 2.731502 / 2.077655 (0.653847) | 1.473901 / 1.504120 (-0.030219) | 1.351165 / 1.541195 (-0.190030) | 1.390582 / 1.468490 (-0.077908) | 0.398443 / 4.584777 (-4.186334) | 2.360497 / 3.745712 (-1.385215) | 2.548158 / 5.269862 (-2.721703) | 1.552416 / 4.565676 (-3.013260) | 0.045659 / 0.424275 (-0.378616) | 0.004778 / 0.007607 (-0.002829) | 0.330191 / 0.226044 (0.104146) | 3.262510 / 2.268929 (0.993582) | 1.823076 / 55.444624 (-53.621549) | 1.541206 / 6.876477 (-5.335271) | 1.589069 / 2.142072 (-0.553004) | 0.472265 / 4.805227 (-4.332963) | 0.099712 / 6.500664 (-6.400952) | 0.042803 / 0.075469 (-0.032666) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.963022 / 1.841788 (-0.878766) | 11.998807 / 8.074308 (3.924499) | 10.526006 / 10.191392 (0.334614) | 0.140965 / 0.680424 (-0.539459) | 0.014197 / 0.534201 (-0.520004) | 0.271668 / 0.579283 (-0.307615) | 0.263993 / 0.434364 (-0.170371) | 0.307213 / 0.540337 (-0.233124) | 0.427411 / 1.386936 (-0.959525) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004761 / 0.011353 (-0.006592) | 0.002652 / 0.011008 (-0.008357) | 0.047949 / 0.038508 (0.009441) | 0.049714 / 0.023109 (0.026604) | 0.274021 / 0.275898 (-0.001877) | 0.292413 / 0.323480 (-0.031067) | 0.003912 / 0.007986 (-0.004074) | 0.002290 / 0.004328 (-0.002038) | 0.047320 / 0.004250 (0.043069) | 0.038061 / 0.037052 (0.001009) | 0.279318 / 0.258489 (0.020829) | 0.305167 / 0.293841 (0.011326) | 0.024595 / 0.128546 (-0.103952) | 0.006976 / 0.075646 (-0.068671) | 0.052987 / 0.419271 (-0.366285) | 0.032454 / 0.043533 (-0.011079) | 0.273986 / 0.255139 (0.018847) | 0.297641 / 0.283200 (0.014442) | 0.017680 / 0.141683 (-0.124003) | 1.141218 / 1.452155 (-0.310937) | 1.222543 / 1.492716 (-0.270173) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092880 / 0.018006 (0.074873) | 0.305080 / 0.000490 (0.304590) | 0.000215 / 0.000200 (0.000016) | 0.000051 / 0.000054 (-0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021050 / 0.037411 (-0.016362) | 0.069676 / 0.014526 (0.055150) | 0.081082 / 0.176557 (-0.095475) | 0.119234 / 0.737135 (-0.617902) | 0.081242 / 0.296338 (-0.215096) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.295916 / 0.215209 (0.080707) | 2.909769 / 2.077655 (0.832115) | 1.623118 / 1.504120 (0.118998) | 1.502297 / 1.541195 (-0.038898) | 1.540290 / 1.468490 (0.071800) | 0.401176 / 4.584777 (-4.183601) | 2.427764 / 3.745712 (-1.317948) | 2.568610 / 5.269862 (-2.701252) | 1.550486 / 4.565676 (-3.015190) | 0.046895 / 0.424275 (-0.377380) | 0.004800 / 0.007607 (-0.002807) | 0.344524 / 0.226044 (0.118479) | 3.429189 / 2.268929 (1.160261) | 1.949738 / 55.444624 (-53.494887) | 1.681440 / 6.876477 (-5.195037) | 1.675304 / 2.142072 (-0.466769) | 0.469663 / 4.805227 (-4.335564) | 0.097470 / 6.500664 (-6.403194) | 0.040121 / 0.075469 (-0.035348) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.957947 / 1.841788 (-0.883841) | 11.968455 / 8.074308 (3.894147) | 10.809763 / 10.191392 (0.618371) | 0.140603 / 0.680424 (-0.539820) | 0.015562 / 0.534201 (-0.518638) | 0.276406 / 0.579283 (-0.302877) | 0.295267 / 0.434364 (-0.139097) | 0.315744 / 0.540337 (-0.224593) | 0.417985 / 1.386936 (-0.968951) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#12e01642a6978cbb9d5778c8b7f1c6b20a9887d5 \"CML watermark\")\n", "I've opened #6704 with a cleaner fix for the issue :)" ]

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[ "I have looked at the doc failures, and I do not think that my change caused the doc build failure, but I'm not 100% sure about that.\r\nI have high confidence that the integration test failures are not something I introduced:-)", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008557 / 0.011353 (-0.002796) | 0.005224 / 0.011008 (-0.005784) | 0.109402 / 0.038508 (0.070893) | 0.075008 / 0.023109 (0.051899) | 0.388910 / 0.275898 (0.113012) | 0.425481 / 0.323480 (0.102002) | 0.005046 / 0.007986 (-0.002939) | 0.004166 / 0.004328 (-0.000162) | 0.079890 / 0.004250 (0.075639) | 0.061992 / 0.037052 (0.024940) | 0.409933 / 0.258489 (0.151444) | 0.444096 / 0.293841 (0.150255) | 0.043958 / 0.128546 (-0.084588) | 0.013655 / 0.075646 (-0.061991) | 0.402620 / 0.419271 (-0.016651) | 0.062784 / 0.043533 (0.019251) | 0.399653 / 0.255139 (0.144514) | 0.432926 / 0.283200 (0.149727) | 0.034631 / 0.141683 (-0.107052) | 1.801450 / 1.452155 (0.349296) | 1.965007 / 1.492716 (0.472290) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.305744 / 0.018006 (0.287738) | 0.590825 / 0.000490 (0.590335) | 0.014561 / 0.000200 (0.014361) | 0.000430 / 0.000054 (0.000375) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030449 / 0.037411 (-0.006962) | 0.091753 / 0.014526 (0.077227) | 0.106259 / 0.176557 (-0.070298) | 0.174599 / 0.737135 (-0.562537) | 0.107069 / 0.296338 (-0.189269) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.607544 / 0.215209 (0.392335) | 6.182592 / 2.077655 (4.104937) | 2.699782 / 1.504120 (1.195663) | 2.386915 / 1.541195 (0.845720) | 2.441763 / 1.468490 (0.973273) | 0.811360 / 4.584777 (-3.773417) | 5.253799 / 3.745712 (1.508087) | 4.762054 / 5.269862 (-0.507807) | 3.045161 / 4.565676 (-1.520515) | 0.095983 / 0.424275 (-0.328292) | 0.008653 / 0.007607 (0.001046) | 0.714218 / 0.226044 (0.488174) | 7.279325 / 2.268929 (5.010397) | 3.356107 / 55.444624 (-52.088517) | 2.765867 / 6.876477 (-4.110610) | 2.997756 / 2.142072 (0.855684) | 1.008740 / 4.805227 (-3.796487) | 0.201462 / 6.500664 (-6.299202) | 0.075780 / 0.075469 (0.000311) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.677034 / 1.841788 (-0.164754) | 23.546919 / 8.074308 (15.472610) | 21.576985 / 10.191392 (11.385593) | 0.239253 / 0.680424 (-0.441171) | 0.028740 / 0.534201 (-0.505460) | 0.468519 / 0.579283 (-0.110765) | 0.593935 / 0.434364 (0.159571) | 0.536830 / 0.540337 (-0.003507) | 0.779925 / 1.386936 (-0.607011) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009582 / 0.011353 (-0.001771) | 0.004971 / 0.011008 (-0.006037) | 0.081304 / 0.038508 (0.042796) | 0.077588 / 0.023109 (0.054478) | 0.486610 / 0.275898 (0.210712) | 0.580228 / 0.323480 (0.256748) | 0.006707 / 0.007986 (-0.001279) | 0.004325 / 0.004328 (-0.000004) | 0.086170 / 0.004250 (0.081920) | 0.060591 / 0.037052 (0.023539) | 0.501723 / 0.258489 (0.243234) | 0.548633 / 0.293841 (0.254793) | 0.050306 / 0.128546 (-0.078240) | 0.017458 / 0.075646 (-0.058188) | 0.093295 / 0.419271 (-0.325977) | 0.064588 / 0.043533 (0.021056) | 0.519395 / 0.255139 (0.264256) | 0.526021 / 0.283200 (0.242821) | 0.035795 / 0.141683 (-0.105888) | 1.792927 / 1.452155 (0.340772) | 1.956499 / 1.492716 (0.463783) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.296249 / 0.018006 (0.278243) | 0.594482 / 0.000490 (0.593992) | 0.007318 / 0.000200 (0.007118) | 0.000182 / 0.000054 (0.000128) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.036110 / 0.037411 (-0.001301) | 0.107924 / 0.014526 (0.093399) | 0.119975 / 0.176557 (-0.056582) | 0.177499 / 0.737135 (-0.559636) | 0.123299 / 0.296338 (-0.173039) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.632994 / 0.215209 (0.417785) | 6.481663 / 2.077655 (4.404008) | 3.231259 / 1.504120 (1.727139) | 2.768298 / 1.541195 (1.227103) | 2.694543 / 1.468490 (1.226053) | 0.837384 / 4.584777 (-3.747393) | 5.405278 / 3.745712 (1.659566) | 4.639424 / 5.269862 (-0.630437) | 2.944251 / 4.565676 (-1.621426) | 0.094978 / 0.424275 (-0.329297) | 0.008716 / 0.007607 (0.001108) | 0.795820 / 0.226044 (0.569776) | 8.514233 / 2.268929 (6.245304) | 3.800463 / 55.444624 (-51.644161) | 3.000005 / 6.876477 (-3.876472) | 3.298853 / 2.142072 (1.156781) | 0.994112 / 4.805227 (-3.811115) | 0.209435 / 6.500664 (-6.291229) | 0.075610 / 0.075469 (0.000141) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.681127 / 1.841788 (-0.160661) | 23.874465 / 8.074308 (15.800156) | 21.638567 / 10.191392 (11.447175) | 0.233303 / 0.680424 (-0.447121) | 0.032504 / 0.534201 (-0.501697) | 0.460462 / 0.579283 (-0.118821) | 0.560043 / 0.434364 (0.125679) | 0.555059 / 0.540337 (0.014721) | 0.831444 / 1.386936 (-0.555492) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#faada1742e1f25fce9cc5691ec11d3f91d4aa120 \"CML watermark\")\n" ]

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

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

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[ "This is exactly what I was looking for. It would also be very useful for me :-)", "This issue is really smashing the selling point of HF datasets... The only workaround I've found so far is to create a customized IterableDataloader which improves the loading speed to some extent.\r\n\r\nFor example I've a HF dataset `dt_train` with `len(dt_train) == 1M`. Using plain DataLoader is extremely slow:\r\n```\r\n%%time\r\ndl_train = DataLoader(dt_train, batch_size=128, shuffle = True)\r\nfor batch in dl_train:\r\n pass\r\n``` \r\n\r\n```\r\nCPU times: user 24min 35s, sys: 704 ms, total: 24min 36s\r\nWall time: 24min 37s\r\n```\r\nAnd DataLoader works even worse with HF's iterable_dataset:\r\n```\r\n%%time\r\ndt_train_ = dt_train.with_format(None).to_iterable_dataset(num_shards=64).shuffle(buffer_size=10_000)\r\ndl_train = DataLoader(dt_train_, batch_size=128)\r\nfor batch in dl_train:\r\n pass\r\n```\r\n```\r\nCPU times: user 1h 6min 2s, sys: 4.28 s, total: 1h 6min 6s\r\nWall time: 1h 7min 53s\r\n```\r\nWorkaround by running a customized wrapper:\r\n```\r\n%%time\r\nfrom torch.utils.data import DataLoader, IterableDataset\r\n\r\nclass Dataset2Iterable(IterableDataset):\r\n \"\"\"\r\n Wrapper to use a HF dataset as pytorch IterableDataset to speed up data loading.\r\n \"\"\"\r\n def __init__(self, dataset, batch_size=1, shuffle=True):\r\n super(Dataset2Iterable).__init__()\r\n self.dataset = dataset\r\n self.batch_size = batch_size\r\n self.shuffle = shuffle\r\n\r\n def __iter__(self):\r\n if self.shuffle: self.dataset.shuffle()\r\n return self.dataset.iter(batch_size=self.batch_size)\r\n\r\ndl_train = DataLoader(Dataset2Iterable(dt_train, batch_size = 128), batch_size=1, num_workers=0)\r\nfor n in range(2):\r\n for batch in dl_train:\r\n pass\r\n```\r\nThe speed still is slower than using tensorflow's loader but improved a lot than previous code:\r\n```\r\nCPU times: user 4min 18s, sys: 0 ns, total: 4min 18s\r\nWall time: 4min 20s\r\n```\r\nNote that the way I implemented `Dataset2Iterable` will only work with `num_workers=0`.", "I can confirm that @zhh210's solution works with `num_workers=0`. However, for my use case, this was still slower than tokenizing on the fly through a collator and leveraging multiple workers in the dataloder.\r\n\r\n@lhoestq I think this is an important use case (e.g., streaming from a large dataset, online or stored on disk). What do you think might be the best solution to move forward?", "I guess it can be implemented using a batched`.map()` under the hood that returns a single item containing the input batch.\r\n\r\nIn the meantime you can use this:\r\n\r\n```python\r\ndef batch(unbatched: dict[str, list]) -> dict[str, list]:\r\n return {k: [v] for k, v in unbatched}\r\n\r\nbatched_dataset = dataset.map(batch, batched=True, batch_size=batch_size)\r\n```\r\n\r\nThough it would be great to have a `.batch()` method indeed, I'd be happy to help with anyone wants to open a PR", "If no one else is planning to work on this, I can take it on. I'll wait until next week, and if no one has started a PR by then, I'll go ahead and open one." ]

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[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009624 / 0.011353 (-0.001729) | 0.005121 / 0.011008 (-0.005887) | 0.105560 / 0.038508 (0.067052) | 0.090749 / 0.023109 (0.067640) | 0.430274 / 0.275898 (0.154376) | 0.443399 / 0.323480 (0.119919) | 0.006575 / 0.007986 (-0.001411) | 0.004396 / 0.004328 (0.000068) | 0.080900 / 0.004250 (0.076649) | 0.064921 / 0.037052 (0.027868) | 0.410092 / 0.258489 (0.151603) | 0.470058 / 0.293841 (0.176217) | 0.054160 / 0.128546 (-0.074386) | 0.014367 / 0.075646 (-0.061279) | 0.384844 / 0.419271 (-0.034428) | 0.072818 / 0.043533 (0.029285) | 0.429341 / 0.255139 (0.174202) | 0.430968 / 0.283200 (0.147769) | 0.038437 / 0.141683 (-0.103246) | 1.814456 / 1.452155 (0.362301) | 1.832122 / 1.492716 (0.339406) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.329266 / 0.018006 (0.311260) | 0.596848 / 0.000490 (0.596358) | 0.018291 / 0.000200 (0.018091) | 0.000113 / 0.000054 (0.000058) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030505 / 0.037411 (-0.006907) | 0.097394 / 0.014526 (0.082869) | 0.127144 / 0.176557 (-0.049412) | 0.190251 / 0.737135 (-0.546884) | 0.116543 / 0.296338 (-0.179795) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.592124 / 0.215209 (0.376915) | 5.979801 / 2.077655 (3.902146) | 2.837753 / 1.504120 (1.333633) | 2.492942 / 1.541195 (0.951747) | 2.548083 / 1.468490 (1.079593) | 0.870446 / 4.584777 (-3.714330) | 5.493718 / 3.745712 (1.748006) | 4.945135 / 5.269862 (-0.324727) | 3.133994 / 4.565676 (-1.431683) | 0.097742 / 0.424275 (-0.326533) | 0.008750 / 0.007607 (0.001143) | 0.723304 / 0.226044 (0.497260) | 7.353766 / 2.268929 (5.084838) | 3.504808 / 55.444624 (-51.939816) | 2.872490 / 6.876477 (-4.003987) | 3.186628 / 2.142072 (1.044556) | 1.035470 / 4.805227 (-3.769758) | 0.211980 / 6.500664 (-6.288684) | 0.080356 / 0.075469 (0.004887) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.623389 / 1.841788 (-0.218399) | 23.492350 / 8.074308 (15.418042) | 21.053525 / 10.191392 (10.862133) | 0.225668 / 0.680424 (-0.454756) | 0.028311 / 0.534201 (-0.505890) | 0.472672 / 0.579283 (-0.106611) | 0.581536 / 0.434364 (0.147172) | 0.525180 / 0.540337 (-0.015158) | 0.790420 / 1.386936 (-0.596516) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009091 / 0.011353 (-0.002262) | 0.004978 / 0.011008 (-0.006030) | 0.077633 / 0.038508 (0.039125) | 0.103189 / 0.023109 (0.080080) | 0.500194 / 0.275898 (0.224296) | 0.524310 / 0.323480 (0.200831) | 0.006656 / 0.007986 (-0.001329) | 0.004586 / 0.004328 (0.000257) | 0.075535 / 0.004250 (0.071284) | 0.065100 / 0.037052 (0.028048) | 0.513776 / 0.258489 (0.255287) | 0.528483 / 0.293841 (0.234642) | 0.049877 / 0.128546 (-0.078669) | 0.012494 / 0.075646 (-0.063152) | 0.090225 / 0.419271 (-0.329046) | 0.054648 / 0.043533 (0.011116) | 0.510369 / 0.255139 (0.255230) | 0.540042 / 0.283200 (0.256842) | 0.035966 / 0.141683 (-0.105717) | 1.825965 / 1.452155 (0.373810) | 1.965647 / 1.492716 (0.472931) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.295921 / 0.018006 (0.277914) | 0.605751 / 0.000490 (0.605262) | 0.007243 / 0.000200 (0.007043) | 0.000134 / 0.000054 (0.000079) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032954 / 0.037411 (-0.004457) | 0.093613 / 0.014526 (0.079087) | 0.120010 / 0.176557 (-0.056546) | 0.176168 / 0.737135 (-0.560967) | 0.113978 / 0.296338 (-0.182360) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.682904 / 0.215209 (0.467695) | 6.674640 / 2.077655 (4.596986) | 3.360660 / 1.504120 (1.856540) | 3.227246 / 1.541195 (1.686051) | 3.188852 / 1.468490 (1.720362) | 0.862293 / 4.584777 (-3.722484) | 5.518455 / 3.745712 (1.772743) | 4.881904 / 5.269862 (-0.387957) | 3.066964 / 4.565676 (-1.498712) | 0.099284 / 0.424275 (-0.324991) | 0.008644 / 0.007607 (0.001037) | 0.789231 / 0.226044 (0.563186) | 7.872017 / 2.268929 (5.603089) | 4.037105 / 55.444624 (-51.407519) | 3.318921 / 6.876477 (-3.557555) | 3.621953 / 2.142072 (1.479881) | 1.012049 / 4.805227 (-3.793178) | 0.204541 / 6.500664 (-6.296123) | 0.074509 / 0.075469 (-0.000960) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.748215 / 1.841788 (-0.093573) | 24.274974 / 8.074308 (16.200665) | 20.582389 / 10.191392 (10.390997) | 0.251001 / 0.680424 (-0.429423) | 0.032390 / 0.534201 (-0.501811) | 0.479211 / 0.579283 (-0.100072) | 0.607482 / 0.434364 (0.173118) | 0.587867 / 0.540337 (0.047530) | 0.822399 / 1.386936 (-0.564537) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f2b6b2fd90ba47f19e9ab125f6f7656903dd065f \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009715 / 0.011353 (-0.001638) | 0.005449 / 0.011008 (-0.005559) | 0.108556 / 0.038508 (0.070048) | 0.080512 / 0.023109 (0.057403) | 0.450736 / 0.275898 (0.174838) | 0.487771 / 0.323480 (0.164291) | 0.005155 / 0.007986 (-0.002830) | 0.004213 / 0.004328 (-0.000115) | 0.087247 / 0.004250 (0.082997) | 0.063962 / 0.037052 (0.026909) | 0.454153 / 0.258489 (0.195664) | 0.499917 / 0.293841 (0.206076) | 0.052605 / 0.128546 (-0.075942) | 0.013019 / 0.075646 (-0.062627) | 0.379716 / 0.419271 (-0.039555) | 0.073241 / 0.043533 (0.029708) | 0.473488 / 0.255139 (0.218349) | 0.482944 / 0.283200 (0.199745) | 0.041541 / 0.141683 (-0.100142) | 1.829415 / 1.452155 (0.377261) | 1.953280 / 1.492716 (0.460564) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.313725 / 0.018006 (0.295719) | 0.591336 / 0.000490 (0.590847) | 0.021224 / 0.000200 (0.021025) | 0.000969 / 0.000054 (0.000914) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031874 / 0.037411 (-0.005537) | 0.099786 / 0.014526 (0.085260) | 0.116987 / 0.176557 (-0.059569) | 0.205538 / 0.737135 (-0.531597) | 0.118716 / 0.296338 (-0.177622) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.617145 / 0.215209 (0.401936) | 6.079144 / 2.077655 (4.001489) | 2.567233 / 1.504120 (1.063113) | 2.265301 / 1.541195 (0.724107) | 2.314001 / 1.468490 (0.845511) | 0.871561 / 4.584777 (-3.713216) | 5.477049 / 3.745712 (1.731337) | 4.720552 / 5.269862 (-0.549309) | 3.107515 / 4.565676 (-1.458162) | 0.100438 / 0.424275 (-0.323838) | 0.008586 / 0.007607 (0.000979) | 0.716913 / 0.226044 (0.490869) | 7.108417 / 2.268929 (4.839489) | 3.391336 / 55.444624 (-52.053288) | 2.734052 / 6.876477 (-4.142425) | 2.857226 / 2.142072 (0.715153) | 1.024121 / 4.805227 (-3.781106) | 0.216735 / 6.500664 (-6.283929) | 0.081605 / 0.075469 (0.006136) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.678176 / 1.841788 (-0.163611) | 23.606037 / 8.074308 (15.531729) | 21.485331 / 10.191392 (11.293939) | 0.218312 / 0.680424 (-0.462112) | 0.027061 / 0.534201 (-0.507140) | 0.481188 / 0.579283 (-0.098096) | 0.620592 / 0.434364 (0.186228) | 0.574778 / 0.540337 (0.034441) | 0.831529 / 1.386936 (-0.555407) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.011666 / 0.011353 (0.000313) | 0.005187 / 0.011008 (-0.005821) | 0.080692 / 0.038508 (0.042184) | 0.079159 / 0.023109 (0.056049) | 0.530823 / 0.275898 (0.254925) | 0.577807 / 0.323480 (0.254327) | 0.006246 / 0.007986 (-0.001740) | 0.004355 / 0.004328 (0.000026) | 0.080702 / 0.004250 (0.076452) | 0.062279 / 0.037052 (0.025226) | 0.553712 / 0.258489 (0.295223) | 0.579112 / 0.293841 (0.285271) | 0.056374 / 0.128546 (-0.072172) | 0.014681 / 0.075646 (-0.060966) | 0.097110 / 0.419271 (-0.322161) | 0.061040 / 0.043533 (0.017507) | 0.524718 / 0.255139 (0.269579) | 0.568586 / 0.283200 (0.285386) | 0.035774 / 0.141683 (-0.105909) | 1.864590 / 1.452155 (0.412435) | 1.953715 / 1.492716 (0.460998) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.271315 / 0.018006 (0.253309) | 0.571343 / 0.000490 (0.570854) | 0.015812 / 0.000200 (0.015612) | 0.000115 / 0.000054 (0.000060) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.038582 / 0.037411 (0.001170) | 0.117523 / 0.014526 (0.102997) | 0.128864 / 0.176557 (-0.047693) | 0.191164 / 0.737135 (-0.545971) | 0.133161 / 0.296338 (-0.163178) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.679305 / 0.215209 (0.464096) | 6.814451 / 2.077655 (4.736796) | 3.377431 / 1.504120 (1.873311) | 3.011008 / 1.541195 (1.469813) | 3.093200 / 1.468490 (1.624710) | 0.905827 / 4.584777 (-3.678950) | 5.456094 / 3.745712 (1.710382) | 4.848511 / 5.269862 (-0.421351) | 3.064230 / 4.565676 (-1.501447) | 0.107478 / 0.424275 (-0.316798) | 0.009234 / 0.007607 (0.001627) | 0.833944 / 0.226044 (0.607899) | 8.286100 / 2.268929 (6.017171) | 4.241455 / 55.444624 (-51.203169) | 3.405460 / 6.876477 (-3.471017) | 3.660618 / 2.142072 (1.518546) | 1.046310 / 4.805227 (-3.758917) | 0.210891 / 6.500664 (-6.289773) | 0.079413 / 0.075469 (0.003944) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.825448 / 1.841788 (-0.016340) | 24.639059 / 8.074308 (16.564750) | 21.970417 / 10.191392 (11.779025) | 0.247708 / 0.680424 (-0.432715) | 0.033810 / 0.534201 (-0.500391) | 0.495517 / 0.579283 (-0.083766) | 0.601820 / 0.434364 (0.167456) | 0.585618 / 0.540337 (0.045280) | 0.858722 / 1.386936 (-0.528214) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0477e20dccb77b68f0add77fd5c9b4cb05473235 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006137 / 0.011353 (-0.005216) | 0.003685 / 0.011008 (-0.007324) | 0.079985 / 0.038508 (0.041476) | 0.060937 / 0.023109 (0.037828) | 0.390583 / 0.275898 (0.114685) | 0.425307 / 0.323480 (0.101827) | 0.003433 / 0.007986 (-0.004552) | 0.002868 / 0.004328 (-0.001461) | 0.062572 / 0.004250 (0.058322) | 0.048642 / 0.037052 (0.011590) | 0.401096 / 0.258489 (0.142607) | 0.436988 / 0.293841 (0.143147) | 0.027645 / 0.128546 (-0.100901) | 0.007973 / 0.075646 (-0.067673) | 0.261997 / 0.419271 (-0.157275) | 0.045393 / 0.043533 (0.001860) | 0.394266 / 0.255139 (0.139127) | 0.414448 / 0.283200 (0.131248) | 0.022551 / 0.141683 (-0.119131) | 1.438458 / 1.452155 (-0.013697) | 1.501568 / 1.492716 (0.008852) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.224335 / 0.018006 (0.206329) | 0.421918 / 0.000490 (0.421428) | 0.006883 / 0.000200 (0.006683) | 0.000210 / 0.000054 (0.000155) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023505 / 0.037411 (-0.013906) | 0.072438 / 0.014526 (0.057912) | 0.083576 / 0.176557 (-0.092981) | 0.142906 / 0.737135 (-0.594229) | 0.083910 / 0.296338 (-0.212428) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.396004 / 0.215209 (0.180795) | 3.969852 / 2.077655 (1.892197) | 1.966000 / 1.504120 (0.461880) | 1.786453 / 1.541195 (0.245258) | 1.866082 / 1.468490 (0.397592) | 0.502633 / 4.584777 (-4.082144) | 3.114331 / 3.745712 (-0.631382) | 2.940003 / 5.269862 (-2.329859) | 1.901844 / 4.565676 (-2.663832) | 0.058109 / 0.424275 (-0.366166) | 0.006502 / 0.007607 (-0.001105) | 0.463465 / 0.226044 (0.237420) | 4.641531 / 2.268929 (2.372603) | 2.315759 / 55.444624 (-53.128865) | 2.253088 / 6.876477 (-4.623389) | 2.151399 / 2.142072 (0.009326) | 0.592225 / 4.805227 (-4.213002) | 0.125072 / 6.500664 (-6.375592) | 0.059966 / 0.075469 (-0.015503) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.231392 / 1.841788 (-0.610396) | 17.533893 / 8.074308 (9.459585) | 13.710478 / 10.191392 (3.519086) | 0.147389 / 0.680424 (-0.533035) | 0.017932 / 0.534201 (-0.516269) | 0.334144 / 0.579283 (-0.245139) | 0.368817 / 0.434364 (-0.065547) | 0.383790 / 0.540337 (-0.156547) | 0.540262 / 1.386936 (-0.846674) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006066 / 0.011353 (-0.005287) | 0.003804 / 0.011008 (-0.007205) | 0.062474 / 0.038508 (0.023966) | 0.060547 / 0.023109 (0.037437) | 0.448643 / 0.275898 (0.172745) | 0.487005 / 0.323480 (0.163525) | 0.004884 / 0.007986 (-0.003102) | 0.002911 / 0.004328 (-0.001418) | 0.062950 / 0.004250 (0.058700) | 0.049672 / 0.037052 (0.012620) | 0.477491 / 0.258489 (0.219002) | 0.488234 / 0.293841 (0.194393) | 0.028711 / 0.128546 (-0.099835) | 0.008101 / 0.075646 (-0.067545) | 0.068333 / 0.419271 (-0.350939) | 0.040959 / 0.043533 (-0.002574) | 0.450716 / 0.255139 (0.195577) | 0.471089 / 0.283200 (0.187890) | 0.020710 / 0.141683 (-0.120973) | 1.474850 / 1.452155 (0.022695) | 1.540115 / 1.492716 (0.047399) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.229811 / 0.018006 (0.211805) | 0.419526 / 0.000490 (0.419036) | 0.003818 / 0.000200 (0.003618) | 0.000084 / 0.000054 (0.000030) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026045 / 0.037411 (-0.011366) | 0.080325 / 0.014526 (0.065799) | 0.091549 / 0.176557 (-0.085007) | 0.145253 / 0.737135 (-0.591882) | 0.091849 / 0.296338 (-0.204489) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.463047 / 0.215209 (0.247838) | 4.598727 / 2.077655 (2.521072) | 2.558996 / 1.504120 (1.054877) | 2.405896 / 1.541195 (0.864701) | 2.447291 / 1.468490 (0.978801) | 0.510393 / 4.584777 (-4.074384) | 3.173344 / 3.745712 (-0.572368) | 2.901201 / 5.269862 (-2.368661) | 1.896440 / 4.565676 (-2.669236) | 0.058374 / 0.424275 (-0.365901) | 0.006449 / 0.007607 (-0.001158) | 0.539653 / 0.226044 (0.313608) | 5.408217 / 2.268929 (3.139289) | 3.042453 / 55.444624 (-52.402172) | 2.656724 / 6.876477 (-4.219753) | 2.838165 / 2.142072 (0.696092) | 0.598663 / 4.805227 (-4.206565) | 0.126211 / 6.500664 (-6.374453) | 0.062830 / 0.075469 (-0.012639) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.392412 / 1.841788 (-0.449376) | 18.195170 / 8.074308 (10.120862) | 14.788251 / 10.191392 (4.596859) | 0.132579 / 0.680424 (-0.547845) | 0.017867 / 0.534201 (-0.516334) | 0.340020 / 0.579283 (-0.239263) | 0.386719 / 0.434364 (-0.047645) | 0.398863 / 0.540337 (-0.141475) | 0.579320 / 1.386936 (-0.807617) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a2569fdfcf387f8885974a35fafa409fbc6dd059 \"CML watermark\")\n", "closing in favor of https://github.com/huggingface/datasets/pull/6282" ]

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

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

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

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[ "Please tell me if the above info is not enough for solving the problem. I will then make my dataset public temporarily so that you can really reproduce the bug. ", "Hi! \r\nCould you share how to solve this problem? \r\nI faced this same error. " ]

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[ "This has already been reported in the HF Course repo (https://github.com/huggingface/course/issues/623).", "@lhoestq @albertvillanova @lewtun I don't think we are allowed to host these data files on the Hub (due to DMCA), which means the only option is to use a different dataset in the course (and to re-record the video 🙂), no?", "Keeping the video is maybe fine, we can add a note on youtube to suggest to load a dataset with a different name. Maybe C4 ? And update the code snippets on the website ?", "Maybe you want to try it with the PUBMED dataset that I reproduced based on the The [PubMed Abstract GitHub Site](http://github.com/thoppe/The-Pile-PubMed) and uploaded on the HuggingFace:\r\n\r\n```\r\nfrom datasets import load_dataset\r\npubmed_dataset = load_dataset(\"hwang2006/PUBMED_title_abstracts_2020_baseline\")\r\npubmed_dataset\r\n\r\n#Downloading data: 100%\r\n#7.98G/7.98G [11:47<00:00, 9.68MB/s]\r\n#Generating train split: 17722096/0 [00:36<00:00, 505376.37 examples/s]\r\n\r\n#DatasetDict({\r\n# train: Dataset({\r\n# features: ['meta', 'text'],\r\n# num_rows: 17722096\r\n# })\r\n#})\r\n```", "孔令涛说感谢感谢" ]

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

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[ "Also reported in https://github.com/huggingface/datasets/issues/6259", "I think it's best to drop duplicates with a `set` (as a temporary fix) and improve the patterns when/if https://github.com/fsspec/filesystem_spec/pull/1382 gets merged. @lhoestq Do you have some other ideas?", "Alternatively we could just use this no ?\r\n\r\n```python\r\nif config.FSSPEC_VERSION < version.parse(\"2023.9.0\"):\r\n KEYWORDS_IN_PATH_NAME_BASE_PATTERNS = [\r\n \"{keyword}[{sep}/]**\",\r\n \"**[{sep}]{keyword}[{sep}/]**\",\r\n \"**/{keyword}[{sep}/]**\",\r\n ]\r\nelse:\r\n KEYWORDS_IN_PATH_NAME_BASE_PATTERNS = [\r\n \"{keyword}[{sep}/]**\",\r\n \"**/*[{sep}]{keyword}[{sep}/]**\",\r\n \"**/*/{keyword}[{sep}/]**\",\r\n ]\r\n```\r\n\r\nThis way no need to implement sets, which would require a bit of work since we've always considered a list of pattern to be resolved as the concatenated list of resolved files for each pattern (including duplicates)\r\n", "Arf `\"**/*/{keyword}[{sep}/]**\"` does return `data/keyword.txt` in latest `fsspec` but not in `glob.glob`\r\n\r\nEDIT: actually forgot to set `recursive=True`", "Actually `glob.glob` does return it with `recursive=True` ! my bad", "Pff just tested and my idea sucks, pattern 1 and 3 obviously give duplicates ", "> I think it's best to drop duplicates with a set (as a temporary fix)\r\n\r\nI started https://github.com/huggingface/datasets/pull/6278 to use DataFilesSet objects instead of DataFilesList" ]

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[ "`gen_kwargs` should be a `dict`, as stated in the docstring, but you are passing a `list`.\r\n\r\nSo, to fix the error, replace the list of dicts with a dict of lists (and slightly modify the generator function):\r\n```python\r\nfrom pathlib import Path\r\nimport datasets\r\n\r\ndef process_yaml(files):\r\n for f in files:\r\n # process\r\n yield dict(...)\r\n\r\n\r\nif __name__ == '__main__':\r\n import sys\r\n dir = Path(sys.argv[0]).parent\r\n ds = datasets.Dataset.from_generator(process_yaml, gen_kwargs={'files': [f for f in dir.glob('*.yml')]})\r\n ds.to_json('training.jsonl')\r\n```", "That runs, and because my dataset is small, it's what I did to get past the problem.\r\nHowever, it does not produce a sharded dataset. From the doc string I expect there ought to be a way to call from_generator such that num_shards in the resulting data set is equal to the number of items in the list.\r\nThe part of the doc string that your suggestion is not responsive to is:\r\n` You can define a sharded dataset by passing the list of shards in *g\r\nen_kwargs*.\r\n`\r\n\r\nWhat your suggestion does is calls the generator once, with the list argument, and produces a single shard dataset.\r\n", "The sharding mentioned here refers to using this function with `num_proc` (multiprocessing splits the `kwargs` into shards and passes them to the generator function)\r\n\r\n> That runs, and because my dataset is small, it's what I did to get past the problem.\r\n\r\n`from_generator` generates a memory-mapped dataset (can be larger than RAM), so the dataset size should not be an issue unless the generator function's implementation does not properly free the memory.\r\n", "It sounds like you are saying that num_proc affects the form of gen_kwargs.\r\nAre you saying that for non-zero num_proc gen_kwargs should be a list whose length is the same as num_proc?\r\nOr are you saying that for non-zero num_proc, gen_kwargs should be a dict whose elements are lists the length of num_proc?\r\n", "I ran some tests. So, it looks like with num_proc greater than 1, gen_kwargs is expected to be a dict of lists. It calls the generator also with a dict of lists, but the lists are split.\r\nI.E. if my original has `gen_kwargs=dict(a=[0,1,2])`, then my generator might get called with `gen_kwalrgs=dict([0])`.\r\nThat all makes sense, but I definitely think there is room for improvement in the doc string here.\r\nIn order to suggest improvements to the doc string, I need to look at how the gen_kwargs are split, and figure out if:\r\n* num_proc needs to exactly equal the length of the lists\r\n* num_proc needs to evenly divide the length of the lists\r\n* Or there's no required relationship.\r\nI'll look into that and then propose an improved doc string if no one else gets to it first.", "Okay, that was fun; I took a dive through the dataset code and feel like I have a much better understanding.\r\nHere is my understanding of the behavior:\r\n* max_proc is an upper limit on the number of shards that `from_generator` produces\r\n* If `max_proc` is greater than 1, then all lists in *gen_kwargs* must be the same length\r\n* If the lists in *gen_kwargs* are shorter than *num_proc* elements, *num_proc* will be reduced and a warning produced. Put another way, `min(list_length, num_shards)` shards will be produced\r\n* The members of the lists in *gen_kwargs* will be partitioned among the created jobs.\r\nTo validate the above, take a look at\r\n`_number_of_shards_in_gen_kwargs` and `_distribute_shards` and `_split_gen_kwargs` in utils/sharding.py.\r\nI've also chased down starting at *from_generator* all the way through to GeneratorBuilder and the calls to the functions in sharding.py.\r\nTomorrow I'll take a look at the contributing guidelines and see what's involved in putting together a PR to improve the doc string." ]

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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) | 0.005864 / 0.011353 (-0.005489) | 0.003535 / 0.011008 (-0.007474) | 0.080732 / 0.038508 (0.042224) | 0.057072 / 0.023109 (0.033963) | 0.334342 / 0.275898 (0.058444) | 0.361345 / 0.323480 (0.037865) | 0.003290 / 0.007986 (-0.004696) | 0.003794 / 0.004328 (-0.000534) | 0.063414 / 0.004250 (0.059163) | 0.046901 / 0.037052 (0.009848) | 0.335973 / 0.258489 (0.077484) | 0.377929 / 0.293841 (0.084088) | 0.027199 / 0.128546 (-0.101348) | 0.008049 / 0.075646 (-0.067597) | 0.261810 / 0.419271 (-0.157462) | 0.044669 / 0.043533 (0.001136) | 0.333600 / 0.255139 (0.078461) | 0.356362 / 0.283200 (0.073162) | 0.020325 / 0.141683 (-0.121358) | 1.458138 / 1.452155 (0.005984) | 1.505923 / 1.492716 (0.013207) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.216456 / 0.018006 (0.198450) | 0.421750 / 0.000490 (0.421261) | 0.007359 / 0.000200 (0.007159) | 0.000246 / 0.000054 (0.000191) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023400 / 0.037411 (-0.014012) | 0.073363 / 0.014526 (0.058838) | 0.083533 / 0.176557 (-0.093023) | 0.144045 / 0.737135 (-0.593090) | 0.084050 / 0.296338 (-0.212288) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.398354 / 0.215209 (0.183145) | 3.982875 / 2.077655 (1.905220) | 2.047299 / 1.504120 (0.543180) | 1.873780 / 1.541195 (0.332585) | 1.977044 / 1.468490 (0.508554) | 0.497038 / 4.584777 (-4.087739) | 3.039743 / 3.745712 (-0.705969) | 2.832885 / 5.269862 (-2.436977) | 1.827300 / 4.565676 (-2.738377) | 0.057503 / 0.424275 (-0.366772) | 0.006272 / 0.007607 (-0.001335) | 0.468681 / 0.226044 (0.242637) | 4.696551 / 2.268929 (2.427622) | 2.413805 / 55.444624 (-53.030819) | 2.157199 / 6.876477 (-4.719278) | 2.345986 / 2.142072 (0.203914) | 0.584632 / 4.805227 (-4.220595) | 0.124684 / 6.500664 (-6.375980) | 0.060090 / 0.075469 (-0.015379) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.293551 / 1.841788 (-0.548236) | 17.198292 / 8.074308 (9.123984) | 13.677910 / 10.191392 (3.486518) | 0.146633 / 0.680424 (-0.533791) | 0.016711 / 0.534201 (-0.517490) | 0.331644 / 0.579283 (-0.247639) | 0.360148 / 0.434364 (-0.074215) | 0.381194 / 0.540337 (-0.159143) | 0.537952 / 1.386936 (-0.848984) |\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) | 0.006020 / 0.011353 (-0.005333) | 0.003557 / 0.011008 (-0.007451) | 0.061926 / 0.038508 (0.023418) | 0.056246 / 0.023109 (0.033137) | 0.446679 / 0.275898 (0.170781) | 0.479843 / 0.323480 (0.156363) | 0.004656 / 0.007986 (-0.003330) | 0.002823 / 0.004328 (-0.001505) | 0.061366 / 0.004250 (0.057115) | 0.045793 / 0.037052 (0.008740) | 0.460807 / 0.258489 (0.202318) | 0.485467 / 0.293841 (0.191626) | 0.028555 / 0.128546 (-0.099991) | 0.007973 / 0.075646 (-0.067674) | 0.068305 / 0.419271 (-0.350966) | 0.040844 / 0.043533 (-0.002689) | 0.463715 / 0.255139 (0.208576) | 0.474553 / 0.283200 (0.191354) | 0.019959 / 0.141683 (-0.121723) | 1.432527 / 1.452155 (-0.019628) | 1.485410 / 1.492716 (-0.007307) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.205555 / 0.018006 (0.187549) | 0.408271 / 0.000490 (0.407781) | 0.004325 / 0.000200 (0.004125) | 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) | 0.026338 / 0.037411 (-0.011074) | 0.080534 / 0.014526 (0.066008) | 0.093935 / 0.176557 (-0.082622) | 0.146446 / 0.737135 (-0.590689) | 0.092890 / 0.296338 (-0.203448) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.463879 / 0.215209 (0.248670) | 4.646411 / 2.077655 (2.568756) | 2.567320 / 1.504120 (1.063200) | 2.384376 / 1.541195 (0.843181) | 2.412738 / 1.468490 (0.944248) | 0.510240 / 4.584777 (-4.074537) | 3.094988 / 3.745712 (-0.650724) | 2.837700 / 5.269862 (-2.432161) | 1.850163 / 4.565676 (-2.715513) | 0.059320 / 0.424275 (-0.364955) | 0.006330 / 0.007607 (-0.001277) | 0.537770 / 0.226044 (0.311726) | 5.385556 / 2.268929 (3.116627) | 3.036088 / 55.444624 (-52.408536) | 2.650464 / 6.876477 (-4.226013) | 2.755676 / 2.142072 (0.613603) | 0.607353 / 4.805227 (-4.197875) | 0.124589 / 6.500664 (-6.376075) | 0.060778 / 0.075469 (-0.014691) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.343243 / 1.841788 (-0.498545) | 17.630281 / 8.074308 (9.555973) | 14.401219 / 10.191392 (4.209827) | 0.143252 / 0.680424 (-0.537172) | 0.017880 / 0.534201 (-0.516321) | 0.337391 / 0.579283 (-0.241892) | 0.373531 / 0.434364 (-0.060833) | 0.398408 / 0.540337 (-0.141929) | 0.558925 / 1.386936 (-0.828011) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a8f511638b486b9f83b17fd69a505fe606ad257b \"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) | 0.006552 / 0.011353 (-0.004801) | 0.003853 / 0.011008 (-0.007155) | 0.077673 / 0.038508 (0.039165) | 0.066043 / 0.023109 (0.042934) | 0.289858 / 0.275898 (0.013960) | 0.299009 / 0.323480 (-0.024471) | 0.004806 / 0.007986 (-0.003179) | 0.003517 / 0.004328 (-0.000811) | 0.058227 / 0.004250 (0.053977) | 0.052134 / 0.037052 (0.015082) | 0.328800 / 0.258489 (0.070311) | 0.317616 / 0.293841 (0.023776) | 0.028344 / 0.128546 (-0.100202) | 0.007853 / 0.075646 (-0.067794) | 0.291207 / 0.419271 (-0.128065) | 0.052977 / 0.043533 (0.009444) | 0.287548 / 0.255139 (0.032409) | 0.307647 / 0.283200 (0.024448) | 0.023899 / 0.141683 (-0.117784) | 1.382267 / 1.452155 (-0.069888) | 1.589915 / 1.492716 (0.097199) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.246244 / 0.018006 (0.228238) | 0.478255 / 0.000490 (0.477766) | 0.014115 / 0.000200 (0.013915) | 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) | 0.027033 / 0.037411 (-0.010378) | 0.073988 / 0.014526 (0.059462) | 0.088337 / 0.176557 (-0.088219) | 0.144067 / 0.737135 (-0.593069) | 0.091295 / 0.296338 (-0.205043) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.365904 / 0.215209 (0.150695) | 3.537330 / 2.077655 (1.459675) | 1.678341 / 1.504120 (0.174221) | 1.530297 / 1.541195 (-0.010898) | 1.605634 / 1.468490 (0.137144) | 0.437461 / 4.584777 (-4.147316) | 3.419040 / 3.745712 (-0.326672) | 3.203549 / 5.269862 (-2.066312) | 1.913214 / 4.565676 (-2.652463) | 0.052675 / 0.424275 (-0.371600) | 0.006681 / 0.007607 (-0.000926) | 0.429269 / 0.226044 (0.203225) | 4.214051 / 2.268929 (1.945122) | 2.217928 / 55.444624 (-53.226696) | 1.842679 / 6.876477 (-5.033798) | 1.867961 / 2.142072 (-0.274111) | 0.550566 / 4.805227 (-4.254661) | 0.118015 / 6.500664 (-6.382649) | 0.054749 / 0.075469 (-0.020720) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.170547 / 1.841788 (-0.671241) | 18.410567 / 8.074308 (10.336259) | 12.729992 / 10.191392 (2.538600) | 0.160426 / 0.680424 (-0.519998) | 0.021259 / 0.534201 (-0.512942) | 0.369573 / 0.579283 (-0.209710) | 0.440350 / 0.434364 (0.005986) | 0.443755 / 0.540337 (-0.096582) | 0.645614 / 1.386936 (-0.741322) |\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) | 0.005913 / 0.011353 (-0.005440) | 0.003542 / 0.011008 (-0.007466) | 0.057621 / 0.038508 (0.019113) | 0.065822 / 0.023109 (0.042713) | 0.390847 / 0.275898 (0.114949) | 0.393127 / 0.323480 (0.069647) | 0.005040 / 0.007986 (-0.002945) | 0.002944 / 0.004328 (-0.001384) | 0.069058 / 0.004250 (0.064808) | 0.051594 / 0.037052 (0.014542) | 0.383745 / 0.258489 (0.125256) | 0.414372 / 0.293841 (0.120531) | 0.030038 / 0.128546 (-0.098508) | 0.008109 / 0.075646 (-0.067538) | 0.065444 / 0.419271 (-0.353828) | 0.045974 / 0.043533 (0.002441) | 0.401695 / 0.255139 (0.146556) | 0.417834 / 0.283200 (0.134635) | 0.020137 / 0.141683 (-0.121546) | 1.452130 / 1.452155 (-0.000025) | 1.455259 / 1.492716 (-0.037458) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.228262 / 0.018006 (0.210255) | 0.455155 / 0.000490 (0.454665) | 0.006667 / 0.000200 (0.006467) | 0.000207 / 0.000054 (0.000153) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030159 / 0.037411 (-0.007252) | 0.098478 / 0.014526 (0.083952) | 0.101409 / 0.176557 (-0.075147) | 0.148689 / 0.737135 (-0.588446) | 0.103067 / 0.296338 (-0.193272) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.444095 / 0.215209 (0.228886) | 3.991588 / 2.077655 (1.913934) | 2.147845 / 1.504120 (0.643725) | 2.007871 / 1.541195 (0.466676) | 2.042074 / 1.468490 (0.573584) | 0.451592 / 4.584777 (-4.133185) | 3.439400 / 3.745712 (-0.306312) | 3.107756 / 5.269862 (-2.162106) | 1.909785 / 4.565676 (-2.655891) | 0.051718 / 0.424275 (-0.372558) | 0.006597 / 0.007607 (-0.001010) | 0.480822 / 0.226044 (0.254777) | 4.913235 / 2.268929 (2.644307) | 2.631882 / 55.444624 (-52.812742) | 2.397209 / 6.876477 (-4.479267) | 2.487191 / 2.142072 (0.345119) | 0.566321 / 4.805227 (-4.238906) | 0.121741 / 6.500664 (-6.378924) | 0.053399 / 0.075469 (-0.022070) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.256599 / 1.841788 (-0.585189) | 18.891127 / 8.074308 (10.816819) | 13.219662 / 10.191392 (3.028270) | 0.154570 / 0.680424 (-0.525854) | 0.022599 / 0.534201 (-0.511602) | 0.361998 / 0.579283 (-0.217286) | 0.413287 / 0.434364 (-0.021077) | 0.464867 / 0.540337 (-0.075470) | 0.638880 / 1.386936 (-0.748056) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#869e6bc775cf4dff1b92834426e1a286b104432b \"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) | 0.010625 / 0.011353 (-0.000728) | 0.005129 / 0.011008 (-0.005879) | 0.119975 / 0.038508 (0.081467) | 0.100128 / 0.023109 (0.077019) | 0.448678 / 0.275898 (0.172780) | 0.533150 / 0.323480 (0.209670) | 0.005881 / 0.007986 (-0.002105) | 0.007451 / 0.004328 (0.003123) | 0.090792 / 0.004250 (0.086542) | 0.073416 / 0.037052 (0.036363) | 0.455395 / 0.258489 (0.196906) | 0.497572 / 0.293841 (0.203731) | 0.053112 / 0.128546 (-0.075434) | 0.014619 / 0.075646 (-0.061027) | 0.388023 / 0.419271 (-0.031248) | 0.074004 / 0.043533 (0.030471) | 0.435319 / 0.255139 (0.180180) | 0.465985 / 0.283200 (0.182785) | 0.046991 / 0.141683 (-0.094692) | 1.895717 / 1.452155 (0.443563) | 2.086600 / 1.492716 (0.593884) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.334412 / 0.018006 (0.316406) | 0.645510 / 0.000490 (0.645020) | 0.019175 / 0.000200 (0.018975) | 0.000429 / 0.000054 (0.000374) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034385 / 0.037411 (-0.003026) | 0.108939 / 0.014526 (0.094413) | 0.125937 / 0.176557 (-0.050619) | 0.205643 / 0.737135 (-0.531493) | 0.127662 / 0.296338 (-0.168676) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.674093 / 0.215209 (0.458884) | 6.646554 / 2.077655 (4.568900) | 2.837698 / 1.504120 (1.333578) | 2.397199 / 1.541195 (0.856004) | 2.485856 / 1.468490 (1.017366) | 0.955142 / 4.584777 (-3.629635) | 5.667462 / 3.745712 (1.921750) | 5.354129 / 5.269862 (0.084268) | 3.301609 / 4.565676 (-1.264068) | 0.106051 / 0.424275 (-0.318224) | 0.009287 / 0.007607 (0.001680) | 0.766678 / 0.226044 (0.540634) | 7.786701 / 2.268929 (5.517772) | 3.665463 / 55.444624 (-51.779161) | 2.982912 / 6.876477 (-3.893564) | 3.053363 / 2.142072 (0.911290) | 1.141090 / 4.805227 (-3.664137) | 0.223975 / 6.500664 (-6.276689) | 0.093024 / 0.075469 (0.017555) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.728175 / 1.841788 (-0.113613) | 25.640134 / 8.074308 (17.565826) | 22.124769 / 10.191392 (11.933377) | 0.237489 / 0.680424 (-0.442935) | 0.030353 / 0.534201 (-0.503848) | 0.509371 / 0.579283 (-0.069913) | 0.642320 / 0.434364 (0.207956) | 0.576889 / 0.540337 (0.036552) | 0.899377 / 1.386936 (-0.487559) |\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) | 0.010846 / 0.011353 (-0.000507) | 0.005876 / 0.011008 (-0.005132) | 0.090810 / 0.038508 (0.052302) | 0.106651 / 0.023109 (0.083542) | 0.551064 / 0.275898 (0.275166) | 0.608328 / 0.323480 (0.284848) | 0.007563 / 0.007986 (-0.000423) | 0.004595 / 0.004328 (0.000267) | 0.089125 / 0.004250 (0.084874) | 0.076577 / 0.037052 (0.039525) | 0.579970 / 0.258489 (0.321481) | 0.620214 / 0.293841 (0.326373) | 0.052577 / 0.128546 (-0.075970) | 0.013734 / 0.075646 (-0.061912) | 0.099825 / 0.419271 (-0.319447) | 0.068391 / 0.043533 (0.024858) | 0.564733 / 0.255139 (0.309594) | 0.593925 / 0.283200 (0.310726) | 0.037201 / 0.141683 (-0.104482) | 1.880969 / 1.452155 (0.428815) | 2.065094 / 1.492716 (0.572377) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.426148 / 0.018006 (0.408141) | 0.673935 / 0.000490 (0.673445) | 0.124190 / 0.000200 (0.123990) | 0.001219 / 0.000054 (0.001164) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.040280 / 0.037411 (0.002868) | 0.122042 / 0.014526 (0.107516) | 0.131333 / 0.176557 (-0.045223) | 0.203039 / 0.737135 (-0.534096) | 0.134851 / 0.296338 (-0.161487) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.684599 / 0.215209 (0.469390) | 6.727529 / 2.077655 (4.649874) | 3.255228 / 1.504120 (1.751108) | 2.925865 / 1.541195 (1.384670) | 2.978762 / 1.468490 (1.510272) | 0.931769 / 4.584777 (-3.653008) | 5.988956 / 3.745712 (2.243244) | 5.228049 / 5.269862 (-0.041812) | 3.341470 / 4.565676 (-1.224206) | 0.106737 / 0.424275 (-0.317539) | 0.009847 / 0.007607 (0.002240) | 0.813954 / 0.226044 (0.587909) | 8.137071 / 2.268929 (5.868143) | 4.140725 / 55.444624 (-51.303899) | 3.500579 / 6.876477 (-3.375898) | 3.623120 / 2.142072 (1.481047) | 1.096634 / 4.805227 (-3.708593) | 0.236938 / 6.500664 (-6.263726) | 0.083099 / 0.075469 (0.007630) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.856112 / 1.841788 (0.014324) | 26.531325 / 8.074308 (18.457017) | 24.435866 / 10.191392 (14.244474) | 0.264093 / 0.680424 (-0.416331) | 0.034872 / 0.534201 (-0.499329) | 0.520682 / 0.579283 (-0.058601) | 0.635010 / 0.434364 (0.200646) | 0.645451 / 0.540337 (0.105113) | 0.914616 / 1.386936 (-0.472320) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d8c29b9416371283e8aaabee235a91b2f45a05ee \"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) | 0.005928 / 0.011353 (-0.005425) | 0.003633 / 0.011008 (-0.007375) | 0.079554 / 0.038508 (0.041046) | 0.057093 / 0.023109 (0.033984) | 0.311374 / 0.275898 (0.035476) | 0.343778 / 0.323480 (0.020298) | 0.004634 / 0.007986 (-0.003352) | 0.002886 / 0.004328 (-0.001443) | 0.061888 / 0.004250 (0.057637) | 0.045895 / 0.037052 (0.008843) | 0.316447 / 0.258489 (0.057958) | 0.358141 / 0.293841 (0.064300) | 0.027247 / 0.128546 (-0.101300) | 0.007947 / 0.075646 (-0.067699) | 0.259070 / 0.419271 (-0.160201) | 0.043802 / 0.043533 (0.000269) | 0.315453 / 0.255139 (0.060314) | 0.335282 / 0.283200 (0.052082) | 0.021096 / 0.141683 (-0.120587) | 1.443219 / 1.452155 (-0.008936) | 1.523140 / 1.492716 (0.030423) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.222957 / 0.018006 (0.204951) | 0.414611 / 0.000490 (0.414122) | 0.008354 / 0.000200 (0.008154) | 0.000249 / 0.000054 (0.000195) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023880 / 0.037411 (-0.013532) | 0.074523 / 0.014526 (0.059997) | 0.084803 / 0.176557 (-0.091754) | 0.146701 / 0.737135 (-0.590435) | 0.084990 / 0.296338 (-0.211348) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.397736 / 0.215209 (0.182527) | 3.961740 / 2.077655 (1.884086) | 1.909014 / 1.504120 (0.404894) | 1.823026 / 1.541195 (0.281831) | 1.966235 / 1.468490 (0.497745) | 0.498056 / 4.584777 (-4.086721) | 3.041408 / 3.745712 (-0.704304) | 2.998010 / 5.269862 (-2.271852) | 1.887293 / 4.565676 (-2.678384) | 0.057096 / 0.424275 (-0.367179) | 0.006338 / 0.007607 (-0.001269) | 0.465166 / 0.226044 (0.239122) | 4.667710 / 2.268929 (2.398781) | 2.480798 / 55.444624 (-52.963826) | 2.270701 / 6.876477 (-4.605776) | 2.376470 / 2.142072 (0.234397) | 0.579873 / 4.805227 (-4.225355) | 0.125032 / 6.500664 (-6.375632) | 0.061057 / 0.075469 (-0.014412) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.229916 / 1.841788 (-0.611872) | 17.829628 / 8.074308 (9.755320) | 13.860184 / 10.191392 (3.668792) | 0.143507 / 0.680424 (-0.536917) | 0.016943 / 0.534201 (-0.517258) | 0.350106 / 0.579283 (-0.229178) | 0.364547 / 0.434364 (-0.069817) | 0.398889 / 0.540337 (-0.141448) | 0.557948 / 1.386936 (-0.828988) |\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) | 0.006052 / 0.011353 (-0.005301) | 0.003636 / 0.011008 (-0.007372) | 0.062705 / 0.038508 (0.024197) | 0.057753 / 0.023109 (0.034644) | 0.453219 / 0.275898 (0.177321) | 0.485179 / 0.323480 (0.161699) | 0.004886 / 0.007986 (-0.003100) | 0.002838 / 0.004328 (-0.001490) | 0.062593 / 0.004250 (0.058343) | 0.047476 / 0.037052 (0.010423) | 0.454266 / 0.258489 (0.195777) | 0.487939 / 0.293841 (0.194098) | 0.028124 / 0.128546 (-0.100422) | 0.008000 / 0.075646 (-0.067647) | 0.068335 / 0.419271 (-0.350937) | 0.040491 / 0.043533 (-0.003042) | 0.457868 / 0.255139 (0.202729) | 0.476355 / 0.283200 (0.193155) | 0.019557 / 0.141683 (-0.122126) | 1.507111 / 1.452155 (0.054956) | 1.569720 / 1.492716 (0.077003) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.209205 / 0.018006 (0.191199) | 0.411782 / 0.000490 (0.411292) | 0.003544 / 0.000200 (0.003344) | 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) | 0.026569 / 0.037411 (-0.010842) | 0.081213 / 0.014526 (0.066687) | 0.090971 / 0.176557 (-0.085585) | 0.145287 / 0.737135 (-0.591849) | 0.091792 / 0.296338 (-0.204546) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.458329 / 0.215209 (0.243120) | 4.574463 / 2.077655 (2.496808) | 2.516693 / 1.504120 (1.012573) | 2.329463 / 1.541195 (0.788269) | 2.386704 / 1.468490 (0.918214) | 0.503526 / 4.584777 (-4.081251) | 3.113382 / 3.745712 (-0.632331) | 2.872538 / 5.269862 (-2.397323) | 1.865483 / 4.565676 (-2.700194) | 0.058292 / 0.424275 (-0.365983) | 0.006434 / 0.007607 (-0.001173) | 0.530804 / 0.226044 (0.304760) | 5.312666 / 2.268929 (3.043738) | 2.992569 / 55.444624 (-52.452055) | 2.611524 / 6.876477 (-4.264953) | 2.779569 / 2.142072 (0.637497) | 0.595200 / 4.805227 (-4.210028) | 0.123957 / 6.500664 (-6.376707) | 0.060601 / 0.075469 (-0.014868) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.345536 / 1.841788 (-0.496252) | 18.183827 / 8.074308 (10.109519) | 14.814084 / 10.191392 (4.622692) | 0.145305 / 0.680424 (-0.535119) | 0.018812 / 0.534201 (-0.515389) | 0.334793 / 0.579283 (-0.244490) | 0.375331 / 0.434364 (-0.059033) | 0.392499 / 0.540337 (-0.147839) | 0.563286 / 1.386936 (-0.823650) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1e186f0b7fe851f1f474020f0d6b1dc35114f994 \"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) | 0.008922 / 0.011353 (-0.002431) | 0.005169 / 0.011008 (-0.005840) | 0.106275 / 0.038508 (0.067767) | 0.076446 / 0.023109 (0.053337) | 0.400207 / 0.275898 (0.124309) | 0.476262 / 0.323480 (0.152782) | 0.006032 / 0.007986 (-0.001954) | 0.004266 / 0.004328 (-0.000063) | 0.083518 / 0.004250 (0.079267) | 0.059644 / 0.037052 (0.022592) | 0.409094 / 0.258489 (0.150605) | 0.470400 / 0.293841 (0.176559) | 0.050161 / 0.128546 (-0.078385) | 0.013580 / 0.075646 (-0.062066) | 0.375047 / 0.419271 (-0.044224) | 0.068319 / 0.043533 (0.024786) | 0.433765 / 0.255139 (0.178626) | 0.449221 / 0.283200 (0.166021) | 0.037636 / 0.141683 (-0.104047) | 1.825855 / 1.452155 (0.373700) | 1.889665 / 1.492716 (0.396948) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.319622 / 0.018006 (0.301616) | 0.588878 / 0.000490 (0.588388) | 0.017790 / 0.000200 (0.017590) | 0.000532 / 0.000054 (0.000477) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031152 / 0.037411 (-0.006259) | 0.093808 / 0.014526 (0.079282) | 0.119296 / 0.176557 (-0.057261) | 0.181845 / 0.737135 (-0.555291) | 0.108527 / 0.296338 (-0.187811) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.575106 / 0.215209 (0.359896) | 5.776322 / 2.077655 (3.698668) | 2.592913 / 1.504120 (1.088793) | 2.389481 / 1.541195 (0.848286) | 2.390117 / 1.468490 (0.921627) | 0.852420 / 4.584777 (-3.732357) | 5.474171 / 3.745712 (1.728459) | 4.967188 / 5.269862 (-0.302674) | 3.053712 / 4.565676 (-1.511965) | 0.098128 / 0.424275 (-0.326147) | 0.008722 / 0.007607 (0.001115) | 0.699838 / 0.226044 (0.473794) | 7.103622 / 2.268929 (4.834693) | 3.359326 / 55.444624 (-52.085299) | 2.733943 / 6.876477 (-4.142534) | 2.770001 / 2.142072 (0.627929) | 1.058217 / 4.805227 (-3.747011) | 0.215845 / 6.500664 (-6.284820) | 0.078532 / 0.075469 (0.003063) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.633173 / 1.841788 (-0.208614) | 23.795045 / 8.074308 (15.720737) | 21.094433 / 10.191392 (10.903041) | 0.234522 / 0.680424 (-0.445902) | 0.033632 / 0.534201 (-0.500569) | 0.496701 / 0.579283 (-0.082582) | 0.626861 / 0.434364 (0.192497) | 0.558267 / 0.540337 (0.017930) | 0.807461 / 1.386936 (-0.579475) |\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) | 0.009136 / 0.011353 (-0.002217) | 0.005425 / 0.011008 (-0.005584) | 0.081478 / 0.038508 (0.042970) | 0.077240 / 0.023109 (0.054130) | 0.512156 / 0.275898 (0.236258) | 0.561593 / 0.323480 (0.238113) | 0.006499 / 0.007986 (-0.001486) | 0.004080 / 0.004328 (-0.000248) | 0.082121 / 0.004250 (0.077870) | 0.063774 / 0.037052 (0.026722) | 0.509801 / 0.258489 (0.251312) | 0.572826 / 0.293841 (0.278985) | 0.050969 / 0.128546 (-0.077578) | 0.014876 / 0.075646 (-0.060771) | 0.094815 / 0.419271 (-0.324456) | 0.063904 / 0.043533 (0.020371) | 0.530572 / 0.255139 (0.275433) | 0.545940 / 0.283200 (0.262741) | 0.036729 / 0.141683 (-0.104954) | 1.799493 / 1.452155 (0.347339) | 1.931955 / 1.492716 (0.439239) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.291405 / 0.018006 (0.273398) | 0.590257 / 0.000490 (0.589767) | 0.008394 / 0.000200 (0.008194) | 0.000112 / 0.000054 (0.000058) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.037613 / 0.037411 (0.000201) | 0.103136 / 0.014526 (0.088610) | 0.121744 / 0.176557 (-0.054813) | 0.198503 / 0.737135 (-0.538632) | 0.120183 / 0.296338 (-0.176156) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.659872 / 0.215209 (0.444663) | 6.616775 / 2.077655 (4.539120) | 3.031679 / 1.504120 (1.527559) | 2.743489 / 1.541195 (1.202294) | 2.786786 / 1.468490 (1.318296) | 0.866625 / 4.584777 (-3.718152) | 5.637705 / 3.745712 (1.891993) | 4.702563 / 5.269862 (-0.567298) | 3.017797 / 4.565676 (-1.547879) | 0.100107 / 0.424275 (-0.324169) | 0.008443 / 0.007607 (0.000836) | 0.791385 / 0.226044 (0.565341) | 7.869504 / 2.268929 (5.600576) | 3.856634 / 55.444624 (-51.587991) | 3.140089 / 6.876477 (-3.736388) | 3.489339 / 2.142072 (1.347267) | 1.132170 / 4.805227 (-3.673058) | 0.219630 / 6.500664 (-6.281034) | 0.082289 / 0.075469 (0.006820) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.781902 / 1.841788 (-0.059885) | 24.912604 / 8.074308 (16.838296) | 21.626512 / 10.191392 (11.435120) | 0.228194 / 0.680424 (-0.452230) | 0.032799 / 0.534201 (-0.501402) | 0.483683 / 0.579283 (-0.095600) | 0.604966 / 0.434364 (0.170602) | 0.617278 / 0.540337 (0.076940) | 0.887337 / 1.386936 (-0.499599) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#579c31fda7182ca6fc33ab1e95db9e3a21fb5518 \"CML watermark\")\n", "I used [this](https://colab.research.google.com/drive/1q2FYnkJFDMM3OZbhnYeZkfzmBa6ksofQ?usp=sharing) Colab to test the new `push_to_hub` on a large dataset (55 GB). It works great. \r\n\r\nOne thing that could be improved is the performance of `dataset.data.nbytes` - it takes ≈ 3 minutes to compute for the dataset in question (50k array chunks per column). It probably makes sense to store larger chunks locally. But this can be addressed in a subsequent 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) | 0.007190 / 0.011353 (-0.004163) | 0.004394 / 0.011008 (-0.006614) | 0.085506 / 0.038508 (0.046998) | 0.092177 / 0.023109 (0.069068) | 0.351636 / 0.275898 (0.075738) | 0.389716 / 0.323480 (0.066236) | 0.004443 / 0.007986 (-0.003543) | 0.003641 / 0.004328 (-0.000687) | 0.066578 / 0.004250 (0.062328) | 0.061399 / 0.037052 (0.024346) | 0.356008 / 0.258489 (0.097519) | 0.398677 / 0.293841 (0.104836) | 0.031958 / 0.128546 (-0.096588) | 0.008857 / 0.075646 (-0.066789) | 0.289613 / 0.419271 (-0.129659) | 0.053555 / 0.043533 (0.010022) | 0.349268 / 0.255139 (0.094129) | 0.368666 / 0.283200 (0.085466) | 0.028267 / 0.141683 (-0.113416) | 1.502857 / 1.452155 (0.050702) | 1.598422 / 1.492716 (0.105705) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.319938 / 0.018006 (0.301931) | 0.566925 / 0.000490 (0.566435) | 0.014625 / 0.000200 (0.014425) | 0.000372 / 0.000054 (0.000318) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030156 / 0.037411 (-0.007255) | 0.083128 / 0.014526 (0.068602) | 0.101435 / 0.176557 (-0.075122) | 0.158971 / 0.737135 (-0.578165) | 0.101488 / 0.296338 (-0.194851) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.383904 / 0.215209 (0.168695) | 3.829201 / 2.077655 (1.751546) | 1.815224 / 1.504120 (0.311104) | 1.647865 / 1.541195 (0.106670) | 1.738411 / 1.468490 (0.269921) | 0.484963 / 4.584777 (-4.099814) | 3.494811 / 3.745712 (-0.250901) | 3.505811 / 5.269862 (-1.764051) | 2.115467 / 4.565676 (-2.450210) | 0.057271 / 0.424275 (-0.367004) | 0.007285 / 0.007607 (-0.000322) | 0.467162 / 0.226044 (0.241118) | 4.661572 / 2.268929 (2.392643) | 2.330443 / 55.444624 (-53.114182) | 1.986116 / 6.876477 (-4.890361) | 2.055350 / 2.142072 (-0.086723) | 0.580369 / 4.805227 (-4.224858) | 0.132700 / 6.500664 (-6.367964) | 0.061219 / 0.075469 (-0.014251) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.270843 / 1.841788 (-0.570945) | 19.870723 / 8.074308 (11.796415) | 14.368932 / 10.191392 (4.177540) | 0.167345 / 0.680424 (-0.513079) | 0.018358 / 0.534201 (-0.515843) | 0.390833 / 0.579283 (-0.188450) | 0.419884 / 0.434364 (-0.014480) | 0.465683 / 0.540337 (-0.074655) | 0.646101 / 1.386936 (-0.740835) |\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) | 0.007027 / 0.011353 (-0.004326) | 0.004578 / 0.011008 (-0.006430) | 0.066468 / 0.038508 (0.027960) | 0.081576 / 0.023109 (0.058466) | 0.414928 / 0.275898 (0.139030) | 0.452130 / 0.323480 (0.128651) | 0.005861 / 0.007986 (-0.002124) | 0.003740 / 0.004328 (-0.000588) | 0.066943 / 0.004250 (0.062692) | 0.060100 / 0.037052 (0.023048) | 0.418697 / 0.258489 (0.160208) | 0.466604 / 0.293841 (0.172764) | 0.031887 / 0.128546 (-0.096660) | 0.009119 / 0.075646 (-0.066527) | 0.072285 / 0.419271 (-0.346986) | 0.047599 / 0.043533 (0.004066) | 0.410791 / 0.255139 (0.155652) | 0.434182 / 0.283200 (0.150982) | 0.024799 / 0.141683 (-0.116884) | 1.500310 / 1.452155 (0.048155) | 1.567151 / 1.492716 (0.074434) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.322482 / 0.018006 (0.304476) | 0.550234 / 0.000490 (0.549744) | 0.007796 / 0.000200 (0.007596) | 0.000088 / 0.000054 (0.000033) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.036013 / 0.037411 (-0.001398) | 0.098482 / 0.014526 (0.083956) | 0.111641 / 0.176557 (-0.064916) | 0.166251 / 0.737135 (-0.570884) | 0.112426 / 0.296338 (-0.183912) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.429181 / 0.215209 (0.213972) | 4.273126 / 2.077655 (2.195472) | 2.277440 / 1.504120 (0.773321) | 2.112567 / 1.541195 (0.571372) | 2.224118 / 1.468490 (0.755628) | 0.488876 / 4.584777 (-4.095901) | 3.711638 / 3.745712 (-0.034074) | 3.480995 / 5.269862 (-1.788867) | 2.122114 / 4.565676 (-2.443563) | 0.057538 / 0.424275 (-0.366737) | 0.007416 / 0.007607 (-0.000191) | 0.506881 / 0.226044 (0.280836) | 5.067601 / 2.268929 (2.798672) | 2.769216 / 55.444624 (-52.675408) | 2.420448 / 6.876477 (-4.456029) | 2.694225 / 2.142072 (0.552153) | 0.588911 / 4.805227 (-4.216316) | 0.133542 / 6.500664 (-6.367122) | 0.061135 / 0.075469 (-0.014334) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.378029 / 1.841788 (-0.463758) | 20.660942 / 8.074308 (12.586634) | 15.725969 / 10.191392 (5.534577) | 0.169078 / 0.680424 (-0.511346) | 0.020540 / 0.534201 (-0.513661) | 0.399409 / 0.579283 (-0.179874) | 0.432572 / 0.434364 (-0.001792) | 0.477106 / 0.540337 (-0.063231) | 0.675593 / 1.386936 (-0.711343) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9764c49d8bfdad5439e16faa6c52e510feabf6fa \"CML watermark\")\n", "@lhoestq \r\n\r\n> single commit can fail (time out) if there are too many operations so we might have to do multi commits anyway in that case\r\n\r\nMultiple commits complicate the logic significantly. Maybe, let's keep things simple and emit a warning if there are more than 100 additions (we can suggest increasing `max_shard_size` in that case). Additionally, we can set the default `max_shard_size` to a higher value, e.g., 5GB. I think handling up to 500GB of data in the default case seems reasonable. In rare cases where this is a problem, one could increase the default `max_shard_size` even further (if RAM is not a limiting factor) or use `to_parquet` + `huggingface_hub` (we could have a docstring or a doc note that explains this).\r\n\r\nNote that we split the dataset based on the Arrow data size, which means Parquet shards will be considerably smaller unless there are binary fields such as image JPEGs in the dataset, which are hard to compress efficiently.\r\n\r\n> how to let users resume a push_to_hub that failed mid-way because of a connection error for example\r\n\r\nThey can resume by rerunning the failed `push_to_hub`.\r\n\r\n`preupload_lfs_files` will be instant in that scenario, as explained in https://github.com/huggingface/huggingface_hub/pull/1699#discussion_r1342446406", "> Multiple commits complicate the logic significantly. Maybe, let's keep things simple and emit a warning if there are more than 100 additions (we can suggest increasing max_shard_size in that case)\r\n\r\nI don't think we can do that, many people are uploading files with 100+ files and it would break their workflow", "Indeed, we should not break this, considering the number of datasets with more than 100 shards on the Hub (over 1k)", "<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) | 0.006834 / 0.011353 (-0.004519) | 0.004424 / 0.011008 (-0.006584) | 0.085199 / 0.038508 (0.046691) | 0.080237 / 0.023109 (0.057128) | 0.308800 / 0.275898 (0.032902) | 0.346314 / 0.323480 (0.022835) | 0.004399 / 0.007986 (-0.003586) | 0.003773 / 0.004328 (-0.000556) | 0.065886 / 0.004250 (0.061636) | 0.057830 / 0.037052 (0.020777) | 0.312035 / 0.258489 (0.053546) | 0.362646 / 0.293841 (0.068805) | 0.031223 / 0.128546 (-0.097323) | 0.008851 / 0.075646 (-0.066795) | 0.288264 / 0.419271 (-0.131007) | 0.052600 / 0.043533 (0.009067) | 0.316127 / 0.255139 (0.060988) | 0.328539 / 0.283200 (0.045340) | 0.026068 / 0.141683 (-0.115615) | 1.458928 / 1.452155 (0.006773) | 1.547619 / 1.492716 (0.054902) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.274382 / 0.018006 (0.256375) | 0.591192 / 0.000490 (0.590703) | 0.009290 / 0.000200 (0.009090) | 0.000327 / 0.000054 (0.000273) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031428 / 0.037411 (-0.005983) | 0.087523 / 0.014526 (0.072997) | 0.101427 / 0.176557 (-0.075130) | 0.159228 / 0.737135 (-0.577907) | 0.101430 / 0.296338 (-0.194909) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.393914 / 0.215209 (0.178705) | 3.917323 / 2.077655 (1.839668) | 1.940577 / 1.504120 (0.436457) | 1.760996 / 1.541195 (0.219801) | 1.865858 / 1.468490 (0.397368) | 0.488920 / 4.584777 (-4.095857) | 3.513465 / 3.745712 (-0.232248) | 3.506600 / 5.269862 (-1.763261) | 2.072583 / 4.565676 (-2.493093) | 0.058256 / 0.424275 (-0.366019) | 0.007420 / 0.007607 (-0.000187) | 0.467241 / 0.226044 (0.241197) | 4.671470 / 2.268929 (2.402542) | 2.422717 / 55.444624 (-53.021908) | 2.069501 / 6.876477 (-4.806975) | 2.159257 / 2.142072 (0.017184) | 0.583808 / 4.805227 (-4.221419) | 0.134160 / 6.500664 (-6.366504) | 0.068855 / 0.075469 (-0.006614) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.305299 / 1.841788 (-0.536488) | 19.913902 / 8.074308 (11.839593) | 14.708057 / 10.191392 (4.516665) | 0.160113 / 0.680424 (-0.520311) | 0.018431 / 0.534201 (-0.515770) | 0.396147 / 0.579283 (-0.183136) | 0.411738 / 0.434364 (-0.022626) | 0.459297 / 0.540337 (-0.081041) | 0.636599 / 1.386936 (-0.750337) |\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) | 0.006936 / 0.011353 (-0.004417) | 0.004290 / 0.011008 (-0.006718) | 0.065754 / 0.038508 (0.027246) | 0.080655 / 0.023109 (0.057546) | 0.399701 / 0.275898 (0.123803) | 0.435999 / 0.323480 (0.112519) | 0.005690 / 0.007986 (-0.002295) | 0.003580 / 0.004328 (-0.000748) | 0.065685 / 0.004250 (0.061434) | 0.059299 / 0.037052 (0.022246) | 0.404295 / 0.258489 (0.145806) | 0.438745 / 0.293841 (0.144904) | 0.032241 / 0.128546 (-0.096305) | 0.008699 / 0.075646 (-0.066947) | 0.072053 / 0.419271 (-0.347218) | 0.047489 / 0.043533 (0.003956) | 0.395638 / 0.255139 (0.140499) | 0.417224 / 0.283200 (0.134025) | 0.022734 / 0.141683 (-0.118949) | 1.507519 / 1.452155 (0.055364) | 1.570459 / 1.492716 (0.077743) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.260442 / 0.018006 (0.242435) | 0.551933 / 0.000490 (0.551444) | 0.005240 / 0.000200 (0.005040) | 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) | 0.033718 / 0.037411 (-0.003694) | 0.095710 / 0.014526 (0.081184) | 0.109970 / 0.176557 (-0.066586) | 0.167930 / 0.737135 (-0.569205) | 0.109977 / 0.296338 (-0.186362) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.430067 / 0.215209 (0.214857) | 4.292564 / 2.077655 (2.214910) | 2.313511 / 1.504120 (0.809391) | 2.158153 / 1.541195 (0.616959) | 2.262486 / 1.468490 (0.793996) | 0.492376 / 4.584777 (-4.092401) | 3.622287 / 3.745712 (-0.123425) | 3.380162 / 5.269862 (-1.889699) | 2.111874 / 4.565676 (-2.453803) | 0.057882 / 0.424275 (-0.366393) | 0.007317 / 0.007607 (-0.000290) | 0.504722 / 0.226044 (0.278678) | 5.039009 / 2.268929 (2.770080) | 2.772162 / 55.444624 (-52.672463) | 2.430928 / 6.876477 (-4.445549) | 2.666556 / 2.142072 (0.524484) | 0.586722 / 4.805227 (-4.218505) | 0.133780 / 6.500664 (-6.366884) | 0.060269 / 0.075469 (-0.015200) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.339064 / 1.841788 (-0.502724) | 20.743931 / 8.074308 (12.669623) | 15.491066 / 10.191392 (5.299674) | 0.159236 / 0.680424 (-0.521188) | 0.020722 / 0.534201 (-0.513479) | 0.399440 / 0.579283 (-0.179843) | 0.424501 / 0.434364 (-0.009863) | 0.474026 / 0.540337 (-0.066311) | 0.685239 / 1.386936 (-0.701697) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#58406f61c52e7ff064ac6c19ebdb3e5247c70862 \"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) | 0.005930 / 0.011353 (-0.005422) | 0.003496 / 0.011008 (-0.007512) | 0.079631 / 0.038508 (0.041123) | 0.058250 / 0.023109 (0.035141) | 0.310108 / 0.275898 (0.034210) | 0.352747 / 0.323480 (0.029267) | 0.005367 / 0.007986 (-0.002619) | 0.002943 / 0.004328 (-0.001386) | 0.062449 / 0.004250 (0.058199) | 0.046433 / 0.037052 (0.009381) | 0.311020 / 0.258489 (0.052531) | 0.361033 / 0.293841 (0.067192) | 0.027419 / 0.128546 (-0.101128) | 0.008073 / 0.075646 (-0.067574) | 0.261403 / 0.419271 (-0.157869) | 0.045059 / 0.043533 (0.001527) | 0.310622 / 0.255139 (0.055483) | 0.344361 / 0.283200 (0.061161) | 0.020561 / 0.141683 (-0.121122) | 1.427409 / 1.452155 (-0.024746) | 1.506612 / 1.492716 (0.013896) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.234095 / 0.018006 (0.216089) | 0.432603 / 0.000490 (0.432113) | 0.010283 / 0.000200 (0.010083) | 0.000289 / 0.000054 (0.000235) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024263 / 0.037411 (-0.013148) | 0.073672 / 0.014526 (0.059146) | 0.084080 / 0.176557 (-0.092476) | 0.146679 / 0.737135 (-0.590457) | 0.084337 / 0.296338 (-0.212001) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.434297 / 0.215209 (0.219088) | 4.358287 / 2.077655 (2.280633) | 2.268461 / 1.504120 (0.764341) | 2.107924 / 1.541195 (0.566729) | 2.165136 / 1.468490 (0.696646) | 0.498421 / 4.584777 (-4.086356) | 3.094414 / 3.745712 (-0.651298) | 2.991511 / 5.269862 (-2.278351) | 1.998052 / 4.565676 (-2.567624) | 0.057363 / 0.424275 (-0.366912) | 0.006405 / 0.007607 (-0.001203) | 0.508396 / 0.226044 (0.282351) | 5.104756 / 2.268929 (2.835828) | 2.720462 / 55.444624 (-52.724163) | 2.391840 / 6.876477 (-4.484637) | 2.443063 / 2.142072 (0.300991) | 0.590015 / 4.805227 (-4.215212) | 0.125414 / 6.500664 (-6.375250) | 0.061122 / 0.075469 (-0.014347) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.221883 / 1.841788 (-0.619904) | 17.788248 / 8.074308 (9.713940) | 13.753315 / 10.191392 (3.561923) | 0.146388 / 0.680424 (-0.534036) | 0.017038 / 0.534201 (-0.517163) | 0.339162 / 0.579283 (-0.240121) | 0.372054 / 0.434364 (-0.062309) | 0.381507 / 0.540337 (-0.158830) | 0.538603 / 1.386936 (-0.848333) |\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) | 0.006044 / 0.011353 (-0.005309) | 0.003654 / 0.011008 (-0.007354) | 0.062956 / 0.038508 (0.024448) | 0.061325 / 0.023109 (0.038216) | 0.450006 / 0.275898 (0.174108) | 0.474560 / 0.323480 (0.151080) | 0.004846 / 0.007986 (-0.003140) | 0.002904 / 0.004328 (-0.001425) | 0.064206 / 0.004250 (0.059956) | 0.047850 / 0.037052 (0.010798) | 0.448431 / 0.258489 (0.189942) | 0.481363 / 0.293841 (0.187523) | 0.028622 / 0.128546 (-0.099925) | 0.008255 / 0.075646 (-0.067391) | 0.068461 / 0.419271 (-0.350810) | 0.040234 / 0.043533 (-0.003299) | 0.447396 / 0.255139 (0.192257) | 0.465383 / 0.283200 (0.182184) | 0.021864 / 0.141683 (-0.119819) | 1.402197 / 1.452155 (-0.049957) | 1.475337 / 1.492716 (-0.017379) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.227093 / 0.018006 (0.209087) | 0.407908 / 0.000490 (0.407419) | 0.006709 / 0.000200 (0.006509) | 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) | 0.026560 / 0.037411 (-0.010851) | 0.080926 / 0.014526 (0.066400) | 0.091531 / 0.176557 (-0.085026) | 0.145742 / 0.737135 (-0.591393) | 0.092203 / 0.296338 (-0.204135) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.473029 / 0.215209 (0.257820) | 4.703613 / 2.077655 (2.625958) | 2.642622 / 1.504120 (1.138502) | 2.465376 / 1.541195 (0.924181) | 2.510125 / 1.468490 (1.041635) | 0.512606 / 4.584777 (-4.072171) | 3.132127 / 3.745712 (-0.613585) | 2.890098 / 5.269862 (-2.379763) | 1.908140 / 4.565676 (-2.657537) | 0.058938 / 0.424275 (-0.365337) | 0.006486 / 0.007607 (-0.001121) | 0.542279 / 0.226044 (0.316235) | 5.435621 / 2.268929 (3.166693) | 3.083943 / 55.444624 (-52.360681) | 2.761575 / 6.876477 (-4.114901) | 2.919672 / 2.142072 (0.777599) | 0.608022 / 4.805227 (-4.197205) | 0.126821 / 6.500664 (-6.373843) | 0.061374 / 0.075469 (-0.014095) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.348848 / 1.841788 (-0.492940) | 18.323507 / 8.074308 (10.249199) | 14.713411 / 10.191392 (4.522019) | 0.155277 / 0.680424 (-0.525146) | 0.017739 / 0.534201 (-0.516462) | 0.337357 / 0.579283 (-0.241926) | 0.376519 / 0.434364 (-0.057844) | 0.398011 / 0.540337 (-0.142327) | 0.589797 / 1.386936 (-0.797139) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#26d8bfca337e01bd78d5590d5e49c6d8d022a3ff \"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) | 0.007823 / 0.011353 (-0.003530) | 0.004136 / 0.011008 (-0.006872) | 0.087282 / 0.038508 (0.048774) | 0.086352 / 0.023109 (0.063243) | 0.328107 / 0.275898 (0.052209) | 0.368717 / 0.323480 (0.045237) | 0.005452 / 0.007986 (-0.002533) | 0.003460 / 0.004328 (-0.000868) | 0.064360 / 0.004250 (0.060110) | 0.062215 / 0.037052 (0.025162) | 0.334666 / 0.258489 (0.076177) | 0.388688 / 0.293841 (0.094847) | 0.031093 / 0.128546 (-0.097454) | 0.008510 / 0.075646 (-0.067137) | 0.295965 / 0.419271 (-0.123306) | 0.052858 / 0.043533 (0.009325) | 0.320104 / 0.255139 (0.064965) | 0.346761 / 0.283200 (0.063562) | 0.024864 / 0.141683 (-0.116819) | 1.483164 / 1.452155 (0.031010) | 1.580363 / 1.492716 (0.087647) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.243523 / 0.018006 (0.225516) | 0.459741 / 0.000490 (0.459251) | 0.010508 / 0.000200 (0.010308) | 0.000384 / 0.000054 (0.000330) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029896 / 0.037411 (-0.007515) | 0.089150 / 0.014526 (0.074624) | 0.098855 / 0.176557 (-0.077702) | 0.154469 / 0.737135 (-0.582667) | 0.099546 / 0.296338 (-0.196792) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.403547 / 0.215209 (0.188338) | 4.036711 / 2.077655 (1.959056) | 2.030882 / 1.504120 (0.526762) | 1.850432 / 1.541195 (0.309238) | 1.924248 / 1.468490 (0.455758) | 0.493153 / 4.584777 (-4.091624) | 3.634074 / 3.745712 (-0.111638) | 3.546145 / 5.269862 (-1.723717) | 2.120819 / 4.565676 (-2.444858) | 0.057137 / 0.424275 (-0.367138) | 0.007454 / 0.007607 (-0.000153) | 0.481687 / 0.226044 (0.255642) | 4.813203 / 2.268929 (2.544275) | 2.481260 / 55.444624 (-52.963364) | 2.194185 / 6.876477 (-4.682292) | 2.255381 / 2.142072 (0.113308) | 0.575160 / 4.805227 (-4.230068) | 0.132310 / 6.500664 (-6.368355) | 0.061917 / 0.075469 (-0.013553) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.265722 / 1.841788 (-0.576066) | 19.949624 / 8.074308 (11.875315) | 14.804356 / 10.191392 (4.612964) | 0.170485 / 0.680424 (-0.509939) | 0.018831 / 0.534201 (-0.515370) | 0.407051 / 0.579283 (-0.172233) | 0.420560 / 0.434364 (-0.013804) | 0.470721 / 0.540337 (-0.069616) | 0.651665 / 1.386936 (-0.735271) |\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) | 0.007113 / 0.011353 (-0.004240) | 0.004186 / 0.011008 (-0.006822) | 0.065082 / 0.038508 (0.026574) | 0.080275 / 0.023109 (0.057166) | 0.393460 / 0.275898 (0.117562) | 0.426702 / 0.323480 (0.103223) | 0.005639 / 0.007986 (-0.002347) | 0.003492 / 0.004328 (-0.000836) | 0.065774 / 0.004250 (0.061523) | 0.059708 / 0.037052 (0.022656) | 0.395598 / 0.258489 (0.137109) | 0.437088 / 0.293841 (0.143247) | 0.033165 / 0.128546 (-0.095381) | 0.008559 / 0.075646 (-0.067087) | 0.071782 / 0.419271 (-0.347490) | 0.048672 / 0.043533 (0.005139) | 0.393883 / 0.255139 (0.138744) | 0.412817 / 0.283200 (0.129617) | 0.024115 / 0.141683 (-0.117568) | 1.522752 / 1.452155 (0.070597) | 1.577311 / 1.492716 (0.084595) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.225569 / 0.018006 (0.207563) | 0.460310 / 0.000490 (0.459820) | 0.004733 / 0.000200 (0.004533) | 0.000115 / 0.000054 (0.000060) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035241 / 0.037411 (-0.002170) | 0.098092 / 0.014526 (0.083566) | 0.108025 / 0.176557 (-0.068531) | 0.162910 / 0.737135 (-0.574225) | 0.108649 / 0.296338 (-0.187689) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.441723 / 0.215209 (0.226514) | 4.400656 / 2.077655 (2.323001) | 2.413588 / 1.504120 (0.909468) | 2.261890 / 1.541195 (0.720696) | 2.420878 / 1.468490 (0.952388) | 0.496456 / 4.584777 (-4.088321) | 3.679930 / 3.745712 (-0.065782) | 3.390539 / 5.269862 (-1.879322) | 2.109599 / 4.565676 (-2.456078) | 0.058896 / 0.424275 (-0.365379) | 0.007483 / 0.007607 (-0.000125) | 0.521108 / 0.226044 (0.295064) | 5.209468 / 2.268929 (2.940540) | 2.948595 / 55.444624 (-52.496029) | 2.658864 / 6.876477 (-4.217613) | 2.913653 / 2.142072 (0.771580) | 0.602776 / 4.805227 (-4.202451) | 0.136166 / 6.500664 (-6.364498) | 0.063812 / 0.075469 (-0.011657) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.350306 / 1.841788 (-0.491482) | 20.453980 / 8.074308 (12.379672) | 15.758719 / 10.191392 (5.567327) | 0.165847 / 0.680424 (-0.514577) | 0.020254 / 0.534201 (-0.513947) | 0.400006 / 0.579283 (-0.179277) | 0.440336 / 0.434364 (0.005972) | 0.480122 / 0.540337 (-0.060215) | 0.688994 / 1.386936 (-0.697942) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#997082a2a3c599ea1b23a70759d3af98a78f7f33 \"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) | 0.008633 / 0.011353 (-0.002720) | 0.004851 / 0.011008 (-0.006157) | 0.100647 / 0.038508 (0.062139) | 0.084701 / 0.023109 (0.061592) | 0.410489 / 0.275898 (0.134590) | 0.440231 / 0.323480 (0.116751) | 0.004679 / 0.007986 (-0.003307) | 0.004172 / 0.004328 (-0.000157) | 0.079911 / 0.004250 (0.075661) | 0.069537 / 0.037052 (0.032485) | 0.423506 / 0.258489 (0.165017) | 0.466098 / 0.293841 (0.172257) | 0.048773 / 0.128546 (-0.079773) | 0.014446 / 0.075646 (-0.061200) | 0.342776 / 0.419271 (-0.076495) | 0.065672 / 0.043533 (0.022139) | 0.411845 / 0.255139 (0.156706) | 0.466662 / 0.283200 (0.183462) | 0.035752 / 0.141683 (-0.105931) | 1.684956 / 1.452155 (0.232801) | 1.832173 / 1.492716 (0.339456) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.250744 / 0.018006 (0.232738) | 0.528860 / 0.000490 (0.528371) | 0.013301 / 0.000200 (0.013101) | 0.000413 / 0.000054 (0.000359) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032376 / 0.037411 (-0.005035) | 0.094630 / 0.014526 (0.080104) | 0.107163 / 0.176557 (-0.069394) | 0.172503 / 0.737135 (-0.564633) | 0.108407 / 0.296338 (-0.187932) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.671251 / 0.215209 (0.456042) | 6.235361 / 2.077655 (4.157706) | 2.650328 / 1.504120 (1.146208) | 2.341199 / 1.541195 (0.800004) | 2.368803 / 1.468490 (0.900313) | 0.841347 / 4.584777 (-3.743430) | 5.042508 / 3.745712 (1.296796) | 4.807565 / 5.269862 (-0.462296) | 3.007420 / 4.565676 (-1.558257) | 0.099953 / 0.424275 (-0.324322) | 0.008412 / 0.007607 (0.000805) | 0.747803 / 0.226044 (0.521759) | 7.481245 / 2.268929 (5.212316) | 3.416157 / 55.444624 (-52.028467) | 2.724608 / 6.876477 (-4.151869) | 2.832982 / 2.142072 (0.690910) | 1.072423 / 4.805227 (-3.732804) | 0.211314 / 6.500664 (-6.289351) | 0.074098 / 0.075469 (-0.001371) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.566010 / 1.841788 (-0.275778) | 23.137708 / 8.074308 (15.063400) | 21.440132 / 10.191392 (11.248740) | 0.230713 / 0.680424 (-0.449711) | 0.028271 / 0.534201 (-0.505930) | 0.450821 / 0.579283 (-0.128463) | 0.548399 / 0.434364 (0.114035) | 0.543588 / 0.540337 (0.003250) | 0.805522 / 1.386936 (-0.581414) |\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) | 0.008969 / 0.011353 (-0.002384) | 0.004793 / 0.011008 (-0.006216) | 0.075804 / 0.038508 (0.037296) | 0.079893 / 0.023109 (0.056783) | 0.464358 / 0.275898 (0.188460) | 0.507243 / 0.323480 (0.183763) | 0.005945 / 0.007986 (-0.002040) | 0.005341 / 0.004328 (0.001012) | 0.077952 / 0.004250 (0.073701) | 0.059965 / 0.037052 (0.022913) | 0.478947 / 0.258489 (0.220458) | 0.528444 / 0.293841 (0.234603) | 0.052878 / 0.128546 (-0.075668) | 0.013939 / 0.075646 (-0.061707) | 0.087351 / 0.419271 (-0.331920) | 0.058448 / 0.043533 (0.014916) | 0.478664 / 0.255139 (0.223525) | 0.491239 / 0.283200 (0.208039) | 0.032674 / 0.141683 (-0.109008) | 1.753911 / 1.452155 (0.301756) | 1.858923 / 1.492716 (0.366206) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.239278 / 0.018006 (0.221271) | 0.507372 / 0.000490 (0.506882) | 0.005489 / 0.000200 (0.005289) | 0.000142 / 0.000054 (0.000087) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032919 / 0.037411 (-0.004493) | 0.097726 / 0.014526 (0.083200) | 0.119159 / 0.176557 (-0.057398) | 0.174545 / 0.737135 (-0.562590) | 0.115319 / 0.296338 (-0.181020) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.627107 / 0.215209 (0.411898) | 6.211925 / 2.077655 (4.134270) | 2.731484 / 1.504120 (1.227365) | 2.488847 / 1.541195 (0.947652) | 2.372445 / 1.468490 (0.903955) | 0.822663 / 4.584777 (-3.762114) | 4.924001 / 3.745712 (1.178289) | 4.371161 / 5.269862 (-0.898700) | 2.850314 / 4.565676 (-1.715363) | 0.099156 / 0.424275 (-0.325119) | 0.007941 / 0.007607 (0.000334) | 0.721539 / 0.226044 (0.495495) | 7.260874 / 2.268929 (4.991946) | 3.351072 / 55.444624 (-52.093552) | 2.757115 / 6.876477 (-4.119362) | 2.858899 / 2.142072 (0.716827) | 0.994054 / 4.805227 (-3.811173) | 0.209186 / 6.500664 (-6.291478) | 0.072070 / 0.075469 (-0.003399) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.748073 / 1.841788 (-0.093714) | 23.514638 / 8.074308 (15.440330) | 20.372037 / 10.191392 (10.180645) | 0.220020 / 0.680424 (-0.460404) | 0.057130 / 0.534201 (-0.477071) | 0.458204 / 0.579283 (-0.121079) | 0.600509 / 0.434364 (0.166145) | 0.557100 / 0.540337 (0.016762) | 0.814360 / 1.386936 (-0.572576) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#072f0ceafde60c16516fe1297e4aba981fc56052 \"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) | 0.007341 / 0.011353 (-0.004012) | 0.004606 / 0.011008 (-0.006402) | 0.087903 / 0.038508 (0.049395) | 0.094090 / 0.023109 (0.070981) | 0.322278 / 0.275898 (0.046380) | 0.356770 / 0.323480 (0.033290) | 0.005988 / 0.007986 (-0.001997) | 0.003667 / 0.004328 (-0.000662) | 0.066105 / 0.004250 (0.061854) | 0.061220 / 0.037052 (0.024167) | 0.331190 / 0.258489 (0.072701) | 0.381402 / 0.293841 (0.087561) | 0.032261 / 0.128546 (-0.096285) | 0.009281 / 0.075646 (-0.066366) | 0.293694 / 0.419271 (-0.125577) | 0.055041 / 0.043533 (0.011508) | 0.318080 / 0.255139 (0.062941) | 0.348763 / 0.283200 (0.065563) | 0.027379 / 0.141683 (-0.114304) | 1.496294 / 1.452155 (0.044139) | 1.581942 / 1.492716 (0.089226) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.307592 / 0.018006 (0.289586) | 0.591805 / 0.000490 (0.591316) | 0.017082 / 0.000200 (0.016882) | 0.000721 / 0.000054 (0.000666) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032157 / 0.037411 (-0.005254) | 0.096249 / 0.014526 (0.081724) | 0.106656 / 0.176557 (-0.069901) | 0.162966 / 0.737135 (-0.574169) | 0.107068 / 0.296338 (-0.189271) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.409083 / 0.215209 (0.193874) | 4.044307 / 2.077655 (1.966652) | 2.062887 / 1.504120 (0.558767) | 1.900568 / 1.541195 (0.359373) | 2.011862 / 1.468490 (0.543372) | 0.489250 / 4.584777 (-4.095527) | 3.519531 / 3.745712 (-0.226182) | 3.631713 / 5.269862 (-1.638149) | 2.163967 / 4.565676 (-2.401709) | 0.057723 / 0.424275 (-0.366552) | 0.007474 / 0.007607 (-0.000133) | 0.479562 / 0.226044 (0.253517) | 4.799825 / 2.268929 (2.530897) | 2.530036 / 55.444624 (-52.914588) | 2.195344 / 6.876477 (-4.681133) | 2.341046 / 2.142072 (0.198974) | 0.625105 / 4.805227 (-4.180122) | 0.132823 / 6.500664 (-6.367841) | 0.061721 / 0.075469 (-0.013748) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.301313 / 1.841788 (-0.540475) | 21.218468 / 8.074308 (13.144159) | 15.466347 / 10.191392 (5.274955) | 0.166115 / 0.680424 (-0.514309) | 0.018866 / 0.534201 (-0.515335) | 0.399307 / 0.579283 (-0.179976) | 0.430537 / 0.434364 (-0.003827) | 0.467110 / 0.540337 (-0.073228) | 0.645686 / 1.386936 (-0.741250) |\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) | 0.004298 / 0.011008 (-0.006710) | 0.065515 / 0.038508 (0.027007) | 0.089948 / 0.023109 (0.066839) | 0.410121 / 0.275898 (0.134223) | 0.449312 / 0.323480 (0.125832) | 0.006749 / 0.007986 (-0.001237) | 0.003927 / 0.004328 (-0.000401) | 0.065321 / 0.004250 (0.061071) | 0.062480 / 0.037052 (0.025428) | 0.410796 / 0.258489 (0.152307) | 0.457356 / 0.293841 (0.163515) | 0.032632 / 0.128546 (-0.095914) | 0.008798 / 0.075646 (-0.066849) | 0.075936 / 0.419271 (-0.343335) | 0.048402 / 0.043533 (0.004869) | 0.403385 / 0.255139 (0.148246) | 0.426094 / 0.283200 (0.142895) | 0.025326 / 0.141683 (-0.116357) | 1.551550 / 1.452155 (0.099395) | 1.628622 / 1.492716 (0.135905) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.279689 / 0.018006 (0.261682) | 0.583754 / 0.000490 (0.583265) | 0.006579 / 0.000200 (0.006379) | 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) | 0.034906 / 0.037411 (-0.002505) | 0.099232 / 0.014526 (0.084706) | 0.113093 / 0.176557 (-0.063464) | 0.165499 / 0.737135 (-0.571636) | 0.113398 / 0.296338 (-0.182941) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.439154 / 0.215209 (0.223945) | 4.377041 / 2.077655 (2.299387) | 2.395058 / 1.504120 (0.890938) | 2.233359 / 1.541195 (0.692164) | 2.357281 / 1.468490 (0.888791) | 0.486036 / 4.584777 (-4.098741) | 3.568794 / 3.745712 (-0.176918) | 3.485421 / 5.269862 (-1.784440) | 2.174325 / 4.565676 (-2.391351) | 0.057855 / 0.424275 (-0.366420) | 0.007545 / 0.007607 (-0.000062) | 0.516853 / 0.226044 (0.290808) | 5.173340 / 2.268929 (2.904412) | 2.931475 / 55.444624 (-52.513149) | 2.566814 / 6.876477 (-4.309663) | 2.873304 / 2.142072 (0.731232) | 0.597072 / 4.805227 (-4.208155) | 0.133589 / 6.500664 (-6.367075) | 0.061882 / 0.075469 (-0.013587) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.382845 / 1.841788 (-0.458943) | 21.608316 / 8.074308 (13.534008) | 15.702152 / 10.191392 (5.510759) | 0.190629 / 0.680424 (-0.489795) | 0.020572 / 0.534201 (-0.513629) | 0.396207 / 0.579283 (-0.183076) | 0.421184 / 0.434364 (-0.013180) | 0.477700 / 0.540337 (-0.062638) | 0.690828 / 1.386936 (-0.696108) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5e7374b453911cda5e0f866ad45b51c3fbe267c9 \"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) | 0.008450 / 0.011353 (-0.002903) | 0.004958 / 0.011008 (-0.006051) | 0.105397 / 0.038508 (0.066889) | 0.079508 / 0.023109 (0.056399) | 0.403050 / 0.275898 (0.127152) | 0.443679 / 0.323480 (0.120199) | 0.004654 / 0.007986 (-0.003332) | 0.005629 / 0.004328 (0.001301) | 0.078755 / 0.004250 (0.074505) | 0.055694 / 0.037052 (0.018642) | 0.409952 / 0.258489 (0.151463) | 0.454931 / 0.293841 (0.161090) | 0.045124 / 0.128546 (-0.083422) | 0.014031 / 0.075646 (-0.061616) | 0.347340 / 0.419271 (-0.071931) | 0.064359 / 0.043533 (0.020826) | 0.414158 / 0.255139 (0.159019) | 0.428442 / 0.283200 (0.145243) | 0.033726 / 0.141683 (-0.107957) | 1.770483 / 1.452155 (0.318328) | 1.795267 / 1.492716 (0.302551) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.251020 / 0.018006 (0.233014) | 0.507066 / 0.000490 (0.506576) | 0.015751 / 0.000200 (0.015551) | 0.000531 / 0.000054 (0.000477) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028897 / 0.037411 (-0.008515) | 0.087393 / 0.014526 (0.072867) | 0.097365 / 0.176557 (-0.079192) | 0.164833 / 0.737135 (-0.572303) | 0.101281 / 0.296338 (-0.195058) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.610806 / 0.215209 (0.395597) | 6.011697 / 2.077655 (3.934042) | 2.544268 / 1.504120 (1.040148) | 2.127103 / 1.541195 (0.585908) | 2.133330 / 1.468490 (0.664839) | 0.860964 / 4.584777 (-3.723813) | 4.982374 / 3.745712 (1.236662) | 5.073026 / 5.269862 (-0.196836) | 3.033056 / 4.565676 (-1.532621) | 0.118835 / 0.424275 (-0.305440) | 0.010122 / 0.007607 (0.002515) | 0.805807 / 0.226044 (0.579763) | 7.839166 / 2.268929 (5.570238) | 3.512405 / 55.444624 (-51.932219) | 2.767578 / 6.876477 (-4.108898) | 2.936885 / 2.142072 (0.794813) | 1.058533 / 4.805227 (-3.746695) | 0.222260 / 6.500664 (-6.278404) | 0.073890 / 0.075469 (-0.001580) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.628307 / 1.841788 (-0.213480) | 22.827116 / 8.074308 (14.752808) | 21.809759 / 10.191392 (11.618367) | 0.220637 / 0.680424 (-0.459786) | 0.028030 / 0.534201 (-0.506171) | 0.448620 / 0.579283 (-0.130663) | 0.540442 / 0.434364 (0.106078) | 0.548601 / 0.540337 (0.008264) | 0.770387 / 1.386936 (-0.616549) |\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) | 0.009198 / 0.011353 (-0.002155) | 0.004935 / 0.011008 (-0.006073) | 0.079095 / 0.038508 (0.040587) | 0.090490 / 0.023109 (0.067381) | 0.453374 / 0.275898 (0.177476) | 0.519483 / 0.323480 (0.196003) | 0.006539 / 0.007986 (-0.001447) | 0.004160 / 0.004328 (-0.000169) | 0.078433 / 0.004250 (0.074182) | 0.068022 / 0.037052 (0.030969) | 0.467686 / 0.258489 (0.209197) | 0.523863 / 0.293841 (0.230022) | 0.050926 / 0.128546 (-0.077620) | 0.013664 / 0.075646 (-0.061982) | 0.088787 / 0.419271 (-0.330485) | 0.060503 / 0.043533 (0.016971) | 0.474692 / 0.255139 (0.219553) | 0.516461 / 0.283200 (0.233261) | 0.034482 / 0.141683 (-0.107200) | 1.747939 / 1.452155 (0.295784) | 1.915212 / 1.492716 (0.422496) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.247400 / 0.018006 (0.229394) | 0.516829 / 0.000490 (0.516339) | 0.005770 / 0.000200 (0.005570) | 0.000121 / 0.000054 (0.000067) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034334 / 0.037411 (-0.003077) | 0.102397 / 0.014526 (0.087871) | 0.114187 / 0.176557 (-0.062370) | 0.171093 / 0.737135 (-0.566043) | 0.117281 / 0.296338 (-0.179058) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.635710 / 0.215209 (0.420501) | 6.400656 / 2.077655 (4.323002) | 2.896896 / 1.504120 (1.392776) | 2.682890 / 1.541195 (1.141696) | 2.656445 / 1.468490 (1.187955) | 1.044244 / 4.584777 (-3.540533) | 5.393212 / 3.745712 (1.647500) | 4.592928 / 5.269862 (-0.676934) | 2.798525 / 4.565676 (-1.767151) | 0.103720 / 0.424275 (-0.320555) | 0.010196 / 0.007607 (0.002589) | 0.762756 / 0.226044 (0.536711) | 7.232939 / 2.268929 (4.964011) | 3.714015 / 55.444624 (-51.730609) | 3.050766 / 6.876477 (-3.825711) | 3.149715 / 2.142072 (1.007643) | 1.058827 / 4.805227 (-3.746400) | 0.214079 / 6.500664 (-6.286585) | 0.076712 / 0.075469 (0.001243) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.701032 / 1.841788 (-0.140755) | 23.742023 / 8.074308 (15.667715) | 22.486043 / 10.191392 (12.294651) | 0.249757 / 0.680424 (-0.430667) | 0.031714 / 0.534201 (-0.502486) | 0.479914 / 0.579283 (-0.099369) | 0.593315 / 0.434364 (0.158951) | 0.562897 / 0.540337 (0.022560) | 0.826636 / 1.386936 (-0.560300) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#429f9c69d1813ec643c316313b69ff23aaf208f6 \"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) | 0.007816 / 0.011353 (-0.003537) | 0.004541 / 0.011008 (-0.006467) | 0.097256 / 0.038508 (0.058748) | 0.081376 / 0.023109 (0.058267) | 0.356635 / 0.275898 (0.080737) | 0.394969 / 0.323480 (0.071489) | 0.004670 / 0.007986 (-0.003316) | 0.003537 / 0.004328 (-0.000791) | 0.075564 / 0.004250 (0.071314) | 0.063459 / 0.037052 (0.026407) | 0.363846 / 0.258489 (0.105357) | 0.416337 / 0.293841 (0.122496) | 0.036690 / 0.128546 (-0.091857) | 0.009653 / 0.075646 (-0.065993) | 0.337265 / 0.419271 (-0.082007) | 0.061446 / 0.043533 (0.017913) | 0.359190 / 0.255139 (0.104051) | 0.385866 / 0.283200 (0.102666) | 0.030474 / 0.141683 (-0.111209) | 1.796903 / 1.452155 (0.344748) | 1.852332 / 1.492716 (0.359616) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.264008 / 0.018006 (0.246002) | 0.507387 / 0.000490 (0.506897) | 0.012309 / 0.000200 (0.012109) | 0.000377 / 0.000054 (0.000323) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033224 / 0.037411 (-0.004188) | 0.097136 / 0.014526 (0.082610) | 0.113035 / 0.176557 (-0.063522) | 0.181778 / 0.737135 (-0.555357) | 0.130511 / 0.296338 (-0.165827) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.444512 / 0.215209 (0.229303) | 4.453285 / 2.077655 (2.375631) | 2.154123 / 1.504120 (0.650003) | 1.955451 / 1.541195 (0.414256) | 2.015089 / 1.468490 (0.546599) | 0.567824 / 4.584777 (-4.016953) | 4.083084 / 3.745712 (0.337371) | 3.912417 / 5.269862 (-1.357445) | 2.366197 / 4.565676 (-2.199480) | 0.066468 / 0.424275 (-0.357807) | 0.008478 / 0.007607 (0.000870) | 0.531196 / 0.226044 (0.305152) | 5.311285 / 2.268929 (3.042356) | 2.743252 / 55.444624 (-52.701372) | 2.322353 / 6.876477 (-4.554124) | 2.368168 / 2.142072 (0.226095) | 0.679223 / 4.805227 (-4.126004) | 0.152401 / 6.500664 (-6.348263) | 0.071954 / 0.075469 (-0.003515) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.489114 / 1.841788 (-0.352674) | 22.114956 / 8.074308 (14.040648) | 16.072564 / 10.191392 (5.881172) | 0.164303 / 0.680424 (-0.516121) | 0.021317 / 0.534201 (-0.512884) | 0.460250 / 0.579283 (-0.119033) | 0.467554 / 0.434364 (0.033190) | 0.539773 / 0.540337 (-0.000564) | 0.751904 / 1.386936 (-0.635032) |\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) | 0.007520 / 0.011353 (-0.003833) | 0.004487 / 0.011008 (-0.006521) | 0.075074 / 0.038508 (0.036566) | 0.083135 / 0.023109 (0.060026) | 0.474052 / 0.275898 (0.198154) | 0.524051 / 0.323480 (0.200571) | 0.006192 / 0.007986 (-0.001793) | 0.003835 / 0.004328 (-0.000494) | 0.074643 / 0.004250 (0.070392) | 0.065334 / 0.037052 (0.028282) | 0.507033 / 0.258489 (0.248544) | 0.519846 / 0.293841 (0.226005) | 0.036985 / 0.128546 (-0.091561) | 0.009828 / 0.075646 (-0.065818) | 0.082992 / 0.419271 (-0.336279) | 0.055942 / 0.043533 (0.012409) | 0.480652 / 0.255139 (0.225513) | 0.503683 / 0.283200 (0.220483) | 0.025560 / 0.141683 (-0.116123) | 1.801390 / 1.452155 (0.349235) | 1.892929 / 1.492716 (0.400213) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.246771 / 0.018006 (0.228765) | 0.498901 / 0.000490 (0.498411) | 0.008186 / 0.000200 (0.007986) | 0.000166 / 0.000054 (0.000112) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.038666 / 0.037411 (0.001254) | 0.110317 / 0.014526 (0.095791) | 0.122995 / 0.176557 (-0.053562) | 0.185355 / 0.737135 (-0.551781) | 0.123720 / 0.296338 (-0.172619) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.508421 / 0.215209 (0.293212) | 5.046464 / 2.077655 (2.968809) | 2.660004 / 1.504120 (1.155884) | 2.482841 / 1.541195 (0.941646) | 2.573941 / 1.468490 (1.105451) | 0.565702 / 4.584777 (-4.019075) | 4.197895 / 3.745712 (0.452183) | 3.755480 / 5.269862 (-1.514381) | 2.308066 / 4.565676 (-2.257610) | 0.066559 / 0.424275 (-0.357716) | 0.008436 / 0.007607 (0.000829) | 0.589858 / 0.226044 (0.363814) | 5.873488 / 2.268929 (3.604559) | 3.241810 / 55.444624 (-52.202814) | 2.789831 / 6.876477 (-4.086645) | 3.008989 / 2.142072 (0.866917) | 0.679624 / 4.805227 (-4.125603) | 0.150868 / 6.500664 (-6.349796) | 0.068581 / 0.075469 (-0.006889) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.582955 / 1.841788 (-0.258833) | 22.684969 / 8.074308 (14.610661) | 16.829855 / 10.191392 (6.638463) | 0.201599 / 0.680424 (-0.478825) | 0.023261 / 0.534201 (-0.510940) | 0.465009 / 0.579283 (-0.114274) | 0.497701 / 0.434364 (0.063337) | 0.557822 / 0.540337 (0.017485) | 0.803234 / 1.386936 (-0.583702) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9241c1070b5c9021705c17b12548b6fea75782d8 \"CML watermark\")\n", "Well done! :clap: :fire: ", "<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) | 0.008866 / 0.011353 (-0.002487) | 0.005910 / 0.011008 (-0.005098) | 0.099916 / 0.038508 (0.061408) | 0.085787 / 0.023109 (0.062678) | 0.391028 / 0.275898 (0.115130) | 0.412689 / 0.323480 (0.089209) | 0.006527 / 0.007986 (-0.001459) | 0.004629 / 0.004328 (0.000301) | 0.084627 / 0.004250 (0.080377) | 0.063404 / 0.037052 (0.026352) | 0.408923 / 0.258489 (0.150434) | 0.437130 / 0.293841 (0.143289) | 0.050256 / 0.128546 (-0.078290) | 0.013914 / 0.075646 (-0.061732) | 0.350893 / 0.419271 (-0.068379) | 0.067931 / 0.043533 (0.024398) | 0.383807 / 0.255139 (0.128668) | 0.424150 / 0.283200 (0.140950) | 0.039978 / 0.141683 (-0.101705) | 1.697631 / 1.452155 (0.245476) | 1.925568 / 1.492716 (0.432851) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.315417 / 0.018006 (0.297410) | 0.607050 / 0.000490 (0.606560) | 0.017314 / 0.000200 (0.017114) | 0.000514 / 0.000054 (0.000459) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032994 / 0.037411 (-0.004417) | 0.103993 / 0.014526 (0.089467) | 0.125369 / 0.176557 (-0.051187) | 0.185984 / 0.737135 (-0.551151) | 0.139192 / 0.296338 (-0.157146) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.639769 / 0.215209 (0.424560) | 6.236187 / 2.077655 (4.158532) | 2.775777 / 1.504120 (1.271657) | 2.599683 / 1.541195 (1.058488) | 2.780064 / 1.468490 (1.311574) | 1.107247 / 4.584777 (-3.477530) | 5.724223 / 3.745712 (1.978511) | 5.284786 / 5.269862 (0.014925) | 3.342465 / 4.565676 (-1.223211) | 0.107685 / 0.424275 (-0.316590) | 0.009237 / 0.007607 (0.001630) | 0.760282 / 0.226044 (0.534238) | 7.570859 / 2.268929 (5.301930) | 3.572498 / 55.444624 (-51.872126) | 2.997482 / 6.876477 (-3.878995) | 2.910001 / 2.142072 (0.767929) | 1.249272 / 4.805227 (-3.555955) | 0.229425 / 6.500664 (-6.271239) | 0.091974 / 0.075469 (0.016505) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.663859 / 1.841788 (-0.177929) | 25.283961 / 8.074308 (17.209653) | 20.793389 / 10.191392 (10.601997) | 0.239263 / 0.680424 (-0.441161) | 0.028808 / 0.534201 (-0.505393) | 0.521045 / 0.579283 (-0.058238) | 0.602451 / 0.434364 (0.168087) | 0.544536 / 0.540337 (0.004198) | 0.819732 / 1.386936 (-0.567204) |\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) | 0.008970 / 0.011353 (-0.002383) | 0.009663 / 0.011008 (-0.001345) | 0.083471 / 0.038508 (0.044963) | 0.090695 / 0.023109 (0.067585) | 0.562539 / 0.275898 (0.286641) | 0.572092 / 0.323480 (0.248612) | 0.007269 / 0.007986 (-0.000717) | 0.004664 / 0.004328 (0.000335) | 0.084212 / 0.004250 (0.079961) | 0.072716 / 0.037052 (0.035664) | 0.559810 / 0.258489 (0.301320) | 0.574296 / 0.293841 (0.280455) | 0.048555 / 0.128546 (-0.079991) | 0.015901 / 0.075646 (-0.059746) | 0.107815 / 0.419271 (-0.311456) | 0.065404 / 0.043533 (0.021871) | 0.544787 / 0.255139 (0.289648) | 0.586993 / 0.283200 (0.303794) | 0.042613 / 0.141683 (-0.099069) | 1.919266 / 1.452155 (0.467111) | 2.095189 / 1.492716 (0.602473) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.298512 / 0.018006 (0.280506) | 0.597745 / 0.000490 (0.597256) | 0.008806 / 0.000200 (0.008606) | 0.000119 / 0.000054 (0.000064) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.039420 / 0.037411 (0.002009) | 0.111378 / 0.014526 (0.096852) | 0.136421 / 0.176557 (-0.040135) | 0.192006 / 0.737135 (-0.545129) | 0.130037 / 0.296338 (-0.166301) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.679169 / 0.215209 (0.463960) | 6.750881 / 2.077655 (4.673226) | 3.220411 / 1.504120 (1.716291) | 2.851988 / 1.541195 (1.310794) | 2.974247 / 1.468490 (1.505757) | 0.892593 / 4.584777 (-3.692184) | 5.659975 / 3.745712 (1.914263) | 5.172641 / 5.269862 (-0.097220) | 3.308429 / 4.565676 (-1.257248) | 0.100580 / 0.424275 (-0.323695) | 0.009320 / 0.007607 (0.001713) | 0.833290 / 0.226044 (0.607245) | 8.091847 / 2.268929 (5.822918) | 4.023734 / 55.444624 (-51.420890) | 3.441583 / 6.876477 (-3.434894) | 3.763562 / 2.142072 (1.621489) | 1.055105 / 4.805227 (-3.750122) | 0.239218 / 6.500664 (-6.261446) | 0.081922 / 0.075469 (0.006453) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.796495 / 1.841788 (-0.045293) | 25.942492 / 8.074308 (17.868184) | 23.211617 / 10.191392 (13.020225) | 0.256054 / 0.680424 (-0.424370) | 0.030491 / 0.534201 (-0.503710) | 0.520474 / 0.579283 (-0.058809) | 0.626331 / 0.434364 (0.191967) | 0.619897 / 0.540337 (0.079560) | 0.900833 / 1.386936 (-0.486103) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e74f80255700c4b8cde383a426c4b2def6db1253 \"CML watermark\")\n", "Congrats on merging this! 👏 " ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6268). All of your documentation changes will be reflected on that endpoint.", "In https://github.com/huggingface/datasets/issues/4129 we want to track the origin of a dataset, e.g. if it comes from multiple datasets.\r\n\r\nI think it's out of scope of DatasetInfo alone, which has info for one dataset only.\r\nTherefore it makes sense to add repo_id, which is for one dataset only.\r\n\r\nIMO if we want to track multiple origins we will need a new DatasetInfo that would have fields relevant to a mix of datasets (out of scope of this PR)\r\n\r\ncc @mariosasko I'd like your opinion on this", "<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) | 0.009009 / 0.011353 (-0.002344) | 0.004169 / 0.011008 (-0.006840) | 0.098634 / 0.038508 (0.060126) | 0.069526 / 0.023109 (0.046417) | 0.337963 / 0.275898 (0.062065) | 0.379737 / 0.323480 (0.056257) | 0.004318 / 0.007986 (-0.003668) | 0.005347 / 0.004328 (0.001019) | 0.069875 / 0.004250 (0.065624) | 0.055964 / 0.037052 (0.018912) | 0.340305 / 0.258489 (0.081816) | 0.429718 / 0.293841 (0.135877) | 0.045101 / 0.128546 (-0.083445) | 0.012610 / 0.075646 (-0.063036) | 0.312366 / 0.419271 (-0.106905) | 0.064711 / 0.043533 (0.021178) | 0.345216 / 0.255139 (0.090077) | 0.367245 / 0.283200 (0.084046) | 0.034638 / 0.141683 (-0.107045) | 1.541947 / 1.452155 (0.089793) | 1.645268 / 1.492716 (0.152551) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.233501 / 0.018006 (0.215495) | 0.514207 / 0.000490 (0.513717) | 0.014271 / 0.000200 (0.014072) | 0.000366 / 0.000054 (0.000311) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026288 / 0.037411 (-0.011124) | 0.083206 / 0.014526 (0.068680) | 0.098172 / 0.176557 (-0.078385) | 0.158529 / 0.737135 (-0.578606) | 0.095183 / 0.296338 (-0.201155) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.538300 / 0.215209 (0.323091) | 5.486939 / 2.077655 (3.409285) | 2.321812 / 1.504120 (0.817692) | 2.002124 / 1.541195 (0.460929) | 2.045043 / 1.468490 (0.576553) | 0.852772 / 4.584777 (-3.732005) | 5.014897 / 3.745712 (1.269185) | 4.428115 / 5.269862 (-0.841746) | 2.750126 / 4.565676 (-1.815550) | 0.099028 / 0.424275 (-0.325247) | 0.007678 / 0.007607 (0.000070) | 0.664463 / 0.226044 (0.438418) | 6.617811 / 2.268929 (4.348883) | 2.888382 / 55.444624 (-52.556242) | 2.190753 / 6.876477 (-4.685724) | 2.414586 / 2.142072 (0.272513) | 1.010302 / 4.805227 (-3.794925) | 0.194925 / 6.500664 (-6.305739) | 0.063490 / 0.075469 (-0.011979) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.543464 / 1.841788 (-0.298323) | 20.566666 / 8.074308 (12.492358) | 19.410745 / 10.191392 (9.219353) | 0.207077 / 0.680424 (-0.473347) | 0.028895 / 0.534201 (-0.505306) | 0.427525 / 0.579283 (-0.151758) | 0.535450 / 0.434364 (0.101086) | 0.494632 / 0.540337 (-0.045705) | 0.723705 / 1.386936 (-0.663231) |\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) | 0.008209 / 0.011353 (-0.003144) | 0.004184 / 0.011008 (-0.006824) | 0.072420 / 0.038508 (0.033912) | 0.066851 / 0.023109 (0.043742) | 0.424137 / 0.275898 (0.148239) | 0.473156 / 0.323480 (0.149676) | 0.005394 / 0.007986 (-0.002591) | 0.003898 / 0.004328 (-0.000430) | 0.069996 / 0.004250 (0.065746) | 0.053113 / 0.037052 (0.016061) | 0.453214 / 0.258489 (0.194725) | 0.495921 / 0.293841 (0.202080) | 0.043028 / 0.128546 (-0.085519) | 0.012320 / 0.075646 (-0.063326) | 0.080270 / 0.419271 (-0.339002) | 0.053337 / 0.043533 (0.009804) | 0.436604 / 0.255139 (0.181465) | 0.463422 / 0.283200 (0.180223) | 0.030277 / 0.141683 (-0.111406) | 1.560261 / 1.452155 (0.108106) | 1.647209 / 1.492716 (0.154493) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.232556 / 0.018006 (0.214550) | 0.502387 / 0.000490 (0.501897) | 0.006688 / 0.000200 (0.006488) | 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) | 0.030204 / 0.037411 (-0.007207) | 0.089438 / 0.014526 (0.074912) | 0.118939 / 0.176557 (-0.057617) | 0.160537 / 0.737135 (-0.576598) | 0.113432 / 0.296338 (-0.182906) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.586469 / 0.215209 (0.371260) | 5.916156 / 2.077655 (3.838502) | 2.904960 / 1.504120 (1.400840) | 2.346838 / 1.541195 (0.805644) | 2.373688 / 1.468490 (0.905198) | 0.829917 / 4.584777 (-3.754860) | 4.851283 / 3.745712 (1.105571) | 4.220103 / 5.269862 (-1.049758) | 2.706139 / 4.565676 (-1.859538) | 0.094095 / 0.424275 (-0.330180) | 0.008201 / 0.007607 (0.000594) | 0.699099 / 0.226044 (0.473054) | 7.046940 / 2.268929 (4.778011) | 3.374837 / 55.444624 (-52.069788) | 2.690839 / 6.876477 (-4.185638) | 2.845717 / 2.142072 (0.703645) | 0.989698 / 4.805227 (-3.815529) | 0.190413 / 6.500664 (-6.310251) | 0.066233 / 0.075469 (-0.009236) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.513607 / 1.841788 (-0.328180) | 21.544200 / 8.074308 (13.469892) | 20.297337 / 10.191392 (10.105945) | 0.216390 / 0.680424 (-0.464034) | 0.029962 / 0.534201 (-0.504239) | 0.451531 / 0.579283 (-0.127752) | 0.530147 / 0.434364 (0.095783) | 0.520739 / 0.540337 (-0.019598) | 0.716431 / 1.386936 (-0.670505) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#fcaa9f218ad1505bb5474060889b4b9578e24423 \"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) | 0.006509 / 0.011353 (-0.004844) | 0.003987 / 0.011008 (-0.007022) | 0.085233 / 0.038508 (0.046725) | 0.077765 / 0.023109 (0.054656) | 0.310467 / 0.275898 (0.034569) | 0.343363 / 0.323480 (0.019883) | 0.005557 / 0.007986 (-0.002429) | 0.003430 / 0.004328 (-0.000898) | 0.064948 / 0.004250 (0.060697) | 0.056864 / 0.037052 (0.019812) | 0.314005 / 0.258489 (0.055516) | 0.360638 / 0.293841 (0.066798) | 0.031134 / 0.128546 (-0.097412) | 0.008869 / 0.075646 (-0.066777) | 0.286409 / 0.419271 (-0.132862) | 0.051338 / 0.043533 (0.007805) | 0.311329 / 0.255139 (0.056190) | 0.334373 / 0.283200 (0.051174) | 0.024816 / 0.141683 (-0.116867) | 1.502872 / 1.452155 (0.050718) | 1.569941 / 1.492716 (0.077224) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.269639 / 0.018006 (0.251633) | 0.558510 / 0.000490 (0.558020) | 0.011748 / 0.000200 (0.011548) | 0.000234 / 0.000054 (0.000180) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029139 / 0.037411 (-0.008272) | 0.083586 / 0.014526 (0.069060) | 0.102426 / 0.176557 (-0.074131) | 0.162398 / 0.737135 (-0.574737) | 0.101364 / 0.296338 (-0.194975) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.382281 / 0.215209 (0.167072) | 3.826412 / 2.077655 (1.748758) | 1.815911 / 1.504120 (0.311791) | 1.644539 / 1.541195 (0.103344) | 1.688487 / 1.468490 (0.219996) | 0.482115 / 4.584777 (-4.102662) | 3.574773 / 3.745712 (-0.170939) | 3.262733 / 5.269862 (-2.007129) | 2.058115 / 4.565676 (-2.507562) | 0.056367 / 0.424275 (-0.367908) | 0.007233 / 0.007607 (-0.000374) | 0.456859 / 0.226044 (0.230815) | 4.565935 / 2.268929 (2.297006) | 2.311802 / 55.444624 (-53.132823) | 1.943936 / 6.876477 (-4.932541) | 2.129811 / 2.142072 (-0.012261) | 0.575098 / 4.805227 (-4.230129) | 0.130495 / 6.500664 (-6.370169) | 0.059757 / 0.075469 (-0.015712) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.238495 / 1.841788 (-0.603293) | 18.940000 / 8.074308 (10.865692) | 14.034240 / 10.191392 (3.842848) | 0.166418 / 0.680424 (-0.514006) | 0.018420 / 0.534201 (-0.515781) | 0.395330 / 0.579283 (-0.183953) | 0.413518 / 0.434364 (-0.020846) | 0.461499 / 0.540337 (-0.078838) | 0.661371 / 1.386936 (-0.725565) |\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) | 0.006673 / 0.011353 (-0.004680) | 0.004335 / 0.011008 (-0.006673) | 0.064568 / 0.038508 (0.026060) | 0.072763 / 0.023109 (0.049653) | 0.429488 / 0.275898 (0.153590) | 0.456900 / 0.323480 (0.133420) | 0.005481 / 0.007986 (-0.002505) | 0.003649 / 0.004328 (-0.000680) | 0.064975 / 0.004250 (0.060724) | 0.056839 / 0.037052 (0.019786) | 0.439451 / 0.258489 (0.180962) | 0.461691 / 0.293841 (0.167850) | 0.031455 / 0.128546 (-0.097092) | 0.008848 / 0.075646 (-0.066798) | 0.071719 / 0.419271 (-0.347553) | 0.047116 / 0.043533 (0.003583) | 0.429055 / 0.255139 (0.173916) | 0.434204 / 0.283200 (0.151004) | 0.022594 / 0.141683 (-0.119089) | 1.539231 / 1.452155 (0.087077) | 1.568111 / 1.492716 (0.075394) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.267374 / 0.018006 (0.249368) | 0.553202 / 0.000490 (0.552712) | 0.005410 / 0.000200 (0.005210) | 0.000101 / 0.000054 (0.000046) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031478 / 0.037411 (-0.005933) | 0.092438 / 0.014526 (0.077912) | 0.103874 / 0.176557 (-0.072682) | 0.158428 / 0.737135 (-0.578708) | 0.111617 / 0.296338 (-0.184721) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.434783 / 0.215209 (0.219574) | 4.332536 / 2.077655 (2.254881) | 2.354522 / 1.504120 (0.850402) | 2.220271 / 1.541195 (0.679076) | 2.338524 / 1.468490 (0.870034) | 0.494508 / 4.584777 (-4.090269) | 3.619592 / 3.745712 (-0.126120) | 3.320897 / 5.269862 (-1.948964) | 2.107475 / 4.565676 (-2.458202) | 0.058479 / 0.424275 (-0.365796) | 0.007427 / 0.007607 (-0.000180) | 0.509298 / 0.226044 (0.283254) | 5.067940 / 2.268929 (2.799012) | 2.815336 / 55.444624 (-52.629288) | 2.470958 / 6.876477 (-4.405519) | 2.672801 / 2.142072 (0.530728) | 0.588199 / 4.805227 (-4.217028) | 0.134062 / 6.500664 (-6.366602) | 0.060951 / 0.075469 (-0.014518) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.353955 / 1.841788 (-0.487832) | 20.386012 / 8.074308 (12.311704) | 15.032463 / 10.191392 (4.841071) | 0.167243 / 0.680424 (-0.513181) | 0.020426 / 0.534201 (-0.513775) | 0.396815 / 0.579283 (-0.182469) | 0.421806 / 0.434364 (-0.012558) | 0.471866 / 0.540337 (-0.068471) | 0.667206 / 1.386936 (-0.719730) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#aade5a0c79398c84632a3ff253111e694c7b598b \"CML watermark\")\n", "Really happy to see this! It could also be helpful to track some other metadata about how the dataset was built in the future. i.e. for the Stack loaded like this:\r\n\r\n```\r\nds = load_dataset(\"bigcode/the-stack\", data_dir=\"data/dockerfile\", split=\"train\")\r\n```\r\nIt could be helpful to have easy access to the `data_dir` argument used during loading since that changes the training data quite a bit vs. loading the full dataset. You can also recover this from `download_checksums`, which seems a bit hacky. That is not necessary for this PR, though.\r\n", "Perhaps it is also interesting to track the revision? I suppose the version also kind of covers that.\r\n\r\nThat said, this is looking great already! I'm quite excited about this. Losing the `repo_id` after merging (different) datasets also makes sense to me, well done.", "One other thought. Is it worth tracking if a `token` was passed during loading? \r\n\r\nThe Hub ID for private datasets could in some cases contain information someone wouldn't want to make public i.e. `davanstrien/super_secret_dataset_using_GPT_created_data`. \r\n\r\nAdding a bool like `is_private` could then be used by another library to determine if the dataset ID should be shared or not (or default to not sharing the ID for private datasets). i.e. in SpanMarker @tomaarsen might do a check like \r\n\r\n```python\r\nif ds.is_private and not push_hub_id_for_private_ds:\r\n\tds_name = None\r\n```\r\nPotentially this is overkill but could be useful for downstream libraries who might use this information for creating automatic model cards. \r\n\r\n\r\n", "We should probably find a way to remove `DatasetInfo`, as (most of) its attributes are outdated (homepage, description, etc.), not introduce new ones :). But I guess storing `repo_id` there is the simplest solution for now, so I'm OK with it.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007757 / 0.011353 (-0.003595) | 0.004543 / 0.011008 (-0.006465) | 0.100193 / 0.038508 (0.061685) | 0.082333 / 0.023109 (0.059224) | 0.374586 / 0.275898 (0.098688) | 0.412617 / 0.323480 (0.089137) | 0.006148 / 0.007986 (-0.001838) | 0.003826 / 0.004328 (-0.000503) | 0.077077 / 0.004250 (0.072827) | 0.064057 / 0.037052 (0.027005) | 0.391435 / 0.258489 (0.132946) | 0.436439 / 0.293841 (0.142599) | 0.036534 / 0.128546 (-0.092012) | 0.009986 / 0.075646 (-0.065660) | 0.344243 / 0.419271 (-0.075028) | 0.062013 / 0.043533 (0.018480) | 0.378113 / 0.255139 (0.122974) | 0.398476 / 0.283200 (0.115276) | 0.026552 / 0.141683 (-0.115131) | 1.740505 / 1.452155 (0.288350) | 1.835684 / 1.492716 (0.342968) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.267917 / 0.018006 (0.249911) | 0.510676 / 0.000490 (0.510186) | 0.010810 / 0.000200 (0.010610) | 0.000383 / 0.000054 (0.000328) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032113 / 0.037411 (-0.005299) | 0.097679 / 0.014526 (0.083153) | 0.113213 / 0.176557 (-0.063344) | 0.177897 / 0.737135 (-0.559238) | 0.111761 / 0.296338 (-0.184577) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.450544 / 0.215209 (0.235335) | 4.476746 / 2.077655 (2.399091) | 2.205391 / 1.504120 (0.701271) | 2.006457 / 1.541195 (0.465262) | 2.058859 / 1.468490 (0.590369) | 0.571549 / 4.584777 (-4.013228) | 4.175039 / 3.745712 (0.429327) | 3.815445 / 5.269862 (-1.454416) | 2.376673 / 4.565676 (-2.189004) | 0.067048 / 0.424275 (-0.357227) | 0.008544 / 0.007607 (0.000937) | 0.536384 / 0.226044 (0.310340) | 5.386232 / 2.268929 (3.117304) | 2.825620 / 55.444624 (-52.619004) | 2.339821 / 6.876477 (-4.536656) | 2.535736 / 2.142072 (0.393663) | 0.679572 / 4.805227 (-4.125655) | 0.156799 / 6.500664 (-6.343865) | 0.071667 / 0.075469 (-0.003802) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.512198 / 1.841788 (-0.329590) | 21.786760 / 8.074308 (13.712452) | 16.386274 / 10.191392 (6.194882) | 0.169108 / 0.680424 (-0.511316) | 0.021312 / 0.534201 (-0.512889) | 0.466153 / 0.579283 (-0.113130) | 0.496192 / 0.434364 (0.061829) | 0.549420 / 0.540337 (0.009082) | 0.780974 / 1.386936 (-0.605962) |\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) | 0.007644 / 0.011353 (-0.003709) | 0.004654 / 0.011008 (-0.006354) | 0.075280 / 0.038508 (0.036772) | 0.083044 / 0.023109 (0.059935) | 0.481704 / 0.275898 (0.205805) | 0.514828 / 0.323480 (0.191348) | 0.006245 / 0.007986 (-0.001740) | 0.003715 / 0.004328 (-0.000614) | 0.074498 / 0.004250 (0.070248) | 0.064406 / 0.037052 (0.027353) | 0.481874 / 0.258489 (0.223385) | 0.518527 / 0.293841 (0.224686) | 0.037549 / 0.128546 (-0.090997) | 0.010106 / 0.075646 (-0.065541) | 0.084266 / 0.419271 (-0.335006) | 0.056659 / 0.043533 (0.013126) | 0.497707 / 0.255139 (0.242568) | 0.503201 / 0.283200 (0.220001) | 0.027086 / 0.141683 (-0.114597) | 1.834715 / 1.452155 (0.382560) | 1.919927 / 1.492716 (0.427210) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.249288 / 0.018006 (0.231282) | 0.500950 / 0.000490 (0.500460) | 0.005856 / 0.000200 (0.005656) | 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) | 0.037674 / 0.037411 (0.000263) | 0.111141 / 0.014526 (0.096615) | 0.123408 / 0.176557 (-0.053149) | 0.186604 / 0.737135 (-0.550531) | 0.125360 / 0.296338 (-0.170979) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.520480 / 0.215209 (0.305271) | 5.171108 / 2.077655 (3.093453) | 2.812746 / 1.504120 (1.308626) | 2.602941 / 1.541195 (1.061746) | 2.666196 / 1.468490 (1.197706) | 0.578684 / 4.584777 (-4.006092) | 4.238722 / 3.745712 (0.493010) | 3.844361 / 5.269862 (-1.425501) | 2.369214 / 4.565676 (-2.196462) | 0.068543 / 0.424275 (-0.355732) | 0.008695 / 0.007607 (0.001088) | 0.621869 / 0.226044 (0.395825) | 6.200566 / 2.268929 (3.931637) | 3.340846 / 55.444624 (-52.103779) | 2.920691 / 6.876477 (-3.955786) | 3.132438 / 2.142072 (0.990366) | 0.697394 / 4.805227 (-4.107834) | 0.158385 / 6.500664 (-6.342280) | 0.072566 / 0.075469 (-0.002903) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.599070 / 1.841788 (-0.242717) | 22.767139 / 8.074308 (14.692831) | 17.053988 / 10.191392 (6.862596) | 0.188414 / 0.680424 (-0.492009) | 0.023409 / 0.534201 (-0.510792) | 0.472092 / 0.579283 (-0.107191) | 0.486107 / 0.434364 (0.051743) | 0.562190 / 0.540337 (0.021852) | 0.791606 / 1.386936 (-0.595330) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#aacbaf45c93f88e8c95924f6224153fb37c3064a \"CML watermark\")\n" ]

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

[]

[ "You can use a `Sequence(ClassLabel(...))` feature type to represent a list of labels, and `cast_column`/`cast` to perform the \"string to label\" conversion (`class_encode_column` does support nested fields), e.g., in your case:\r\n```python\r\nfrom datasets import Dataset, Sequence, ClassLabel\r\ndata = {\r\n 'text': ['one', 'two', 'three', 'four'],\r\n 'labels': [['a', 'b'], ['b'], ['b', 'c'], ['a', 'd']]\r\n}\r\n\r\ndataset = Dataset.from_dict(data)\r\ndataset = dataset.cast_column('labels', Sequence(ClassLabel(names=[\"a\", \"b\", \"c\", \"d\"])))\r\n```", "Great! Can you elaborate on \"class_encode_column does support nested fields\"? Do you mean that there is a way to `class_encode_column` on a Sequence?", "Yes, exactly! This would be a nice contribution, though.", "Sorry, I'm still not following. Are you saying that there currently exists a way to call `class_encode_column` on a `Sequence(ClassLabel)` type? Or that the underlying data structures support it and a contribution of a method to do that would be welcome?", "`class_encode_column ` currently does not support `Sequence(ClassLabel)`. Implementing support for this would be a nice contribution.\r\n\r\nIn the meantime, this limitation can be circumvented by fetching (unique) labels and calling `.cast_column(col, Sequence(ClassLabel(names=labels)))`.", "Ok makes sense, can you take a look at the POC implementation I did [here](https://github.com/huggingface/datasets/commit/15443098e9ce053943172f7ec6fce3769d7dff6e)? Happy to take another pass / submit as a PR but would be helpful if I got a thumbs up that this was directionally correct with respect to implementation / architecture. ", "There is no need to introduce a new type (`MultiLabel`) for this feature. Also, I think we can keep the logic inside a single method instead of separating the two cases.\r\n\r\nMaybe https://github.com/huggingface/datasets/pull/4277 can help with the implementation. We extended `align_labels_with_mapping` to support `Sequence(ClassLabel(...))` in that PR (initially, it only worked with `ClassLabel(...)`)" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6266). All of your documentation changes will be reflected on that endpoint.", "On Ubuntu, if `libyaml-dev` is installed, you can install PyYAML 6.0.1 with LibYAML with the following command (as it's automatically detected):\r\n\r\n```bash\r\npip install git+https://github.com/yaml/pyyaml.git@6.0.1\r\n```", "Are the failing tests flaky?", "We use `huggingface_hub`'s RepoCard API instead of these modules to parse the YAML block (notice the deprecations), so the `huggingface_hub` repo is the right place to suggest these changes.\r\n\r\nPersonally, I'm not a fan of these changes, as a single non-standard usage of the `ClassLabel` type is not a sufficient reason to merge them. Also, the dataset in question stores data in a single Parquet file, with the features info embedded in its (schema) metadata, which means the YAML parsing can be skipped while preserving the features by directly loading the Parquet file:\r\n```python\r\nfrom datasets import load_dataset\r\nds = load_dataset(\"parquet\", data_files=\"https://huggingface.co/datasets/HuggingFaceM4/SugarCrepe_swap_obj/resolve/main/data/test-00000-of-00001-ca2ae6017a2336d7.parquet\")\r\n```\r\n\r\nPS: Yes, these tests are flaky. We are working on fixing them.", "Oh, I didn't realize they were deprecated. Thanks for the tip on how to work around this issue!\r\n\r\nFor future reference, the places to change the code in `huggingface_hub` would be:\r\n\r\nhttps://github.com/huggingface/huggingface_hub/blob/89cc69105074f1d071e0471144605f3cdfe1dab3/src/huggingface_hub/repocard.py#L506\r\n\r\nhttps://github.com/huggingface/huggingface_hub/blob/89cc69105074f1d071e0471144605f3cdfe1dab3/src/huggingface_hub/utils/_fixes.py#L34" ]

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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) | 0.005896 / 0.011353 (-0.005457) | 0.003642 / 0.011008 (-0.007366) | 0.081917 / 0.038508 (0.043409) | 0.059513 / 0.023109 (0.036404) | 0.341422 / 0.275898 (0.065524) | 0.359278 / 0.323480 (0.035798) | 0.004707 / 0.007986 (-0.003279) | 0.002938 / 0.004328 (-0.001390) | 0.063095 / 0.004250 (0.058845) | 0.051777 / 0.037052 (0.014725) | 0.321114 / 0.258489 (0.062625) | 0.363823 / 0.293841 (0.069982) | 0.027590 / 0.128546 (-0.100957) | 0.007846 / 0.075646 (-0.067800) | 0.261197 / 0.419271 (-0.158074) | 0.045812 / 0.043533 (0.002279) | 0.319787 / 0.255139 (0.064648) | 0.341839 / 0.283200 (0.058640) | 0.021913 / 0.141683 (-0.119770) | 1.397525 / 1.452155 (-0.054630) | 1.495902 / 1.492716 (0.003186) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.224815 / 0.018006 (0.206809) | 0.425780 / 0.000490 (0.425290) | 0.006934 / 0.000200 (0.006734) | 0.000225 / 0.000054 (0.000171) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024342 / 0.037411 (-0.013070) | 0.073923 / 0.014526 (0.059398) | 0.082108 / 0.176557 (-0.094448) | 0.143017 / 0.737135 (-0.594119) | 0.083163 / 0.296338 (-0.213175) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.398244 / 0.215209 (0.183035) | 3.957688 / 2.077655 (1.880033) | 1.904615 / 1.504120 (0.400495) | 1.710353 / 1.541195 (0.169158) | 1.798980 / 1.468490 (0.330490) | 0.499307 / 4.584777 (-4.085470) | 3.026734 / 3.745712 (-0.718978) | 2.923940 / 5.269862 (-2.345922) | 1.831870 / 4.565676 (-2.733807) | 0.058551 / 0.424275 (-0.365724) | 0.006403 / 0.007607 (-0.001204) | 0.464164 / 0.226044 (0.238119) | 4.644556 / 2.268929 (2.375628) | 2.341455 / 55.444624 (-53.103169) | 2.004385 / 6.876477 (-4.872092) | 2.051819 / 2.142072 (-0.090253) | 0.585610 / 4.805227 (-4.219617) | 0.124735 / 6.500664 (-6.375929) | 0.061150 / 0.075469 (-0.014319) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.224665 / 1.841788 (-0.617122) | 17.476227 / 8.074308 (9.401919) | 13.867617 / 10.191392 (3.676225) | 0.144177 / 0.680424 (-0.536247) | 0.017045 / 0.534201 (-0.517156) | 0.337468 / 0.579283 (-0.241815) | 0.374476 / 0.434364 (-0.059888) | 0.393428 / 0.540337 (-0.146910) | 0.535335 / 1.386936 (-0.851601) |\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) | 0.006208 / 0.011353 (-0.005145) | 0.003650 / 0.011008 (-0.007359) | 0.062843 / 0.038508 (0.024335) | 0.062272 / 0.023109 (0.039162) | 0.446336 / 0.275898 (0.170438) | 0.477476 / 0.323480 (0.153996) | 0.004862 / 0.007986 (-0.003124) | 0.002822 / 0.004328 (-0.001506) | 0.063427 / 0.004250 (0.059177) | 0.049023 / 0.037052 (0.011971) | 0.453633 / 0.258489 (0.195144) | 0.486494 / 0.293841 (0.192653) | 0.028634 / 0.128546 (-0.099912) | 0.008187 / 0.075646 (-0.067460) | 0.068846 / 0.419271 (-0.350425) | 0.041104 / 0.043533 (-0.002429) | 0.446646 / 0.255139 (0.191507) | 0.468860 / 0.283200 (0.185660) | 0.020980 / 0.141683 (-0.120703) | 1.455565 / 1.452155 (0.003410) | 1.511142 / 1.492716 (0.018426) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.224242 / 0.018006 (0.206236) | 0.408483 / 0.000490 (0.407993) | 0.003495 / 0.000200 (0.003296) | 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) | 0.027286 / 0.037411 (-0.010125) | 0.081151 / 0.014526 (0.066625) | 0.096598 / 0.176557 (-0.079959) | 0.146193 / 0.737135 (-0.590942) | 0.092213 / 0.296338 (-0.204125) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.463837 / 0.215209 (0.248628) | 4.636820 / 2.077655 (2.559165) | 2.576100 / 1.504120 (1.071980) | 2.396974 / 1.541195 (0.855779) | 2.461526 / 1.468490 (0.993036) | 0.502360 / 4.584777 (-4.082417) | 3.099973 / 3.745712 (-0.645739) | 2.937260 / 5.269862 (-2.332602) | 1.871274 / 4.565676 (-2.694402) | 0.057913 / 0.424275 (-0.366362) | 0.006511 / 0.007607 (-0.001096) | 0.536917 / 0.226044 (0.310873) | 5.396966 / 2.268929 (3.128038) | 3.015646 / 55.444624 (-52.428978) | 2.673793 / 6.876477 (-4.202684) | 2.712376 / 2.142072 (0.570304) | 0.591632 / 4.805227 (-4.213595) | 0.124872 / 6.500664 (-6.375792) | 0.061820 / 0.075469 (-0.013649) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.356828 / 1.841788 (-0.484960) | 18.076995 / 8.074308 (10.002687) | 15.116482 / 10.191392 (4.925090) | 0.151375 / 0.680424 (-0.529049) | 0.017867 / 0.534201 (-0.516334) | 0.335012 / 0.579283 (-0.244271) | 0.384137 / 0.434364 (-0.050226) | 0.397792 / 0.540337 (-0.142546) | 0.551521 / 1.386936 (-0.835415) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#46a0506765d0f92916ed5c37eb19e5fa1a77736a \"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) | 0.009418 / 0.011353 (-0.001935) | 0.005186 / 0.011008 (-0.005822) | 0.112270 / 0.038508 (0.073761) | 0.114856 / 0.023109 (0.091747) | 0.402267 / 0.275898 (0.126369) | 0.445213 / 0.323480 (0.121733) | 0.005588 / 0.007986 (-0.002398) | 0.004315 / 0.004328 (-0.000013) | 0.083561 / 0.004250 (0.079311) | 0.087319 / 0.037052 (0.050267) | 0.400989 / 0.258489 (0.142500) | 0.455636 / 0.293841 (0.161795) | 0.045168 / 0.128546 (-0.083378) | 0.010939 / 0.075646 (-0.064707) | 0.400120 / 0.419271 (-0.019151) | 0.071599 / 0.043533 (0.028066) | 0.418112 / 0.255139 (0.162973) | 0.443889 / 0.283200 (0.160690) | 0.032433 / 0.141683 (-0.109250) | 1.886313 / 1.452155 (0.434159) | 2.012909 / 1.492716 (0.520193) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.306991 / 0.018006 (0.288985) | 0.590426 / 0.000490 (0.589937) | 0.011811 / 0.000200 (0.011611) | 0.000596 / 0.000054 (0.000542) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.042520 / 0.037411 (0.005108) | 0.129808 / 0.014526 (0.115283) | 0.125481 / 0.176557 (-0.051075) | 0.199181 / 0.737135 (-0.537954) | 0.130426 / 0.296338 (-0.165913) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.526455 / 0.215209 (0.311246) | 5.213304 / 2.077655 (3.135649) | 2.643406 / 1.504120 (1.139286) | 2.611214 / 1.541195 (1.070019) | 2.586730 / 1.468490 (1.118240) | 0.639103 / 4.584777 (-3.945674) | 5.197421 / 3.745712 (1.451709) | 4.634642 / 5.269862 (-0.635220) | 2.741079 / 4.565676 (-1.824598) | 0.073064 / 0.424275 (-0.351211) | 0.009441 / 0.007607 (0.001834) | 0.635984 / 0.226044 (0.409940) | 6.283268 / 2.268929 (4.014339) | 3.337205 / 55.444624 (-52.107419) | 3.192362 / 6.876477 (-3.684114) | 2.910367 / 2.142072 (0.768294) | 0.767937 / 4.805227 (-4.037290) | 0.177467 / 6.500664 (-6.323198) | 0.081162 / 0.075469 (0.005693) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.803717 / 1.841788 (-0.038071) | 26.823235 / 8.074308 (18.748927) | 19.714471 / 10.191392 (9.523079) | 0.204048 / 0.680424 (-0.476376) | 0.025992 / 0.534201 (-0.508209) | 0.521438 / 0.579283 (-0.057845) | 0.596524 / 0.434364 (0.162160) | 0.600763 / 0.540337 (0.060425) | 0.945971 / 1.386936 (-0.440965) |\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) | 0.009126 / 0.011353 (-0.002226) | 0.005109 / 0.011008 (-0.005899) | 0.083046 / 0.038508 (0.044538) | 0.115930 / 0.023109 (0.092821) | 0.534311 / 0.275898 (0.258413) | 0.552846 / 0.323480 (0.229366) | 0.007240 / 0.007986 (-0.000746) | 0.004617 / 0.004328 (0.000289) | 0.083927 / 0.004250 (0.079676) | 0.075926 / 0.037052 (0.038873) | 0.534750 / 0.258489 (0.276261) | 0.575122 / 0.293841 (0.281281) | 0.041001 / 0.128546 (-0.087545) | 0.010851 / 0.075646 (-0.064795) | 0.096574 / 0.419271 (-0.322697) | 0.063533 / 0.043533 (0.020001) | 0.546850 / 0.255139 (0.291711) | 0.547122 / 0.283200 (0.263922) | 0.032437 / 0.141683 (-0.109245) | 1.926191 / 1.452155 (0.474036) | 2.029841 / 1.492716 (0.537125) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.275582 / 0.018006 (0.257576) | 0.574212 / 0.000490 (0.573722) | 0.006863 / 0.000200 (0.006663) | 0.000236 / 0.000054 (0.000181) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.045340 / 0.037411 (0.007928) | 0.129196 / 0.014526 (0.114670) | 0.136637 / 0.176557 (-0.039920) | 0.200040 / 0.737135 (-0.537096) | 0.136328 / 0.296338 (-0.160011) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.612379 / 0.215209 (0.397170) | 5.874664 / 2.077655 (3.797010) | 3.070626 / 1.504120 (1.566506) | 2.999319 / 1.541195 (1.458124) | 3.000571 / 1.468490 (1.532081) | 0.732119 / 4.584777 (-3.852658) | 5.193226 / 3.745712 (1.447514) | 4.714571 / 5.269862 (-0.555291) | 2.870438 / 4.565676 (-1.695239) | 0.075793 / 0.424275 (-0.348482) | 0.009238 / 0.007607 (0.001631) | 0.695192 / 0.226044 (0.469148) | 6.897996 / 2.268929 (4.629067) | 3.923474 / 55.444624 (-51.521150) | 3.458326 / 6.876477 (-3.418151) | 3.331652 / 2.142072 (1.189579) | 0.821132 / 4.805227 (-3.984095) | 0.182252 / 6.500664 (-6.318412) | 0.084730 / 0.075469 (0.009260) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.919861 / 1.841788 (0.078073) | 27.437228 / 8.074308 (19.362920) | 21.109899 / 10.191392 (10.918507) | 0.245998 / 0.680424 (-0.434426) | 0.025817 / 0.534201 (-0.508384) | 0.517757 / 0.579283 (-0.061526) | 0.576375 / 0.434364 (0.142011) | 0.625283 / 0.540337 (0.084945) | 0.956877 / 1.386936 (-0.430059) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8ddee15a8650a0ea52073477036d8c973da50f11 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008099 / 0.011353 (-0.003254) | 0.004815 / 0.011008 (-0.006194) | 0.099657 / 0.038508 (0.061149) | 0.064737 / 0.023109 (0.041628) | 0.461773 / 0.275898 (0.185875) | 0.444810 / 0.323480 (0.121330) | 0.004247 / 0.007986 (-0.003739) | 0.004956 / 0.004328 (0.000628) | 0.068664 / 0.004250 (0.064414) | 0.052039 / 0.037052 (0.014986) | 0.406750 / 0.258489 (0.148261) | 0.452832 / 0.293841 (0.158991) | 0.044518 / 0.128546 (-0.084028) | 0.013220 / 0.075646 (-0.062426) | 0.317713 / 0.419271 (-0.101558) | 0.061897 / 0.043533 (0.018364) | 0.398664 / 0.255139 (0.143525) | 0.531494 / 0.283200 (0.248294) | 0.064033 / 0.141683 (-0.077650) | 1.590385 / 1.452155 (0.138231) | 1.769918 / 1.492716 (0.277202) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.230795 / 0.018006 (0.212789) | 0.568797 / 0.000490 (0.568308) | 0.013498 / 0.000200 (0.013298) | 0.000448 / 0.000054 (0.000393) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028394 / 0.037411 (-0.009017) | 0.081973 / 0.014526 (0.067447) | 0.097623 / 0.176557 (-0.078934) | 0.158691 / 0.737135 (-0.578445) | 0.101548 / 0.296338 (-0.194791) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.574459 / 0.215209 (0.359249) | 5.709871 / 2.077655 (3.632217) | 2.521460 / 1.504120 (1.017340) | 2.239463 / 1.541195 (0.698268) | 2.195067 / 1.468490 (0.726577) | 0.792390 / 4.584777 (-3.792387) | 4.841665 / 3.745712 (1.095952) | 4.201620 / 5.269862 (-1.068241) | 2.664081 / 4.565676 (-1.901595) | 0.097661 / 0.424275 (-0.326614) | 0.008428 / 0.007607 (0.000821) | 0.698729 / 0.226044 (0.472684) | 6.908867 / 2.268929 (4.639939) | 3.247480 / 55.444624 (-52.197145) | 2.563921 / 6.876477 (-4.312556) | 2.738249 / 2.142072 (0.596177) | 0.972066 / 4.805227 (-3.833161) | 0.191196 / 6.500664 (-6.309468) | 0.064732 / 0.075469 (-0.010737) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.421910 / 1.841788 (-0.419877) | 20.633538 / 8.074308 (12.559230) | 18.054562 / 10.191392 (7.863170) | 0.194125 / 0.680424 (-0.486299) | 0.028097 / 0.534201 (-0.506104) | 0.417857 / 0.579283 (-0.161426) | 0.518758 / 0.434364 (0.084394) | 0.500199 / 0.540337 (-0.040138) | 0.754662 / 1.386936 (-0.632274) |\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) | 0.008452 / 0.011353 (-0.002901) | 0.004646 / 0.011008 (-0.006362) | 0.077286 / 0.038508 (0.038778) | 0.072507 / 0.023109 (0.049398) | 0.439580 / 0.275898 (0.163682) | 0.506166 / 0.323480 (0.182686) | 0.006035 / 0.007986 (-0.001950) | 0.003886 / 0.004328 (-0.000442) | 0.075091 / 0.004250 (0.070841) | 0.063163 / 0.037052 (0.026110) | 0.468550 / 0.258489 (0.210061) | 0.523273 / 0.293841 (0.229432) | 0.048728 / 0.128546 (-0.079818) | 0.012991 / 0.075646 (-0.062655) | 0.087964 / 0.419271 (-0.331308) | 0.058920 / 0.043533 (0.015387) | 0.451247 / 0.255139 (0.196108) | 0.489827 / 0.283200 (0.206628) | 0.031164 / 0.141683 (-0.110519) | 1.675504 / 1.452155 (0.223349) | 1.806098 / 1.492716 (0.313382) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.253567 / 0.018006 (0.235561) | 0.508971 / 0.000490 (0.508481) | 0.010882 / 0.000200 (0.010682) | 0.000111 / 0.000054 (0.000057) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029490 / 0.037411 (-0.007921) | 0.090255 / 0.014526 (0.075729) | 0.110075 / 0.176557 (-0.066482) | 0.159375 / 0.737135 (-0.577760) | 0.109313 / 0.296338 (-0.187025) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.580252 / 0.215209 (0.365043) | 5.911741 / 2.077655 (3.834086) | 2.659405 / 1.504120 (1.155285) | 2.344943 / 1.541195 (0.803749) | 2.390748 / 1.468490 (0.922258) | 0.827823 / 4.584777 (-3.756954) | 4.973544 / 3.745712 (1.227832) | 4.300220 / 5.269862 (-0.969642) | 2.826181 / 4.565676 (-1.739495) | 0.101013 / 0.424275 (-0.323263) | 0.008025 / 0.007607 (0.000418) | 0.728414 / 0.226044 (0.502369) | 7.508045 / 2.268929 (5.239117) | 3.687627 / 55.444624 (-51.756997) | 2.902953 / 6.876477 (-3.973524) | 3.094624 / 2.142072 (0.952551) | 1.054696 / 4.805227 (-3.750531) | 0.212297 / 6.500664 (-6.288367) | 0.070211 / 0.075469 (-0.005258) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.567117 / 1.841788 (-0.274670) | 21.420746 / 8.074308 (13.346438) | 19.857467 / 10.191392 (9.666075) | 0.228554 / 0.680424 (-0.451870) | 0.032278 / 0.534201 (-0.501923) | 0.459966 / 0.579283 (-0.119317) | 0.541219 / 0.434364 (0.106855) | 0.549599 / 0.540337 (0.009261) | 0.731476 / 1.386936 (-0.655460) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0cc77d7f45c73698c31eab4f8cfff901044d0020 \"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) | 0.008356 / 0.011353 (-0.002997) | 0.004553 / 0.011008 (-0.006455) | 0.101025 / 0.038508 (0.062517) | 0.090194 / 0.023109 (0.067085) | 0.427127 / 0.275898 (0.151229) | 0.469116 / 0.323480 (0.145636) | 0.007593 / 0.007986 (-0.000393) | 0.003751 / 0.004328 (-0.000578) | 0.077432 / 0.004250 (0.073182) | 0.082744 / 0.037052 (0.045692) | 0.433638 / 0.258489 (0.175149) | 0.482387 / 0.293841 (0.188546) | 0.040658 / 0.128546 (-0.087888) | 0.009799 / 0.075646 (-0.065848) | 0.345274 / 0.419271 (-0.073998) | 0.076642 / 0.043533 (0.033109) | 0.424417 / 0.255139 (0.169278) | 0.457045 / 0.283200 (0.173846) | 0.033642 / 0.141683 (-0.108041) | 1.765446 / 1.452155 (0.313291) | 1.859279 / 1.492716 (0.366562) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.273629 / 0.018006 (0.255623) | 0.505743 / 0.000490 (0.505253) | 0.009300 / 0.000200 (0.009100) | 0.000359 / 0.000054 (0.000305) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032510 / 0.037411 (-0.004901) | 0.099628 / 0.014526 (0.085103) | 0.112904 / 0.176557 (-0.063652) | 0.179118 / 0.737135 (-0.558018) | 0.115946 / 0.296338 (-0.180393) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.456431 / 0.215209 (0.241222) | 4.556559 / 2.077655 (2.478904) | 2.207893 / 1.504120 (0.703773) | 2.024706 / 1.541195 (0.483512) | 2.165424 / 1.468490 (0.696934) | 0.571745 / 4.584777 (-4.013031) | 4.341017 / 3.745712 (0.595305) | 3.980520 / 5.269862 (-1.289342) | 2.333077 / 4.565676 (-2.232599) | 0.067200 / 0.424275 (-0.357075) | 0.008563 / 0.007607 (0.000956) | 0.545294 / 0.226044 (0.319250) | 5.445152 / 2.268929 (3.176224) | 2.740657 / 55.444624 (-52.703968) | 2.370635 / 6.876477 (-4.505842) | 2.451642 / 2.142072 (0.309570) | 0.679385 / 4.805227 (-4.125842) | 0.155967 / 6.500664 (-6.344697) | 0.072812 / 0.075469 (-0.002657) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.494483 / 1.841788 (-0.347305) | 23.673700 / 8.074308 (15.599392) | 16.608529 / 10.191392 (6.417137) | 0.170220 / 0.680424 (-0.510204) | 0.021630 / 0.534201 (-0.512571) | 0.470771 / 0.579283 (-0.108512) | 0.535874 / 0.434364 (0.101510) | 0.550376 / 0.540337 (0.010039) | 0.776633 / 1.386936 (-0.610303) |\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) | 0.007899 / 0.011353 (-0.003454) | 0.004581 / 0.011008 (-0.006427) | 0.076520 / 0.038508 (0.038012) | 0.090374 / 0.023109 (0.067265) | 0.495016 / 0.275898 (0.219118) | 0.532384 / 0.323480 (0.208904) | 0.006160 / 0.007986 (-0.001825) | 0.003780 / 0.004328 (-0.000548) | 0.077164 / 0.004250 (0.072914) | 0.064444 / 0.037052 (0.027391) | 0.501642 / 0.258489 (0.243153) | 0.549170 / 0.293841 (0.255329) | 0.038051 / 0.128546 (-0.090495) | 0.010081 / 0.075646 (-0.065565) | 0.083752 / 0.419271 (-0.335520) | 0.061334 / 0.043533 (0.017801) | 0.493502 / 0.255139 (0.238363) | 0.518018 / 0.283200 (0.234818) | 0.029534 / 0.141683 (-0.112149) | 1.929432 / 1.452155 (0.477277) | 1.889985 / 1.492716 (0.397268) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.254802 / 0.018006 (0.236795) | 0.494463 / 0.000490 (0.493974) | 0.005040 / 0.000200 (0.004840) | 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) | 0.038372 / 0.037411 (0.000960) | 0.112247 / 0.014526 (0.097721) | 0.124365 / 0.176557 (-0.052191) | 0.187142 / 0.737135 (-0.549993) | 0.126070 / 0.296338 (-0.170269) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.513418 / 0.215209 (0.298209) | 5.132267 / 2.077655 (3.054613) | 2.773676 / 1.504120 (1.269556) | 2.576840 / 1.541195 (1.035645) | 2.681729 / 1.468490 (1.213238) | 0.581809 / 4.584777 (-4.002968) | 4.327075 / 3.745712 (0.581363) | 4.040264 / 5.269862 (-1.229598) | 2.436192 / 4.565676 (-2.129484) | 0.067819 / 0.424275 (-0.356456) | 0.008760 / 0.007607 (0.001153) | 0.610765 / 0.226044 (0.384720) | 6.105679 / 2.268929 (3.836750) | 3.341341 / 55.444624 (-52.103284) | 2.926695 / 6.876477 (-3.949781) | 3.017269 / 2.142072 (0.875196) | 0.707289 / 4.805227 (-4.097938) | 0.157379 / 6.500664 (-6.343285) | 0.072549 / 0.075469 (-0.002920) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.666738 / 1.841788 (-0.175050) | 23.698567 / 8.074308 (15.624259) | 17.806437 / 10.191392 (7.615045) | 0.172103 / 0.680424 (-0.508321) | 0.023508 / 0.534201 (-0.510693) | 0.473171 / 0.579283 (-0.106112) | 0.524834 / 0.434364 (0.090470) | 0.562562 / 0.540337 (0.022224) | 0.788667 / 1.386936 (-0.598269) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1e7338259b26b32a095d251d5cdbc779c3573307 \"CML watermark\")\n", "CI 404 errors are unrelated. See:\r\n- #6262 ", "<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) | 0.006657 / 0.011353 (-0.004696) | 0.003975 / 0.011008 (-0.007033) | 0.084614 / 0.038508 (0.046106) | 0.074557 / 0.023109 (0.051448) | 0.309213 / 0.275898 (0.033315) | 0.338245 / 0.323480 (0.014765) | 0.005375 / 0.007986 (-0.002610) | 0.003355 / 0.004328 (-0.000973) | 0.064406 / 0.004250 (0.060156) | 0.061763 / 0.037052 (0.024711) | 0.313405 / 0.258489 (0.054916) | 0.352149 / 0.293841 (0.058308) | 0.031597 / 0.128546 (-0.096949) | 0.008499 / 0.075646 (-0.067147) | 0.289098 / 0.419271 (-0.130174) | 0.054415 / 0.043533 (0.010882) | 0.313210 / 0.255139 (0.058071) | 0.326728 / 0.283200 (0.043528) | 0.024597 / 0.141683 (-0.117086) | 1.449916 / 1.452155 (-0.002239) | 1.526314 / 1.492716 (0.033598) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.231435 / 0.018006 (0.213429) | 0.537224 / 0.000490 (0.536734) | 0.007287 / 0.000200 (0.007088) | 0.000227 / 0.000054 (0.000172) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028340 / 0.037411 (-0.009071) | 0.084085 / 0.014526 (0.069560) | 0.428211 / 0.176557 (0.251655) | 0.157360 / 0.737135 (-0.579775) | 0.139470 / 0.296338 (-0.156868) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.389311 / 0.215209 (0.174102) | 3.871329 / 2.077655 (1.793674) | 1.861533 / 1.504120 (0.357413) | 1.688082 / 1.541195 (0.146887) | 1.804036 / 1.468490 (0.335546) | 0.489154 / 4.584777 (-4.095623) | 3.603843 / 3.745712 (-0.141869) | 3.424868 / 5.269862 (-1.844994) | 2.013525 / 4.565676 (-2.552152) | 0.057387 / 0.424275 (-0.366888) | 0.007274 / 0.007607 (-0.000333) | 0.462340 / 0.226044 (0.236295) | 4.620095 / 2.268929 (2.351167) | 2.326641 / 55.444624 (-53.117984) | 1.990082 / 6.876477 (-4.886395) | 2.037841 / 2.142072 (-0.104232) | 0.581973 / 4.805227 (-4.223254) | 0.135932 / 6.500664 (-6.364732) | 0.061092 / 0.075469 (-0.014377) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.249586 / 1.841788 (-0.592202) | 19.036233 / 8.074308 (10.961925) | 14.083365 / 10.191392 (3.891973) | 0.169802 / 0.680424 (-0.510622) | 0.018547 / 0.534201 (-0.515654) | 0.392926 / 0.579283 (-0.186357) | 0.409993 / 0.434364 (-0.024371) | 0.460081 / 0.540337 (-0.080257) | 0.643836 / 1.386936 (-0.743100) |\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) | 0.006889 / 0.011353 (-0.004464) | 0.004060 / 0.011008 (-0.006948) | 0.064332 / 0.038508 (0.025824) | 0.077067 / 0.023109 (0.053958) | 0.401235 / 0.275898 (0.125337) | 0.437139 / 0.323480 (0.113659) | 0.005510 / 0.007986 (-0.002476) | 0.003338 / 0.004328 (-0.000991) | 0.064446 / 0.004250 (0.060195) | 0.055537 / 0.037052 (0.018485) | 0.432689 / 0.258489 (0.174200) | 0.460005 / 0.293841 (0.166164) | 0.033122 / 0.128546 (-0.095424) | 0.008637 / 0.075646 (-0.067010) | 0.071088 / 0.419271 (-0.348183) | 0.049024 / 0.043533 (0.005491) | 0.400258 / 0.255139 (0.145119) | 0.419324 / 0.283200 (0.136124) | 0.022050 / 0.141683 (-0.119632) | 1.475744 / 1.452155 (0.023589) | 1.546565 / 1.492716 (0.053848) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.226241 / 0.018006 (0.208235) | 0.448574 / 0.000490 (0.448085) | 0.004732 / 0.000200 (0.004533) | 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) | 0.033260 / 0.037411 (-0.004151) | 0.092622 / 0.014526 (0.078096) | 0.105501 / 0.176557 (-0.071056) | 0.157981 / 0.737135 (-0.579155) | 0.105993 / 0.296338 (-0.190345) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.445716 / 0.215209 (0.230507) | 4.451848 / 2.077655 (2.374194) | 2.404769 / 1.504120 (0.900649) | 2.232594 / 1.541195 (0.691399) | 2.312735 / 1.468490 (0.844245) | 0.491208 / 4.584777 (-4.093569) | 3.561629 / 3.745712 (-0.184083) | 3.444269 / 5.269862 (-1.825592) | 2.060365 / 4.565676 (-2.505311) | 0.057723 / 0.424275 (-0.366552) | 0.007392 / 0.007607 (-0.000215) | 0.526447 / 0.226044 (0.300403) | 5.264307 / 2.268929 (2.995379) | 2.951481 / 55.444624 (-52.493143) | 2.593178 / 6.876477 (-4.283299) | 2.689780 / 2.142072 (0.547707) | 0.588649 / 4.805227 (-4.216579) | 0.133566 / 6.500664 (-6.367098) | 0.060462 / 0.075469 (-0.015008) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.381008 / 1.841788 (-0.460780) | 19.452394 / 8.074308 (11.378086) | 15.255912 / 10.191392 (5.064520) | 0.171043 / 0.680424 (-0.509381) | 0.020395 / 0.534201 (-0.513806) | 0.396429 / 0.579283 (-0.182854) | 0.422820 / 0.434364 (-0.011544) | 0.477305 / 0.540337 (-0.063032) | 0.658274 / 1.386936 (-0.728663) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#faedc670ca896584d0f8edcb1fd9c13d1d6cc903 \"CML watermark\")\n" ]

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[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008220 / 0.011353 (-0.003133) | 0.005560 / 0.011008 (-0.005448) | 0.100147 / 0.038508 (0.061639) | 0.070106 / 0.023109 (0.046996) | 0.411906 / 0.275898 (0.136008) | 0.432825 / 0.323480 (0.109345) | 0.004795 / 0.007986 (-0.003190) | 0.004094 / 0.004328 (-0.000235) | 0.075719 / 0.004250 (0.071468) | 0.067426 / 0.037052 (0.030374) | 0.428531 / 0.258489 (0.170042) | 0.437114 / 0.293841 (0.143273) | 0.045603 / 0.128546 (-0.082943) | 0.013333 / 0.075646 (-0.062313) | 0.353137 / 0.419271 (-0.066134) | 0.067902 / 0.043533 (0.024369) | 0.396633 / 0.255139 (0.141494) | 0.399185 / 0.283200 (0.115985) | 0.036377 / 0.141683 (-0.105306) | 1.624249 / 1.452155 (0.172094) | 1.792575 / 1.492716 (0.299859) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.315847 / 0.018006 (0.297840) | 0.595009 / 0.000490 (0.594519) | 0.018876 / 0.000200 (0.018676) | 0.000613 / 0.000054 (0.000558) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029886 / 0.037411 (-0.007526) | 0.085765 / 0.014526 (0.071239) | 0.108680 / 0.176557 (-0.067877) | 0.174588 / 0.737135 (-0.562548) | 0.104494 / 0.296338 (-0.191844) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.594429 / 0.215209 (0.379220) | 5.912352 / 2.077655 (3.834698) | 2.408501 / 1.504120 (0.904381) | 2.050914 / 1.541195 (0.509720) | 2.199349 / 1.468490 (0.730859) | 0.813797 / 4.584777 (-3.770980) | 5.169577 / 3.745712 (1.423864) | 4.653951 / 5.269862 (-0.615911) | 2.805423 / 4.565676 (-1.760253) | 0.092278 / 0.424275 (-0.331997) | 0.007394 / 0.007607 (-0.000213) | 0.684029 / 0.226044 (0.457985) | 6.964260 / 2.268929 (4.695331) | 3.108408 / 55.444624 (-52.336217) | 2.470907 / 6.876477 (-4.405569) | 2.460153 / 2.142072 (0.318081) | 0.986445 / 4.805227 (-3.818782) | 0.213069 / 6.500664 (-6.287596) | 0.074061 / 0.075469 (-0.001408) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.590732 / 1.841788 (-0.251056) | 23.736918 / 8.074308 (15.662609) | 21.223910 / 10.191392 (11.032518) | 0.236173 / 0.680424 (-0.444251) | 0.030056 / 0.534201 (-0.504145) | 0.489461 / 0.579283 (-0.089822) | 0.607582 / 0.434364 (0.173218) | 0.539889 / 0.540337 (-0.000449) | 0.817942 / 1.386936 (-0.568994) |\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) | 0.008042 / 0.011353 (-0.003311) | 0.004836 / 0.011008 (-0.006173) | 0.075434 / 0.038508 (0.036926) | 0.080818 / 0.023109 (0.057709) | 0.474797 / 0.275898 (0.198899) | 0.526168 / 0.323480 (0.202689) | 0.006463 / 0.007986 (-0.001522) | 0.004031 / 0.004328 (-0.000297) | 0.074141 / 0.004250 (0.069891) | 0.068265 / 0.037052 (0.031212) | 0.562550 / 0.258489 (0.304061) | 0.544820 / 0.293841 (0.250979) | 0.047263 / 0.128546 (-0.081283) | 0.014113 / 0.075646 (-0.061534) | 0.086061 / 0.419271 (-0.333210) | 0.062475 / 0.043533 (0.018942) | 0.479912 / 0.255139 (0.224773) | 0.494784 / 0.283200 (0.211584) | 0.035847 / 0.141683 (-0.105836) | 1.726452 / 1.452155 (0.274297) | 1.770113 / 1.492716 (0.277396) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.286713 / 0.018006 (0.268707) | 0.609704 / 0.000490 (0.609214) | 0.009342 / 0.000200 (0.009143) | 0.000134 / 0.000054 (0.000080) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035137 / 0.037411 (-0.002275) | 0.099331 / 0.014526 (0.084805) | 0.108971 / 0.176557 (-0.067586) | 0.170952 / 0.737135 (-0.566183) | 0.111736 / 0.296338 (-0.184603) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.617434 / 0.215209 (0.402225) | 6.204351 / 2.077655 (4.126697) | 2.854347 / 1.504120 (1.350227) | 2.557424 / 1.541195 (1.016229) | 2.638173 / 1.468490 (1.169683) | 0.854234 / 4.584777 (-3.730543) | 5.383288 / 3.745712 (1.637576) | 4.698098 / 5.269862 (-0.571763) | 2.903860 / 4.565676 (-1.661817) | 0.094689 / 0.424275 (-0.329586) | 0.007892 / 0.007607 (0.000285) | 0.729420 / 0.226044 (0.503376) | 7.356691 / 2.268929 (5.087763) | 3.708039 / 55.444624 (-51.736585) | 2.979734 / 6.876477 (-3.896743) | 2.978983 / 2.142072 (0.836911) | 1.040554 / 4.805227 (-3.764673) | 0.211246 / 6.500664 (-6.289418) | 0.079880 / 0.075469 (0.004411) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.676057 / 1.841788 (-0.165731) | 23.428443 / 8.074308 (15.354135) | 21.016293 / 10.191392 (10.824901) | 0.260927 / 0.680424 (-0.419497) | 0.030689 / 0.534201 (-0.503512) | 0.495652 / 0.579283 (-0.083632) | 0.622976 / 0.434364 (0.188612) | 0.561175 / 0.540337 (0.020837) | 0.786733 / 1.386936 (-0.600203) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#fb621b9630a69643255d25f192fdb011935122b1 \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005942 / 0.011353 (-0.005410) | 0.003706 / 0.011008 (-0.007302) | 0.081002 / 0.038508 (0.042493) | 0.056854 / 0.023109 (0.033745) | 0.358668 / 0.275898 (0.082770) | 0.369718 / 0.323480 (0.046238) | 0.005202 / 0.007986 (-0.002784) | 0.002841 / 0.004328 (-0.001487) | 0.062976 / 0.004250 (0.058726) | 0.051308 / 0.037052 (0.014255) | 0.373636 / 0.258489 (0.115147) | 0.390480 / 0.293841 (0.096639) | 0.027480 / 0.128546 (-0.101067) | 0.007960 / 0.075646 (-0.067686) | 0.262719 / 0.419271 (-0.156552) | 0.046488 / 0.043533 (0.002955) | 0.347299 / 0.255139 (0.092160) | 0.393448 / 0.283200 (0.110249) | 0.019445 / 0.141683 (-0.122238) | 1.431314 / 1.452155 (-0.020841) | 1.495578 / 1.492716 (0.002862) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.223724 / 0.018006 (0.205718) | 0.416929 / 0.000490 (0.416440) | 0.005253 / 0.000200 (0.005053) | 0.000217 / 0.000054 (0.000163) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023571 / 0.037411 (-0.013841) | 0.073503 / 0.014526 (0.058978) | 0.081366 / 0.176557 (-0.095190) | 0.142716 / 0.737135 (-0.594420) | 0.082612 / 0.296338 (-0.213727) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.407319 / 0.215209 (0.192109) | 4.141404 / 2.077655 (2.063749) | 1.910842 / 1.504120 (0.406722) | 1.731694 / 1.541195 (0.190499) | 1.805228 / 1.468490 (0.336738) | 0.497109 / 4.584777 (-4.087668) | 3.107624 / 3.745712 (-0.638088) | 2.890687 / 5.269862 (-2.379174) | 1.795913 / 4.565676 (-2.769763) | 0.057099 / 0.424275 (-0.367176) | 0.006414 / 0.007607 (-0.001194) | 0.482127 / 0.226044 (0.256083) | 4.835158 / 2.268929 (2.566229) | 2.368909 / 55.444624 (-53.075715) | 2.001608 / 6.876477 (-4.874868) | 2.004492 / 2.142072 (-0.137580) | 0.579910 / 4.805227 (-4.225317) | 0.123541 / 6.500664 (-6.377123) | 0.059651 / 0.075469 (-0.015818) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.242364 / 1.841788 (-0.599424) | 16.982676 / 8.074308 (8.908368) | 13.718885 / 10.191392 (3.527493) | 0.132759 / 0.680424 (-0.547665) | 0.017012 / 0.534201 (-0.517189) | 0.333447 / 0.579283 (-0.245836) | 0.360149 / 0.434364 (-0.074215) | 0.385526 / 0.540337 (-0.154811) | 0.536915 / 1.386936 (-0.850021) |\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) | 0.005946 / 0.011353 (-0.005407) | 0.003442 / 0.011008 (-0.007566) | 0.062595 / 0.038508 (0.024087) | 0.058699 / 0.023109 (0.035590) | 0.442626 / 0.275898 (0.166728) | 0.473773 / 0.323480 (0.150293) | 0.004622 / 0.007986 (-0.003364) | 0.002812 / 0.004328 (-0.001516) | 0.064099 / 0.004250 (0.059849) | 0.046784 / 0.037052 (0.009731) | 0.466049 / 0.258489 (0.207560) | 0.487912 / 0.293841 (0.194071) | 0.028372 / 0.128546 (-0.100174) | 0.007992 / 0.075646 (-0.067654) | 0.068151 / 0.419271 (-0.351120) | 0.041010 / 0.043533 (-0.002523) | 0.442331 / 0.255139 (0.187192) | 0.469686 / 0.283200 (0.186487) | 0.019694 / 0.141683 (-0.121989) | 1.467928 / 1.452155 (0.015774) | 1.525635 / 1.492716 (0.032918) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.204459 / 0.018006 (0.186453) | 0.407766 / 0.000490 (0.407276) | 0.003898 / 0.000200 (0.003698) | 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) | 0.025909 / 0.037411 (-0.011503) | 0.080341 / 0.014526 (0.065816) | 0.088231 / 0.176557 (-0.088325) | 0.144056 / 0.737135 (-0.593079) | 0.089769 / 0.296338 (-0.206569) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.462876 / 0.215209 (0.247667) | 4.625983 / 2.077655 (2.548329) | 2.580079 / 1.504120 (1.075959) | 2.402792 / 1.541195 (0.861597) | 2.424982 / 1.468490 (0.956491) | 0.503654 / 4.584777 (-4.081123) | 3.178995 / 3.745712 (-0.566717) | 2.956126 / 5.269862 (-2.313735) | 1.847837 / 4.565676 (-2.717840) | 0.057964 / 0.424275 (-0.366311) | 0.006405 / 0.007607 (-0.001202) | 0.536036 / 0.226044 (0.309992) | 5.374416 / 2.268929 (3.105487) | 3.036440 / 55.444624 (-52.408184) | 2.682054 / 6.876477 (-4.194422) | 2.683462 / 2.142072 (0.541390) | 0.592751 / 4.805227 (-4.212477) | 0.124313 / 6.500664 (-6.376351) | 0.061127 / 0.075469 (-0.014342) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.383539 / 1.841788 (-0.458249) | 17.766221 / 8.074308 (9.691913) | 15.306600 / 10.191392 (5.115208) | 0.145035 / 0.680424 (-0.535389) | 0.018078 / 0.534201 (-0.516123) | 0.330102 / 0.579283 (-0.249181) | 0.375380 / 0.434364 (-0.058984) | 0.388531 / 0.540337 (-0.151807) | 0.548720 / 1.386936 (-0.838216) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0082342ac792a05f4a615e4985d1c791e155115a \"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) | 0.006757 / 0.011353 (-0.004596) | 0.004110 / 0.011008 (-0.006898) | 0.084727 / 0.038508 (0.046219) | 0.074328 / 0.023109 (0.051219) | 0.310467 / 0.275898 (0.034569) | 0.343209 / 0.323480 (0.019729) | 0.004228 / 0.007986 (-0.003757) | 0.003400 / 0.004328 (-0.000929) | 0.065546 / 0.004250 (0.061296) | 0.063057 / 0.037052 (0.026005) | 0.315023 / 0.258489 (0.056534) | 0.356395 / 0.293841 (0.062554) | 0.031959 / 0.128546 (-0.096588) | 0.008577 / 0.075646 (-0.067069) | 0.289075 / 0.419271 (-0.130196) | 0.055011 / 0.043533 (0.011478) | 0.308861 / 0.255139 (0.053722) | 0.328691 / 0.283200 (0.045491) | 0.027037 / 0.141683 (-0.114646) | 1.464314 / 1.452155 (0.012159) | 1.549644 / 1.492716 (0.056927) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.238330 / 0.018006 (0.220324) | 0.451570 / 0.000490 (0.451080) | 0.010873 / 0.000200 (0.010673) | 0.000341 / 0.000054 (0.000286) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029909 / 0.037411 (-0.007503) | 0.085222 / 0.014526 (0.070696) | 0.100180 / 0.176557 (-0.076377) | 0.154842 / 0.737135 (-0.582293) | 0.099253 / 0.296338 (-0.197086) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.401603 / 0.215209 (0.186394) | 4.009781 / 2.077655 (1.932126) | 2.021807 / 1.504120 (0.517687) | 1.861017 / 1.541195 (0.319822) | 2.009072 / 1.468490 (0.540582) | 0.483798 / 4.584777 (-4.100979) | 3.580394 / 3.745712 (-0.165318) | 3.464587 / 5.269862 (-1.805275) | 2.018400 / 4.565676 (-2.547276) | 0.057134 / 0.424275 (-0.367141) | 0.007303 / 0.007607 (-0.000304) | 0.473627 / 0.226044 (0.247582) | 4.722634 / 2.268929 (2.453706) | 2.490884 / 55.444624 (-52.953741) | 2.121568 / 6.876477 (-4.754909) | 2.200699 / 2.142072 (0.058626) | 0.576728 / 4.805227 (-4.228499) | 0.135633 / 6.500664 (-6.365032) | 0.061625 / 0.075469 (-0.013844) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.250545 / 1.841788 (-0.591243) | 19.167642 / 8.074308 (11.093334) | 14.189891 / 10.191392 (3.998499) | 0.164552 / 0.680424 (-0.515872) | 0.018215 / 0.534201 (-0.515986) | 0.389962 / 0.579283 (-0.189321) | 0.413972 / 0.434364 (-0.020392) | 0.460253 / 0.540337 (-0.080085) | 0.647897 / 1.386936 (-0.739039) |\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) | 0.006714 / 0.011353 (-0.004639) | 0.004081 / 0.011008 (-0.006927) | 0.065627 / 0.038508 (0.027119) | 0.077644 / 0.023109 (0.054535) | 0.409950 / 0.275898 (0.134052) | 0.442940 / 0.323480 (0.119460) | 0.005523 / 0.007986 (-0.002463) | 0.003366 / 0.004328 (-0.000962) | 0.065425 / 0.004250 (0.061174) | 0.056222 / 0.037052 (0.019169) | 0.429928 / 0.258489 (0.171439) | 0.457136 / 0.293841 (0.163296) | 0.032356 / 0.128546 (-0.096190) | 0.008676 / 0.075646 (-0.066970) | 0.071785 / 0.419271 (-0.347486) | 0.048458 / 0.043533 (0.004925) | 0.408003 / 0.255139 (0.152864) | 0.433529 / 0.283200 (0.150330) | 0.023232 / 0.141683 (-0.118450) | 1.483640 / 1.452155 (0.031485) | 1.552425 / 1.492716 (0.059709) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.282347 / 0.018006 (0.264341) | 0.448742 / 0.000490 (0.448253) | 0.039590 / 0.000200 (0.039390) | 0.000407 / 0.000054 (0.000353) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032516 / 0.037411 (-0.004896) | 0.095269 / 0.014526 (0.080744) | 0.106363 / 0.176557 (-0.070193) | 0.157945 / 0.737135 (-0.579191) | 0.106783 / 0.296338 (-0.189556) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.436334 / 0.215209 (0.221125) | 4.348147 / 2.077655 (2.270492) | 2.326830 / 1.504120 (0.822710) | 2.162586 / 1.541195 (0.621391) | 2.257769 / 1.468490 (0.789279) | 0.491677 / 4.584777 (-4.093099) | 3.707385 / 3.745712 (-0.038328) | 3.567147 / 5.269862 (-1.702715) | 2.099451 / 4.565676 (-2.466226) | 0.058486 / 0.424275 (-0.365789) | 0.007324 / 0.007607 (-0.000283) | 0.510962 / 0.226044 (0.284917) | 5.106550 / 2.268929 (2.837622) | 2.785723 / 55.444624 (-52.658901) | 2.452928 / 6.876477 (-4.423548) | 2.545034 / 2.142072 (0.402961) | 0.611124 / 4.805227 (-4.194103) | 0.133503 / 6.500664 (-6.367161) | 0.061118 / 0.075469 (-0.014351) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.386640 / 1.841788 (-0.455148) | 20.485670 / 8.074308 (12.411362) | 15.332223 / 10.191392 (5.140831) | 0.164070 / 0.680424 (-0.516354) | 0.019962 / 0.534201 (-0.514239) | 0.394217 / 0.579283 (-0.185066) | 0.428442 / 0.434364 (-0.005922) | 0.473784 / 0.540337 (-0.066553) | 0.665141 / 1.386936 (-0.721795) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c722eb75a6cc56eac530c44a17ff679ca805aa89 \"CML watermark\")\n", "The CI errors seem unrelated to this PR but I think they need further investigation in another PR.\r\n```\r\nFAILED tests/test_upstream_hub.py::TestPushToHub::test_push_dataset_dict_to_hub_multiple_files - KeyError: 'url'\r\n```", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008766 / 0.011353 (-0.002587) | 0.005289 / 0.011008 (-0.005720) | 0.097220 / 0.038508 (0.058712) | 0.072246 / 0.023109 (0.049137) | 0.369359 / 0.275898 (0.093461) | 0.422571 / 0.323480 (0.099091) | 0.004941 / 0.007986 (-0.003044) | 0.006103 / 0.004328 (0.001774) | 0.075828 / 0.004250 (0.071578) | 0.065795 / 0.037052 (0.028743) | 0.412835 / 0.258489 (0.154346) | 0.430062 / 0.293841 (0.136221) | 0.045806 / 0.128546 (-0.082741) | 0.013760 / 0.075646 (-0.061887) | 0.351542 / 0.419271 (-0.067729) | 0.064836 / 0.043533 (0.021304) | 0.370162 / 0.255139 (0.115023) | 0.434949 / 0.283200 (0.151749) | 0.039198 / 0.141683 (-0.102485) | 1.670940 / 1.452155 (0.218785) | 1.809677 / 1.492716 (0.316961) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.295104 / 0.018006 (0.277097) | 0.594584 / 0.000490 (0.594095) | 0.010923 / 0.000200 (0.010723) | 0.000479 / 0.000054 (0.000425) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029174 / 0.037411 (-0.008237) | 0.094637 / 0.014526 (0.080111) | 0.102948 / 0.176557 (-0.073608) | 0.171048 / 0.737135 (-0.566087) | 0.111465 / 0.296338 (-0.184873) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.582017 / 0.215209 (0.366808) | 5.727008 / 2.077655 (3.649354) | 2.563211 / 1.504120 (1.059091) | 2.308912 / 1.541195 (0.767717) | 2.301258 / 1.468490 (0.832768) | 0.819594 / 4.584777 (-3.765183) | 5.177536 / 3.745712 (1.431824) | 4.473602 / 5.269862 (-0.796260) | 2.743819 / 4.565676 (-1.821857) | 0.090052 / 0.424275 (-0.334223) | 0.007903 / 0.007607 (0.000295) | 0.679142 / 0.226044 (0.453097) | 6.887891 / 2.268929 (4.618962) | 3.337926 / 55.444624 (-52.106699) | 2.659228 / 6.876477 (-4.217249) | 2.641289 / 2.142072 (0.499216) | 0.974829 / 4.805227 (-3.830398) | 0.205775 / 6.500664 (-6.294890) | 0.075268 / 0.075469 (-0.000201) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.500562 / 1.841788 (-0.341226) | 22.688483 / 8.074308 (14.614175) | 19.634878 / 10.191392 (9.443486) | 0.227409 / 0.680424 (-0.453015) | 0.029794 / 0.534201 (-0.504407) | 0.475204 / 0.579283 (-0.104079) | 0.579379 / 0.434364 (0.145016) | 0.541244 / 0.540337 (0.000907) | 0.739187 / 1.386936 (-0.647749) |\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) | 0.008641 / 0.011353 (-0.002712) | 0.006139 / 0.011008 (-0.004870) | 0.075048 / 0.038508 (0.036540) | 0.074070 / 0.023109 (0.050961) | 0.508288 / 0.275898 (0.232390) | 0.539770 / 0.323480 (0.216290) | 0.006092 / 0.007986 (-0.001894) | 0.003748 / 0.004328 (-0.000581) | 0.077945 / 0.004250 (0.073695) | 0.056989 / 0.037052 (0.019936) | 0.526889 / 0.258489 (0.268400) | 0.560862 / 0.293841 (0.267021) | 0.046507 / 0.128546 (-0.082040) | 0.013249 / 0.075646 (-0.062397) | 0.088363 / 0.419271 (-0.330908) | 0.058776 / 0.043533 (0.015243) | 0.495869 / 0.255139 (0.240730) | 0.538615 / 0.283200 (0.255415) | 0.034055 / 0.141683 (-0.107628) | 1.658713 / 1.452155 (0.206558) | 1.736599 / 1.492716 (0.243883) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.288355 / 0.018006 (0.270349) | 0.571481 / 0.000490 (0.570991) | 0.006765 / 0.000200 (0.006565) | 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) | 0.031836 / 0.037411 (-0.005575) | 0.101312 / 0.014526 (0.086786) | 0.111433 / 0.176557 (-0.065124) | 0.169599 / 0.737135 (-0.567536) | 0.114595 / 0.296338 (-0.181743) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.645258 / 0.215209 (0.430049) | 6.446653 / 2.077655 (4.368998) | 2.983498 / 1.504120 (1.479379) | 2.573820 / 1.541195 (1.032625) | 2.624286 / 1.468490 (1.155796) | 0.815997 / 4.584777 (-3.768780) | 5.140248 / 3.745712 (1.394536) | 4.636915 / 5.269862 (-0.632947) | 2.866313 / 4.565676 (-1.699364) | 0.096643 / 0.424275 (-0.327633) | 0.008452 / 0.007607 (0.000845) | 0.765837 / 0.226044 (0.539793) | 7.622897 / 2.268929 (5.353968) | 3.796247 / 55.444624 (-51.648378) | 3.019349 / 6.876477 (-3.857128) | 3.034187 / 2.142072 (0.892115) | 1.001682 / 4.805227 (-3.803546) | 0.211841 / 6.500664 (-6.288823) | 0.073351 / 0.075469 (-0.002119) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.740254 / 1.841788 (-0.101534) | 23.465619 / 8.074308 (15.391311) | 21.651670 / 10.191392 (11.460278) | 0.226129 / 0.680424 (-0.454294) | 0.029611 / 0.534201 (-0.504590) | 0.441140 / 0.579283 (-0.138143) | 0.605591 / 0.434364 (0.171227) | 0.552427 / 0.540337 (0.012090) | 0.771975 / 1.386936 (-0.614961) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ef5751522c424c758df0647ff9a449b8b0404b6a \"CML watermark\")\n", "> The CI errors seem unrelated to this PR but I think they need further investigation in another PR.\r\n> \r\n> ```\r\n> FAILED tests/test_upstream_hub.py::TestPushToHub::test_push_dataset_dict_to_hub_multiple_files - KeyError: 'url'\r\n> ```\r\n\r\nWe need to wait for `huggingface_hub`'s next release to fix this (see https://github.com/huggingface/huggingface_hub/pull/1675; 409 error is currently ignored, hence the `KeyError`)\r\n\r\nAlso, we should be able to fix `test_push_dataset_dict_to_hub_overwrite_files` by inserting `gc.collect()` (to drop the \"reference\" to an Arrow file) between the `load_dataset` calls to avoid the `PermissionError` (also reported in https://github.com/huggingface/datasets/issues/3139)\r\n\r\n(Indeed, this can be addressed in subsequent PRs.)\r\n\r\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008988 / 0.011353 (-0.002365) | 0.005270 / 0.011008 (-0.005738) | 0.114577 / 0.038508 (0.076068) | 0.091630 / 0.023109 (0.068521) | 0.409217 / 0.275898 (0.133319) | 0.440903 / 0.323480 (0.117424) | 0.005226 / 0.007986 (-0.002760) | 0.004289 / 0.004328 (-0.000040) | 0.082246 / 0.004250 (0.077995) | 0.084926 / 0.037052 (0.047873) | 0.407822 / 0.258489 (0.149333) | 0.440891 / 0.293841 (0.147051) | 0.052225 / 0.128546 (-0.076321) | 0.014218 / 0.075646 (-0.061429) | 0.436994 / 0.419271 (0.017722) | 0.066433 / 0.043533 (0.022901) | 0.413909 / 0.255139 (0.158770) | 0.425729 / 0.283200 (0.142530) | 0.039576 / 0.141683 (-0.102107) | 1.905604 / 1.452155 (0.453449) | 1.907032 / 1.492716 (0.414315) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.313662 / 0.018006 (0.295655) | 0.614541 / 0.000490 (0.614051) | 0.015631 / 0.000200 (0.015431) | 0.000507 / 0.000054 (0.000453) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029049 / 0.037411 (-0.008362) | 0.094626 / 0.014526 (0.080100) | 0.104718 / 0.176557 (-0.071838) | 0.187346 / 0.737135 (-0.549790) | 0.108001 / 0.296338 (-0.188337) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.578997 / 0.215209 (0.363788) | 5.815546 / 2.077655 (3.737892) | 2.411301 / 1.504120 (0.907181) | 2.110088 / 1.541195 (0.568893) | 2.147839 / 1.468490 (0.679349) | 0.861285 / 4.584777 (-3.723492) | 5.264245 / 3.745712 (1.518533) | 4.695786 / 5.269862 (-0.574076) | 2.867522 / 4.565676 (-1.698154) | 0.096523 / 0.424275 (-0.327752) | 0.008777 / 0.007607 (0.001170) | 0.716316 / 0.226044 (0.490272) | 7.257574 / 2.268929 (4.988645) | 3.141502 / 55.444624 (-52.303123) | 2.480604 / 6.876477 (-4.395872) | 2.530031 / 2.142072 (0.387958) | 1.054274 / 4.805227 (-3.750953) | 0.210781 / 6.500664 (-6.289883) | 0.073837 / 0.075469 (-0.001632) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.607689 / 1.841788 (-0.234099) | 23.856780 / 8.074308 (15.782472) | 19.507196 / 10.191392 (9.315804) | 0.232712 / 0.680424 (-0.447712) | 0.027037 / 0.534201 (-0.507164) | 0.466613 / 0.579283 (-0.112670) | 0.571139 / 0.434364 (0.136775) | 0.543109 / 0.540337 (0.002771) | 0.785558 / 1.386936 (-0.601378) |\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) | 0.008104 / 0.011353 (-0.003249) | 0.004923 / 0.011008 (-0.006086) | 0.075093 / 0.038508 (0.036585) | 0.075218 / 0.023109 (0.052109) | 0.476615 / 0.275898 (0.200717) | 0.506984 / 0.323480 (0.183504) | 0.006371 / 0.007986 (-0.001614) | 0.004818 / 0.004328 (0.000489) | 0.075634 / 0.004250 (0.071383) | 0.059513 / 0.037052 (0.022461) | 0.523763 / 0.258489 (0.265274) | 0.531858 / 0.293841 (0.238017) | 0.048168 / 0.128546 (-0.080379) | 0.014110 / 0.075646 (-0.061537) | 0.086052 / 0.419271 (-0.333219) | 0.058369 / 0.043533 (0.014836) | 0.475537 / 0.255139 (0.220398) | 0.509429 / 0.283200 (0.226229) | 0.033924 / 0.141683 (-0.107758) | 1.657490 / 1.452155 (0.205336) | 1.762544 / 1.492716 (0.269828) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.263863 / 0.018006 (0.245857) | 0.584468 / 0.000490 (0.583978) | 0.007063 / 0.000200 (0.006863) | 0.000181 / 0.000054 (0.000126) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032229 / 0.037411 (-0.005183) | 0.096750 / 0.014526 (0.082224) | 0.117798 / 0.176557 (-0.058758) | 0.173376 / 0.737135 (-0.563760) | 0.117241 / 0.296338 (-0.179098) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.701935 / 0.215209 (0.486726) | 6.544655 / 2.077655 (4.467001) | 3.055531 / 1.504120 (1.551411) | 2.896339 / 1.541195 (1.355144) | 3.013157 / 1.468490 (1.544667) | 0.852989 / 4.584777 (-3.731788) | 5.399355 / 3.745712 (1.653643) | 5.119811 / 5.269862 (-0.150051) | 3.167269 / 4.565676 (-1.398407) | 0.096962 / 0.424275 (-0.327313) | 0.008843 / 0.007607 (0.001236) | 0.776170 / 0.226044 (0.550125) | 7.735093 / 2.268929 (5.466164) | 3.792629 / 55.444624 (-51.651996) | 3.249911 / 6.876477 (-3.626565) | 3.235590 / 2.142072 (1.093517) | 1.046426 / 4.805227 (-3.758801) | 0.239854 / 6.500664 (-6.260810) | 0.100648 / 0.075469 (0.025179) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.774488 / 1.841788 (-0.067300) | 25.646958 / 8.074308 (17.572650) | 23.181577 / 10.191392 (12.990185) | 0.231948 / 0.680424 (-0.448476) | 0.030147 / 0.534201 (-0.504054) | 0.464161 / 0.579283 (-0.115122) | 0.598980 / 0.434364 (0.164616) | 0.571156 / 0.540337 (0.030819) | 0.833221 / 1.386936 (-0.553715) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ad876e8908188dcd56759a35c4da182bf121535a \"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) | 0.006010 / 0.011353 (-0.005343) | 0.003662 / 0.011008 (-0.007346) | 0.079971 / 0.038508 (0.041463) | 0.066790 / 0.023109 (0.043681) | 0.311387 / 0.275898 (0.035489) | 0.346781 / 0.323480 (0.023301) | 0.003500 / 0.007986 (-0.004485) | 0.002831 / 0.004328 (-0.001498) | 0.063238 / 0.004250 (0.058988) | 0.056163 / 0.037052 (0.019110) | 0.317456 / 0.258489 (0.058967) | 0.356106 / 0.293841 (0.062265) | 0.027358 / 0.128546 (-0.101188) | 0.007906 / 0.075646 (-0.067741) | 0.261779 / 0.419271 (-0.157492) | 0.046385 / 0.043533 (0.002852) | 0.312587 / 0.255139 (0.057448) | 0.339513 / 0.283200 (0.056314) | 0.021474 / 0.141683 (-0.120209) | 1.418637 / 1.452155 (-0.033518) | 1.510257 / 1.492716 (0.017540) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.211761 / 0.018006 (0.193755) | 0.424387 / 0.000490 (0.423898) | 0.002579 / 0.000200 (0.002379) | 0.000065 / 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) | 0.024038 / 0.037411 (-0.013374) | 0.072524 / 0.014526 (0.057998) | 0.083443 / 0.176557 (-0.093113) | 0.144835 / 0.737135 (-0.592300) | 0.084754 / 0.296338 (-0.211585) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.392423 / 0.215209 (0.177214) | 3.927220 / 2.077655 (1.849565) | 1.877853 / 1.504120 (0.373733) | 1.699275 / 1.541195 (0.158081) | 1.793144 / 1.468490 (0.324654) | 0.503809 / 4.584777 (-4.080968) | 3.052569 / 3.745712 (-0.693143) | 2.907432 / 5.269862 (-2.362429) | 1.811220 / 4.565676 (-2.754457) | 0.057249 / 0.424275 (-0.367026) | 0.006433 / 0.007607 (-0.001174) | 0.463257 / 0.226044 (0.237213) | 4.631038 / 2.268929 (2.362109) | 2.315870 / 55.444624 (-53.128754) | 2.000476 / 6.876477 (-4.876001) | 2.043581 / 2.142072 (-0.098492) | 0.588911 / 4.805227 (-4.216317) | 0.125370 / 6.500664 (-6.375295) | 0.061721 / 0.075469 (-0.013748) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.244486 / 1.841788 (-0.597301) | 17.862422 / 8.074308 (9.788114) | 13.890205 / 10.191392 (3.698813) | 0.145467 / 0.680424 (-0.534957) | 0.016856 / 0.534201 (-0.517345) | 0.329357 / 0.579283 (-0.249926) | 0.367550 / 0.434364 (-0.066814) | 0.377541 / 0.540337 (-0.162796) | 0.534087 / 1.386936 (-0.852849) |\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) | 0.006030 / 0.011353 (-0.005323) | 0.003650 / 0.011008 (-0.007359) | 0.063300 / 0.038508 (0.024792) | 0.058877 / 0.023109 (0.035767) | 0.454662 / 0.275898 (0.178764) | 0.489362 / 0.323480 (0.165882) | 0.004856 / 0.007986 (-0.003130) | 0.002909 / 0.004328 (-0.001420) | 0.063356 / 0.004250 (0.059105) | 0.047867 / 0.037052 (0.010814) | 0.465461 / 0.258489 (0.206972) | 0.506684 / 0.293841 (0.212843) | 0.028599 / 0.128546 (-0.099947) | 0.008076 / 0.075646 (-0.067570) | 0.068695 / 0.419271 (-0.350576) | 0.041487 / 0.043533 (-0.002045) | 0.448676 / 0.255139 (0.193537) | 0.471206 / 0.283200 (0.188007) | 0.020401 / 0.141683 (-0.121282) | 1.461181 / 1.452155 (0.009026) | 1.517079 / 1.492716 (0.024363) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.222827 / 0.018006 (0.204821) | 0.425074 / 0.000490 (0.424585) | 0.004153 / 0.000200 (0.003953) | 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) | 0.026980 / 0.037411 (-0.010431) | 0.080786 / 0.014526 (0.066260) | 0.092040 / 0.176557 (-0.084517) | 0.146082 / 0.737135 (-0.591053) | 0.092739 / 0.296338 (-0.203600) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.461663 / 0.215209 (0.246454) | 4.604828 / 2.077655 (2.527173) | 2.566926 / 1.504120 (1.062806) | 2.394419 / 1.541195 (0.853224) | 2.458375 / 1.468490 (0.989885) | 0.505140 / 4.584777 (-4.079637) | 3.155916 / 3.745712 (-0.589796) | 3.014474 / 5.269862 (-2.255388) | 1.900296 / 4.565676 (-2.665380) | 0.058063 / 0.424275 (-0.366212) | 0.006409 / 0.007607 (-0.001198) | 0.541165 / 0.226044 (0.315120) | 5.410700 / 2.268929 (3.141772) | 3.010239 / 55.444624 (-52.434386) | 2.668103 / 6.876477 (-4.208373) | 2.730418 / 2.142072 (0.588346) | 0.603471 / 4.805227 (-4.201756) | 0.129852 / 6.500664 (-6.370812) | 0.061507 / 0.075469 (-0.013962) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.355272 / 1.841788 (-0.486516) | 18.170088 / 8.074308 (10.095780) | 15.583855 / 10.191392 (5.392463) | 0.146246 / 0.680424 (-0.534178) | 0.018093 / 0.534201 (-0.516108) | 0.331695 / 0.579283 (-0.247588) | 0.380845 / 0.434364 (-0.053519) | 0.388564 / 0.540337 (-0.151774) | 0.551465 / 1.386936 (-0.835471) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#afc3c2b034481a3502f5476186a110cf8613a248 \"CML watermark\")\n" ]

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

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[ "Hi! Unfortunately, the current behavior is to delete the downloaded data when this error happens. So, I've opened a PR that removes the problematic import to avoid losing data due to `apache_beam` not being installed (we host the preprocessed version of `natual_questions` on the HF GCS, so requiring `apache_beam` in that case doesn't make sense)", "Thanks for your reply. I met another question that I set `export HF_DATASETS_CACHE=/data/lxy/.cache` , but each time I run load_datasets, the datasets module still looking for NQ in the wrong default cache dir '/home/lxy/.cache' 。How to avoid this incorrect behavior. I am sure HF_DATASETS_CACHE was set correctly since I use echo & to check it.\r\n\r\nby the way I delete the file in '/home/lxy/.cache' since I found there has some kb size file seems useless.", "You need to set this variable before the `datasets` import. Then, you can use `import datasets; datasets.config.HF_DATASETS_CACHE` to verify the cache location." ]

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[ "Thanks for reporting this issue! We should be able to avoid this by making our `glob` patterns more precise. In the meantime, you can load the dataset by directly assigning splits to the data files: \r\n```python\r\nfrom datasets import load_dataset\r\nds = load_dataset(\"parquet\", data_files={\"train\": \"testing123/train/output_train.parquet\", \"validation\": \"testing123/val/output_val.parquet\"})\r\n```" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006131 / 0.011353 (-0.005222) | 0.003682 / 0.011008 (-0.007327) | 0.081108 / 0.038508 (0.042600) | 0.061580 / 0.023109 (0.038471) | 0.395880 / 0.275898 (0.119982) | 0.427429 / 0.323480 (0.103949) | 0.003570 / 0.007986 (-0.004416) | 0.003874 / 0.004328 (-0.000455) | 0.063322 / 0.004250 (0.059072) | 0.049742 / 0.037052 (0.012690) | 0.396547 / 0.258489 (0.138058) | 0.434759 / 0.293841 (0.140918) | 0.028137 / 0.128546 (-0.100409) | 0.008103 / 0.075646 (-0.067544) | 0.262504 / 0.419271 (-0.156767) | 0.045944 / 0.043533 (0.002411) | 0.397659 / 0.255139 (0.142520) | 0.416479 / 0.283200 (0.133280) | 0.022870 / 0.141683 (-0.118813) | 1.478280 / 1.452155 (0.026126) | 1.543748 / 1.492716 (0.051031) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.228851 / 0.018006 (0.210845) | 0.432845 / 0.000490 (0.432355) | 0.005922 / 0.000200 (0.005722) | 0.000227 / 0.000054 (0.000172) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025545 / 0.037411 (-0.011867) | 0.073506 / 0.014526 (0.058980) | 0.087622 / 0.176557 (-0.088935) | 0.145455 / 0.737135 (-0.591680) | 0.085236 / 0.296338 (-0.211102) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.433083 / 0.215209 (0.217874) | 4.323121 / 2.077655 (2.245466) | 2.297947 / 1.504120 (0.793827) | 2.126405 / 1.541195 (0.585211) | 2.201635 / 1.468490 (0.733145) | 0.509902 / 4.584777 (-4.074875) | 3.116877 / 3.745712 (-0.628835) | 2.892949 / 5.269862 (-2.376912) | 1.866833 / 4.565676 (-2.698844) | 0.058087 / 0.424275 (-0.366189) | 0.006464 / 0.007607 (-0.001143) | 0.503594 / 0.226044 (0.277550) | 5.027634 / 2.268929 (2.758705) | 2.718030 / 55.444624 (-52.726595) | 2.373876 / 6.876477 (-4.502600) | 2.515496 / 2.142072 (0.373423) | 0.602648 / 4.805227 (-4.202579) | 0.126119 / 6.500664 (-6.374545) | 0.060623 / 0.075469 (-0.014846) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.236429 / 1.841788 (-0.605359) | 17.760532 / 8.074308 (9.686224) | 13.970093 / 10.191392 (3.778701) | 0.145455 / 0.680424 (-0.534969) | 0.017110 / 0.534201 (-0.517091) | 0.329649 / 0.579283 (-0.249634) | 0.366942 / 0.434364 (-0.067421) | 0.384418 / 0.540337 (-0.155920) | 0.552330 / 1.386936 (-0.834606) |\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) | 0.006302 / 0.011353 (-0.005051) | 0.003677 / 0.011008 (-0.007331) | 0.062836 / 0.038508 (0.024328) | 0.063317 / 0.023109 (0.040207) | 0.449970 / 0.275898 (0.174072) | 0.480903 / 0.323480 (0.157423) | 0.005013 / 0.007986 (-0.002972) | 0.002934 / 0.004328 (-0.001394) | 0.062975 / 0.004250 (0.058724) | 0.051285 / 0.037052 (0.014233) | 0.448417 / 0.258489 (0.189928) | 0.486022 / 0.293841 (0.192181) | 0.029215 / 0.128546 (-0.099332) | 0.008189 / 0.075646 (-0.067457) | 0.068203 / 0.419271 (-0.351068) | 0.041942 / 0.043533 (-0.001591) | 0.445749 / 0.255139 (0.190610) | 0.465442 / 0.283200 (0.182243) | 0.020681 / 0.141683 (-0.121002) | 1.500704 / 1.452155 (0.048549) | 1.550511 / 1.492716 (0.057795) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.224922 / 0.018006 (0.206915) | 0.419714 / 0.000490 (0.419224) | 0.003804 / 0.000200 (0.003604) | 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) | 0.026924 / 0.037411 (-0.010487) | 0.082400 / 0.014526 (0.067874) | 0.092193 / 0.176557 (-0.084363) | 0.147045 / 0.737135 (-0.590090) | 0.093173 / 0.296338 (-0.203166) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.462510 / 0.215209 (0.247300) | 4.635249 / 2.077655 (2.557594) | 2.627127 / 1.504120 (1.123007) | 2.442879 / 1.541195 (0.901684) | 2.502456 / 1.468490 (1.033966) | 0.506607 / 4.584777 (-4.078170) | 3.127348 / 3.745712 (-0.618364) | 2.901818 / 5.269862 (-2.368044) | 1.906876 / 4.565676 (-2.658801) | 0.058025 / 0.424275 (-0.366250) | 0.006442 / 0.007607 (-0.001165) | 0.534438 / 0.226044 (0.308394) | 5.352481 / 2.268929 (3.083553) | 3.058068 / 55.444624 (-52.386556) | 2.697310 / 6.876477 (-4.179167) | 2.873141 / 2.142072 (0.731069) | 0.594517 / 4.805227 (-4.210710) | 0.125369 / 6.500664 (-6.375295) | 0.061411 / 0.075469 (-0.014058) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.369549 / 1.841788 (-0.472238) | 17.933507 / 8.074308 (9.859199) | 14.890107 / 10.191392 (4.698715) | 0.154398 / 0.680424 (-0.526026) | 0.018021 / 0.534201 (-0.516180) | 0.335163 / 0.579283 (-0.244120) | 0.350396 / 0.434364 (-0.083968) | 0.397694 / 0.540337 (-0.142643) | 0.554853 / 1.386936 (-0.832083) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f56fd9d6c877ffa6fb44fb832c13b61227c9cc5b \"CML watermark\")\n" ]

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

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[ "Can you share the error message?\r\n\r\nAlso, it would help if you could check whether `huggingface_hub`'s download behaves the same:\r\n```python\r\nfrom huggingface_hub import snapshot_download\r\nsnapshot_download(\"trec\", repo_type=\"dataset\", cache_dir='/path/to/my/dir)\r\n```\r\n\r\nIn the next major release, we aim to switch to `huggingface_hub` for file download/caching, but we could align the `cache_dir`'s `umask` behavior earlier than this if their solution works for your use case." ]

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[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005905 / 0.011353 (-0.005448) | 0.003623 / 0.011008 (-0.007385) | 0.079616 / 0.038508 (0.041108) | 0.059840 / 0.023109 (0.036730) | 0.392281 / 0.275898 (0.116383) | 0.434539 / 0.323480 (0.111059) | 0.004746 / 0.007986 (-0.003239) | 0.002935 / 0.004328 (-0.001394) | 0.062907 / 0.004250 (0.058657) | 0.048233 / 0.037052 (0.011181) | 0.394170 / 0.258489 (0.135681) | 0.427430 / 0.293841 (0.133589) | 0.027382 / 0.128546 (-0.101164) | 0.007890 / 0.075646 (-0.067756) | 0.259681 / 0.419271 (-0.159591) | 0.044085 / 0.043533 (0.000552) | 0.388640 / 0.255139 (0.133501) | 0.412665 / 0.283200 (0.129465) | 0.021256 / 0.141683 (-0.120427) | 1.485672 / 1.452155 (0.033518) | 1.531410 / 1.492716 (0.038694) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.220346 / 0.018006 (0.202340) | 0.425329 / 0.000490 (0.424840) | 0.006224 / 0.000200 (0.006024) | 0.000208 / 0.000054 (0.000153) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024864 / 0.037411 (-0.012547) | 0.072925 / 0.014526 (0.058399) | 0.083711 / 0.176557 (-0.092845) | 0.144213 / 0.737135 (-0.592923) | 0.084201 / 0.296338 (-0.212137) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.399467 / 0.215209 (0.184258) | 3.978979 / 2.077655 (1.901325) | 1.916994 / 1.504120 (0.412874) | 1.753098 / 1.541195 (0.211903) | 1.809866 / 1.468490 (0.341376) | 0.506806 / 4.584777 (-4.077971) | 3.051044 / 3.745712 (-0.694668) | 2.857624 / 5.269862 (-2.412237) | 1.872033 / 4.565676 (-2.693644) | 0.058543 / 0.424275 (-0.365732) | 0.006569 / 0.007607 (-0.001038) | 0.472630 / 0.226044 (0.246586) | 4.724862 / 2.268929 (2.455934) | 2.413068 / 55.444624 (-53.031556) | 2.046910 / 6.876477 (-4.829567) | 2.190455 / 2.142072 (0.048383) | 0.595228 / 4.805227 (-4.210000) | 0.125942 / 6.500664 (-6.374722) | 0.059474 / 0.075469 (-0.015995) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.235927 / 1.841788 (-0.605861) | 17.367803 / 8.074308 (9.293495) | 13.550362 / 10.191392 (3.358970) | 0.131664 / 0.680424 (-0.548760) | 0.016331 / 0.534201 (-0.517870) | 0.331295 / 0.579283 (-0.247988) | 0.367641 / 0.434364 (-0.066723) | 0.382595 / 0.540337 (-0.157742) | 0.540361 / 1.386936 (-0.846575) |\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) | 0.006120 / 0.011353 (-0.005233) | 0.003691 / 0.011008 (-0.007318) | 0.062768 / 0.038508 (0.024259) | 0.058045 / 0.023109 (0.034936) | 0.443616 / 0.275898 (0.167718) | 0.473854 / 0.323480 (0.150374) | 0.004710 / 0.007986 (-0.003275) | 0.002915 / 0.004328 (-0.001414) | 0.062922 / 0.004250 (0.058672) | 0.048557 / 0.037052 (0.011505) | 0.446136 / 0.258489 (0.187647) | 0.479235 / 0.293841 (0.185394) | 0.028704 / 0.128546 (-0.099842) | 0.008170 / 0.075646 (-0.067477) | 0.068853 / 0.419271 (-0.350419) | 0.041393 / 0.043533 (-0.002140) | 0.444683 / 0.255139 (0.189544) | 0.466607 / 0.283200 (0.183407) | 0.020890 / 0.141683 (-0.120793) | 1.473745 / 1.452155 (0.021590) | 1.498772 / 1.492716 (0.006055) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.216875 / 0.018006 (0.198868) | 0.411700 / 0.000490 (0.411211) | 0.003337 / 0.000200 (0.003137) | 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) | 0.027054 / 0.037411 (-0.010357) | 0.080617 / 0.014526 (0.066092) | 0.091052 / 0.176557 (-0.085505) | 0.144126 / 0.737135 (-0.593009) | 0.090123 / 0.296338 (-0.206216) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.461132 / 0.215209 (0.245922) | 4.598662 / 2.077655 (2.521008) | 2.539213 / 1.504120 (1.035093) | 2.362782 / 1.541195 (0.821587) | 2.428648 / 1.468490 (0.960157) | 0.506305 / 4.584777 (-4.078472) | 3.091132 / 3.745712 (-0.654581) | 2.884870 / 5.269862 (-2.384992) | 1.880806 / 4.565676 (-2.684870) | 0.058727 / 0.424275 (-0.365548) | 0.006452 / 0.007607 (-0.001155) | 0.533519 / 0.226044 (0.307474) | 5.346406 / 2.268929 (3.077478) | 2.987920 / 55.444624 (-52.456704) | 2.667591 / 6.876477 (-4.208885) | 2.848696 / 2.142072 (0.706623) | 0.601018 / 4.805227 (-4.204209) | 0.124929 / 6.500664 (-6.375735) | 0.061583 / 0.075469 (-0.013886) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.356825 / 1.841788 (-0.484962) | 17.964503 / 8.074308 (9.890195) | 14.691471 / 10.191392 (4.500079) | 0.132525 / 0.680424 (-0.547899) | 0.018061 / 0.534201 (-0.516140) | 0.335459 / 0.579283 (-0.243824) | 0.378260 / 0.434364 (-0.056104) | 0.390681 / 0.540337 (-0.149657) | 0.547030 / 1.386936 (-0.839906) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8c55213a6c5fcff9b3dacce491caa68eacebe10d \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006624 / 0.011353 (-0.004729) | 0.004039 / 0.011008 (-0.006970) | 0.085862 / 0.038508 (0.047354) | 0.077183 / 0.023109 (0.054074) | 0.319132 / 0.275898 (0.043234) | 0.350818 / 0.323480 (0.027338) | 0.004122 / 0.007986 (-0.003864) | 0.003395 / 0.004328 (-0.000934) | 0.065237 / 0.004250 (0.060987) | 0.056675 / 0.037052 (0.019623) | 0.321040 / 0.258489 (0.062551) | 0.362011 / 0.293841 (0.068170) | 0.030988 / 0.128546 (-0.097559) | 0.008623 / 0.075646 (-0.067023) | 0.289433 / 0.419271 (-0.129839) | 0.052755 / 0.043533 (0.009222) | 0.323291 / 0.255139 (0.068152) | 0.340110 / 0.283200 (0.056911) | 0.026299 / 0.141683 (-0.115383) | 1.509405 / 1.452155 (0.057250) | 1.559993 / 1.492716 (0.067277) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.233285 / 0.018006 (0.215279) | 0.451633 / 0.000490 (0.451143) | 0.009954 / 0.000200 (0.009754) | 0.000098 / 0.000054 (0.000043) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029623 / 0.037411 (-0.007788) | 0.083942 / 0.014526 (0.069416) | 0.097378 / 0.176557 (-0.079178) | 0.152630 / 0.737135 (-0.584506) | 0.098379 / 0.296338 (-0.197959) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.386237 / 0.215209 (0.171028) | 3.850805 / 2.077655 (1.773150) | 1.896032 / 1.504120 (0.391912) | 1.729746 / 1.541195 (0.188551) | 1.867831 / 1.468490 (0.399341) | 0.481496 / 4.584777 (-4.103281) | 3.564432 / 3.745712 (-0.181280) | 3.336084 / 5.269862 (-1.933777) | 2.040944 / 4.565676 (-2.524732) | 0.057247 / 0.424275 (-0.367028) | 0.007275 / 0.007607 (-0.000332) | 0.464600 / 0.226044 (0.238556) | 4.648562 / 2.268929 (2.379634) | 2.394430 / 55.444624 (-53.050195) | 2.029748 / 6.876477 (-4.846728) | 2.280975 / 2.142072 (0.138902) | 0.619073 / 4.805227 (-4.186154) | 0.150504 / 6.500664 (-6.350160) | 0.061206 / 0.075469 (-0.014263) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.267309 / 1.841788 (-0.574479) | 19.062725 / 8.074308 (10.988417) | 14.192565 / 10.191392 (4.001173) | 0.162908 / 0.680424 (-0.517515) | 0.018445 / 0.534201 (-0.515756) | 0.392110 / 0.579283 (-0.187173) | 0.415340 / 0.434364 (-0.019024) | 0.456783 / 0.540337 (-0.083554) | 0.653019 / 1.386936 (-0.733917) |\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) | 0.006995 / 0.011353 (-0.004358) | 0.004027 / 0.011008 (-0.006981) | 0.064124 / 0.038508 (0.025616) | 0.076004 / 0.023109 (0.052895) | 0.401760 / 0.275898 (0.125862) | 0.432339 / 0.323480 (0.108859) | 0.005471 / 0.007986 (-0.002515) | 0.003335 / 0.004328 (-0.000993) | 0.064164 / 0.004250 (0.059913) | 0.058101 / 0.037052 (0.021048) | 0.401698 / 0.258489 (0.143209) | 0.436033 / 0.293841 (0.142192) | 0.032789 / 0.128546 (-0.095757) | 0.008482 / 0.075646 (-0.067165) | 0.070707 / 0.419271 (-0.348565) | 0.048287 / 0.043533 (0.004755) | 0.395501 / 0.255139 (0.140362) | 0.419385 / 0.283200 (0.136186) | 0.024043 / 0.141683 (-0.117640) | 1.503310 / 1.452155 (0.051156) | 1.562160 / 1.492716 (0.069444) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.227629 / 0.018006 (0.209623) | 0.457306 / 0.000490 (0.456816) | 0.005835 / 0.000200 (0.005635) | 0.000109 / 0.000054 (0.000054) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032991 / 0.037411 (-0.004420) | 0.093265 / 0.014526 (0.078739) | 0.106595 / 0.176557 (-0.069961) | 0.158557 / 0.737135 (-0.578578) | 0.106805 / 0.296338 (-0.189533) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.436573 / 0.215209 (0.221364) | 4.355777 / 2.077655 (2.278122) | 2.323151 / 1.504120 (0.819031) | 2.164101 / 1.541195 (0.622906) | 2.252808 / 1.468490 (0.784318) | 0.494902 / 4.584777 (-4.089875) | 3.615073 / 3.745712 (-0.130639) | 3.329738 / 5.269862 (-1.940124) | 2.059137 / 4.565676 (-2.506539) | 0.058384 / 0.424275 (-0.365891) | 0.007330 / 0.007607 (-0.000277) | 0.512326 / 0.226044 (0.286281) | 5.125652 / 2.268929 (2.856724) | 2.861981 / 55.444624 (-52.582644) | 2.500172 / 6.876477 (-4.376305) | 2.715862 / 2.142072 (0.573789) | 0.597299 / 4.805227 (-4.207928) | 0.134346 / 6.500664 (-6.366318) | 0.060396 / 0.075469 (-0.015074) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.353771 / 1.841788 (-0.488017) | 19.334801 / 8.074308 (11.260493) | 14.669875 / 10.191392 (4.478483) | 0.167607 / 0.680424 (-0.512817) | 0.019839 / 0.534201 (-0.514362) | 0.395473 / 0.579283 (-0.183810) | 0.419822 / 0.434364 (-0.014542) | 0.471400 / 0.540337 (-0.068938) | 0.648297 / 1.386936 (-0.738639) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d5a112e7f1ce1635725773d911c825adca7bcfe0 \"CML watermark\")\n", "@mariosasko let me know what you think or if you have better ideas to make it faster", "Yea lazy data files resolution seems a better approach actually" ]

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[ "Answered on the forum: https://discuss.huggingface.co/t/dataset-from-generator-cost-much-more-time-in-vscode-debugging-mode-then-running-mode/56005/2" ]

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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==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) | 0.006591 / 0.011353 (-0.004762) | 0.003991 / 0.011008 (-0.007017) | 0.085197 / 0.038508 (0.046689) | 0.080312 / 0.023109 (0.057202) | 0.342026 / 0.275898 (0.066128) | 0.370749 / 0.323480 (0.047269) | 0.004124 / 0.007986 (-0.003861) | 0.003413 / 0.004328 (-0.000916) | 0.064363 / 0.004250 (0.060113) | 0.055920 / 0.037052 (0.018868) | 0.340667 / 0.258489 (0.082178) | 0.380138 / 0.293841 (0.086297) | 0.031115 / 0.128546 (-0.097431) | 0.008511 / 0.075646 (-0.067135) | 0.289065 / 0.419271 (-0.130207) | 0.052266 / 0.043533 (0.008734) | 0.343808 / 0.255139 (0.088669) | 0.353578 / 0.283200 (0.070378) | 0.024006 / 0.141683 (-0.117676) | 1.490322 / 1.452155 (0.038168) | 1.591133 / 1.492716 (0.098417) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.234718 / 0.018006 (0.216712) | 0.447023 / 0.000490 (0.446533) | 0.009343 / 0.000200 (0.009143) | 0.000259 / 0.000054 (0.000204) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030466 / 0.037411 (-0.006945) | 0.083367 / 0.014526 (0.068841) | 0.100532 / 0.176557 (-0.076024) | 0.158018 / 0.737135 (-0.579117) | 0.098280 / 0.296338 (-0.198059) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.408501 / 0.215209 (0.193292) | 4.066937 / 2.077655 (1.989282) | 2.034029 / 1.504120 (0.529909) | 1.842982 / 1.541195 (0.301788) | 1.987319 / 1.468490 (0.518829) | 0.492126 / 4.584777 (-4.092651) | 3.554027 / 3.745712 (-0.191685) | 3.289023 / 5.269862 (-1.980839) | 2.069796 / 4.565676 (-2.495880) | 0.057930 / 0.424275 (-0.366346) | 0.007308 / 0.007607 (-0.000299) | 0.482596 / 0.226044 (0.256552) | 4.830714 / 2.268929 (2.561785) | 2.506787 / 55.444624 (-52.937838) | 2.163498 / 6.876477 (-4.712979) | 2.389135 / 2.142072 (0.247062) | 0.597538 / 4.805227 (-4.207689) | 0.134268 / 6.500664 (-6.366396) | 0.061189 / 0.075469 (-0.014280) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.245328 / 1.841788 (-0.596460) | 19.145151 / 8.074308 (11.070843) | 14.742121 / 10.191392 (4.550729) | 0.144749 / 0.680424 (-0.535675) | 0.018433 / 0.534201 (-0.515768) | 0.391867 / 0.579283 (-0.187416) | 0.416555 / 0.434364 (-0.017809) | 0.454341 / 0.540337 (-0.085997) | 0.646833 / 1.386936 (-0.740103) |\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) | 0.004031 / 0.011008 (-0.006978) | 0.064347 / 0.038508 (0.025839) | 0.076857 / 0.023109 (0.053748) | 0.415864 / 0.275898 (0.139966) | 0.468615 / 0.323480 (0.145135) | 0.005383 / 0.007986 (-0.002603) | 0.003314 / 0.004328 (-0.001015) | 0.064829 / 0.004250 (0.060578) | 0.057182 / 0.037052 (0.020129) | 0.417055 / 0.258489 (0.158566) | 0.472725 / 0.293841 (0.178884) | 0.031938 / 0.128546 (-0.096608) | 0.008564 / 0.075646 (-0.067082) | 0.070649 / 0.419271 (-0.348623) | 0.047439 / 0.043533 (0.003906) | 0.409589 / 0.255139 (0.154450) | 0.433700 / 0.283200 (0.150500) | 0.024132 / 0.141683 (-0.117551) | 1.500825 / 1.452155 (0.048670) | 1.592059 / 1.492716 (0.099343) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.225652 / 0.018006 (0.207646) | 0.444188 / 0.000490 (0.443698) | 0.004581 / 0.000200 (0.004381) | 0.000104 / 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) | 0.033272 / 0.037411 (-0.004139) | 0.096833 / 0.014526 (0.082307) | 0.107134 / 0.176557 (-0.069422) | 0.159299 / 0.737135 (-0.577836) | 0.107533 / 0.296338 (-0.188806) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.429100 / 0.215209 (0.213890) | 4.281051 / 2.077655 (2.203396) | 2.318713 / 1.504120 (0.814593) | 2.165645 / 1.541195 (0.624451) | 2.250224 / 1.468490 (0.781734) | 0.495791 / 4.584777 (-4.088986) | 3.591953 / 3.745712 (-0.153760) | 3.303426 / 5.269862 (-1.966436) | 2.076861 / 4.565676 (-2.488816) | 0.058369 / 0.424275 (-0.365906) | 0.007387 / 0.007607 (-0.000220) | 0.501270 / 0.226044 (0.275225) | 5.014987 / 2.268929 (2.746059) | 2.800951 / 55.444624 (-52.643673) | 2.464316 / 6.876477 (-4.412161) | 2.685259 / 2.142072 (0.543187) | 0.584797 / 4.805227 (-4.220430) | 0.131889 / 6.500664 (-6.368775) | 0.061021 / 0.075469 (-0.014448) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.366982 / 1.841788 (-0.474806) | 19.820376 / 8.074308 (11.746068) | 14.968664 / 10.191392 (4.777272) | 0.165344 / 0.680424 (-0.515080) | 0.019956 / 0.534201 (-0.514245) | 0.395843 / 0.579283 (-0.183441) | 0.420854 / 0.434364 (-0.013510) | 0.465065 / 0.540337 (-0.075272) | 0.651531 / 1.386936 (-0.735405) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#980ca0e13300f5392cd87189d5afd5942927afc7 \"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) | 0.005974 / 0.011353 (-0.005379) | 0.003714 / 0.011008 (-0.007294) | 0.080049 / 0.038508 (0.041541) | 0.061233 / 0.023109 (0.038124) | 0.317187 / 0.275898 (0.041289) | 0.352725 / 0.323480 (0.029245) | 0.004867 / 0.007986 (-0.003119) | 0.002953 / 0.004328 (-0.001376) | 0.063156 / 0.004250 (0.058905) | 0.046752 / 0.037052 (0.009700) | 0.320171 / 0.258489 (0.061682) | 0.367572 / 0.293841 (0.073731) | 0.027253 / 0.128546 (-0.101293) | 0.008100 / 0.075646 (-0.067546) | 0.261206 / 0.419271 (-0.158066) | 0.044581 / 0.043533 (0.001048) | 0.331169 / 0.255139 (0.076030) | 0.348719 / 0.283200 (0.065519) | 0.021397 / 0.141683 (-0.120286) | 1.528315 / 1.452155 (0.076160) | 1.533789 / 1.492716 (0.041073) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.233336 / 0.018006 (0.215329) | 0.416866 / 0.000490 (0.416376) | 0.008805 / 0.000200 (0.008605) | 0.000240 / 0.000054 (0.000186) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024754 / 0.037411 (-0.012657) | 0.073311 / 0.014526 (0.058785) | 0.085419 / 0.176557 (-0.091138) | 0.146380 / 0.737135 (-0.590756) | 0.085545 / 0.296338 (-0.210793) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.431426 / 0.215209 (0.216217) | 4.315899 / 2.077655 (2.238244) | 2.232492 / 1.504120 (0.728372) | 2.064174 / 1.541195 (0.522979) | 2.158982 / 1.468490 (0.690492) | 0.499375 / 4.584777 (-4.085402) | 3.093259 / 3.745712 (-0.652454) | 2.848260 / 5.269862 (-2.421601) | 1.853097 / 4.565676 (-2.712579) | 0.057143 / 0.424275 (-0.367132) | 0.006349 / 0.007607 (-0.001258) | 0.507747 / 0.226044 (0.281702) | 5.078872 / 2.268929 (2.809944) | 2.717697 / 55.444624 (-52.726927) | 2.363564 / 6.876477 (-4.512913) | 2.485756 / 2.142072 (0.343684) | 0.595888 / 4.805227 (-4.209340) | 0.127285 / 6.500664 (-6.373379) | 0.060639 / 0.075469 (-0.014830) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.219287 / 1.841788 (-0.622501) | 17.300038 / 8.074308 (9.225730) | 13.747230 / 10.191392 (3.555838) | 0.144841 / 0.680424 (-0.535583) | 0.016587 / 0.534201 (-0.517614) | 0.336891 / 0.579283 (-0.242392) | 0.376128 / 0.434364 (-0.058236) | 0.385749 / 0.540337 (-0.154588) | 0.552218 / 1.386936 (-0.834718) |\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) | 0.006477 / 0.011353 (-0.004876) | 0.003709 / 0.011008 (-0.007299) | 0.064708 / 0.038508 (0.026200) | 0.062627 / 0.023109 (0.039518) | 0.444721 / 0.275898 (0.168823) | 0.477825 / 0.323480 (0.154345) | 0.004890 / 0.007986 (-0.003096) | 0.002896 / 0.004328 (-0.001432) | 0.063781 / 0.004250 (0.059530) | 0.050488 / 0.037052 (0.013436) | 0.453466 / 0.258489 (0.194977) | 0.483303 / 0.293841 (0.189462) | 0.028814 / 0.128546 (-0.099732) | 0.008207 / 0.075646 (-0.067440) | 0.070140 / 0.419271 (-0.349131) | 0.041487 / 0.043533 (-0.002045) | 0.454599 / 0.255139 (0.199460) | 0.468374 / 0.283200 (0.185174) | 0.019758 / 0.141683 (-0.121925) | 1.437542 / 1.452155 (-0.014613) | 1.507965 / 1.492716 (0.015249) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.223358 / 0.018006 (0.205352) | 0.413824 / 0.000490 (0.413334) | 0.004593 / 0.000200 (0.004393) | 0.000089 / 0.000054 (0.000035) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026278 / 0.037411 (-0.011134) | 0.081992 / 0.014526 (0.067466) | 0.089969 / 0.176557 (-0.086587) | 0.143668 / 0.737135 (-0.593467) | 0.091273 / 0.296338 (-0.205066) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.461198 / 0.215209 (0.245989) | 4.615398 / 2.077655 (2.537743) | 2.552291 / 1.504120 (1.048171) | 2.373789 / 1.541195 (0.832595) | 2.431591 / 1.468490 (0.963101) | 0.507683 / 4.584777 (-4.077094) | 3.148771 / 3.745712 (-0.596941) | 2.849118 / 5.269862 (-2.420744) | 1.883001 / 4.565676 (-2.682675) | 0.059423 / 0.424275 (-0.364852) | 0.006463 / 0.007607 (-0.001144) | 0.535129 / 0.226044 (0.309085) | 5.362870 / 2.268929 (3.093941) | 3.016548 / 55.444624 (-52.428076) | 2.666205 / 6.876477 (-4.210271) | 2.821396 / 2.142072 (0.679324) | 0.606596 / 4.805227 (-4.198631) | 0.125991 / 6.500664 (-6.374673) | 0.063566 / 0.075469 (-0.011903) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.364771 / 1.841788 (-0.477017) | 18.000713 / 8.074308 (9.926404) | 14.840330 / 10.191392 (4.648937) | 0.144770 / 0.680424 (-0.535653) | 0.018060 / 0.534201 (-0.516141) | 0.334470 / 0.579283 (-0.244813) | 0.387386 / 0.434364 (-0.046978) | 0.398743 / 0.540337 (-0.141595) | 0.555437 / 1.386936 (-0.831499) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5b974c9af6b45b6ebdbbf4b3418f25506c1c0618 \"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) | 0.006491 / 0.011353 (-0.004862) | 0.004058 / 0.011008 (-0.006950) | 0.084462 / 0.038508 (0.045954) | 0.072310 / 0.023109 (0.049201) | 0.352458 / 0.275898 (0.076560) | 0.385829 / 0.323480 (0.062350) | 0.003978 / 0.007986 (-0.004007) | 0.003455 / 0.004328 (-0.000873) | 0.064070 / 0.004250 (0.059819) | 0.055577 / 0.037052 (0.018525) | 0.361288 / 0.258489 (0.102799) | 0.400147 / 0.293841 (0.106306) | 0.030785 / 0.128546 (-0.097761) | 0.008676 / 0.075646 (-0.066971) | 0.287481 / 0.419271 (-0.131791) | 0.052643 / 0.043533 (0.009110) | 0.354670 / 0.255139 (0.099531) | 0.382322 / 0.283200 (0.099122) | 0.025657 / 0.141683 (-0.116026) | 1.486798 / 1.452155 (0.034643) | 1.588439 / 1.492716 (0.095723) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.240881 / 0.018006 (0.222875) | 0.463997 / 0.000490 (0.463507) | 0.009688 / 0.000200 (0.009488) | 0.000601 / 0.000054 (0.000546) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029071 / 0.037411 (-0.008340) | 0.083077 / 0.014526 (0.068551) | 0.119857 / 0.176557 (-0.056699) | 0.153387 / 0.737135 (-0.583749) | 0.132162 / 0.296338 (-0.164177) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.383822 / 0.215209 (0.168613) | 3.828572 / 2.077655 (1.750918) | 1.877629 / 1.504120 (0.373509) | 1.708757 / 1.541195 (0.167562) | 1.771658 / 1.468490 (0.303168) | 0.482439 / 4.584777 (-4.102338) | 3.496247 / 3.745712 (-0.249466) | 3.282055 / 5.269862 (-1.987807) | 2.053069 / 4.565676 (-2.512607) | 0.056626 / 0.424275 (-0.367649) | 0.007338 / 0.007607 (-0.000269) | 0.461257 / 0.226044 (0.235213) | 4.605326 / 2.268929 (2.336397) | 2.408365 / 55.444624 (-53.036260) | 1.986550 / 6.876477 (-4.889926) | 2.225220 / 2.142072 (0.083148) | 0.601301 / 4.805227 (-4.203927) | 0.132217 / 6.500664 (-6.368447) | 0.061217 / 0.075469 (-0.014252) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.268706 / 1.841788 (-0.573081) | 18.892026 / 8.074308 (10.817717) | 14.093892 / 10.191392 (3.902500) | 0.162483 / 0.680424 (-0.517941) | 0.018372 / 0.534201 (-0.515829) | 0.391901 / 0.579283 (-0.187382) | 0.401578 / 0.434364 (-0.032786) | 0.456741 / 0.540337 (-0.083596) | 0.646760 / 1.386936 (-0.740176) |\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) | 0.006657 / 0.011353 (-0.004696) | 0.003981 / 0.011008 (-0.007027) | 0.066126 / 0.038508 (0.027617) | 0.072673 / 0.023109 (0.049564) | 0.409970 / 0.275898 (0.134072) | 0.430797 / 0.323480 (0.107317) | 0.005477 / 0.007986 (-0.002508) | 0.003362 / 0.004328 (-0.000966) | 0.065532 / 0.004250 (0.061282) | 0.056018 / 0.037052 (0.018966) | 0.406676 / 0.258489 (0.148187) | 0.438516 / 0.293841 (0.144675) | 0.032795 / 0.128546 (-0.095751) | 0.008580 / 0.075646 (-0.067066) | 0.072692 / 0.419271 (-0.346579) | 0.048110 / 0.043533 (0.004577) | 0.396826 / 0.255139 (0.141687) | 0.418442 / 0.283200 (0.135242) | 0.023269 / 0.141683 (-0.118414) | 1.499438 / 1.452155 (0.047283) | 1.568842 / 1.492716 (0.076126) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.218729 / 0.018006 (0.200723) | 0.450771 / 0.000490 (0.450281) | 0.004996 / 0.000200 (0.004796) | 0.000086 / 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) | 0.031484 / 0.037411 (-0.005928) | 0.092927 / 0.014526 (0.078401) | 0.107849 / 0.176557 (-0.068707) | 0.156658 / 0.737135 (-0.580478) | 0.106373 / 0.296338 (-0.189965) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.434658 / 0.215209 (0.219449) | 4.336386 / 2.077655 (2.258731) | 2.322577 / 1.504120 (0.818457) | 2.149505 / 1.541195 (0.608310) | 2.201967 / 1.468490 (0.733476) | 0.496994 / 4.584777 (-4.087783) | 3.533065 / 3.745712 (-0.212647) | 3.235750 / 5.269862 (-2.034112) | 2.034854 / 4.565676 (-2.530823) | 0.058258 / 0.424275 (-0.366017) | 0.007260 / 0.007607 (-0.000347) | 0.509115 / 0.226044 (0.283071) | 5.088427 / 2.268929 (2.819499) | 2.793551 / 55.444624 (-52.651073) | 2.430588 / 6.876477 (-4.445889) | 2.625998 / 2.142072 (0.483926) | 0.611676 / 4.805227 (-4.193552) | 0.133343 / 6.500664 (-6.367321) | 0.059888 / 0.075469 (-0.015581) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.377292 / 1.841788 (-0.464496) | 19.214299 / 8.074308 (11.139991) | 14.629146 / 10.191392 (4.437754) | 0.171283 / 0.680424 (-0.509141) | 0.020348 / 0.534201 (-0.513853) | 0.397823 / 0.579283 (-0.181461) | 0.411590 / 0.434364 (-0.022774) | 0.470850 / 0.540337 (-0.069487) | 0.658667 / 1.386936 (-0.728269) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a1e1867e932f14233244fb25713f3c94c46ff50a \"CML watermark\")\n" ]

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

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[ "Indeed, it makes sense to do this by default. \r\n\r\nIn the meantime, you can use `.with_transform` to transpose the images when accessing them:\r\n\r\n```python\r\nimport PIL.ImageOps\r\n\r\ndef exif_transpose_transform(batch):\r\n batch[\"image\"] = [PIL.ImageOps.exif_transpose(image) for image in batch[\"image\"]]\r\n return batch\r\n\r\ndataset = dataset.with_transform(exif_transpose_transform)\r\n```", "This operation sets some `Image` attributes to `None` (`.format`, `.filename`, etc.), causing our tests to fail, so I think we should wait for Datasets 3.0 to make this change. In version 3.0, storing image paths will be replaced by embedding image bytes, so there will be fewer instances where we use the `.filename` attribute." ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "This function reads an entire Arrow table in one go, which is not ideal memory-wise, so I don't think we should encourage using this function, considering we want to keep RAM usage as low as possible in the streaming mode. \r\n\r\n(Note that Parquet files are compressed, meaning the loaded table can be significantly larger than the size in Parquet.)\r\n\r\nInstead, we should suggest the authors to use:\r\n```python\r\nwith open(doc_path, \"rb\") as f:\r\n parquet_file = pq.ParquetFile(f)\r\n for batch in parquet_file.iter_batches():\r\n pa_table = pa.Table.from_batches([batch])\r\n yield idx, pa_table\r\n idx += 1\r\n```", "@mariosasko I think the potential problem you evoke is independent of whether or not we support streaming mode:\r\n- if the user's script with `read_table` works in non-streaming mode, it will also work in streaming mode after this PR\r\n\r\nIn fact, what we should suggest instead is to follow the scriptless approach, so that our `parquet` packaged module is used, with all the optimizations implemented. But this approach is not possible in all cases, and some use cases need to implement a script. And if they have small Parquet files and use `read_table`, I think we should support streaming.\r\n\r\nIn summary, let me clarify the goal and the scope of this PR:\r\n- a user needs using a loading script\r\n- their files are small enough so that they use `read_table`\r\n- their loading script works in non-streaming mode\r\n- therefore, this PR allows loading their dataset in streaming mode as well", "Yes, the no-script approach with metadata configs makes the most sense.\r\n\r\n> their files are small enough so that they use read_table\r\n\r\nSome of the Parquet files in that repo are larger than 1 GB ...\r\n\r\nAlso, I'd wait for more instances of people using the `read_table` function on the Hub before merging this PR.", "@mariosasko, yes, this solution is not specifically for the \"uonlp/CulturaX\" dataset, but for other use cases as I explained above: indeed, they finally removed the use of `read_table` because their data files are too large.\r\n\r\n> Also, I'd wait for more instances of people using the `read_table` function on the Hub before merging this PR.\r\n\r\nDo you know how many datasets are currently using `read_table`?", "> Do you know how many datasets are currently using read_table?\r\n\r\nZero (based on the script that checks the script contents of the public Hub datasets). ", "I see... Thanks! :hugs: ", "@mariosasko thanks for pointing the script! :hugs: \r\n\r\nHowever, I have found some Hub datasets that are using `read_table`, e.g.:\r\n- https://huggingface.co/datasets/jglaser/protein_ligand_contacts\r\n- https://huggingface.co/datasets/AresEkb/prof_standards_sbert_large_mt_nlu_ru\r\n- https://huggingface.co/datasets/victorcosta/pt_legislation\r\n- https://huggingface.co/datasets/jglaser/binding_affinity\r\n- https://huggingface.co/datasets/jglaser/pdbbind_complexes\r\n- https://huggingface.co/datasets/victorcosta/ria_pt__proems_format", "I'm merging this PR as discussed in private.", "<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) | 0.008267 / 0.011353 (-0.003086) | 0.005813 / 0.011008 (-0.005195) | 0.108802 / 0.038508 (0.070294) | 0.093996 / 0.023109 (0.070886) | 0.403115 / 0.275898 (0.127217) | 0.457299 / 0.323480 (0.133819) | 0.006277 / 0.007986 (-0.001709) | 0.004701 / 0.004328 (0.000373) | 0.080700 / 0.004250 (0.076449) | 0.077906 / 0.037052 (0.040854) | 0.409972 / 0.258489 (0.151483) | 0.477707 / 0.293841 (0.183867) | 0.041816 / 0.128546 (-0.086731) | 0.011250 / 0.075646 (-0.064397) | 0.390634 / 0.419271 (-0.028637) | 0.065361 / 0.043533 (0.021828) | 0.404501 / 0.255139 (0.149362) | 0.448162 / 0.283200 (0.164962) | 0.032823 / 0.141683 (-0.108860) | 1.899892 / 1.452155 (0.447737) | 2.044561 / 1.492716 (0.551844) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.241093 / 0.018006 (0.223086) | 0.482111 / 0.000490 (0.481622) | 0.005505 / 0.000200 (0.005305) | 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) | 0.034861 / 0.037411 (-0.002551) | 0.109296 / 0.014526 (0.094770) | 0.127594 / 0.176557 (-0.048962) | 0.191815 / 0.737135 (-0.545320) | 0.122630 / 0.296338 (-0.173709) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.452194 / 0.215209 (0.236985) | 4.486200 / 2.077655 (2.408545) | 2.155635 / 1.504120 (0.651515) | 2.004569 / 1.541195 (0.463374) | 2.142570 / 1.468490 (0.674080) | 0.561488 / 4.584777 (-4.023289) | 4.381102 / 3.745712 (0.635390) | 3.914920 / 5.269862 (-1.354942) | 2.474271 / 4.565676 (-2.091406) | 0.067528 / 0.424275 (-0.356747) | 0.008723 / 0.007607 (0.001116) | 0.536077 / 0.226044 (0.310033) | 5.342050 / 2.268929 (3.073122) | 2.735747 / 55.444624 (-52.708877) | 2.353938 / 6.876477 (-4.522539) | 2.442878 / 2.142072 (0.300805) | 0.685404 / 4.805227 (-4.119823) | 0.156657 / 6.500664 (-6.344007) | 0.071714 / 0.075469 (-0.003755) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.562852 / 1.841788 (-0.278935) | 24.538203 / 8.074308 (16.463895) | 16.857777 / 10.191392 (6.666385) | 0.184221 / 0.680424 (-0.496203) | 0.021688 / 0.534201 (-0.512513) | 0.470700 / 0.579283 (-0.108583) | 0.470593 / 0.434364 (0.036229) | 0.645066 / 0.540337 (0.104729) | 0.756075 / 1.386936 (-0.630861) |\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) | 0.009486 / 0.011353 (-0.001867) | 0.004694 / 0.011008 (-0.006314) | 0.080216 / 0.038508 (0.041708) | 0.093479 / 0.023109 (0.070369) | 0.537353 / 0.275898 (0.261455) | 0.551631 / 0.323480 (0.228151) | 0.007373 / 0.007986 (-0.000613) | 0.004044 / 0.004328 (-0.000285) | 0.075301 / 0.004250 (0.071051) | 0.069408 / 0.037052 (0.032355) | 0.527962 / 0.258489 (0.269473) | 0.559423 / 0.293841 (0.265582) | 0.039351 / 0.128546 (-0.089195) | 0.010801 / 0.075646 (-0.064845) | 0.092803 / 0.419271 (-0.326468) | 0.058876 / 0.043533 (0.015343) | 0.513742 / 0.255139 (0.258603) | 0.574666 / 0.283200 (0.291466) | 0.030277 / 0.141683 (-0.111406) | 1.884936 / 1.452155 (0.432782) | 2.008260 / 1.492716 (0.515543) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.242162 / 0.018006 (0.224156) | 0.467400 / 0.000490 (0.466910) | 0.005348 / 0.000200 (0.005148) | 0.000103 / 0.000054 (0.000049) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.038022 / 0.037411 (0.000611) | 0.108239 / 0.014526 (0.093713) | 0.121514 / 0.176557 (-0.055042) | 0.184951 / 0.737135 (-0.552184) | 0.123138 / 0.296338 (-0.173200) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.558587 / 0.215209 (0.343377) | 5.740312 / 2.077655 (3.662657) | 3.172164 / 1.504120 (1.668044) | 2.852908 / 1.541195 (1.311713) | 2.894435 / 1.468490 (1.425945) | 0.586399 / 4.584777 (-3.998378) | 4.498342 / 3.745712 (0.752630) | 4.000569 / 5.269862 (-1.269292) | 2.610988 / 4.565676 (-1.954688) | 0.068415 / 0.424275 (-0.355860) | 0.008602 / 0.007607 (0.000994) | 0.614731 / 0.226044 (0.388686) | 6.068158 / 2.268929 (3.799229) | 3.301070 / 55.444624 (-52.143554) | 2.868034 / 6.876477 (-4.008443) | 2.959072 / 2.142072 (0.816999) | 0.684174 / 4.805227 (-4.121053) | 0.154099 / 6.500664 (-6.346565) | 0.070641 / 0.075469 (-0.004828) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.835667 / 1.841788 (-0.006120) | 24.981645 / 8.074308 (16.907337) | 17.218517 / 10.191392 (7.027125) | 0.197055 / 0.680424 (-0.483368) | 0.025465 / 0.534201 (-0.508736) | 0.523498 / 0.579283 (-0.055785) | 0.528268 / 0.434364 (0.093904) | 0.599518 / 0.540337 (0.059180) | 0.887206 / 1.386936 (-0.499730) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#dd786d3b8dc94f1ab717327e88f65879b525091d \"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) | 0.008892 / 0.011353 (-0.002461) | 0.005140 / 0.011008 (-0.005868) | 0.110951 / 0.038508 (0.072442) | 0.086159 / 0.023109 (0.063050) | 0.391117 / 0.275898 (0.115218) | 0.440884 / 0.323480 (0.117404) | 0.006562 / 0.007986 (-0.001423) | 0.003711 / 0.004328 (-0.000618) | 0.081848 / 0.004250 (0.077598) | 0.063187 / 0.037052 (0.026135) | 0.369771 / 0.258489 (0.111282) | 0.447685 / 0.293841 (0.153844) | 0.046623 / 0.128546 (-0.081923) | 0.014024 / 0.075646 (-0.061622) | 0.418556 / 0.419271 (-0.000715) | 0.064660 / 0.043533 (0.021127) | 0.379416 / 0.255139 (0.124277) | 0.415800 / 0.283200 (0.132600) | 0.036899 / 0.141683 (-0.104784) | 1.710280 / 1.452155 (0.258125) | 1.932326 / 1.492716 (0.439610) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.311351 / 0.018006 (0.293345) | 0.621121 / 0.000490 (0.620631) | 0.013677 / 0.000200 (0.013477) | 0.000543 / 0.000054 (0.000488) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031310 / 0.037411 (-0.006102) | 0.099546 / 0.014526 (0.085020) | 0.122100 / 0.176557 (-0.054457) | 0.186477 / 0.737135 (-0.550659) | 0.116634 / 0.296338 (-0.179704) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.574639 / 0.215209 (0.359430) | 5.976678 / 2.077655 (3.899023) | 2.535482 / 1.504120 (1.031362) | 2.248873 / 1.541195 (0.707678) | 2.361696 / 1.468490 (0.893205) | 0.866700 / 4.584777 (-3.718077) | 5.298018 / 3.745712 (1.552306) | 4.753240 / 5.269862 (-0.516622) | 3.124698 / 4.565676 (-1.440979) | 0.101852 / 0.424275 (-0.322423) | 0.009117 / 0.007607 (0.001510) | 0.723730 / 0.226044 (0.497685) | 7.172649 / 2.268929 (4.903720) | 3.400410 / 55.444624 (-52.044214) | 2.626619 / 6.876477 (-4.249857) | 2.948692 / 2.142072 (0.806620) | 0.991589 / 4.805227 (-3.813638) | 0.208902 / 6.500664 (-6.291762) | 0.076172 / 0.075469 (0.000703) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.621880 / 1.841788 (-0.219907) | 22.735673 / 8.074308 (14.661365) | 20.376990 / 10.191392 (10.185598) | 0.232219 / 0.680424 (-0.448204) | 0.028616 / 0.534201 (-0.505585) | 0.455725 / 0.579283 (-0.123558) | 0.562796 / 0.434364 (0.128432) | 0.545344 / 0.540337 (0.005007) | 0.759440 / 1.386936 (-0.627496) |\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) | 0.009845 / 0.011353 (-0.001508) | 0.005289 / 0.011008 (-0.005719) | 0.083117 / 0.038508 (0.044609) | 0.098467 / 0.023109 (0.075357) | 0.532345 / 0.275898 (0.256447) | 0.571000 / 0.323480 (0.247520) | 0.007223 / 0.007986 (-0.000763) | 0.004442 / 0.004328 (0.000114) | 0.081710 / 0.004250 (0.077459) | 0.071132 / 0.037052 (0.034080) | 0.540093 / 0.258489 (0.281604) | 0.582244 / 0.293841 (0.288403) | 0.048509 / 0.128546 (-0.080038) | 0.013897 / 0.075646 (-0.061749) | 0.092579 / 0.419271 (-0.326692) | 0.073409 / 0.043533 (0.029876) | 0.537369 / 0.255139 (0.282230) | 0.551403 / 0.283200 (0.268203) | 0.038847 / 0.141683 (-0.102835) | 1.940848 / 1.452155 (0.488693) | 2.045597 / 1.492716 (0.552881) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.303883 / 0.018006 (0.285877) | 0.600237 / 0.000490 (0.599748) | 0.006030 / 0.000200 (0.005830) | 0.000124 / 0.000054 (0.000070) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.036633 / 0.037411 (-0.000778) | 0.105853 / 0.014526 (0.091327) | 0.126289 / 0.176557 (-0.050267) | 0.190022 / 0.737135 (-0.547113) | 0.123251 / 0.296338 (-0.173087) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.711893 / 0.215209 (0.496684) | 6.979781 / 2.077655 (4.902126) | 3.491514 / 1.504120 (1.987394) | 3.268077 / 1.541195 (1.726882) | 3.241777 / 1.468490 (1.773287) | 0.875913 / 4.584777 (-3.708864) | 5.458421 / 3.745712 (1.712709) | 4.818355 / 5.269862 (-0.451507) | 3.256046 / 4.565676 (-1.309631) | 0.095000 / 0.424275 (-0.329275) | 0.009072 / 0.007607 (0.001465) | 0.818468 / 0.226044 (0.592424) | 8.027702 / 2.268929 (5.758773) | 4.363234 / 55.444624 (-51.081390) | 3.695269 / 6.876477 (-3.181207) | 3.902601 / 2.142072 (1.760528) | 1.039007 / 4.805227 (-3.766220) | 0.212050 / 6.500664 (-6.288614) | 0.081438 / 0.075469 (0.005969) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.746945 / 1.841788 (-0.094842) | 25.274283 / 8.074308 (17.199975) | 23.514717 / 10.191392 (13.323325) | 0.232580 / 0.680424 (-0.447843) | 0.032083 / 0.534201 (-0.502118) | 0.482873 / 0.579283 (-0.096410) | 0.585730 / 0.434364 (0.151366) | 0.602066 / 0.540337 (0.061729) | 0.796391 / 1.386936 (-0.590546) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0d7cb68fe37dbfd81e5f82e19d8f9847c337788d \"CML watermark\")\n" ]

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[ "I think it's an issue with the code.\r\n\r\nSpecifically:\r\n```python\r\ndataset = dataset['train'].add_column(\"/workspace/data\", new_column)\r\n```\r\n\r\nNow `dataset` is the train set with a new column. \r\nTo fix this, you can do:\r\n\r\n```python\r\ndataset['train'] = dataset['train'].add_column(\"/workspace/data\", new_column)\r\n```", "> I think it's an issue with the code.\r\n> \r\n> Specifically:\r\n> \r\n> ```python\r\n> dataset = dataset['train'].add_column(\"/workspace/data\", new_column)\r\n> ```\r\n> \r\n> Now `dataset` is the train set with a new column. To fix this, you can do:\r\n> \r\n> ```python\r\n> dataset['train'] = dataset['train'].add_column(\"/workspace/data\", new_column)\r\n> ```\r\n\r\nThanks for your response, but i can not access mask images, please let me know why the problem still persists. Here is the notebook for reference: https://colab.research.google.com/drive/10lZ_zLtU4itYVmIVTvIEVbjfOtCZaAZy?usp=sharing ", "I think there is a slight misunderstanding.\r\n```python\r\nnew_column = [\"mask\"] * len(dataset[\"train\"])\r\ndataset['train'] = dataset['train'].add_column(\"/workspace/data\", new_column)\r\n```\r\n\r\nadds a column with the string `mask` to your dataset.\r\nIf you're trying to load the images `\"mask_{idx}.png\"` in your dataset, you could try:\r\n\r\n```\r\nfrom datasets import Image\r\n\r\ndataset['train'] = dataset['train'].map(lambda u, idx: {'mask': f\"/workspace/data/mask_{idx}.png\", with_indices=True).cast_column(\"mask\", Image())\r\n```\r\n\r\nWhat this does is that it adds a column to your dataset name `mask` with the path to the mask, then it cast the column as an `Image` feature.\r\n\r\nThis [link](https://huggingface.co/docs/datasets/v2.5.1/en/image_load) explains how to load images.\r\n\r\nHope this helps!", "> I think there is a slight misunderstanding.\r\n> \r\n> ```python\r\n> new_column = [\"mask\"] * len(dataset[\"train\"])\r\n> dataset['train'] = dataset['train'].add_column(\"/workspace/data\", new_column)\r\n> ```\r\n> \r\n> adds a column with the string `mask` to your dataset. If you're trying to load the images `\"mask_{idx}.png\"` in your dataset, you could try:\r\n> \r\n> ```\r\n> from datasets import Image\r\n> \r\n> dataset['train'] = dataset['train'].map(lambda u, idx: {'mask': f\"/workspace/data/mask_{idx}.png\", with_indices=True).cast_column(\"mask\", Image())\r\n> ```\r\n> \r\n> What this does is that it adds a column to your dataset name `mask` with the path to the mask, then it cast the column as an `Image` feature.\r\n> \r\n> This [link](https://huggingface.co/docs/datasets/v2.5.1/en/image_load) explains how to load images.\r\n> \r\n> Hope this helps!\r\n\r\nThank you very much, this is really helpful...\r\ni made some changes for it to work:\r\n```\r\ndataset['train'] = dataset['train'].map(lambda u, idx: {'mask': f\"/content/data/mask_{idx}.png\"}, with_indices=True).cast_column(\"mask\", Image())\r\n```\r\nThanks Again @Dref360 " ]

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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) | 0.006410 / 0.011353 (-0.004943) | 0.003995 / 0.011008 (-0.007013) | 0.083585 / 0.038508 (0.045076) | 0.074285 / 0.023109 (0.051176) | 0.307163 / 0.275898 (0.031265) | 0.344691 / 0.323480 (0.021212) | 0.004277 / 0.007986 (-0.003708) | 0.004192 / 0.004328 (-0.000136) | 0.065156 / 0.004250 (0.060905) | 0.056774 / 0.037052 (0.019721) | 0.315483 / 0.258489 (0.056994) | 0.361911 / 0.293841 (0.068070) | 0.030454 / 0.128546 (-0.098092) | 0.008600 / 0.075646 (-0.067047) | 0.286692 / 0.419271 (-0.132579) | 0.052354 / 0.043533 (0.008821) | 0.308997 / 0.255139 (0.053858) | 0.337847 / 0.283200 (0.054647) | 0.022459 / 0.141683 (-0.119224) | 1.482758 / 1.452155 (0.030604) | 1.572853 / 1.492716 (0.080137) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.288603 / 0.018006 (0.270597) | 0.632903 / 0.000490 (0.632413) | 0.013702 / 0.000200 (0.013502) | 0.000284 / 0.000054 (0.000230) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028448 / 0.037411 (-0.008964) | 0.082441 / 0.014526 (0.067916) | 0.099048 / 0.176557 (-0.077508) | 0.154370 / 0.737135 (-0.582765) | 0.146143 / 0.296338 (-0.150195) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.399250 / 0.215209 (0.184040) | 3.986683 / 2.077655 (1.909028) | 1.962606 / 1.504120 (0.458486) | 1.782653 / 1.541195 (0.241459) | 1.830251 / 1.468490 (0.361761) | 0.492498 / 4.584777 (-4.092278) | 3.549581 / 3.745712 (-0.196131) | 3.200056 / 5.269862 (-2.069806) | 2.028109 / 4.565676 (-2.537568) | 0.058222 / 0.424275 (-0.366053) | 0.007629 / 0.007607 (0.000022) | 0.482083 / 0.226044 (0.256039) | 4.824728 / 2.268929 (2.555800) | 2.448772 / 55.444624 (-52.995852) | 2.079629 / 6.876477 (-4.796848) | 2.267739 / 2.142072 (0.125667) | 0.586712 / 4.805227 (-4.218515) | 0.134073 / 6.500664 (-6.366591) | 0.060565 / 0.075469 (-0.014904) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.263244 / 1.841788 (-0.578544) | 18.964498 / 8.074308 (10.890190) | 14.125062 / 10.191392 (3.933670) | 0.167635 / 0.680424 (-0.512789) | 0.018469 / 0.534201 (-0.515732) | 0.390395 / 0.579283 (-0.188888) | 0.406055 / 0.434364 (-0.028309) | 0.460717 / 0.540337 (-0.079620) | 0.642746 / 1.386936 (-0.744190) |\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) | 0.003972 / 0.011008 (-0.007036) | 0.064569 / 0.038508 (0.026061) | 0.075450 / 0.023109 (0.052341) | 0.405250 / 0.275898 (0.129352) | 0.433530 / 0.323480 (0.110050) | 0.005625 / 0.007986 (-0.002361) | 0.004118 / 0.004328 (-0.000211) | 0.065092 / 0.004250 (0.060842) | 0.057979 / 0.037052 (0.020927) | 0.413732 / 0.258489 (0.155243) | 0.451983 / 0.293841 (0.158142) | 0.032170 / 0.128546 (-0.096377) | 0.008690 / 0.075646 (-0.066957) | 0.071792 / 0.419271 (-0.347479) | 0.048560 / 0.043533 (0.005027) | 0.410312 / 0.255139 (0.155173) | 0.427294 / 0.283200 (0.144095) | 0.023006 / 0.141683 (-0.118677) | 1.496319 / 1.452155 (0.044164) | 1.566744 / 1.492716 (0.074027) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.266812 / 0.018006 (0.248805) | 0.540277 / 0.000490 (0.539788) | 0.008998 / 0.000200 (0.008799) | 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) | 0.032496 / 0.037411 (-0.004915) | 0.091387 / 0.014526 (0.076861) | 0.107516 / 0.176557 (-0.069041) | 0.160019 / 0.737135 (-0.577116) | 0.107686 / 0.296338 (-0.188652) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.433321 / 0.215209 (0.218111) | 4.330221 / 2.077655 (2.252566) | 2.367215 / 1.504120 (0.863095) | 2.192464 / 1.541195 (0.651269) | 2.200204 / 1.468490 (0.731714) | 0.488057 / 4.584777 (-4.096720) | 3.625429 / 3.745712 (-0.120283) | 3.282859 / 5.269862 (-1.987003) | 2.038716 / 4.565676 (-2.526960) | 0.057968 / 0.424275 (-0.366307) | 0.007753 / 0.007607 (0.000146) | 0.509133 / 0.226044 (0.283089) | 5.086445 / 2.268929 (2.817516) | 2.846017 / 55.444624 (-52.598607) | 2.469546 / 6.876477 (-4.406931) | 2.673218 / 2.142072 (0.531145) | 0.591228 / 4.805227 (-4.213999) | 0.131920 / 6.500664 (-6.368744) | 0.059967 / 0.075469 (-0.015502) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.375634 / 1.841788 (-0.466153) | 19.506752 / 8.074308 (11.432444) | 14.677876 / 10.191392 (4.486484) | 0.165071 / 0.680424 (-0.515353) | 0.020614 / 0.534201 (-0.513587) | 0.395967 / 0.579283 (-0.183316) | 0.424358 / 0.434364 (-0.010006) | 0.469954 / 0.540337 (-0.070384) | 0.643169 / 1.386936 (-0.743767) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#887a854f03c4ac6d2e99b9ef4d89e6fe8c46d6f1 \"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) | 0.006072 / 0.011353 (-0.005281) | 0.003691 / 0.011008 (-0.007318) | 0.081683 / 0.038508 (0.043175) | 0.059114 / 0.023109 (0.036005) | 0.317053 / 0.275898 (0.041155) | 0.357672 / 0.323480 (0.034192) | 0.003577 / 0.007986 (-0.004408) | 0.003890 / 0.004328 (-0.000438) | 0.063667 / 0.004250 (0.059417) | 0.048233 / 0.037052 (0.011181) | 0.322854 / 0.258489 (0.064365) | 0.368014 / 0.293841 (0.074173) | 0.027750 / 0.128546 (-0.100796) | 0.008137 / 0.075646 (-0.067509) | 0.263906 / 0.419271 (-0.155366) | 0.045402 / 0.043533 (0.001870) | 0.315414 / 0.255139 (0.060275) | 0.340906 / 0.283200 (0.057707) | 0.023475 / 0.141683 (-0.118208) | 1.443922 / 1.452155 (-0.008233) | 1.550332 / 1.492716 (0.057616) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.211914 / 0.018006 (0.193908) | 0.423577 / 0.000490 (0.423088) | 0.003436 / 0.000200 (0.003236) | 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) | 0.024675 / 0.037411 (-0.012737) | 0.072550 / 0.014526 (0.058024) | 0.084533 / 0.176557 (-0.092024) | 0.146106 / 0.737135 (-0.591029) | 0.085523 / 0.296338 (-0.210816) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.403498 / 0.215209 (0.188289) | 4.019000 / 2.077655 (1.941345) | 1.984821 / 1.504120 (0.480701) | 1.805071 / 1.541195 (0.263876) | 1.860906 / 1.468490 (0.392416) | 0.499570 / 4.584777 (-4.085207) | 3.088424 / 3.745712 (-0.657288) | 2.833693 / 5.269862 (-2.436169) | 1.869731 / 4.565676 (-2.695945) | 0.057606 / 0.424275 (-0.366669) | 0.006960 / 0.007607 (-0.000647) | 0.476085 / 0.226044 (0.250040) | 4.774063 / 2.268929 (2.505134) | 2.458079 / 55.444624 (-52.986545) | 2.106075 / 6.876477 (-4.770402) | 2.248373 / 2.142072 (0.106301) | 0.589767 / 4.805227 (-4.215460) | 0.124382 / 6.500664 (-6.376282) | 0.060705 / 0.075469 (-0.014764) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.287031 / 1.841788 (-0.554756) | 17.662455 / 8.074308 (9.588147) | 14.288812 / 10.191392 (4.097420) | 0.156168 / 0.680424 (-0.524256) | 0.016795 / 0.534201 (-0.517406) | 0.333726 / 0.579283 (-0.245557) | 0.362327 / 0.434364 (-0.072037) | 0.387773 / 0.540337 (-0.152564) | 0.547232 / 1.386936 (-0.839704) |\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) | 0.006494 / 0.011353 (-0.004859) | 0.003762 / 0.011008 (-0.007247) | 0.062373 / 0.038508 (0.023864) | 0.066357 / 0.023109 (0.043247) | 0.448687 / 0.275898 (0.172789) | 0.482445 / 0.323480 (0.158965) | 0.004990 / 0.007986 (-0.002996) | 0.002945 / 0.004328 (-0.001384) | 0.062444 / 0.004250 (0.058194) | 0.051381 / 0.037052 (0.014329) | 0.449310 / 0.258489 (0.190821) | 0.483188 / 0.293841 (0.189347) | 0.029078 / 0.128546 (-0.099468) | 0.008146 / 0.075646 (-0.067501) | 0.067369 / 0.419271 (-0.351903) | 0.041732 / 0.043533 (-0.001801) | 0.451675 / 0.255139 (0.196536) | 0.470445 / 0.283200 (0.187246) | 0.021053 / 0.141683 (-0.120630) | 1.483627 / 1.452155 (0.031472) | 1.541594 / 1.492716 (0.048878) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.210247 / 0.018006 (0.192240) | 0.424663 / 0.000490 (0.424173) | 0.005394 / 0.000200 (0.005194) | 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) | 0.026894 / 0.037411 (-0.010517) | 0.081324 / 0.014526 (0.066798) | 0.091362 / 0.176557 (-0.085195) | 0.145602 / 0.737135 (-0.591533) | 0.091896 / 0.296338 (-0.204443) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.469662 / 0.215209 (0.254453) | 4.689495 / 2.077655 (2.611840) | 2.596462 / 1.504120 (1.092342) | 2.422584 / 1.541195 (0.881389) | 2.476710 / 1.468490 (1.008220) | 0.507049 / 4.584777 (-4.077728) | 3.185519 / 3.745712 (-0.560193) | 2.879842 / 5.269862 (-2.390019) | 1.882643 / 4.565676 (-2.683034) | 0.058046 / 0.424275 (-0.366229) | 0.006797 / 0.007607 (-0.000811) | 0.545245 / 0.226044 (0.319201) | 5.449248 / 2.268929 (3.180319) | 3.057341 / 55.444624 (-52.387283) | 2.728385 / 6.876477 (-4.148092) | 2.898945 / 2.142072 (0.756873) | 0.600035 / 4.805227 (-4.205192) | 0.126337 / 6.500664 (-6.374327) | 0.061333 / 0.075469 (-0.014136) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.332966 / 1.841788 (-0.508821) | 17.960805 / 8.074308 (9.886497) | 14.978838 / 10.191392 (4.787446) | 0.148852 / 0.680424 (-0.531572) | 0.018307 / 0.534201 (-0.515894) | 0.335234 / 0.579283 (-0.244050) | 0.389659 / 0.434364 (-0.044704) | 0.393259 / 0.540337 (-0.147078) | 0.549237 / 1.386936 (-0.837699) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#278a5673172c30b915a9ebf64cc7aff9667b58fd \"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) | 0.008808 / 0.011353 (-0.002545) | 0.005001 / 0.011008 (-0.006008) | 0.110022 / 0.038508 (0.071514) | 0.078015 / 0.023109 (0.054906) | 0.384724 / 0.275898 (0.108826) | 0.441354 / 0.323480 (0.117874) | 0.005116 / 0.007986 (-0.002870) | 0.004308 / 0.004328 (-0.000020) | 0.081679 / 0.004250 (0.077429) | 0.061386 / 0.037052 (0.024333) | 0.398149 / 0.258489 (0.139660) | 0.464859 / 0.293841 (0.171018) | 0.047443 / 0.128546 (-0.081104) | 0.014693 / 0.075646 (-0.060954) | 0.365438 / 0.419271 (-0.053833) | 0.081689 / 0.043533 (0.038156) | 0.400458 / 0.255139 (0.145319) | 0.449958 / 0.283200 (0.166758) | 0.038266 / 0.141683 (-0.103417) | 1.795043 / 1.452155 (0.342888) | 1.908819 / 1.492716 (0.416102) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.297911 / 0.018006 (0.279905) | 0.601640 / 0.000490 (0.601150) | 0.015406 / 0.000200 (0.015206) | 0.000163 / 0.000054 (0.000108) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034520 / 0.037411 (-0.002891) | 0.092657 / 0.014526 (0.078131) | 0.113992 / 0.176557 (-0.062564) | 0.189075 / 0.737135 (-0.548061) | 0.106602 / 0.296338 (-0.189736) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.585838 / 0.215209 (0.370629) | 5.719281 / 2.077655 (3.641627) | 2.525914 / 1.504120 (1.021794) | 2.231908 / 1.541195 (0.690713) | 2.215272 / 1.468490 (0.746782) | 0.814425 / 4.584777 (-3.770352) | 5.243406 / 3.745712 (1.497694) | 4.476642 / 5.269862 (-0.793220) | 2.929438 / 4.565676 (-1.636239) | 0.092070 / 0.424275 (-0.332205) | 0.009358 / 0.007607 (0.001751) | 0.713975 / 0.226044 (0.487931) | 6.948846 / 2.268929 (4.679918) | 3.361125 / 55.444624 (-52.083500) | 2.575224 / 6.876477 (-4.301253) | 2.783082 / 2.142072 (0.641009) | 1.016205 / 4.805227 (-3.789022) | 0.202578 / 6.500664 (-6.298086) | 0.076696 / 0.075469 (0.001227) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.650889 / 1.841788 (-0.190898) | 23.358273 / 8.074308 (15.283965) | 19.882450 / 10.191392 (9.691058) | 0.228971 / 0.680424 (-0.451453) | 0.027736 / 0.534201 (-0.506465) | 0.472405 / 0.579283 (-0.106878) | 0.581799 / 0.434364 (0.147435) | 0.533000 / 0.540337 (-0.007338) | 0.815588 / 1.386936 (-0.571348) |\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) | 0.009151 / 0.011353 (-0.002202) | 0.005074 / 0.011008 (-0.005934) | 0.078709 / 0.038508 (0.040201) | 0.077696 / 0.023109 (0.054586) | 0.522356 / 0.275898 (0.246458) | 0.562345 / 0.323480 (0.238865) | 0.006411 / 0.007986 (-0.001575) | 0.004379 / 0.004328 (0.000051) | 0.082402 / 0.004250 (0.078151) | 0.064223 / 0.037052 (0.027170) | 0.518184 / 0.258489 (0.259695) | 0.566221 / 0.293841 (0.272380) | 0.046796 / 0.128546 (-0.081750) | 0.013987 / 0.075646 (-0.061659) | 0.094925 / 0.419271 (-0.324346) | 0.058810 / 0.043533 (0.015277) | 0.520252 / 0.255139 (0.265113) | 0.566403 / 0.283200 (0.283203) | 0.034720 / 0.141683 (-0.106963) | 1.796809 / 1.452155 (0.344654) | 1.913787 / 1.492716 (0.421070) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.317449 / 0.018006 (0.299443) | 0.620154 / 0.000490 (0.619664) | 0.007066 / 0.000200 (0.006866) | 0.000126 / 0.000054 (0.000072) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035252 / 0.037411 (-0.002160) | 0.111648 / 0.014526 (0.097122) | 0.120692 / 0.176557 (-0.055864) | 0.193202 / 0.737135 (-0.543933) | 0.127905 / 0.296338 (-0.168434) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.661012 / 0.215209 (0.445803) | 6.626680 / 2.077655 (4.549026) | 3.243065 / 1.504120 (1.738945) | 2.904053 / 1.541195 (1.362858) | 2.880516 / 1.468490 (1.412026) | 0.875650 / 4.584777 (-3.709127) | 5.381993 / 3.745712 (1.636281) | 4.743997 / 5.269862 (-0.525864) | 3.020736 / 4.565676 (-1.544940) | 0.106573 / 0.424275 (-0.317702) | 0.011151 / 0.007607 (0.003544) | 0.821990 / 0.226044 (0.595946) | 8.225383 / 2.268929 (5.956454) | 3.963232 / 55.444624 (-51.481392) | 3.288916 / 6.876477 (-3.587561) | 3.579435 / 2.142072 (1.437363) | 1.043379 / 4.805227 (-3.761848) | 0.207508 / 6.500664 (-6.293156) | 0.085109 / 0.075469 (0.009640) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.723798 / 1.841788 (-0.117990) | 24.709848 / 8.074308 (16.635540) | 22.484674 / 10.191392 (12.293282) | 0.260357 / 0.680424 (-0.420067) | 0.033539 / 0.534201 (-0.500662) | 0.487814 / 0.579283 (-0.091469) | 0.610171 / 0.434364 (0.175807) | 0.585012 / 0.540337 (0.044674) | 0.803764 / 1.386936 (-0.583172) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f611e5815ce1bdcb4fa8556f55d85a6739cba0ea \"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) | 0.006661 / 0.011353 (-0.004692) | 0.004022 / 0.011008 (-0.006987) | 0.084269 / 0.038508 (0.045760) | 0.070707 / 0.023109 (0.047598) | 0.315035 / 0.275898 (0.039137) | 0.339830 / 0.323480 (0.016350) | 0.003994 / 0.007986 (-0.003991) | 0.004129 / 0.004328 (-0.000199) | 0.065383 / 0.004250 (0.061133) | 0.055493 / 0.037052 (0.018441) | 0.320521 / 0.258489 (0.062032) | 0.354301 / 0.293841 (0.060460) | 0.031177 / 0.128546 (-0.097370) | 0.008724 / 0.075646 (-0.066922) | 0.288298 / 0.419271 (-0.130974) | 0.052418 / 0.043533 (0.008885) | 0.319122 / 0.255139 (0.063983) | 0.335859 / 0.283200 (0.052659) | 0.026260 / 0.141683 (-0.115423) | 1.450039 / 1.452155 (-0.002115) | 1.545172 / 1.492716 (0.052455) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.234232 / 0.018006 (0.216226) | 0.454983 / 0.000490 (0.454493) | 0.007590 / 0.000200 (0.007390) | 0.000550 / 0.000054 (0.000495) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028714 / 0.037411 (-0.008698) | 0.083686 / 0.014526 (0.069160) | 0.162986 / 0.176557 (-0.013570) | 0.167574 / 0.737135 (-0.569561) | 0.273158 / 0.296338 (-0.023180) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.388275 / 0.215209 (0.173066) | 3.862034 / 2.077655 (1.784379) | 1.843561 / 1.504120 (0.339441) | 1.675224 / 1.541195 (0.134029) | 1.730394 / 1.468490 (0.261904) | 0.495259 / 4.584777 (-4.089518) | 3.627155 / 3.745712 (-0.118557) | 3.290590 / 5.269862 (-1.979272) | 2.032432 / 4.565676 (-2.533245) | 0.058212 / 0.424275 (-0.366063) | 0.007815 / 0.007607 (0.000208) | 0.460625 / 0.226044 (0.234580) | 4.616845 / 2.268929 (2.347916) | 2.339280 / 55.444624 (-53.105344) | 1.957216 / 6.876477 (-4.919261) | 2.129511 / 2.142072 (-0.012562) | 0.591782 / 4.805227 (-4.213446) | 0.136391 / 6.500664 (-6.364273) | 0.059627 / 0.075469 (-0.015842) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.278998 / 1.841788 (-0.562789) | 18.485496 / 8.074308 (10.411188) | 14.161273 / 10.191392 (3.969881) | 0.164346 / 0.680424 (-0.516078) | 0.018144 / 0.534201 (-0.516057) | 0.391601 / 0.579283 (-0.187682) | 0.424391 / 0.434364 (-0.009973) | 0.458209 / 0.540337 (-0.082129) | 0.645124 / 1.386936 (-0.741812) |\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) | 0.006799 / 0.011353 (-0.004554) | 0.004023 / 0.011008 (-0.006985) | 0.065206 / 0.038508 (0.026698) | 0.074386 / 0.023109 (0.051277) | 0.437399 / 0.275898 (0.161501) | 0.467382 / 0.323480 (0.143903) | 0.005467 / 0.007986 (-0.002519) | 0.003324 / 0.004328 (-0.001005) | 0.064289 / 0.004250 (0.060039) | 0.057257 / 0.037052 (0.020205) | 0.440035 / 0.258489 (0.181546) | 0.477138 / 0.293841 (0.183298) | 0.032171 / 0.128546 (-0.096375) | 0.008400 / 0.075646 (-0.067247) | 0.070877 / 0.419271 (-0.348395) | 0.048180 / 0.043533 (0.004648) | 0.441274 / 0.255139 (0.186135) | 0.461386 / 0.283200 (0.178187) | 0.022576 / 0.141683 (-0.119106) | 1.520914 / 1.452155 (0.068759) | 1.575593 / 1.492716 (0.082877) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.221551 / 0.018006 (0.203545) | 0.447213 / 0.000490 (0.446723) | 0.004435 / 0.000200 (0.004235) | 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) | 0.032123 / 0.037411 (-0.005288) | 0.091809 / 0.014526 (0.077283) | 0.103938 / 0.176557 (-0.072618) | 0.156878 / 0.737135 (-0.580258) | 0.105071 / 0.296338 (-0.191268) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.430389 / 0.215209 (0.215180) | 4.293496 / 2.077655 (2.215841) | 2.292801 / 1.504120 (0.788681) | 2.135320 / 1.541195 (0.594126) | 2.195720 / 1.468490 (0.727229) | 0.493277 / 4.584777 (-4.091500) | 3.685617 / 3.745712 (-0.060096) | 3.278897 / 5.269862 (-1.990965) | 2.036939 / 4.565676 (-2.528737) | 0.058766 / 0.424275 (-0.365509) | 0.007783 / 0.007607 (0.000176) | 0.511165 / 0.226044 (0.285120) | 5.126757 / 2.268929 (2.857829) | 2.756690 / 55.444624 (-52.687935) | 2.421745 / 6.876477 (-4.454732) | 2.597249 / 2.142072 (0.455177) | 0.647206 / 4.805227 (-4.158021) | 0.143392 / 6.500664 (-6.357273) | 0.060110 / 0.075469 (-0.015359) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.340289 / 1.841788 (-0.501499) | 19.057620 / 8.074308 (10.983312) | 14.832892 / 10.191392 (4.641500) | 0.167730 / 0.680424 (-0.512694) | 0.020178 / 0.534201 (-0.514023) | 0.394060 / 0.579283 (-0.185223) | 0.433976 / 0.434364 (-0.000388) | 0.474417 / 0.540337 (-0.065921) | 0.640653 / 1.386936 (-0.746283) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d0519c6a1988a3344ecae37f7348c208bcbc99d6 \"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) | 0.007661 / 0.011353 (-0.003692) | 0.004541 / 0.011008 (-0.006467) | 0.100547 / 0.038508 (0.062039) | 0.084257 / 0.023109 (0.061148) | 0.377627 / 0.275898 (0.101729) | 0.433764 / 0.323480 (0.110284) | 0.005995 / 0.007986 (-0.001990) | 0.003810 / 0.004328 (-0.000518) | 0.076409 / 0.004250 (0.072158) | 0.063411 / 0.037052 (0.026359) | 0.382504 / 0.258489 (0.124015) | 0.449721 / 0.293841 (0.155880) | 0.036499 / 0.128546 (-0.092047) | 0.009942 / 0.075646 (-0.065705) | 0.343839 / 0.419271 (-0.075433) | 0.062147 / 0.043533 (0.018614) | 0.383244 / 0.255139 (0.128105) | 0.415606 / 0.283200 (0.132406) | 0.027475 / 0.141683 (-0.114207) | 1.740413 / 1.452155 (0.288258) | 1.862210 / 1.492716 (0.369493) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.260064 / 0.018006 (0.242058) | 0.499001 / 0.000490 (0.498511) | 0.015811 / 0.000200 (0.015611) | 0.000119 / 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) | 0.033599 / 0.037411 (-0.003812) | 0.099354 / 0.014526 (0.084828) | 0.114693 / 0.176557 (-0.061864) | 0.180231 / 0.737135 (-0.556904) | 0.114715 / 0.296338 (-0.181623) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.459884 / 0.215209 (0.244675) | 4.580806 / 2.077655 (2.503151) | 2.270770 / 1.504120 (0.766650) | 2.077127 / 1.541195 (0.535932) | 2.167175 / 1.468490 (0.698685) | 0.570593 / 4.584777 (-4.014184) | 4.120926 / 3.745712 (0.375214) | 3.817595 / 5.269862 (-1.452267) | 2.404782 / 4.565676 (-2.160894) | 0.067972 / 0.424275 (-0.356304) | 0.009378 / 0.007607 (0.001771) | 0.549642 / 0.226044 (0.323597) | 5.490369 / 2.268929 (3.221440) | 2.905264 / 55.444624 (-52.539361) | 2.452935 / 6.876477 (-4.423542) | 2.700760 / 2.142072 (0.558688) | 0.700407 / 4.805227 (-4.104820) | 0.159349 / 6.500664 (-6.341315) | 0.074605 / 0.075469 (-0.000864) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.517803 / 1.841788 (-0.323985) | 22.343700 / 8.074308 (14.269392) | 16.411639 / 10.191392 (6.220247) | 0.169816 / 0.680424 (-0.510608) | 0.021532 / 0.534201 (-0.512668) | 0.470161 / 0.579283 (-0.109122) | 0.473412 / 0.434364 (0.039048) | 0.539690 / 0.540337 (-0.000647) | 0.774011 / 1.386936 (-0.612925) |\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) | 0.007629 / 0.011353 (-0.003724) | 0.004651 / 0.011008 (-0.006357) | 0.075162 / 0.038508 (0.036654) | 0.085365 / 0.023109 (0.062256) | 0.493272 / 0.275898 (0.217374) | 0.535776 / 0.323480 (0.212296) | 0.006323 / 0.007986 (-0.001663) | 0.003785 / 0.004328 (-0.000544) | 0.076161 / 0.004250 (0.071911) | 0.065982 / 0.037052 (0.028929) | 0.513355 / 0.258489 (0.254866) | 0.549219 / 0.293841 (0.255378) | 0.038052 / 0.128546 (-0.090494) | 0.010055 / 0.075646 (-0.065592) | 0.083744 / 0.419271 (-0.335527) | 0.056708 / 0.043533 (0.013175) | 0.496273 / 0.255139 (0.241135) | 0.523709 / 0.283200 (0.240509) | 0.026502 / 0.141683 (-0.115181) | 1.793032 / 1.452155 (0.340877) | 1.870534 / 1.492716 (0.377817) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.252288 / 0.018006 (0.234281) | 0.490380 / 0.000490 (0.489890) | 0.005884 / 0.000200 (0.005684) | 0.000109 / 0.000054 (0.000054) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.038238 / 0.037411 (0.000827) | 0.110010 / 0.014526 (0.095485) | 0.125497 / 0.176557 (-0.051059) | 0.188154 / 0.737135 (-0.548981) | 0.126112 / 0.296338 (-0.170227) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.515837 / 0.215209 (0.300628) | 5.135153 / 2.077655 (3.057498) | 2.761740 / 1.504120 (1.257620) | 2.552718 / 1.541195 (1.011523) | 2.636425 / 1.468490 (1.167935) | 0.588442 / 4.584777 (-3.996335) | 4.220833 / 3.745712 (0.475120) | 3.874637 / 5.269862 (-1.395225) | 2.424668 / 4.565676 (-2.141009) | 0.069979 / 0.424275 (-0.354296) | 0.009349 / 0.007607 (0.001742) | 0.608936 / 0.226044 (0.382891) | 6.081209 / 2.268929 (3.812280) | 3.348067 / 55.444624 (-52.096557) | 2.919130 / 6.876477 (-3.957347) | 3.159093 / 2.142072 (1.017020) | 0.704059 / 4.805227 (-4.101169) | 0.158417 / 6.500664 (-6.342247) | 0.071321 / 0.075469 (-0.004148) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.595287 / 1.841788 (-0.246501) | 23.096619 / 8.074308 (15.022311) | 17.258041 / 10.191392 (7.066649) | 0.186197 / 0.680424 (-0.494227) | 0.023633 / 0.534201 (-0.510567) | 0.472181 / 0.579283 (-0.107102) | 0.493817 / 0.434364 (0.059453) | 0.567657 / 0.540337 (0.027320) | 0.793789 / 1.386936 (-0.593147) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e0bd8444689c5d82344a62ddf79e5dc103fc67b8 \"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) | 0.007084 / 0.011353 (-0.004268) | 0.004093 / 0.011008 (-0.006915) | 0.086395 / 0.038508 (0.047887) | 0.087734 / 0.023109 (0.064625) | 0.356936 / 0.275898 (0.081038) | 0.386413 / 0.323480 (0.062933) | 0.005531 / 0.007986 (-0.002454) | 0.003462 / 0.004328 (-0.000866) | 0.065503 / 0.004250 (0.061252) | 0.058973 / 0.037052 (0.021920) | 0.354151 / 0.258489 (0.095662) | 0.398812 / 0.293841 (0.104971) | 0.031508 / 0.128546 (-0.097038) | 0.008537 / 0.075646 (-0.067109) | 0.290942 / 0.419271 (-0.128329) | 0.053537 / 0.043533 (0.010004) | 0.352067 / 0.255139 (0.096928) | 0.375142 / 0.283200 (0.091943) | 0.025658 / 0.141683 (-0.116025) | 1.468496 / 1.452155 (0.016341) | 1.556926 / 1.492716 (0.064210) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.238858 / 0.018006 (0.220852) | 0.460018 / 0.000490 (0.459528) | 0.009613 / 0.000200 (0.009414) | 0.000326 / 0.000054 (0.000272) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030333 / 0.037411 (-0.007078) | 0.088431 / 0.014526 (0.073905) | 0.098130 / 0.176557 (-0.078427) | 0.155160 / 0.737135 (-0.581975) | 0.099963 / 0.296338 (-0.196375) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.385769 / 0.215209 (0.170560) | 3.836723 / 2.077655 (1.759069) | 1.861065 / 1.504120 (0.356945) | 1.685159 / 1.541195 (0.143965) | 1.780679 / 1.468490 (0.312189) | 0.491865 / 4.584777 (-4.092912) | 3.581139 / 3.745712 (-0.164573) | 3.366278 / 5.269862 (-1.903584) | 2.093094 / 4.565676 (-2.472583) | 0.058063 / 0.424275 (-0.366212) | 0.007903 / 0.007607 (0.000296) | 0.464866 / 0.226044 (0.238821) | 4.647754 / 2.268929 (2.378825) | 2.316466 / 55.444624 (-53.128158) | 1.984079 / 6.876477 (-4.892398) | 2.235020 / 2.142072 (0.092948) | 0.592591 / 4.805227 (-4.212636) | 0.135586 / 6.500664 (-6.365078) | 0.061434 / 0.075469 (-0.014035) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.282940 / 1.841788 (-0.558848) | 19.635975 / 8.074308 (11.561667) | 14.426135 / 10.191392 (4.234743) | 0.166732 / 0.680424 (-0.513692) | 0.018438 / 0.534201 (-0.515763) | 0.393173 / 0.579283 (-0.186110) | 0.417291 / 0.434364 (-0.017073) | 0.459188 / 0.540337 (-0.081149) | 0.632568 / 1.386936 (-0.754368) |\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) | 0.007166 / 0.011353 (-0.004187) | 0.004254 / 0.011008 (-0.006754) | 0.064667 / 0.038508 (0.026159) | 0.085142 / 0.023109 (0.062033) | 0.410081 / 0.275898 (0.134183) | 0.445803 / 0.323480 (0.122323) | 0.005600 / 0.007986 (-0.002385) | 0.003520 / 0.004328 (-0.000809) | 0.064148 / 0.004250 (0.059897) | 0.059869 / 0.037052 (0.022817) | 0.407439 / 0.258489 (0.148950) | 0.451169 / 0.293841 (0.157329) | 0.032619 / 0.128546 (-0.095927) | 0.008706 / 0.075646 (-0.066940) | 0.071230 / 0.419271 (-0.348041) | 0.048499 / 0.043533 (0.004966) | 0.416401 / 0.255139 (0.161262) | 0.430737 / 0.283200 (0.147537) | 0.022511 / 0.141683 (-0.119172) | 1.517296 / 1.452155 (0.065141) | 1.581704 / 1.492716 (0.088988) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.220738 / 0.018006 (0.202732) | 0.454026 / 0.000490 (0.453536) | 0.004695 / 0.000200 (0.004495) | 0.000087 / 0.000054 (0.000033) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033202 / 0.037411 (-0.004209) | 0.097506 / 0.014526 (0.082980) | 0.106661 / 0.176557 (-0.069896) | 0.160554 / 0.737135 (-0.576581) | 0.109203 / 0.296338 (-0.187135) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.432013 / 0.215209 (0.216804) | 4.310399 / 2.077655 (2.232744) | 2.296529 / 1.504120 (0.792409) | 2.139929 / 1.541195 (0.598734) | 2.227432 / 1.468490 (0.758942) | 0.493697 / 4.584777 (-4.091080) | 3.639877 / 3.745712 (-0.105835) | 3.323165 / 5.269862 (-1.946697) | 2.084527 / 4.565676 (-2.481150) | 0.058812 / 0.424275 (-0.365463) | 0.007813 / 0.007607 (0.000206) | 0.512366 / 0.226044 (0.286321) | 5.119660 / 2.268929 (2.850732) | 2.783819 / 55.444624 (-52.660806) | 2.490669 / 6.876477 (-4.385808) | 2.696653 / 2.142072 (0.554581) | 0.627161 / 4.805227 (-4.178066) | 0.137032 / 6.500664 (-6.363632) | 0.064040 / 0.075469 (-0.011429) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.369578 / 1.841788 (-0.472210) | 20.421182 / 8.074308 (12.346873) | 15.719347 / 10.191392 (5.527955) | 0.166150 / 0.680424 (-0.514274) | 0.020262 / 0.534201 (-0.513939) | 0.395645 / 0.579283 (-0.183638) | 0.430363 / 0.434364 (-0.004001) | 0.477843 / 0.540337 (-0.062494) | 0.638501 / 1.386936 (-0.748435) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c89e60cc50563dfc41ea039c6d3a1f6e43033e8e \"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) | 0.006141 / 0.011353 (-0.005211) | 0.003683 / 0.011008 (-0.007325) | 0.081127 / 0.038508 (0.042618) | 0.064285 / 0.023109 (0.041176) | 0.323038 / 0.275898 (0.047140) | 0.347280 / 0.323480 (0.023800) | 0.003518 / 0.007986 (-0.004467) | 0.002958 / 0.004328 (-0.001370) | 0.063093 / 0.004250 (0.058843) | 0.050682 / 0.037052 (0.013629) | 0.321222 / 0.258489 (0.062733) | 0.359266 / 0.293841 (0.065425) | 0.027515 / 0.128546 (-0.101032) | 0.007964 / 0.075646 (-0.067682) | 0.261305 / 0.419271 (-0.157966) | 0.044897 / 0.043533 (0.001365) | 0.320684 / 0.255139 (0.065545) | 0.335722 / 0.283200 (0.052522) | 0.023378 / 0.141683 (-0.118305) | 1.418211 / 1.452155 (-0.033943) | 1.523728 / 1.492716 (0.031011) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.222316 / 0.018006 (0.204310) | 0.426943 / 0.000490 (0.426454) | 0.008785 / 0.000200 (0.008585) | 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) | 0.024716 / 0.037411 (-0.012695) | 0.075341 / 0.014526 (0.060816) | 0.089532 / 0.176557 (-0.087024) | 0.147638 / 0.737135 (-0.589498) | 0.085697 / 0.296338 (-0.210641) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.396395 / 0.215209 (0.181186) | 3.947280 / 2.077655 (1.869625) | 1.894762 / 1.504120 (0.390642) | 1.712094 / 1.541195 (0.170899) | 1.779049 / 1.468490 (0.310559) | 0.509206 / 4.584777 (-4.075571) | 3.073951 / 3.745712 (-0.671761) | 2.886826 / 5.269862 (-2.383035) | 1.894444 / 4.565676 (-2.671232) | 0.059519 / 0.424275 (-0.364756) | 0.006951 / 0.007607 (-0.000656) | 0.468213 / 0.226044 (0.242169) | 4.667134 / 2.268929 (2.398206) | 2.342516 / 55.444624 (-53.102108) | 1.992047 / 6.876477 (-4.884430) | 2.142059 / 2.142072 (-0.000014) | 0.600507 / 4.805227 (-4.204720) | 0.128982 / 6.500664 (-6.371682) | 0.062100 / 0.075469 (-0.013369) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.234500 / 1.841788 (-0.607288) | 17.951646 / 8.074308 (9.877338) | 13.862763 / 10.191392 (3.671371) | 0.143133 / 0.680424 (-0.537291) | 0.016643 / 0.534201 (-0.517558) | 0.333174 / 0.579283 (-0.246109) | 0.366956 / 0.434364 (-0.067408) | 0.384569 / 0.540337 (-0.155769) | 0.546830 / 1.386936 (-0.840106) |\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) | 0.006146 / 0.011353 (-0.005207) | 0.003725 / 0.011008 (-0.007283) | 0.062099 / 0.038508 (0.023591) | 0.064117 / 0.023109 (0.041008) | 0.456100 / 0.275898 (0.180202) | 0.490794 / 0.323480 (0.167314) | 0.005652 / 0.007986 (-0.002334) | 0.002897 / 0.004328 (-0.001432) | 0.061909 / 0.004250 (0.057659) | 0.050634 / 0.037052 (0.013582) | 0.454422 / 0.258489 (0.195933) | 0.493208 / 0.293841 (0.199367) | 0.028822 / 0.128546 (-0.099724) | 0.008115 / 0.075646 (-0.067531) | 0.067214 / 0.419271 (-0.352058) | 0.041529 / 0.043533 (-0.002004) | 0.458016 / 0.255139 (0.202877) | 0.476059 / 0.283200 (0.192859) | 0.019926 / 0.141683 (-0.121757) | 1.465345 / 1.452155 (0.013190) | 1.533518 / 1.492716 (0.040802) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.218830 / 0.018006 (0.200823) | 0.418869 / 0.000490 (0.418380) | 0.005154 / 0.000200 (0.004954) | 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) | 0.027648 / 0.037411 (-0.009763) | 0.083842 / 0.014526 (0.069316) | 0.092300 / 0.176557 (-0.084257) | 0.146098 / 0.737135 (-0.591037) | 0.093441 / 0.296338 (-0.202898) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.464426 / 0.215209 (0.249217) | 4.632705 / 2.077655 (2.555051) | 2.642091 / 1.504120 (1.137971) | 2.461768 / 1.541195 (0.920573) | 2.535554 / 1.468490 (1.067064) | 0.507506 / 4.584777 (-4.077271) | 3.095485 / 3.745712 (-0.650227) | 2.884261 / 5.269862 (-2.385601) | 1.908943 / 4.565676 (-2.656734) | 0.058622 / 0.424275 (-0.365653) | 0.006892 / 0.007607 (-0.000715) | 0.536045 / 0.226044 (0.310001) | 5.377448 / 2.268929 (3.108519) | 3.076023 / 55.444624 (-52.368602) | 2.745586 / 6.876477 (-4.130890) | 2.939582 / 2.142072 (0.797510) | 0.595639 / 4.805227 (-4.209589) | 0.125086 / 6.500664 (-6.375578) | 0.061075 / 0.075469 (-0.014394) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.342820 / 1.841788 (-0.498968) | 18.326240 / 8.074308 (10.251932) | 15.007094 / 10.191392 (4.815702) | 0.133037 / 0.680424 (-0.547387) | 0.018702 / 0.534201 (-0.515499) | 0.330245 / 0.579283 (-0.249038) | 0.381494 / 0.434364 (-0.052870) | 0.393705 / 0.540337 (-0.146633) | 0.533676 / 1.386936 (-0.853260) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#45291275d84448c235829fb62aa951070aa4061d \"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) | 0.007644 / 0.011353 (-0.003709) | 0.004759 / 0.011008 (-0.006249) | 0.100569 / 0.038508 (0.062061) | 0.089645 / 0.023109 (0.066536) | 0.376679 / 0.275898 (0.100781) | 0.413214 / 0.323480 (0.089735) | 0.006087 / 0.007986 (-0.001899) | 0.003832 / 0.004328 (-0.000496) | 0.075892 / 0.004250 (0.071641) | 0.064635 / 0.037052 (0.027582) | 0.376874 / 0.258489 (0.118385) | 0.436756 / 0.293841 (0.142915) | 0.036372 / 0.128546 (-0.092174) | 0.010047 / 0.075646 (-0.065599) | 0.345073 / 0.419271 (-0.074198) | 0.062092 / 0.043533 (0.018559) | 0.380503 / 0.255139 (0.125364) | 0.414800 / 0.283200 (0.131600) | 0.028274 / 0.141683 (-0.113409) | 1.732463 / 1.452155 (0.280308) | 1.859049 / 1.492716 (0.366333) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.267129 / 0.018006 (0.249123) | 0.509109 / 0.000490 (0.508619) | 0.012329 / 0.000200 (0.012130) | 0.000432 / 0.000054 (0.000377) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033773 / 0.037411 (-0.003638) | 0.102800 / 0.014526 (0.088274) | 0.114256 / 0.176557 (-0.062300) | 0.182048 / 0.737135 (-0.555087) | 0.118225 / 0.296338 (-0.178113) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.457553 / 0.215209 (0.242344) | 4.588212 / 2.077655 (2.510557) | 2.184138 / 1.504120 (0.680018) | 2.003570 / 1.541195 (0.462375) | 2.093217 / 1.468490 (0.624727) | 0.585679 / 4.584777 (-3.999098) | 4.175319 / 3.745712 (0.429607) | 3.914168 / 5.269862 (-1.355693) | 2.452992 / 4.565676 (-2.112684) | 0.068363 / 0.424275 (-0.355912) | 0.009314 / 0.007607 (0.001707) | 0.543640 / 0.226044 (0.317595) | 5.440853 / 2.268929 (3.171925) | 2.782415 / 55.444624 (-52.662210) | 2.332359 / 6.876477 (-4.544118) | 2.628520 / 2.142072 (0.486448) | 0.696838 / 4.805227 (-4.108389) | 0.160653 / 6.500664 (-6.340012) | 0.075599 / 0.075469 (0.000130) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.545305 / 1.841788 (-0.296483) | 23.073174 / 8.074308 (14.998866) | 16.974977 / 10.191392 (6.783585) | 0.183719 / 0.680424 (-0.496705) | 0.021633 / 0.534201 (-0.512568) | 0.471202 / 0.579283 (-0.108081) | 0.479385 / 0.434364 (0.045021) | 0.550872 / 0.540337 (0.010535) | 0.766825 / 1.386936 (-0.620111) |\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) | 0.007918 / 0.011353 (-0.003435) | 0.004793 / 0.011008 (-0.006215) | 0.077273 / 0.038508 (0.038765) | 0.092079 / 0.023109 (0.068969) | 0.483269 / 0.275898 (0.207371) | 0.524919 / 0.323480 (0.201439) | 0.006273 / 0.007986 (-0.001713) | 0.004018 / 0.004328 (-0.000310) | 0.077188 / 0.004250 (0.072937) | 0.067891 / 0.037052 (0.030839) | 0.478531 / 0.258489 (0.220042) | 0.526956 / 0.293841 (0.233115) | 0.038309 / 0.128546 (-0.090237) | 0.010133 / 0.075646 (-0.065513) | 0.083892 / 0.419271 (-0.335379) | 0.057369 / 0.043533 (0.013836) | 0.509427 / 0.255139 (0.254288) | 0.506574 / 0.283200 (0.223374) | 0.027987 / 0.141683 (-0.113696) | 1.897469 / 1.452155 (0.445314) | 1.893102 / 1.492716 (0.400385) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.243003 / 0.018006 (0.224997) | 0.500267 / 0.000490 (0.499777) | 0.007442 / 0.000200 (0.007242) | 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) | 0.039266 / 0.037411 (0.001855) | 0.114438 / 0.014526 (0.099912) | 0.124528 / 0.176557 (-0.052029) | 0.189399 / 0.737135 (-0.547736) | 0.126703 / 0.296338 (-0.169635) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.518139 / 0.215209 (0.302930) | 5.162058 / 2.077655 (3.084403) | 2.835111 / 1.504120 (1.330991) | 2.640919 / 1.541195 (1.099724) | 2.736800 / 1.468490 (1.268310) | 0.582813 / 4.584777 (-4.001964) | 4.246269 / 3.745712 (0.500557) | 3.891161 / 5.269862 (-1.378701) | 2.445392 / 4.565676 (-2.120285) | 0.068943 / 0.424275 (-0.355332) | 0.009248 / 0.007607 (0.001641) | 0.604859 / 0.226044 (0.378815) | 6.030660 / 2.268929 (3.761731) | 3.409778 / 55.444624 (-52.034846) | 2.990488 / 6.876477 (-3.885988) | 3.281317 / 2.142072 (1.139245) | 0.697705 / 4.805227 (-4.107523) | 0.159502 / 6.500664 (-6.341162) | 0.072471 / 0.075469 (-0.002999) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.625428 / 1.841788 (-0.216360) | 23.602509 / 8.074308 (15.528201) | 18.091474 / 10.191392 (7.900082) | 0.172816 / 0.680424 (-0.507608) | 0.023708 / 0.534201 (-0.510493) | 0.473768 / 0.579283 (-0.105515) | 0.493713 / 0.434364 (0.059349) | 0.566326 / 0.540337 (0.025989) | 0.788670 / 1.386936 (-0.598266) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1ee2359c17ccb35b57e195f2bfe8478f49630039 \"CML watermark\")\n", "> Thanks. Any comment on my comment below?\r\n> \r\n> >Maybe we should update the docstring of get_data_patterns accordingly? Currently it only gives examples of outputs with ** not in a single path segment (i.e. not with a / as prefix or suffix).\r\n\r\nYea right we need to update it indeed, the outputs are the ones from older versions of fsspec, and from older patterns that we don't use anymore.\r\n\r\nIn general in docstrings I also think we should encourage users to use `**/*` instead of `**` (which has a behavior that is unique to fsspec)", "Also just noticed that `KEYWORDS_IN_DIR_NAME_BASE_PATTERNS` seems to include `KEYWORDS_IN_FILENAME_BASE_PATTERNS`. I guess we can try to remove the filename one in another PR to remove this redundancy \r\n\r\n(noticed this by checking that the data pattern is the same for both the dir name and filename examples in the get_data_patterns docstring)", "<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) | 0.006922 / 0.011353 (-0.004431) | 0.004459 / 0.011008 (-0.006549) | 0.084742 / 0.038508 (0.046234) | 0.089002 / 0.023109 (0.065893) | 0.310886 / 0.275898 (0.034988) | 0.340518 / 0.323480 (0.017038) | 0.007011 / 0.007986 (-0.000975) | 0.004566 / 0.004328 (0.000237) | 0.067260 / 0.004250 (0.063009) | 0.066349 / 0.037052 (0.029297) | 0.324029 / 0.258489 (0.065540) | 0.373785 / 0.293841 (0.079944) | 0.031780 / 0.128546 (-0.096766) | 0.009208 / 0.075646 (-0.066438) | 0.288871 / 0.419271 (-0.130401) | 0.054548 / 0.043533 (0.011015) | 0.313344 / 0.255139 (0.058205) | 0.336430 / 0.283200 (0.053231) | 0.029037 / 0.141683 (-0.112646) | 1.483797 / 1.452155 (0.031642) | 1.581884 / 1.492716 (0.089167) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.370520 / 0.018006 (0.352514) | 0.796720 / 0.000490 (0.796230) | 0.009329 / 0.000200 (0.009129) | 0.000109 / 0.000054 (0.000055) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033002 / 0.037411 (-0.004410) | 0.083442 / 0.014526 (0.068916) | 0.106468 / 0.176557 (-0.070088) | 0.165315 / 0.737135 (-0.571820) | 0.103048 / 0.296338 (-0.193291) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.386800 / 0.215209 (0.171591) | 3.843312 / 2.077655 (1.765658) | 1.848953 / 1.504120 (0.344834) | 1.679508 / 1.541195 (0.138313) | 1.733578 / 1.468490 (0.265088) | 0.488455 / 4.584777 (-4.096322) | 3.613594 / 3.745712 (-0.132118) | 3.533334 / 5.269862 (-1.736528) | 2.176216 / 4.565676 (-2.389460) | 0.056915 / 0.424275 (-0.367360) | 0.007349 / 0.007607 (-0.000258) | 0.465132 / 0.226044 (0.239088) | 4.638479 / 2.268929 (2.369550) | 2.354741 / 55.444624 (-53.089883) | 1.991777 / 6.876477 (-4.884700) | 2.249823 / 2.142072 (0.107751) | 0.582748 / 4.805227 (-4.222480) | 0.133829 / 6.500664 (-6.366835) | 0.060949 / 0.075469 (-0.014520) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.252027 / 1.841788 (-0.589760) | 20.660234 / 8.074308 (12.585926) | 14.328496 / 10.191392 (4.137104) | 0.164872 / 0.680424 (-0.515552) | 0.018867 / 0.534201 (-0.515334) | 0.392850 / 0.579283 (-0.186433) | 0.425684 / 0.434364 (-0.008679) | 0.461776 / 0.540337 (-0.078562) | 0.663688 / 1.386936 (-0.723248) |\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) | 0.007010 / 0.011353 (-0.004343) | 0.004791 / 0.011008 (-0.006217) | 0.064738 / 0.038508 (0.026230) | 0.088648 / 0.023109 (0.065539) | 0.418106 / 0.275898 (0.142208) | 0.446767 / 0.323480 (0.123287) | 0.006761 / 0.007986 (-0.001224) | 0.004649 / 0.004328 (0.000320) | 0.066345 / 0.004250 (0.062094) | 0.068326 / 0.037052 (0.031274) | 0.423426 / 0.258489 (0.164937) | 0.463160 / 0.293841 (0.169319) | 0.032689 / 0.128546 (-0.095858) | 0.009299 / 0.075646 (-0.066347) | 0.071321 / 0.419271 (-0.347951) | 0.048752 / 0.043533 (0.005219) | 0.418932 / 0.255139 (0.163793) | 0.440673 / 0.283200 (0.157473) | 0.027898 / 0.141683 (-0.113785) | 1.531860 / 1.452155 (0.079705) | 1.620456 / 1.492716 (0.127739) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.354917 / 0.018006 (0.336911) | 0.792432 / 0.000490 (0.791943) | 0.006626 / 0.000200 (0.006426) | 0.000124 / 0.000054 (0.000070) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.036190 / 0.037411 (-0.001222) | 0.093052 / 0.014526 (0.078526) | 0.111927 / 0.176557 (-0.064629) | 0.165571 / 0.737135 (-0.571564) | 0.112159 / 0.296338 (-0.184180) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.437798 / 0.215209 (0.222589) | 4.367166 / 2.077655 (2.289511) | 2.343292 / 1.504120 (0.839172) | 2.169298 / 1.541195 (0.628103) | 2.224471 / 1.468490 (0.755981) | 0.487317 / 4.584777 (-4.097460) | 3.627825 / 3.745712 (-0.117887) | 3.500914 / 5.269862 (-1.768947) | 2.175862 / 4.565676 (-2.389815) | 0.057975 / 0.424275 (-0.366300) | 0.007509 / 0.007607 (-0.000098) | 0.517389 / 0.226044 (0.291345) | 5.169694 / 2.268929 (2.900766) | 2.850993 / 55.444624 (-52.593631) | 2.473111 / 6.876477 (-4.403366) | 2.746731 / 2.142072 (0.604659) | 0.586597 / 4.805227 (-4.218630) | 0.134082 / 6.500664 (-6.366582) | 0.061035 / 0.075469 (-0.014434) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.375186 / 1.841788 (-0.466602) | 20.960817 / 8.074308 (12.886509) | 15.035071 / 10.191392 (4.843679) | 0.169494 / 0.680424 (-0.510930) | 0.020654 / 0.534201 (-0.513547) | 0.398047 / 0.579283 (-0.181236) | 0.438117 / 0.434364 (0.003753) | 0.483896 / 0.540337 (-0.056441) | 0.690728 / 1.386936 (-0.696208) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3e7fc64af912e5fcdcf949ed09d954332f0ae94a \"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) | 0.006892 / 0.011353 (-0.004461) | 0.004087 / 0.011008 (-0.006921) | 0.084695 / 0.038508 (0.046187) | 0.078084 / 0.023109 (0.054975) | 0.322976 / 0.275898 (0.047078) | 0.355332 / 0.323480 (0.031852) | 0.004235 / 0.007986 (-0.003750) | 0.003450 / 0.004328 (-0.000879) | 0.065355 / 0.004250 (0.061104) | 0.058593 / 0.037052 (0.021541) | 0.335761 / 0.258489 (0.077272) | 0.370392 / 0.293841 (0.076551) | 0.031720 / 0.128546 (-0.096827) | 0.008611 / 0.075646 (-0.067036) | 0.288213 / 0.419271 (-0.131059) | 0.053374 / 0.043533 (0.009842) | 0.321863 / 0.255139 (0.066724) | 0.341587 / 0.283200 (0.058387) | 0.025694 / 0.141683 (-0.115989) | 1.470502 / 1.452155 (0.018348) | 1.565068 / 1.492716 (0.072352) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.231063 / 0.018006 (0.213057) | 0.464996 / 0.000490 (0.464506) | 0.007316 / 0.000200 (0.007116) | 0.000288 / 0.000054 (0.000233) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029244 / 0.037411 (-0.008167) | 0.086303 / 0.014526 (0.071777) | 0.097281 / 0.176557 (-0.079276) | 0.153552 / 0.737135 (-0.583583) | 0.098488 / 0.296338 (-0.197850) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.382753 / 0.215209 (0.167544) | 3.826503 / 2.077655 (1.748848) | 1.848439 / 1.504120 (0.344319) | 1.688519 / 1.541195 (0.147324) | 1.787867 / 1.468490 (0.319377) | 0.489708 / 4.584777 (-4.095069) | 3.576780 / 3.745712 (-0.168932) | 3.341536 / 5.269862 (-1.928325) | 2.108787 / 4.565676 (-2.456889) | 0.057409 / 0.424275 (-0.366866) | 0.007325 / 0.007607 (-0.000282) | 0.459536 / 0.226044 (0.233492) | 4.590609 / 2.268929 (2.321681) | 2.313005 / 55.444624 (-53.131620) | 1.972389 / 6.876477 (-4.904087) | 2.218511 / 2.142072 (0.076439) | 0.613817 / 4.805227 (-4.191410) | 0.133846 / 6.500664 (-6.366818) | 0.062190 / 0.075469 (-0.013279) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.279860 / 1.841788 (-0.561928) | 19.549777 / 8.074308 (11.475469) | 14.225844 / 10.191392 (4.034452) | 0.164682 / 0.680424 (-0.515741) | 0.018321 / 0.534201 (-0.515880) | 0.389874 / 0.579283 (-0.189409) | 0.408597 / 0.434364 (-0.025767) | 0.454327 / 0.540337 (-0.086011) | 0.645571 / 1.386936 (-0.741365) |\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) | 0.007021 / 0.011353 (-0.004332) | 0.004119 / 0.011008 (-0.006889) | 0.065393 / 0.038508 (0.026885) | 0.085005 / 0.023109 (0.061896) | 0.412221 / 0.275898 (0.136323) | 0.438266 / 0.323480 (0.114786) | 0.005594 / 0.007986 (-0.002392) | 0.003499 / 0.004328 (-0.000829) | 0.065053 / 0.004250 (0.060802) | 0.060608 / 0.037052 (0.023555) | 0.413938 / 0.258489 (0.155449) | 0.446192 / 0.293841 (0.152351) | 0.032232 / 0.128546 (-0.096314) | 0.008617 / 0.075646 (-0.067029) | 0.071296 / 0.419271 (-0.347976) | 0.048756 / 0.043533 (0.005223) | 0.404977 / 0.255139 (0.149838) | 0.426801 / 0.283200 (0.143602) | 0.023650 / 0.141683 (-0.118033) | 1.526928 / 1.452155 (0.074773) | 1.627504 / 1.492716 (0.134787) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.224318 / 0.018006 (0.206312) | 0.469717 / 0.000490 (0.469227) | 0.005539 / 0.000200 (0.005339) | 0.000098 / 0.000054 (0.000043) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034240 / 0.037411 (-0.003171) | 0.096449 / 0.014526 (0.081923) | 0.107309 / 0.176557 (-0.069247) | 0.160246 / 0.737135 (-0.576889) | 0.107595 / 0.296338 (-0.188743) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.434266 / 0.215209 (0.219057) | 4.325571 / 2.077655 (2.247916) | 2.324066 / 1.504120 (0.819946) | 2.140238 / 1.541195 (0.599044) | 2.244593 / 1.468490 (0.776103) | 0.486259 / 4.584777 (-4.098518) | 3.644120 / 3.745712 (-0.101592) | 3.372330 / 5.269862 (-1.897531) | 2.074779 / 4.565676 (-2.490897) | 0.057154 / 0.424275 (-0.367121) | 0.007304 / 0.007607 (-0.000303) | 0.516944 / 0.226044 (0.290899) | 5.174300 / 2.268929 (2.905372) | 2.816269 / 55.444624 (-52.628356) | 2.462943 / 6.876477 (-4.413534) | 2.735851 / 2.142072 (0.593779) | 0.589028 / 4.805227 (-4.216200) | 0.131804 / 6.500664 (-6.368860) | 0.060173 / 0.075469 (-0.015296) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.354540 / 1.841788 (-0.487248) | 20.436511 / 8.074308 (12.362203) | 15.541981 / 10.191392 (5.350589) | 0.168399 / 0.680424 (-0.512025) | 0.020716 / 0.534201 (-0.513485) | 0.396275 / 0.579283 (-0.183008) | 0.427232 / 0.434364 (-0.007132) | 0.475121 / 0.540337 (-0.065216) | 0.648579 / 1.386936 (-0.738357) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4fa138fc0d9aa1536194fd46566840e698ccde03 \"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) | 0.009071 / 0.011353 (-0.002282) | 0.005820 / 0.011008 (-0.005188) | 0.119974 / 0.038508 (0.081466) | 0.092145 / 0.023109 (0.069036) | 0.445349 / 0.275898 (0.169451) | 0.442488 / 0.323480 (0.119008) | 0.005352 / 0.007986 (-0.002634) | 0.004332 / 0.004328 (0.000003) | 0.084397 / 0.004250 (0.080147) | 0.064624 / 0.037052 (0.027572) | 0.430938 / 0.258489 (0.172448) | 0.503574 / 0.293841 (0.209733) | 0.047900 / 0.128546 (-0.080647) | 0.014237 / 0.075646 (-0.061409) | 0.366145 / 0.419271 (-0.053127) | 0.066344 / 0.043533 (0.022811) | 0.424582 / 0.255139 (0.169443) | 0.451845 / 0.283200 (0.168646) | 0.041409 / 0.141683 (-0.100274) | 1.886998 / 1.452155 (0.434843) | 2.011676 / 1.492716 (0.518960) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.301008 / 0.018006 (0.283001) | 0.608670 / 0.000490 (0.608180) | 0.011963 / 0.000200 (0.011763) | 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) | 0.031996 / 0.037411 (-0.005415) | 0.102274 / 0.014526 (0.087748) | 0.121437 / 0.176557 (-0.055120) | 0.181647 / 0.737135 (-0.555489) | 0.121634 / 0.296338 (-0.174704) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.597070 / 0.215209 (0.381861) | 5.973808 / 2.077655 (3.896154) | 2.486345 / 1.504120 (0.982225) | 2.125395 / 1.541195 (0.584201) | 2.270864 / 1.468490 (0.802374) | 0.880031 / 4.584777 (-3.704746) | 5.396522 / 3.745712 (1.650809) | 4.702005 / 5.269862 (-0.567857) | 3.023087 / 4.565676 (-1.542589) | 0.097093 / 0.424275 (-0.327182) | 0.008457 / 0.007607 (0.000850) | 0.712164 / 0.226044 (0.486120) | 7.112867 / 2.268929 (4.843938) | 3.364509 / 55.444624 (-52.080115) | 2.646953 / 6.876477 (-4.229524) | 2.795967 / 2.142072 (0.653894) | 1.067182 / 4.805227 (-3.738046) | 0.218297 / 6.500664 (-6.282368) | 0.071720 / 0.075469 (-0.003750) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.640477 / 1.841788 (-0.201311) | 24.875163 / 8.074308 (16.800855) | 22.125706 / 10.191392 (11.934314) | 0.247267 / 0.680424 (-0.433157) | 0.033717 / 0.534201 (-0.500484) | 0.492422 / 0.579283 (-0.086862) | 0.578323 / 0.434364 (0.143959) | 0.579503 / 0.540337 (0.039165) | 0.816721 / 1.386936 (-0.570215) |\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) | 0.009372 / 0.011353 (-0.001981) | 0.005449 / 0.011008 (-0.005559) | 0.095371 / 0.038508 (0.056863) | 0.086320 / 0.023109 (0.063211) | 0.539573 / 0.275898 (0.263675) | 0.580338 / 0.323480 (0.256858) | 0.007028 / 0.007986 (-0.000958) | 0.004196 / 0.004328 (-0.000133) | 0.082710 / 0.004250 (0.078460) | 0.064336 / 0.037052 (0.027284) | 0.521490 / 0.258489 (0.263001) | 0.567942 / 0.293841 (0.274101) | 0.049659 / 0.128546 (-0.078887) | 0.017297 / 0.075646 (-0.058350) | 0.093874 / 0.419271 (-0.325398) | 0.061664 / 0.043533 (0.018131) | 0.524476 / 0.255139 (0.269337) | 0.563255 / 0.283200 (0.280055) | 0.039990 / 0.141683 (-0.101693) | 1.854438 / 1.452155 (0.402283) | 1.819321 / 1.492716 (0.326605) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.298817 / 0.018006 (0.280811) | 0.629381 / 0.000490 (0.628891) | 0.006259 / 0.000200 (0.006059) | 0.000690 / 0.000054 (0.000635) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.041009 / 0.037411 (0.003598) | 0.123845 / 0.014526 (0.109319) | 0.138606 / 0.176557 (-0.037951) | 0.215042 / 0.737135 (-0.522093) | 0.129572 / 0.296338 (-0.166767) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.668823 / 0.215209 (0.453614) | 6.596762 / 2.077655 (4.519108) | 3.275429 / 1.504120 (1.771309) | 2.921747 / 1.541195 (1.380553) | 2.963748 / 1.468490 (1.495258) | 0.897588 / 4.584777 (-3.687188) | 5.683618 / 3.745712 (1.937906) | 5.051102 / 5.269862 (-0.218760) | 3.178855 / 4.565676 (-1.386822) | 0.107446 / 0.424275 (-0.316829) | 0.008967 / 0.007607 (0.001360) | 0.785577 / 0.226044 (0.559532) | 8.236556 / 2.268929 (5.967628) | 3.914725 / 55.444624 (-51.529899) | 3.129068 / 6.876477 (-3.747409) | 3.368383 / 2.142072 (1.226310) | 1.004307 / 4.805227 (-3.800920) | 0.204788 / 6.500664 (-6.295876) | 0.078250 / 0.075469 (0.002780) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.778574 / 1.841788 (-0.063213) | 25.583659 / 8.074308 (17.509351) | 23.505866 / 10.191392 (13.314474) | 0.228759 / 0.680424 (-0.451665) | 0.038348 / 0.534201 (-0.495853) | 0.468980 / 0.579283 (-0.110303) | 0.630194 / 0.434364 (0.195830) | 0.587535 / 0.540337 (0.047198) | 0.831761 / 1.386936 (-0.555175) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#68f4f847f3248f02fc99458310d9d786906d7a6f \"CML watermark\")\n", "I've addressed the comments. Let me know if it looks all good now :)", "Actually just found out that the current `**/*[-._ 0-9/]train[-._ 0-9/]**` doesn't match `data/train.csv` in bash (but does match in fsspec right now).\r\n\r\nSo there might be a risk that this pattern breaks in the future no ?", "@lhoestq `fsspec` has tests to check their specific (non-posix) behavior, so I think merging in the current state is fine. And if they make a breaking change in the future, we can align the patterns once again :) ", "Yea after more thoughts I also think it's fine. Feel free to merge !", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006920 / 0.011353 (-0.004433) | 0.004182 / 0.011008 (-0.006826) | 0.084629 / 0.038508 (0.046121) | 0.086052 / 0.023109 (0.062943) | 0.326062 / 0.275898 (0.050164) | 0.344190 / 0.323480 (0.020710) | 0.005393 / 0.007986 (-0.002593) | 0.003410 / 0.004328 (-0.000918) | 0.064327 / 0.004250 (0.060076) | 0.056556 / 0.037052 (0.019504) | 0.319255 / 0.258489 (0.060766) | 0.357943 / 0.293841 (0.064102) | 0.032097 / 0.128546 (-0.096450) | 0.008778 / 0.075646 (-0.066868) | 0.291057 / 0.419271 (-0.128215) | 0.053225 / 0.043533 (0.009692) | 0.307713 / 0.255139 (0.052574) | 0.350058 / 0.283200 (0.066858) | 0.024380 / 0.141683 (-0.117303) | 1.459482 / 1.452155 (0.007328) | 1.555711 / 1.492716 (0.062994) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.239487 / 0.018006 (0.221480) | 0.467604 / 0.000490 (0.467114) | 0.010742 / 0.000200 (0.010542) | 0.000285 / 0.000054 (0.000230) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029394 / 0.037411 (-0.008018) | 0.087404 / 0.014526 (0.072879) | 0.098701 / 0.176557 (-0.077855) | 0.154145 / 0.737135 (-0.582990) | 0.099726 / 0.296338 (-0.196612) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.389008 / 0.215209 (0.173799) | 3.873165 / 2.077655 (1.795510) | 1.860676 / 1.504120 (0.356556) | 1.679668 / 1.541195 (0.138474) | 1.782347 / 1.468490 (0.313857) | 0.489469 / 4.584777 (-4.095308) | 3.678706 / 3.745712 (-0.067006) | 3.404076 / 5.269862 (-1.865785) | 2.110972 / 4.565676 (-2.454704) | 0.057478 / 0.424275 (-0.366797) | 0.007443 / 0.007607 (-0.000164) | 0.464780 / 0.226044 (0.238736) | 4.643606 / 2.268929 (2.374678) | 2.355744 / 55.444624 (-53.088881) | 1.993992 / 6.876477 (-4.882485) | 2.245520 / 2.142072 (0.103447) | 0.592773 / 4.805227 (-4.212454) | 0.135369 / 6.500664 (-6.365295) | 0.062478 / 0.075469 (-0.012991) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.257537 / 1.841788 (-0.584251) | 19.828010 / 8.074308 (11.753702) | 14.709260 / 10.191392 (4.517868) | 0.168359 / 0.680424 (-0.512065) | 0.018907 / 0.534201 (-0.515294) | 0.397223 / 0.579283 (-0.182060) | 0.421760 / 0.434364 (-0.012604) | 0.464597 / 0.540337 (-0.075740) | 0.665905 / 1.386936 (-0.721031) |\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) | 0.007247 / 0.011353 (-0.004106) | 0.004104 / 0.011008 (-0.006904) | 0.065008 / 0.038508 (0.026500) | 0.083485 / 0.023109 (0.060376) | 0.399808 / 0.275898 (0.123910) | 0.433374 / 0.323480 (0.109894) | 0.005453 / 0.007986 (-0.002532) | 0.003479 / 0.004328 (-0.000850) | 0.065126 / 0.004250 (0.060876) | 0.059945 / 0.037052 (0.022893) | 0.402018 / 0.258489 (0.143529) | 0.437927 / 0.293841 (0.144086) | 0.032654 / 0.128546 (-0.095892) | 0.008717 / 0.075646 (-0.066929) | 0.071737 / 0.419271 (-0.347534) | 0.048903 / 0.043533 (0.005370) | 0.402107 / 0.255139 (0.146968) | 0.417602 / 0.283200 (0.134402) | 0.024821 / 0.141683 (-0.116862) | 1.474471 / 1.452155 (0.022316) | 1.559571 / 1.492716 (0.066855) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.232010 / 0.018006 (0.214003) | 0.460768 / 0.000490 (0.460278) | 0.005250 / 0.000200 (0.005050) | 0.000109 / 0.000054 (0.000055) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033839 / 0.037411 (-0.003573) | 0.101617 / 0.014526 (0.087091) | 0.107984 / 0.176557 (-0.068573) | 0.160923 / 0.737135 (-0.576212) | 0.110367 / 0.296338 (-0.185971) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.433087 / 0.215209 (0.217878) | 4.324100 / 2.077655 (2.246445) | 2.312937 / 1.504120 (0.808817) | 2.159903 / 1.541195 (0.618708) | 2.240235 / 1.468490 (0.771745) | 0.500659 / 4.584777 (-4.084118) | 3.743801 / 3.745712 (-0.001911) | 3.441350 / 5.269862 (-1.828512) | 2.141370 / 4.565676 (-2.424306) | 0.059078 / 0.424275 (-0.365197) | 0.007468 / 0.007607 (-0.000139) | 0.508108 / 0.226044 (0.282064) | 5.076738 / 2.268929 (2.807809) | 2.825939 / 55.444624 (-52.618685) | 2.467762 / 6.876477 (-4.408715) | 2.705079 / 2.142072 (0.563006) | 0.603363 / 4.805227 (-4.201864) | 0.136267 / 6.500664 (-6.364397) | 0.062887 / 0.075469 (-0.012582) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.359344 / 1.841788 (-0.482443) | 20.581510 / 8.074308 (12.507202) | 15.534489 / 10.191392 (5.343097) | 0.192068 / 0.680424 (-0.488356) | 0.020831 / 0.534201 (-0.513370) | 0.403330 / 0.579283 (-0.175953) | 0.429536 / 0.434364 (-0.004828) | 0.479906 / 0.540337 (-0.060431) | 0.674170 / 1.386936 (-0.712766) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#33ac74c2df928dece49ca2cf25e14172896b442e \"CML watermark\")\n" ]

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

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[ "While this issue may seem specific, it led to a silent problem in my workflow that took days to diagnose. If this feature is not intended to be supported, an error should be raised when encountering this configuration to prevent such issues.", "Thanks for reporting! This is a MRE:\r\n\r\n```python\r\nimport pyarrow as pa\r\nfrom datasets.table import cast_array_to_feature\r\nfrom datasets import Sequence, Value\r\ndata = [\r\n [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]],\r\n [[7.0, 8.0, 9.0], [10.0, 11.0, 12.0]],\r\n]\r\narr = pa.array(data, pa.list_(pa.list_(pa.float32(), 3)))\r\ncast_array_to_feature(arr, Sequence(Sequence(Value(\"float32\"))))\r\n```\r\n\r\nI've opened a PR with a fix." ]

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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) | 0.006753 / 0.011353 (-0.004600) | 0.004027 / 0.011008 (-0.006982) | 0.084200 / 0.038508 (0.045692) | 0.072233 / 0.023109 (0.049124) | 0.361535 / 0.275898 (0.085637) | 0.386196 / 0.323480 (0.062716) | 0.004047 / 0.007986 (-0.003939) | 0.003416 / 0.004328 (-0.000912) | 0.064724 / 0.004250 (0.060474) | 0.055740 / 0.037052 (0.018688) | 0.360422 / 0.258489 (0.101933) | 0.399230 / 0.293841 (0.105389) | 0.031537 / 0.128546 (-0.097009) | 0.008630 / 0.075646 (-0.067016) | 0.289652 / 0.419271 (-0.129620) | 0.052881 / 0.043533 (0.009348) | 0.359538 / 0.255139 (0.104399) | 0.379410 / 0.283200 (0.096211) | 0.024539 / 0.141683 (-0.117144) | 1.470891 / 1.452155 (0.018736) | 1.578879 / 1.492716 (0.086163) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.239200 / 0.018006 (0.221194) | 0.462100 / 0.000490 (0.461610) | 0.009055 / 0.000200 (0.008856) | 0.000406 / 0.000054 (0.000352) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028736 / 0.037411 (-0.008675) | 0.088051 / 0.014526 (0.073525) | 0.098101 / 0.176557 (-0.078456) | 0.152399 / 0.737135 (-0.584737) | 0.098776 / 0.296338 (-0.197563) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.401761 / 0.215209 (0.186552) | 4.014143 / 2.077655 (1.936488) | 2.033255 / 1.504120 (0.529135) | 1.855347 / 1.541195 (0.314152) | 1.996144 / 1.468490 (0.527654) | 0.488545 / 4.584777 (-4.096232) | 3.712030 / 3.745712 (-0.033682) | 3.439725 / 5.269862 (-1.830137) | 2.119289 / 4.565676 (-2.446388) | 0.057523 / 0.424275 (-0.366752) | 0.007780 / 0.007607 (0.000173) | 0.479522 / 0.226044 (0.253477) | 4.798218 / 2.268929 (2.529290) | 2.543816 / 55.444624 (-52.900809) | 2.180392 / 6.876477 (-4.696085) | 2.427195 / 2.142072 (0.285122) | 0.602071 / 4.805227 (-4.203156) | 0.133450 / 6.500664 (-6.367214) | 0.061975 / 0.075469 (-0.013494) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.250040 / 1.841788 (-0.591748) | 19.532327 / 8.074308 (11.458019) | 14.200298 / 10.191392 (4.008906) | 0.165165 / 0.680424 (-0.515259) | 0.018326 / 0.534201 (-0.515875) | 0.389788 / 0.579283 (-0.189495) | 0.419301 / 0.434364 (-0.015063) | 0.452645 / 0.540337 (-0.087693) | 0.643409 / 1.386936 (-0.743527) |\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) | 0.007040 / 0.011353 (-0.004313) | 0.004157 / 0.011008 (-0.006851) | 0.065439 / 0.038508 (0.026931) | 0.083210 / 0.023109 (0.060101) | 0.406707 / 0.275898 (0.130809) | 0.442759 / 0.323480 (0.119279) | 0.006321 / 0.007986 (-0.001665) | 0.003684 / 0.004328 (-0.000645) | 0.064517 / 0.004250 (0.060266) | 0.060676 / 0.037052 (0.023624) | 0.413395 / 0.258489 (0.154906) | 0.446776 / 0.293841 (0.152935) | 0.032542 / 0.128546 (-0.096004) | 0.008614 / 0.075646 (-0.067033) | 0.071760 / 0.419271 (-0.347511) | 0.049646 / 0.043533 (0.006113) | 0.402409 / 0.255139 (0.147270) | 0.422775 / 0.283200 (0.139575) | 0.024846 / 0.141683 (-0.116836) | 1.522915 / 1.452155 (0.070761) | 1.566518 / 1.492716 (0.073802) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.234478 / 0.018006 (0.216472) | 0.461318 / 0.000490 (0.460828) | 0.006304 / 0.000200 (0.006105) | 0.000105 / 0.000054 (0.000051) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.036904 / 0.037411 (-0.000508) | 0.102144 / 0.014526 (0.087619) | 0.108985 / 0.176557 (-0.067572) | 0.162609 / 0.737135 (-0.574526) | 0.110295 / 0.296338 (-0.186044) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.438735 / 0.215209 (0.223526) | 4.377602 / 2.077655 (2.299948) | 2.375305 / 1.504120 (0.871185) | 2.215877 / 1.541195 (0.674682) | 2.317468 / 1.468490 (0.848978) | 0.495137 / 4.584777 (-4.089640) | 3.726323 / 3.745712 (-0.019389) | 3.493785 / 5.269862 (-1.776077) | 2.177891 / 4.565676 (-2.387785) | 0.058975 / 0.424275 (-0.365300) | 0.007897 / 0.007607 (0.000290) | 0.514063 / 0.226044 (0.288019) | 5.132714 / 2.268929 (2.863786) | 2.914125 / 55.444624 (-52.530499) | 2.532912 / 6.876477 (-4.343564) | 2.776438 / 2.142072 (0.634365) | 0.624831 / 4.805227 (-4.180396) | 0.135023 / 6.500664 (-6.365641) | 0.062040 / 0.075469 (-0.013429) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.359970 / 1.841788 (-0.481818) | 20.816464 / 8.074308 (12.742156) | 16.103544 / 10.191392 (5.912152) | 0.149120 / 0.680424 (-0.531304) | 0.020279 / 0.534201 (-0.513922) | 0.408727 / 0.579283 (-0.170556) | 0.436191 / 0.434364 (0.001827) | 0.485056 / 0.540337 (-0.055281) | 0.737727 / 1.386936 (-0.649209) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d15280f435b7e27c9350a0cc37a07dbc5e2ea9ca \"CML watermark\")\n", "CI failures are unrelated", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008102 / 0.011353 (-0.003251) | 0.004886 / 0.011008 (-0.006123) | 0.090482 / 0.038508 (0.051974) | 0.071594 / 0.023109 (0.048485) | 0.428678 / 0.275898 (0.152780) | 0.442179 / 0.323480 (0.118699) | 0.004329 / 0.007986 (-0.003657) | 0.003756 / 0.004328 (-0.000573) | 0.087125 / 0.004250 (0.082874) | 0.055159 / 0.037052 (0.018107) | 0.437646 / 0.258489 (0.179157) | 0.446665 / 0.293841 (0.152824) | 0.046402 / 0.128546 (-0.082145) | 0.014248 / 0.075646 (-0.061398) | 0.331401 / 0.419271 (-0.087871) | 0.062010 / 0.043533 (0.018478) | 0.434774 / 0.255139 (0.179635) | 0.441063 / 0.283200 (0.157863) | 0.037424 / 0.141683 (-0.104258) | 1.720276 / 1.452155 (0.268121) | 1.731491 / 1.492716 (0.238775) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.302935 / 0.018006 (0.284929) | 0.590556 / 0.000490 (0.590067) | 0.014473 / 0.000200 (0.014274) | 0.000712 / 0.000054 (0.000658) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031289 / 0.037411 (-0.006122) | 0.091175 / 0.014526 (0.076649) | 0.112895 / 0.176557 (-0.063661) | 0.199558 / 0.737135 (-0.537577) | 0.113397 / 0.296338 (-0.182942) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.571586 / 0.215209 (0.356377) | 5.706894 / 2.077655 (3.629240) | 2.512701 / 1.504120 (1.008581) | 2.151705 / 1.541195 (0.610510) | 2.252738 / 1.468490 (0.784248) | 0.857524 / 4.584777 (-3.727253) | 5.189027 / 3.745712 (1.443315) | 4.464979 / 5.269862 (-0.804882) | 2.787486 / 4.565676 (-1.778190) | 0.090161 / 0.424275 (-0.334115) | 0.008649 / 0.007607 (0.001042) | 0.703367 / 0.226044 (0.477322) | 7.128971 / 2.268929 (4.860043) | 3.437475 / 55.444624 (-52.007149) | 2.562291 / 6.876477 (-4.314186) | 2.753419 / 2.142072 (0.611346) | 0.981964 / 4.805227 (-3.823263) | 0.194533 / 6.500664 (-6.306131) | 0.069659 / 0.075469 (-0.005810) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.510356 / 1.841788 (-0.331431) | 22.414117 / 8.074308 (14.339809) | 20.325418 / 10.191392 (10.134025) | 0.226823 / 0.680424 (-0.453601) | 0.029123 / 0.534201 (-0.505078) | 0.454656 / 0.579283 (-0.124627) | 0.559588 / 0.434364 (0.125224) | 0.547386 / 0.540337 (0.007048) | 0.770169 / 1.386936 (-0.616767) |\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) | 0.010167 / 0.011353 (-0.001186) | 0.005164 / 0.011008 (-0.005844) | 0.094897 / 0.038508 (0.056388) | 0.078027 / 0.023109 (0.054918) | 0.474442 / 0.275898 (0.198544) | 0.503362 / 0.323480 (0.179882) | 0.006988 / 0.007986 (-0.000998) | 0.005369 / 0.004328 (0.001041) | 0.079547 / 0.004250 (0.075297) | 0.059382 / 0.037052 (0.022329) | 0.468759 / 0.258489 (0.210270) | 0.566780 / 0.293841 (0.272939) | 0.050791 / 0.128546 (-0.077755) | 0.013191 / 0.075646 (-0.062455) | 0.086086 / 0.419271 (-0.333186) | 0.060399 / 0.043533 (0.016866) | 0.492985 / 0.255139 (0.237846) | 0.509139 / 0.283200 (0.225940) | 0.034537 / 0.141683 (-0.107146) | 1.699166 / 1.452155 (0.247011) | 1.789781 / 1.492716 (0.297065) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.278776 / 0.018006 (0.260769) | 0.615877 / 0.000490 (0.615387) | 0.009062 / 0.000200 (0.008862) | 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) | 0.032931 / 0.037411 (-0.004481) | 0.094796 / 0.014526 (0.080270) | 0.126697 / 0.176557 (-0.049859) | 0.168172 / 0.737135 (-0.568963) | 0.113906 / 0.296338 (-0.182433) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.602378 / 0.215209 (0.387169) | 5.987708 / 2.077655 (3.910054) | 2.800339 / 1.504120 (1.296219) | 2.474127 / 1.541195 (0.932932) | 2.502387 / 1.468490 (1.033897) | 0.808147 / 4.584777 (-3.776630) | 5.212691 / 3.745712 (1.466979) | 4.479452 / 5.269862 (-0.790409) | 2.831960 / 4.565676 (-1.733717) | 0.086777 / 0.424275 (-0.337498) | 0.009492 / 0.007607 (0.001885) | 0.716848 / 0.226044 (0.490803) | 7.099904 / 2.268929 (4.830975) | 3.794708 / 55.444624 (-51.649916) | 2.859826 / 6.876477 (-4.016650) | 3.109673 / 2.142072 (0.967600) | 0.936776 / 4.805227 (-3.868451) | 0.195152 / 6.500664 (-6.305512) | 0.074184 / 0.075469 (-0.001285) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.585419 / 1.841788 (-0.256369) | 22.420377 / 8.074308 (14.346068) | 20.761533 / 10.191392 (10.570141) | 0.228480 / 0.680424 (-0.451943) | 0.030944 / 0.534201 (-0.503257) | 0.444717 / 0.579283 (-0.134566) | 0.579632 / 0.434364 (0.145268) | 0.521669 / 0.540337 (-0.018669) | 0.748274 / 1.386936 (-0.638662) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#94e07965a400e6901f12e6f0f25c7090656c828c \"CML watermark\")\n" ]

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[ "What type of dataset are you using in this script? `torch.utils.data.Dataset` or `datasets.Dataset`? Please share the `datasets` package version if it's the latter. Otherwise, it's better to move this issue to the `accelerate` repo.", "Very sorry, I thought I had a repo in `accelerate!`\r\nI will close this issue and repo the issue in the appropriate place." ]

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[ "I think this is the same issue as https://github.com/huggingface/datasets/issues/4776. Maybe installing `ffmpeg` can fix it:\r\n```python\r\nadd-apt-repository -y ppa:savoury1/ffmpeg4\r\napt-get -qq install -y ffmpeg\r\n```\r\n\r\nHowever, the best solution is to use a newer version of `datasets`. In the recent releases, we've replaced `torchaudio` with `soundfile`, which is easier to install and faster.", "@mariosasko \r\nThanks for your help" ]

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[ "`filter` treats the function's output as a (selection) mask - `True` keeps the sample, and `False` drops it. In your case, `bool(0)` evaluates to `False`, so dropping the first sample is the correct behavior.", "Oh gosh! 🤦 I totally misunderstood the API! My apologies!" ]

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[ "[This](https://huggingface.co/docs/datasets/nlp_process#map) is the most performant way to tokenize a dataset (`batched=True, num_proc=None, return_tensors=\"np\"`) \r\n\r\nIf`tokenizer.is_fast` returns `True`, `num_proc` must be `None/1` to benefit from the fast tokenizers' parallelism (the fast tokenizers are implemented in Rust, and Rust multi-threading doesn't work well with Python multi-processing)" ]

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

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[ "cc @Rocketknight1 ", "Hey! You can implement this yourself, just:\r\n\r\n1) Create the dataset with `to_tf_dataset()` with `shuffle=False`\r\n2) Add an `unbatch()` at the end (or use batch_size=1)\r\n3) Add a `shuffle()` to the resulting dataset with your desired buffer size\r\n4) Add a `batch()` at the end again to re-batch your dataset.\r\n\r\nNote that the way we construct datasets in `to_tf_dataset()`, we don't actually shuffle the entire dataset in-memory, using `tf.data.Dataset.shuffle()`! Instead, we shuffle an index array and then load from the dataset with that. This means that shuffling with `tf.data.Dataset.shuffle()` will probably be slower and use more memory than our approach - I don't think adding the option for smaller shuffle buffers will actually save you memory on this!", "Thanks for your reply! @Rocketknight1 \r\n\"We don't actually shuffle the entire dataset in-memory, using tf.data.Dataset.shuffle()! Instead, we shuffle an index array and then load from the dataset with that.\"\r\nIn such case, there will be random access to dataset data during shuffling. When the dataset is large, the performance can be X10 times slow. I have tried many ways with to_tf_dataset() trying to achieve comparable performance with tf.data.Dataset().shuffle(buffer_size).batch(). But the performance with to_tf_dataset() is still slow. \r\n" ]

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

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[ "_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) | 0.008370 / 0.011353 (-0.002983) | 0.004674 / 0.011008 (-0.006334) | 0.103912 / 0.038508 (0.065404) | 0.101668 / 0.023109 (0.078559) | 0.417945 / 0.275898 (0.142047) | 0.454805 / 0.323480 (0.131325) | 0.004763 / 0.007986 (-0.003223) | 0.003934 / 0.004328 (-0.000394) | 0.078446 / 0.004250 (0.074196) | 0.068383 / 0.037052 (0.031331) | 0.415100 / 0.258489 (0.156611) | 0.475272 / 0.293841 (0.181431) | 0.036884 / 0.128546 (-0.091662) | 0.010097 / 0.075646 (-0.065549) | 0.354962 / 0.419271 (-0.064309) | 0.062688 / 0.043533 (0.019155) | 0.420643 / 0.255139 (0.165504) | 0.446504 / 0.283200 (0.163304) | 0.029075 / 0.141683 (-0.112608) | 1.791517 / 1.452155 (0.339363) | 1.859820 / 1.492716 (0.367104) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.246929 / 0.018006 (0.228923) | 0.519593 / 0.000490 (0.519103) | 0.006848 / 0.000200 (0.006648) | 0.000168 / 0.000054 (0.000114) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035179 / 0.037411 (-0.002232) | 0.115582 / 0.014526 (0.101057) | 0.128235 / 0.176557 (-0.048321) | 0.187123 / 0.737135 (-0.550012) | 0.120862 / 0.296338 (-0.175477) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.463406 / 0.215209 (0.248197) | 4.615517 / 2.077655 (2.537863) | 2.250513 / 1.504120 (0.746393) | 2.061226 / 1.541195 (0.520032) | 2.189938 / 1.468490 (0.721448) | 0.582984 / 4.584777 (-4.001793) | 4.299464 / 3.745712 (0.553751) | 4.037274 / 5.269862 (-1.232588) | 2.608967 / 4.565676 (-1.956710) | 0.068944 / 0.424275 (-0.355331) | 0.009501 / 0.007607 (0.001894) | 0.567436 / 0.226044 (0.341392) | 5.662738 / 2.268929 (3.393809) | 2.849094 / 55.444624 (-52.595530) | 2.461013 / 6.876477 (-4.415464) | 2.663245 / 2.142072 (0.521172) | 0.704528 / 4.805227 (-4.100699) | 0.163583 / 6.500664 (-6.337081) | 0.075719 / 0.075469 (0.000250) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.604743 / 1.841788 (-0.237044) | 24.512054 / 8.074308 (16.437746) | 17.870939 / 10.191392 (7.679547) | 0.199188 / 0.680424 (-0.481236) | 0.023820 / 0.534201 (-0.510381) | 0.487520 / 0.579283 (-0.091763) | 0.512543 / 0.434364 (0.078179) | 0.575138 / 0.540337 (0.034801) | 0.759863 / 1.386936 (-0.627073) |\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) | 0.010516 / 0.011353 (-0.000837) | 0.004779 / 0.011008 (-0.006229) | 0.078482 / 0.038508 (0.039974) | 0.108533 / 0.023109 (0.085424) | 0.498692 / 0.275898 (0.222794) | 0.534698 / 0.323480 (0.211218) | 0.007624 / 0.007986 (-0.000362) | 0.003938 / 0.004328 (-0.000391) | 0.077317 / 0.004250 (0.073067) | 0.078056 / 0.037052 (0.041004) | 0.493648 / 0.258489 (0.235159) | 0.540891 / 0.293841 (0.247050) | 0.040377 / 0.128546 (-0.088169) | 0.010155 / 0.075646 (-0.065491) | 0.084384 / 0.419271 (-0.334888) | 0.061419 / 0.043533 (0.017886) | 0.494474 / 0.255139 (0.239335) | 0.524656 / 0.283200 (0.241456) | 0.029052 / 0.141683 (-0.112631) | 1.794584 / 1.452155 (0.342429) | 1.939987 / 1.492716 (0.447270) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.377404 / 0.018006 (0.359398) | 0.516562 / 0.000490 (0.516072) | 0.109555 / 0.000200 (0.109356) | 0.001126 / 0.000054 (0.001071) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.039793 / 0.037411 (0.002382) | 0.123001 / 0.014526 (0.108475) | 0.127536 / 0.176557 (-0.049021) | 0.191681 / 0.737135 (-0.545455) | 0.128590 / 0.296338 (-0.167748) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.513689 / 0.215209 (0.298480) | 5.135114 / 2.077655 (3.057459) | 2.797885 / 1.504120 (1.293765) | 2.715332 / 1.541195 (1.174137) | 2.746437 / 1.468490 (1.277947) | 0.596480 / 4.584777 (-3.988297) | 4.382013 / 3.745712 (0.636301) | 3.965956 / 5.269862 (-1.303906) | 2.545206 / 4.565676 (-2.020471) | 0.069620 / 0.424275 (-0.354655) | 0.009321 / 0.007607 (0.001714) | 0.612424 / 0.226044 (0.386379) | 6.107037 / 2.268929 (3.838109) | 3.447246 / 55.444624 (-51.997379) | 3.073262 / 6.876477 (-3.803215) | 3.280185 / 2.142072 (1.138113) | 0.704776 / 4.805227 (-4.100451) | 0.160488 / 6.500664 (-6.340176) | 0.075730 / 0.075469 (0.000261) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.697035 / 1.841788 (-0.144753) | 24.766118 / 8.074308 (16.691809) | 18.476699 / 10.191392 (8.285307) | 0.176594 / 0.680424 (-0.503830) | 0.024249 / 0.534201 (-0.509952) | 0.478743 / 0.579283 (-0.100541) | 0.518774 / 0.434364 (0.084410) | 0.581498 / 0.540337 (0.041161) | 0.797784 / 1.386936 (-0.589152) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#722cea0f4929ff4ffcdbb7ca6b72cba229b9701a \"CML watermark\")\n" ]

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[ "_The documentation is not available anymore as the PR was closed or merged._", "CI errors are unrelated", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006681 / 0.011353 (-0.004672) | 0.004132 / 0.011008 (-0.006876) | 0.085045 / 0.038508 (0.046536) | 0.077680 / 0.023109 (0.054571) | 0.382042 / 0.275898 (0.106144) | 0.412932 / 0.323480 (0.089452) | 0.005339 / 0.007986 (-0.002646) | 0.003408 / 0.004328 (-0.000921) | 0.065280 / 0.004250 (0.061030) | 0.055732 / 0.037052 (0.018680) | 0.400231 / 0.258489 (0.141742) | 0.432497 / 0.293841 (0.138656) | 0.031532 / 0.128546 (-0.097014) | 0.008721 / 0.075646 (-0.066925) | 0.289612 / 0.419271 (-0.129660) | 0.053089 / 0.043533 (0.009556) | 0.383300 / 0.255139 (0.128161) | 0.401204 / 0.283200 (0.118004) | 0.023582 / 0.141683 (-0.118100) | 1.493854 / 1.452155 (0.041699) | 1.583497 / 1.492716 (0.090781) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.239163 / 0.018006 (0.221157) | 0.469555 / 0.000490 (0.469065) | 0.008325 / 0.000200 (0.008125) | 0.000113 / 0.000054 (0.000059) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028975 / 0.037411 (-0.008436) | 0.084195 / 0.014526 (0.069669) | 0.189394 / 0.176557 (0.012837) | 0.158010 / 0.737135 (-0.579125) | 0.097502 / 0.296338 (-0.198837) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.383085 / 0.215209 (0.167876) | 3.827030 / 2.077655 (1.749375) | 1.872279 / 1.504120 (0.368159) | 1.705808 / 1.541195 (0.164613) | 1.833706 / 1.468490 (0.365216) | 0.484744 / 4.584777 (-4.100033) | 3.658221 / 3.745712 (-0.087491) | 3.398462 / 5.269862 (-1.871399) | 2.064974 / 4.565676 (-2.500703) | 0.057740 / 0.424275 (-0.366535) | 0.007926 / 0.007607 (0.000319) | 0.465358 / 0.226044 (0.239314) | 4.652951 / 2.268929 (2.384022) | 2.328390 / 55.444624 (-53.116235) | 2.000606 / 6.876477 (-4.875870) | 2.268391 / 2.142072 (0.126318) | 0.586537 / 4.805227 (-4.218690) | 0.134749 / 6.500664 (-6.365915) | 0.061276 / 0.075469 (-0.014193) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.337913 / 1.841788 (-0.503875) | 20.232122 / 8.074308 (12.157814) | 14.478579 / 10.191392 (4.287187) | 0.167545 / 0.680424 (-0.512878) | 0.018745 / 0.534201 (-0.515456) | 0.401209 / 0.579283 (-0.178074) | 0.425748 / 0.434364 (-0.008616) | 0.462539 / 0.540337 (-0.077798) | 0.652446 / 1.386936 (-0.734490) |\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) | 0.007159 / 0.011353 (-0.004194) | 0.004091 / 0.011008 (-0.006917) | 0.066202 / 0.038508 (0.027694) | 0.083096 / 0.023109 (0.059987) | 0.402160 / 0.275898 (0.126261) | 0.440565 / 0.323480 (0.117085) | 0.005757 / 0.007986 (-0.002228) | 0.003445 / 0.004328 (-0.000884) | 0.065498 / 0.004250 (0.061248) | 0.059787 / 0.037052 (0.022735) | 0.407017 / 0.258489 (0.148528) | 0.448270 / 0.293841 (0.154429) | 0.033606 / 0.128546 (-0.094941) | 0.008744 / 0.075646 (-0.066902) | 0.072902 / 0.419271 (-0.346369) | 0.050144 / 0.043533 (0.006611) | 0.401069 / 0.255139 (0.145930) | 0.426389 / 0.283200 (0.143189) | 0.023297 / 0.141683 (-0.118386) | 1.506152 / 1.452155 (0.053998) | 1.570211 / 1.492716 (0.077495) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.235759 / 0.018006 (0.217753) | 0.488410 / 0.000490 (0.487921) | 0.004587 / 0.000200 (0.004387) | 0.000115 / 0.000054 (0.000060) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034123 / 0.037411 (-0.003289) | 0.102163 / 0.014526 (0.087638) | 0.110892 / 0.176557 (-0.065664) | 0.166000 / 0.737135 (-0.571135) | 0.110845 / 0.296338 (-0.185494) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.431397 / 0.215209 (0.216188) | 4.291540 / 2.077655 (2.213885) | 2.298248 / 1.504120 (0.794128) | 2.134752 / 1.541195 (0.593557) | 2.207913 / 1.468490 (0.739423) | 0.490607 / 4.584777 (-4.094170) | 3.683078 / 3.745712 (-0.062635) | 3.314266 / 5.269862 (-1.955596) | 2.059488 / 4.565676 (-2.506188) | 0.057876 / 0.424275 (-0.366399) | 0.007696 / 0.007607 (0.000089) | 0.512186 / 0.226044 (0.286142) | 5.124071 / 2.268929 (2.855142) | 2.803913 / 55.444624 (-52.640711) | 2.428558 / 6.876477 (-4.447919) | 2.655207 / 2.142072 (0.513135) | 0.584589 / 4.805227 (-4.220638) | 0.133518 / 6.500664 (-6.367146) | 0.060729 / 0.075469 (-0.014740) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.352916 / 1.841788 (-0.488872) | 20.249632 / 8.074308 (12.175323) | 15.283079 / 10.191392 (5.091686) | 0.157601 / 0.680424 (-0.522823) | 0.019650 / 0.534201 (-0.514551) | 0.396398 / 0.579283 (-0.182885) | 0.430111 / 0.434364 (-0.004252) | 0.480627 / 0.540337 (-0.059710) | 0.642165 / 1.386936 (-0.744771) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#9b21e181b642bd55b3ef68c1948bfbcd388136d6 \"CML watermark\")\n" ]

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6231). All of your documentation changes will be reflected on that endpoint.", "realized that this pr is still not merged, @lhoestq maybe you can take a look at it? ", "I think https://github.com/huggingface/datasets/pull/6218 fixed the issue (a bit differently though)", "ah actually nope, let me check", "@lhoestq yeah the pr you're referencing doesn't fix the problem when two semantically analogous configs occur in datasets_info.json, i suggest to rewrite the legacy one if it exists during .push_to_hub", "Only the old versions of `datasets` use the JSON file over the README and they can only load one config so the name doesn't really matter.\r\n\r\nThat's why I chose to load the info from the JSON no matter the name (no check to see if it's \"username--dataset_name\") in my previous PR.\r\n\r\nI think you can remove the old info without even checking the name. In this case maybe no need to update load.py ", "(also minor: not checking the name makes it more robust to dataset renaming)", "@lhoestq okay makes sense... so you think it's not a problem that in some cases we might end up with `dataset_infos.json` having two keys in it?", "> @lhoestq okay makes sense... so you think it's not a problem that in some cases we might end up with dataset_infos.json having two keys in it?\r\n\r\nIdeally they should have only one config no ? Since old versions of `datasets` simply load the first config in the JSON.\r\nWe can overwrite it with the new default one (and no matter the name of the outdated config in the JSON)\r\n\r\n" ]

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[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005894 / 0.011353 (-0.005459) | 0.003621 / 0.011008 (-0.007387) | 0.080446 / 0.038508 (0.041938) | 0.056800 / 0.023109 (0.033691) | 0.326485 / 0.275898 (0.050587) | 0.376207 / 0.323480 (0.052727) | 0.004640 / 0.007986 (-0.003346) | 0.002795 / 0.004328 (-0.001533) | 0.062815 / 0.004250 (0.058565) | 0.045761 / 0.037052 (0.008709) | 0.341417 / 0.258489 (0.082928) | 0.373129 / 0.293841 (0.079288) | 0.027226 / 0.128546 (-0.101321) | 0.007873 / 0.075646 (-0.067774) | 0.261737 / 0.419271 (-0.157535) | 0.044648 / 0.043533 (0.001115) | 0.320195 / 0.255139 (0.065056) | 0.381892 / 0.283200 (0.098692) | 0.020431 / 0.141683 (-0.121252) | 1.405332 / 1.452155 (-0.046823) | 1.455592 / 1.492716 (-0.037125) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.191539 / 0.018006 (0.173533) | 0.423655 / 0.000490 (0.423165) | 0.002741 / 0.000200 (0.002541) | 0.000069 / 0.000054 (0.000014) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023952 / 0.037411 (-0.013459) | 0.073387 / 0.014526 (0.058861) | 0.083746 / 0.176557 (-0.092810) | 0.144977 / 0.737135 (-0.592159) | 0.083808 / 0.296338 (-0.212530) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.436228 / 0.215209 (0.221019) | 4.370510 / 2.077655 (2.292855) | 2.340426 / 1.504120 (0.836306) | 2.202215 / 1.541195 (0.661021) | 2.258528 / 1.468490 (0.790037) | 0.503455 / 4.584777 (-4.081322) | 3.043695 / 3.745712 (-0.702017) | 2.784033 / 5.269862 (-2.485829) | 1.847956 / 4.565676 (-2.717721) | 0.057702 / 0.424275 (-0.366573) | 0.006703 / 0.007607 (-0.000904) | 0.510628 / 0.226044 (0.284583) | 5.101890 / 2.268929 (2.832961) | 2.816469 / 55.444624 (-52.628155) | 2.474220 / 6.876477 (-4.402257) | 2.617851 / 2.142072 (0.475779) | 0.593585 / 4.805227 (-4.211642) | 0.125895 / 6.500664 (-6.374769) | 0.062170 / 0.075469 (-0.013299) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.238792 / 1.841788 (-0.602996) | 18.096417 / 8.074308 (10.022108) | 13.548778 / 10.191392 (3.357386) | 0.144878 / 0.680424 (-0.535546) | 0.016644 / 0.534201 (-0.517557) | 0.334556 / 0.579283 (-0.244728) | 0.343680 / 0.434364 (-0.090684) | 0.383093 / 0.540337 (-0.157244) | 0.525075 / 1.386936 (-0.861861) |\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) | 0.006125 / 0.011353 (-0.005228) | 0.003668 / 0.011008 (-0.007340) | 0.062650 / 0.038508 (0.024142) | 0.058882 / 0.023109 (0.035772) | 0.454643 / 0.275898 (0.178745) | 0.486659 / 0.323480 (0.163179) | 0.005558 / 0.007986 (-0.002427) | 0.002858 / 0.004328 (-0.001471) | 0.062603 / 0.004250 (0.058353) | 0.049701 / 0.037052 (0.012649) | 0.455903 / 0.258489 (0.197413) | 0.491544 / 0.293841 (0.197703) | 0.028581 / 0.128546 (-0.099965) | 0.008040 / 0.075646 (-0.067607) | 0.068314 / 0.419271 (-0.350957) | 0.040637 / 0.043533 (-0.002896) | 0.450288 / 0.255139 (0.195149) | 0.476330 / 0.283200 (0.193131) | 0.018989 / 0.141683 (-0.122693) | 1.455122 / 1.452155 (0.002967) | 1.496941 / 1.492716 (0.004225) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.227382 / 0.018006 (0.209376) | 0.432637 / 0.000490 (0.432147) | 0.002727 / 0.000200 (0.002527) | 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) | 0.026125 / 0.037411 (-0.011286) | 0.081342 / 0.014526 (0.066817) | 0.091227 / 0.176557 (-0.085329) | 0.145175 / 0.737135 (-0.591960) | 0.091988 / 0.296338 (-0.204351) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.454293 / 0.215209 (0.239083) | 4.537912 / 2.077655 (2.460257) | 2.489146 / 1.504120 (0.985026) | 2.307166 / 1.541195 (0.765971) | 2.380866 / 1.468490 (0.912376) | 0.509015 / 4.584777 (-4.075762) | 3.111069 / 3.745712 (-0.634644) | 2.839181 / 5.269862 (-2.430681) | 1.874630 / 4.565676 (-2.691047) | 0.058540 / 0.424275 (-0.365735) | 0.006693 / 0.007607 (-0.000914) | 0.528408 / 0.226044 (0.302363) | 5.285802 / 2.268929 (3.016874) | 2.952090 / 55.444624 (-52.492534) | 2.591496 / 6.876477 (-4.284980) | 2.741080 / 2.142072 (0.599007) | 0.595610 / 4.805227 (-4.209617) | 0.124387 / 6.500664 (-6.376277) | 0.061032 / 0.075469 (-0.014437) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.365816 / 1.841788 (-0.475972) | 18.684534 / 8.074308 (10.610226) | 14.540438 / 10.191392 (4.349046) | 0.146793 / 0.680424 (-0.533631) | 0.018165 / 0.534201 (-0.516036) | 0.333794 / 0.579283 (-0.245489) | 0.345533 / 0.434364 (-0.088830) | 0.384453 / 0.540337 (-0.155885) | 0.529104 / 1.386936 (-0.857832) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6c884967dd5f4e8aa3d1f3c2e3a414ae53afe261 \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006121 / 0.011353 (-0.005232) | 0.003683 / 0.011008 (-0.007325) | 0.083329 / 0.038508 (0.044821) | 0.063350 / 0.023109 (0.040241) | 0.329959 / 0.275898 (0.054061) | 0.396111 / 0.323480 (0.072631) | 0.003554 / 0.007986 (-0.004432) | 0.002907 / 0.004328 (-0.001421) | 0.064152 / 0.004250 (0.059902) | 0.049182 / 0.037052 (0.012130) | 0.343862 / 0.258489 (0.085373) | 0.414568 / 0.293841 (0.120727) | 0.027157 / 0.128546 (-0.101389) | 0.007957 / 0.075646 (-0.067689) | 0.261868 / 0.419271 (-0.157404) | 0.044938 / 0.043533 (0.001405) | 0.318470 / 0.255139 (0.063331) | 0.393319 / 0.283200 (0.110119) | 0.022848 / 0.141683 (-0.118835) | 1.419916 / 1.452155 (-0.032238) | 1.508783 / 1.492716 (0.016067) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.200530 / 0.018006 (0.182523) | 0.433586 / 0.000490 (0.433097) | 0.002063 / 0.000200 (0.001863) | 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) | 0.024803 / 0.037411 (-0.012609) | 0.075894 / 0.014526 (0.061368) | 0.086488 / 0.176557 (-0.090069) | 0.149058 / 0.737135 (-0.588077) | 0.087046 / 0.296338 (-0.209292) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.390771 / 0.215209 (0.175562) | 3.886178 / 2.077655 (1.808523) | 1.868626 / 1.504120 (0.364506) | 1.708532 / 1.541195 (0.167338) | 1.788491 / 1.468490 (0.320001) | 0.505706 / 4.584777 (-4.079071) | 3.062094 / 3.745712 (-0.683618) | 2.898559 / 5.269862 (-2.371302) | 1.901225 / 4.565676 (-2.664452) | 0.058366 / 0.424275 (-0.365909) | 0.006851 / 0.007607 (-0.000756) | 0.465382 / 0.226044 (0.239337) | 4.650187 / 2.268929 (2.381258) | 2.316152 / 55.444624 (-53.128472) | 1.989597 / 6.876477 (-4.886879) | 2.169266 / 2.142072 (0.027194) | 0.593257 / 4.805227 (-4.211970) | 0.126440 / 6.500664 (-6.374224) | 0.062227 / 0.075469 (-0.013242) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.283591 / 1.841788 (-0.558197) | 18.384667 / 8.074308 (10.310358) | 14.079611 / 10.191392 (3.888219) | 0.150453 / 0.680424 (-0.529971) | 0.017100 / 0.534201 (-0.517101) | 0.330503 / 0.579283 (-0.248780) | 0.348134 / 0.434364 (-0.086230) | 0.385726 / 0.540337 (-0.154612) | 0.529147 / 1.386936 (-0.857789) |\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) | 0.006168 / 0.011353 (-0.005185) | 0.003801 / 0.011008 (-0.007208) | 0.063168 / 0.038508 (0.024660) | 0.062331 / 0.023109 (0.039221) | 0.448321 / 0.275898 (0.172423) | 0.484416 / 0.323480 (0.160937) | 0.004827 / 0.007986 (-0.003159) | 0.002848 / 0.004328 (-0.001480) | 0.062736 / 0.004250 (0.058486) | 0.049128 / 0.037052 (0.012075) | 0.449276 / 0.258489 (0.190787) | 0.499035 / 0.293841 (0.205194) | 0.028577 / 0.128546 (-0.099969) | 0.008114 / 0.075646 (-0.067532) | 0.068297 / 0.419271 (-0.350974) | 0.040835 / 0.043533 (-0.002698) | 0.453556 / 0.255139 (0.198417) | 0.475420 / 0.283200 (0.192220) | 0.020292 / 0.141683 (-0.121390) | 1.472226 / 1.452155 (0.020071) | 1.523809 / 1.492716 (0.031093) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.230662 / 0.018006 (0.212655) | 0.439697 / 0.000490 (0.439207) | 0.009899 / 0.000200 (0.009699) | 0.000087 / 0.000054 (0.000033) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.026418 / 0.037411 (-0.010993) | 0.082188 / 0.014526 (0.067662) | 0.091039 / 0.176557 (-0.085518) | 0.146646 / 0.737135 (-0.590489) | 0.091693 / 0.296338 (-0.204645) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.462086 / 0.215209 (0.246877) | 4.620925 / 2.077655 (2.543271) | 2.539234 / 1.504120 (1.035114) | 2.371178 / 1.541195 (0.829983) | 2.440538 / 1.468490 (0.972048) | 0.511047 / 4.584777 (-4.073730) | 3.082088 / 3.745712 (-0.663624) | 2.918162 / 5.269862 (-2.351700) | 1.899651 / 4.565676 (-2.666025) | 0.059003 / 0.424275 (-0.365272) | 0.006746 / 0.007607 (-0.000861) | 0.537863 / 0.226044 (0.311819) | 5.382355 / 2.268929 (3.113426) | 3.060091 / 55.444624 (-52.384534) | 2.754969 / 6.876477 (-4.121507) | 2.863156 / 2.142072 (0.721084) | 0.606888 / 4.805227 (-4.198339) | 0.127448 / 6.500664 (-6.373216) | 0.062975 / 0.075469 (-0.012494) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.336065 / 1.841788 (-0.505722) | 19.019902 / 8.074308 (10.945594) | 15.057979 / 10.191392 (4.866587) | 0.160646 / 0.680424 (-0.519778) | 0.018340 / 0.534201 (-0.515861) | 0.341664 / 0.579283 (-0.237619) | 0.356536 / 0.434364 (-0.077828) | 0.393974 / 0.540337 (-0.146363) | 0.546036 / 1.386936 (-0.840900) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#fd04e445bd36d7eb4af4d5a6b8519ab8e306ecf5 \"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) | 0.007220 / 0.011353 (-0.004132) | 0.004537 / 0.011008 (-0.006471) | 0.087333 / 0.038508 (0.048825) | 0.095637 / 0.023109 (0.072528) | 0.323819 / 0.275898 (0.047921) | 0.358838 / 0.323480 (0.035358) | 0.005910 / 0.007986 (-0.002076) | 0.003781 / 0.004328 (-0.000548) | 0.064565 / 0.004250 (0.060315) | 0.062818 / 0.037052 (0.025766) | 0.322595 / 0.258489 (0.064106) | 0.371865 / 0.293841 (0.078024) | 0.031667 / 0.128546 (-0.096880) | 0.009068 / 0.075646 (-0.066579) | 0.290574 / 0.419271 (-0.128697) | 0.054618 / 0.043533 (0.011085) | 0.314708 / 0.255139 (0.059569) | 0.336647 / 0.283200 (0.053447) | 0.027070 / 0.141683 (-0.114613) | 1.500640 / 1.452155 (0.048485) | 1.586775 / 1.492716 (0.094059) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.294461 / 0.018006 (0.276455) | 0.580125 / 0.000490 (0.579635) | 0.008165 / 0.000200 (0.007965) | 0.000320 / 0.000054 (0.000266) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032352 / 0.037411 (-0.005059) | 0.092187 / 0.014526 (0.077661) | 0.104993 / 0.176557 (-0.071564) | 0.162738 / 0.737135 (-0.574397) | 0.103242 / 0.296338 (-0.193096) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.396732 / 0.215209 (0.181523) | 3.955049 / 2.077655 (1.877394) | 1.876762 / 1.504120 (0.372642) | 1.698477 / 1.541195 (0.157282) | 1.847086 / 1.468490 (0.378596) | 0.488306 / 4.584777 (-4.096471) | 3.658922 / 3.745712 (-0.086790) | 3.559050 / 5.269862 (-1.710812) | 2.187363 / 4.565676 (-2.378313) | 0.059795 / 0.424275 (-0.364480) | 0.008966 / 0.007607 (0.001359) | 0.474212 / 0.226044 (0.248168) | 4.732540 / 2.268929 (2.463611) | 2.466370 / 55.444624 (-52.978254) | 2.112105 / 6.876477 (-4.764372) | 2.414624 / 2.142072 (0.272552) | 0.595447 / 4.805227 (-4.209780) | 0.136705 / 6.500664 (-6.363959) | 0.062267 / 0.075469 (-0.013202) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.266518 / 1.841788 (-0.575270) | 21.009975 / 8.074308 (12.935666) | 14.823960 / 10.191392 (4.632568) | 0.165630 / 0.680424 (-0.514793) | 0.018499 / 0.534201 (-0.515702) | 0.396720 / 0.579283 (-0.182563) | 0.424807 / 0.434364 (-0.009557) | 0.463326 / 0.540337 (-0.077011) | 0.653132 / 1.386936 (-0.733804) |\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) | 0.007789 / 0.011353 (-0.003564) | 0.004720 / 0.011008 (-0.006288) | 0.066656 / 0.038508 (0.028148) | 0.094219 / 0.023109 (0.071109) | 0.414965 / 0.275898 (0.139067) | 0.454808 / 0.323480 (0.131328) | 0.006088 / 0.007986 (-0.001898) | 0.003980 / 0.004328 (-0.000349) | 0.066048 / 0.004250 (0.061797) | 0.065875 / 0.037052 (0.028823) | 0.419994 / 0.258489 (0.161505) | 0.462001 / 0.293841 (0.168160) | 0.033534 / 0.128546 (-0.095013) | 0.009010 / 0.075646 (-0.066636) | 0.072778 / 0.419271 (-0.346493) | 0.049834 / 0.043533 (0.006301) | 0.411003 / 0.255139 (0.155864) | 0.430918 / 0.283200 (0.147718) | 0.025664 / 0.141683 (-0.116019) | 1.526771 / 1.452155 (0.074616) | 1.634767 / 1.492716 (0.142051) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.271180 / 0.018006 (0.253174) | 0.576704 / 0.000490 (0.576214) | 0.004362 / 0.000200 (0.004162) | 0.000112 / 0.000054 (0.000058) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035648 / 0.037411 (-0.001763) | 0.102407 / 0.014526 (0.087881) | 0.111613 / 0.176557 (-0.064944) | 0.166173 / 0.737135 (-0.570962) | 0.113371 / 0.296338 (-0.182967) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.436031 / 0.215209 (0.220822) | 4.347071 / 2.077655 (2.269416) | 2.366937 / 1.504120 (0.862817) | 2.216356 / 1.541195 (0.675161) | 2.335933 / 1.468490 (0.867443) | 0.490484 / 4.584777 (-4.094293) | 3.730656 / 3.745712 (-0.015056) | 3.497248 / 5.269862 (-1.772613) | 2.215729 / 4.565676 (-2.349947) | 0.057905 / 0.424275 (-0.366370) | 0.007983 / 0.007607 (0.000376) | 0.510413 / 0.226044 (0.284369) | 5.114502 / 2.268929 (2.845574) | 2.871599 / 55.444624 (-52.573026) | 2.537514 / 6.876477 (-4.338962) | 2.819135 / 2.142072 (0.677063) | 0.588397 / 4.805227 (-4.216830) | 0.134665 / 6.500664 (-6.365999) | 0.063349 / 0.075469 (-0.012120) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.352962 / 1.841788 (-0.488826) | 21.628664 / 8.074308 (13.554356) | 15.962105 / 10.191392 (5.770713) | 0.167781 / 0.680424 (-0.512643) | 0.020965 / 0.534201 (-0.513236) | 0.402809 / 0.579283 (-0.176474) | 0.435153 / 0.434364 (0.000789) | 0.481394 / 0.540337 (-0.058944) | 0.658068 / 1.386936 (-0.728868) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#12adf38b90fde8e2a4e46fcbb023ee23b5c4e98c \"CML watermark\")\n" ]

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[ "From what I see, `MMSegInferencer` supports NumPy arrays, so replace the line `image_path = example['image']` with `image_path = np.array(example['image'])` to fix the issue (`example[\"image\"]` is a `PIL.Image` object). ", "> From what I see, `MMSegInferencer` supports NumPy arrays, so replace the line `image_path = example['image']` with `image_path = np.array(example['image'])` to fix the issue (`example[\"image\"]` is a `PIL.Image` object).\r\n\r\nThanks @mariosasko for your reply...\r\ni tried :\r\n```\r\n# Define a function to apply the code to each image in the dataset\r\ndef process_image(image_path):\r\n print(\"Processing image:\", image_path)\r\n result = inferencer(image_path)['predictions']\r\n mask = np.where(result == 12, 255, 0).astype('uint8')\r\n return Image.fromarray(mask)\r\n\r\n# Process and save masks for each image in the dataset\r\nfor idx, example in enumerate(dataset['train']):\r\n image_path = np.array(example['image'])\r\n mask_image = process_image(image_path)\r\n mask_image.save(f\"mask_{idx}.png\")\r\n```\r\nand got\r\n```\r\nProcessing image: [[[202 165 87]\r\n [203 166 88]\r\n [207 168 91]\r\n ...\r\n [243 205 122]\r\n [244 202 120]\r\n [242 200 118]]\r\n\r\n [[202 165 87]\r\n [203 166 88]\r\n [207 168 91]\r\n ...\r\n [244 206 123]\r\n [245 203 121]\r\n [243 201 119]]\r\n\r\n [[203 164 87]\r\n [204 165 88]\r\n [207 168 91]\r\n ...\r\n [245 207 126]\r\n [246 204 122]\r\n [245 203 121]]\r\n\r\n ...\r\n\r\n [[154 123 56]\r\n [155 124 57]\r\n [158 125 56]\r\n ...\r\n [ 3 3 1]\r\n [ 3 3 1]\r\n [ 3 3 1]]\r\n\r\n [[154 123 56]\r\n [154 123 56]\r\n [155 124 57]\r\n ...\r\n [ 2 2 0]\r\n [ 2 2 0]\r\n [ 2 2 0]]\r\n\r\n [[152 121 54]\r\n [152 121 54]\r\n [153 122 55]\r\n ...\r\n [ 2 2 0]\r\n [ 2 2 0]\r\n [ 2 2 0]]]\r\nInference ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ \r\nProcessing image: [[[ 39 44 40]\r\n [ 39 44 40]\r\n [ 39 43 44]\r\n ...\r\n [187 185 164]\r\n [208 204 175]\r\n [203 198 166]]\r\n\r\n [[ 42 47 43]\r\n [ 40 45 41]\r\n [ 40 44 45]\r\n ...\r\n [188 186 165]\r\n [202 198 169]\r\n [201 196 164]]\r\n\r\n [[ 41 46 42]\r\n [ 39 44 40]\r\n [ 40 44 45]\r\n ...\r\n [187 184 165]\r\n [197 193 166]\r\n [201 196 166]]\r\n\r\n ...\r\n\r\n [[ 29 27 30]\r\n [ 28 26 29]\r\n [ 25 23 26]\r\n ...\r\n [ 48 33 28]\r\n [ 44 31 25]\r\n [ 39 26 20]]\r\n\r\n [[ 34 29 33]\r\n [ 32 27 31]\r\n [ 29 24 28]\r\n ...\r\n [ 30 17 11]\r\n [ 36 23 15]\r\n [ 41 28 20]]\r\n\r\n [[ 35 30 34]\r\n [ 33 28 32]\r\n [ 28 23 27]\r\n ...\r\n [ 28 15 9]\r\n [ 41 28 20]\r\n [ 46 33 25]]]\r\nInference ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ \r\nProcessing image: [[[ 65 53 55]\r\n [ 65 53 55]\r\n [ 51 39 41]\r\n ...\r\n [133 127 111]\r\n [150 141 124]\r\n [133 124 107]]\r\n\r\n [[ 58 45 52]\r\n [ 61 48 55]\r\n [ 51 38 45]\r\n ...\r\n [148 141 123]\r\n [178 169 152]\r\n [144 135 118]]\r\n\r\n [[ 79 66 83]\r\n [ 73 60 77]\r\n [ 65 51 66]\r\n ...\r\n [140 131 114]\r\n [142 133 116]\r\n [147 136 118]]\r\n\r\n ...\r\n\r\n [[132 122 133]\r\n [ 95 85 94]\r\n [ 61 51 60]\r\n ...\r\n [ 39 28 42]\r\n [ 46 36 45]\r\n [ 25 16 21]]\r\n\r\n [[150 143 151]\r\n [114 107 115]\r\n [ 64 54 63]\r\n ...\r\n [ 47 35 47]\r\n [ 38 27 35]\r\n [140 129 133]]\r\n\r\n [[145 138 146]\r\n [115 108 116]\r\n [ 69 59 67]\r\n ...\r\n [ 31 19 31]\r\n [128 117 123]\r\n [196 185 189]]]\r\nInference ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ \r\nProcessing image: [[[159 151 140]\r\n [171 163 152]\r\n [161 148 142]\r\n ...\r\n [198 184 171]\r\n [189 175 162]\r\n [183 169 156]]\r\n\r\n [[128 118 106]\r\n [138 128 116]\r\n [138 125 116]\r\n ...\r\n [200 186 173]\r\n [190 176 163]\r\n [187 173 160]]\r\n\r\n [[165 153 137]\r\n [170 158 142]\r\n [174 162 148]\r\n ...\r\n [200 187 171]\r\n [188 175 159]\r\n [182 169 153]]\r\n```\r\nHowever , when trying to add to:\r\n```\r\nfrom datasets import load_dataset\r\ndataset = load_dataset('Andyrasika/cat_kingdom')\r\ndataset\r\n```\r\ni did \r\n```\r\nnew_column = [\"mask\"] * len(dataset[\"train\"])\r\nnew_column\r\ndataset = dataset.add_column(\"/workspace/data\", new_column)\r\n\r\nprint(dataset)\r\n```\r\ngot error:\r\n```\r\n---------------------------------------------------------------------------\r\nAttributeError Traceback (most recent call last)\r\nCell In[11], line 3\r\n 1 new_column = [\"mask\"] * len(dataset[\"train\"])\r\n 2 new_column\r\n----> 3 dataset = dataset.add_column(\"/workspace/data\", new_column)\r\n 5 print(dataset)\r\n\r\nAttributeError: 'DatasetDict' object has no attribute 'add_column'\r\n```", "https://github.com/huggingface/datasets/issues/6246 resolved the `add_column` error, so I'm closing this issue :) " ]

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