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alex-atelo

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"2023-11-02T10:15:58Z"

"2023-12-04T16:53:34Z"

"2023-12-04T16:53:33Z"

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[ "Thanks for reporting! Can you also share the error message printed in step 5?", "I did not store it at the time but I'll try to re-do a mwe next week to get it again", "I haven't managed to reproduce this issue using a [notebook](https://colab.research.google.com/drive/1m6eduYun7pFTkigrCJAFgw0BghlbvXIL?usp=sharing) that follows the steps to reproduce the bug. So, I'm closing it.\r\n\r\nBut feel free to re-open it if you have a better reproducer." ]

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"2023-11-02T09:48:58Z"

"2023-11-02T10:22:33Z"

"2023-11-02T10:11:19Z"

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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) | 0.008947 / 0.011353 (-0.002406) | 0.005602 / 0.011008 (-0.005406) | 0.111208 / 0.038508 (0.072700) | 0.082750 / 0.023109 (0.059641) | 0.453277 / 0.275898 (0.177379) | 0.480072 / 0.323480 (0.156592) | 0.005254 / 0.007986 (-0.002731) | 0.005421 / 0.004328 (0.001092) | 0.082899 / 0.004250 (0.078648) | 0.062859 / 0.037052 (0.025807) | 0.466703 / 0.258489 (0.208214) | 0.478241 / 0.293841 (0.184400) | 0.050754 / 0.128546 (-0.077792) | 0.017726 / 0.075646 (-0.057920) | 0.374830 / 0.419271 (-0.044442) | 0.068577 / 0.043533 (0.025044) | 0.453643 / 0.255139 (0.198504) | 0.453736 / 0.283200 (0.170537) | 0.037313 / 0.141683 (-0.104369) | 1.741215 / 1.452155 (0.289060) | 1.862247 / 1.492716 (0.369531) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.314174 / 0.018006 (0.296168) | 0.644439 / 0.000490 (0.643949) | 0.013914 / 0.000200 (0.013715) | 0.000478 / 0.000054 (0.000424) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030462 / 0.037411 (-0.006949) | 0.096789 / 0.014526 (0.082263) | 0.109999 / 0.176557 (-0.066557) | 0.184610 / 0.737135 (-0.552525) | 0.113846 / 0.296338 (-0.182493) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.586508 / 0.215209 (0.371299) | 5.785138 / 2.077655 (3.707484) | 2.578512 / 1.504120 (1.074392) | 2.266981 / 1.541195 (0.725786) | 2.442463 / 1.468490 (0.973973) | 0.880973 / 4.584777 (-3.703804) | 5.410327 / 3.745712 (1.664615) | 4.976842 / 5.269862 (-0.293020) | 3.020535 / 4.565676 (-1.545142) | 0.089640 / 0.424275 (-0.334635) | 0.009126 / 0.007607 (0.001519) | 0.682364 / 0.226044 (0.456319) | 6.840507 / 2.268929 (4.571579) | 3.313314 / 55.444624 (-52.131310) | 2.815313 / 6.876477 (-4.061164) | 2.851787 / 2.142072 (0.709715) | 1.044916 / 4.805227 (-3.760312) | 0.218346 / 6.500664 (-6.282318) | 0.075655 / 0.075469 (0.000186) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.641767 / 1.841788 (-0.200020) | 24.618096 / 8.074308 (16.543788) | 21.557652 / 10.191392 (11.366260) | 0.211622 / 0.680424 (-0.468801) | 0.028775 / 0.534201 (-0.505426) | 0.480469 / 0.579283 (-0.098814) | 0.593311 / 0.434364 (0.158948) | 0.560620 / 0.540337 (0.020283) | 0.827026 / 1.386936 (-0.559910) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009347 / 0.011353 (-0.002006) | 0.005184 / 0.011008 (-0.005824) | 0.078878 / 0.038508 (0.040370) | 0.083067 / 0.023109 (0.059957) | 0.446591 / 0.275898 (0.170693) | 0.512934 / 0.323480 (0.189454) | 0.006614 / 0.007986 (-0.001372) | 0.004477 / 0.004328 (0.000148) | 0.087403 / 0.004250 (0.083153) | 0.060710 / 0.037052 (0.023658) | 0.451811 / 0.258489 (0.193322) | 0.482031 / 0.293841 (0.188190) | 0.051685 / 0.128546 (-0.076862) | 0.013436 / 0.075646 (-0.062210) | 0.109012 / 0.419271 (-0.310259) | 0.059654 / 0.043533 (0.016121) | 0.439041 / 0.255139 (0.183902) | 0.481708 / 0.283200 (0.198508) | 0.037393 / 0.141683 (-0.104290) | 1.761704 / 1.452155 (0.309549) | 1.946711 / 1.492716 (0.453995) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.287981 / 0.018006 (0.269975) | 0.610219 / 0.000490 (0.609729) | 0.006733 / 0.000200 (0.006533) | 0.000128 / 0.000054 (0.000074) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.038999 / 0.037411 (0.001588) | 0.100613 / 0.014526 (0.086087) | 0.126445 / 0.176557 (-0.050111) | 0.187596 / 0.737135 (-0.549540) | 0.122130 / 0.296338 (-0.174208) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.647686 / 0.215209 (0.432477) | 6.176079 / 2.077655 (4.098424) | 2.800232 / 1.504120 (1.296112) | 2.434625 / 1.541195 (0.893430) | 2.460646 / 1.468490 (0.992155) | 0.923736 / 4.584777 (-3.661041) | 5.480197 / 3.745712 (1.734485) | 4.849250 / 5.269862 (-0.420612) | 3.031576 / 4.565676 (-1.534101) | 0.102525 / 0.424275 (-0.321750) | 0.008688 / 0.007607 (0.001081) | 0.766097 / 0.226044 (0.540052) | 7.626822 / 2.268929 (5.357893) | 3.719155 / 55.444624 (-51.725469) | 2.967121 / 6.876477 (-3.909356) | 3.182464 / 2.142072 (1.040392) | 1.018315 / 4.805227 (-3.786912) | 0.211300 / 6.500664 (-6.289364) | 0.083055 / 0.075469 (0.007586) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.731619 / 1.841788 (-0.110168) | 25.315978 / 8.074308 (17.241669) | 22.736306 / 10.191392 (12.544914) | 0.270330 / 0.680424 (-0.410094) | 0.034790 / 0.534201 (-0.499411) | 0.488675 / 0.579283 (-0.090608) | 0.603426 / 0.434364 (0.169062) | 0.572547 / 0.540337 (0.032210) | 0.825719 / 1.386936 (-0.561217) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1eaa85a4ad79aa0e411218d61a8894cc14a75fa0 \"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) | 0.008992 / 0.011353 (-0.002360) | 0.005086 / 0.011008 (-0.005923) | 0.107400 / 0.038508 (0.068892) | 0.091894 / 0.023109 (0.068785) | 0.382347 / 0.275898 (0.106449) | 0.446581 / 0.323480 (0.123101) | 0.005179 / 0.007986 (-0.002807) | 0.006356 / 0.004328 (0.002028) | 0.084979 / 0.004250 (0.080729) | 0.060647 / 0.037052 (0.023594) | 0.385940 / 0.258489 (0.127451) | 0.444817 / 0.293841 (0.150976) | 0.049484 / 0.128546 (-0.079062) | 0.014173 / 0.075646 (-0.061473) | 0.345704 / 0.419271 (-0.073567) | 0.068082 / 0.043533 (0.024550) | 0.377170 / 0.255139 (0.122031) | 0.411816 / 0.283200 (0.128616) | 0.043049 / 0.141683 (-0.098633) | 1.681499 / 1.452155 (0.229344) | 1.805428 / 1.492716 (0.312712) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.323170 / 0.018006 (0.305164) | 0.693845 / 0.000490 (0.693355) | 0.015499 / 0.000200 (0.015299) | 0.000603 / 0.000054 (0.000548) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031629 / 0.037411 (-0.005783) | 0.093511 / 0.014526 (0.078985) | 0.112400 / 0.176557 (-0.064157) | 0.173731 / 0.737135 (-0.563405) | 0.116013 / 0.296338 (-0.180325) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.576724 / 0.215209 (0.361515) | 5.775055 / 2.077655 (3.697400) | 2.755869 / 1.504120 (1.251749) | 2.430253 / 1.541195 (0.889058) | 2.479629 / 1.468490 (1.011139) | 0.841472 / 4.584777 (-3.743305) | 5.120536 / 3.745712 (1.374824) | 4.813281 / 5.269862 (-0.456581) | 3.054617 / 4.565676 (-1.511059) | 0.091459 / 0.424275 (-0.332816) | 0.009072 / 0.007607 (0.001465) | 0.742674 / 0.226044 (0.516629) | 7.137861 / 2.268929 (4.868933) | 3.497568 / 55.444624 (-51.947056) | 2.814658 / 6.876477 (-4.061819) | 2.934415 / 2.142072 (0.792343) | 0.970855 / 4.805227 (-3.834372) | 0.213366 / 6.500664 (-6.287299) | 0.078763 / 0.075469 (0.003293) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.584716 / 1.841788 (-0.257072) | 24.098173 / 8.074308 (16.023865) | 20.746352 / 10.191392 (10.554960) | 0.215313 / 0.680424 (-0.465111) | 0.029538 / 0.534201 (-0.504663) | 0.448672 / 0.579283 (-0.130611) | 0.580023 / 0.434364 (0.145659) | 0.537867 / 0.540337 (-0.002471) | 0.804622 / 1.386936 (-0.582314) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008965 / 0.011353 (-0.002388) | 0.005544 / 0.011008 (-0.005464) | 0.076806 / 0.038508 (0.038298) | 0.085333 / 0.023109 (0.062224) | 0.509974 / 0.275898 (0.234076) | 0.511548 / 0.323480 (0.188068) | 0.007136 / 0.007986 (-0.000849) | 0.004491 / 0.004328 (0.000163) | 0.086687 / 0.004250 (0.082437) | 0.066539 / 0.037052 (0.029486) | 0.483663 / 0.258489 (0.225174) | 0.529480 / 0.293841 (0.235639) | 0.046296 / 0.128546 (-0.082250) | 0.014736 / 0.075646 (-0.060910) | 0.088261 / 0.419271 (-0.331010) | 0.056753 / 0.043533 (0.013220) | 0.511698 / 0.255139 (0.256559) | 0.497956 / 0.283200 (0.214756) | 0.034753 / 0.141683 (-0.106930) | 1.828354 / 1.452155 (0.376199) | 1.799211 / 1.492716 (0.306494) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.389652 / 0.018006 (0.371645) | 0.602522 / 0.000490 (0.602033) | 0.068363 / 0.000200 (0.068163) | 0.000493 / 0.000054 (0.000439) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.036431 / 0.037411 (-0.000980) | 0.102162 / 0.014526 (0.087636) | 0.122466 / 0.176557 (-0.054091) | 0.181001 / 0.737135 (-0.556134) | 0.125743 / 0.296338 (-0.170596) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.583847 / 0.215209 (0.368638) | 5.913008 / 2.077655 (3.835354) | 2.716088 / 1.504120 (1.211968) | 2.328631 / 1.541195 (0.787437) | 2.459953 / 1.468490 (0.991463) | 0.792829 / 4.584777 (-3.791948) | 5.183965 / 3.745712 (1.438253) | 4.508264 / 5.269862 (-0.761598) | 2.855444 / 4.565676 (-1.710232) | 0.090704 / 0.424275 (-0.333571) | 0.009303 / 0.007607 (0.001696) | 0.694303 / 0.226044 (0.468258) | 6.951876 / 2.268929 (4.682947) | 3.418244 / 55.444624 (-52.026381) | 2.799743 / 6.876477 (-4.076734) | 3.043657 / 2.142072 (0.901584) | 0.921537 / 4.805227 (-3.883691) | 0.191774 / 6.500664 (-6.308890) | 0.068602 / 0.075469 (-0.006867) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.624842 / 1.841788 (-0.216946) | 24.570622 / 8.074308 (16.496314) | 21.207566 / 10.191392 (11.016174) | 0.217734 / 0.680424 (-0.462689) | 0.033109 / 0.534201 (-0.501091) | 0.451651 / 0.579283 (-0.127632) | 0.590890 / 0.434364 (0.156526) | 0.546195 / 0.540337 (0.005858) | 0.730298 / 1.386936 (-0.656638) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f6bdecff73303cf97f279a4e36622faf53133f9c \"CML watermark\")\n" ]

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"2023-11-02T09:37:06Z"

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

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

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"2023-11-02T01:36:49Z"

"2023-11-02T15:18:22Z"

"2023-11-02T10:11:38Z"

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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) | 0.006709 / 0.011353 (-0.004643) | 0.004102 / 0.011008 (-0.006906) | 0.084449 / 0.038508 (0.045941) | 0.076078 / 0.023109 (0.052969) | 0.319831 / 0.275898 (0.043933) | 0.359918 / 0.323480 (0.036438) | 0.006092 / 0.007986 (-0.001894) | 0.003402 / 0.004328 (-0.000926) | 0.064715 / 0.004250 (0.060465) | 0.054541 / 0.037052 (0.017488) | 0.330394 / 0.258489 (0.071905) | 0.366048 / 0.293841 (0.072207) | 0.031594 / 0.128546 (-0.096952) | 0.008591 / 0.075646 (-0.067056) | 0.292983 / 0.419271 (-0.126288) | 0.052986 / 0.043533 (0.009453) | 0.322253 / 0.255139 (0.067114) | 0.340082 / 0.283200 (0.056882) | 0.023390 / 0.141683 (-0.118293) | 1.459038 / 1.452155 (0.006883) | 1.536256 / 1.492716 (0.043540) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.233527 / 0.018006 (0.215521) | 0.459145 / 0.000490 (0.458655) | 0.007471 / 0.000200 (0.007271) | 0.000281 / 0.000054 (0.000227) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028158 / 0.037411 (-0.009253) | 0.083079 / 0.014526 (0.068553) | 0.097159 / 0.176557 (-0.079397) | 0.151927 / 0.737135 (-0.585208) | 0.098024 / 0.296338 (-0.198314) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.386882 / 0.215209 (0.171673) | 3.849635 / 2.077655 (1.771981) | 1.832885 / 1.504120 (0.328765) | 1.668356 / 1.541195 (0.127162) | 1.745066 / 1.468490 (0.276576) | 0.484476 / 4.584777 (-4.100301) | 3.547604 / 3.745712 (-0.198108) | 3.480338 / 5.269862 (-1.789523) | 2.066837 / 4.565676 (-2.498840) | 0.056755 / 0.424275 (-0.367520) | 0.007747 / 0.007607 (0.000140) | 0.467999 / 0.226044 (0.241955) | 4.678875 / 2.268929 (2.409946) | 2.341930 / 55.444624 (-53.102695) | 1.985632 / 6.876477 (-4.890844) | 2.046998 / 2.142072 (-0.095074) | 0.579860 / 4.805227 (-4.225367) | 0.131488 / 6.500664 (-6.369176) | 0.060193 / 0.075469 (-0.015276) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.249656 / 1.841788 (-0.592132) | 19.079517 / 8.074308 (11.005209) | 14.328827 / 10.191392 (4.137435) | 0.173707 / 0.680424 (-0.506717) | 0.018250 / 0.534201 (-0.515951) | 0.392225 / 0.579283 (-0.187058) | 0.413920 / 0.434364 (-0.020444) | 0.464124 / 0.540337 (-0.076214) | 0.640283 / 1.386936 (-0.746653) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006859 / 0.011353 (-0.004494) | 0.004068 / 0.011008 (-0.006940) | 0.063936 / 0.038508 (0.025428) | 0.077187 / 0.023109 (0.054078) | 0.365098 / 0.275898 (0.089200) | 0.391003 / 0.323480 (0.067523) | 0.005571 / 0.007986 (-0.002415) | 0.003425 / 0.004328 (-0.000904) | 0.063220 / 0.004250 (0.058970) | 0.056964 / 0.037052 (0.019912) | 0.367793 / 0.258489 (0.109304) | 0.398776 / 0.293841 (0.104935) | 0.033182 / 0.128546 (-0.095364) | 0.008601 / 0.075646 (-0.067045) | 0.070276 / 0.419271 (-0.348996) | 0.048383 / 0.043533 (0.004850) | 0.360414 / 0.255139 (0.105275) | 0.368171 / 0.283200 (0.084971) | 0.023114 / 0.141683 (-0.118569) | 1.503503 / 1.452155 (0.051349) | 1.567279 / 1.492716 (0.074562) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.224296 / 0.018006 (0.206290) | 0.455138 / 0.000490 (0.454648) | 0.004014 / 0.000200 (0.003814) | 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) | 0.032337 / 0.037411 (-0.005074) | 0.094385 / 0.014526 (0.079859) | 0.109870 / 0.176557 (-0.066687) | 0.156978 / 0.737135 (-0.580157) | 0.107559 / 0.296338 (-0.188780) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.427409 / 0.215209 (0.212200) | 4.261772 / 2.077655 (2.184117) | 2.276106 / 1.504120 (0.771986) | 2.115232 / 1.541195 (0.574038) | 2.192048 / 1.468490 (0.723558) | 0.488459 / 4.584777 (-4.096318) | 3.675463 / 3.745712 (-0.070249) | 3.322475 / 5.269862 (-1.947387) | 2.072253 / 4.565676 (-2.493424) | 0.058259 / 0.424275 (-0.366017) | 0.007319 / 0.007607 (-0.000288) | 0.499513 / 0.226044 (0.273469) | 4.994774 / 2.268929 (2.725845) | 2.760927 / 55.444624 (-52.683697) | 2.391947 / 6.876477 (-4.484530) | 2.600557 / 2.142072 (0.458484) | 0.587597 / 4.805227 (-4.217630) | 0.131444 / 6.500664 (-6.369220) | 0.057334 / 0.075469 (-0.018135) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.354636 / 1.841788 (-0.487152) | 19.685735 / 8.074308 (11.611427) | 14.295920 / 10.191392 (4.104528) | 0.171921 / 0.680424 (-0.508503) | 0.019926 / 0.534201 (-0.514274) | 0.395216 / 0.579283 (-0.184068) | 0.432791 / 0.434364 (-0.001573) | 0.473055 / 0.540337 (-0.067282) | 0.638633 / 1.386936 (-0.748303) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#fad7c899ec9218a717311223aa6ef5c09a6c7885 \"CML watermark\")\n" ]

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[]

[]

"2023-11-01T17:57:11Z"

"2023-11-02T16:02:52Z"

"2023-11-02T15:52:09Z"

{ "diff_url": "https://github.com/huggingface/datasets/pull/6372.diff", "html_url": "https://github.com/huggingface/datasets/pull/6372", "merged_at": "2023-11-02T15:52:09Z", "patch_url": "https://github.com/huggingface/datasets/pull/6372.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6372" }

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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) | 0.007617 / 0.011353 (-0.003735) | 0.005371 / 0.011008 (-0.005638) | 0.092110 / 0.038508 (0.053602) | 0.070654 / 0.023109 (0.047544) | 0.362501 / 0.275898 (0.086603) | 0.412835 / 0.323480 (0.089355) | 0.006752 / 0.007986 (-0.001234) | 0.003752 / 0.004328 (-0.000576) | 0.075644 / 0.004250 (0.071394) | 0.055666 / 0.037052 (0.018614) | 0.355906 / 0.258489 (0.097417) | 0.405078 / 0.293841 (0.111237) | 0.045767 / 0.128546 (-0.082779) | 0.013778 / 0.075646 (-0.061868) | 0.324696 / 0.419271 (-0.094575) | 0.062200 / 0.043533 (0.018667) | 0.359571 / 0.255139 (0.104432) | 0.387274 / 0.283200 (0.104075) | 0.035323 / 0.141683 (-0.106360) | 1.586294 / 1.452155 (0.134139) | 1.707564 / 1.492716 (0.214847) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.303940 / 0.018006 (0.285934) | 0.583349 / 0.000490 (0.582859) | 0.014845 / 0.000200 (0.014645) | 0.000698 / 0.000054 (0.000643) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028994 / 0.037411 (-0.008417) | 0.085555 / 0.014526 (0.071029) | 0.097856 / 0.176557 (-0.078701) | 0.161480 / 0.737135 (-0.575655) | 0.098573 / 0.296338 (-0.197766) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.591294 / 0.215209 (0.376085) | 5.751350 / 2.077655 (3.673695) | 2.241620 / 1.504120 (0.737500) | 1.991083 / 1.541195 (0.449888) | 2.006711 / 1.468490 (0.538221) | 0.832339 / 4.584777 (-3.752438) | 5.213808 / 3.745712 (1.468095) | 4.650355 / 5.269862 (-0.619506) | 2.860494 / 4.565676 (-1.705182) | 0.093090 / 0.424275 (-0.331185) | 0.009740 / 0.007607 (0.002133) | 0.693509 / 0.226044 (0.467464) | 6.828735 / 2.268929 (4.559807) | 2.967763 / 55.444624 (-52.476862) | 2.311461 / 6.876477 (-4.565016) | 2.400051 / 2.142072 (0.257979) | 0.914753 / 4.805227 (-3.890474) | 0.202804 / 6.500664 (-6.297860) | 0.076905 / 0.075469 (0.001436) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.576424 / 1.841788 (-0.265363) | 22.963472 / 8.074308 (14.889164) | 19.948105 / 10.191392 (9.756713) | 0.228982 / 0.680424 (-0.451442) | 0.029038 / 0.534201 (-0.505163) | 0.477715 / 0.579283 (-0.101568) | 0.554924 / 0.434364 (0.120560) | 0.532118 / 0.540337 (-0.008219) | 0.775096 / 1.386936 (-0.611840) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009127 / 0.011353 (-0.002226) | 0.004978 / 0.011008 (-0.006030) | 0.084166 / 0.038508 (0.045658) | 0.083391 / 0.023109 (0.060282) | 0.420760 / 0.275898 (0.144862) | 0.459072 / 0.323480 (0.135592) | 0.007102 / 0.007986 (-0.000883) | 0.004175 / 0.004328 (-0.000154) | 0.082922 / 0.004250 (0.078672) | 0.059010 / 0.037052 (0.021957) | 0.416959 / 0.258489 (0.158470) | 0.472220 / 0.293841 (0.178379) | 0.049999 / 0.128546 (-0.078547) | 0.014126 / 0.075646 (-0.061520) | 0.096894 / 0.419271 (-0.322378) | 0.057920 / 0.043533 (0.014387) | 0.405779 / 0.255139 (0.150640) | 0.464286 / 0.283200 (0.181087) | 0.034957 / 0.141683 (-0.106726) | 1.637921 / 1.452155 (0.185767) | 1.768231 / 1.492716 (0.275515) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.354875 / 0.018006 (0.336868) | 0.554667 / 0.000490 (0.554177) | 0.074127 / 0.000200 (0.073927) | 0.000411 / 0.000054 (0.000357) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027681 / 0.037411 (-0.009730) | 0.087746 / 0.014526 (0.073220) | 0.093714 / 0.176557 (-0.082843) | 0.145380 / 0.737135 (-0.591755) | 0.095686 / 0.296338 (-0.200652) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.522079 / 0.215209 (0.306870) | 5.197366 / 2.077655 (3.119711) | 2.300744 / 1.504120 (0.796624) | 2.056846 / 1.541195 (0.515652) | 2.009897 / 1.468490 (0.541407) | 0.813025 / 4.584777 (-3.771751) | 5.177732 / 3.745712 (1.432020) | 4.076749 / 5.269862 (-1.193112) | 2.545588 / 4.565676 (-2.020088) | 0.083507 / 0.424275 (-0.340769) | 0.007011 / 0.007607 (-0.000596) | 0.598820 / 0.226044 (0.372776) | 6.203730 / 2.268929 (3.934801) | 2.945385 / 55.444624 (-52.499239) | 2.304849 / 6.876477 (-4.571628) | 2.599035 / 2.142072 (0.456962) | 1.002721 / 4.805227 (-3.802506) | 0.191781 / 6.500664 (-6.308883) | 0.064178 / 0.075469 (-0.011292) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.549560 / 1.841788 (-0.292228) | 22.395727 / 8.074308 (14.321418) | 20.537895 / 10.191392 (10.346503) | 0.246542 / 0.680424 (-0.433882) | 0.031673 / 0.534201 (-0.502528) | 0.442490 / 0.579283 (-0.136793) | 0.589838 / 0.434364 (0.155474) | 0.535201 / 0.540337 (-0.005136) | 0.733660 / 1.386936 (-0.653276) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e742718e504a372cce0b1f87c2cac65eb8c35792 \"CML watermark\")\n" ]

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[]

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"2023-11-01T17:36:17Z"

"2023-11-02T15:52:10Z"

"2023-11-02T15:52:10Z"

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[ "Indeed, setting `try_from_gcs` to `False` makes sense for `from_generator`.\r\n\r\nWe plan to deprecate and remove `try_from_hf_gcs` soon, as we can use Hub for file hosting now, but this is a good temporary fix.\r\n" ]

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"2023-11-01T10:09:54Z"

"2023-11-29T16:31:08Z"

"2023-11-29T16:31:08Z"

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[ "I figured it out. I found that `Trainer` does not work with TensorDataset even though the document says it uses it. Instead, I ended up creating a dictionary and converting it to a dataset using `dataset.Dataset.from_dict()`.\r\n\r\nI will leave this post open for a while. If someone knows a better approach, please leave a comment.", "Only issues directly related to the HF datasets library should be reported here. ~So, I'm transferring this issue to the `transformers` repo.~ I'm not a `transformers` maintainer, so GitHub doesn't let me transfer it there :(. This means you need to do it manually." ]

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"2023-11-01T06:36:54Z"

"2023-11-30T16:04:46Z"

"2023-11-30T16:04:45Z"

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[ "The inconsistency may be caused by the usage of \"update_fingerprint\" and setting \"trust_remote_code\" to \"True.\"\r\nWhen the tokenizer employs \"trust_remote_code,\" the behavior of the map function varies with each code execution. Even if the remote code of the tokenizer remains the same, the result of \"asher.hexdigest()\" is found to be inconsistent each time.\r\nThis may result in different processes executing multiple maps\r\n\r\n\r\n\r\n", "The issue may be related to problems previously discussed in GitHub issues [#3847](https://github.com/huggingface/datasets/issues/3847) and [#6318](https://github.com/huggingface/datasets/pull/6318). \r\nThis arises from the fact that tokenizer.tokens_trie._tokens is an unordered set, leading to varying hash results:\r\n`value = hash_bytes(dumps(tokenizer.tokens_trie._tokens))`\r\nConsequently, this results in different outcomes each time for:\r\n`new_fingerprint = update_fingerprint(datasets._fingerprint, transform, kwargs_for_fingerprint)`\r\n\r\nTo address this issue, it's essential to make `Trie._tokens` a deterministic set while ensuring a consistent order after the final update of `_tokens`.\r\n", "We now sort `set` and `dict` items to make their hashes deterministic (install from `main` with `pip install git+https://github.com/huggingface/datasets` to test this). Consequently, this should also make the `tokenizer.tokens_trie`'s hash deterministic. Feel free to re-open the issue if this is not the case." ]

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"2023-10-31T19:48:08Z"

"2023-11-02T14:42:28Z"

"2023-11-02T14:21:16Z"

{ "diff_url": "https://github.com/huggingface/datasets/pull/6368.diff", "html_url": "https://github.com/huggingface/datasets/pull/6368", "merged_at": "2023-11-02T14:21:16Z", "patch_url": "https://github.com/huggingface/datasets/pull/6368.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6368" }

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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) | 0.008047 / 0.011353 (-0.003305) | 0.004649 / 0.011008 (-0.006359) | 0.100275 / 0.038508 (0.061767) | 0.089551 / 0.023109 (0.066442) | 0.369831 / 0.275898 (0.093933) | 0.431023 / 0.323480 (0.107544) | 0.004721 / 0.007986 (-0.003265) | 0.004904 / 0.004328 (0.000575) | 0.076345 / 0.004250 (0.072095) | 0.066902 / 0.037052 (0.029849) | 0.377208 / 0.258489 (0.118718) | 0.430989 / 0.293841 (0.137148) | 0.036260 / 0.128546 (-0.092287) | 0.010158 / 0.075646 (-0.065488) | 0.344923 / 0.419271 (-0.074349) | 0.062504 / 0.043533 (0.018971) | 0.373038 / 0.255139 (0.117899) | 0.399918 / 0.283200 (0.116718) | 0.028257 / 0.141683 (-0.113425) | 1.782546 / 1.452155 (0.330391) | 1.920010 / 1.492716 (0.427293) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.277670 / 0.018006 (0.259664) | 0.500543 / 0.000490 (0.500053) | 0.018256 / 0.000200 (0.018056) | 0.000343 / 0.000054 (0.000289) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033337 / 0.037411 (-0.004074) | 0.100542 / 0.014526 (0.086017) | 0.114903 / 0.176557 (-0.061654) | 0.181267 / 0.737135 (-0.555868) | 0.115019 / 0.296338 (-0.181320) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.457333 / 0.215209 (0.242124) | 4.542082 / 2.077655 (2.464427) | 2.231817 / 1.504120 (0.727697) | 2.028523 / 1.541195 (0.487328) | 2.110715 / 1.468490 (0.642225) | 0.583162 / 4.584777 (-4.001615) | 4.179413 / 3.745712 (0.433701) | 4.145620 / 5.269862 (-1.124241) | 2.452458 / 4.565676 (-2.113218) | 0.068229 / 0.424275 (-0.356046) | 0.009027 / 0.007607 (0.001420) | 0.549002 / 0.226044 (0.322957) | 5.485707 / 2.268929 (3.216779) | 2.789467 / 55.444624 (-52.655157) | 2.397499 / 6.876477 (-4.478977) | 2.492083 / 2.142072 (0.350010) | 0.692445 / 4.805227 (-4.112782) | 0.160527 / 6.500664 (-6.340137) | 0.071597 / 0.075469 (-0.003872) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.486043 / 1.841788 (-0.355744) | 22.377207 / 8.074308 (14.302899) | 16.443719 / 10.191392 (6.252327) | 0.170740 / 0.680424 (-0.509684) | 0.021511 / 0.534201 (-0.512690) | 0.470798 / 0.579283 (-0.108485) | 0.511851 / 0.434364 (0.077487) | 0.551154 / 0.540337 (0.010817) | 0.768420 / 1.386936 (-0.618516) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008049 / 0.011353 (-0.003303) | 0.004676 / 0.011008 (-0.006332) | 0.076360 / 0.038508 (0.037852) | 0.093648 / 0.023109 (0.070539) | 0.480597 / 0.275898 (0.204699) | 0.524674 / 0.323480 (0.201194) | 0.006242 / 0.007986 (-0.001744) | 0.003827 / 0.004328 (-0.000501) | 0.077039 / 0.004250 (0.072788) | 0.067992 / 0.037052 (0.030940) | 0.480287 / 0.258489 (0.221798) | 0.528546 / 0.293841 (0.234706) | 0.038347 / 0.128546 (-0.090199) | 0.010036 / 0.075646 (-0.065611) | 0.084386 / 0.419271 (-0.334885) | 0.057211 / 0.043533 (0.013678) | 0.475993 / 0.255139 (0.220854) | 0.504881 / 0.283200 (0.221682) | 0.026658 / 0.141683 (-0.115025) | 1.777095 / 1.452155 (0.324940) | 1.896446 / 1.492716 (0.403730) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.242450 / 0.018006 (0.224443) | 0.488864 / 0.000490 (0.488374) | 0.007329 / 0.000200 (0.007129) | 0.000108 / 0.000054 (0.000053) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.039093 / 0.037411 (0.001682) | 0.114724 / 0.014526 (0.100198) | 0.124965 / 0.176557 (-0.051591) | 0.188165 / 0.737135 (-0.548971) | 0.125336 / 0.296338 (-0.171002) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.515718 / 0.215209 (0.300509) | 5.150865 / 2.077655 (3.073210) | 2.767866 / 1.504120 (1.263746) | 2.571003 / 1.541195 (1.029808) | 2.656224 / 1.468490 (1.187734) | 0.583771 / 4.584777 (-4.001006) | 4.268713 / 3.745712 (0.523001) | 3.938699 / 5.269862 (-1.331163) | 2.413569 / 4.565676 (-2.152108) | 0.068848 / 0.424275 (-0.355427) | 0.008758 / 0.007607 (0.001151) | 0.610831 / 0.226044 (0.384786) | 6.099965 / 2.268929 (3.831037) | 3.337530 / 55.444624 (-52.107095) | 2.910962 / 6.876477 (-3.965514) | 3.149813 / 2.142072 (1.007740) | 0.700576 / 4.805227 (-4.104651) | 0.157569 / 6.500664 (-6.343095) | 0.072237 / 0.075469 (-0.003232) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.655840 / 1.841788 (-0.185947) | 23.639061 / 8.074308 (15.564753) | 17.301593 / 10.191392 (7.110201) | 0.201717 / 0.680424 (-0.478707) | 0.023836 / 0.534201 (-0.510365) | 0.470941 / 0.579283 (-0.108342) | 0.498157 / 0.434364 (0.063794) | 0.581195 / 0.540337 (0.040857) | 0.788304 / 1.386936 (-0.598632) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#f657900acfd8ea1afaf47267e552a7ad2c6ef28b \"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) | 0.004823 / 0.011353 (-0.006530) | 0.002976 / 0.011008 (-0.008032) | 0.062070 / 0.038508 (0.023562) | 0.051623 / 0.023109 (0.028513) | 0.242249 / 0.275898 (-0.033649) | 0.271223 / 0.323480 (-0.052257) | 0.003906 / 0.007986 (-0.004079) | 0.002709 / 0.004328 (-0.001620) | 0.047874 / 0.004250 (0.043624) | 0.038123 / 0.037052 (0.001071) | 0.253737 / 0.258489 (-0.004752) | 0.281942 / 0.293841 (-0.011899) | 0.023750 / 0.128546 (-0.104797) | 0.007227 / 0.075646 (-0.068420) | 0.203137 / 0.419271 (-0.216134) | 0.036254 / 0.043533 (-0.007278) | 0.243923 / 0.255139 (-0.011216) | 0.263908 / 0.283200 (-0.019291) | 0.017795 / 0.141683 (-0.123888) | 1.105680 / 1.452155 (-0.346475) | 1.166804 / 1.492716 (-0.325912) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.097388 / 0.018006 (0.079381) | 0.305481 / 0.000490 (0.304991) | 0.000210 / 0.000200 (0.000010) | 0.000043 / 0.000054 (-0.000012) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.020096 / 0.037411 (-0.017315) | 0.063990 / 0.014526 (0.049464) | 0.073694 / 0.176557 (-0.102863) | 0.122909 / 0.737135 (-0.614227) | 0.076199 / 0.296338 (-0.220140) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.285612 / 0.215209 (0.070403) | 2.770524 / 2.077655 (0.692869) | 1.451624 / 1.504120 (-0.052496) | 1.329223 / 1.541195 (-0.211972) | 1.369980 / 1.468490 (-0.098510) | 0.398269 / 4.584777 (-4.186507) | 2.418740 / 3.745712 (-1.326972) | 2.796384 / 5.269862 (-2.473478) | 1.686490 / 4.565676 (-2.879186) | 0.046417 / 0.424275 (-0.377858) | 0.005414 / 0.007607 (-0.002193) | 0.345505 / 0.226044 (0.119460) | 3.391857 / 2.268929 (1.122929) | 1.856696 / 55.444624 (-53.587929) | 1.538061 / 6.876477 (-5.338416) | 1.631489 / 2.142072 (-0.510584) | 0.479188 / 4.805227 (-4.326039) | 0.101549 / 6.500664 (-6.399116) | 0.042150 / 0.075469 (-0.033319) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.957961 / 1.841788 (-0.883827) | 12.349371 / 8.074308 (4.275063) | 10.778214 / 10.191392 (0.586822) | 0.141265 / 0.680424 (-0.539158) | 0.014559 / 0.534201 (-0.519642) | 0.272071 / 0.579283 (-0.307212) | 0.262493 / 0.434364 (-0.171871) | 0.310351 / 0.540337 (-0.229986) | 0.399220 / 1.386936 (-0.987716) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005127 / 0.011353 (-0.006226) | 0.002926 / 0.011008 (-0.008082) | 0.048320 / 0.038508 (0.009812) | 0.063082 / 0.023109 (0.039973) | 0.269846 / 0.275898 (-0.006052) | 0.294470 / 0.323480 (-0.029010) | 0.004201 / 0.007986 (-0.003784) | 0.002434 / 0.004328 (-0.001894) | 0.048020 / 0.004250 (0.043770) | 0.043909 / 0.037052 (0.006856) | 0.271328 / 0.258489 (0.012839) | 0.298820 / 0.293841 (0.004979) | 0.024565 / 0.128546 (-0.103981) | 0.007752 / 0.075646 (-0.067894) | 0.054171 / 0.419271 (-0.365101) | 0.033147 / 0.043533 (-0.010386) | 0.266628 / 0.255139 (0.011489) | 0.288651 / 0.283200 (0.005452) | 0.018910 / 0.141683 (-0.122773) | 1.153679 / 1.452155 (-0.298476) | 1.214979 / 1.492716 (-0.277737) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.097064 / 0.018006 (0.079057) | 0.307504 / 0.000490 (0.307014) | 0.000230 / 0.000200 (0.000030) | 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) | 0.021848 / 0.037411 (-0.015563) | 0.071159 / 0.014526 (0.056633) | 0.081310 / 0.176557 (-0.095247) | 0.120175 / 0.737135 (-0.616961) | 0.082619 / 0.296338 (-0.213720) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.296606 / 0.215209 (0.081397) | 2.908495 / 2.077655 (0.830840) | 1.606522 / 1.504120 (0.102402) | 1.528599 / 1.541195 (-0.012596) | 1.508332 / 1.468490 (0.039842) | 0.396336 / 4.584777 (-4.188441) | 2.449163 / 3.745712 (-1.296549) | 2.533372 / 5.269862 (-2.736490) | 1.623061 / 4.565676 (-2.942615) | 0.046723 / 0.424275 (-0.377552) | 0.005120 / 0.007607 (-0.002487) | 0.345763 / 0.226044 (0.119718) | 3.427382 / 2.268929 (1.158454) | 1.962806 / 55.444624 (-53.481819) | 1.678548 / 6.876477 (-5.197929) | 1.865773 / 2.142072 (-0.276300) | 0.477932 / 4.805227 (-4.327295) | 0.100994 / 6.500664 (-6.399670) | 0.042212 / 0.075469 (-0.033258) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.992766 / 1.841788 (-0.849022) | 12.764885 / 8.074308 (4.690577) | 10.892094 / 10.191392 (0.700702) | 0.143211 / 0.680424 (-0.537213) | 0.016347 / 0.534201 (-0.517853) | 0.270181 / 0.579283 (-0.309102) | 0.278658 / 0.434364 (-0.155706) | 0.307134 / 0.540337 (-0.233203) | 0.396792 / 1.386936 (-0.990144) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#6d2f2a5e0fea3827eccfd1717d8021c15fc4292a \"CML watermark\")\n", "Thanks for the fix ! It was probably my mistake (forgot to re-apply the features)" ]

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[]

[]

"2023-10-31T17:57:17Z"

"2023-10-31T18:35:53Z"

"2023-10-31T18:24:02Z"

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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) | 0.007683 / 0.011353 (-0.003670) | 0.004159 / 0.011008 (-0.006849) | 0.097017 / 0.038508 (0.058509) | 0.074216 / 0.023109 (0.051107) | 0.323115 / 0.275898 (0.047217) | 0.412836 / 0.323480 (0.089356) | 0.005151 / 0.007986 (-0.002834) | 0.004037 / 0.004328 (-0.000292) | 0.067881 / 0.004250 (0.063631) | 0.051395 / 0.037052 (0.014342) | 0.356391 / 0.258489 (0.097901) | 0.386744 / 0.293841 (0.092903) | 0.043571 / 0.128546 (-0.084975) | 0.012844 / 0.075646 (-0.062803) | 0.369440 / 0.419271 (-0.049832) | 0.056944 / 0.043533 (0.013411) | 0.316159 / 0.255139 (0.061020) | 0.435530 / 0.283200 (0.152330) | 0.033622 / 0.141683 (-0.108061) | 1.379602 / 1.452155 (-0.072553) | 1.766400 / 1.492716 (0.273683) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.304151 / 0.018006 (0.286145) | 0.616365 / 0.000490 (0.615875) | 0.013588 / 0.000200 (0.013389) | 0.000441 / 0.000054 (0.000387) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032812 / 0.037411 (-0.004600) | 0.100914 / 0.014526 (0.086388) | 0.124004 / 0.176557 (-0.052552) | 0.195087 / 0.737135 (-0.542048) | 0.124388 / 0.296338 (-0.171951) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.575649 / 0.215209 (0.360440) | 5.665461 / 2.077655 (3.587806) | 2.474892 / 1.504120 (0.970773) | 2.142687 / 1.541195 (0.601492) | 2.254962 / 1.468490 (0.786472) | 0.816635 / 4.584777 (-3.768141) | 5.044279 / 3.745712 (1.298567) | 4.566728 / 5.269862 (-0.703134) | 2.867146 / 4.565676 (-1.698531) | 0.092994 / 0.424275 (-0.331281) | 0.008395 / 0.007607 (0.000788) | 0.680346 / 0.226044 (0.454302) | 6.909875 / 2.268929 (4.640946) | 3.275602 / 55.444624 (-52.169022) | 2.556000 / 6.876477 (-4.320477) | 2.581337 / 2.142072 (0.439264) | 0.997883 / 4.805227 (-3.807344) | 0.204109 / 6.500664 (-6.296555) | 0.069705 / 0.075469 (-0.005764) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.504573 / 1.841788 (-0.337215) | 22.219363 / 8.074308 (14.145055) | 19.078040 / 10.191392 (8.886648) | 0.234970 / 0.680424 (-0.445454) | 0.027324 / 0.534201 (-0.506877) | 0.427960 / 0.579283 (-0.151323) | 0.570258 / 0.434364 (0.135894) | 0.502335 / 0.540337 (-0.038003) | 0.788078 / 1.386936 (-0.598858) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated 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 (-0.002982) | 0.004573 / 0.011008 (-0.006435) | 0.073080 / 0.038508 (0.034572) | 0.068752 / 0.023109 (0.045643) | 0.439648 / 0.275898 (0.163750) | 0.499700 / 0.323480 (0.176220) | 0.006119 / 0.007986 (-0.001866) | 0.004300 / 0.004328 (-0.000028) | 0.073173 / 0.004250 (0.068923) | 0.055676 / 0.037052 (0.018624) | 0.464152 / 0.258489 (0.205663) | 0.476954 / 0.293841 (0.183113) | 0.046335 / 0.128546 (-0.082211) | 0.013373 / 0.075646 (-0.062274) | 0.092006 / 0.419271 (-0.327265) | 0.054802 / 0.043533 (0.011269) | 0.456594 / 0.255139 (0.201455) | 0.491931 / 0.283200 (0.208732) | 0.034021 / 0.141683 (-0.107662) | 1.575200 / 1.452155 (0.123045) | 1.689742 / 1.492716 (0.197026) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.299432 / 0.018006 (0.281426) | 0.605643 / 0.000490 (0.605153) | 0.006280 / 0.000200 (0.006080) | 0.000120 / 0.000054 (0.000066) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028414 / 0.037411 (-0.008997) | 0.085812 / 0.014526 (0.071286) | 0.109142 / 0.176557 (-0.067414) | 0.163458 / 0.737135 (-0.573677) | 0.100837 / 0.296338 (-0.195501) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.615557 / 0.215209 (0.400348) | 6.051599 / 2.077655 (3.973944) | 2.872353 / 1.504120 (1.368234) | 2.508322 / 1.541195 (0.967128) | 2.550073 / 1.468490 (1.081583) | 0.835793 / 4.584777 (-3.748983) | 5.208484 / 3.745712 (1.462772) | 4.361846 / 5.269862 (-0.908016) | 2.776164 / 4.565676 (-1.789513) | 0.090831 / 0.424275 (-0.333444) | 0.007320 / 0.007607 (-0.000287) | 0.725533 / 0.226044 (0.499488) | 7.051321 / 2.268929 (4.782393) | 3.515464 / 55.444624 (-51.929160) | 2.798193 / 6.876477 (-4.078284) | 3.022512 / 2.142072 (0.880440) | 0.986744 / 4.805227 (-3.818484) | 0.198050 / 6.500664 (-6.302615) | 0.069200 / 0.075469 (-0.006269) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.623759 / 1.841788 (-0.218029) | 22.269700 / 8.074308 (14.195392) | 19.577429 / 10.191392 (9.386037) | 0.215990 / 0.680424 (-0.464434) | 0.033005 / 0.534201 (-0.501196) | 0.436848 / 0.579283 (-0.142435) | 0.591442 / 0.434364 (0.157078) | 0.547701 / 0.540337 (0.007364) | 0.741695 / 1.386936 (-0.645241) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7e17e139b1323aca3321a5d2c2da40d82c458bae \"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) | 0.009027 / 0.011353 (-0.002326) | 0.006118 / 0.011008 (-0.004890) | 0.118939 / 0.038508 (0.080431) | 0.089979 / 0.023109 (0.066869) | 0.412425 / 0.275898 (0.136527) | 0.455706 / 0.323480 (0.132227) | 0.006762 / 0.007986 (-0.001224) | 0.004409 / 0.004328 (0.000080) | 0.088002 / 0.004250 (0.083751) | 0.063708 / 0.037052 (0.026656) | 0.417373 / 0.258489 (0.158884) | 0.489582 / 0.293841 (0.195741) | 0.050222 / 0.128546 (-0.078324) | 0.014386 / 0.075646 (-0.061260) | 0.435363 / 0.419271 (0.016092) | 0.069375 / 0.043533 (0.025842) | 0.410242 / 0.255139 (0.155103) | 0.436439 / 0.283200 (0.153239) | 0.039318 / 0.141683 (-0.102365) | 1.857574 / 1.452155 (0.405419) | 1.919402 / 1.492716 (0.426686) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.343916 / 0.018006 (0.325910) | 0.633639 / 0.000490 (0.633150) | 0.014756 / 0.000200 (0.014557) | 0.000707 / 0.000054 (0.000652) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031983 / 0.037411 (-0.005429) | 0.097222 / 0.014526 (0.082697) | 0.114644 / 0.176557 (-0.061912) | 0.187787 / 0.737135 (-0.549348) | 0.120595 / 0.296338 (-0.175743) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.605861 / 0.215209 (0.390652) | 6.039318 / 2.077655 (3.961664) | 2.699251 / 1.504120 (1.195132) | 2.436398 / 1.541195 (0.895203) | 2.493653 / 1.468490 (1.025163) | 0.889423 / 4.584777 (-3.695354) | 5.384769 / 3.745712 (1.639056) | 5.033033 / 5.269862 (-0.236829) | 3.056894 / 4.565676 (-1.508783) | 0.100683 / 0.424275 (-0.323592) | 0.009103 / 0.007607 (0.001495) | 0.737066 / 0.226044 (0.511021) | 7.370485 / 2.268929 (5.101556) | 3.422670 / 55.444624 (-52.021954) | 2.830392 / 6.876477 (-4.046084) | 2.985789 / 2.142072 (0.843717) | 0.999239 / 4.805227 (-3.805989) | 0.203506 / 6.500664 (-6.297158) | 0.076135 / 0.075469 (0.000666) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.697001 / 1.841788 (-0.144787) | 24.653975 / 8.074308 (16.579667) | 22.241622 / 10.191392 (12.050230) | 0.257075 / 0.680424 (-0.423349) | 0.029159 / 0.534201 (-0.505041) | 0.493329 / 0.579283 (-0.085954) | 0.596661 / 0.434364 (0.162297) | 0.569431 / 0.540337 (0.029094) | 0.812231 / 1.386936 (-0.574705) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009815 / 0.011353 (-0.001538) | 0.005136 / 0.011008 (-0.005872) | 0.078224 / 0.038508 (0.039716) | 0.103276 / 0.023109 (0.080166) | 0.512742 / 0.275898 (0.236844) | 0.544010 / 0.323480 (0.220530) | 0.007957 / 0.007986 (-0.000029) | 0.004629 / 0.004328 (0.000300) | 0.074983 / 0.004250 (0.070733) | 0.071831 / 0.037052 (0.034778) | 0.542752 / 0.258489 (0.284262) | 0.573176 / 0.293841 (0.279335) | 0.053939 / 0.128546 (-0.074607) | 0.015007 / 0.075646 (-0.060640) | 0.085389 / 0.419271 (-0.333882) | 0.063587 / 0.043533 (0.020055) | 0.509580 / 0.255139 (0.254441) | 0.563374 / 0.283200 (0.280174) | 0.037575 / 0.141683 (-0.104108) | 1.840740 / 1.452155 (0.388585) | 1.836414 / 1.492716 (0.343698) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.310188 / 0.018006 (0.292182) | 0.641478 / 0.000490 (0.640988) | 0.011057 / 0.000200 (0.010857) | 0.000173 / 0.000054 (0.000119) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.043280 / 0.037411 (0.005869) | 0.109256 / 0.014526 (0.094730) | 0.126701 / 0.176557 (-0.049856) | 0.199172 / 0.737135 (-0.537963) | 0.123584 / 0.296338 (-0.172755) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.649272 / 0.215209 (0.434063) | 6.487501 / 2.077655 (4.409846) | 3.170330 / 1.504120 (1.666210) | 2.960912 / 1.541195 (1.419718) | 3.024531 / 1.468490 (1.556041) | 0.905112 / 4.584777 (-3.679665) | 5.560961 / 3.745712 (1.815249) | 4.920463 / 5.269862 (-0.349399) | 3.158989 / 4.565676 (-1.406687) | 0.095444 / 0.424275 (-0.328831) | 0.008264 / 0.007607 (0.000657) | 0.819292 / 0.226044 (0.593247) | 7.982695 / 2.268929 (5.713767) | 4.098704 / 55.444624 (-51.345921) | 3.442330 / 6.876477 (-3.434147) | 3.763426 / 2.142072 (1.621354) | 1.065464 / 4.805227 (-3.739763) | 0.215089 / 6.500664 (-6.285575) | 0.085280 / 0.075469 (0.009811) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.881770 / 1.841788 (0.039983) | 25.671479 / 8.074308 (17.597171) | 22.367019 / 10.191392 (12.175627) | 0.241377 / 0.680424 (-0.439047) | 0.033555 / 0.534201 (-0.500646) | 0.501786 / 0.579283 (-0.077497) | 0.596376 / 0.434364 (0.162012) | 0.579674 / 0.540337 (0.039337) | 0.855534 / 1.386936 (-0.531402) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c9c1166e1cf81d38534020f9c167b326585339e5 \"CML watermark\")\n" ]

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"2023-10-31T11:10:48Z"

"2023-11-02T14:21:17Z"

"2023-11-02T14:21:17Z"

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

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[]

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"2023-10-31T10:29:02Z"

"2023-10-31T10:49:17Z"

"2023-10-31T10:49:17Z"

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[ "Wrong repo." ]

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[]

[]

"2023-10-30T20:14:01Z"

"2023-10-31T19:21:23Z"

"2023-10-31T19:21:23Z"

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[ "You can use the following code to load this CSV with the list values preserved:\r\n```python\r\nfrom datasets import load_dataset\r\nimport ast\r\n\r\nconverters = {\r\n \"contexts\" : ast.literal_eval,\r\n \"ground_truths\" : ast.literal_eval,\r\n}\r\n\r\nds = load_dataset(\"csv\", data_files=\"golden_dataset.csv\", converters=converters)\r\n```", "Thank you! it worked :)" ]

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[]

[]

"2023-10-30T17:34:05Z"

"2023-11-22T00:29:21Z"

"2023-11-22T00:29:21Z"

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[ "I think the code hangs on the `accelerator.main_process_first()` context manager exit. To verify this, you can append a print statement to the end of the `accelerator.main_process_first()` block. \r\n\r\n\r\nIf the problem is in `with_transform`, it would help if you could share the error stack trace printed when you interrupt the process (while it hangs)", "@bhosalems Were you able to fix that ? I face this issue as well", "@matankley No I am not able to resolve this issue yet.", "@mariosasko yes the problem seems to be to exit from accelerator.main_process_first(). Is there any known problem?", "NCCL debug info I get below output, if it helps.\r\n```\r\n11/09/2023 13:36:44 - INFO - __main__ - Distributed environment: MULTI_GPU Backend: nccl\r\nNum processes: 2\r\nProcess index: 1\r\nLocal process index: 1\r\nDevice: cuda:1\r\n\r\nMixed precision type: fp16\r\n\r\nDetected kernel version 5.4.0, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher.\r\n11/09/2023 13:36:44 - INFO - __main__ - Distributed environment: MULTI_GPU Backend: nccl\r\nNum processes: 2\r\nProcess index: 0\r\nLocal process index: 0\r\nDevice: cuda:0\r\n\r\nMixed precision type: fp16\r\n\r\n{'timestep_spacing', 'thresholding', 'variance_type', 'clip_sample_range', 'prediction_type', 'dynamic_thresholding_ratio', 'sample_max_value'} was not found in config. Values will be initialized to default values.\r\n{'norm_num_groups', 'force_upcast'} was not found in config. Values will be initialized to default values.\r\n{'num_attention_heads', 'projection_class_embeddings_input_dim', 'addition_embed_type_num_heads', 'mid_block_only_cross_attention', 'addition_embed_type', 'num_class_embeds', 'upcast_attention', 'cross_attention_norm', 'addition_time_embed_dim', 'time_embedding_dim', 'class_embeddings_concat', 'encoder_hid_dim', 'encoder_hid_dim_type', 'resnet_out_scale_factor', 'attention_type', 'conv_out_kernel', 'only_cross_attention', 'resnet_time_scale_shift', 'resnet_skip_time_act', 'reverse_transformer_layers_per_block', 'conv_in_kernel', 'time_cond_proj_dim', 'use_linear_projection', 'mid_block_type', 'time_embedding_act_fn', 'dropout', 'timestep_post_act', 'dual_cross_attention', 'class_embed_type', 'transformer_layers_per_block', 'time_embedding_type'} was not found in config. Values will be initialized to default values.\r\n{'num_attention_heads', 'projection_class_embeddings_input_dim', 'addition_embed_type_num_heads', 'mid_block_only_cross_attention', 'addition_embed_type', 'num_class_embeds', 'upcast_attention', 'cross_attention_norm', 'addition_time_embed_dim', 'time_embedding_dim', 'class_embeddings_concat', 'encoder_hid_dim', 'encoder_hid_dim_type', 'resnet_out_scale_factor', 'attention_type', 'conv_out_kernel', 'only_cross_attention', 'resnet_time_scale_shift', 'resnet_skip_time_act', 'reverse_transformer_layers_per_block', 'conv_in_kernel', 'time_cond_proj_dim', 'use_linear_projection', 'mid_block_type', 'time_embedding_act_fn', 'dropout', 'timestep_post_act', 'dual_cross_attention', 'class_embed_type', 'transformer_layers_per_block', 'time_embedding_type'} was not found in config. Values will be initialized to default values.\r\ndeepbull5:1311249:1311249 [0] NCCL INFO Bootstrap : Using enp194s0f0:128.205.43.171<0>\r\ndeepbull5:1311249:1311249 [0] NCCL INFO NET/Plugin : No plugin found (libnccl-net.so), using internal implementation\r\ndeepbull5:1311249:1311249 [0] NCCL INFO cudaDriverVersion 11070\r\nNCCL version 2.14.3+cuda11.7\r\ndeepbull5:1311250:1311250 [1] NCCL INFO cudaDriverVersion 11070\r\ndeepbull5:1311249:1311365 [0] NCCL INFO NET/IB : No device found.\r\ndeepbull5:1311249:1311365 [0] NCCL INFO NET/Socket : Using [0]enp194s0f0:128.205.43.171<0>\r\ndeepbull5:1311249:1311365 [0] NCCL INFO Using network Socket\r\ndeepbull5:1311250:1311250 [1] NCCL INFO Bootstrap : Using enp194s0f0:128.205.43.171<0>\r\ndeepbull5:1311250:1311250 [1] NCCL INFO NET/Plugin : No plugin found (libnccl-net.so), using internal implementation\r\ndeepbull5:1311250:1311366 [1] NCCL INFO NET/IB : No device found.\r\ndeepbull5:1311250:1311366 [1] NCCL INFO NET/Socket : Using [0]enp194s0f0:128.205.43.171<0>\r\ndeepbull5:1311250:1311366 [1] NCCL INFO Using network Socket\r\ndeepbull5:1311250:1311366 [1] NCCL INFO Setting affinity for GPU 1 to ff,ffff0000,00ffffff\r\ndeepbull5:1311249:1311365 [0] NCCL INFO Setting affinity for GPU 0 to ff,ffff0000,00ffffff\r\ndeepbull5:1311249:1311365 [0] NCCL INFO Channel 00/04 : 0 1\r\ndeepbull5:1311250:1311366 [1] NCCL INFO Trees [0] -1/-1/-1->1->0 [1] 0/-1/-1->1->-1 [2] -1/-1/-1->1->0 [3] 0/-1/-1->1->-1\r\ndeepbull5:1311249:1311365 [0] NCCL INFO Channel 01/04 : 0 1\r\ndeepbull5:1311249:1311365 [0] NCCL INFO Channel 02/04 : 0 1\r\ndeepbull5:1311249:1311365 [0] NCCL INFO Channel 03/04 : 0 1\r\ndeepbull5:1311249:1311365 [0] NCCL INFO Trees [0] 1/-1/-1->0->-1 [1] -1/-1/-1->0->1 [2] 1/-1/-1->0->-1 [3] -1/-1/-1->0->1\r\ndeepbull5:1311249:1311365 [0] NCCL INFO Channel 00/0 : 0[1000] -> 1[24000] via P2P/IPC\r\ndeepbull5:1311250:1311366 [1] NCCL INFO Channel 00/0 : 1[24000] -> 0[1000] via P2P/IPC\r\ndeepbull5:1311249:1311365 [0] NCCL INFO Channel 01/0 : 0[1000] -> 1[24000] via P2P/IPC\r\ndeepbull5:1311250:1311366 [1] NCCL INFO Channel 01/0 : 1[24000] -> 0[1000] via P2P/IPC\r\ndeepbull5:1311250:1311366 [1] NCCL INFO Channel 02/0 : 1[24000] -> 0[1000] via P2P/IPC\r\ndeepbull5:1311249:1311365 [0] NCCL INFO Channel 02/0 : 0[1000] -> 1[24000] via P2P/IPC\r\ndeepbull5:1311250:1311366 [1] NCCL INFO Channel 03/0 : 1[24000] -> 0[1000] via P2P/IPC\r\ndeepbull5:1311249:1311365 [0] NCCL INFO Channel 03/0 : 0[1000] -> 1[24000] via P2P/IPC\r\ndeepbull5:1311249:1311365 [0] NCCL INFO Connected all rings\r\ndeepbull5:1311249:1311365 [0] NCCL INFO Connected all trees\r\ndeepbull5:1311249:1311365 [0] NCCL INFO threadThresholds 8/8/64 | 16/8/64 | 512 | 512\r\ndeepbull5:1311249:1311365 [0] NCCL INFO 4 coll channels, 4 p2p channels, 2 p2p channels per peer\r\ndeepbull5:1311250:1311366 [1] NCCL INFO Connected all rings\r\ndeepbull5:1311250:1311366 [1] NCCL INFO Connected all trees\r\ndeepbull5:1311250:1311366 [1] NCCL INFO threadThresholds 8/8/64 | 16/8/64 | 512 | 512\r\ndeepbull5:1311250:1311366 [1] NCCL INFO 4 coll channels, 4 p2p channels, 2 p2p channels per peer\r\ndeepbull5:1311249:1311365 [0] NCCL INFO comm 0x88a84ee0 rank 0 nranks 2 cudaDev 0 busId 1000 - Init COMPLETE\r\ndeepbull5:1311250:1311366 [1] NCCL INFO comm 0x89a42f60 rank 1 nranks 2 cudaDev 1 busId 24000 - Init COMPLETE\r\n\r\n```", "Maybe @muellerzr can help as an `accelerate` maintainer.", "I don't know what the issue was, but after going through the thread here I loved the issue with https://github.com/huggingface/accelerate/issues/314#issuecomment-1565259831" ]

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[]

[]

"2023-10-27T15:54:18Z"

"2023-11-15T14:08:29Z"

"2023-11-15T14:02:02Z"

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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) | 0.008852 / 0.011353 (-0.002501) | 0.004613 / 0.011008 (-0.006396) | 0.096153 / 0.038508 (0.057645) | 0.074945 / 0.023109 (0.051836) | 0.365960 / 0.275898 (0.090062) | 0.385450 / 0.323480 (0.061970) | 0.004757 / 0.007986 (-0.003229) | 0.003453 / 0.004328 (-0.000876) | 0.069944 / 0.004250 (0.065693) | 0.057781 / 0.037052 (0.020729) | 0.361056 / 0.258489 (0.102567) | 0.409218 / 0.293841 (0.115377) | 0.045714 / 0.128546 (-0.082833) | 0.013776 / 0.075646 (-0.061871) | 0.328797 / 0.419271 (-0.090474) | 0.063431 / 0.043533 (0.019899) | 0.370799 / 0.255139 (0.115660) | 0.370701 / 0.283200 (0.087502) | 0.034894 / 0.141683 (-0.106789) | 1.730290 / 1.452155 (0.278136) | 1.863600 / 1.492716 (0.370883) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.245571 / 0.018006 (0.227565) | 0.509666 / 0.000490 (0.509176) | 0.008051 / 0.000200 (0.007851) | 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) | 0.027854 / 0.037411 (-0.009557) | 0.090735 / 0.014526 (0.076209) | 0.100100 / 0.176557 (-0.076457) | 0.158267 / 0.737135 (-0.578868) | 0.107537 / 0.296338 (-0.188801) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.565455 / 0.215209 (0.350246) | 5.671436 / 2.077655 (3.593781) | 2.438078 / 1.504120 (0.933958) | 2.072403 / 1.541195 (0.531208) | 2.127830 / 1.468490 (0.659340) | 0.840101 / 4.584777 (-3.744675) | 4.945952 / 3.745712 (1.200240) | 4.840904 / 5.269862 (-0.428957) | 3.037936 / 4.565676 (-1.527740) | 0.099027 / 0.424275 (-0.325248) | 0.008448 / 0.007607 (0.000841) | 0.703315 / 0.226044 (0.477271) | 6.837550 / 2.268929 (4.568621) | 3.204232 / 55.444624 (-52.240393) | 2.492985 / 6.876477 (-4.383492) | 2.426792 / 2.142072 (0.284720) | 0.998430 / 4.805227 (-3.806797) | 0.203854 / 6.500664 (-6.296811) | 0.072386 / 0.075469 (-0.003083) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.606627 / 1.841788 (-0.235161) | 22.287391 / 8.074308 (14.213082) | 20.245654 / 10.191392 (10.054262) | 0.229377 / 0.680424 (-0.451046) | 0.028399 / 0.534201 (-0.505802) | 0.446567 / 0.579283 (-0.132716) | 0.565277 / 0.434364 (0.130913) | 0.502957 / 0.540337 (-0.037381) | 0.749268 / 1.386936 (-0.637668) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008253 / 0.011353 (-0.003100) | 0.004432 / 0.011008 (-0.006576) | 0.081995 / 0.038508 (0.043487) | 0.075443 / 0.023109 (0.052334) | 0.442139 / 0.275898 (0.166241) | 0.507308 / 0.323480 (0.183829) | 0.007343 / 0.007986 (-0.000643) | 0.003850 / 0.004328 (-0.000478) | 0.072656 / 0.004250 (0.068406) | 0.054585 / 0.037052 (0.017533) | 0.430057 / 0.258489 (0.171568) | 0.466953 / 0.293841 (0.173112) | 0.050350 / 0.128546 (-0.078196) | 0.013682 / 0.075646 (-0.061965) | 0.088164 / 0.419271 (-0.331107) | 0.061726 / 0.043533 (0.018193) | 0.444420 / 0.255139 (0.189281) | 0.470406 / 0.283200 (0.187206) | 0.033258 / 0.141683 (-0.108425) | 1.635977 / 1.452155 (0.183823) | 1.732767 / 1.492716 (0.240051) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.227350 / 0.018006 (0.209344) | 0.500805 / 0.000490 (0.500316) | 0.006473 / 0.000200 (0.006273) | 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) | 0.034456 / 0.037411 (-0.002955) | 0.094832 / 0.014526 (0.080306) | 0.118549 / 0.176557 (-0.058008) | 0.177971 / 0.737135 (-0.559164) | 0.114165 / 0.296338 (-0.182174) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.664805 / 0.215209 (0.449596) | 6.509756 / 2.077655 (4.432101) | 2.936840 / 1.504120 (1.432720) | 2.662645 / 1.541195 (1.121450) | 2.659957 / 1.468490 (1.191467) | 0.903019 / 4.584777 (-3.681758) | 5.237191 / 3.745712 (1.491479) | 4.791917 / 5.269862 (-0.477945) | 3.130905 / 4.565676 (-1.434772) | 0.100953 / 0.424275 (-0.323322) | 0.008388 / 0.007607 (0.000781) | 0.776393 / 0.226044 (0.550348) | 7.726230 / 2.268929 (5.457301) | 3.669223 / 55.444624 (-51.775401) | 2.904556 / 6.876477 (-3.971921) | 3.205546 / 2.142072 (1.063473) | 1.058899 / 4.805227 (-3.746329) | 0.213733 / 6.500664 (-6.286931) | 0.071374 / 0.075469 (-0.004096) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.713384 / 1.841788 (-0.128403) | 23.325498 / 8.074308 (15.251190) | 20.140510 / 10.191392 (9.949118) | 0.211565 / 0.680424 (-0.468859) | 0.032916 / 0.534201 (-0.501285) | 0.460504 / 0.579283 (-0.118779) | 0.594352 / 0.434364 (0.159988) | 0.556384 / 0.540337 (0.016047) | 0.788586 / 1.386936 (-0.598350) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3c7249c11d8330ce49b1fe119c34fc6100f10774 \"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) | 0.008840 / 0.011353 (-0.002513) | 0.005045 / 0.011008 (-0.005963) | 0.110777 / 0.038508 (0.072269) | 0.100495 / 0.023109 (0.077386) | 0.420302 / 0.275898 (0.144404) | 0.456423 / 0.323480 (0.132943) | 0.006873 / 0.007986 (-0.001113) | 0.005230 / 0.004328 (0.000902) | 0.081316 / 0.004250 (0.077066) | 0.063047 / 0.037052 (0.025995) | 0.439469 / 0.258489 (0.180979) | 0.488477 / 0.293841 (0.194636) | 0.048553 / 0.128546 (-0.079994) | 0.014984 / 0.075646 (-0.060662) | 0.401317 / 0.419271 (-0.017955) | 0.074578 / 0.043533 (0.031045) | 0.435298 / 0.255139 (0.180159) | 0.464406 / 0.283200 (0.181206) | 0.048788 / 0.141683 (-0.092895) | 1.836166 / 1.452155 (0.384011) | 1.959808 / 1.492716 (0.467091) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.321419 / 0.018006 (0.303412) | 0.595736 / 0.000490 (0.595246) | 0.021144 / 0.000200 (0.020944) | 0.000626 / 0.000054 (0.000571) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033033 / 0.037411 (-0.004379) | 0.112621 / 0.014526 (0.098095) | 0.118736 / 0.176557 (-0.057821) | 0.195533 / 0.737135 (-0.541602) | 0.120807 / 0.296338 (-0.175531) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.616692 / 0.215209 (0.401483) | 6.033674 / 2.077655 (3.956019) | 2.630106 / 1.504120 (1.125986) | 2.316739 / 1.541195 (0.775544) | 2.387525 / 1.468490 (0.919035) | 0.863385 / 4.584777 (-3.721392) | 5.288193 / 3.745712 (1.542481) | 5.115766 / 5.269862 (-0.154096) | 3.083055 / 4.565676 (-1.482621) | 0.104885 / 0.424275 (-0.319391) | 0.012233 / 0.007607 (0.004626) | 0.739924 / 0.226044 (0.513880) | 7.422996 / 2.268929 (5.154067) | 3.403316 / 55.444624 (-52.041309) | 2.778740 / 6.876477 (-4.097736) | 2.836937 / 2.142072 (0.694864) | 1.059683 / 4.805227 (-3.745544) | 0.235838 / 6.500664 (-6.264826) | 0.083725 / 0.075469 (0.008256) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.755843 / 1.841788 (-0.085944) | 25.186642 / 8.074308 (17.112334) | 24.133582 / 10.191392 (13.942190) | 0.240511 / 0.680424 (-0.439913) | 0.029563 / 0.534201 (-0.504638) | 0.486049 / 0.579283 (-0.093234) | 0.610064 / 0.434364 (0.175700) | 0.559521 / 0.540337 (0.019184) | 0.828289 / 1.386936 (-0.558647) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.012134 / 0.011353 (0.000781) | 0.005133 / 0.011008 (-0.005875) | 0.084521 / 0.038508 (0.046013) | 0.095172 / 0.023109 (0.072063) | 0.527298 / 0.275898 (0.251400) | 0.558915 / 0.323480 (0.235435) | 0.006996 / 0.007986 (-0.000989) | 0.004283 / 0.004328 (-0.000045) | 0.082975 / 0.004250 (0.078725) | 0.067976 / 0.037052 (0.030924) | 0.534020 / 0.258489 (0.275531) | 0.560810 / 0.293841 (0.266969) | 0.051603 / 0.128546 (-0.076943) | 0.013330 / 0.075646 (-0.062316) | 0.094093 / 0.419271 (-0.325178) | 0.068967 / 0.043533 (0.025434) | 0.512527 / 0.255139 (0.257388) | 0.542182 / 0.283200 (0.258982) | 0.039159 / 0.141683 (-0.102524) | 1.858841 / 1.452155 (0.406686) | 1.915450 / 1.492716 (0.422734) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.269013 / 0.018006 (0.251007) | 0.601711 / 0.000490 (0.601222) | 0.013950 / 0.000200 (0.013750) | 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) | 0.038817 / 0.037411 (0.001405) | 0.138528 / 0.014526 (0.124002) | 0.130691 / 0.176557 (-0.045865) | 0.192825 / 0.737135 (-0.544310) | 0.128337 / 0.296338 (-0.168002) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.678725 / 0.215209 (0.463516) | 6.869763 / 2.077655 (4.792108) | 3.416224 / 1.504120 (1.912104) | 3.106971 / 1.541195 (1.565776) | 3.117248 / 1.468490 (1.648757) | 0.895004 / 4.584777 (-3.689773) | 5.551618 / 3.745712 (1.805906) | 4.964811 / 5.269862 (-0.305051) | 3.239555 / 4.565676 (-1.326121) | 0.099776 / 0.424275 (-0.324500) | 0.008723 / 0.007607 (0.001116) | 0.818554 / 0.226044 (0.592510) | 8.015976 / 2.268929 (5.747047) | 4.200392 / 55.444624 (-51.244232) | 3.566942 / 6.876477 (-3.309535) | 3.766249 / 2.142072 (1.624177) | 1.083428 / 4.805227 (-3.721799) | 0.214614 / 6.500664 (-6.286050) | 0.081951 / 0.075469 (0.006482) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.854400 / 1.841788 (0.012612) | 26.002556 / 8.074308 (17.928248) | 24.315194 / 10.191392 (14.123802) | 0.249012 / 0.680424 (-0.431412) | 0.032681 / 0.534201 (-0.501520) | 0.502360 / 0.579283 (-0.076923) | 0.606014 / 0.434364 (0.171650) | 0.616852 / 0.540337 (0.076514) | 0.861785 / 1.386936 (-0.525151) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#53c5c01583153cc112a507082aff4679433a1cce \"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) | 0.006723 / 0.011353 (-0.004630) | 0.004135 / 0.011008 (-0.006873) | 0.079241 / 0.038508 (0.040733) | 0.065484 / 0.023109 (0.042374) | 0.302831 / 0.275898 (0.026933) | 0.343747 / 0.323480 (0.020268) | 0.005910 / 0.007986 (-0.002076) | 0.006028 / 0.004328 (0.001699) | 0.064000 / 0.004250 (0.059750) | 0.047872 / 0.037052 (0.010820) | 0.336928 / 0.258489 (0.078439) | 0.357726 / 0.293841 (0.063885) | 0.039375 / 0.128546 (-0.089171) | 0.010439 / 0.075646 (-0.065207) | 0.310453 / 0.419271 (-0.108819) | 0.055320 / 0.043533 (0.011787) | 0.294722 / 0.255139 (0.039583) | 0.314649 / 0.283200 (0.031450) | 0.033223 / 0.141683 (-0.108460) | 1.386705 / 1.452155 (-0.065450) | 1.420546 / 1.492716 (-0.072170) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.262649 / 0.018006 (0.244643) | 0.536764 / 0.000490 (0.536274) | 0.011090 / 0.000200 (0.010891) | 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) | 0.023822 / 0.037411 (-0.013590) | 0.074279 / 0.014526 (0.059753) | 0.081295 / 0.176557 (-0.095262) | 0.135853 / 0.737135 (-0.601282) | 0.080193 / 0.296338 (-0.216146) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.468577 / 0.215209 (0.253368) | 4.615975 / 2.077655 (2.538321) | 2.059232 / 1.504120 (0.555112) | 1.798578 / 1.541195 (0.257383) | 1.801436 / 1.468490 (0.332946) | 0.660489 / 4.584777 (-3.924288) | 4.394652 / 3.745712 (0.648940) | 3.956277 / 5.269862 (-1.313585) | 2.406700 / 4.565676 (-2.158976) | 0.077174 / 0.424275 (-0.347101) | 0.007121 / 0.007607 (-0.000486) | 0.568213 / 0.226044 (0.342168) | 5.721217 / 2.268929 (3.452289) | 2.662741 / 55.444624 (-52.781883) | 2.207333 / 6.876477 (-4.669144) | 2.165279 / 2.142072 (0.023206) | 0.772566 / 4.805227 (-4.032661) | 0.162845 / 6.500664 (-6.337819) | 0.057515 / 0.075469 (-0.017954) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.313565 / 1.841788 (-0.528223) | 19.298926 / 8.074308 (11.224618) | 17.194320 / 10.191392 (7.002928) | 0.223404 / 0.680424 (-0.457020) | 0.024735 / 0.534201 (-0.509466) | 0.388452 / 0.579283 (-0.190831) | 0.489354 / 0.434364 (0.054990) | 0.427962 / 0.540337 (-0.112375) | 0.629483 / 1.386936 (-0.757453) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007404 / 0.011353 (-0.003949) | 0.004434 / 0.011008 (-0.006574) | 0.061633 / 0.038508 (0.023125) | 0.058446 / 0.023109 (0.035336) | 0.386107 / 0.275898 (0.110209) | 0.397676 / 0.323480 (0.074197) | 0.005463 / 0.007986 (-0.002523) | 0.003797 / 0.004328 (-0.000531) | 0.067323 / 0.004250 (0.063072) | 0.053826 / 0.037052 (0.016774) | 0.387910 / 0.258489 (0.129421) | 0.409364 / 0.293841 (0.115523) | 0.039836 / 0.128546 (-0.088710) | 0.011940 / 0.075646 (-0.063706) | 0.071812 / 0.419271 (-0.347459) | 0.047952 / 0.043533 (0.004419) | 0.386826 / 0.255139 (0.131687) | 0.392845 / 0.283200 (0.109645) | 0.029430 / 0.141683 (-0.112253) | 1.390961 / 1.452155 (-0.061194) | 1.482744 / 1.492716 (-0.009972) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.258814 / 0.018006 (0.240807) | 0.535505 / 0.000490 (0.535015) | 0.006097 / 0.000200 (0.005897) | 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) | 0.028046 / 0.037411 (-0.009365) | 0.078077 / 0.014526 (0.063552) | 0.087713 / 0.176557 (-0.088843) | 0.140856 / 0.737135 (-0.596279) | 0.090565 / 0.296338 (-0.205773) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.504375 / 0.215209 (0.289165) | 5.133472 / 2.077655 (3.055817) | 2.368968 / 1.504120 (0.864848) | 2.176939 / 1.541195 (0.635744) | 2.151976 / 1.468490 (0.683486) | 0.720566 / 4.584777 (-3.864211) | 5.050505 / 3.745712 (1.304793) | 3.993614 / 5.269862 (-1.276248) | 2.492234 / 4.565676 (-2.073443) | 0.089629 / 0.424275 (-0.334646) | 0.008074 / 0.007607 (0.000467) | 0.677706 / 0.226044 (0.451661) | 6.208332 / 2.268929 (3.939403) | 3.058299 / 55.444624 (-52.386325) | 2.461078 / 6.876477 (-4.415399) | 2.622681 / 2.142072 (0.480609) | 0.873573 / 4.805227 (-3.931654) | 0.176321 / 6.500664 (-6.324343) | 0.062410 / 0.075469 (-0.013059) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.454767 / 1.841788 (-0.387021) | 19.544225 / 8.074308 (11.469917) | 17.365997 / 10.191392 (7.174605) | 0.225461 / 0.680424 (-0.454963) | 0.027679 / 0.534201 (-0.506522) | 0.396419 / 0.579283 (-0.182864) | 0.513244 / 0.434364 (0.078880) | 0.469054 / 0.540337 (-0.071283) | 0.676458 / 1.386936 (-0.710478) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d44d8a649b541cd0b10ea99fbfe7a02c3ba50a63 \"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) | 0.007606 / 0.011353 (-0.003747) | 0.004692 / 0.011008 (-0.006317) | 0.100525 / 0.038508 (0.062017) | 0.085426 / 0.023109 (0.062317) | 0.378568 / 0.275898 (0.102670) | 0.412268 / 0.323480 (0.088788) | 0.004756 / 0.007986 (-0.003230) | 0.003871 / 0.004328 (-0.000457) | 0.075244 / 0.004250 (0.070994) | 0.064969 / 0.037052 (0.027916) | 0.385569 / 0.258489 (0.127079) | 0.429117 / 0.293841 (0.135276) | 0.035798 / 0.128546 (-0.092749) | 0.009999 / 0.075646 (-0.065647) | 0.351380 / 0.419271 (-0.067891) | 0.060850 / 0.043533 (0.017317) | 0.381327 / 0.255139 (0.126188) | 0.403663 / 0.283200 (0.120464) | 0.028103 / 0.141683 (-0.113580) | 1.814143 / 1.452155 (0.361988) | 1.895062 / 1.492716 (0.402346) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.263581 / 0.018006 (0.245575) | 0.506988 / 0.000490 (0.506499) | 0.012775 / 0.000200 (0.012575) | 0.000456 / 0.000054 (0.000402) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033452 / 0.037411 (-0.003959) | 0.104950 / 0.014526 (0.090425) | 0.114803 / 0.176557 (-0.061754) | 0.182465 / 0.737135 (-0.554671) | 0.116156 / 0.296338 (-0.180183) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.441574 / 0.215209 (0.226365) | 4.394601 / 2.077655 (2.316946) | 2.170797 / 1.504120 (0.666677) | 1.926675 / 1.541195 (0.385480) | 1.974867 / 1.468490 (0.506377) | 0.546777 / 4.584777 (-4.038000) | 4.053612 / 3.745712 (0.307900) | 3.934278 / 5.269862 (-1.335583) | 2.354660 / 4.565676 (-2.211017) | 0.067706 / 0.424275 (-0.356569) | 0.009217 / 0.007607 (0.001610) | 0.539261 / 0.226044 (0.313217) | 5.409552 / 2.268929 (3.140623) | 2.835739 / 55.444624 (-52.608886) | 2.282246 / 6.876477 (-4.594230) | 2.359930 / 2.142072 (0.217858) | 0.696363 / 4.805227 (-4.108864) | 0.155947 / 6.500664 (-6.344717) | 0.071293 / 0.075469 (-0.004176) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.495512 / 1.841788 (-0.346275) | 22.027128 / 8.074308 (13.952820) | 16.226068 / 10.191392 (6.034676) | 0.180281 / 0.680424 (-0.500142) | 0.021839 / 0.534201 (-0.512362) | 0.446151 / 0.579283 (-0.133132) | 0.476872 / 0.434364 (0.042508) | 0.515171 / 0.540337 (-0.025166) | 0.731372 / 1.386936 (-0.655564) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006843 / 0.011353 (-0.004510) | 0.004286 / 0.011008 (-0.006722) | 0.074104 / 0.038508 (0.035596) | 0.076789 / 0.023109 (0.053680) | 0.441506 / 0.275898 (0.165608) | 0.500999 / 0.323480 (0.177519) | 0.006041 / 0.007986 (-0.001945) | 0.003718 / 0.004328 (-0.000610) | 0.074189 / 0.004250 (0.069938) | 0.060513 / 0.037052 (0.023461) | 0.460812 / 0.258489 (0.202323) | 0.503631 / 0.293841 (0.209790) | 0.037026 / 0.128546 (-0.091520) | 0.009611 / 0.075646 (-0.066035) | 0.077037 / 0.419271 (-0.342234) | 0.052191 / 0.043533 (0.008658) | 0.444567 / 0.255139 (0.189428) | 0.486730 / 0.283200 (0.203530) | 0.023846 / 0.141683 (-0.117837) | 1.692422 / 1.452155 (0.240267) | 1.809648 / 1.492716 (0.316932) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.240007 / 0.018006 (0.222001) | 0.481980 / 0.000490 (0.481490) | 0.006945 / 0.000200 (0.006746) | 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) | 0.037198 / 0.037411 (-0.000213) | 0.119413 / 0.014526 (0.104887) | 0.137409 / 0.176557 (-0.039148) | 0.199130 / 0.737135 (-0.538005) | 0.133137 / 0.296338 (-0.163202) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.521747 / 0.215209 (0.306538) | 4.955653 / 2.077655 (2.877999) | 2.694323 / 1.504120 (1.190203) | 2.496629 / 1.541195 (0.955434) | 2.661151 / 1.468490 (1.192660) | 0.576687 / 4.584777 (-4.008089) | 4.251437 / 3.745712 (0.505725) | 3.683020 / 5.269862 (-1.586842) | 2.363951 / 4.565676 (-2.201726) | 0.064631 / 0.424275 (-0.359644) | 0.007958 / 0.007607 (0.000351) | 0.616498 / 0.226044 (0.390454) | 5.919424 / 2.268929 (3.650496) | 3.255936 / 55.444624 (-52.188689) | 2.866167 / 6.876477 (-4.010309) | 3.007272 / 2.142072 (0.865199) | 0.660259 / 4.805227 (-4.144968) | 0.152469 / 6.500664 (-6.348195) | 0.065254 / 0.075469 (-0.010215) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.547912 / 1.841788 (-0.293876) | 22.494611 / 8.074308 (14.420303) | 16.400746 / 10.191392 (6.209354) | 0.184137 / 0.680424 (-0.496287) | 0.023615 / 0.534201 (-0.510586) | 0.473923 / 0.579283 (-0.105360) | 0.473030 / 0.434364 (0.038666) | 0.534264 / 0.540337 (-0.006073) | 0.770178 / 1.386936 (-0.616758) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#565efb7f43839072ef01247681645ca404ba0b94 \"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) | 0.006812 / 0.011353 (-0.004541) | 0.004254 / 0.011008 (-0.006754) | 0.084271 / 0.038508 (0.045763) | 0.084299 / 0.023109 (0.061189) | 0.317437 / 0.275898 (0.041539) | 0.350855 / 0.323480 (0.027375) | 0.004296 / 0.007986 (-0.003690) | 0.003610 / 0.004328 (-0.000718) | 0.065205 / 0.004250 (0.060955) | 0.057734 / 0.037052 (0.020682) | 0.324049 / 0.258489 (0.065560) | 0.365042 / 0.293841 (0.071201) | 0.031454 / 0.128546 (-0.097092) | 0.008703 / 0.075646 (-0.066943) | 0.286603 / 0.419271 (-0.132668) | 0.052251 / 0.043533 (0.008719) | 0.312404 / 0.255139 (0.057265) | 0.335902 / 0.283200 (0.052703) | 0.025087 / 0.141683 (-0.116595) | 1.478573 / 1.452155 (0.026418) | 1.559548 / 1.492716 (0.066831) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.307637 / 0.018006 (0.289631) | 0.567169 / 0.000490 (0.566679) | 0.006782 / 0.000200 (0.006582) | 0.000235 / 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) | 0.030979 / 0.037411 (-0.006433) | 0.089972 / 0.014526 (0.075446) | 0.101689 / 0.176557 (-0.074868) | 0.162038 / 0.737135 (-0.575097) | 0.103107 / 0.296338 (-0.193232) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.382458 / 0.215209 (0.167248) | 3.813105 / 2.077655 (1.735450) | 1.855198 / 1.504120 (0.351078) | 1.699850 / 1.541195 (0.158656) | 1.902818 / 1.468490 (0.434328) | 0.478654 / 4.584777 (-4.106123) | 3.536926 / 3.745712 (-0.208786) | 3.558557 / 5.269862 (-1.711304) | 2.121098 / 4.565676 (-2.444579) | 0.056584 / 0.424275 (-0.367691) | 0.007693 / 0.007607 (0.000086) | 0.471157 / 0.226044 (0.245112) | 4.717742 / 2.268929 (2.448813) | 2.389033 / 55.444624 (-53.055591) | 2.102898 / 6.876477 (-4.773579) | 2.233404 / 2.142072 (0.091332) | 0.585829 / 4.805227 (-4.219398) | 0.133784 / 6.500664 (-6.366880) | 0.063963 / 0.075469 (-0.011506) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.272234 / 1.841788 (-0.569554) | 19.897647 / 8.074308 (11.823339) | 14.808090 / 10.191392 (4.616698) | 0.167199 / 0.680424 (-0.513224) | 0.018357 / 0.534201 (-0.515844) | 0.391635 / 0.579283 (-0.187648) | 0.409603 / 0.434364 (-0.024761) | 0.467670 / 0.540337 (-0.072668) | 0.639763 / 1.386936 (-0.747173) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006794 / 0.011353 (-0.004559) | 0.004317 / 0.011008 (-0.006692) | 0.065434 / 0.038508 (0.026926) | 0.079066 / 0.023109 (0.055957) | 0.415486 / 0.275898 (0.139588) | 0.448072 / 0.323480 (0.124593) | 0.005705 / 0.007986 (-0.002281) | 0.003589 / 0.004328 (-0.000739) | 0.065195 / 0.004250 (0.060945) | 0.058951 / 0.037052 (0.021899) | 0.414466 / 0.258489 (0.155977) | 0.453844 / 0.293841 (0.160003) | 0.032437 / 0.128546 (-0.096110) | 0.008805 / 0.075646 (-0.066841) | 0.071741 / 0.419271 (-0.347530) | 0.048051 / 0.043533 (0.004518) | 0.413197 / 0.255139 (0.158058) | 0.430071 / 0.283200 (0.146872) | 0.023144 / 0.141683 (-0.118539) | 1.507756 / 1.452155 (0.055601) | 1.572180 / 1.492716 (0.079464) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.326556 / 0.018006 (0.308550) | 0.533664 / 0.000490 (0.533174) | 0.007400 / 0.000200 (0.007200) | 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) | 0.033397 / 0.037411 (-0.004014) | 0.092486 / 0.014526 (0.077960) | 0.108454 / 0.176557 (-0.068103) | 0.163885 / 0.737135 (-0.573250) | 0.109682 / 0.296338 (-0.186657) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.429283 / 0.215209 (0.214074) | 4.285774 / 2.077655 (2.208119) | 2.245646 / 1.504120 (0.741526) | 2.088460 / 1.541195 (0.547265) | 2.217908 / 1.468490 (0.749418) | 0.500126 / 4.584777 (-4.084651) | 3.640253 / 3.745712 (-0.105459) | 3.435069 / 5.269862 (-1.834793) | 2.158015 / 4.565676 (-2.407662) | 0.059087 / 0.424275 (-0.365188) | 0.007479 / 0.007607 (-0.000128) | 0.518067 / 0.226044 (0.292023) | 5.181891 / 2.268929 (2.912963) | 2.759156 / 55.444624 (-52.685468) | 2.452164 / 6.876477 (-4.424313) | 2.712764 / 2.142072 (0.570692) | 0.604871 / 4.805227 (-4.200356) | 0.137810 / 6.500664 (-6.362854) | 0.061999 / 0.075469 (-0.013470) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.338081 / 1.841788 (-0.503706) | 19.934668 / 8.074308 (11.860360) | 14.482526 / 10.191392 (4.291134) | 0.167615 / 0.680424 (-0.512809) | 0.020257 / 0.534201 (-0.513944) | 0.399103 / 0.579283 (-0.180180) | 0.431785 / 0.434364 (-0.002579) | 0.475470 / 0.540337 (-0.064868) | 0.648003 / 1.386936 (-0.738933) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#153a6c0b0897fc30203ded5a6d6c358c53aa3a0e \"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) | 0.011916 / 0.011353 (0.000563) | 0.004696 / 0.011008 (-0.006313) | 0.101061 / 0.038508 (0.062553) | 0.093383 / 0.023109 (0.070274) | 0.391517 / 0.275898 (0.115619) | 0.434374 / 0.323480 (0.110894) | 0.006193 / 0.007986 (-0.001792) | 0.003840 / 0.004328 (-0.000489) | 0.077946 / 0.004250 (0.073696) | 0.066332 / 0.037052 (0.029280) | 0.413103 / 0.258489 (0.154614) | 0.452988 / 0.293841 (0.159148) | 0.044899 / 0.128546 (-0.083647) | 0.009969 / 0.075646 (-0.065677) | 0.344569 / 0.419271 (-0.074703) | 0.064688 / 0.043533 (0.021155) | 0.388042 / 0.255139 (0.132903) | 0.417615 / 0.283200 (0.134416) | 0.032899 / 0.141683 (-0.108784) | 1.738834 / 1.452155 (0.286679) | 1.837562 / 1.492716 (0.344845) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.255265 / 0.018006 (0.237259) | 0.547550 / 0.000490 (0.547061) | 0.009018 / 0.000200 (0.008818) | 0.001232 / 0.000054 (0.001178) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033171 / 0.037411 (-0.004241) | 0.102569 / 0.014526 (0.088043) | 0.113611 / 0.176557 (-0.062946) | 0.181805 / 0.737135 (-0.555330) | 0.115015 / 0.296338 (-0.181323) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.456430 / 0.215209 (0.241221) | 4.536000 / 2.077655 (2.458346) | 2.220554 / 1.504120 (0.716434) | 2.037965 / 1.541195 (0.496770) | 2.223780 / 1.468490 (0.755290) | 0.565732 / 4.584777 (-4.019045) | 4.574917 / 3.745712 (0.829205) | 4.085683 / 5.269862 (-1.184178) | 2.529052 / 4.565676 (-2.036624) | 0.067061 / 0.424275 (-0.357214) | 0.009161 / 0.007607 (0.001554) | 0.551377 / 0.226044 (0.325332) | 5.510422 / 2.268929 (3.241493) | 2.788264 / 55.444624 (-52.656360) | 2.432821 / 6.876477 (-4.443656) | 2.500835 / 2.142072 (0.358762) | 0.683645 / 4.805227 (-4.121582) | 0.155595 / 6.500664 (-6.345069) | 0.072265 / 0.075469 (-0.003204) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.512571 / 1.841788 (-0.329217) | 23.752582 / 8.074308 (15.678273) | 16.798834 / 10.191392 (6.607442) | 0.210325 / 0.680424 (-0.470099) | 0.023446 / 0.534201 (-0.510755) | 0.472964 / 0.579283 (-0.106319) | 0.518003 / 0.434364 (0.083639) | 0.588422 / 0.540337 (0.048085) | 0.830762 / 1.386936 (-0.556174) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008075 / 0.011353 (-0.003278) | 0.004569 / 0.011008 (-0.006439) | 0.079786 / 0.038508 (0.041278) | 0.092741 / 0.023109 (0.069632) | 0.500732 / 0.275898 (0.224834) | 0.544108 / 0.323480 (0.220628) | 0.006305 / 0.007986 (-0.001680) | 0.003843 / 0.004328 (-0.000486) | 0.078347 / 0.004250 (0.074096) | 0.066969 / 0.037052 (0.029916) | 0.504116 / 0.258489 (0.245627) | 0.548109 / 0.293841 (0.254268) | 0.038263 / 0.128546 (-0.090283) | 0.010006 / 0.075646 (-0.065640) | 0.085582 / 0.419271 (-0.333690) | 0.056937 / 0.043533 (0.013404) | 0.502861 / 0.255139 (0.247722) | 0.532002 / 0.283200 (0.248802) | 0.027003 / 0.141683 (-0.114679) | 1.811658 / 1.452155 (0.359503) | 1.878863 / 1.492716 (0.386147) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.242297 / 0.018006 (0.224291) | 0.489060 / 0.000490 (0.488570) | 0.005770 / 0.000200 (0.005570) | 0.000129 / 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) | 0.040368 / 0.037411 (0.002956) | 0.116221 / 0.014526 (0.101695) | 0.125195 / 0.176557 (-0.051361) | 0.188616 / 0.737135 (-0.548519) | 0.126473 / 0.296338 (-0.169866) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.513975 / 0.215209 (0.298766) | 5.122407 / 2.077655 (3.044752) | 2.854024 / 1.504120 (1.349904) | 2.611101 / 1.541195 (1.069906) | 2.704880 / 1.468490 (1.236390) | 0.581568 / 4.584777 (-4.003209) | 4.628965 / 3.745712 (0.883253) | 4.069359 / 5.269862 (-1.200503) | 2.433793 / 4.565676 (-2.131883) | 0.068624 / 0.424275 (-0.355651) | 0.008843 / 0.007607 (0.001235) | 0.609147 / 0.226044 (0.383102) | 6.096923 / 2.268929 (3.827995) | 3.411687 / 55.444624 (-52.032937) | 2.972037 / 6.876477 (-3.904440) | 3.210266 / 2.142072 (1.068194) | 0.697935 / 4.805227 (-4.107292) | 0.156855 / 6.500664 (-6.343809) | 0.072600 / 0.075469 (-0.002869) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.673126 / 1.841788 (-0.168661) | 24.782231 / 8.074308 (16.707923) | 17.945937 / 10.191392 (7.754545) | 0.229063 / 0.680424 (-0.451361) | 0.024264 / 0.534201 (-0.509937) | 0.474904 / 0.579283 (-0.104379) | 0.616602 / 0.434364 (0.182238) | 0.587687 / 0.540337 (0.047350) | 0.875600 / 1.386936 (-0.511336) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a46ae63ad72c0733c947fa0f2996fa739d80e1ef \"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) | 0.004866 / 0.011353 (-0.006487) | 0.002877 / 0.011008 (-0.008132) | 0.061786 / 0.038508 (0.023277) | 0.051555 / 0.023109 (0.028446) | 0.262182 / 0.275898 (-0.013716) | 0.288908 / 0.323480 (-0.034572) | 0.002929 / 0.007986 (-0.005057) | 0.002358 / 0.004328 (-0.001971) | 0.048246 / 0.004250 (0.043995) | 0.040391 / 0.037052 (0.003339) | 0.268165 / 0.258489 (0.009675) | 0.304844 / 0.293841 (0.011003) | 0.023280 / 0.128546 (-0.105266) | 0.007274 / 0.075646 (-0.068372) | 0.200698 / 0.419271 (-0.218574) | 0.036181 / 0.043533 (-0.007352) | 0.267292 / 0.255139 (0.012153) | 0.286981 / 0.283200 (0.003781) | 0.018686 / 0.141683 (-0.122996) | 1.131903 / 1.452155 (-0.320251) | 1.196631 / 1.492716 (-0.296086) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.092158 / 0.018006 (0.074152) | 0.300621 / 0.000490 (0.300132) | 0.000205 / 0.000200 (0.000006) | 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) | 0.018101 / 0.037411 (-0.019310) | 0.062478 / 0.014526 (0.047952) | 0.073092 / 0.176557 (-0.103464) | 0.119397 / 0.737135 (-0.617738) | 0.073768 / 0.296338 (-0.222570) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.286711 / 0.215209 (0.071502) | 2.766663 / 2.077655 (0.689008) | 1.431238 / 1.504120 (-0.072882) | 1.308312 / 1.541195 (-0.232883) | 1.344886 / 1.468490 (-0.123605) | 0.396719 / 4.584777 (-4.188058) | 2.371154 / 3.745712 (-1.374558) | 2.626471 / 5.269862 (-2.643391) | 1.574837 / 4.565676 (-2.990840) | 0.046344 / 0.424275 (-0.377931) | 0.005108 / 0.007607 (-0.002499) | 0.334200 / 0.226044 (0.108156) | 3.277034 / 2.268929 (1.008106) | 1.789338 / 55.444624 (-53.655286) | 1.527584 / 6.876477 (-5.348892) | 1.570417 / 2.142072 (-0.571656) | 0.472663 / 4.805227 (-4.332564) | 0.100825 / 6.500664 (-6.399839) | 0.042270 / 0.075469 (-0.033199) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.965416 / 1.841788 (-0.876372) | 11.827406 / 8.074308 (3.753098) | 10.820703 / 10.191392 (0.629311) | 0.128636 / 0.680424 (-0.551788) | 0.014696 / 0.534201 (-0.519505) | 0.271019 / 0.579283 (-0.308264) | 0.270077 / 0.434364 (-0.164287) | 0.313054 / 0.540337 (-0.227284) | 0.402941 / 1.386936 (-0.983995) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005204 / 0.011353 (-0.006149) | 0.002976 / 0.011008 (-0.008032) | 0.047723 / 0.038508 (0.009215) | 0.056180 / 0.023109 (0.033071) | 0.277751 / 0.275898 (0.001853) | 0.304109 / 0.323480 (-0.019371) | 0.004254 / 0.007986 (-0.003732) | 0.002386 / 0.004328 (-0.001943) | 0.047815 / 0.004250 (0.043564) | 0.041553 / 0.037052 (0.004501) | 0.280958 / 0.258489 (0.022469) | 0.308639 / 0.293841 (0.014799) | 0.023549 / 0.128546 (-0.104997) | 0.007846 / 0.075646 (-0.067800) | 0.053762 / 0.419271 (-0.365509) | 0.031763 / 0.043533 (-0.011770) | 0.278208 / 0.255139 (0.023069) | 0.294024 / 0.283200 (0.010825) | 0.018648 / 0.141683 (-0.123035) | 1.140664 / 1.452155 (-0.311490) | 1.206706 / 1.492716 (-0.286010) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093211 / 0.018006 (0.075205) | 0.303067 / 0.000490 (0.302577) | 0.000222 / 0.000200 (0.000022) | 0.000055 / 0.000054 (0.000001) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021745 / 0.037411 (-0.015666) | 0.070400 / 0.014526 (0.055874) | 0.083250 / 0.176557 (-0.093307) | 0.119745 / 0.737135 (-0.617391) | 0.083004 / 0.296338 (-0.213335) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.305841 / 0.215209 (0.090632) | 2.958171 / 2.077655 (0.880516) | 1.596990 / 1.504120 (0.092870) | 1.466522 / 1.541195 (-0.074673) | 1.487050 / 1.468490 (0.018560) | 0.402866 / 4.584777 (-4.181911) | 2.425415 / 3.745712 (-1.320297) | 2.545245 / 5.269862 (-2.724617) | 1.569719 / 4.565676 (-2.995958) | 0.046344 / 0.424275 (-0.377931) | 0.005275 / 0.007607 (-0.002332) | 0.362024 / 0.226044 (0.135980) | 3.556721 / 2.268929 (1.287792) | 1.961359 / 55.444624 (-53.483266) | 1.672835 / 6.876477 (-5.203641) | 1.814036 / 2.142072 (-0.328036) | 0.482012 / 4.805227 (-4.323215) | 0.099275 / 6.500664 (-6.401389) | 0.040988 / 0.075469 (-0.034481) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.984368 / 1.841788 (-0.857420) | 12.251555 / 8.074308 (4.177247) | 10.645975 / 10.191392 (0.454583) | 0.128955 / 0.680424 (-0.551468) | 0.015355 / 0.534201 (-0.518846) | 0.272498 / 0.579283 (-0.306785) | 0.279342 / 0.434364 (-0.155022) | 0.303055 / 0.540337 (-0.237282) | 0.392437 / 1.386936 (-0.994499) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#56458fa98d2670ff6bf47a782b6f418785c017fd \"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) | 0.009502 / 0.011353 (-0.001851) | 0.004957 / 0.011008 (-0.006052) | 0.111062 / 0.038508 (0.072553) | 0.100012 / 0.023109 (0.076903) | 0.415747 / 0.275898 (0.139849) | 0.453910 / 0.323480 (0.130430) | 0.006030 / 0.007986 (-0.001956) | 0.004271 / 0.004328 (-0.000057) | 0.088694 / 0.004250 (0.084444) | 0.064529 / 0.037052 (0.027477) | 0.414999 / 0.258489 (0.156510) | 0.477115 / 0.293841 (0.183274) | 0.047565 / 0.128546 (-0.080982) | 0.013352 / 0.075646 (-0.062294) | 0.367948 / 0.419271 (-0.051324) | 0.067577 / 0.043533 (0.024044) | 0.405107 / 0.255139 (0.149968) | 0.430281 / 0.283200 (0.147081) | 0.041629 / 0.141683 (-0.100054) | 1.784746 / 1.452155 (0.332591) | 1.901539 / 1.492716 (0.408822) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.308456 / 0.018006 (0.290450) | 0.623253 / 0.000490 (0.622763) | 0.014966 / 0.000200 (0.014766) | 0.000393 / 0.000054 (0.000338) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031538 / 0.037411 (-0.005873) | 0.100321 / 0.014526 (0.085796) | 0.112788 / 0.176557 (-0.063769) | 0.180998 / 0.737135 (-0.556138) | 0.111589 / 0.296338 (-0.184750) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.603121 / 0.215209 (0.387912) | 5.769795 / 2.077655 (3.692140) | 2.501168 / 1.504120 (0.997048) | 2.240982 / 1.541195 (0.699787) | 2.333123 / 1.468490 (0.864633) | 0.799246 / 4.584777 (-3.785531) | 5.148529 / 3.745712 (1.402817) | 4.737782 / 5.269862 (-0.532080) | 3.003032 / 4.565676 (-1.562644) | 0.087457 / 0.424275 (-0.336818) | 0.008777 / 0.007607 (0.001170) | 0.692961 / 0.226044 (0.466916) | 7.235537 / 2.268929 (4.966608) | 3.464074 / 55.444624 (-51.980551) | 2.817360 / 6.876477 (-4.059116) | 2.903121 / 2.142072 (0.761049) | 1.026150 / 4.805227 (-3.779077) | 0.231814 / 6.500664 (-6.268850) | 0.088358 / 0.075469 (0.012888) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.527889 / 1.841788 (-0.313898) | 24.374770 / 8.074308 (16.300462) | 21.720415 / 10.191392 (11.529023) | 0.209357 / 0.680424 (-0.471067) | 0.027587 / 0.534201 (-0.506614) | 0.479136 / 0.579283 (-0.100147) | 0.573005 / 0.434364 (0.138641) | 0.537713 / 0.540337 (-0.002625) | 0.753628 / 1.386936 (-0.633308) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009724 / 0.011353 (-0.001629) | 0.004798 / 0.011008 (-0.006210) | 0.076423 / 0.038508 (0.037915) | 0.085693 / 0.023109 (0.062584) | 0.446864 / 0.275898 (0.170966) | 0.482700 / 0.323480 (0.159220) | 0.006448 / 0.007986 (-0.001537) | 0.004451 / 0.004328 (0.000122) | 0.078295 / 0.004250 (0.074045) | 0.061940 / 0.037052 (0.024888) | 0.446091 / 0.258489 (0.187601) | 0.478567 / 0.293841 (0.184726) | 0.047206 / 0.128546 (-0.081340) | 0.012608 / 0.075646 (-0.063038) | 0.089719 / 0.419271 (-0.329552) | 0.057791 / 0.043533 (0.014258) | 0.438357 / 0.255139 (0.183218) | 0.475060 / 0.283200 (0.191860) | 0.035466 / 0.141683 (-0.106216) | 1.691982 / 1.452155 (0.239827) | 1.773834 / 1.492716 (0.281118) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.290053 / 0.018006 (0.272047) | 0.595465 / 0.000490 (0.594976) | 0.007531 / 0.000200 (0.007331) | 0.000179 / 0.000054 (0.000124) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034625 / 0.037411 (-0.002786) | 0.098725 / 0.014526 (0.084200) | 0.111248 / 0.176557 (-0.065308) | 0.172113 / 0.737135 (-0.565022) | 0.111299 / 0.296338 (-0.185040) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.581773 / 0.215209 (0.366564) | 6.150993 / 2.077655 (4.073338) | 2.761099 / 1.504120 (1.256980) | 2.431459 / 1.541195 (0.890264) | 2.501471 / 1.468490 (1.032981) | 0.805751 / 4.584777 (-3.779026) | 5.375406 / 3.745712 (1.629693) | 4.829323 / 5.269862 (-0.440538) | 3.095235 / 4.565676 (-1.470442) | 0.103336 / 0.424275 (-0.320939) | 0.012678 / 0.007607 (0.005071) | 0.730121 / 0.226044 (0.504077) | 7.272025 / 2.268929 (5.003097) | 3.607889 / 55.444624 (-51.836735) | 2.904797 / 6.876477 (-3.971680) | 3.179139 / 2.142072 (1.037067) | 0.997510 / 4.805227 (-3.807717) | 0.219023 / 6.500664 (-6.281641) | 0.076680 / 0.075469 (0.001211) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.712838 / 1.841788 (-0.128950) | 24.242240 / 8.074308 (16.167932) | 19.746825 / 10.191392 (9.555433) | 0.234590 / 0.680424 (-0.445833) | 0.032015 / 0.534201 (-0.502186) | 0.462554 / 0.579283 (-0.116729) | 0.604529 / 0.434364 (0.170165) | 0.537779 / 0.540337 (-0.002558) | 0.777386 / 1.386936 (-0.609550) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#89c1b13d85b8971925440deb84f558e23c224a47 \"CML watermark\")\n", "Cool ! Nice to simplify 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) | 0.004659 / 0.011353 (-0.006693) | 0.002672 / 0.011008 (-0.008337) | 0.062385 / 0.038508 (0.023877) | 0.030581 / 0.023109 (0.007471) | 0.243210 / 0.275898 (-0.032688) | 0.271441 / 0.323480 (-0.052039) | 0.002909 / 0.007986 (-0.005076) | 0.002371 / 0.004328 (-0.001957) | 0.049213 / 0.004250 (0.044962) | 0.043952 / 0.037052 (0.006900) | 0.250257 / 0.258489 (-0.008232) | 0.280470 / 0.293841 (-0.013371) | 0.023048 / 0.128546 (-0.105499) | 0.006893 / 0.075646 (-0.068754) | 0.204026 / 0.419271 (-0.215245) | 0.054067 / 0.043533 (0.010534) | 0.248730 / 0.255139 (-0.006409) | 0.272325 / 0.283200 (-0.010874) | 0.019028 / 0.141683 (-0.122655) | 1.103477 / 1.452155 (-0.348678) | 1.185775 / 1.492716 (-0.306942) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.097295 / 0.018006 (0.079289) | 0.302997 / 0.000490 (0.302507) | 0.000216 / 0.000200 (0.000016) | 0.000044 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.018653 / 0.037411 (-0.018759) | 0.062604 / 0.014526 (0.048079) | 0.075652 / 0.176557 (-0.100904) | 0.121298 / 0.737135 (-0.615838) | 0.074129 / 0.296338 (-0.222209) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.283315 / 0.215209 (0.068106) | 2.833975 / 2.077655 (0.756320) | 1.463877 / 1.504120 (-0.040243) | 1.352197 / 1.541195 (-0.188998) | 1.337623 / 1.468490 (-0.130867) | 0.405282 / 4.584777 (-4.179495) | 2.371381 / 3.745712 (-1.374331) | 2.584853 / 5.269862 (-2.685009) | 1.565902 / 4.565676 (-2.999775) | 0.046398 / 0.424275 (-0.377877) | 0.004795 / 0.007607 (-0.002812) | 0.345949 / 0.226044 (0.119905) | 3.326662 / 2.268929 (1.057733) | 1.778394 / 55.444624 (-53.666230) | 1.520788 / 6.876477 (-5.355688) | 1.526517 / 2.142072 (-0.615556) | 0.471788 / 4.805227 (-4.333439) | 0.099236 / 6.500664 (-6.401428) | 0.041886 / 0.075469 (-0.033583) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.958183 / 1.841788 (-0.883605) | 11.474476 / 8.074308 (3.400168) | 10.547550 / 10.191392 (0.356158) | 0.129316 / 0.680424 (-0.551108) | 0.013969 / 0.534201 (-0.520232) | 0.272028 / 0.579283 (-0.307255) | 0.271027 / 0.434364 (-0.163337) | 0.312124 / 0.540337 (-0.228214) | 0.423879 / 1.386936 (-0.963057) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004743 / 0.011353 (-0.006610) | 0.002724 / 0.011008 (-0.008284) | 0.049526 / 0.038508 (0.011018) | 0.051429 / 0.023109 (0.028319) | 0.265202 / 0.275898 (-0.010696) | 0.287498 / 0.323480 (-0.035981) | 0.004034 / 0.007986 (-0.003951) | 0.002460 / 0.004328 (-0.001868) | 0.049367 / 0.004250 (0.045116) | 0.038526 / 0.037052 (0.001474) | 0.271496 / 0.258489 (0.013007) | 0.300969 / 0.293841 (0.007128) | 0.024159 / 0.128546 (-0.104387) | 0.006959 / 0.075646 (-0.068687) | 0.055316 / 0.419271 (-0.363955) | 0.032409 / 0.043533 (-0.011124) | 0.267524 / 0.255139 (0.012385) | 0.284667 / 0.283200 (0.001467) | 0.017305 / 0.141683 (-0.124378) | 1.127560 / 1.452155 (-0.324595) | 1.188271 / 1.492716 (-0.304445) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093587 / 0.018006 (0.075581) | 0.301834 / 0.000490 (0.301344) | 0.000211 / 0.000200 (0.000011) | 0.000043 / 0.000054 (-0.000011) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.020899 / 0.037411 (-0.016512) | 0.069999 / 0.014526 (0.055473) | 0.081434 / 0.176557 (-0.095123) | 0.120538 / 0.737135 (-0.616598) | 0.082708 / 0.296338 (-0.213630) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.291845 / 0.215209 (0.076636) | 2.872476 / 2.077655 (0.794822) | 1.579330 / 1.504120 (0.075210) | 1.453083 / 1.541195 (-0.088112) | 1.496675 / 1.468490 (0.028185) | 0.406178 / 4.584777 (-4.178599) | 2.434121 / 3.745712 (-1.311592) | 2.519760 / 5.269862 (-2.750101) | 1.535781 / 4.565676 (-3.029895) | 0.046331 / 0.424275 (-0.377944) | 0.004749 / 0.007607 (-0.002858) | 0.340862 / 0.226044 (0.114817) | 3.362750 / 2.268929 (1.093822) | 1.924707 / 55.444624 (-53.519917) | 1.646820 / 6.876477 (-5.229657) | 1.630885 / 2.142072 (-0.511188) | 0.478623 / 4.805227 (-4.326605) | 0.098235 / 6.500664 (-6.402429) | 0.040741 / 0.075469 (-0.034728) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.989858 / 1.841788 (-0.851929) | 12.111035 / 8.074308 (4.036727) | 11.065284 / 10.191392 (0.873892) | 0.143443 / 0.680424 (-0.536981) | 0.015873 / 0.534201 (-0.518328) | 0.271932 / 0.579283 (-0.307351) | 0.281440 / 0.434364 (-0.152924) | 0.309518 / 0.540337 (-0.230819) | 0.414701 / 1.386936 (-0.972235) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#322ee4bd7d460a5789f9991a45453b9fb5f5aed1 \"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) | 0.005840 / 0.011353 (-0.005513) | 0.003580 / 0.011008 (-0.007428) | 0.079921 / 0.038508 (0.041413) | 0.036316 / 0.023109 (0.013206) | 0.321065 / 0.275898 (0.045167) | 0.348594 / 0.323480 (0.025115) | 0.004662 / 0.007986 (-0.003324) | 0.002884 / 0.004328 (-0.001444) | 0.062964 / 0.004250 (0.058714) | 0.052856 / 0.037052 (0.015804) | 0.322087 / 0.258489 (0.063598) | 0.355546 / 0.293841 (0.061705) | 0.027025 / 0.128546 (-0.101521) | 0.007969 / 0.075646 (-0.067678) | 0.261416 / 0.419271 (-0.157855) | 0.066612 / 0.043533 (0.023079) | 0.314631 / 0.255139 (0.059492) | 0.340939 / 0.283200 (0.057739) | 0.019710 / 0.141683 (-0.121972) | 1.446068 / 1.452155 (-0.006086) | 1.510342 / 1.492716 (0.017625) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.219742 / 0.018006 (0.201736) | 0.431794 / 0.000490 (0.431304) | 0.005717 / 0.000200 (0.005517) | 0.000195 / 0.000054 (0.000141) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024486 / 0.037411 (-0.012926) | 0.073231 / 0.014526 (0.058706) | 0.084053 / 0.176557 (-0.092503) | 0.145857 / 0.737135 (-0.591279) | 0.083050 / 0.296338 (-0.213289) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.400532 / 0.215209 (0.185323) | 3.989293 / 2.077655 (1.911638) | 1.935520 / 1.504120 (0.431400) | 1.754146 / 1.541195 (0.212951) | 1.821060 / 1.468490 (0.352570) | 0.512603 / 4.584777 (-4.072173) | 3.070974 / 3.745712 (-0.674738) | 2.984617 / 5.269862 (-2.285245) | 1.875790 / 4.565676 (-2.689886) | 0.057881 / 0.424275 (-0.366394) | 0.006403 / 0.007607 (-0.001204) | 0.465542 / 0.226044 (0.239498) | 4.659589 / 2.268929 (2.390661) | 2.349637 / 55.444624 (-53.094987) | 2.011511 / 6.876477 (-4.864965) | 2.071893 / 2.142072 (-0.070179) | 0.591113 / 4.805227 (-4.214114) | 0.125000 / 6.500664 (-6.375664) | 0.061372 / 0.075469 (-0.014097) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.237068 / 1.841788 (-0.604720) | 17.493192 / 8.074308 (9.418884) | 13.600688 / 10.191392 (3.409296) | 0.142508 / 0.680424 (-0.537916) | 0.017305 / 0.534201 (-0.516896) | 0.333352 / 0.579283 (-0.245931) | 0.366699 / 0.434364 (-0.067665) | 0.381104 / 0.540337 (-0.159233) | 0.562645 / 1.386936 (-0.824291) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006337 / 0.011353 (-0.005016) | 0.003584 / 0.011008 (-0.007424) | 0.063351 / 0.038508 (0.024843) | 0.061351 / 0.023109 (0.038242) | 0.430690 / 0.275898 (0.154792) | 0.462158 / 0.323480 (0.138678) | 0.004922 / 0.007986 (-0.003064) | 0.002898 / 0.004328 (-0.001430) | 0.063722 / 0.004250 (0.059472) | 0.046970 / 0.037052 (0.009918) | 0.436340 / 0.258489 (0.177851) | 0.472842 / 0.293841 (0.179001) | 0.029238 / 0.128546 (-0.099309) | 0.008079 / 0.075646 (-0.067568) | 0.068425 / 0.419271 (-0.350846) | 0.041272 / 0.043533 (-0.002261) | 0.429150 / 0.255139 (0.174011) | 0.451859 / 0.283200 (0.168659) | 0.020135 / 0.141683 (-0.121547) | 1.440388 / 1.452155 (-0.011767) | 1.506784 / 1.492716 (0.014068) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.225810 / 0.018006 (0.207804) | 0.408447 / 0.000490 (0.407957) | 0.002484 / 0.000200 (0.002284) | 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) | 0.026162 / 0.037411 (-0.011250) | 0.079292 / 0.014526 (0.064766) | 0.091126 / 0.176557 (-0.085431) | 0.141607 / 0.737135 (-0.595528) | 0.090073 / 0.296338 (-0.206266) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.420689 / 0.215209 (0.205479) | 4.207631 / 2.077655 (2.129976) | 2.163469 / 1.504120 (0.659350) | 2.098208 / 1.541195 (0.557013) | 2.217340 / 1.468490 (0.748850) | 0.502599 / 4.584777 (-4.082178) | 3.128151 / 3.745712 (-0.617561) | 2.921041 / 5.269862 (-2.348820) | 1.808352 / 4.565676 (-2.757325) | 0.057724 / 0.424275 (-0.366551) | 0.006423 / 0.007607 (-0.001184) | 0.490631 / 0.226044 (0.264587) | 4.878761 / 2.268929 (2.609833) | 2.614831 / 55.444624 (-52.829793) | 2.214611 / 6.876477 (-4.661866) | 2.253313 / 2.142072 (0.111241) | 0.585643 / 4.805227 (-4.219584) | 0.122436 / 6.500664 (-6.378228) | 0.057974 / 0.075469 (-0.017495) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.334290 / 1.841788 (-0.507498) | 17.778981 / 8.074308 (9.704672) | 14.982837 / 10.191392 (4.791445) | 0.135731 / 0.680424 (-0.544693) | 0.018314 / 0.534201 (-0.515887) | 0.332318 / 0.579283 (-0.246966) | 0.380185 / 0.434364 (-0.054179) | 0.391430 / 0.540337 (-0.148907) | 0.554577 / 1.386936 (-0.832359) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1715b61a096cafb20caeb136111522432aba04f5 \"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) | 0.005248 / 0.011353 (-0.006105) | 0.003188 / 0.011008 (-0.007820) | 0.063045 / 0.038508 (0.024537) | 0.033620 / 0.023109 (0.010511) | 0.244725 / 0.275898 (-0.031173) | 0.283259 / 0.323480 (-0.040220) | 0.003013 / 0.007986 (-0.004973) | 0.002486 / 0.004328 (-0.001842) | 0.048873 / 0.004250 (0.044623) | 0.049431 / 0.037052 (0.012379) | 0.245297 / 0.258489 (-0.013192) | 0.283127 / 0.293841 (-0.010714) | 0.024204 / 0.128546 (-0.104342) | 0.007542 / 0.075646 (-0.068104) | 0.204831 / 0.419271 (-0.214440) | 0.067487 / 0.043533 (0.023954) | 0.251477 / 0.255139 (-0.003662) | 0.273108 / 0.283200 (-0.010091) | 0.021035 / 0.141683 (-0.120648) | 1.108361 / 1.452155 (-0.343793) | 1.172923 / 1.492716 (-0.319793) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.094729 / 0.018006 (0.076722) | 0.301877 / 0.000490 (0.301388) | 0.000223 / 0.000200 (0.000023) | 0.000050 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019901 / 0.037411 (-0.017511) | 0.068059 / 0.014526 (0.053534) | 0.075333 / 0.176557 (-0.101224) | 0.123276 / 0.737135 (-0.613859) | 0.076810 / 0.296338 (-0.219528) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.283421 / 0.215209 (0.068211) | 2.775511 / 2.077655 (0.697857) | 1.430927 / 1.504120 (-0.073193) | 1.317334 / 1.541195 (-0.223860) | 1.359483 / 1.468490 (-0.109007) | 0.403186 / 4.584777 (-4.181591) | 2.405789 / 3.745712 (-1.339923) | 2.773039 / 5.269862 (-2.496823) | 1.666722 / 4.565676 (-2.898954) | 0.047937 / 0.424275 (-0.376338) | 0.004879 / 0.007607 (-0.002728) | 0.347225 / 0.226044 (0.121180) | 3.380860 / 2.268929 (1.111931) | 1.838532 / 55.444624 (-53.606092) | 1.597681 / 6.876477 (-5.278796) | 1.600123 / 2.142072 (-0.541949) | 0.478836 / 4.805227 (-4.326391) | 0.100332 / 6.500664 (-6.400332) | 0.043334 / 0.075469 (-0.032135) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.942591 / 1.841788 (-0.899196) | 12.588886 / 8.074308 (4.514578) | 11.375666 / 10.191392 (1.184274) | 0.143460 / 0.680424 (-0.536964) | 0.014990 / 0.534201 (-0.519211) | 0.271068 / 0.579283 (-0.308216) | 0.265478 / 0.434364 (-0.168885) | 0.310914 / 0.540337 (-0.229423) | 0.428310 / 1.386936 (-0.958626) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004986 / 0.011353 (-0.006367) | 0.003263 / 0.011008 (-0.007745) | 0.049076 / 0.038508 (0.010567) | 0.063665 / 0.023109 (0.040556) | 0.270352 / 0.275898 (-0.005546) | 0.298849 / 0.323480 (-0.024631) | 0.004083 / 0.007986 (-0.003903) | 0.002503 / 0.004328 (-0.001826) | 0.048586 / 0.004250 (0.044335) | 0.040701 / 0.037052 (0.003648) | 0.274082 / 0.258489 (0.015593) | 0.308279 / 0.293841 (0.014438) | 0.024734 / 0.128546 (-0.103812) | 0.007535 / 0.075646 (-0.068111) | 0.054670 / 0.419271 (-0.364602) | 0.032828 / 0.043533 (-0.010705) | 0.276226 / 0.255139 (0.021087) | 0.289322 / 0.283200 (0.006122) | 0.018789 / 0.141683 (-0.122893) | 1.279837 / 1.452155 (-0.172318) | 1.203010 / 1.492716 (-0.289706) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.095674 / 0.018006 (0.077667) | 0.309754 / 0.000490 (0.309265) | 0.000229 / 0.000200 (0.000029) | 0.000052 / 0.000054 (-0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021733 / 0.037411 (-0.015678) | 0.074858 / 0.014526 (0.060332) | 0.081845 / 0.176557 (-0.094711) | 0.121991 / 0.737135 (-0.615145) | 0.084057 / 0.296338 (-0.212281) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.298456 / 0.215209 (0.083246) | 2.884930 / 2.077655 (0.807276) | 1.574875 / 1.504120 (0.070755) | 1.451598 / 1.541195 (-0.089597) | 1.548106 / 1.468490 (0.079616) | 0.408662 / 4.584777 (-4.176115) | 2.444306 / 3.745712 (-1.301406) | 2.737027 / 5.269862 (-2.532835) | 1.633085 / 4.565676 (-2.932592) | 0.047349 / 0.424275 (-0.376926) | 0.004864 / 0.007607 (-0.002744) | 0.355434 / 0.226044 (0.129389) | 3.495531 / 2.268929 (1.226603) | 1.972737 / 55.444624 (-53.471888) | 1.706973 / 6.876477 (-5.169504) | 1.798985 / 2.142072 (-0.343087) | 0.490353 / 4.805227 (-4.314874) | 0.099533 / 6.500664 (-6.401131) | 0.042397 / 0.075469 (-0.033073) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.978092 / 1.841788 (-0.863696) | 13.166220 / 8.074308 (5.091912) | 11.673518 / 10.191392 (1.482126) | 0.134253 / 0.680424 (-0.546171) | 0.016478 / 0.534201 (-0.517723) | 0.271629 / 0.579283 (-0.307654) | 0.284082 / 0.434364 (-0.150282) | 0.313352 / 0.540337 (-0.226986) | 0.416913 / 1.386936 (-0.970023) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#01e7144a02825fea3418872c51a8ca93950f3080 \"CML watermark\")\n" ]

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"2023-10-27T14:39:57Z"

"2024-02-06T19:24:20Z"

"2024-02-06T19:24:20Z"

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[ "This issue stems from https://github.com/huggingface/datasets/blob/6d2f2a5e0fea3827eccfd1717d8021c15fc4292a/src/datasets/table.py#L2203-L2205\r\n\r\nI'll address it as part of https://github.com/huggingface/datasets/pull/6283.\r\n\r\nIn the meantime, this should work\r\n\r\n```python\r\nimport pyarrow as pa\r\nfrom datasets import Image\r\n\r\ndataset = dataset.with_format(\"arrow\")\r\n\r\ndef embed_images(pa_table):\r\n images_arr = pa.chunked_array(\r\n [\r\n pa.ListArray.from_arrays(chunk.offsets, Image().embed_storage(chunk.values), mask=chunk.is_null())\r\n for chunk in pa_table[\"images\"].chunks\r\n ]\r\n )\r\n return pa_table.set_column(pa_table.schema.get_field_index(\"images\"), \"images\", images_arr)\r\n\r\ndataset = dataset.map(embed_images, batched=True)\r\n\r\ndataset = dataset.with_format(\"python\")\r\n\r\ndataset.push_to_hub(...)\r\n```" ]

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[]

"2023-10-27T12:01:51Z"

"2024-01-24T15:02:06Z"

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[ "Most likely, the data file inference logic is the problem here.\r\n\r\nYou can run the following code to verify this:\r\n```python\r\nimport time\r\nfrom datasets.data_files import get_data_patterns\r\nstart_time = time.time()\r\nget_data_patterns(\"/path/to/img_dir\")\r\nend_time = time.time()\r\nprint(f\"Elapsed time: {end_time - start_time:.2f}s\")\r\n```\r\n \r\nWe plan to optimize this for the next version (or version after that). In the meantime, specifying the split patterns manually should give better performance:\r\n```python\r\nds = load_dataset(\"imagefolder\", data_files={\"train\": \"path/to/img_dir/train/**\", ...}, split=\"train\")\r\n```", "Hi, @mariosasko, you are right; data file inference logic is extremely slow.\r\n\r\nI have done a similar test, that is I modify the source code of datasets/load.py to measure the cost of two suspicious operations:\r\n```python\r\ndef get_module(self) -> DatasetModule:\r\n base_path = Path(self.data_dir or \"\").expanduser().resolve().as_posix()\r\n start = time.time()\r\n patterns = sanitize_patterns(self.data_files) if self.data_files is not None else get_data_patterns(base_path)\r\n print(f\"patterns: {time.time() - start}\")\r\n start = time.time()\r\n data_files = DataFilesDict.from_patterns(\r\n patterns,\r\n download_config=self.download_config,\r\n base_path=base_path,\r\n )\r\n print(f\"data_files: {time.time() - start}\")\r\n```\r\nIt gaves:\r\npatterns: 3062.2050700187683\r\ndata_files: 413.9576675891876\r\n\r\nThus, these two operations contribute to almost all of load time. What's going on in them?", "Furthermore, what's my current workaround about this problem? Should I save it by `save_to_disk()` and load dataset through `load_from_disk`?", "were you able to solve this issue?, I am facing the same issue" ]

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"2023-10-27T08:20:27Z"

"2023-11-28T14:47:12Z"

"2023-11-28T14:47:12Z"

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[ "You may be able to make it work by tweaking some environment variables, such as [`HF_HOME`](https://huggingface.co/docs/huggingface_hub/main/en/package_reference/environment_variables#hfhome) or [`HF_DATASETS_CACHE`](https://huggingface.co/docs/datasets/cache#cache-directory).", "> You may be able to make it work by tweaking some environment variables, such as [`HF_HOME`](https://huggingface.co/docs/huggingface_hub/main/en/package_reference/environment_variables#hfhome) or [`HF_DATASETS_CACHE`](https://huggingface.co/docs/datasets/cache#cache-directory).\r\n\r\nI am already doing this. The problem is that, while this seemingly allows flexibility, the absolute paths written into the cache still have the old cache directory. The paths written into the cache should be relative to the cache location to allow this sort of flexibility. Sorry, I omitted this in the reproduction steps, I have now added it.", "I'm unable to reproduce this with the cache\r\n```bash\r\nexport HF_CACHE=$PWD/hf_cache\r\npython -c \"import datasets; datasets.load_dataset('imdb')\"\r\n```\r\nimported inside a dummy container that is built from\r\n```bash\r\nFROM python:3.9\r\n\r\nWORKDIR /usr/src/app\r\n\r\nRUN pip install datasets\r\n\r\nCOPY ./hf_cache ./hf_cache\r\n\r\nENV HF_HOME=./hf_cache\r\nENV HF_DATASETS_OFFLINE=1\r\n\r\nCMD [\"python\"]\r\n```\r\nWhat do you mean by \"absolute paths written into the cache\"? Paths inside the HF cache paths are based on hash (hashed URL of the downloaded files, etc.)" ]

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"2023-10-27T02:31:16Z"

"2023-10-27T02:31:16Z"

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"2023-10-26T17:19:25Z"

"2023-10-26T18:42:56Z"

"2023-10-26T18:32:21Z"

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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) | 0.008775 / 0.011353 (-0.002578) | 0.005304 / 0.011008 (-0.005704) | 0.108912 / 0.038508 (0.070404) | 0.075589 / 0.023109 (0.052479) | 0.456612 / 0.275898 (0.180713) | 0.502303 / 0.323480 (0.178823) | 0.006695 / 0.007986 (-0.001291) | 0.004404 / 0.004328 (0.000076) | 0.084802 / 0.004250 (0.080552) | 0.062711 / 0.037052 (0.025659) | 0.465062 / 0.258489 (0.206573) | 0.505321 / 0.293841 (0.211480) | 0.049401 / 0.128546 (-0.079146) | 0.014784 / 0.075646 (-0.060862) | 0.378202 / 0.419271 (-0.041069) | 0.069826 / 0.043533 (0.026293) | 0.461161 / 0.255139 (0.206022) | 0.484616 / 0.283200 (0.201416) | 0.035998 / 0.141683 (-0.105685) | 1.846343 / 1.452155 (0.394189) | 1.999439 / 1.492716 (0.506723) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.317779 / 0.018006 (0.299773) | 0.605967 / 0.000490 (0.605477) | 0.011412 / 0.000200 (0.011212) | 0.000410 / 0.000054 (0.000356) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031118 / 0.037411 (-0.006293) | 0.095425 / 0.014526 (0.080900) | 0.108002 / 0.176557 (-0.068554) | 0.184625 / 0.737135 (-0.552511) | 0.108180 / 0.296338 (-0.188159) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.587497 / 0.215209 (0.372288) | 5.818632 / 2.077655 (3.740977) | 2.629776 / 1.504120 (1.125656) | 2.266129 / 1.541195 (0.724934) | 2.324618 / 1.468490 (0.856128) | 0.830049 / 4.584777 (-3.754728) | 5.380062 / 3.745712 (1.634350) | 4.808525 / 5.269862 (-0.461336) | 2.960368 / 4.565676 (-1.605309) | 0.093637 / 0.424275 (-0.330638) | 0.009187 / 0.007607 (0.001580) | 0.703468 / 0.226044 (0.477424) | 6.924509 / 2.268929 (4.655580) | 3.380582 / 55.444624 (-52.064043) | 2.689118 / 6.876477 (-4.187358) | 2.712418 / 2.142072 (0.570345) | 1.017144 / 4.805227 (-3.788084) | 0.212874 / 6.500664 (-6.287791) | 0.080053 / 0.075469 (0.004584) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.623663 / 1.841788 (-0.218125) | 23.668872 / 8.074308 (15.594564) | 20.245972 / 10.191392 (10.054580) | 0.236448 / 0.680424 (-0.443976) | 0.029730 / 0.534201 (-0.504470) | 0.491525 / 0.579283 (-0.087758) | 0.593780 / 0.434364 (0.159416) | 0.548776 / 0.540337 (0.008438) | 0.799370 / 1.386936 (-0.587566) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009714 / 0.011353 (-0.001639) | 0.005328 / 0.011008 (-0.005681) | 0.078460 / 0.038508 (0.039952) | 0.077791 / 0.023109 (0.054682) | 0.510124 / 0.275898 (0.234226) | 0.547769 / 0.323480 (0.224289) | 0.006868 / 0.007986 (-0.001118) | 0.004145 / 0.004328 (-0.000183) | 0.088696 / 0.004250 (0.084445) | 0.072387 / 0.037052 (0.035334) | 0.527373 / 0.258489 (0.268884) | 0.561948 / 0.293841 (0.268107) | 0.049769 / 0.128546 (-0.078777) | 0.014401 / 0.075646 (-0.061246) | 0.097541 / 0.419271 (-0.321731) | 0.062237 / 0.043533 (0.018705) | 0.531001 / 0.255139 (0.275862) | 0.561797 / 0.283200 (0.278597) | 0.038482 / 0.141683 (-0.103201) | 1.783558 / 1.452155 (0.331404) | 1.864339 / 1.492716 (0.371622) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.289389 / 0.018006 (0.271383) | 0.595326 / 0.000490 (0.594836) | 0.004583 / 0.000200 (0.004383) | 0.000114 / 0.000054 (0.000060) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034492 / 0.037411 (-0.002919) | 0.102934 / 0.014526 (0.088409) | 0.121689 / 0.176557 (-0.054868) | 0.182121 / 0.737135 (-0.555015) | 0.127087 / 0.296338 (-0.169252) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.645726 / 0.215209 (0.430517) | 6.462235 / 2.077655 (4.384580) | 3.044176 / 1.504120 (1.540056) | 2.731181 / 1.541195 (1.189986) | 2.805508 / 1.468490 (1.337018) | 0.846324 / 4.584777 (-3.738453) | 5.341074 / 3.745712 (1.595362) | 4.687111 / 5.269862 (-0.582751) | 3.035472 / 4.565676 (-1.530205) | 0.099193 / 0.424275 (-0.325082) | 0.008825 / 0.007607 (0.001218) | 0.795102 / 0.226044 (0.569058) | 7.895770 / 2.268929 (5.626842) | 3.826752 / 55.444624 (-51.617873) | 3.112217 / 6.876477 (-3.764259) | 3.526878 / 2.142072 (1.384806) | 1.011352 / 4.805227 (-3.793875) | 0.213424 / 6.500664 (-6.287240) | 0.076228 / 0.075469 (0.000759) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.805232 / 1.841788 (-0.036556) | 24.049100 / 8.074308 (15.974792) | 23.056011 / 10.191392 (12.864619) | 0.261656 / 0.680424 (-0.418767) | 0.032021 / 0.534201 (-0.502179) | 0.483829 / 0.579283 (-0.095454) | 0.602208 / 0.434364 (0.167844) | 0.565848 / 0.540337 (0.025511) | 0.818678 / 1.386936 (-0.568258) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#71fc5e2ca41f5f725b9117f4cf99f348534902f3 \"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) | 0.008043 / 0.011353 (-0.003310) | 0.004642 / 0.011008 (-0.006366) | 0.102592 / 0.038508 (0.064084) | 0.099508 / 0.023109 (0.076399) | 0.377692 / 0.275898 (0.101794) | 0.409929 / 0.323480 (0.086450) | 0.006363 / 0.007986 (-0.001622) | 0.003881 / 0.004328 (-0.000447) | 0.076636 / 0.004250 (0.072386) | 0.067021 / 0.037052 (0.029969) | 0.371454 / 0.258489 (0.112964) | 0.423637 / 0.293841 (0.129796) | 0.038632 / 0.128546 (-0.089914) | 0.010055 / 0.075646 (-0.065591) | 0.352021 / 0.419271 (-0.067251) | 0.064988 / 0.043533 (0.021456) | 0.369614 / 0.255139 (0.114475) | 0.396972 / 0.283200 (0.113773) | 0.028866 / 0.141683 (-0.112817) | 1.757620 / 1.452155 (0.305465) | 1.886283 / 1.492716 (0.393567) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.257579 / 0.018006 (0.239572) | 0.529859 / 0.000490 (0.529369) | 0.011720 / 0.000200 (0.011520) | 0.000455 / 0.000054 (0.000401) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034163 / 0.037411 (-0.003248) | 0.101422 / 0.014526 (0.086896) | 0.114858 / 0.176557 (-0.061698) | 0.180265 / 0.737135 (-0.556870) | 0.116034 / 0.296338 (-0.180305) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.477609 / 0.215209 (0.262400) | 4.830116 / 2.077655 (2.752461) | 2.323844 / 1.504120 (0.819724) | 2.174496 / 1.541195 (0.633301) | 2.268594 / 1.468490 (0.800104) | 0.612429 / 4.584777 (-3.972348) | 4.265277 / 3.745712 (0.519565) | 4.095741 / 5.269862 (-1.174121) | 2.561532 / 4.565676 (-2.004144) | 0.068043 / 0.424275 (-0.356233) | 0.009139 / 0.007607 (0.001532) | 0.545512 / 0.226044 (0.319467) | 5.456403 / 2.268929 (3.187475) | 2.778937 / 55.444624 (-52.665688) | 2.428560 / 6.876477 (-4.447917) | 2.557483 / 2.142072 (0.415411) | 0.696721 / 4.805227 (-4.108506) | 0.157217 / 6.500664 (-6.343447) | 0.071334 / 0.075469 (-0.004135) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.617755 / 1.841788 (-0.224032) | 23.368508 / 8.074308 (15.294200) | 17.028591 / 10.191392 (6.837199) | 0.195881 / 0.680424 (-0.484542) | 0.021788 / 0.534201 (-0.512413) | 0.468484 / 0.579283 (-0.110799) | 0.474604 / 0.434364 (0.040240) | 0.544738 / 0.540337 (0.004400) | 0.771722 / 1.386936 (-0.615214) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007939 / 0.011353 (-0.003414) | 0.004684 / 0.011008 (-0.006324) | 0.077273 / 0.038508 (0.038765) | 0.088763 / 0.023109 (0.065654) | 0.489178 / 0.275898 (0.213280) | 0.531547 / 0.323480 (0.208067) | 0.006214 / 0.007986 (-0.001772) | 0.003988 / 0.004328 (-0.000340) | 0.076685 / 0.004250 (0.072434) | 0.066628 / 0.037052 (0.029576) | 0.497153 / 0.258489 (0.238664) | 0.538301 / 0.293841 (0.244460) | 0.037939 / 0.128546 (-0.090607) | 0.010054 / 0.075646 (-0.065592) | 0.084642 / 0.419271 (-0.334629) | 0.057140 / 0.043533 (0.013608) | 0.487701 / 0.255139 (0.232562) | 0.519676 / 0.283200 (0.236477) | 0.026560 / 0.141683 (-0.115123) | 1.809676 / 1.452155 (0.357521) | 1.864884 / 1.492716 (0.372168) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.259005 / 0.018006 (0.240998) | 0.522900 / 0.000490 (0.522410) | 0.006885 / 0.000200 (0.006685) | 0.000156 / 0.000054 (0.000102) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.039838 / 0.037411 (0.002426) | 0.117777 / 0.014526 (0.103251) | 0.129189 / 0.176557 (-0.047368) | 0.198584 / 0.737135 (-0.538552) | 0.129753 / 0.296338 (-0.166586) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.543366 / 0.215209 (0.328157) | 5.241502 / 2.077655 (3.163847) | 2.719079 / 1.504120 (1.214959) | 2.525337 / 1.541195 (0.984142) | 2.648908 / 1.468490 (1.180418) | 0.589239 / 4.584777 (-3.995538) | 4.379856 / 3.745712 (0.634144) | 4.139919 / 5.269862 (-1.129943) | 2.633412 / 4.565676 (-1.932264) | 0.074582 / 0.424275 (-0.349693) | 0.009106 / 0.007607 (0.001499) | 0.635540 / 0.226044 (0.409495) | 6.072965 / 2.268929 (3.804037) | 3.327233 / 55.444624 (-52.117391) | 3.012637 / 6.876477 (-3.863840) | 3.113226 / 2.142072 (0.971154) | 0.712705 / 4.805227 (-4.092523) | 0.159550 / 6.500664 (-6.341114) | 0.073446 / 0.075469 (-0.002023) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.718732 / 1.841788 (-0.123055) | 23.249445 / 8.074308 (15.175137) | 17.630643 / 10.191392 (7.439251) | 0.201017 / 0.680424 (-0.479407) | 0.024162 / 0.534201 (-0.510039) | 0.475054 / 0.579283 (-0.104229) | 0.492348 / 0.434364 (0.057985) | 0.587118 / 0.540337 (0.046781) | 0.777462 / 1.386936 (-0.609474) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#feb036956a592b9a9ecdf048cc801549f233dbef \"CML watermark\")\n" ]

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"2023-10-26T16:54:46Z"

"2024-01-11T06:34:16Z"

"2023-10-30T17:32:57Z"

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006941 / 0.011353 (-0.004412) | 0.004255 / 0.011008 (-0.006753) | 0.085237 / 0.038508 (0.046729) | 0.080962 / 0.023109 (0.057853) | 0.312016 / 0.275898 (0.036118) | 0.353161 / 0.323480 (0.029681) | 0.005756 / 0.007986 (-0.002230) | 0.003591 / 0.004328 (-0.000738) | 0.065416 / 0.004250 (0.061166) | 0.057837 / 0.037052 (0.020785) | 0.316169 / 0.258489 (0.057680) | 0.372345 / 0.293841 (0.078504) | 0.031958 / 0.128546 (-0.096588) | 0.008798 / 0.075646 (-0.066848) | 0.294764 / 0.419271 (-0.124507) | 0.053954 / 0.043533 (0.010421) | 0.310961 / 0.255139 (0.055822) | 0.330063 / 0.283200 (0.046864) | 0.025298 / 0.141683 (-0.116385) | 1.454715 / 1.452155 (0.002560) | 1.557915 / 1.492716 (0.065198) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.274830 / 0.018006 (0.256824) | 0.565890 / 0.000490 (0.565400) | 0.009242 / 0.000200 (0.009042) | 0.000321 / 0.000054 (0.000266) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031092 / 0.037411 (-0.006320) | 0.087558 / 0.014526 (0.073033) | 0.103395 / 0.176557 (-0.073162) | 0.160078 / 0.737135 (-0.577057) | 0.102356 / 0.296338 (-0.193983) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.402912 / 0.215209 (0.187703) | 4.029374 / 2.077655 (1.951719) | 2.048237 / 1.504120 (0.544117) | 1.887470 / 1.541195 (0.346276) | 1.994807 / 1.468490 (0.526316) | 0.491109 / 4.584777 (-4.093668) | 3.645059 / 3.745712 (-0.100653) | 3.516376 / 5.269862 (-1.753486) | 2.103267 / 4.565676 (-2.462409) | 0.058072 / 0.424275 (-0.366203) | 0.007796 / 0.007607 (0.000189) | 0.480544 / 0.226044 (0.254499) | 4.795422 / 2.268929 (2.526494) | 2.507770 / 55.444624 (-52.936854) | 2.187106 / 6.876477 (-4.689371) | 2.271005 / 2.142072 (0.128933) | 0.585376 / 4.805227 (-4.219851) | 0.134741 / 6.500664 (-6.365923) | 0.060684 / 0.075469 (-0.014785) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.264349 / 1.841788 (-0.577439) | 19.448735 / 8.074308 (11.374427) | 14.521197 / 10.191392 (4.329805) | 0.167295 / 0.680424 (-0.513129) | 0.018352 / 0.534201 (-0.515849) | 0.396345 / 0.579283 (-0.182938) | 0.418690 / 0.434364 (-0.015674) | 0.469703 / 0.540337 (-0.070635) | 0.637852 / 1.386936 (-0.749084) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006939 / 0.011353 (-0.004414) | 0.004196 / 0.011008 (-0.006812) | 0.064719 / 0.038508 (0.026211) | 0.077517 / 0.023109 (0.054407) | 0.401977 / 0.275898 (0.126079) | 0.431089 / 0.323480 (0.107609) | 0.005624 / 0.007986 (-0.002362) | 0.003680 / 0.004328 (-0.000649) | 0.065817 / 0.004250 (0.061567) | 0.058297 / 0.037052 (0.021245) | 0.399614 / 0.258489 (0.141125) | 0.440089 / 0.293841 (0.146248) | 0.032492 / 0.128546 (-0.096054) | 0.008974 / 0.075646 (-0.066672) | 0.071311 / 0.419271 (-0.347961) | 0.048001 / 0.043533 (0.004468) | 0.394763 / 0.255139 (0.139624) | 0.416754 / 0.283200 (0.133554) | 0.023730 / 0.141683 (-0.117953) | 1.509677 / 1.452155 (0.057522) | 1.605711 / 1.492716 (0.112994) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.265490 / 0.018006 (0.247483) | 0.561745 / 0.000490 (0.561255) | 0.004616 / 0.000200 (0.004417) | 0.000105 / 0.000054 (0.000051) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033371 / 0.037411 (-0.004040) | 0.092763 / 0.014526 (0.078238) | 0.108905 / 0.176557 (-0.067652) | 0.160380 / 0.737135 (-0.576756) | 0.106968 / 0.296338 (-0.189370) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.430268 / 0.215209 (0.215059) | 4.299313 / 2.077655 (2.221658) | 2.308971 / 1.504120 (0.804851) | 2.155855 / 1.541195 (0.614661) | 2.392698 / 1.468490 (0.924208) | 0.498464 / 4.584777 (-4.086313) | 3.694473 / 3.745712 (-0.051239) | 3.409625 / 5.269862 (-1.860236) | 2.106144 / 4.565676 (-2.459532) | 0.058992 / 0.424275 (-0.365283) | 0.007395 / 0.007607 (-0.000212) | 0.511291 / 0.226044 (0.285247) | 5.101806 / 2.268929 (2.832877) | 2.853100 / 55.444624 (-52.591524) | 2.527216 / 6.876477 (-4.349260) | 2.819380 / 2.142072 (0.677308) | 0.635155 / 4.805227 (-4.170072) | 0.135816 / 6.500664 (-6.364848) | 0.062056 / 0.075469 (-0.013413) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.353479 / 1.841788 (-0.488308) | 20.318513 / 8.074308 (12.244205) | 15.105336 / 10.191392 (4.913944) | 0.166186 / 0.680424 (-0.514238) | 0.020742 / 0.534201 (-0.513459) | 0.399286 / 0.579283 (-0.179997) | 0.431785 / 0.434364 (-0.002579) | 0.478667 / 0.540337 (-0.061671) | 0.654683 / 1.386936 (-0.732253) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b39d1ce0b8f231649752f28cb724971f4df1c7ae \"CML watermark\")\n", "Yea I think some of it should be in the Hub docs indeed, let me open a new PR there.\r\n\r\nThen I'll update the `datasets` docs anyway to avoid redundant stuff and add redirects instead" ]

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"2023-10-26T12:43:36Z"

"2023-11-14T18:46:03Z"

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

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"2023-10-26T03:47:06Z"

"2024-02-02T15:23:14Z"

"2023-10-26T10:18:04Z"

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

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"2023-10-25T21:55:31Z"

"2023-11-07T07:26:54Z"

"2023-11-07T07:26:54Z"

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

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[]

[]

"2023-10-25T18:21:08Z"

"2023-10-26T17:19:49Z"

"2023-10-26T17:10:27Z"

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007718 / 0.011353 (-0.003635) | 0.004730 / 0.011008 (-0.006278) | 0.097262 / 0.038508 (0.058754) | 0.077880 / 0.023109 (0.054771) | 0.363855 / 0.275898 (0.087957) | 0.394470 / 0.323480 (0.070990) | 0.006416 / 0.007986 (-0.001570) | 0.003596 / 0.004328 (-0.000732) | 0.076494 / 0.004250 (0.072243) | 0.062656 / 0.037052 (0.025603) | 0.366160 / 0.258489 (0.107671) | 0.421383 / 0.293841 (0.127542) | 0.035756 / 0.128546 (-0.092791) | 0.009430 / 0.075646 (-0.066217) | 0.327722 / 0.419271 (-0.091550) | 0.061252 / 0.043533 (0.017719) | 0.352167 / 0.255139 (0.097028) | 0.385166 / 0.283200 (0.101966) | 0.026656 / 0.141683 (-0.115027) | 1.718533 / 1.452155 (0.266378) | 1.886646 / 1.492716 (0.393930) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.254564 / 0.018006 (0.236558) | 0.490942 / 0.000490 (0.490452) | 0.011656 / 0.000200 (0.011456) | 0.000313 / 0.000054 (0.000259) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028753 / 0.037411 (-0.008659) | 0.093076 / 0.014526 (0.078550) | 0.096441 / 0.176557 (-0.080116) | 0.154848 / 0.737135 (-0.582287) | 0.092903 / 0.296338 (-0.203435) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.395611 / 0.215209 (0.180402) | 3.860736 / 2.077655 (1.783082) | 1.908808 / 1.504120 (0.404688) | 1.708975 / 1.541195 (0.167781) | 1.848173 / 1.468490 (0.379683) | 0.527022 / 4.584777 (-4.057755) | 3.815171 / 3.745712 (0.069459) | 3.621132 / 5.269862 (-1.648730) | 2.220238 / 4.565676 (-2.345439) | 0.063169 / 0.424275 (-0.361106) | 0.008906 / 0.007607 (0.001299) | 0.510478 / 0.226044 (0.284433) | 4.828116 / 2.268929 (2.559187) | 2.340801 / 55.444624 (-53.103824) | 2.040834 / 6.876477 (-4.835642) | 2.092316 / 2.142072 (-0.049757) | 0.579194 / 4.805227 (-4.226033) | 0.135525 / 6.500664 (-6.365139) | 0.062720 / 0.075469 (-0.012749) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.393091 / 1.841788 (-0.448697) | 19.751526 / 8.074308 (11.677218) | 14.161795 / 10.191392 (3.970403) | 0.163340 / 0.680424 (-0.517084) | 0.021504 / 0.534201 (-0.512697) | 0.393183 / 0.579283 (-0.186100) | 0.448407 / 0.434364 (0.014043) | 0.504169 / 0.540337 (-0.036169) | 0.663698 / 1.386936 (-0.723238) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007390 / 0.011353 (-0.003962) | 0.004381 / 0.011008 (-0.006628) | 0.074501 / 0.038508 (0.035993) | 0.078242 / 0.023109 (0.055133) | 0.481108 / 0.275898 (0.205210) | 0.512111 / 0.323480 (0.188631) | 0.006280 / 0.007986 (-0.001705) | 0.003820 / 0.004328 (-0.000509) | 0.071602 / 0.004250 (0.067351) | 0.068359 / 0.037052 (0.031307) | 0.478484 / 0.258489 (0.219995) | 0.519543 / 0.293841 (0.225702) | 0.036211 / 0.128546 (-0.092335) | 0.009433 / 0.075646 (-0.066213) | 0.086140 / 0.419271 (-0.333132) | 0.054177 / 0.043533 (0.010644) | 0.466726 / 0.255139 (0.211587) | 0.514085 / 0.283200 (0.230885) | 0.026729 / 0.141683 (-0.114954) | 1.743770 / 1.452155 (0.291615) | 1.833469 / 1.492716 (0.340753) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.251339 / 0.018006 (0.233333) | 0.472294 / 0.000490 (0.471804) | 0.013381 / 0.000200 (0.013181) | 0.000117 / 0.000054 (0.000062) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.037845 / 0.037411 (0.000433) | 0.105977 / 0.014526 (0.091451) | 0.124446 / 0.176557 (-0.052111) | 0.180432 / 0.737135 (-0.556703) | 0.120844 / 0.296338 (-0.175495) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.470928 / 0.215209 (0.255719) | 4.738154 / 2.077655 (2.660499) | 2.558618 / 1.504120 (1.054498) | 2.359745 / 1.541195 (0.818550) | 2.458438 / 1.468490 (0.989948) | 0.548580 / 4.584777 (-4.036197) | 3.912145 / 3.745712 (0.166433) | 3.764174 / 5.269862 (-1.505687) | 2.325265 / 4.565676 (-2.240411) | 0.078022 / 0.424275 (-0.346254) | 0.008279 / 0.007607 (0.000672) | 0.571635 / 0.226044 (0.345590) | 5.672445 / 2.268929 (3.403517) | 2.760577 / 55.444624 (-52.684047) | 2.544229 / 6.876477 (-4.332248) | 2.537509 / 2.142072 (0.395436) | 0.609858 / 4.805227 (-4.195369) | 0.131053 / 6.500664 (-6.369611) | 0.056433 / 0.075469 (-0.019036) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.567231 / 1.841788 (-0.274556) | 21.415586 / 8.074308 (13.341278) | 15.982328 / 10.191392 (5.790936) | 0.167648 / 0.680424 (-0.512776) | 0.023562 / 0.534201 (-0.510639) | 0.477307 / 0.579283 (-0.101976) | 0.471929 / 0.434364 (0.037566) | 0.549996 / 0.540337 (0.009659) | 0.753927 / 1.386936 (-0.633009) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1fb2785be9198997e8b9006225b0e231f4d8ed31 \"CML watermark\")\n" ]

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"2023-10-25T17:08:39Z"

"2023-10-26T21:03:06Z"

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

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"2023-10-25T13:29:51Z"

"2024-01-26T15:31:36Z"

"2023-10-31T19:59:35Z"

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

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[]

"2023-10-25T12:12:44Z"

"2023-10-25T12:13:07Z"

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[]

"2023-10-24T11:01:21Z"

"2023-10-25T13:05:21Z"

"2023-10-25T13:05:21Z"

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

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[]

"2023-10-24T08:38:43Z"

"2023-10-25T17:39:17Z"

"2023-10-25T16:36:38Z"

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009286 / 0.011353 (-0.002067) | 0.005478 / 0.011008 (-0.005530) | 0.109768 / 0.038508 (0.071260) | 0.088460 / 0.023109 (0.065351) | 0.387664 / 0.275898 (0.111766) | 0.457379 / 0.323480 (0.133899) | 0.006517 / 0.007986 (-0.001469) | 0.004037 / 0.004328 (-0.000292) | 0.083911 / 0.004250 (0.079661) | 0.071658 / 0.037052 (0.034605) | 0.385065 / 0.258489 (0.126576) | 0.460928 / 0.293841 (0.167087) | 0.048062 / 0.128546 (-0.080484) | 0.016343 / 0.075646 (-0.059303) | 0.373675 / 0.419271 (-0.045597) | 0.067640 / 0.043533 (0.024108) | 0.391730 / 0.255139 (0.136591) | 0.432908 / 0.283200 (0.149708) | 0.035748 / 0.141683 (-0.105935) | 1.767625 / 1.452155 (0.315471) | 1.965606 / 1.492716 (0.472889) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.277405 / 0.018006 (0.259399) | 0.538448 / 0.000490 (0.537958) | 0.013795 / 0.000200 (0.013595) | 0.000518 / 0.000054 (0.000464) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.043962 / 0.037411 (0.006550) | 0.115305 / 0.014526 (0.100780) | 0.117572 / 0.176557 (-0.058985) | 0.182168 / 0.737135 (-0.554968) | 0.114833 / 0.296338 (-0.181505) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.604209 / 0.215209 (0.389000) | 6.186113 / 2.077655 (4.108458) | 2.771067 / 1.504120 (1.266947) | 2.425420 / 1.541195 (0.884226) | 2.475200 / 1.468490 (1.006710) | 0.887096 / 4.584777 (-3.697681) | 5.214349 / 3.745712 (1.468637) | 4.989606 / 5.269862 (-0.280256) | 3.092135 / 4.565676 (-1.473541) | 0.104464 / 0.424275 (-0.319811) | 0.008994 / 0.007607 (0.001387) | 0.732819 / 0.226044 (0.506775) | 7.396007 / 2.268929 (5.127078) | 3.371167 / 55.444624 (-52.073457) | 2.645475 / 6.876477 (-4.231001) | 2.704215 / 2.142072 (0.562143) | 1.034724 / 4.805227 (-3.770504) | 0.219063 / 6.500664 (-6.281601) | 0.073863 / 0.075469 (-0.001606) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.625020 / 1.841788 (-0.216768) | 23.369980 / 8.074308 (15.295671) | 22.480951 / 10.191392 (12.289559) | 0.228219 / 0.680424 (-0.452204) | 0.026981 / 0.534201 (-0.507220) | 0.487670 / 0.579283 (-0.091613) | 0.582310 / 0.434364 (0.147946) | 0.539182 / 0.540337 (-0.001156) | 0.791962 / 1.386936 (-0.594974) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008657 / 0.011353 (-0.002696) | 0.004971 / 0.011008 (-0.006037) | 0.089499 / 0.038508 (0.050991) | 0.075963 / 0.023109 (0.052854) | 0.497719 / 0.275898 (0.221821) | 0.507912 / 0.323480 (0.184432) | 0.006067 / 0.007986 (-0.001919) | 0.004118 / 0.004328 (-0.000210) | 0.079397 / 0.004250 (0.075146) | 0.059181 / 0.037052 (0.022129) | 0.501108 / 0.258489 (0.242619) | 0.565792 / 0.293841 (0.271951) | 0.048818 / 0.128546 (-0.079729) | 0.014813 / 0.075646 (-0.060833) | 0.093863 / 0.419271 (-0.325409) | 0.060824 / 0.043533 (0.017292) | 0.489289 / 0.255139 (0.234150) | 0.533624 / 0.283200 (0.250425) | 0.034997 / 0.141683 (-0.106685) | 1.770574 / 1.452155 (0.318419) | 1.837213 / 1.492716 (0.344496) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.237319 / 0.018006 (0.219313) | 0.594976 / 0.000490 (0.594486) | 0.008888 / 0.000200 (0.008688) | 0.000124 / 0.000054 (0.000070) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.036955 / 0.037411 (-0.000456) | 0.097825 / 0.014526 (0.083299) | 0.111139 / 0.176557 (-0.065418) | 0.174776 / 0.737135 (-0.562359) | 0.117755 / 0.296338 (-0.178584) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.606498 / 0.215209 (0.391289) | 6.089874 / 2.077655 (4.012219) | 2.811135 / 1.504120 (1.307015) | 2.428486 / 1.541195 (0.887292) | 2.399512 / 1.468490 (0.931022) | 0.823492 / 4.584777 (-3.761285) | 4.897107 / 3.745712 (1.151395) | 4.407589 / 5.269862 (-0.862272) | 2.868442 / 4.565676 (-1.697235) | 0.098774 / 0.424275 (-0.325502) | 0.007998 / 0.007607 (0.000391) | 0.699489 / 0.226044 (0.473445) | 7.139214 / 2.268929 (4.870285) | 3.511158 / 55.444624 (-51.933466) | 2.775459 / 6.876477 (-4.101018) | 2.951549 / 2.142072 (0.809477) | 1.006921 / 4.805227 (-3.798306) | 0.200105 / 6.500664 (-6.300559) | 0.071064 / 0.075469 (-0.004405) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.680599 / 1.841788 (-0.161189) | 23.399777 / 8.074308 (15.325469) | 21.776357 / 10.191392 (11.584965) | 0.264697 / 0.680424 (-0.415726) | 0.034272 / 0.534201 (-0.499929) | 0.506984 / 0.579283 (-0.072299) | 0.609556 / 0.434364 (0.175192) | 0.599014 / 0.540337 (0.058677) | 0.824068 / 1.386936 (-0.562868) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3ab9de69420de8bd5d057579d71d07187b3a2c60 \"CML watermark\")\n" ]

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[]

"2023-10-23T17:55:37Z"

"2023-10-23T17:55:37Z"

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"2023-10-23T15:13:28Z"

"2023-10-23T15:24:31Z"

"2023-10-23T15:13:38Z"

{ "diff_url": "https://github.com/huggingface/datasets/pull/6344.diff", "html_url": "https://github.com/huggingface/datasets/pull/6344", "merged_at": "2023-10-23T15:13:38Z", "patch_url": "https://github.com/huggingface/datasets/pull/6344.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6344" }

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6344). All of your documentation changes will be reflected on that endpoint.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008237 / 0.011353 (-0.003116) | 0.004658 / 0.011008 (-0.006351) | 0.105902 / 0.038508 (0.067394) | 0.082690 / 0.023109 (0.059581) | 0.471745 / 0.275898 (0.195847) | 0.464772 / 0.323480 (0.141292) | 0.006373 / 0.007986 (-0.001613) | 0.003823 / 0.004328 (-0.000505) | 0.077721 / 0.004250 (0.073471) | 0.068371 / 0.037052 (0.031318) | 0.457004 / 0.258489 (0.198515) | 0.500989 / 0.293841 (0.207148) | 0.036688 / 0.128546 (-0.091858) | 0.010004 / 0.075646 (-0.065643) | 0.363398 / 0.419271 (-0.055874) | 0.065354 / 0.043533 (0.021821) | 0.440326 / 0.255139 (0.185187) | 0.475314 / 0.283200 (0.192115) | 0.029024 / 0.141683 (-0.112659) | 1.851005 / 1.452155 (0.398851) | 1.939997 / 1.492716 (0.447281) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.269739 / 0.018006 (0.251732) | 0.510411 / 0.000490 (0.509922) | 0.013423 / 0.000200 (0.013223) | 0.000513 / 0.000054 (0.000458) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032912 / 0.037411 (-0.004499) | 0.097497 / 0.014526 (0.082971) | 0.111945 / 0.176557 (-0.064612) | 0.179264 / 0.737135 (-0.557871) | 0.111901 / 0.296338 (-0.184437) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.480994 / 0.215209 (0.265785) | 4.800969 / 2.077655 (2.723314) | 2.467390 / 1.504120 (0.963270) | 2.283219 / 1.541195 (0.742024) | 2.407735 / 1.468490 (0.939245) | 0.573862 / 4.584777 (-4.010915) | 4.213394 / 3.745712 (0.467682) | 4.120092 / 5.269862 (-1.149770) | 2.479549 / 4.565676 (-2.086128) | 0.077204 / 0.424275 (-0.347071) | 0.009165 / 0.007607 (0.001558) | 0.583887 / 0.226044 (0.357842) | 5.760759 / 2.268929 (3.491830) | 3.089220 / 55.444624 (-52.355404) | 2.652330 / 6.876477 (-4.224146) | 2.746255 / 2.142072 (0.604182) | 0.689010 / 4.805227 (-4.116217) | 0.158042 / 6.500664 (-6.342622) | 0.072789 / 0.075469 (-0.002680) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.658877 / 1.841788 (-0.182911) | 22.928756 / 8.074308 (14.854448) | 17.231823 / 10.191392 (7.040431) | 0.201475 / 0.680424 (-0.478949) | 0.025533 / 0.534201 (-0.508668) | 0.467023 / 0.579283 (-0.112260) | 0.470779 / 0.434364 (0.036415) | 0.643192 / 0.540337 (0.102855) | 0.822006 / 1.386936 (-0.564930) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008096 / 0.011353 (-0.003257) | 0.004708 / 0.011008 (-0.006300) | 0.076607 / 0.038508 (0.038099) | 0.086278 / 0.023109 (0.063168) | 0.478027 / 0.275898 (0.202129) | 0.533121 / 0.323480 (0.209641) | 0.006331 / 0.007986 (-0.001654) | 0.004005 / 0.004328 (-0.000324) | 0.076018 / 0.004250 (0.071767) | 0.067240 / 0.037052 (0.030188) | 0.484882 / 0.258489 (0.226393) | 0.536924 / 0.293841 (0.243083) | 0.045064 / 0.128546 (-0.083482) | 0.010071 / 0.075646 (-0.065575) | 0.084319 / 0.419271 (-0.334953) | 0.066267 / 0.043533 (0.022734) | 0.479283 / 0.255139 (0.224144) | 0.507832 / 0.283200 (0.224633) | 0.026436 / 0.141683 (-0.115247) | 1.820043 / 1.452155 (0.367889) | 1.954663 / 1.492716 (0.461947) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.292672 / 0.018006 (0.274666) | 0.495523 / 0.000490 (0.495033) | 0.020836 / 0.000200 (0.020636) | 0.000143 / 0.000054 (0.000088) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.038326 / 0.037411 (0.000915) | 0.114629 / 0.014526 (0.100103) | 0.126036 / 0.176557 (-0.050521) | 0.191498 / 0.737135 (-0.545638) | 0.128763 / 0.296338 (-0.167575) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.507657 / 0.215209 (0.292448) | 5.062056 / 2.077655 (2.984401) | 2.765895 / 1.504120 (1.261775) | 2.590335 / 1.541195 (1.049141) | 2.790912 / 1.468490 (1.322422) | 0.582819 / 4.584777 (-4.001958) | 4.350034 / 3.745712 (0.604322) | 3.899466 / 5.269862 (-1.370396) | 2.499655 / 4.565676 (-2.066021) | 0.068909 / 0.424275 (-0.355366) | 0.008633 / 0.007607 (0.001026) | 0.593597 / 0.226044 (0.367553) | 5.934398 / 2.268929 (3.665470) | 3.358549 / 55.444624 (-52.086075) | 3.145686 / 6.876477 (-3.730791) | 3.232153 / 2.142072 (1.090080) | 0.753039 / 4.805227 (-4.052188) | 0.164043 / 6.500664 (-6.336621) | 0.072084 / 0.075469 (-0.003385) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.632702 / 1.841788 (-0.209086) | 23.411084 / 8.074308 (15.336776) | 17.035726 / 10.191392 (6.844334) | 0.223460 / 0.680424 (-0.456964) | 0.023723 / 0.534201 (-0.510478) | 0.474160 / 0.579283 (-0.105124) | 0.538638 / 0.434364 (0.104274) | 0.595591 / 0.540337 (0.055254) | 0.803324 / 1.386936 (-0.583612) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#84855c8ddc8d3e33b516f04b687e01d498d0906e \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008300 / 0.011353 (-0.003053) | 0.004667 / 0.011008 (-0.006341) | 0.101028 / 0.038508 (0.062520) | 0.100269 / 0.023109 (0.077160) | 0.418651 / 0.275898 (0.142752) | 0.459061 / 0.323480 (0.135581) | 0.006786 / 0.007986 (-0.001199) | 0.003926 / 0.004328 (-0.000403) | 0.076682 / 0.004250 (0.072432) | 0.066173 / 0.037052 (0.029120) | 0.430644 / 0.258489 (0.172155) | 0.466244 / 0.293841 (0.172403) | 0.040601 / 0.128546 (-0.087946) | 0.009856 / 0.075646 (-0.065790) | 0.351467 / 0.419271 (-0.067805) | 0.068727 / 0.043533 (0.025194) | 0.419527 / 0.255139 (0.164388) | 0.431245 / 0.283200 (0.148045) | 0.028933 / 0.141683 (-0.112750) | 1.749540 / 1.452155 (0.297386) | 1.829076 / 1.492716 (0.336360) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.282248 / 0.018006 (0.264242) | 0.587293 / 0.000490 (0.586803) | 0.014497 / 0.000200 (0.014297) | 0.000383 / 0.000054 (0.000329) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031861 / 0.037411 (-0.005550) | 0.097395 / 0.014526 (0.082869) | 0.113610 / 0.176557 (-0.062946) | 0.181208 / 0.737135 (-0.555927) | 0.115340 / 0.296338 (-0.180999) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.459746 / 0.215209 (0.244537) | 4.582387 / 2.077655 (2.504733) | 2.247968 / 1.504120 (0.743848) | 2.032340 / 1.541195 (0.491145) | 2.151766 / 1.468490 (0.683276) | 0.567664 / 4.584777 (-4.017113) | 4.491732 / 3.745712 (0.746020) | 4.000651 / 5.269862 (-1.269211) | 2.429113 / 4.565676 (-2.136564) | 0.067052 / 0.424275 (-0.357223) | 0.009095 / 0.007607 (0.001488) | 0.546461 / 0.226044 (0.320417) | 5.473524 / 2.268929 (3.204595) | 2.902091 / 55.444624 (-52.542533) | 2.517510 / 6.876477 (-4.358966) | 2.572537 / 2.142072 (0.430464) | 0.683499 / 4.805227 (-4.121728) | 0.154863 / 6.500664 (-6.345801) | 0.071298 / 0.075469 (-0.004171) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.625236 / 1.841788 (-0.216552) | 23.531541 / 8.074308 (15.457233) | 16.762514 / 10.191392 (6.571122) | 0.215922 / 0.680424 (-0.464502) | 0.021928 / 0.534201 (-0.512273) | 0.466055 / 0.579283 (-0.113228) | 0.553036 / 0.434364 (0.118672) | 0.590063 / 0.540337 (0.049725) | 0.789959 / 1.386936 (-0.596977) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008240 / 0.011353 (-0.003113) | 0.004151 / 0.011008 (-0.006858) | 0.077988 / 0.038508 (0.039479) | 0.092865 / 0.023109 (0.069756) | 0.468238 / 0.275898 (0.192340) | 0.512882 / 0.323480 (0.189402) | 0.006632 / 0.007986 (-0.001354) | 0.003879 / 0.004328 (-0.000450) | 0.076238 / 0.004250 (0.071988) | 0.069372 / 0.037052 (0.032319) | 0.481040 / 0.258489 (0.222550) | 0.526332 / 0.293841 (0.232491) | 0.036768 / 0.128546 (-0.091778) | 0.009891 / 0.075646 (-0.065756) | 0.084426 / 0.419271 (-0.334846) | 0.062382 / 0.043533 (0.018849) | 0.480667 / 0.255139 (0.225528) | 0.509001 / 0.283200 (0.225802) | 0.029215 / 0.141683 (-0.112468) | 1.776075 / 1.452155 (0.323920) | 1.948558 / 1.492716 (0.455841) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.257879 / 0.018006 (0.239873) | 0.471038 / 0.000490 (0.470548) | 0.009273 / 0.000200 (0.009073) | 0.000208 / 0.000054 (0.000154) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.039249 / 0.037411 (0.001838) | 0.133281 / 0.014526 (0.118755) | 0.138261 / 0.176557 (-0.038296) | 0.191051 / 0.737135 (-0.546084) | 0.134493 / 0.296338 (-0.161845) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.507165 / 0.215209 (0.291955) | 5.081018 / 2.077655 (3.003364) | 2.747633 / 1.504120 (1.243513) | 2.558265 / 1.541195 (1.017070) | 2.710839 / 1.468490 (1.242348) | 0.579913 / 4.584777 (-4.004864) | 4.843657 / 3.745712 (1.097945) | 3.942503 / 5.269862 (-1.327358) | 2.529641 / 4.565676 (-2.036036) | 0.068826 / 0.424275 (-0.355449) | 0.008847 / 0.007607 (0.001240) | 0.605332 / 0.226044 (0.379287) | 6.039574 / 2.268929 (3.770646) | 3.437291 / 55.444624 (-52.007333) | 3.086631 / 6.876477 (-3.789846) | 3.189340 / 2.142072 (1.047267) | 0.702650 / 4.805227 (-4.102578) | 0.157403 / 6.500664 (-6.343261) | 0.074637 / 0.075469 (-0.000832) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.816532 / 1.841788 (-0.025256) | 24.526675 / 8.074308 (16.452367) | 17.371691 / 10.191392 (7.180299) | 0.236044 / 0.680424 (-0.444380) | 0.024759 / 0.534201 (-0.509442) | 0.530578 / 0.579283 (-0.048705) | 0.527424 / 0.434364 (0.093060) | 0.620267 / 0.540337 (0.079929) | 0.791159 / 1.386936 (-0.595777) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#78cfce823b98b6cce79a9297fe6fa9e8f80a869c \"CML watermark\")\n" ]

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[]

[]

"2023-10-23T14:54:18Z"

"2023-11-16T09:09:42Z"

"2023-11-16T09:03:39Z"

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006584 / 0.011353 (-0.004769) | 0.004197 / 0.011008 (-0.006812) | 0.083598 / 0.038508 (0.045090) | 0.075502 / 0.023109 (0.052392) | 0.312986 / 0.275898 (0.037088) | 0.344630 / 0.323480 (0.021150) | 0.005394 / 0.007986 (-0.002591) | 0.003485 / 0.004328 (-0.000843) | 0.064529 / 0.004250 (0.060279) | 0.055003 / 0.037052 (0.017950) | 0.320522 / 0.258489 (0.062033) | 0.362623 / 0.293841 (0.068782) | 0.030900 / 0.128546 (-0.097646) | 0.008459 / 0.075646 (-0.067187) | 0.286986 / 0.419271 (-0.132285) | 0.052310 / 0.043533 (0.008777) | 0.315873 / 0.255139 (0.060734) | 0.333962 / 0.283200 (0.050762) | 0.023836 / 0.141683 (-0.117847) | 1.481806 / 1.452155 (0.029651) | 1.567926 / 1.492716 (0.075209) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.268188 / 0.018006 (0.250182) | 0.520542 / 0.000490 (0.520052) | 0.017617 / 0.000200 (0.017417) | 0.000631 / 0.000054 (0.000577) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028828 / 0.037411 (-0.008584) | 0.083028 / 0.014526 (0.068502) | 0.099808 / 0.176557 (-0.076748) | 0.154282 / 0.737135 (-0.582853) | 0.098590 / 0.296338 (-0.197748) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.407548 / 0.215209 (0.192339) | 4.066128 / 2.077655 (1.988474) | 2.036757 / 1.504120 (0.532637) | 1.870130 / 1.541195 (0.328935) | 1.949031 / 1.468490 (0.480541) | 0.489263 / 4.584777 (-4.095514) | 3.506269 / 3.745712 (-0.239443) | 3.457232 / 5.269862 (-1.812629) | 2.060097 / 4.565676 (-2.505580) | 0.057252 / 0.424275 (-0.367024) | 0.007727 / 0.007607 (0.000120) | 0.480229 / 0.226044 (0.254185) | 4.807064 / 2.268929 (2.538135) | 2.495438 / 55.444624 (-52.949186) | 2.186194 / 6.876477 (-4.690283) | 2.243372 / 2.142072 (0.101300) | 0.580550 / 4.805227 (-4.224678) | 0.135398 / 6.500664 (-6.365266) | 0.061878 / 0.075469 (-0.013591) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.305635 / 1.841788 (-0.536152) | 19.194421 / 8.074308 (11.120113) | 14.531699 / 10.191392 (4.340307) | 0.167144 / 0.680424 (-0.513280) | 0.018270 / 0.534201 (-0.515931) | 0.393702 / 0.579283 (-0.185581) | 0.406518 / 0.434364 (-0.027846) | 0.458126 / 0.540337 (-0.082211) | 0.639839 / 1.386936 (-0.747097) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006742 / 0.011353 (-0.004611) | 0.004092 / 0.011008 (-0.006916) | 0.065547 / 0.038508 (0.027039) | 0.076293 / 0.023109 (0.053184) | 0.389701 / 0.275898 (0.113803) | 0.429158 / 0.323480 (0.105678) | 0.005606 / 0.007986 (-0.002380) | 0.003491 / 0.004328 (-0.000837) | 0.065903 / 0.004250 (0.061653) | 0.057346 / 0.037052 (0.020293) | 0.393233 / 0.258489 (0.134744) | 0.433106 / 0.293841 (0.139265) | 0.032612 / 0.128546 (-0.095934) | 0.008777 / 0.075646 (-0.066869) | 0.073135 / 0.419271 (-0.346137) | 0.048167 / 0.043533 (0.004635) | 0.389309 / 0.255139 (0.134170) | 0.416442 / 0.283200 (0.133242) | 0.022839 / 0.141683 (-0.118844) | 1.531607 / 1.452155 (0.079453) | 1.598950 / 1.492716 (0.106234) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.254856 / 0.018006 (0.236850) | 0.528186 / 0.000490 (0.527697) | 0.006975 / 0.000200 (0.006775) | 0.000102 / 0.000054 (0.000048) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032377 / 0.037411 (-0.005034) | 0.092706 / 0.014526 (0.078180) | 0.107618 / 0.176557 (-0.068939) | 0.160103 / 0.737135 (-0.577032) | 0.107226 / 0.296338 (-0.189112) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.430922 / 0.215209 (0.215713) | 4.312556 / 2.077655 (2.234901) | 2.287686 / 1.504120 (0.783567) | 2.111103 / 1.541195 (0.569908) | 2.284105 / 1.468490 (0.815614) | 0.485987 / 4.584777 (-4.098790) | 3.557320 / 3.745712 (-0.188392) | 3.341150 / 5.269862 (-1.928711) | 2.056705 / 4.565676 (-2.508972) | 0.057265 / 0.424275 (-0.367010) | 0.007264 / 0.007607 (-0.000344) | 0.505191 / 0.226044 (0.279146) | 5.045379 / 2.268929 (2.776450) | 2.732357 / 55.444624 (-52.712267) | 2.390256 / 6.876477 (-4.486220) | 2.643676 / 2.142072 (0.501604) | 0.584630 / 4.805227 (-4.220597) | 0.132402 / 6.500664 (-6.368262) | 0.061387 / 0.075469 (-0.014082) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.340721 / 1.841788 (-0.501066) | 19.744145 / 8.074308 (11.669837) | 14.694482 / 10.191392 (4.503090) | 0.166294 / 0.680424 (-0.514129) | 0.020691 / 0.534201 (-0.513510) | 0.398359 / 0.579283 (-0.180924) | 0.423831 / 0.434364 (-0.010533) | 0.474365 / 0.540337 (-0.065972) | 0.649410 / 1.386936 (-0.737526) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b29bc9cef6237eb0d18f77c56686705f468bed25 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004369 / 0.011353 (-0.006984) | 0.002728 / 0.011008 (-0.008280) | 0.063754 / 0.038508 (0.025246) | 0.029396 / 0.023109 (0.006287) | 0.269409 / 0.275898 (-0.006489) | 0.287654 / 0.323480 (-0.035826) | 0.003926 / 0.007986 (-0.004060) | 0.002366 / 0.004328 (-0.001963) | 0.048910 / 0.004250 (0.044660) | 0.043126 / 0.037052 (0.006074) | 0.260774 / 0.258489 (0.002285) | 0.299996 / 0.293841 (0.006155) | 0.023359 / 0.128546 (-0.105187) | 0.007259 / 0.075646 (-0.068388) | 0.211412 / 0.419271 (-0.207860) | 0.053883 / 0.043533 (0.010350) | 0.268946 / 0.255139 (0.013807) | 0.287664 / 0.283200 (0.004465) | 0.017600 / 0.141683 (-0.124083) | 1.096478 / 1.452155 (-0.355676) | 1.193063 / 1.492716 (-0.299653) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.090985 / 0.018006 (0.072979) | 0.287168 / 0.000490 (0.286678) | 0.000208 / 0.000200 (0.000009) | 0.000052 / 0.000054 (-0.000002) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019238 / 0.037411 (-0.018173) | 0.062660 / 0.014526 (0.048134) | 0.073414 / 0.176557 (-0.103143) | 0.120842 / 0.737135 (-0.616294) | 0.077658 / 0.296338 (-0.218681) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.280285 / 0.215209 (0.065076) | 2.729807 / 2.077655 (0.652152) | 1.430686 / 1.504120 (-0.073434) | 1.307260 / 1.541195 (-0.233935) | 1.321013 / 1.468490 (-0.147477) | 0.387253 / 4.584777 (-4.197524) | 2.415635 / 3.745712 (-1.330077) | 2.557206 / 5.269862 (-2.712656) | 1.553224 / 4.565676 (-3.012453) | 0.045402 / 0.424275 (-0.378873) | 0.004798 / 0.007607 (-0.002809) | 0.330493 / 0.226044 (0.104449) | 3.226835 / 2.268929 (0.957906) | 1.739068 / 55.444624 (-53.705557) | 1.494841 / 6.876477 (-5.381636) | 1.528253 / 2.142072 (-0.613820) | 0.451525 / 4.805227 (-4.353702) | 0.096620 / 6.500664 (-6.404044) | 0.041176 / 0.075469 (-0.034293) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.930892 / 1.841788 (-0.910896) | 11.343351 / 8.074308 (3.269043) | 10.420327 / 10.191392 (0.228935) | 0.137629 / 0.680424 (-0.542795) | 0.013907 / 0.534201 (-0.520293) | 0.267778 / 0.579283 (-0.311505) | 0.260774 / 0.434364 (-0.173590) | 0.308213 / 0.540337 (-0.232124) | 0.419659 / 1.386936 (-0.967277) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004867 / 0.011353 (-0.006486) | 0.002830 / 0.011008 (-0.008178) | 0.048506 / 0.038508 (0.009998) | 0.048190 / 0.023109 (0.025080) | 0.279995 / 0.275898 (0.004097) | 0.296396 / 0.323480 (-0.027083) | 0.004700 / 0.007986 (-0.003285) | 0.003546 / 0.004328 (-0.000782) | 0.048237 / 0.004250 (0.043987) | 0.037102 / 0.037052 (0.000050) | 0.284582 / 0.258489 (0.026093) | 0.315896 / 0.293841 (0.022055) | 0.024699 / 0.128546 (-0.103848) | 0.007077 / 0.075646 (-0.068569) | 0.054471 / 0.419271 (-0.364800) | 0.032537 / 0.043533 (-0.010996) | 0.276761 / 0.255139 (0.021622) | 0.294741 / 0.283200 (0.011542) | 0.017766 / 0.141683 (-0.123917) | 1.118377 / 1.452155 (-0.333778) | 1.186617 / 1.492716 (-0.306100) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.088981 / 0.018006 (0.070975) | 0.297793 / 0.000490 (0.297303) | 0.000220 / 0.000200 (0.000020) | 0.000050 / 0.000054 (-0.000004) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021300 / 0.037411 (-0.016111) | 0.070059 / 0.014526 (0.055533) | 0.080452 / 0.176557 (-0.096104) | 0.118461 / 0.737135 (-0.618674) | 0.081099 / 0.296338 (-0.215240) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.300560 / 0.215209 (0.085351) | 2.951461 / 2.077655 (0.873806) | 1.621978 / 1.504120 (0.117858) | 1.478871 / 1.541195 (-0.062324) | 1.520732 / 1.468490 (0.052242) | 0.408625 / 4.584777 (-4.176152) | 2.407253 / 3.745712 (-1.338459) | 2.546000 / 5.269862 (-2.723861) | 1.525920 / 4.565676 (-3.039757) | 0.046817 / 0.424275 (-0.377458) | 0.004880 / 0.007607 (-0.002727) | 0.350866 / 0.226044 (0.124821) | 3.489379 / 2.268929 (1.220451) | 1.967197 / 55.444624 (-53.477427) | 1.686083 / 6.876477 (-5.190394) | 1.699307 / 2.142072 (-0.442766) | 0.479659 / 4.805227 (-4.325568) | 0.098853 / 6.500664 (-6.401811) | 0.040718 / 0.075469 (-0.034751) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.018352 / 1.841788 (-0.823436) | 12.022551 / 8.074308 (3.948243) | 10.841890 / 10.191392 (0.650498) | 0.130732 / 0.680424 (-0.549692) | 0.016334 / 0.534201 (-0.517867) | 0.271984 / 0.579283 (-0.307299) | 0.276733 / 0.434364 (-0.157631) | 0.308049 / 0.540337 (-0.232289) | 0.415428 / 1.386936 (-0.971508) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#31d95717e4e5fc6dd7699878720f063d51f1d595 \"CML watermark\")\n" ]

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"2023-10-23T14:43:26Z"

"2023-10-23T15:21:54Z"

"2023-10-23T15:07:25Z"

{ "diff_url": "https://github.com/huggingface/datasets/pull/6342.diff", "html_url": "https://github.com/huggingface/datasets/pull/6342", "merged_at": "2023-10-23T15:07:25Z", "patch_url": "https://github.com/huggingface/datasets/pull/6342.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6342" }

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[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007051 / 0.011353 (-0.004302) | 0.004291 / 0.011008 (-0.006717) | 0.085557 / 0.038508 (0.047048) | 0.087919 / 0.023109 (0.064810) | 0.356912 / 0.275898 (0.081014) | 0.394835 / 0.323480 (0.071355) | 0.004464 / 0.007986 (-0.003522) | 0.003688 / 0.004328 (-0.000640) | 0.065437 / 0.004250 (0.061186) | 0.060156 / 0.037052 (0.023103) | 0.361807 / 0.258489 (0.103318) | 0.420917 / 0.293841 (0.127076) | 0.031704 / 0.128546 (-0.096842) | 0.008921 / 0.075646 (-0.066726) | 0.287828 / 0.419271 (-0.131443) | 0.053600 / 0.043533 (0.010067) | 0.361833 / 0.255139 (0.106694) | 0.396732 / 0.283200 (0.113532) | 0.025874 / 0.141683 (-0.115809) | 1.474926 / 1.452155 (0.022771) | 1.563186 / 1.492716 (0.070469) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.316823 / 0.018006 (0.298817) | 0.604085 / 0.000490 (0.603595) | 0.020828 / 0.000200 (0.020628) | 0.000351 / 0.000054 (0.000297) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030468 / 0.037411 (-0.006943) | 0.083904 / 0.014526 (0.069378) | 0.103019 / 0.176557 (-0.073537) | 0.159018 / 0.737135 (-0.578117) | 0.102737 / 0.296338 (-0.193602) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.405311 / 0.215209 (0.190102) | 4.029060 / 2.077655 (1.951406) | 2.046590 / 1.504120 (0.542470) | 1.919335 / 1.541195 (0.378140) | 2.030371 / 1.468490 (0.561881) | 0.484209 / 4.584777 (-4.100568) | 3.486888 / 3.745712 (-0.258824) | 3.390777 / 5.269862 (-1.879084) | 2.110744 / 4.565676 (-2.454933) | 0.056587 / 0.424275 (-0.367688) | 0.007766 / 0.007607 (0.000159) | 0.488217 / 0.226044 (0.262173) | 4.853904 / 2.268929 (2.584976) | 2.595122 / 55.444624 (-52.849502) | 2.217712 / 6.876477 (-4.658765) | 2.500368 / 2.142072 (0.358296) | 0.580843 / 4.805227 (-4.224384) | 0.132719 / 6.500664 (-6.367945) | 0.060202 / 0.075469 (-0.015267) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.260748 / 1.841788 (-0.581040) | 20.148848 / 8.074308 (12.074540) | 14.738779 / 10.191392 (4.547387) | 0.167562 / 0.680424 (-0.512862) | 0.018944 / 0.534201 (-0.515257) | 0.394314 / 0.579283 (-0.184969) | 0.409345 / 0.434364 (-0.025019) | 0.458743 / 0.540337 (-0.081594) | 0.638175 / 1.386936 (-0.748761) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007097 / 0.011353 (-0.004256) | 0.004304 / 0.011008 (-0.006705) | 0.065539 / 0.038508 (0.027030) | 0.094078 / 0.023109 (0.070969) | 0.412411 / 0.275898 (0.136513) | 0.441900 / 0.323480 (0.118420) | 0.006038 / 0.007986 (-0.001948) | 0.003647 / 0.004328 (-0.000682) | 0.065298 / 0.004250 (0.061048) | 0.062571 / 0.037052 (0.025518) | 0.405156 / 0.258489 (0.146667) | 0.443779 / 0.293841 (0.149938) | 0.034470 / 0.128546 (-0.094077) | 0.008858 / 0.075646 (-0.066789) | 0.071840 / 0.419271 (-0.347431) | 0.050468 / 0.043533 (0.006935) | 0.404198 / 0.255139 (0.149059) | 0.430196 / 0.283200 (0.146997) | 0.025710 / 0.141683 (-0.115973) | 1.525374 / 1.452155 (0.073219) | 1.591830 / 1.492716 (0.099114) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.294330 / 0.018006 (0.276324) | 0.516943 / 0.000490 (0.516453) | 0.004807 / 0.000200 (0.004607) | 0.000103 / 0.000054 (0.000048) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034505 / 0.037411 (-0.002907) | 0.096645 / 0.014526 (0.082119) | 0.111926 / 0.176557 (-0.064630) | 0.165241 / 0.737135 (-0.571894) | 0.111834 / 0.296338 (-0.184504) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.436370 / 0.215209 (0.221161) | 4.357568 / 2.077655 (2.279913) | 2.360529 / 1.504120 (0.856409) | 2.196375 / 1.541195 (0.655180) | 2.307481 / 1.468490 (0.838991) | 0.494072 / 4.584777 (-4.090705) | 3.565078 / 3.745712 (-0.180634) | 3.405174 / 5.269862 (-1.864688) | 2.203307 / 4.565676 (-2.362369) | 0.058582 / 0.424275 (-0.365693) | 0.007410 / 0.007607 (-0.000197) | 0.514323 / 0.226044 (0.288279) | 5.139834 / 2.268929 (2.870905) | 2.884111 / 55.444624 (-52.560513) | 2.589021 / 6.876477 (-4.287456) | 2.787577 / 2.142072 (0.645504) | 0.590765 / 4.805227 (-4.214462) | 0.135237 / 6.500664 (-6.365427) | 0.061078 / 0.075469 (-0.014391) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.346938 / 1.841788 (-0.494850) | 21.009948 / 8.074308 (12.935640) | 15.203281 / 10.191392 (5.011889) | 0.166208 / 0.680424 (-0.514216) | 0.020634 / 0.534201 (-0.513567) | 0.413825 / 0.579283 (-0.165458) | 0.416477 / 0.434364 (-0.017887) | 0.485888 / 0.540337 (-0.054449) | 0.664941 / 1.386936 (-0.721995) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#395b30ee2c0f6088e28fe78a3e61b591e40a4668 \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.005927 / 0.011353 (-0.005425) | 0.003622 / 0.011008 (-0.007386) | 0.081414 / 0.038508 (0.042906) | 0.061031 / 0.023109 (0.037922) | 0.358323 / 0.275898 (0.082425) | 0.394192 / 0.323480 (0.070712) | 0.003471 / 0.007986 (-0.004515) | 0.002930 / 0.004328 (-0.001399) | 0.064215 / 0.004250 (0.059964) | 0.048678 / 0.037052 (0.011625) | 0.367966 / 0.258489 (0.109477) | 0.412618 / 0.293841 (0.118777) | 0.027192 / 0.128546 (-0.101355) | 0.007921 / 0.075646 (-0.067725) | 0.262213 / 0.419271 (-0.157059) | 0.044750 / 0.043533 (0.001217) | 0.351573 / 0.255139 (0.096434) | 0.389000 / 0.283200 (0.105800) | 0.020842 / 0.141683 (-0.120840) | 1.448925 / 1.452155 (-0.003229) | 1.530478 / 1.492716 (0.037761) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.227787 / 0.018006 (0.209780) | 0.423161 / 0.000490 (0.422671) | 0.007557 / 0.000200 (0.007357) | 0.000205 / 0.000054 (0.000150) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024703 / 0.037411 (-0.012709) | 0.074044 / 0.014526 (0.059518) | 0.085520 / 0.176557 (-0.091037) | 0.146132 / 0.737135 (-0.591003) | 0.085637 / 0.296338 (-0.210701) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.393177 / 0.215209 (0.177968) | 3.926740 / 2.077655 (1.849085) | 1.892420 / 1.504120 (0.388300) | 1.716844 / 1.541195 (0.175650) | 1.784040 / 1.468490 (0.315550) | 0.499570 / 4.584777 (-4.085207) | 3.057764 / 3.745712 (-0.687948) | 2.885463 / 5.269862 (-2.384399) | 1.905206 / 4.565676 (-2.660471) | 0.058216 / 0.424275 (-0.366059) | 0.006805 / 0.007607 (-0.000802) | 0.465406 / 0.226044 (0.239361) | 4.658569 / 2.268929 (2.389641) | 2.461737 / 55.444624 (-52.982887) | 2.170620 / 6.876477 (-4.705856) | 2.373715 / 2.142072 (0.231643) | 0.592818 / 4.805227 (-4.212409) | 0.127960 / 6.500664 (-6.372704) | 0.061696 / 0.075469 (-0.013773) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.229073 / 1.841788 (-0.612715) | 17.832087 / 8.074308 (9.757778) | 13.889485 / 10.191392 (3.698093) | 0.142237 / 0.680424 (-0.538187) | 0.016752 / 0.534201 (-0.517449) | 0.338342 / 0.579283 (-0.240941) | 0.383933 / 0.434364 (-0.050431) | 0.393017 / 0.540337 (-0.147320) | 0.557621 / 1.386936 (-0.829315) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006218 / 0.011353 (-0.005135) | 0.003679 / 0.011008 (-0.007329) | 0.062934 / 0.038508 (0.024426) | 0.066764 / 0.023109 (0.043655) | 0.482737 / 0.275898 (0.206839) | 0.483241 / 0.323480 (0.159761) | 0.004828 / 0.007986 (-0.003158) | 0.002880 / 0.004328 (-0.001448) | 0.063111 / 0.004250 (0.058861) | 0.049500 / 0.037052 (0.012448) | 0.453155 / 0.258489 (0.194666) | 0.488776 / 0.293841 (0.194935) | 0.028568 / 0.128546 (-0.099978) | 0.008490 / 0.075646 (-0.067157) | 0.068202 / 0.419271 (-0.351069) | 0.040695 / 0.043533 (-0.002838) | 0.457473 / 0.255139 (0.202334) | 0.471968 / 0.283200 (0.188768) | 0.021261 / 0.141683 (-0.120422) | 1.476304 / 1.452155 (0.024150) | 1.503433 / 1.492716 (0.010716) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.227108 / 0.018006 (0.209102) | 0.428330 / 0.000490 (0.427840) | 0.004637 / 0.000200 (0.004437) | 0.000074 / 0.000054 (0.000020) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027253 / 0.037411 (-0.010158) | 0.081990 / 0.014526 (0.067464) | 0.092763 / 0.176557 (-0.083794) | 0.146155 / 0.737135 (-0.590981) | 0.093175 / 0.296338 (-0.203164) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.464585 / 0.215209 (0.249376) | 4.630704 / 2.077655 (2.553050) | 2.583272 / 1.504120 (1.079152) | 2.393810 / 1.541195 (0.852615) | 2.463255 / 1.468490 (0.994765) | 0.507045 / 4.584777 (-4.077732) | 3.181972 / 3.745712 (-0.563740) | 2.902321 / 5.269862 (-2.367541) | 1.905431 / 4.565676 (-2.660246) | 0.059427 / 0.424275 (-0.364848) | 0.006387 / 0.007607 (-0.001220) | 0.542247 / 0.226044 (0.316203) | 5.426868 / 2.268929 (3.157939) | 3.073489 / 55.444624 (-52.371136) | 2.719620 / 6.876477 (-4.156857) | 2.861865 / 2.142072 (0.719793) | 0.593757 / 4.805227 (-4.211471) | 0.125439 / 6.500664 (-6.375225) | 0.060901 / 0.075469 (-0.014568) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.359938 / 1.841788 (-0.481850) | 18.484867 / 8.074308 (10.410559) | 14.685645 / 10.191392 (4.494253) | 0.164098 / 0.680424 (-0.516325) | 0.018090 / 0.534201 (-0.516111) | 0.339760 / 0.579283 (-0.239523) | 0.376668 / 0.434364 (-0.057696) | 0.396963 / 0.540337 (-0.143374) | 0.549305 / 1.386936 (-0.837631) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0c896f4195ec8a91e09f8bb9a57950bcec8b8450 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006052 / 0.011353 (-0.005301) | 0.003715 / 0.011008 (-0.007293) | 0.079646 / 0.038508 (0.041138) | 0.059053 / 0.023109 (0.035944) | 0.393016 / 0.275898 (0.117118) | 0.424758 / 0.323480 (0.101278) | 0.005407 / 0.007986 (-0.002578) | 0.002920 / 0.004328 (-0.001408) | 0.062145 / 0.004250 (0.057894) | 0.047289 / 0.037052 (0.010237) | 0.399848 / 0.258489 (0.141359) | 0.434239 / 0.293841 (0.140398) | 0.027388 / 0.128546 (-0.101158) | 0.007967 / 0.075646 (-0.067680) | 0.262546 / 0.419271 (-0.156725) | 0.045014 / 0.043533 (0.001482) | 0.398086 / 0.255139 (0.142947) | 0.414615 / 0.283200 (0.131415) | 0.020410 / 0.141683 (-0.121272) | 1.447276 / 1.452155 (-0.004879) | 1.512390 / 1.492716 (0.019673) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.224854 / 0.018006 (0.206847) | 0.434173 / 0.000490 (0.433683) | 0.010091 / 0.000200 (0.009891) | 0.000259 / 0.000054 (0.000205) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025316 / 0.037411 (-0.012095) | 0.073284 / 0.014526 (0.058758) | 0.085177 / 0.176557 (-0.091379) | 0.148905 / 0.737135 (-0.588230) | 0.084696 / 0.296338 (-0.211642) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.438259 / 0.215209 (0.223050) | 4.380679 / 2.077655 (2.303025) | 2.310329 / 1.504120 (0.806209) | 2.144002 / 1.541195 (0.602807) | 2.203761 / 1.468490 (0.735270) | 0.500559 / 4.584777 (-4.084218) | 3.031172 / 3.745712 (-0.714540) | 2.839425 / 5.269862 (-2.430436) | 1.878391 / 4.565676 (-2.687285) | 0.057325 / 0.424275 (-0.366950) | 0.006719 / 0.007607 (-0.000888) | 0.510122 / 0.226044 (0.284078) | 5.108632 / 2.268929 (2.839704) | 2.805716 / 55.444624 (-52.638909) | 2.422183 / 6.876477 (-4.454293) | 2.635280 / 2.142072 (0.493207) | 0.589351 / 4.805227 (-4.215876) | 0.125416 / 6.500664 (-6.375248) | 0.061142 / 0.075469 (-0.014327) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.234997 / 1.841788 (-0.606791) | 17.731828 / 8.074308 (9.657520) | 13.858081 / 10.191392 (3.666689) | 0.145975 / 0.680424 (-0.534449) | 0.016827 / 0.534201 (-0.517374) | 0.335701 / 0.579283 (-0.243582) | 0.361867 / 0.434364 (-0.072497) | 0.394620 / 0.540337 (-0.145718) | 0.532146 / 1.386936 (-0.854790) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006091 / 0.011353 (-0.005262) | 0.003663 / 0.011008 (-0.007345) | 0.062596 / 0.038508 (0.024088) | 0.061649 / 0.023109 (0.038539) | 0.440647 / 0.275898 (0.164749) | 0.472974 / 0.323480 (0.149494) | 0.005009 / 0.007986 (-0.002976) | 0.002879 / 0.004328 (-0.001449) | 0.062815 / 0.004250 (0.058565) | 0.049000 / 0.037052 (0.011947) | 0.442990 / 0.258489 (0.184501) | 0.477622 / 0.293841 (0.183781) | 0.028512 / 0.128546 (-0.100034) | 0.008031 / 0.075646 (-0.067615) | 0.067853 / 0.419271 (-0.351418) | 0.040823 / 0.043533 (-0.002710) | 0.437811 / 0.255139 (0.182672) | 0.464615 / 0.283200 (0.181416) | 0.021348 / 0.141683 (-0.120334) | 1.479230 / 1.452155 (0.027075) | 1.544053 / 1.492716 (0.051337) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.210697 / 0.018006 (0.192691) | 0.436450 / 0.000490 (0.435960) | 0.003413 / 0.000200 (0.003213) | 0.000089 / 0.000054 (0.000035) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027190 / 0.037411 (-0.010222) | 0.083254 / 0.014526 (0.068728) | 0.092936 / 0.176557 (-0.083620) | 0.147261 / 0.737135 (-0.589874) | 0.092910 / 0.296338 (-0.203429) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.454195 / 0.215209 (0.238986) | 4.569122 / 2.077655 (2.491468) | 2.497198 / 1.504120 (0.993079) | 2.314337 / 1.541195 (0.773142) | 2.378471 / 1.468490 (0.909981) | 0.515402 / 4.584777 (-4.069375) | 3.199374 / 3.745712 (-0.546338) | 2.899300 / 5.269862 (-2.370562) | 1.873314 / 4.565676 (-2.692362) | 0.058820 / 0.424275 (-0.365455) | 0.006651 / 0.007607 (-0.000957) | 0.526681 / 0.226044 (0.300636) | 5.275232 / 2.268929 (3.006303) | 2.969107 / 55.444624 (-52.475517) | 2.600959 / 6.876477 (-4.275518) | 2.762930 / 2.142072 (0.620858) | 0.605726 / 4.805227 (-4.199501) | 0.127618 / 6.500664 (-6.373046) | 0.062840 / 0.075469 (-0.012629) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.367276 / 1.841788 (-0.474512) | 18.069385 / 8.074308 (9.995077) | 14.691945 / 10.191392 (4.500553) | 0.147203 / 0.680424 (-0.533221) | 0.018484 / 0.534201 (-0.515717) | 0.333759 / 0.579283 (-0.245524) | 0.395503 / 0.434364 (-0.038861) | 0.387031 / 0.540337 (-0.153306) | 0.550428 / 1.386936 (-0.836508) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4c8f7eb79dff66dd03211321dcb55f7a7a05ef38 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007675 / 0.011353 (-0.003678) | 0.004532 / 0.011008 (-0.006476) | 0.088176 / 0.038508 (0.049668) | 0.103257 / 0.023109 (0.080148) | 0.314785 / 0.275898 (0.038887) | 0.354280 / 0.323480 (0.030800) | 0.004638 / 0.007986 (-0.003348) | 0.003736 / 0.004328 (-0.000592) | 0.066744 / 0.004250 (0.062493) | 0.064647 / 0.037052 (0.027595) | 0.320227 / 0.258489 (0.061738) | 0.369581 / 0.293841 (0.075740) | 0.032347 / 0.128546 (-0.096199) | 0.009226 / 0.075646 (-0.066421) | 0.292966 / 0.419271 (-0.126306) | 0.055738 / 0.043533 (0.012206) | 0.316537 / 0.255139 (0.061398) | 0.334699 / 0.283200 (0.051499) | 0.027401 / 0.141683 (-0.114282) | 1.482390 / 1.452155 (0.030236) | 1.594771 / 1.492716 (0.102055) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.322181 / 0.018006 (0.304175) | 0.577701 / 0.000490 (0.577212) | 0.014565 / 0.000200 (0.014365) | 0.000393 / 0.000054 (0.000338) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033255 / 0.037411 (-0.004156) | 0.094271 / 0.014526 (0.079745) | 0.105360 / 0.176557 (-0.071197) | 0.163699 / 0.737135 (-0.573436) | 0.105620 / 0.296338 (-0.190719) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.383449 / 0.215209 (0.168240) | 3.824292 / 2.077655 (1.746637) | 1.861809 / 1.504120 (0.357689) | 1.698153 / 1.541195 (0.156958) | 1.819460 / 1.468490 (0.350970) | 0.488277 / 4.584777 (-4.096500) | 3.622772 / 3.745712 (-0.122940) | 3.486041 / 5.269862 (-1.783821) | 2.211679 / 4.565676 (-2.353998) | 0.057637 / 0.424275 (-0.366638) | 0.008028 / 0.007607 (0.000421) | 0.461917 / 0.226044 (0.235873) | 4.626493 / 2.268929 (2.357565) | 2.374846 / 55.444624 (-53.069779) | 1.976003 / 6.876477 (-4.900473) | 2.325342 / 2.142072 (0.183269) | 0.582538 / 4.805227 (-4.222689) | 0.133575 / 6.500664 (-6.367089) | 0.061696 / 0.075469 (-0.013773) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.271846 / 1.841788 (-0.569941) | 20.944702 / 8.074308 (12.870394) | 15.438119 / 10.191392 (5.246727) | 0.167334 / 0.680424 (-0.513090) | 0.019538 / 0.534201 (-0.514663) | 0.401467 / 0.579283 (-0.177816) | 0.428222 / 0.434364 (-0.006142) | 0.466108 / 0.540337 (-0.074229) | 0.645326 / 1.386936 (-0.741610) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007096 / 0.011353 (-0.004257) | 0.004398 / 0.011008 (-0.006610) | 0.066253 / 0.038508 (0.027745) | 0.089415 / 0.023109 (0.066306) | 0.395760 / 0.275898 (0.119862) | 0.436058 / 0.323480 (0.112579) | 0.005944 / 0.007986 (-0.002042) | 0.003821 / 0.004328 (-0.000507) | 0.065286 / 0.004250 (0.061036) | 0.060990 / 0.037052 (0.023937) | 0.394674 / 0.258489 (0.136185) | 0.437672 / 0.293841 (0.143831) | 0.032370 / 0.128546 (-0.096177) | 0.009025 / 0.075646 (-0.066622) | 0.071365 / 0.419271 (-0.347906) | 0.048232 / 0.043533 (0.004699) | 0.395677 / 0.255139 (0.140538) | 0.415869 / 0.283200 (0.132669) | 0.024632 / 0.141683 (-0.117051) | 1.511386 / 1.452155 (0.059231) | 1.604475 / 1.492716 (0.111759) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.312864 / 0.018006 (0.294858) | 0.535432 / 0.000490 (0.534943) | 0.005195 / 0.000200 (0.004995) | 0.000101 / 0.000054 (0.000047) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035827 / 0.037411 (-0.001584) | 0.099353 / 0.014526 (0.084827) | 0.110796 / 0.176557 (-0.065761) | 0.165224 / 0.737135 (-0.571911) | 0.112111 / 0.296338 (-0.184228) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.428873 / 0.215209 (0.213664) | 4.284264 / 2.077655 (2.206609) | 2.303966 / 1.504120 (0.799847) | 2.153868 / 1.541195 (0.612674) | 2.275669 / 1.468490 (0.807179) | 0.495452 / 4.584777 (-4.089325) | 3.706773 / 3.745712 (-0.038939) | 3.471988 / 5.269862 (-1.797874) | 2.194851 / 4.565676 (-2.370825) | 0.058998 / 0.424275 (-0.365277) | 0.007522 / 0.007607 (-0.000085) | 0.511222 / 0.226044 (0.285177) | 5.097058 / 2.268929 (2.828130) | 2.856793 / 55.444624 (-52.587832) | 2.521907 / 6.876477 (-4.354569) | 2.783133 / 2.142072 (0.641060) | 0.600511 / 4.805227 (-4.204717) | 0.134130 / 6.500664 (-6.366534) | 0.061726 / 0.075469 (-0.013743) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.385272 / 1.841788 (-0.456516) | 21.149260 / 8.074308 (13.074952) | 15.548746 / 10.191392 (5.357354) | 0.167506 / 0.680424 (-0.512918) | 0.020494 / 0.534201 (-0.513707) | 0.400697 / 0.579283 (-0.178586) | 0.427386 / 0.434364 (-0.006978) | 0.478514 / 0.540337 (-0.061824) | 0.655753 / 1.386936 (-0.731183) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4c8f7eb79dff66dd03211321dcb55f7a7a05ef38 \"CML watermark\")\n" ]

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[]

[]

"2023-10-23T11:10:22Z"

"2023-10-23T14:20:46Z"

"2023-10-23T11:12:40Z"

{ "diff_url": "https://github.com/huggingface/datasets/pull/6340.diff", "html_url": "https://github.com/huggingface/datasets/pull/6340", "merged_at": null, "patch_url": "https://github.com/huggingface/datasets/pull/6340.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6340" }

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

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[]

[]

"2023-10-23T11:07:04Z"

"2023-11-07T10:38:54Z"

"2023-11-07T10:32:41Z"

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006572 / 0.011353 (-0.004780) | 0.004019 / 0.011008 (-0.006989) | 0.084080 / 0.038508 (0.045572) | 0.070111 / 0.023109 (0.047002) | 0.340440 / 0.275898 (0.064542) | 0.358839 / 0.323480 (0.035359) | 0.005254 / 0.007986 (-0.002732) | 0.003296 / 0.004328 (-0.001032) | 0.064368 / 0.004250 (0.060117) | 0.054549 / 0.037052 (0.017497) | 0.343817 / 0.258489 (0.085328) | 0.369871 / 0.293841 (0.076030) | 0.030621 / 0.128546 (-0.097925) | 0.008457 / 0.075646 (-0.067189) | 0.287839 / 0.419271 (-0.131432) | 0.051700 / 0.043533 (0.008167) | 0.331602 / 0.255139 (0.076463) | 0.339836 / 0.283200 (0.056636) | 0.023224 / 0.141683 (-0.118459) | 1.494597 / 1.452155 (0.042443) | 1.578640 / 1.492716 (0.085924) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.236985 / 0.018006 (0.218979) | 0.506153 / 0.000490 (0.505664) | 0.009753 / 0.000200 (0.009553) | 0.000345 / 0.000054 (0.000291) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028355 / 0.037411 (-0.009056) | 0.082104 / 0.014526 (0.067578) | 0.095141 / 0.176557 (-0.081415) | 0.151054 / 0.737135 (-0.586081) | 0.095139 / 0.296338 (-0.201200) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.403773 / 0.215209 (0.188564) | 4.025567 / 2.077655 (1.947912) | 2.024641 / 1.504120 (0.520521) | 1.857039 / 1.541195 (0.315845) | 1.957346 / 1.468490 (0.488856) | 0.481486 / 4.584777 (-4.103291) | 3.574463 / 3.745712 (-0.171249) | 3.399311 / 5.269862 (-1.870551) | 1.996806 / 4.565676 (-2.568870) | 0.056644 / 0.424275 (-0.367631) | 0.007503 / 0.007607 (-0.000104) | 0.479480 / 0.226044 (0.253435) | 4.793686 / 2.268929 (2.524757) | 2.481011 / 55.444624 (-52.963613) | 2.176473 / 6.876477 (-4.700004) | 2.203192 / 2.142072 (0.061120) | 0.574071 / 4.805227 (-4.231156) | 0.131852 / 6.500664 (-6.368812) | 0.058883 / 0.075469 (-0.016586) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.249945 / 1.841788 (-0.591842) | 18.439267 / 8.074308 (10.364959) | 14.100934 / 10.191392 (3.909542) | 0.164191 / 0.680424 (-0.516233) | 0.018086 / 0.534201 (-0.516115) | 0.390821 / 0.579283 (-0.188462) | 0.414166 / 0.434364 (-0.020198) | 0.460073 / 0.540337 (-0.080265) | 0.636299 / 1.386936 (-0.750637) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006606 / 0.011353 (-0.004747) | 0.003987 / 0.011008 (-0.007021) | 0.064616 / 0.038508 (0.026108) | 0.070830 / 0.023109 (0.047721) | 0.397340 / 0.275898 (0.121442) | 0.426823 / 0.323480 (0.103343) | 0.005345 / 0.007986 (-0.002641) | 0.003264 / 0.004328 (-0.001065) | 0.064728 / 0.004250 (0.060477) | 0.055763 / 0.037052 (0.018711) | 0.405347 / 0.258489 (0.146858) | 0.433163 / 0.293841 (0.139322) | 0.032394 / 0.128546 (-0.096153) | 0.008474 / 0.075646 (-0.067172) | 0.071583 / 0.419271 (-0.347689) | 0.048424 / 0.043533 (0.004892) | 0.400582 / 0.255139 (0.145443) | 0.418111 / 0.283200 (0.134911) | 0.022257 / 0.141683 (-0.119426) | 1.495521 / 1.452155 (0.043366) | 1.554626 / 1.492716 (0.061910) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.218249 / 0.018006 (0.200242) | 0.438527 / 0.000490 (0.438037) | 0.005406 / 0.000200 (0.005206) | 0.000098 / 0.000054 (0.000044) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031600 / 0.037411 (-0.005812) | 0.090836 / 0.014526 (0.076310) | 0.105000 / 0.176557 (-0.071556) | 0.157648 / 0.737135 (-0.579487) | 0.103827 / 0.296338 (-0.192512) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.426428 / 0.215209 (0.211219) | 4.259435 / 2.077655 (2.181780) | 2.300795 / 1.504120 (0.796675) | 2.121302 / 1.541195 (0.580108) | 2.145602 / 1.468490 (0.677112) | 0.486856 / 4.584777 (-4.097921) | 3.673568 / 3.745712 (-0.072144) | 3.278619 / 5.269862 (-1.991243) | 2.037760 / 4.565676 (-2.527917) | 0.057699 / 0.424275 (-0.366576) | 0.007269 / 0.007607 (-0.000338) | 0.499549 / 0.226044 (0.273505) | 4.996214 / 2.268929 (2.727285) | 2.766480 / 55.444624 (-52.678144) | 2.417308 / 6.876477 (-4.459168) | 2.581026 / 2.142072 (0.438953) | 0.589463 / 4.805227 (-4.215765) | 0.134820 / 6.500664 (-6.365844) | 0.061699 / 0.075469 (-0.013770) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.353704 / 1.841788 (-0.488084) | 19.104167 / 8.074308 (11.029859) | 14.652166 / 10.191392 (4.460774) | 0.171885 / 0.680424 (-0.508539) | 0.020222 / 0.534201 (-0.513978) | 0.396777 / 0.579283 (-0.182506) | 0.426304 / 0.434364 (-0.008060) | 0.471347 / 0.540337 (-0.068991) | 0.635887 / 1.386936 (-0.751049) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ce19ec527c581eddec306a03ad1db554223cc94a \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004686 / 0.011353 (-0.006667) | 0.002998 / 0.011008 (-0.008010) | 0.063604 / 0.038508 (0.025096) | 0.048927 / 0.023109 (0.025818) | 0.247238 / 0.275898 (-0.028660) | 0.272409 / 0.323480 (-0.051071) | 0.003909 / 0.007986 (-0.004077) | 0.002469 / 0.004328 (-0.001859) | 0.048473 / 0.004250 (0.044223) | 0.037514 / 0.037052 (0.000462) | 0.257292 / 0.258489 (-0.001197) | 0.285203 / 0.293841 (-0.008638) | 0.023131 / 0.128546 (-0.105415) | 0.006803 / 0.075646 (-0.068843) | 0.202920 / 0.419271 (-0.216351) | 0.035653 / 0.043533 (-0.007880) | 0.254791 / 0.255139 (-0.000348) | 0.272973 / 0.283200 (-0.010226) | 0.017707 / 0.141683 (-0.123976) | 1.091606 / 1.452155 (-0.360549) | 1.151453 / 1.492716 (-0.341263) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.093701 / 0.018006 (0.075695) | 0.304199 / 0.000490 (0.303709) | 0.000223 / 0.000200 (0.000023) | 0.000051 / 0.000054 (-0.000003) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.019291 / 0.037411 (-0.018120) | 0.062168 / 0.014526 (0.047642) | 0.073273 / 0.176557 (-0.103284) | 0.119497 / 0.737135 (-0.617638) | 0.075008 / 0.296338 (-0.221331) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.279983 / 0.215209 (0.064774) | 2.774413 / 2.077655 (0.696758) | 1.476678 / 1.504120 (-0.027441) | 1.336273 / 1.541195 (-0.204922) | 1.332349 / 1.468490 (-0.136142) | 0.403150 / 4.584777 (-4.181627) | 2.390026 / 3.745712 (-1.355686) | 2.619151 / 5.269862 (-2.650711) | 1.578607 / 4.565676 (-2.987069) | 0.046632 / 0.424275 (-0.377643) | 0.007352 / 0.007607 (-0.000255) | 0.333419 / 0.226044 (0.107375) | 3.288734 / 2.268929 (1.019805) | 1.843677 / 55.444624 (-53.600947) | 1.536746 / 6.876477 (-5.339731) | 1.573005 / 2.142072 (-0.569067) | 0.475699 / 4.805227 (-4.329529) | 0.104742 / 6.500664 (-6.395922) | 0.042450 / 0.075469 (-0.033019) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.949039 / 1.841788 (-0.892749) | 11.895928 / 8.074308 (3.821620) | 10.650521 / 10.191392 (0.459129) | 0.142308 / 0.680424 (-0.538116) | 0.014207 / 0.534201 (-0.519994) | 0.274011 / 0.579283 (-0.305272) | 0.288259 / 0.434364 (-0.146105) | 0.327729 / 0.540337 (-0.212609) | 0.395728 / 1.386936 (-0.991208) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.004830 / 0.011353 (-0.006523) | 0.002978 / 0.011008 (-0.008030) | 0.048623 / 0.038508 (0.010114) | 0.055040 / 0.023109 (0.031930) | 0.276436 / 0.275898 (0.000538) | 0.302403 / 0.323480 (-0.021076) | 0.004080 / 0.007986 (-0.003905) | 0.002479 / 0.004328 (-0.001849) | 0.048078 / 0.004250 (0.043827) | 0.039680 / 0.037052 (0.002627) | 0.279095 / 0.258489 (0.020606) | 0.307399 / 0.293841 (0.013558) | 0.024533 / 0.128546 (-0.104013) | 0.007196 / 0.075646 (-0.068450) | 0.053879 / 0.419271 (-0.365393) | 0.032545 / 0.043533 (-0.010988) | 0.275501 / 0.255139 (0.020362) | 0.298530 / 0.283200 (0.015330) | 0.017992 / 0.141683 (-0.123691) | 1.144191 / 1.452155 (-0.307963) | 1.208309 / 1.492716 (-0.284408) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.095690 / 0.018006 (0.077684) | 0.304932 / 0.000490 (0.304442) | 0.000223 / 0.000200 (0.000023) | 0.000055 / 0.000054 (0.000000) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.021409 / 0.037411 (-0.016003) | 0.069861 / 0.014526 (0.055335) | 0.080959 / 0.176557 (-0.095597) | 0.119432 / 0.737135 (-0.617703) | 0.083649 / 0.296338 (-0.212690) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.297243 / 0.215209 (0.082034) | 2.909288 / 2.077655 (0.831634) | 1.571512 / 1.504120 (0.067392) | 1.452403 / 1.541195 (-0.088792) | 1.481290 / 1.468490 (0.012800) | 0.405795 / 4.584777 (-4.178982) | 2.452923 / 3.745712 (-1.292789) | 2.513371 / 5.269862 (-2.756490) | 1.593216 / 4.565676 (-2.972460) | 0.048073 / 0.424275 (-0.376202) | 0.005312 / 0.007607 (-0.002296) | 0.355783 / 0.226044 (0.129738) | 3.494062 / 2.268929 (1.225133) | 1.947388 / 55.444624 (-53.497236) | 1.651724 / 6.876477 (-5.224753) | 1.789007 / 2.142072 (-0.353065) | 0.487073 / 4.805227 (-4.318154) | 0.100271 / 6.500664 (-6.400393) | 0.041571 / 0.075469 (-0.033898) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.983766 / 1.841788 (-0.858021) | 12.384778 / 8.074308 (4.310469) | 10.669519 / 10.191392 (0.478127) | 0.133105 / 0.680424 (-0.547318) | 0.016665 / 0.534201 (-0.517536) | 0.269479 / 0.579283 (-0.309804) | 0.276498 / 0.434364 (-0.157866) | 0.302105 / 0.540337 (-0.238233) | 0.391204 / 1.386936 (-0.995732) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#12ebe695b4748c5a26e08b44ed51955f74f5801d \"CML watermark\")\n" ]

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[]

[]

"2023-10-23T10:50:54Z"

"2024-01-11T06:32:56Z"

"2023-10-23T10:51:52Z"

{ "diff_url": "https://github.com/huggingface/datasets/pull/6338.diff", "html_url": "https://github.com/huggingface/datasets/pull/6338", "merged_at": null, "patch_url": "https://github.com/huggingface/datasets/pull/6338.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6338" }

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

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[]

"2023-10-23T10:44:16Z"

"2023-10-23T12:13:20Z"

"2023-10-23T12:04:36Z"

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[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006915 / 0.011353 (-0.004438) | 0.004110 / 0.011008 (-0.006898) | 0.084392 / 0.038508 (0.045884) | 0.079649 / 0.023109 (0.056540) | 0.305760 / 0.275898 (0.029862) | 0.343968 / 0.323480 (0.020488) | 0.005402 / 0.007986 (-0.002584) | 0.003342 / 0.004328 (-0.000986) | 0.064774 / 0.004250 (0.060523) | 0.055919 / 0.037052 (0.018866) | 0.315194 / 0.258489 (0.056705) | 0.355014 / 0.293841 (0.061173) | 0.032140 / 0.128546 (-0.096406) | 0.008865 / 0.075646 (-0.066781) | 0.287684 / 0.419271 (-0.131588) | 0.053504 / 0.043533 (0.009971) | 0.306852 / 0.255139 (0.051713) | 0.331125 / 0.283200 (0.047925) | 0.023476 / 0.141683 (-0.118207) | 1.506590 / 1.452155 (0.054435) | 1.574508 / 1.492716 (0.081792) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.239987 / 0.018006 (0.221981) | 0.459144 / 0.000490 (0.458654) | 0.008509 / 0.000200 (0.008309) | 0.000335 / 0.000054 (0.000280) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028353 / 0.037411 (-0.009058) | 0.082345 / 0.014526 (0.067819) | 0.499524 / 0.176557 (0.322967) | 0.152896 / 0.737135 (-0.584239) | 0.096978 / 0.296338 (-0.199360) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.404855 / 0.215209 (0.189646) | 4.053103 / 2.077655 (1.975448) | 2.069638 / 1.504120 (0.565518) | 1.917354 / 1.541195 (0.376159) | 2.035816 / 1.468490 (0.567326) | 0.480358 / 4.584777 (-4.104419) | 3.594316 / 3.745712 (-0.151396) | 3.582952 / 5.269862 (-1.686910) | 2.101142 / 4.565676 (-2.464535) | 0.057004 / 0.424275 (-0.367271) | 0.007715 / 0.007607 (0.000108) | 0.487417 / 0.226044 (0.261372) | 4.863100 / 2.268929 (2.594172) | 2.569038 / 55.444624 (-52.875587) | 2.187167 / 6.876477 (-4.689310) | 2.270034 / 2.142072 (0.127962) | 0.578095 / 4.805227 (-4.227132) | 0.133283 / 6.500664 (-6.367381) | 0.060164 / 0.075469 (-0.015305) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.269120 / 1.841788 (-0.572667) | 19.493072 / 8.074308 (11.418764) | 14.560576 / 10.191392 (4.369184) | 0.167440 / 0.680424 (-0.512984) | 0.018493 / 0.534201 (-0.515708) | 0.392774 / 0.579283 (-0.186509) | 0.420903 / 0.434364 (-0.013461) | 0.461904 / 0.540337 (-0.078433) | 0.643104 / 1.386936 (-0.743832) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006985 / 0.011353 (-0.004368) | 0.004246 / 0.011008 (-0.006762) | 0.066246 / 0.038508 (0.027738) | 0.080757 / 0.023109 (0.057648) | 0.391774 / 0.275898 (0.115876) | 0.424957 / 0.323480 (0.101478) | 0.005575 / 0.007986 (-0.002411) | 0.003447 / 0.004328 (-0.000881) | 0.066565 / 0.004250 (0.062315) | 0.057597 / 0.037052 (0.020544) | 0.394663 / 0.258489 (0.136174) | 0.430310 / 0.293841 (0.136469) | 0.032746 / 0.128546 (-0.095800) | 0.008783 / 0.075646 (-0.066863) | 0.071940 / 0.419271 (-0.347331) | 0.048877 / 0.043533 (0.005344) | 0.390269 / 0.255139 (0.135130) | 0.411867 / 0.283200 (0.128668) | 0.024101 / 0.141683 (-0.117582) | 1.507370 / 1.452155 (0.055215) | 1.585810 / 1.492716 (0.093093) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.222796 / 0.018006 (0.204790) | 0.459035 / 0.000490 (0.458546) | 0.005322 / 0.000200 (0.005122) | 0.000099 / 0.000054 (0.000045) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033237 / 0.037411 (-0.004174) | 0.098244 / 0.014526 (0.083718) | 0.106654 / 0.176557 (-0.069903) | 0.159675 / 0.737135 (-0.577460) | 0.108470 / 0.296338 (-0.187869) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.429085 / 0.215209 (0.213876) | 4.281206 / 2.077655 (2.203551) | 2.320492 / 1.504120 (0.816372) | 2.153218 / 1.541195 (0.612024) | 2.287122 / 1.468490 (0.818632) | 0.497307 / 4.584777 (-4.087470) | 3.799541 / 3.745712 (0.053828) | 3.380053 / 5.269862 (-1.889809) | 2.100009 / 4.565676 (-2.465667) | 0.057988 / 0.424275 (-0.366287) | 0.007381 / 0.007607 (-0.000226) | 0.506843 / 0.226044 (0.280798) | 5.071286 / 2.268929 (2.802357) | 2.750487 / 55.444624 (-52.694137) | 2.415613 / 6.876477 (-4.460864) | 2.667144 / 2.142072 (0.525072) | 0.624889 / 4.805227 (-4.180338) | 0.134191 / 6.500664 (-6.366473) | 0.060704 / 0.075469 (-0.014765) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.353074 / 1.841788 (-0.488714) | 20.507074 / 8.074308 (12.432766) | 14.911788 / 10.191392 (4.720396) | 0.149248 / 0.680424 (-0.531176) | 0.020593 / 0.534201 (-0.513608) | 0.398458 / 0.579283 (-0.180825) | 0.434846 / 0.434364 (0.000482) | 0.478853 / 0.540337 (-0.061484) | 0.648072 / 1.386936 (-0.738864) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b30c72a2d3d9c191a590e0f0a6b3a6363ab15e8f \"CML watermark\")\n", "In particular I expect fsspec to do another breaking change in the next release (switch to glob.glob)", "_The documentation is not available anymore as the PR was closed or merged._", "see https://github.com/huggingface/datasets/pull/6338", "Yes, unfortunately breaking changes are quite usual from their part.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006099 / 0.011353 (-0.005253) | 0.003672 / 0.011008 (-0.007336) | 0.083095 / 0.038508 (0.044587) | 0.059607 / 0.023109 (0.036498) | 0.319591 / 0.275898 (0.043693) | 0.351945 / 0.323480 (0.028465) | 0.004785 / 0.007986 (-0.003201) | 0.002965 / 0.004328 (-0.001364) | 0.062907 / 0.004250 (0.058657) | 0.049122 / 0.037052 (0.012070) | 0.344641 / 0.258489 (0.086152) | 0.361519 / 0.293841 (0.067678) | 0.027254 / 0.128546 (-0.101292) | 0.008081 / 0.075646 (-0.067565) | 0.261569 / 0.419271 (-0.157702) | 0.045101 / 0.043533 (0.001568) | 0.313645 / 0.255139 (0.058506) | 0.337843 / 0.283200 (0.054644) | 0.020968 / 0.141683 (-0.120715) | 1.438450 / 1.452155 (-0.013705) | 1.507567 / 1.492716 (0.014850) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.230826 / 0.018006 (0.212820) | 0.434363 / 0.000490 (0.433873) | 0.008210 / 0.000200 (0.008010) | 0.000212 / 0.000054 (0.000157) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.025278 / 0.037411 (-0.012133) | 0.073659 / 0.014526 (0.059133) | 0.085147 / 0.176557 (-0.091409) | 0.145451 / 0.737135 (-0.591684) | 0.086400 / 0.296338 (-0.209939) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.429887 / 0.215209 (0.214678) | 4.292626 / 2.077655 (2.214971) | 2.266824 / 1.504120 (0.762704) | 2.090472 / 1.541195 (0.549277) | 2.186477 / 1.468490 (0.717987) | 0.503684 / 4.584777 (-4.081093) | 3.100791 / 3.745712 (-0.644921) | 3.008938 / 5.269862 (-2.260923) | 1.885559 / 4.565676 (-2.680118) | 0.057434 / 0.424275 (-0.366841) | 0.006639 / 0.007607 (-0.000969) | 0.506579 / 0.226044 (0.280535) | 5.058905 / 2.268929 (2.789977) | 2.708321 / 55.444624 (-52.736304) | 2.367388 / 6.876477 (-4.509089) | 2.422660 / 2.142072 (0.280587) | 0.587562 / 4.805227 (-4.217665) | 0.125260 / 6.500664 (-6.375404) | 0.061856 / 0.075469 (-0.013613) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.280495 / 1.841788 (-0.561292) | 17.968873 / 8.074308 (9.894565) | 13.922838 / 10.191392 (3.731446) | 0.149907 / 0.680424 (-0.530517) | 0.016736 / 0.534201 (-0.517465) | 0.333417 / 0.579283 (-0.245866) | 0.367710 / 0.434364 (-0.066654) | 0.389648 / 0.540337 (-0.150690) | 0.535625 / 1.386936 (-0.851311) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006237 / 0.011353 (-0.005116) | 0.003787 / 0.011008 (-0.007221) | 0.062536 / 0.038508 (0.024028) | 0.062335 / 0.023109 (0.039226) | 0.455209 / 0.275898 (0.179311) | 0.488961 / 0.323480 (0.165482) | 0.004875 / 0.007986 (-0.003111) | 0.002961 / 0.004328 (-0.001368) | 0.063045 / 0.004250 (0.058795) | 0.048624 / 0.037052 (0.011571) | 0.455743 / 0.258489 (0.197254) | 0.494024 / 0.293841 (0.200183) | 0.028690 / 0.128546 (-0.099856) | 0.008147 / 0.075646 (-0.067499) | 0.069479 / 0.419271 (-0.349792) | 0.041613 / 0.043533 (-0.001919) | 0.460472 / 0.255139 (0.205333) | 0.475606 / 0.283200 (0.192406) | 0.020600 / 0.141683 (-0.121083) | 1.464960 / 1.452155 (0.012805) | 1.540942 / 1.492716 (0.048226) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.214558 / 0.018006 (0.196552) | 0.410482 / 0.000490 (0.409992) | 0.005539 / 0.000200 (0.005339) | 0.000076 / 0.000054 (0.000021) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027044 / 0.037411 (-0.010367) | 0.081512 / 0.014526 (0.066986) | 0.101963 / 0.176557 (-0.074593) | 0.146686 / 0.737135 (-0.590449) | 0.092676 / 0.296338 (-0.203663) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.468766 / 0.215209 (0.253557) | 4.680514 / 2.077655 (2.602859) | 2.562454 / 1.504120 (1.058334) | 2.383692 / 1.541195 (0.842497) | 2.481820 / 1.468490 (1.013330) | 0.509122 / 4.584777 (-4.075655) | 3.201597 / 3.745712 (-0.544115) | 2.853539 / 5.269862 (-2.416323) | 1.891535 / 4.565676 (-2.674141) | 0.058594 / 0.424275 (-0.365681) | 0.006448 / 0.007607 (-0.001159) | 0.535950 / 0.226044 (0.309906) | 5.388239 / 2.268929 (3.119311) | 2.999986 / 55.444624 (-52.444638) | 2.733291 / 6.876477 (-4.143186) | 2.841548 / 2.142072 (0.699475) | 0.602388 / 4.805227 (-4.202840) | 0.126369 / 6.500664 (-6.374295) | 0.061519 / 0.075469 (-0.013951) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.322746 / 1.841788 (-0.519042) | 17.940825 / 8.074308 (9.866517) | 14.679559 / 10.191392 (4.488167) | 0.146481 / 0.680424 (-0.533943) | 0.018060 / 0.534201 (-0.516141) | 0.334924 / 0.579283 (-0.244359) | 0.384735 / 0.434364 (-0.049629) | 0.391834 / 0.540337 (-0.148503) | 0.540011 / 1.386936 (-0.846925) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d82f3c2264436ef60fac8c397fb11c80175c5132 \"CML watermark\")\n" ]

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[]

[]

"2023-10-23T10:16:46Z"

"2024-02-07T12:41:35Z"

"2023-10-23T10:17:48Z"

{ "diff_url": "https://github.com/huggingface/datasets/pull/6336.diff", "html_url": "https://github.com/huggingface/datasets/pull/6336", "merged_at": "2023-10-23T10:17:48Z", "patch_url": "https://github.com/huggingface/datasets/pull/6336.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6336" }

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[ "The docs for this PR live [here](https://moon-ci-docs.huggingface.co/docs/datasets/pr_6336). All of your documentation changes will be reflected on that endpoint.", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006202 / 0.011353 (-0.005151) | 0.003627 / 0.011008 (-0.007381) | 0.080643 / 0.038508 (0.042135) | 0.057135 / 0.023109 (0.034026) | 0.315853 / 0.275898 (0.039955) | 0.348503 / 0.323480 (0.025023) | 0.004762 / 0.007986 (-0.003224) | 0.002884 / 0.004328 (-0.001445) | 0.063208 / 0.004250 (0.058958) | 0.046777 / 0.037052 (0.009725) | 0.321426 / 0.258489 (0.062937) | 0.362128 / 0.293841 (0.068287) | 0.027494 / 0.128546 (-0.101052) | 0.007931 / 0.075646 (-0.067715) | 0.262262 / 0.419271 (-0.157009) | 0.044330 / 0.043533 (0.000797) | 0.310504 / 0.255139 (0.055366) | 0.339409 / 0.283200 (0.056209) | 0.021030 / 0.141683 (-0.120652) | 1.405333 / 1.452155 (-0.046822) | 1.493497 / 1.492716 (0.000781) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.225431 / 0.018006 (0.207425) | 0.451723 / 0.000490 (0.451233) | 0.007763 / 0.000200 (0.007563) | 0.000310 / 0.000054 (0.000256) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023381 / 0.037411 (-0.014031) | 0.074183 / 0.014526 (0.059657) | 0.084003 / 0.176557 (-0.092553) | 0.143628 / 0.737135 (-0.593507) | 0.084543 / 0.296338 (-0.211796) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.393062 / 0.215209 (0.177853) | 3.905649 / 2.077655 (1.827994) | 1.923155 / 1.504120 (0.419035) | 1.751554 / 1.541195 (0.210359) | 1.816141 / 1.468490 (0.347651) | 0.502789 / 4.584777 (-4.081988) | 3.006149 / 3.745712 (-0.739564) | 2.979645 / 5.269862 (-2.290216) | 1.877408 / 4.565676 (-2.688269) | 0.057544 / 0.424275 (-0.366731) | 0.006733 / 0.007607 (-0.000874) | 0.468469 / 0.226044 (0.242425) | 4.695595 / 2.268929 (2.426667) | 2.367238 / 55.444624 (-53.077387) | 2.041035 / 6.876477 (-4.835442) | 2.087396 / 2.142072 (-0.054676) | 0.586866 / 4.805227 (-4.218361) | 0.125616 / 6.500664 (-6.375049) | 0.060535 / 0.075469 (-0.014934) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.244753 / 1.841788 (-0.597035) | 17.652902 / 8.074308 (9.578594) | 13.733195 / 10.191392 (3.541803) | 0.143741 / 0.680424 (-0.536683) | 0.016775 / 0.534201 (-0.517426) | 0.335487 / 0.579283 (-0.243797) | 0.350292 / 0.434364 (-0.084072) | 0.388744 / 0.540337 (-0.151594) | 0.536630 / 1.386936 (-0.850306) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006008 / 0.011353 (-0.005345) | 0.003708 / 0.011008 (-0.007301) | 0.062504 / 0.038508 (0.023996) | 0.058570 / 0.023109 (0.035461) | 0.450549 / 0.275898 (0.174651) | 0.467768 / 0.323480 (0.144288) | 0.004955 / 0.007986 (-0.003031) | 0.002903 / 0.004328 (-0.001426) | 0.062778 / 0.004250 (0.058528) | 0.048750 / 0.037052 (0.011698) | 0.439848 / 0.258489 (0.181359) | 0.471780 / 0.293841 (0.177939) | 0.028472 / 0.128546 (-0.100074) | 0.008221 / 0.075646 (-0.067425) | 0.068325 / 0.419271 (-0.350946) | 0.040612 / 0.043533 (-0.002921) | 0.435530 / 0.255139 (0.180391) | 0.458992 / 0.283200 (0.175792) | 0.020143 / 0.141683 (-0.121539) | 1.479101 / 1.452155 (0.026947) | 1.507408 / 1.492716 (0.014692) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.207723 / 0.018006 (0.189717) | 0.406596 / 0.000490 (0.406106) | 0.004431 / 0.000200 (0.004231) | 0.000078 / 0.000054 (0.000024) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027037 / 0.037411 (-0.010374) | 0.081576 / 0.014526 (0.067050) | 0.091177 / 0.176557 (-0.085379) | 0.146191 / 0.737135 (-0.590944) | 0.092485 / 0.296338 (-0.203854) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.456676 / 0.215209 (0.241467) | 4.556214 / 2.077655 (2.478559) | 2.500146 / 1.504120 (0.996026) | 2.325175 / 1.541195 (0.783981) | 2.421023 / 1.468490 (0.952533) | 0.512135 / 4.584777 (-4.072641) | 3.167070 / 3.745712 (-0.578642) | 2.897697 / 5.269862 (-2.372165) | 1.881974 / 4.565676 (-2.683702) | 0.058453 / 0.424275 (-0.365823) | 0.006515 / 0.007607 (-0.001092) | 0.530742 / 0.226044 (0.304698) | 5.304943 / 2.268929 (3.036014) | 2.928824 / 55.444624 (-52.515800) | 2.598023 / 6.876477 (-4.278454) | 2.758496 / 2.142072 (0.616423) | 0.601777 / 4.805227 (-4.203450) | 0.126701 / 6.500664 (-6.373964) | 0.061808 / 0.075469 (-0.013661) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.357844 / 1.841788 (-0.483943) | 17.887666 / 8.074308 (9.813358) | 14.561904 / 10.191392 (4.370512) | 0.146788 / 0.680424 (-0.533636) | 0.018277 / 0.534201 (-0.515924) | 0.343168 / 0.579283 (-0.236115) | 0.382220 / 0.434364 (-0.052144) | 0.401234 / 0.540337 (-0.139104) | 0.546246 / 1.386936 (-0.840690) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#b0980a74d58098b8b1738e2411f1212161a211b8 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008919 / 0.011353 (-0.002434) | 0.006110 / 0.011008 (-0.004898) | 0.110554 / 0.038508 (0.072046) | 0.075705 / 0.023109 (0.052596) | 0.391235 / 0.275898 (0.115336) | 0.458331 / 0.323480 (0.134851) | 0.007489 / 0.007986 (-0.000497) | 0.003744 / 0.004328 (-0.000585) | 0.078124 / 0.004250 (0.073874) | 0.057244 / 0.037052 (0.020192) | 0.393251 / 0.258489 (0.134762) | 0.460153 / 0.293841 (0.166312) | 0.047245 / 0.128546 (-0.081301) | 0.014086 / 0.075646 (-0.061560) | 0.421272 / 0.419271 (0.002001) | 0.067668 / 0.043533 (0.024135) | 0.397325 / 0.255139 (0.142186) | 0.432683 / 0.283200 (0.149483) | 0.039086 / 0.141683 (-0.102596) | 1.764898 / 1.452155 (0.312744) | 1.848820 / 1.492716 (0.356104) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.258163 / 0.018006 (0.240156) | 0.498655 / 0.000490 (0.498165) | 0.014959 / 0.000200 (0.014759) | 0.000465 / 0.000054 (0.000410) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028889 / 0.037411 (-0.008522) | 0.091568 / 0.014526 (0.077042) | 0.102700 / 0.176557 (-0.073857) | 0.173580 / 0.737135 (-0.563555) | 0.108763 / 0.296338 (-0.187576) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.610147 / 0.215209 (0.394938) | 5.851239 / 2.077655 (3.773584) | 2.467471 / 1.504120 (0.963351) | 2.117189 / 1.541195 (0.575995) | 2.197947 / 1.468490 (0.729457) | 0.851736 / 4.584777 (-3.733041) | 5.163183 / 3.745712 (1.417471) | 5.039564 / 5.269862 (-0.230297) | 3.067215 / 4.565676 (-1.498462) | 0.098593 / 0.424275 (-0.325682) | 0.008646 / 0.007607 (0.001038) | 0.788397 / 0.226044 (0.562352) | 7.340837 / 2.268929 (5.071909) | 3.511611 / 55.444624 (-51.933013) | 2.767479 / 6.876477 (-4.108998) | 2.687368 / 2.142072 (0.545296) | 1.046387 / 4.805227 (-3.758841) | 0.215902 / 6.500664 (-6.284763) | 0.072939 / 0.075469 (-0.002530) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.512795 / 1.841788 (-0.328992) | 22.086131 / 8.074308 (14.011823) | 20.235550 / 10.191392 (10.044158) | 0.240381 / 0.680424 (-0.440043) | 0.029171 / 0.534201 (-0.505030) | 0.465123 / 0.579283 (-0.114160) | 0.569260 / 0.434364 (0.134896) | 0.540967 / 0.540337 (0.000629) | 0.764006 / 1.386936 (-0.622930) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.011024 / 0.011353 (-0.000329) | 0.005915 / 0.011008 (-0.005094) | 0.076455 / 0.038508 (0.037947) | 0.087842 / 0.023109 (0.064733) | 0.471732 / 0.275898 (0.195834) | 0.513666 / 0.323480 (0.190186) | 0.007062 / 0.007986 (-0.000924) | 0.004013 / 0.004328 (-0.000315) | 0.076016 / 0.004250 (0.071766) | 0.061296 / 0.037052 (0.024244) | 0.487277 / 0.258489 (0.228788) | 0.508185 / 0.293841 (0.214344) | 0.049963 / 0.128546 (-0.078583) | 0.013774 / 0.075646 (-0.061873) | 0.089376 / 0.419271 (-0.329895) | 0.067502 / 0.043533 (0.023969) | 0.471283 / 0.255139 (0.216144) | 0.507365 / 0.283200 (0.224165) | 0.033638 / 0.141683 (-0.108045) | 1.785544 / 1.452155 (0.333390) | 1.878765 / 1.492716 (0.386048) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.230462 / 0.018006 (0.212456) | 0.502458 / 0.000490 (0.501968) | 0.005987 / 0.000200 (0.005787) | 0.000114 / 0.000054 (0.000060) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031588 / 0.037411 (-0.005824) | 0.113566 / 0.014526 (0.099040) | 0.115734 / 0.176557 (-0.060822) | 0.174162 / 0.737135 (-0.562974) | 0.121574 / 0.296338 (-0.174764) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.662837 / 0.215209 (0.447628) | 6.420327 / 2.077655 (4.342672) | 3.033522 / 1.504120 (1.529402) | 2.728294 / 1.541195 (1.187099) | 2.790621 / 1.468490 (1.322131) | 0.852478 / 4.584777 (-3.732299) | 5.033637 / 3.745712 (1.287925) | 4.543152 / 5.269862 (-0.726709) | 2.980261 / 4.565676 (-1.585415) | 0.102444 / 0.424275 (-0.321831) | 0.008362 / 0.007607 (0.000755) | 0.786868 / 0.226044 (0.560823) | 7.887665 / 2.268929 (5.618737) | 4.010614 / 55.444624 (-51.434010) | 3.220715 / 6.876477 (-3.655762) | 3.317316 / 2.142072 (1.175244) | 1.098137 / 4.805227 (-3.707090) | 0.218309 / 6.500664 (-6.282355) | 0.078182 / 0.075469 (0.002713) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.696740 / 1.841788 (-0.145047) | 23.762454 / 8.074308 (15.688146) | 21.802645 / 10.191392 (11.611253) | 0.233654 / 0.680424 (-0.446770) | 0.032911 / 0.534201 (-0.501290) | 0.511760 / 0.579283 (-0.067524) | 0.586299 / 0.434364 (0.151935) | 0.583704 / 0.540337 (0.043367) | 0.780762 / 1.386936 (-0.606174) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0a94aa2f738075bbc08291583f1b153220d5e6e7 \"CML watermark\")\n" ]

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[]

[]

"2023-10-23T09:29:17Z"

"2024-01-11T06:33:35Z"

"2023-11-14T14:17:40Z"

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006013 / 0.011353 (-0.005340) | 0.003647 / 0.011008 (-0.007362) | 0.081781 / 0.038508 (0.043273) | 0.059020 / 0.023109 (0.035911) | 0.321823 / 0.275898 (0.045925) | 0.350159 / 0.323480 (0.026679) | 0.003599 / 0.007986 (-0.004386) | 0.002877 / 0.004328 (-0.001452) | 0.063941 / 0.004250 (0.059690) | 0.049460 / 0.037052 (0.012408) | 0.330185 / 0.258489 (0.071696) | 0.362220 / 0.293841 (0.068379) | 0.027613 / 0.128546 (-0.100934) | 0.007976 / 0.075646 (-0.067670) | 0.263386 / 0.419271 (-0.155885) | 0.045504 / 0.043533 (0.001971) | 0.321172 / 0.255139 (0.066033) | 0.345291 / 0.283200 (0.062091) | 0.023133 / 0.141683 (-0.118550) | 1.435816 / 1.452155 (-0.016339) | 1.557241 / 1.492716 (0.064524) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.222228 / 0.018006 (0.204222) | 0.420008 / 0.000490 (0.419518) | 0.008598 / 0.000200 (0.008398) | 0.000343 / 0.000054 (0.000288) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.023725 / 0.037411 (-0.013686) | 0.073023 / 0.014526 (0.058497) | 0.814888 / 0.176557 (0.638332) | 0.294122 / 0.737135 (-0.443013) | 0.088945 / 0.296338 (-0.207393) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.393561 / 0.215209 (0.178352) | 3.946544 / 2.077655 (1.868890) | 1.916476 / 1.504120 (0.412356) | 1.721544 / 1.541195 (0.180349) | 1.768583 / 1.468490 (0.300093) | 0.508067 / 4.584777 (-4.076710) | 3.047832 / 3.745712 (-0.697880) | 2.952842 / 5.269862 (-2.317020) | 1.869337 / 4.565676 (-2.696339) | 0.057812 / 0.424275 (-0.366463) | 0.006694 / 0.007607 (-0.000913) | 0.463007 / 0.226044 (0.236963) | 4.635087 / 2.268929 (2.366158) | 2.419833 / 55.444624 (-53.024792) | 2.018519 / 6.876477 (-4.857958) | 2.043430 / 2.142072 (-0.098643) | 0.590895 / 4.805227 (-4.214333) | 0.126113 / 6.500664 (-6.374552) | 0.061045 / 0.075469 (-0.014424) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.226850 / 1.841788 (-0.614937) | 17.336630 / 8.074308 (9.262322) | 13.651049 / 10.191392 (3.459656) | 0.143308 / 0.680424 (-0.537116) | 0.016938 / 0.534201 (-0.517263) | 0.332829 / 0.579283 (-0.246454) | 0.368684 / 0.434364 (-0.065680) | 0.385848 / 0.540337 (-0.154489) | 0.546391 / 1.386936 (-0.840545) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006149 / 0.011353 (-0.005204) | 0.003818 / 0.011008 (-0.007191) | 0.064012 / 0.038508 (0.025504) | 0.059846 / 0.023109 (0.036737) | 0.455928 / 0.275898 (0.180030) | 0.480736 / 0.323480 (0.157256) | 0.004874 / 0.007986 (-0.003111) | 0.002877 / 0.004328 (-0.001451) | 0.064195 / 0.004250 (0.059944) | 0.048146 / 0.037052 (0.011094) | 0.452638 / 0.258489 (0.194149) | 0.484339 / 0.293841 (0.190499) | 0.028832 / 0.128546 (-0.099715) | 0.008162 / 0.075646 (-0.067485) | 0.069855 / 0.419271 (-0.349417) | 0.041429 / 0.043533 (-0.002104) | 0.453282 / 0.255139 (0.198143) | 0.473812 / 0.283200 (0.190613) | 0.021186 / 0.141683 (-0.120497) | 1.465207 / 1.452155 (0.013052) | 1.508216 / 1.492716 (0.015500) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.242491 / 0.018006 (0.224485) | 0.421219 / 0.000490 (0.420730) | 0.011201 / 0.000200 (0.011001) | 0.000083 / 0.000054 (0.000028) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027015 / 0.037411 (-0.010396) | 0.080465 / 0.014526 (0.065939) | 0.092622 / 0.176557 (-0.083934) | 0.146111 / 0.737135 (-0.591024) | 0.091546 / 0.296338 (-0.204793) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.458351 / 0.215209 (0.243142) | 4.591454 / 2.077655 (2.513799) | 2.508156 / 1.504120 (1.004037) | 2.328771 / 1.541195 (0.787576) | 2.423251 / 1.468490 (0.954761) | 0.508504 / 4.584777 (-4.076273) | 3.133789 / 3.745712 (-0.611923) | 2.862777 / 5.269862 (-2.407084) | 1.886327 / 4.565676 (-2.679350) | 0.058017 / 0.424275 (-0.366258) | 0.006496 / 0.007607 (-0.001111) | 0.529629 / 0.226044 (0.303585) | 5.310338 / 2.268929 (3.041409) | 2.973075 / 55.444624 (-52.471549) | 2.601313 / 6.876477 (-4.275163) | 2.777348 / 2.142072 (0.635275) | 0.593711 / 4.805227 (-4.211516) | 0.125453 / 6.500664 (-6.375211) | 0.061034 / 0.075469 (-0.014435) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.374391 / 1.841788 (-0.467397) | 18.768026 / 8.074308 (10.693718) | 15.053637 / 10.191392 (4.862245) | 0.158253 / 0.680424 (-0.522171) | 0.018126 / 0.534201 (-0.516075) | 0.337427 / 0.579283 (-0.241856) | 0.391678 / 0.434364 (-0.042686) | 0.398524 / 0.540337 (-0.141813) | 0.558629 / 1.386936 (-0.828307) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e0b79660f180c88517884f831eca620bc46a0957 \"CML watermark\")\n", "I think https://github.com/huggingface/datasets/pull/6334 fixes it already no ?", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006432 / 0.011353 (-0.004921) | 0.003861 / 0.011008 (-0.007147) | 0.084132 / 0.038508 (0.045624) | 0.069391 / 0.023109 (0.046282) | 0.341081 / 0.275898 (0.065183) | 0.375975 / 0.323480 (0.052495) | 0.003962 / 0.007986 (-0.004024) | 0.003235 / 0.004328 (-0.001094) | 0.064927 / 0.004250 (0.060677) | 0.054190 / 0.037052 (0.017137) | 0.350719 / 0.258489 (0.092230) | 0.393216 / 0.293841 (0.099375) | 0.031002 / 0.128546 (-0.097544) | 0.008416 / 0.075646 (-0.067230) | 0.289268 / 0.419271 (-0.130003) | 0.052167 / 0.043533 (0.008634) | 0.347559 / 0.255139 (0.092420) | 0.370908 / 0.283200 (0.087709) | 0.022540 / 0.141683 (-0.119142) | 1.486297 / 1.452155 (0.034143) | 1.576968 / 1.492716 (0.084252) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.237048 / 0.018006 (0.219042) | 0.452065 / 0.000490 (0.451575) | 0.013963 / 0.000200 (0.013763) | 0.000242 / 0.000054 (0.000188) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028084 / 0.037411 (-0.009327) | 0.081271 / 0.014526 (0.066745) | 0.096490 / 0.176557 (-0.080067) | 0.152106 / 0.737135 (-0.585030) | 0.096174 / 0.296338 (-0.200164) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.386585 / 0.215209 (0.171375) | 3.854996 / 2.077655 (1.777342) | 1.832898 / 1.504120 (0.328778) | 1.662832 / 1.541195 (0.121638) | 1.730753 / 1.468490 (0.262263) | 0.485286 / 4.584777 (-4.099491) | 3.571410 / 3.745712 (-0.174302) | 3.373035 / 5.269862 (-1.896826) | 1.995570 / 4.565676 (-2.570107) | 0.056711 / 0.424275 (-0.367564) | 0.007447 / 0.007607 (-0.000160) | 0.462985 / 0.226044 (0.236941) | 4.617186 / 2.268929 (2.348257) | 2.313915 / 55.444624 (-53.130709) | 1.961697 / 6.876477 (-4.914780) | 1.990410 / 2.142072 (-0.151662) | 0.580536 / 4.805227 (-4.224692) | 0.146275 / 6.500664 (-6.354389) | 0.059458 / 0.075469 (-0.016011) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.274841 / 1.841788 (-0.566947) | 18.641853 / 8.074308 (10.567545) | 13.977525 / 10.191392 (3.786133) | 0.151469 / 0.680424 (-0.528955) | 0.018111 / 0.534201 (-0.516090) | 0.393243 / 0.579283 (-0.186040) | 0.412310 / 0.434364 (-0.022054) | 0.461646 / 0.540337 (-0.078692) | 0.633016 / 1.386936 (-0.753920) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006496 / 0.011353 (-0.004857) | 0.003973 / 0.011008 (-0.007035) | 0.064527 / 0.038508 (0.026019) | 0.069390 / 0.023109 (0.046281) | 0.401162 / 0.275898 (0.125264) | 0.431031 / 0.323480 (0.107551) | 0.005244 / 0.007986 (-0.002741) | 0.003283 / 0.004328 (-0.001046) | 0.064931 / 0.004250 (0.060680) | 0.054402 / 0.037052 (0.017350) | 0.397917 / 0.258489 (0.139428) | 0.436728 / 0.293841 (0.142887) | 0.031932 / 0.128546 (-0.096614) | 0.008557 / 0.075646 (-0.067089) | 0.073336 / 0.419271 (-0.345935) | 0.047559 / 0.043533 (0.004026) | 0.395825 / 0.255139 (0.140686) | 0.423002 / 0.283200 (0.139802) | 0.021708 / 0.141683 (-0.119975) | 1.501140 / 1.452155 (0.048985) | 1.558376 / 1.492716 (0.065660) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.289522 / 0.018006 (0.271516) | 0.449078 / 0.000490 (0.448589) | 0.034174 / 0.000200 (0.033974) | 0.000396 / 0.000054 (0.000342) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032533 / 0.037411 (-0.004878) | 0.093398 / 0.014526 (0.078872) | 0.106930 / 0.176557 (-0.069626) | 0.158743 / 0.737135 (-0.578393) | 0.106904 / 0.296338 (-0.189435) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.427479 / 0.215209 (0.212270) | 4.271758 / 2.077655 (2.194103) | 2.298770 / 1.504120 (0.794650) | 2.134906 / 1.541195 (0.593712) | 2.220487 / 1.468490 (0.751996) | 0.490506 / 4.584777 (-4.094270) | 3.593876 / 3.745712 (-0.151836) | 3.225656 / 5.269862 (-2.044205) | 2.004434 / 4.565676 (-2.561243) | 0.058015 / 0.424275 (-0.366260) | 0.007221 / 0.007607 (-0.000387) | 0.504928 / 0.226044 (0.278884) | 5.049547 / 2.268929 (2.780618) | 2.743843 / 55.444624 (-52.700781) | 2.398399 / 6.876477 (-4.478078) | 2.562939 / 2.142072 (0.420867) | 0.597229 / 4.805227 (-4.207998) | 0.134664 / 6.500664 (-6.366001) | 0.059612 / 0.075469 (-0.015857) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.369692 / 1.841788 (-0.472095) | 19.065326 / 8.074308 (10.991018) | 14.404508 / 10.191392 (4.213116) | 0.175809 / 0.680424 (-0.504615) | 0.020137 / 0.534201 (-0.514064) | 0.394043 / 0.579283 (-0.185240) | 0.424772 / 0.434364 (-0.009592) | 0.475587 / 0.540337 (-0.064751) | 0.644275 / 1.386936 (-0.742661) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#224977971accd63d97ba0a90cc108c4754055ebb \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007259 / 0.011353 (-0.004094) | 0.004396 / 0.011008 (-0.006612) | 0.096456 / 0.038508 (0.057948) | 0.078752 / 0.023109 (0.055643) | 0.359215 / 0.275898 (0.083317) | 0.396927 / 0.323480 (0.073448) | 0.005611 / 0.007986 (-0.002375) | 0.003687 / 0.004328 (-0.000641) | 0.072794 / 0.004250 (0.068544) | 0.059794 / 0.037052 (0.022741) | 0.372352 / 0.258489 (0.113863) | 0.414038 / 0.293841 (0.120197) | 0.034490 / 0.128546 (-0.094056) | 0.009790 / 0.075646 (-0.065857) | 0.326338 / 0.419271 (-0.092934) | 0.058582 / 0.043533 (0.015049) | 0.354221 / 0.255139 (0.099082) | 0.386669 / 0.283200 (0.103469) | 0.025356 / 0.141683 (-0.116327) | 1.664104 / 1.452155 (0.211950) | 1.766825 / 1.492716 (0.274108) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.251107 / 0.018006 (0.233101) | 0.478833 / 0.000490 (0.478344) | 0.010776 / 0.000200 (0.010577) | 0.000292 / 0.000054 (0.000238) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032869 / 0.037411 (-0.004543) | 0.098449 / 0.014526 (0.083923) | 0.109954 / 0.176557 (-0.066602) | 0.176786 / 0.737135 (-0.560350) | 0.113477 / 0.296338 (-0.182862) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.431169 / 0.215209 (0.215960) | 4.303239 / 2.077655 (2.225585) | 2.088885 / 1.504120 (0.584765) | 1.895900 / 1.541195 (0.354706) | 1.997442 / 1.468490 (0.528952) | 0.541840 / 4.584777 (-4.042937) | 3.991982 / 3.745712 (0.246270) | 3.842421 / 5.269862 (-1.427440) | 2.281150 / 4.565676 (-2.284526) | 0.063851 / 0.424275 (-0.360425) | 0.008470 / 0.007607 (0.000863) | 0.515886 / 0.226044 (0.289841) | 5.202908 / 2.268929 (2.933980) | 2.662789 / 55.444624 (-52.781835) | 2.266731 / 6.876477 (-4.609746) | 2.343760 / 2.142072 (0.201688) | 0.641050 / 4.805227 (-4.164177) | 0.148236 / 6.500664 (-6.352428) | 0.067422 / 0.075469 (-0.008047) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.475729 / 1.841788 (-0.366059) | 22.401583 / 8.074308 (14.327274) | 15.886237 / 10.191392 (5.694845) | 0.171828 / 0.680424 (-0.508595) | 0.022161 / 0.534201 (-0.512040) | 0.465873 / 0.579283 (-0.113411) | 0.476386 / 0.434364 (0.042022) | 0.538317 / 0.540337 (-0.002020) | 0.754375 / 1.386936 (-0.632561) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007429 / 0.011353 (-0.003924) | 0.004592 / 0.011008 (-0.006416) | 0.072315 / 0.038508 (0.033807) | 0.080806 / 0.023109 (0.057697) | 0.444607 / 0.275898 (0.168709) | 0.476970 / 0.323480 (0.153490) | 0.006030 / 0.007986 (-0.001956) | 0.003755 / 0.004328 (-0.000573) | 0.074602 / 0.004250 (0.070352) | 0.061846 / 0.037052 (0.024794) | 0.450928 / 0.258489 (0.192439) | 0.493932 / 0.293841 (0.200091) | 0.037398 / 0.128546 (-0.091148) | 0.009807 / 0.075646 (-0.065840) | 0.080531 / 0.419271 (-0.338741) | 0.054052 / 0.043533 (0.010519) | 0.453034 / 0.255139 (0.197895) | 0.464959 / 0.283200 (0.181760) | 0.024718 / 0.141683 (-0.116965) | 1.687552 / 1.452155 (0.235397) | 1.765746 / 1.492716 (0.273029) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.266998 / 0.018006 (0.248992) | 0.479832 / 0.000490 (0.479342) | 0.005429 / 0.000200 (0.005229) | 0.000117 / 0.000054 (0.000062) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.038885 / 0.037411 (0.001474) | 0.105931 / 0.014526 (0.091405) | 0.120880 / 0.176557 (-0.055677) | 0.184006 / 0.737135 (-0.553130) | 0.120750 / 0.296338 (-0.175589) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.478626 / 0.215209 (0.263417) | 4.797355 / 2.077655 (2.719700) | 2.582758 / 1.504120 (1.078638) | 2.396488 / 1.541195 (0.855293) | 2.515597 / 1.468490 (1.047107) | 0.544541 / 4.584777 (-4.040236) | 4.150702 / 3.745712 (0.404990) | 3.676837 / 5.269862 (-1.593024) | 2.287275 / 4.565676 (-2.278402) | 0.064602 / 0.424275 (-0.359673) | 0.008253 / 0.007607 (0.000646) | 0.576201 / 0.226044 (0.350157) | 5.859839 / 2.268929 (3.590910) | 3.248603 / 55.444624 (-52.196021) | 2.841959 / 6.876477 (-4.034518) | 2.991120 / 2.142072 (0.849047) | 0.667755 / 4.805227 (-4.137472) | 0.151219 / 6.500664 (-6.349445) | 0.068990 / 0.075469 (-0.006479) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.572359 / 1.841788 (-0.269429) | 21.890279 / 8.074308 (13.815971) | 15.927473 / 10.191392 (5.736081) | 0.170388 / 0.680424 (-0.510036) | 0.023282 / 0.534201 (-0.510919) | 0.459371 / 0.579283 (-0.119912) | 0.468838 / 0.434364 (0.034475) | 0.546438 / 0.540337 (0.006101) | 0.746912 / 1.386936 (-0.640024) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#8197ce872d2e24bd1ffbb07213faea25078f1386 \"CML watermark\")\n", "Yes, @lhoestq, you are right. I think we cross-send fixing PRs in a 15 minute interval... :sweat_smile: \r\n\r\nI would say the code in this PR is simpler and easier to understand, but feel free to ignore it.", "I think the correct way it to check if \"file\" in in the tuple if it's a tuple (in case someone adds another protocol name for the local filesystem)" ]

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[]

[]

"2023-10-23T09:17:54Z"

"2024-02-07T12:41:15Z"

"2023-10-23T10:14:10Z"

{ "diff_url": "https://github.com/huggingface/datasets/pull/6334.diff", "html_url": "https://github.com/huggingface/datasets/pull/6334", "merged_at": "2023-10-23T10:14:10Z", "patch_url": "https://github.com/huggingface/datasets/pull/6334.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6334" }

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006648 / 0.011353 (-0.004705) | 0.004104 / 0.011008 (-0.006904) | 0.084718 / 0.038508 (0.046210) | 0.075342 / 0.023109 (0.052232) | 0.332624 / 0.275898 (0.056726) | 0.376758 / 0.323480 (0.053278) | 0.005371 / 0.007986 (-0.002614) | 0.003317 / 0.004328 (-0.001011) | 0.065153 / 0.004250 (0.060902) | 0.055270 / 0.037052 (0.018218) | 0.342410 / 0.258489 (0.083920) | 0.397484 / 0.293841 (0.103643) | 0.031168 / 0.128546 (-0.097379) | 0.008545 / 0.075646 (-0.067101) | 0.297641 / 0.419271 (-0.121631) | 0.052404 / 0.043533 (0.008871) | 0.327633 / 0.255139 (0.072494) | 0.362177 / 0.283200 (0.078977) | 0.025056 / 0.141683 (-0.116627) | 1.459023 / 1.452155 (0.006868) | 1.529651 / 1.492716 (0.036935) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.242838 / 0.018006 (0.224832) | 0.451007 / 0.000490 (0.450517) | 0.013732 / 0.000200 (0.013532) | 0.000345 / 0.000054 (0.000290) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028068 / 0.037411 (-0.009343) | 0.081970 / 0.014526 (0.067444) | 0.096148 / 0.176557 (-0.080409) | 0.151758 / 0.737135 (-0.585377) | 0.095617 / 0.296338 (-0.200721) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.389188 / 0.215209 (0.173979) | 3.867506 / 2.077655 (1.789852) | 1.941912 / 1.504120 (0.437792) | 1.759270 / 1.541195 (0.218076) | 1.774714 / 1.468490 (0.306224) | 0.476587 / 4.584777 (-4.108190) | 3.539342 / 3.745712 (-0.206370) | 3.434389 / 5.269862 (-1.835472) | 2.047581 / 4.565676 (-2.518096) | 0.056322 / 0.424275 (-0.367954) | 0.007286 / 0.007607 (-0.000321) | 0.461826 / 0.226044 (0.235781) | 4.604179 / 2.268929 (2.335251) | 2.405267 / 55.444624 (-53.039357) | 2.133998 / 6.876477 (-4.742479) | 2.187724 / 2.142072 (0.045652) | 0.566578 / 4.805227 (-4.238650) | 0.130007 / 6.500664 (-6.370657) | 0.059685 / 0.075469 (-0.015784) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.256204 / 1.841788 (-0.585584) | 18.829475 / 8.074308 (10.755167) | 13.937879 / 10.191392 (3.746487) | 0.163948 / 0.680424 (-0.516475) | 0.018118 / 0.534201 (-0.516083) | 0.389369 / 0.579283 (-0.189914) | 0.399988 / 0.434364 (-0.034376) | 0.459504 / 0.540337 (-0.080834) | 0.674696 / 1.386936 (-0.712240) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006806 / 0.011353 (-0.004547) | 0.004103 / 0.011008 (-0.006905) | 0.064477 / 0.038508 (0.025969) | 0.079514 / 0.023109 (0.056405) | 0.391657 / 0.275898 (0.115759) | 0.422997 / 0.323480 (0.099517) | 0.005485 / 0.007986 (-0.002501) | 0.003461 / 0.004328 (-0.000868) | 0.064621 / 0.004250 (0.060371) | 0.057686 / 0.037052 (0.020633) | 0.396885 / 0.258489 (0.138396) | 0.431508 / 0.293841 (0.137667) | 0.032305 / 0.128546 (-0.096241) | 0.008617 / 0.075646 (-0.067030) | 0.071577 / 0.419271 (-0.347694) | 0.047769 / 0.043533 (0.004236) | 0.394037 / 0.255139 (0.138898) | 0.412593 / 0.283200 (0.129393) | 0.023800 / 0.141683 (-0.117883) | 1.479114 / 1.452155 (0.026959) | 1.562422 / 1.492716 (0.069706) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.229822 / 0.018006 (0.211816) | 0.452465 / 0.000490 (0.451975) | 0.005877 / 0.000200 (0.005677) | 0.000097 / 0.000054 (0.000043) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033528 / 0.037411 (-0.003884) | 0.091819 / 0.014526 (0.077294) | 0.106188 / 0.176557 (-0.070368) | 0.159480 / 0.737135 (-0.577655) | 0.106326 / 0.296338 (-0.190013) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.427396 / 0.215209 (0.212187) | 4.275196 / 2.077655 (2.197541) | 2.287446 / 1.504120 (0.783326) | 2.137089 / 1.541195 (0.595894) | 2.198439 / 1.468490 (0.729949) | 0.491006 / 4.584777 (-4.093771) | 3.531067 / 3.745712 (-0.214645) | 3.264357 / 5.269862 (-2.005505) | 2.047760 / 4.565676 (-2.517916) | 0.057982 / 0.424275 (-0.366293) | 0.007278 / 0.007607 (-0.000329) | 0.507471 / 0.226044 (0.281426) | 5.073901 / 2.268929 (2.804973) | 2.781799 / 55.444624 (-52.662825) | 2.410759 / 6.876477 (-4.465718) | 2.623331 / 2.142072 (0.481258) | 0.601601 / 4.805227 (-4.203626) | 0.131461 / 6.500664 (-6.369204) | 0.060045 / 0.075469 (-0.015424) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.372946 / 1.841788 (-0.468842) | 19.560818 / 8.074308 (11.486509) | 14.388468 / 10.191392 (4.197076) | 0.177310 / 0.680424 (-0.503114) | 0.020233 / 0.534201 (-0.513967) | 0.395938 / 0.579283 (-0.183345) | 0.418336 / 0.434364 (-0.016028) | 0.471731 / 0.540337 (-0.068607) | 0.684679 / 1.386936 (-0.702257) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#4bedb7dcbaedd292ae5764f0fe6d44c16e1c2c10 \"CML watermark\")\n", "We did a patch release containing your fix @ap-- !" ]

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"2023-10-23T09:14:53Z"

"2024-02-07T12:39:58Z"

"2024-02-07T12:39:58Z"

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

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[]

[]

"2023-10-23T09:05:26Z"

"2023-11-07T08:23:10Z"

"2023-11-07T08:09:06Z"

{ "diff_url": "https://github.com/huggingface/datasets/pull/6332.diff", "html_url": "https://github.com/huggingface/datasets/pull/6332", "merged_at": "2023-11-07T08:09:06Z", "patch_url": "https://github.com/huggingface/datasets/pull/6332.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6332" }

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006884 / 0.011353 (-0.004469) | 0.004132 / 0.011008 (-0.006877) | 0.085993 / 0.038508 (0.047485) | 0.084049 / 0.023109 (0.060940) | 0.346194 / 0.275898 (0.070296) | 0.386999 / 0.323480 (0.063519) | 0.004185 / 0.007986 (-0.003801) | 0.004354 / 0.004328 (0.000026) | 0.065137 / 0.004250 (0.060886) | 0.057629 / 0.037052 (0.020577) | 0.353639 / 0.258489 (0.095150) | 0.400815 / 0.293841 (0.106974) | 0.031370 / 0.128546 (-0.097176) | 0.008719 / 0.075646 (-0.066927) | 0.289579 / 0.419271 (-0.129693) | 0.052826 / 0.043533 (0.009293) | 0.351110 / 0.255139 (0.095971) | 0.375663 / 0.283200 (0.092464) | 0.025892 / 0.141683 (-0.115791) | 1.481943 / 1.452155 (0.029789) | 1.541494 / 1.492716 (0.048778) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.240007 / 0.018006 (0.222000) | 0.456216 / 0.000490 (0.455726) | 0.009348 / 0.000200 (0.009148) | 0.000370 / 0.000054 (0.000315) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029541 / 0.037411 (-0.007870) | 0.088394 / 0.014526 (0.073868) | 0.098460 / 0.176557 (-0.078096) | 0.154053 / 0.737135 (-0.583083) | 0.098821 / 0.296338 (-0.197518) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.386751 / 0.215209 (0.171542) | 3.809818 / 2.077655 (1.732164) | 1.833439 / 1.504120 (0.329319) | 1.686924 / 1.541195 (0.145729) | 1.796882 / 1.468490 (0.328392) | 0.488853 / 4.584777 (-4.095924) | 3.606369 / 3.745712 (-0.139343) | 3.460003 / 5.269862 (-1.809858) | 2.087493 / 4.565676 (-2.478184) | 0.056838 / 0.424275 (-0.367437) | 0.007679 / 0.007607 (0.000072) | 0.455080 / 0.226044 (0.229036) | 4.539227 / 2.268929 (2.270299) | 2.337245 / 55.444624 (-53.107379) | 1.988195 / 6.876477 (-4.888281) | 2.067473 / 2.142072 (-0.074600) | 0.576640 / 4.805227 (-4.228587) | 0.132140 / 6.500664 (-6.368525) | 0.060737 / 0.075469 (-0.014732) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.268866 / 1.841788 (-0.572922) | 19.695296 / 8.074308 (11.620988) | 14.431254 / 10.191392 (4.239862) | 0.166779 / 0.680424 (-0.513645) | 0.018262 / 0.534201 (-0.515939) | 0.390406 / 0.579283 (-0.188877) | 0.411284 / 0.434364 (-0.023080) | 0.456696 / 0.540337 (-0.083642) | 0.629660 / 1.386936 (-0.757276) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007210 / 0.011353 (-0.004143) | 0.004124 / 0.011008 (-0.006884) | 0.065877 / 0.038508 (0.027368) | 0.086242 / 0.023109 (0.063133) | 0.420087 / 0.275898 (0.144189) | 0.454327 / 0.323480 (0.130847) | 0.005586 / 0.007986 (-0.002399) | 0.003465 / 0.004328 (-0.000863) | 0.065153 / 0.004250 (0.060902) | 0.059337 / 0.037052 (0.022285) | 0.420913 / 0.258489 (0.162424) | 0.458552 / 0.293841 (0.164711) | 0.032335 / 0.128546 (-0.096211) | 0.008672 / 0.075646 (-0.066974) | 0.072029 / 0.419271 (-0.347242) | 0.048148 / 0.043533 (0.004615) | 0.423334 / 0.255139 (0.168196) | 0.440616 / 0.283200 (0.157416) | 0.023761 / 0.141683 (-0.117922) | 1.487022 / 1.452155 (0.034868) | 1.554028 / 1.492716 (0.061312) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.216693 / 0.018006 (0.198687) | 0.446359 / 0.000490 (0.445869) | 0.005294 / 0.000200 (0.005094) | 0.000100 / 0.000054 (0.000045) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034655 / 0.037411 (-0.002756) | 0.099479 / 0.014526 (0.084953) | 0.111822 / 0.176557 (-0.064735) | 0.160675 / 0.737135 (-0.576461) | 0.108718 / 0.296338 (-0.187621) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.440270 / 0.215209 (0.225061) | 4.389013 / 2.077655 (2.311358) | 2.408007 / 1.504120 (0.903887) | 2.237233 / 1.541195 (0.696038) | 2.344131 / 1.468490 (0.875641) | 0.493143 / 4.584777 (-4.091634) | 3.620024 / 3.745712 (-0.125688) | 3.335810 / 5.269862 (-1.934052) | 2.079256 / 4.565676 (-2.486420) | 0.058324 / 0.424275 (-0.365951) | 0.007410 / 0.007607 (-0.000197) | 0.512057 / 0.226044 (0.286013) | 5.120629 / 2.268929 (2.851701) | 2.913268 / 55.444624 (-52.531356) | 2.558214 / 6.876477 (-4.318262) | 2.784146 / 2.142072 (0.642074) | 0.593308 / 4.805227 (-4.211920) | 0.134941 / 6.500664 (-6.365723) | 0.062292 / 0.075469 (-0.013177) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.351795 / 1.841788 (-0.489993) | 20.489559 / 8.074308 (12.415251) | 15.046116 / 10.191392 (4.854724) | 0.166339 / 0.680424 (-0.514085) | 0.020449 / 0.534201 (-0.513752) | 0.406570 / 0.579283 (-0.172713) | 0.423405 / 0.434364 (-0.010959) | 0.474541 / 0.540337 (-0.065796) | 0.653280 / 1.386936 (-0.733656) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3bde0f0f0e556e55b95c72b0f83bdcf7145c813c \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006362 / 0.011353 (-0.004991) | 0.003990 / 0.011008 (-0.007018) | 0.084020 / 0.038508 (0.045512) | 0.072198 / 0.023109 (0.049089) | 0.335992 / 0.275898 (0.060094) | 0.362056 / 0.323480 (0.038576) | 0.005298 / 0.007986 (-0.002688) | 0.003421 / 0.004328 (-0.000908) | 0.065343 / 0.004250 (0.061092) | 0.053310 / 0.037052 (0.016258) | 0.344855 / 0.258489 (0.086366) | 0.385524 / 0.293841 (0.091683) | 0.030209 / 0.128546 (-0.098337) | 0.008465 / 0.075646 (-0.067181) | 0.287359 / 0.419271 (-0.131912) | 0.051371 / 0.043533 (0.007838) | 0.338716 / 0.255139 (0.083577) | 0.351730 / 0.283200 (0.068530) | 0.023581 / 0.141683 (-0.118102) | 1.473772 / 1.452155 (0.021617) | 1.560594 / 1.492716 (0.067878) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.309019 / 0.018006 (0.291013) | 0.561428 / 0.000490 (0.560939) | 0.007237 / 0.000200 (0.007038) | 0.000266 / 0.000054 (0.000212) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028172 / 0.037411 (-0.009239) | 0.081050 / 0.014526 (0.066524) | 0.095952 / 0.176557 (-0.080604) | 0.151796 / 0.737135 (-0.585340) | 0.096132 / 0.296338 (-0.200206) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.384287 / 0.215209 (0.169078) | 3.840797 / 2.077655 (1.763142) | 1.891120 / 1.504120 (0.387000) | 1.743498 / 1.541195 (0.202303) | 1.821037 / 1.468490 (0.352547) | 0.484946 / 4.584777 (-4.099831) | 3.586053 / 3.745712 (-0.159659) | 3.446215 / 5.269862 (-1.823647) | 2.054352 / 4.565676 (-2.511325) | 0.057315 / 0.424275 (-0.366960) | 0.007541 / 0.007607 (-0.000066) | 0.464088 / 0.226044 (0.238044) | 4.634005 / 2.268929 (2.365076) | 2.355818 / 55.444624 (-53.088806) | 2.045584 / 6.876477 (-4.830893) | 2.039455 / 2.142072 (-0.102617) | 0.576137 / 4.805227 (-4.229090) | 0.132071 / 6.500664 (-6.368593) | 0.059611 / 0.075469 (-0.015858) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.280078 / 1.841788 (-0.561710) | 19.054079 / 8.074308 (10.979771) | 14.291090 / 10.191392 (4.099698) | 0.170607 / 0.680424 (-0.509817) | 0.018489 / 0.534201 (-0.515712) | 0.391802 / 0.579283 (-0.187481) | 0.418945 / 0.434364 (-0.015419) | 0.464084 / 0.540337 (-0.076254) | 0.638099 / 1.386936 (-0.748837) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006735 / 0.011353 (-0.004618) | 0.004133 / 0.011008 (-0.006876) | 0.064620 / 0.038508 (0.026112) | 0.076395 / 0.023109 (0.053286) | 0.399659 / 0.275898 (0.123761) | 0.426821 / 0.323480 (0.103341) | 0.006407 / 0.007986 (-0.001578) | 0.003472 / 0.004328 (-0.000857) | 0.064922 / 0.004250 (0.060671) | 0.058312 / 0.037052 (0.021260) | 0.403286 / 0.258489 (0.144797) | 0.437772 / 0.293841 (0.143931) | 0.032323 / 0.128546 (-0.096223) | 0.008727 / 0.075646 (-0.066919) | 0.071344 / 0.419271 (-0.347927) | 0.048673 / 0.043533 (0.005141) | 0.400693 / 0.255139 (0.145554) | 0.418668 / 0.283200 (0.135468) | 0.022871 / 0.141683 (-0.118812) | 1.517691 / 1.452155 (0.065536) | 1.552021 / 1.492716 (0.059305) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.305279 / 0.018006 (0.287272) | 0.520054 / 0.000490 (0.519564) | 0.007247 / 0.000200 (0.007047) | 0.000098 / 0.000054 (0.000044) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032001 / 0.037411 (-0.005410) | 0.091273 / 0.014526 (0.076747) | 0.106480 / 0.176557 (-0.070077) | 0.163122 / 0.737135 (-0.574014) | 0.105244 / 0.296338 (-0.191094) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.432207 / 0.215209 (0.216998) | 4.304856 / 2.077655 (2.227202) | 2.326790 / 1.504120 (0.822670) | 2.150081 / 1.541195 (0.608886) | 2.150558 / 1.468490 (0.682068) | 0.488808 / 4.584777 (-4.095969) | 3.690435 / 3.745712 (-0.055277) | 3.302625 / 5.269862 (-1.967236) | 2.044193 / 4.565676 (-2.521483) | 0.057520 / 0.424275 (-0.366755) | 0.007281 / 0.007607 (-0.000326) | 0.521078 / 0.226044 (0.295034) | 5.162620 / 2.268929 (2.893691) | 2.744041 / 55.444624 (-52.700583) | 2.407211 / 6.876477 (-4.469266) | 2.606290 / 2.142072 (0.464217) | 0.586412 / 4.805227 (-4.218815) | 0.132152 / 6.500664 (-6.368512) | 0.059424 / 0.075469 (-0.016045) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.351879 / 1.841788 (-0.489908) | 19.460608 / 8.074308 (11.386299) | 14.643413 / 10.191392 (4.452021) | 0.168062 / 0.680424 (-0.512362) | 0.020396 / 0.534201 (-0.513805) | 0.395885 / 0.579283 (-0.183398) | 0.439551 / 0.434364 (0.005187) | 0.473051 / 0.540337 (-0.067286) | 0.644614 / 1.386936 (-0.742322) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#732b2ed47728fffc8d74f92691c21de8ac7423fe \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.014708 / 0.011353 (0.003355) | 0.008309 / 0.011008 (-0.002699) | 0.138986 / 0.038508 (0.100478) | 0.121781 / 0.023109 (0.098671) | 0.495536 / 0.275898 (0.219637) | 0.565195 / 0.323480 (0.241715) | 0.008018 / 0.007986 (0.000032) | 0.004904 / 0.004328 (0.000575) | 0.080622 / 0.004250 (0.076371) | 0.078917 / 0.037052 (0.041865) | 0.489424 / 0.258489 (0.230935) | 0.540496 / 0.293841 (0.246656) | 0.061110 / 0.128546 (-0.067437) | 0.021443 / 0.075646 (-0.054203) | 0.395789 / 0.419271 (-0.023482) | 0.076727 / 0.043533 (0.033194) | 0.427808 / 0.255139 (0.172669) | 0.519672 / 0.283200 (0.236473) | 0.041607 / 0.141683 (-0.100076) | 2.098675 / 1.452155 (0.646520) | 2.175123 / 1.492716 (0.682407) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.275784 / 0.018006 (0.257777) | 0.707103 / 0.000490 (0.706613) | 0.011524 / 0.000200 (0.011324) | 0.000390 / 0.000054 (0.000336) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032897 / 0.037411 (-0.004514) | 0.123239 / 0.014526 (0.108713) | 0.151815 / 0.176557 (-0.024741) | 0.214790 / 0.737135 (-0.522345) | 0.139166 / 0.296338 (-0.157173) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.740662 / 0.215209 (0.525453) | 7.540376 / 2.077655 (5.462721) | 3.168207 / 1.504120 (1.664087) | 2.745663 / 1.541195 (1.204468) | 2.714020 / 1.468490 (1.245530) | 1.182632 / 4.584777 (-3.402145) | 6.365807 / 3.745712 (2.620095) | 6.317228 / 5.269862 (1.047366) | 4.061107 / 4.565676 (-0.504569) | 0.146939 / 0.424275 (-0.277336) | 0.011765 / 0.007607 (0.004158) | 0.910564 / 0.226044 (0.684519) | 9.020618 / 2.268929 (6.751689) | 4.180748 / 55.444624 (-51.263876) | 3.290257 / 6.876477 (-3.586220) | 3.363172 / 2.142072 (1.221099) | 1.239142 / 4.805227 (-3.566086) | 0.294965 / 6.500664 (-6.205699) | 0.088520 / 0.075469 (0.013051) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.867528 / 1.841788 (0.025741) | 29.494058 / 8.074308 (21.419750) | 31.386703 / 10.191392 (21.195311) | 0.302488 / 0.680424 (-0.377936) | 0.036116 / 0.534201 (-0.498085) | 0.622112 / 0.579283 (0.042829) | 0.775658 / 0.434364 (0.341294) | 0.632452 / 0.540337 (0.092115) | 0.909424 / 1.386936 (-0.477512) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.016002 / 0.011353 (0.004649) | 0.007007 / 0.011008 (-0.004002) | 0.100463 / 0.038508 (0.061955) | 0.124423 / 0.023109 (0.101314) | 0.556014 / 0.275898 (0.280116) | 0.600909 / 0.323480 (0.277429) | 0.007272 / 0.007986 (-0.000714) | 0.006743 / 0.004328 (0.002415) | 0.088575 / 0.004250 (0.084324) | 0.066003 / 0.037052 (0.028951) | 0.580080 / 0.258489 (0.321591) | 0.655567 / 0.293841 (0.361726) | 0.065295 / 0.128546 (-0.063252) | 0.021105 / 0.075646 (-0.054541) | 0.120044 / 0.419271 (-0.299227) | 0.081133 / 0.043533 (0.037600) | 0.570322 / 0.255139 (0.315183) | 0.581134 / 0.283200 (0.297934) | 0.046298 / 0.141683 (-0.095385) | 2.113200 / 1.452155 (0.661045) | 2.344187 / 1.492716 (0.851471) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.284517 / 0.018006 (0.266511) | 0.611834 / 0.000490 (0.611345) | 0.005581 / 0.000200 (0.005381) | 0.000153 / 0.000054 (0.000098) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.042162 / 0.037411 (0.004750) | 0.114496 / 0.014526 (0.099970) | 0.134034 / 0.176557 (-0.042523) | 0.201649 / 0.737135 (-0.535486) | 0.143235 / 0.296338 (-0.153103) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.764863 / 0.215209 (0.549654) | 7.603076 / 2.077655 (5.525421) | 3.318911 / 1.504120 (1.814791) | 2.939815 / 1.541195 (1.398620) | 2.870911 / 1.468490 (1.402421) | 1.171978 / 4.584777 (-3.412799) | 6.479933 / 3.745712 (2.734221) | 5.944387 / 5.269862 (0.674526) | 4.282625 / 4.565676 (-0.283051) | 0.123672 / 0.424275 (-0.300603) | 0.009666 / 0.007607 (0.002059) | 0.870683 / 0.226044 (0.644638) | 9.187788 / 2.268929 (6.918859) | 4.431818 / 55.444624 (-51.012807) | 3.460457 / 6.876477 (-3.416020) | 3.708198 / 2.142072 (1.566126) | 1.353673 / 4.805227 (-3.451554) | 0.264274 / 6.500664 (-6.236390) | 0.074943 / 0.075469 (-0.000526) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 2.073810 / 1.841788 (0.232023) | 29.182464 / 8.074308 (21.108156) | 30.527040 / 10.191392 (20.335648) | 0.307561 / 0.680424 (-0.372863) | 0.047384 / 0.534201 (-0.486817) | 0.662760 / 0.579283 (0.083477) | 0.768321 / 0.434364 (0.333957) | 0.692296 / 0.540337 (0.151959) | 0.955197 / 1.386936 (-0.431739) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1e82d6f017c7fc0ab6b65847c1e34772c880d3b7 \"CML watermark\")\n" ]

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[]

[]

"2023-10-23T08:51:50Z"

"2023-10-23T09:26:42Z"

"2023-10-23T09:17:55Z"

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009605 / 0.011353 (-0.001747) | 0.004864 / 0.011008 (-0.006144) | 0.114605 / 0.038508 (0.076097) | 0.090874 / 0.023109 (0.067765) | 0.429203 / 0.275898 (0.153305) | 0.489888 / 0.323480 (0.166408) | 0.006542 / 0.007986 (-0.001443) | 0.004585 / 0.004328 (0.000257) | 0.090251 / 0.004250 (0.086001) | 0.066612 / 0.037052 (0.029560) | 0.437491 / 0.258489 (0.179002) | 0.515196 / 0.293841 (0.221355) | 0.047756 / 0.128546 (-0.080791) | 0.013587 / 0.075646 (-0.062059) | 0.376960 / 0.419271 (-0.042311) | 0.069701 / 0.043533 (0.026168) | 0.430850 / 0.255139 (0.175711) | 0.475061 / 0.283200 (0.191861) | 0.034800 / 0.141683 (-0.106883) | 1.799947 / 1.452155 (0.347793) | 1.941863 / 1.492716 (0.449147) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.316685 / 0.018006 (0.298679) | 0.595098 / 0.000490 (0.594608) | 0.015447 / 0.000200 (0.015247) | 0.000463 / 0.000054 (0.000409) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.039364 / 0.037411 (0.001953) | 0.091295 / 0.014526 (0.076769) | 0.109380 / 0.176557 (-0.067177) | 0.185454 / 0.737135 (-0.551681) | 0.104476 / 0.296338 (-0.191862) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.626291 / 0.215209 (0.411082) | 5.869948 / 2.077655 (3.792293) | 2.466267 / 1.504120 (0.962147) | 2.183572 / 1.541195 (0.642377) | 2.208286 / 1.468490 (0.739796) | 0.817175 / 4.584777 (-3.767602) | 5.255141 / 3.745712 (1.509429) | 4.878668 / 5.269862 (-0.391193) | 2.917020 / 4.565676 (-1.648657) | 0.104995 / 0.424275 (-0.319280) | 0.008687 / 0.007607 (0.001080) | 0.678993 / 0.226044 (0.452948) | 7.004983 / 2.268929 (4.736054) | 3.444040 / 55.444624 (-52.000584) | 2.745075 / 6.876477 (-4.131402) | 2.720151 / 2.142072 (0.578078) | 0.995803 / 4.805227 (-3.809424) | 0.205928 / 6.500664 (-6.294736) | 0.077053 / 0.075469 (0.001584) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.587354 / 1.841788 (-0.254434) | 23.843227 / 8.074308 (15.768919) | 21.355771 / 10.191392 (11.164379) | 0.225593 / 0.680424 (-0.454831) | 0.029054 / 0.534201 (-0.505147) | 0.469676 / 0.579283 (-0.109607) | 0.582619 / 0.434364 (0.148255) | 0.576932 / 0.540337 (0.036594) | 0.946182 / 1.386936 (-0.440754) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.009819 / 0.011353 (-0.001534) | 0.005562 / 0.011008 (-0.005446) | 0.075512 / 0.038508 (0.037004) | 0.084294 / 0.023109 (0.061185) | 0.549516 / 0.275898 (0.273618) | 0.550364 / 0.323480 (0.226884) | 0.006603 / 0.007986 (-0.001383) | 0.004587 / 0.004328 (0.000259) | 0.084040 / 0.004250 (0.079789) | 0.066815 / 0.037052 (0.029762) | 0.549224 / 0.258489 (0.290735) | 0.556213 / 0.293841 (0.262372) | 0.048538 / 0.128546 (-0.080008) | 0.014050 / 0.075646 (-0.061596) | 0.088955 / 0.419271 (-0.330317) | 0.062393 / 0.043533 (0.018860) | 0.528770 / 0.255139 (0.273631) | 0.564854 / 0.283200 (0.281655) | 0.033976 / 0.141683 (-0.107707) | 1.858558 / 1.452155 (0.406403) | 1.894616 / 1.492716 (0.401899) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.378597 / 0.018006 (0.360591) | 0.650586 / 0.000490 (0.650097) | 0.033179 / 0.000200 (0.032979) | 0.000477 / 0.000054 (0.000423) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031779 / 0.037411 (-0.005632) | 0.103393 / 0.014526 (0.088867) | 0.119810 / 0.176557 (-0.056747) | 0.192188 / 0.737135 (-0.544948) | 0.114545 / 0.296338 (-0.181794) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.623571 / 0.215209 (0.408362) | 6.350249 / 2.077655 (4.272594) | 3.207773 / 1.504120 (1.703653) | 2.861118 / 1.541195 (1.319923) | 2.864445 / 1.468490 (1.395955) | 0.827451 / 4.584777 (-3.757326) | 5.323860 / 3.745712 (1.578148) | 4.569197 / 5.269862 (-0.700665) | 2.967595 / 4.565676 (-1.598081) | 0.090926 / 0.424275 (-0.333349) | 0.007820 / 0.007607 (0.000213) | 0.731610 / 0.226044 (0.505565) | 7.342651 / 2.268929 (5.073723) | 3.781727 / 55.444624 (-51.662897) | 3.222100 / 6.876477 (-3.654377) | 3.546145 / 2.142072 (1.404073) | 1.030500 / 4.805227 (-3.774728) | 0.226563 / 6.500664 (-6.274101) | 0.078633 / 0.075469 (0.003164) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.733677 / 1.841788 (-0.108111) | 24.650616 / 8.074308 (16.576308) | 22.033745 / 10.191392 (11.842353) | 0.211055 / 0.680424 (-0.469369) | 0.031658 / 0.534201 (-0.502543) | 0.467190 / 0.579283 (-0.112094) | 0.598303 / 0.434364 (0.163939) | 0.569318 / 0.540337 (0.028981) | 0.825984 / 1.386936 (-0.560952) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7eef7749031232d0b29f7ca10e3fa9f997b19ef7 \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006873 / 0.011353 (-0.004479) | 0.004174 / 0.011008 (-0.006835) | 0.085874 / 0.038508 (0.047366) | 0.074207 / 0.023109 (0.051098) | 0.307342 / 0.275898 (0.031444) | 0.339972 / 0.323480 (0.016493) | 0.005522 / 0.007986 (-0.002463) | 0.003576 / 0.004328 (-0.000753) | 0.065680 / 0.004250 (0.061430) | 0.056274 / 0.037052 (0.019222) | 0.313121 / 0.258489 (0.054632) | 0.364699 / 0.293841 (0.070858) | 0.031297 / 0.128546 (-0.097249) | 0.008652 / 0.075646 (-0.066994) | 0.288431 / 0.419271 (-0.130840) | 0.053081 / 0.043533 (0.009548) | 0.309076 / 0.255139 (0.053937) | 0.329251 / 0.283200 (0.046052) | 0.024840 / 0.141683 (-0.116843) | 1.484155 / 1.452155 (0.032001) | 1.598665 / 1.492716 (0.105949) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.270933 / 0.018006 (0.252927) | 0.565867 / 0.000490 (0.565377) | 0.006964 / 0.000200 (0.006764) | 0.000298 / 0.000054 (0.000244) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028393 / 0.037411 (-0.009018) | 0.081756 / 0.014526 (0.067230) | 0.095733 / 0.176557 (-0.080823) | 0.152426 / 0.737135 (-0.584710) | 0.096655 / 0.296338 (-0.199683) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.403992 / 0.215209 (0.188783) | 4.027230 / 2.077655 (1.949576) | 2.031102 / 1.504120 (0.526982) | 1.843727 / 1.541195 (0.302532) | 1.898342 / 1.468490 (0.429852) | 0.479186 / 4.584777 (-4.105591) | 3.488153 / 3.745712 (-0.257559) | 3.523953 / 5.269862 (-1.745909) | 2.078392 / 4.565676 (-2.487284) | 0.056104 / 0.424275 (-0.368171) | 0.007368 / 0.007607 (-0.000239) | 0.479630 / 0.226044 (0.253585) | 4.787400 / 2.268929 (2.518471) | 2.488268 / 55.444624 (-52.956356) | 2.229955 / 6.876477 (-4.646522) | 2.260468 / 2.142072 (0.118396) | 0.587934 / 4.805227 (-4.217294) | 0.147124 / 6.500664 (-6.353540) | 0.059954 / 0.075469 (-0.015515) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.283155 / 1.841788 (-0.558632) | 19.013574 / 8.074308 (10.939266) | 13.915188 / 10.191392 (3.723796) | 0.174101 / 0.680424 (-0.506323) | 0.018172 / 0.534201 (-0.516029) | 0.390322 / 0.579283 (-0.188961) | 0.405493 / 0.434364 (-0.028871) | 0.456914 / 0.540337 (-0.083424) | 0.635213 / 1.386936 (-0.751723) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006622 / 0.011353 (-0.004731) | 0.003997 / 0.011008 (-0.007011) | 0.064542 / 0.038508 (0.026034) | 0.074165 / 0.023109 (0.051056) | 0.392285 / 0.275898 (0.116387) | 0.423522 / 0.323480 (0.100042) | 0.006361 / 0.007986 (-0.001625) | 0.003463 / 0.004328 (-0.000866) | 0.064891 / 0.004250 (0.060641) | 0.058485 / 0.037052 (0.021433) | 0.425217 / 0.258489 (0.166728) | 0.435907 / 0.293841 (0.142066) | 0.031501 / 0.128546 (-0.097045) | 0.008575 / 0.075646 (-0.067071) | 0.072094 / 0.419271 (-0.347178) | 0.047904 / 0.043533 (0.004371) | 0.397174 / 0.255139 (0.142035) | 0.417940 / 0.283200 (0.134741) | 0.023324 / 0.141683 (-0.118358) | 1.517245 / 1.452155 (0.065090) | 1.586497 / 1.492716 (0.093781) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.268311 / 0.018006 (0.250305) | 0.561118 / 0.000490 (0.560628) | 0.004352 / 0.000200 (0.004152) | 0.000095 / 0.000054 (0.000041) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033039 / 0.037411 (-0.004373) | 0.091596 / 0.014526 (0.077071) | 0.111520 / 0.176557 (-0.065036) | 0.161755 / 0.737135 (-0.575381) | 0.107681 / 0.296338 (-0.188657) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.427170 / 0.215209 (0.211961) | 4.252648 / 2.077655 (2.174994) | 2.257623 / 1.504120 (0.753503) | 2.098446 / 1.541195 (0.557251) | 2.128544 / 1.468490 (0.660054) | 0.496639 / 4.584777 (-4.088138) | 3.593385 / 3.745712 (-0.152328) | 3.396367 / 5.269862 (-1.873494) | 2.073369 / 4.565676 (-2.492308) | 0.058386 / 0.424275 (-0.365889) | 0.007515 / 0.007607 (-0.000093) | 0.502358 / 0.226044 (0.276313) | 5.015224 / 2.268929 (2.746296) | 2.735740 / 55.444624 (-52.708885) | 2.388368 / 6.876477 (-4.488109) | 2.682857 / 2.142072 (0.540785) | 0.595003 / 4.805227 (-4.210225) | 0.135419 / 6.500664 (-6.365245) | 0.062824 / 0.075469 (-0.012645) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.367507 / 1.841788 (-0.474281) | 19.569288 / 8.074308 (11.494979) | 14.748693 / 10.191392 (4.557301) | 0.198659 / 0.680424 (-0.481765) | 0.020954 / 0.534201 (-0.513247) | 0.414858 / 0.579283 (-0.164426) | 0.421226 / 0.434364 (-0.013138) | 0.477774 / 0.540337 (-0.062563) | 0.676173 / 1.386936 (-0.710763) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#87ed467af77d87ad7e38279cf9a24c341545cbac \"CML watermark\")\n" ]

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"2023-10-22T20:57:10Z"

"2023-11-07T10:02:14Z"

"2023-10-23T09:17:56Z"

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

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"2023-10-22T11:07:46Z"

"2023-10-23T09:22:58Z"

"2023-10-23T09:22:58Z"

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"2023-10-22T11:07:21Z"

"2023-10-23T09:22:38Z"

"2023-10-23T09:22:38Z"

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

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"2023-10-21T12:27:03Z"

"2023-10-23T18:50:07Z"

"2023-10-23T18:50:07Z"

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

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"2023-10-21T10:07:48Z"

"2023-10-23T14:56:20Z"

"2023-10-23T14:56:20Z"

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"2023-10-21T10:06:37Z"

"2023-10-23T14:55:58Z"

"2023-10-23T14:55:58Z"

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"2023-10-20T23:20:58Z"

"2023-10-23T20:52:57Z"

"2023-10-23T20:52:57Z"

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

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"2023-10-20T12:59:55Z"

"2023-10-20T12:59:55Z"

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"2023-10-19T19:45:10Z"

"2023-10-23T14:40:45Z"

"2023-10-23T14:31:21Z"

{ "diff_url": "https://github.com/huggingface/datasets/pull/6322.diff", "html_url": "https://github.com/huggingface/datasets/pull/6322", "merged_at": "2023-10-23T14:31:21Z", "patch_url": "https://github.com/huggingface/datasets/pull/6322.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6322" }

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[ "_The documentation is not available anymore as the PR was closed or merged._", "> The reason why I used the the glob_pattern_to_regex in the entire pattern is because otherwise I got an error for Windows local paths: a base_path like 'C:\\\\Users\\\\runneradmin... made the function string_to_dict raise re.error: incomplete escape \\U at position 2\r\n\r\nWhat is the expected inputs and outputs for the windows `base_path`\r\n\r\n> That issue was fixed once we pass the base_path as POSIX.\r\n\r\nI'm not sure what you meant by that, are there still changes needed?\r\n", "We took the liberty of continuing this PR to include it in today's patch release :)\r\nI hope you don't mind", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007109 / 0.011353 (-0.004244) | 0.004209 / 0.011008 (-0.006799) | 0.097401 / 0.038508 (0.058892) | 0.079532 / 0.023109 (0.056423) | 0.341300 / 0.275898 (0.065402) | 0.402165 / 0.323480 (0.078685) | 0.005838 / 0.007986 (-0.002148) | 0.003310 / 0.004328 (-0.001018) | 0.072804 / 0.004250 (0.068553) | 0.059418 / 0.037052 (0.022366) | 0.339277 / 0.258489 (0.080788) | 0.418495 / 0.293841 (0.124654) | 0.035975 / 0.128546 (-0.092571) | 0.008101 / 0.075646 (-0.067546) | 0.339236 / 0.419271 (-0.080035) | 0.059326 / 0.043533 (0.015794) | 0.326880 / 0.255139 (0.071741) | 0.393614 / 0.283200 (0.110414) | 0.025830 / 0.141683 (-0.115852) | 1.657726 / 1.452155 (0.205571) | 1.817250 / 1.492716 (0.324534) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.256015 / 0.018006 (0.238008) | 0.482447 / 0.000490 (0.481957) | 0.012166 / 0.000200 (0.011966) | 0.000343 / 0.000054 (0.000288) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029898 / 0.037411 (-0.007514) | 0.088218 / 0.014526 (0.073692) | 0.102353 / 0.176557 (-0.074203) | 0.165863 / 0.737135 (-0.571272) | 0.100342 / 0.296338 (-0.195996) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.429362 / 0.215209 (0.214153) | 4.147327 / 2.077655 (2.069672) | 2.014653 / 1.504120 (0.510533) | 1.824394 / 1.541195 (0.283199) | 1.936408 / 1.468490 (0.467917) | 0.542960 / 4.584777 (-4.041817) | 3.917215 / 3.745712 (0.171503) | 3.714825 / 5.269862 (-1.555036) | 2.180279 / 4.565676 (-2.385398) | 0.057808 / 0.424275 (-0.366467) | 0.008426 / 0.007607 (0.000819) | 0.472372 / 0.226044 (0.246327) | 4.879656 / 2.268929 (2.610728) | 2.602729 / 55.444624 (-52.841896) | 2.142593 / 6.876477 (-4.733884) | 2.206070 / 2.142072 (0.063997) | 0.635591 / 4.805227 (-4.169636) | 0.140928 / 6.500664 (-6.359736) | 0.065119 / 0.075469 (-0.010350) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.455909 / 1.841788 (-0.385879) | 20.803592 / 8.074308 (12.729284) | 14.788713 / 10.191392 (4.597321) | 0.170546 / 0.680424 (-0.509878) | 0.021189 / 0.534201 (-0.513012) | 0.432368 / 0.579283 (-0.146915) | 0.444664 / 0.434364 (0.010300) | 0.517744 / 0.540337 (-0.022593) | 0.699265 / 1.386936 (-0.687671) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007592 / 0.011353 (-0.003760) | 0.004045 / 0.011008 (-0.006964) | 0.073434 / 0.038508 (0.034926) | 0.076962 / 0.023109 (0.053853) | 0.468873 / 0.275898 (0.192975) | 0.479968 / 0.323480 (0.156488) | 0.006270 / 0.007986 (-0.001716) | 0.003652 / 0.004328 (-0.000677) | 0.069893 / 0.004250 (0.065643) | 0.061902 / 0.037052 (0.024850) | 0.443379 / 0.258489 (0.184890) | 0.492627 / 0.293841 (0.198786) | 0.035967 / 0.128546 (-0.092579) | 0.009276 / 0.075646 (-0.066370) | 0.083060 / 0.419271 (-0.336212) | 0.050870 / 0.043533 (0.007337) | 0.438246 / 0.255139 (0.183107) | 0.472074 / 0.283200 (0.188874) | 0.023724 / 0.141683 (-0.117959) | 1.677178 / 1.452155 (0.225023) | 1.732273 / 1.492716 (0.239557) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.244693 / 0.018006 (0.226687) | 0.470067 / 0.000490 (0.469577) | 0.005574 / 0.000200 (0.005374) | 0.000105 / 0.000054 (0.000051) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.036242 / 0.037411 (-0.001169) | 0.099166 / 0.014526 (0.084641) | 0.116785 / 0.176557 (-0.059772) | 0.174986 / 0.737135 (-0.562149) | 0.118130 / 0.296338 (-0.178209) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.475907 / 0.215209 (0.260698) | 4.708125 / 2.077655 (2.630470) | 2.600855 / 1.504120 (1.096735) | 2.446498 / 1.541195 (0.905303) | 2.538786 / 1.468490 (1.070296) | 0.566787 / 4.584777 (-4.017990) | 4.066187 / 3.745712 (0.320475) | 3.743632 / 5.269862 (-1.526229) | 2.337737 / 4.565676 (-2.227939) | 0.068402 / 0.424275 (-0.355873) | 0.008674 / 0.007607 (0.001067) | 0.593428 / 0.226044 (0.367384) | 5.840687 / 2.268929 (3.571759) | 3.194937 / 55.444624 (-52.249688) | 2.899033 / 6.876477 (-3.977444) | 2.977870 / 2.142072 (0.835797) | 0.683673 / 4.805227 (-4.121554) | 0.154933 / 6.500664 (-6.345731) | 0.071619 / 0.075469 (-0.003850) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.501895 / 1.841788 (-0.339893) | 21.709792 / 8.074308 (13.635484) | 15.679556 / 10.191392 (5.488164) | 0.188028 / 0.680424 (-0.492396) | 0.022555 / 0.534201 (-0.511646) | 0.439840 / 0.579283 (-0.139443) | 0.452140 / 0.434364 (0.017776) | 0.526421 / 0.540337 (-0.013916) | 0.731692 / 1.386936 (-0.655244) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#02ecc84a2e2ed664f574ccbcab0e525a7377a01d \"CML watermark\")\n" ]

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[]

[]

"2023-10-19T16:24:35Z"

"2023-10-19T17:18:00Z"

"2023-10-19T17:07:35Z"

{ "diff_url": "https://github.com/huggingface/datasets/pull/6321.diff", "html_url": "https://github.com/huggingface/datasets/pull/6321", "merged_at": "2023-10-19T17:07:35Z", "patch_url": "https://github.com/huggingface/datasets/pull/6321.patch", "url": "https://api.github.com/repos/huggingface/datasets/pulls/6321" }

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007809 / 0.011353 (-0.003544) | 0.004573 / 0.011008 (-0.006435) | 0.101201 / 0.038508 (0.062693) | 0.089703 / 0.023109 (0.066594) | 0.416502 / 0.275898 (0.140604) | 0.463352 / 0.323480 (0.139872) | 0.006101 / 0.007986 (-0.001885) | 0.003783 / 0.004328 (-0.000545) | 0.076531 / 0.004250 (0.072281) | 0.064017 / 0.037052 (0.026964) | 0.422453 / 0.258489 (0.163964) | 0.485926 / 0.293841 (0.192085) | 0.036797 / 0.128546 (-0.091749) | 0.010172 / 0.075646 (-0.065474) | 0.344442 / 0.419271 (-0.074829) | 0.062240 / 0.043533 (0.018707) | 0.422685 / 0.255139 (0.167546) | 0.451457 / 0.283200 (0.168257) | 0.027831 / 0.141683 (-0.113852) | 1.737187 / 1.452155 (0.285033) | 1.847631 / 1.492716 (0.354915) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.270336 / 0.018006 (0.252330) | 0.500540 / 0.000490 (0.500050) | 0.017042 / 0.000200 (0.016842) | 0.000704 / 0.000054 (0.000650) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033450 / 0.037411 (-0.003962) | 0.100314 / 0.014526 (0.085788) | 0.117216 / 0.176557 (-0.059340) | 0.182352 / 0.737135 (-0.554784) | 0.114903 / 0.296338 (-0.181436) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.458562 / 0.215209 (0.243353) | 4.570492 / 2.077655 (2.492837) | 2.230286 / 1.504120 (0.726167) | 2.032229 / 1.541195 (0.491034) | 2.130431 / 1.468490 (0.661941) | 0.563254 / 4.584777 (-4.021523) | 4.108455 / 3.745712 (0.362743) | 3.994059 / 5.269862 (-1.275802) | 2.424589 / 4.565676 (-2.141087) | 0.067534 / 0.424275 (-0.356741) | 0.008774 / 0.007607 (0.001167) | 0.546356 / 0.226044 (0.320312) | 5.527772 / 2.268929 (3.258843) | 2.934410 / 55.444624 (-52.510215) | 2.536871 / 6.876477 (-4.339605) | 2.598704 / 2.142072 (0.456632) | 0.676721 / 4.805227 (-4.128506) | 0.155904 / 6.500664 (-6.344760) | 0.073274 / 0.075469 (-0.002195) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.559170 / 1.841788 (-0.282618) | 23.228524 / 8.074308 (15.154216) | 16.743246 / 10.191392 (6.551854) | 0.184113 / 0.680424 (-0.496310) | 0.021804 / 0.534201 (-0.512397) | 0.466158 / 0.579283 (-0.113125) | 0.539911 / 0.434364 (0.105547) | 0.544377 / 0.540337 (0.004040) | 0.765779 / 1.386936 (-0.621157) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008249 / 0.011353 (-0.003104) | 0.004734 / 0.011008 (-0.006275) | 0.077083 / 0.038508 (0.038575) | 0.096959 / 0.023109 (0.073850) | 0.497501 / 0.275898 (0.221603) | 0.530687 / 0.323480 (0.207207) | 0.006379 / 0.007986 (-0.001607) | 0.003899 / 0.004328 (-0.000430) | 0.076165 / 0.004250 (0.071915) | 0.069406 / 0.037052 (0.032354) | 0.515847 / 0.258489 (0.257358) | 0.540639 / 0.293841 (0.246798) | 0.038334 / 0.128546 (-0.090213) | 0.010112 / 0.075646 (-0.065534) | 0.084918 / 0.419271 (-0.334353) | 0.056866 / 0.043533 (0.013333) | 0.495555 / 0.255139 (0.240416) | 0.518988 / 0.283200 (0.235789) | 0.028556 / 0.141683 (-0.113127) | 1.799320 / 1.452155 (0.347165) | 1.874647 / 1.492716 (0.381931) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.264283 / 0.018006 (0.246277) | 0.510278 / 0.000490 (0.509788) | 0.015219 / 0.000200 (0.015019) | 0.000160 / 0.000054 (0.000105) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.038462 / 0.037411 (0.001051) | 0.115420 / 0.014526 (0.100894) | 0.124250 / 0.176557 (-0.052306) | 0.187724 / 0.737135 (-0.549411) | 0.126674 / 0.296338 (-0.169664) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.499345 / 0.215209 (0.284136) | 4.983924 / 2.077655 (2.906269) | 2.705099 / 1.504120 (1.200980) | 2.516344 / 1.541195 (0.975149) | 2.621103 / 1.468490 (1.152613) | 0.583254 / 4.584777 (-4.001523) | 4.231215 / 3.745712 (0.485503) | 4.028326 / 5.269862 (-1.241536) | 2.459171 / 4.565676 (-2.106505) | 0.069194 / 0.424275 (-0.355081) | 0.008850 / 0.007607 (0.001243) | 0.593878 / 0.226044 (0.367834) | 5.926478 / 2.268929 (3.657549) | 3.287435 / 55.444624 (-52.157189) | 2.902104 / 6.876477 (-3.974372) | 3.151307 / 2.142072 (1.009234) | 0.696922 / 4.805227 (-4.108306) | 0.161140 / 6.500664 (-6.339524) | 0.073728 / 0.075469 (-0.001741) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.636456 / 1.841788 (-0.205331) | 23.884606 / 8.074308 (15.810298) | 17.180875 / 10.191392 (6.989483) | 0.176782 / 0.680424 (-0.503642) | 0.023731 / 0.534201 (-0.510470) | 0.475191 / 0.579283 (-0.104092) | 0.506603 / 0.434364 (0.072239) | 0.571976 / 0.540337 (0.031638) | 0.826935 / 1.386936 (-0.560002) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#2b19f6b30f49e09b0d1f0c4a38b10d76f35ac483 \"CML watermark\")\n" ]

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"2023-10-19T16:09:22Z"

"2023-11-30T16:21:15Z"

"2023-11-30T16:21:15Z"

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

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"2023-10-19T12:19:33Z"

"2023-11-30T03:27:26Z"

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

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[]

[]

"2023-10-19T12:19:13Z"

"2023-10-19T16:27:20Z"

"2023-10-19T16:16:31Z"

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006827 / 0.011353 (-0.004526) | 0.004468 / 0.011008 (-0.006540) | 0.088687 / 0.038508 (0.050179) | 0.072560 / 0.023109 (0.049451) | 0.333421 / 0.275898 (0.057523) | 0.374977 / 0.323480 (0.051497) | 0.005829 / 0.007986 (-0.002156) | 0.003284 / 0.004328 (-0.001045) | 0.068929 / 0.004250 (0.064678) | 0.057212 / 0.037052 (0.020160) | 0.328911 / 0.258489 (0.070422) | 0.389107 / 0.293841 (0.095266) | 0.033518 / 0.128546 (-0.095029) | 0.009919 / 0.075646 (-0.065728) | 0.308100 / 0.419271 (-0.111171) | 0.059380 / 0.043533 (0.015847) | 0.345587 / 0.255139 (0.090448) | 0.353703 / 0.283200 (0.070503) | 0.026454 / 0.141683 (-0.115229) | 1.573309 / 1.452155 (0.121155) | 1.663812 / 1.492716 (0.171095) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.255081 / 0.018006 (0.237075) | 0.472613 / 0.000490 (0.472123) | 0.016120 / 0.000200 (0.015920) | 0.000383 / 0.000054 (0.000328) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028219 / 0.037411 (-0.009192) | 0.086600 / 0.014526 (0.072074) | 0.099484 / 0.176557 (-0.077073) | 0.154604 / 0.737135 (-0.582531) | 0.099168 / 0.296338 (-0.197171) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.421703 / 0.215209 (0.206494) | 4.188600 / 2.077655 (2.110945) | 2.037575 / 1.504120 (0.533456) | 1.843389 / 1.541195 (0.302194) | 1.912554 / 1.468490 (0.444064) | 0.517452 / 4.584777 (-4.067325) | 3.838002 / 3.745712 (0.092290) | 3.698899 / 5.269862 (-1.570963) | 2.175393 / 4.565676 (-2.390283) | 0.066059 / 0.424275 (-0.358216) | 0.008455 / 0.007607 (0.000848) | 0.506813 / 0.226044 (0.280768) | 4.826994 / 2.268929 (2.558066) | 2.544437 / 55.444624 (-52.900187) | 2.164938 / 6.876477 (-4.711539) | 2.171725 / 2.142072 (0.029652) | 0.603757 / 4.805227 (-4.201470) | 0.149113 / 6.500664 (-6.351551) | 0.065093 / 0.075469 (-0.010376) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.366887 / 1.841788 (-0.474901) | 20.508089 / 8.074308 (12.433780) | 14.836531 / 10.191392 (4.645139) | 0.167418 / 0.680424 (-0.513006) | 0.019707 / 0.534201 (-0.514494) | 0.409897 / 0.579283 (-0.169387) | 0.439412 / 0.434364 (0.005048) | 0.495784 / 0.540337 (-0.044553) | 0.685367 / 1.386936 (-0.701569) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007604 / 0.011353 (-0.003749) | 0.004368 / 0.011008 (-0.006640) | 0.072628 / 0.038508 (0.034120) | 0.084187 / 0.023109 (0.061077) | 0.461396 / 0.275898 (0.185498) | 0.481429 / 0.323480 (0.157949) | 0.005894 / 0.007986 (-0.002092) | 0.003472 / 0.004328 (-0.000857) | 0.068717 / 0.004250 (0.064466) | 0.061066 / 0.037052 (0.024014) | 0.464217 / 0.258489 (0.205728) | 0.498061 / 0.293841 (0.204220) | 0.035458 / 0.128546 (-0.093089) | 0.009474 / 0.075646 (-0.066173) | 0.079633 / 0.419271 (-0.339639) | 0.053966 / 0.043533 (0.010433) | 0.454911 / 0.255139 (0.199772) | 0.470837 / 0.283200 (0.187637) | 0.026358 / 0.141683 (-0.115325) | 1.665131 / 1.452155 (0.212976) | 1.730365 / 1.492716 (0.237648) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.234810 / 0.018006 (0.216804) | 0.453672 / 0.000490 (0.453183) | 0.004620 / 0.000200 (0.004420) | 0.000119 / 0.000054 (0.000064) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035310 / 0.037411 (-0.002101) | 0.100379 / 0.014526 (0.085853) | 0.118802 / 0.176557 (-0.057754) | 0.173853 / 0.737135 (-0.563282) | 0.115714 / 0.296338 (-0.180624) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.466797 / 0.215209 (0.251588) | 4.698324 / 2.077655 (2.620670) | 2.446897 / 1.504120 (0.942777) | 2.277346 / 1.541195 (0.736151) | 2.347211 / 1.468490 (0.878721) | 0.514377 / 4.584777 (-4.070400) | 3.931269 / 3.745712 (0.185557) | 3.573575 / 5.269862 (-1.696286) | 2.208122 / 4.565676 (-2.357554) | 0.061081 / 0.424275 (-0.363194) | 0.007803 / 0.007607 (0.000196) | 0.544376 / 0.226044 (0.318332) | 5.440003 / 2.268929 (3.171074) | 3.012559 / 55.444624 (-52.432065) | 2.617286 / 6.876477 (-4.259191) | 2.863978 / 2.142072 (0.721906) | 0.610024 / 4.805227 (-4.195203) | 0.133643 / 6.500664 (-6.367021) | 0.064766 / 0.075469 (-0.010703) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.465225 / 1.841788 (-0.376563) | 21.308351 / 8.074308 (13.234043) | 15.176634 / 10.191392 (4.985242) | 0.172701 / 0.680424 (-0.507723) | 0.020345 / 0.534201 (-0.513855) | 0.433923 / 0.579283 (-0.145360) | 0.450183 / 0.434364 (0.015819) | 0.514048 / 0.540337 (-0.026289) | 0.736302 / 1.386936 (-0.650634) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7f1a7d621fff3b08ace02643466097654a5e010f \"CML watermark\")\n", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008305 / 0.011353 (-0.003048) | 0.006007 / 0.011008 (-0.005001) | 0.103521 / 0.038508 (0.065013) | 0.075776 / 0.023109 (0.052666) | 0.378888 / 0.275898 (0.102990) | 0.405245 / 0.323480 (0.081765) | 0.004596 / 0.007986 (-0.003390) | 0.003687 / 0.004328 (-0.000641) | 0.079043 / 0.004250 (0.074792) | 0.055895 / 0.037052 (0.018843) | 0.406565 / 0.258489 (0.148076) | 0.433869 / 0.293841 (0.140028) | 0.045321 / 0.128546 (-0.083226) | 0.014317 / 0.075646 (-0.061329) | 0.345312 / 0.419271 (-0.073960) | 0.064485 / 0.043533 (0.020953) | 0.381744 / 0.255139 (0.126605) | 0.401162 / 0.283200 (0.117962) | 0.035973 / 0.141683 (-0.105709) | 1.829616 / 1.452155 (0.377461) | 1.868487 / 1.492716 (0.375771) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.245432 / 0.018006 (0.227426) | 0.494249 / 0.000490 (0.493759) | 0.010878 / 0.000200 (0.010678) | 0.000492 / 0.000054 (0.000437) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032778 / 0.037411 (-0.004633) | 0.103418 / 0.014526 (0.088892) | 0.108010 / 0.176557 (-0.068547) | 0.176477 / 0.737135 (-0.560658) | 0.107732 / 0.296338 (-0.188606) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.572471 / 0.215209 (0.357262) | 5.647039 / 2.077655 (3.569384) | 2.385069 / 1.504120 (0.880949) | 2.048928 / 1.541195 (0.507733) | 2.108538 / 1.468490 (0.640048) | 0.861436 / 4.584777 (-3.723341) | 4.933452 / 3.745712 (1.187739) | 4.735219 / 5.269862 (-0.534642) | 2.926971 / 4.565676 (-1.638705) | 0.097687 / 0.424275 (-0.326588) | 0.008346 / 0.007607 (0.000739) | 0.677754 / 0.226044 (0.451709) | 6.798433 / 2.268929 (4.529504) | 3.129862 / 55.444624 (-52.314762) | 2.454033 / 6.876477 (-4.422444) | 2.464590 / 2.142072 (0.322517) | 1.034497 / 4.805227 (-3.770730) | 0.205753 / 6.500664 (-6.294911) | 0.076618 / 0.075469 (0.001149) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.617569 / 1.841788 (-0.224219) | 22.091489 / 8.074308 (14.017181) | 20.406312 / 10.191392 (10.214920) | 0.222012 / 0.680424 (-0.458411) | 0.027787 / 0.534201 (-0.506414) | 0.441669 / 0.579283 (-0.137615) | 0.564773 / 0.434364 (0.130409) | 0.510389 / 0.540337 (-0.029948) | 0.753672 / 1.386936 (-0.633264) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.011107 / 0.011353 (-0.000246) | 0.004973 / 0.011008 (-0.006035) | 0.078331 / 0.038508 (0.039823) | 0.083964 / 0.023109 (0.060855) | 0.518980 / 0.275898 (0.243082) | 0.528264 / 0.323480 (0.204784) | 0.007452 / 0.007986 (-0.000534) | 0.003931 / 0.004328 (-0.000397) | 0.079724 / 0.004250 (0.075474) | 0.061739 / 0.037052 (0.024686) | 0.517804 / 0.258489 (0.259315) | 0.582764 / 0.293841 (0.288923) | 0.049674 / 0.128546 (-0.078873) | 0.014540 / 0.075646 (-0.061106) | 0.093130 / 0.419271 (-0.326141) | 0.060647 / 0.043533 (0.017114) | 0.492628 / 0.255139 (0.237489) | 0.549761 / 0.283200 (0.266562) | 0.034313 / 0.141683 (-0.107369) | 1.824574 / 1.452155 (0.372419) | 2.013664 / 1.492716 (0.520947) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.231335 / 0.018006 (0.213329) | 0.521477 / 0.000490 (0.520987) | 0.011314 / 0.000200 (0.011114) | 0.000397 / 0.000054 (0.000343) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033303 / 0.037411 (-0.004108) | 0.098238 / 0.014526 (0.083712) | 0.119527 / 0.176557 (-0.057030) | 0.169163 / 0.737135 (-0.567972) | 0.114536 / 0.296338 (-0.181803) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.578401 / 0.215209 (0.363191) | 5.966438 / 2.077655 (3.888783) | 2.646370 / 1.504120 (1.142250) | 2.361833 / 1.541195 (0.820638) | 2.476573 / 1.468490 (1.008083) | 0.777411 / 4.584777 (-3.807366) | 4.811070 / 3.745712 (1.065357) | 4.314221 / 5.269862 (-0.955641) | 2.743317 / 4.565676 (-1.822359) | 0.110394 / 0.424275 (-0.313881) | 0.008333 / 0.007607 (0.000726) | 0.729588 / 0.226044 (0.503543) | 7.743226 / 2.268929 (5.474298) | 3.606294 / 55.444624 (-51.838330) | 2.838069 / 6.876477 (-4.038408) | 3.087494 / 2.142072 (0.945421) | 1.053341 / 4.805227 (-3.751886) | 0.205105 / 6.500664 (-6.295559) | 0.075204 / 0.075469 (-0.000265) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.561959 / 1.841788 (-0.279829) | 21.407849 / 8.074308 (13.333541) | 19.084263 / 10.191392 (8.892871) | 0.226129 / 0.680424 (-0.454295) | 0.029695 / 0.534201 (-0.504506) | 0.427035 / 0.579283 (-0.152248) | 0.565353 / 0.434364 (0.130989) | 0.526789 / 0.540337 (-0.013548) | 0.734820 / 1.386936 (-0.652116) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5b52536f4e39df3b98f7e0b03ee71b24c4fff49a \"CML watermark\")\n" ]

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"2023-10-19T11:25:21Z"

"2023-10-19T13:04:56Z"

"2023-10-19T13:04:56Z"

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

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"2023-10-19T10:21:34Z"

"2023-10-20T06:23:21Z"

"2023-10-20T06:14:09Z"

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[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008896 / 0.011353 (-0.002456) | 0.005811 / 0.011008 (-0.005197) | 0.108582 / 0.038508 (0.070074) | 0.096509 / 0.023109 (0.073399) | 0.481725 / 0.275898 (0.205827) | 0.534743 / 0.323480 (0.211263) | 0.005517 / 0.007986 (-0.002468) | 0.006479 / 0.004328 (0.002151) | 0.081313 / 0.004250 (0.077062) | 0.063578 / 0.037052 (0.026525) | 0.493977 / 0.258489 (0.235488) | 0.551897 / 0.293841 (0.258056) | 0.051835 / 0.128546 (-0.076711) | 0.014105 / 0.075646 (-0.061541) | 0.385866 / 0.419271 (-0.033405) | 0.069131 / 0.043533 (0.025598) | 0.484780 / 0.255139 (0.229641) | 0.493221 / 0.283200 (0.210021) | 0.039560 / 0.141683 (-0.102123) | 1.782331 / 1.452155 (0.330176) | 1.899193 / 1.492716 (0.406477) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.329978 / 0.018006 (0.311972) | 0.600839 / 0.000490 (0.600349) | 0.013187 / 0.000200 (0.012987) | 0.000499 / 0.000054 (0.000444) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031835 / 0.037411 (-0.005576) | 0.103740 / 0.014526 (0.089214) | 0.115875 / 0.176557 (-0.060681) | 0.189880 / 0.737135 (-0.547255) | 0.132614 / 0.296338 (-0.163725) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.596255 / 0.215209 (0.381046) | 5.967993 / 2.077655 (3.890339) | 2.612675 / 1.504120 (1.108555) | 2.251461 / 1.541195 (0.710266) | 2.308585 / 1.468490 (0.840095) | 0.816516 / 4.584777 (-3.768261) | 5.241791 / 3.745712 (1.496079) | 4.680745 / 5.269862 (-0.589117) | 2.997370 / 4.565676 (-1.568307) | 0.098632 / 0.424275 (-0.325643) | 0.010912 / 0.007607 (0.003305) | 0.659092 / 0.226044 (0.433047) | 6.825562 / 2.268929 (4.556634) | 3.323844 / 55.444624 (-52.120780) | 2.796203 / 6.876477 (-4.080274) | 2.946994 / 2.142072 (0.804922) | 1.002814 / 4.805227 (-3.802413) | 0.202613 / 6.500664 (-6.298051) | 0.072011 / 0.075469 (-0.003459) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.613873 / 1.841788 (-0.227914) | 24.500990 / 8.074308 (16.426682) | 21.941599 / 10.191392 (11.750207) | 0.214450 / 0.680424 (-0.465974) | 0.031227 / 0.534201 (-0.502974) | 0.498297 / 0.579283 (-0.080986) | 0.597460 / 0.434364 (0.163096) | 0.558152 / 0.540337 (0.017815) | 0.789693 / 1.386936 (-0.597243) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.011299 / 0.011353 (-0.000053) | 0.005103 / 0.011008 (-0.005905) | 0.083161 / 0.038508 (0.044653) | 0.094201 / 0.023109 (0.071092) | 0.560457 / 0.275898 (0.284559) | 0.590459 / 0.323480 (0.266980) | 0.007059 / 0.007986 (-0.000926) | 0.004418 / 0.004328 (0.000090) | 0.081343 / 0.004250 (0.077093) | 0.067069 / 0.037052 (0.030016) | 0.538137 / 0.258489 (0.279648) | 0.600416 / 0.293841 (0.306575) | 0.049046 / 0.128546 (-0.079500) | 0.014299 / 0.075646 (-0.061347) | 0.093631 / 0.419271 (-0.325641) | 0.062536 / 0.043533 (0.019003) | 0.557238 / 0.255139 (0.302099) | 0.571050 / 0.283200 (0.287850) | 0.035881 / 0.141683 (-0.105802) | 1.918487 / 1.452155 (0.466332) | 2.013979 / 1.492716 (0.521263) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.400995 / 0.018006 (0.382989) | 0.634898 / 0.000490 (0.634408) | 0.041809 / 0.000200 (0.041609) | 0.000279 / 0.000054 (0.000224) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034160 / 0.037411 (-0.003251) | 0.109996 / 0.014526 (0.095470) | 0.124335 / 0.176557 (-0.052222) | 0.188100 / 0.737135 (-0.549035) | 0.135897 / 0.296338 (-0.160442) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.639751 / 0.215209 (0.424542) | 6.403312 / 2.077655 (4.325657) | 3.146453 / 1.504120 (1.642333) | 2.840358 / 1.541195 (1.299164) | 2.908667 / 1.468490 (1.440177) | 0.818767 / 4.584777 (-3.766010) | 5.416939 / 3.745712 (1.671227) | 4.853498 / 5.269862 (-0.416364) | 3.023526 / 4.565676 (-1.542150) | 0.110850 / 0.424275 (-0.313425) | 0.013103 / 0.007607 (0.005496) | 0.799720 / 0.226044 (0.573676) | 7.837704 / 2.268929 (5.568775) | 4.016526 / 55.444624 (-51.428099) | 3.338965 / 6.876477 (-3.537512) | 3.715721 / 2.142072 (1.573648) | 1.088340 / 4.805227 (-3.716887) | 0.213610 / 6.500664 (-6.287054) | 0.079244 / 0.075469 (0.003775) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.833175 / 1.841788 (-0.008612) | 25.307218 / 8.074308 (17.232910) | 23.716075 / 10.191392 (13.524683) | 0.259114 / 0.680424 (-0.421310) | 0.035171 / 0.534201 (-0.499029) | 0.530128 / 0.579283 (-0.049155) | 0.651484 / 0.434364 (0.217120) | 0.589414 / 0.540337 (0.049077) | 0.862691 / 1.386936 (-0.524245) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#1bdfba93b8a739b9d885b8fb1909d47ff689bbc2 \"CML watermark\")\n", "_The documentation is not available anymore as the PR was closed or merged._", "Me too, I thought the same... quite surprised... :open_mouth: ", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006929 / 0.011353 (-0.004423) | 0.004345 / 0.011008 (-0.006663) | 0.085522 / 0.038508 (0.047014) | 0.083380 / 0.023109 (0.060271) | 0.310332 / 0.275898 (0.034434) | 0.350525 / 0.323480 (0.027045) | 0.004367 / 0.007986 (-0.003618) | 0.005503 / 0.004328 (0.001175) | 0.066311 / 0.004250 (0.062061) | 0.059545 / 0.037052 (0.022492) | 0.314090 / 0.258489 (0.055601) | 0.366661 / 0.293841 (0.072821) | 0.031581 / 0.128546 (-0.096965) | 0.008852 / 0.075646 (-0.066794) | 0.289312 / 0.419271 (-0.129960) | 0.052960 / 0.043533 (0.009427) | 0.308134 / 0.255139 (0.052995) | 0.330342 / 0.283200 (0.047142) | 0.026157 / 0.141683 (-0.115526) | 1.488463 / 1.452155 (0.036308) | 1.561441 / 1.492716 (0.068725) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.327735 / 0.018006 (0.309729) | 0.568162 / 0.000490 (0.567672) | 0.012097 / 0.000200 (0.011897) | 0.000438 / 0.000054 (0.000383) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029503 / 0.037411 (-0.007909) | 0.084327 / 0.014526 (0.069801) | 0.102065 / 0.176557 (-0.074492) | 0.157392 / 0.737135 (-0.579744) | 0.101428 / 0.296338 (-0.194910) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.386767 / 0.215209 (0.171558) | 3.870757 / 2.077655 (1.793102) | 1.870048 / 1.504120 (0.365928) | 1.678221 / 1.541195 (0.137026) | 1.799423 / 1.468490 (0.330933) | 0.477718 / 4.584777 (-4.107059) | 3.618351 / 3.745712 (-0.127361) | 3.577921 / 5.269862 (-1.691941) | 2.146217 / 4.565676 (-2.419459) | 0.056290 / 0.424275 (-0.367985) | 0.007378 / 0.007607 (-0.000229) | 0.460678 / 0.226044 (0.234633) | 4.606243 / 2.268929 (2.337314) | 2.303460 / 55.444624 (-53.141164) | 1.982662 / 6.876477 (-4.893814) | 2.103891 / 2.142072 (-0.038182) | 0.570700 / 4.805227 (-4.234527) | 0.131747 / 6.500664 (-6.368918) | 0.060915 / 0.075469 (-0.014554) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.286364 / 1.841788 (-0.555424) | 20.106330 / 8.074308 (12.032022) | 14.780833 / 10.191392 (4.589441) | 0.164301 / 0.680424 (-0.516123) | 0.018730 / 0.534201 (-0.515471) | 0.398530 / 0.579283 (-0.180754) | 0.418084 / 0.434364 (-0.016280) | 0.468735 / 0.540337 (-0.071602) | 0.690122 / 1.386936 (-0.696814) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007262 / 0.011353 (-0.004091) | 0.004228 / 0.011008 (-0.006780) | 0.065866 / 0.038508 (0.027358) | 0.096151 / 0.023109 (0.073042) | 0.409352 / 0.275898 (0.133454) | 0.441234 / 0.323480 (0.117754) | 0.005946 / 0.007986 (-0.002039) | 0.003630 / 0.004328 (-0.000698) | 0.066271 / 0.004250 (0.062020) | 0.061567 / 0.037052 (0.024515) | 0.409097 / 0.258489 (0.150608) | 0.447675 / 0.293841 (0.153834) | 0.032804 / 0.128546 (-0.095743) | 0.008793 / 0.075646 (-0.066853) | 0.070790 / 0.419271 (-0.348482) | 0.048650 / 0.043533 (0.005117) | 0.411021 / 0.255139 (0.155882) | 0.421398 / 0.283200 (0.138198) | 0.025305 / 0.141683 (-0.116378) | 1.494826 / 1.452155 (0.042671) | 1.580441 / 1.492716 (0.087724) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.321871 / 0.018006 (0.303865) | 0.526471 / 0.000490 (0.525982) | 0.006913 / 0.000200 (0.006713) | 0.000108 / 0.000054 (0.000054) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034889 / 0.037411 (-0.002522) | 0.096096 / 0.014526 (0.081570) | 0.111920 / 0.176557 (-0.064636) | 0.166103 / 0.737135 (-0.571032) | 0.111162 / 0.296338 (-0.185176) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.428037 / 0.215209 (0.212828) | 4.294150 / 2.077655 (2.216495) | 2.270331 / 1.504120 (0.766211) | 2.108235 / 1.541195 (0.567041) | 2.242560 / 1.468490 (0.774070) | 0.489941 / 4.584777 (-4.094836) | 3.688111 / 3.745712 (-0.057601) | 3.450180 / 5.269862 (-1.819681) | 2.175106 / 4.565676 (-2.390570) | 0.057657 / 0.424275 (-0.366619) | 0.007478 / 0.007607 (-0.000130) | 0.505242 / 0.226044 (0.279198) | 5.047817 / 2.268929 (2.778888) | 2.724125 / 55.444624 (-52.720500) | 2.419765 / 6.876477 (-4.456711) | 2.723231 / 2.142072 (0.581159) | 0.602382 / 4.805227 (-4.202846) | 0.132362 / 6.500664 (-6.368302) | 0.060600 / 0.075469 (-0.014869) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.363356 / 1.841788 (-0.478431) | 21.446474 / 8.074308 (13.372165) | 15.074732 / 10.191392 (4.883340) | 0.191837 / 0.680424 (-0.488587) | 0.020565 / 0.534201 (-0.513636) | 0.396692 / 0.579283 (-0.182591) | 0.432390 / 0.434364 (-0.001974) | 0.491747 / 0.540337 (-0.048591) | 0.699203 / 1.386936 (-0.687733) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#c3a8a87c841426495d3a7ed1863c26660a6a551f \"CML watermark\")\n" ]

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"2023-10-19T10:11:29Z"

"2023-10-20T06:14:10Z"

"2023-10-20T06:14:10Z"

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"2023-10-19T09:12:39Z"

"2023-10-19T09:20:06Z"

"2023-10-19T09:19:48Z"

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"2023-10-19T07:53:56Z"

"2023-10-20T14:06:13Z"

"2023-10-20T13:57:39Z"

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[ "_The documentation is not available anymore as the PR was closed or merged._", "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006760 / 0.011353 (-0.004593) | 0.003918 / 0.011008 (-0.007091) | 0.084016 / 0.038508 (0.045508) | 0.069927 / 0.023109 (0.046818) | 0.307898 / 0.275898 (0.032000) | 0.337453 / 0.323480 (0.013973) | 0.004132 / 0.007986 (-0.003854) | 0.003248 / 0.004328 (-0.001081) | 0.064526 / 0.004250 (0.060275) | 0.056424 / 0.037052 (0.019371) | 0.316313 / 0.258489 (0.057824) | 0.356302 / 0.293841 (0.062461) | 0.030634 / 0.128546 (-0.097912) | 0.008467 / 0.075646 (-0.067180) | 0.286676 / 0.419271 (-0.132595) | 0.051813 / 0.043533 (0.008280) | 0.309874 / 0.255139 (0.054735) | 0.332513 / 0.283200 (0.049313) | 0.023919 / 0.141683 (-0.117764) | 1.509033 / 1.452155 (0.056878) | 1.549636 / 1.492716 (0.056920) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.221464 / 0.018006 (0.203458) | 0.447873 / 0.000490 (0.447384) | 0.002408 / 0.000200 (0.002208) | 0.000090 / 0.000054 (0.000035) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027634 / 0.037411 (-0.009777) | 0.081802 / 0.014526 (0.067276) | 0.781489 / 0.176557 (0.604933) | 0.165184 / 0.737135 (-0.571951) | 0.121526 / 0.296338 (-0.174813) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.408215 / 0.215209 (0.193006) | 4.091192 / 2.077655 (2.013538) | 2.062608 / 1.504120 (0.558488) | 1.895747 / 1.541195 (0.354552) | 1.873682 / 1.468490 (0.405192) | 0.484184 / 4.584777 (-4.100593) | 3.469096 / 3.745712 (-0.276616) | 3.365325 / 5.269862 (-1.904537) | 2.000333 / 4.565676 (-2.565343) | 0.056661 / 0.424275 (-0.367614) | 0.007100 / 0.007607 (-0.000507) | 0.478587 / 0.226044 (0.252542) | 4.768703 / 2.268929 (2.499774) | 2.472432 / 55.444624 (-52.972192) | 2.133611 / 6.876477 (-4.742865) | 2.154296 / 2.142072 (0.012223) | 0.582293 / 4.805227 (-4.222934) | 0.131932 / 6.500664 (-6.368732) | 0.060259 / 0.075469 (-0.015211) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.259167 / 1.841788 (-0.582620) | 18.465604 / 8.074308 (10.391296) | 14.024528 / 10.191392 (3.833136) | 0.162320 / 0.680424 (-0.518104) | 0.018144 / 0.534201 (-0.516057) | 0.389931 / 0.579283 (-0.189352) | 0.396456 / 0.434364 (-0.037908) | 0.454734 / 0.540337 (-0.085603) | 0.636406 / 1.386936 (-0.750530) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006565 / 0.011353 (-0.004788) | 0.004008 / 0.011008 (-0.007000) | 0.064526 / 0.038508 (0.026018) | 0.071963 / 0.023109 (0.048854) | 0.415456 / 0.275898 (0.139557) | 0.441199 / 0.323480 (0.117719) | 0.005619 / 0.007986 (-0.002366) | 0.003261 / 0.004328 (-0.001067) | 0.064817 / 0.004250 (0.060567) | 0.055349 / 0.037052 (0.018296) | 0.425172 / 0.258489 (0.166683) | 0.452629 / 0.293841 (0.158788) | 0.031676 / 0.128546 (-0.096870) | 0.008432 / 0.075646 (-0.067214) | 0.071752 / 0.419271 (-0.347519) | 0.047176 / 0.043533 (0.003643) | 0.408641 / 0.255139 (0.153502) | 0.428579 / 0.283200 (0.145380) | 0.021548 / 0.141683 (-0.120135) | 1.495153 / 1.452155 (0.042999) | 1.557933 / 1.492716 (0.065217) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.212749 / 0.018006 (0.194743) | 0.441263 / 0.000490 (0.440773) | 0.005831 / 0.000200 (0.005631) | 0.000092 / 0.000054 (0.000037) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031844 / 0.037411 (-0.005567) | 0.091590 / 0.014526 (0.077064) | 0.102859 / 0.176557 (-0.073697) | 0.155859 / 0.737135 (-0.581276) | 0.104717 / 0.296338 (-0.191622) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.425924 / 0.215209 (0.210715) | 4.292829 / 2.077655 (2.215174) | 2.314350 / 1.504120 (0.810230) | 2.163087 / 1.541195 (0.621892) | 2.217310 / 1.468490 (0.748820) | 0.490889 / 4.584777 (-4.093887) | 3.498287 / 3.745712 (-0.247425) | 3.224980 / 5.269862 (-2.044881) | 1.987739 / 4.565676 (-2.577938) | 0.057486 / 0.424275 (-0.366790) | 0.007199 / 0.007607 (-0.000408) | 0.501194 / 0.226044 (0.275149) | 5.015202 / 2.268929 (2.746273) | 2.816307 / 55.444624 (-52.628318) | 2.474593 / 6.876477 (-4.401884) | 2.649510 / 2.142072 (0.507437) | 0.597167 / 4.805227 (-4.208060) | 0.131199 / 6.500664 (-6.369465) | 0.059532 / 0.075469 (-0.015938) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.384053 / 1.841788 (-0.457734) | 18.964201 / 8.074308 (10.889893) | 14.336209 / 10.191392 (4.144817) | 0.187522 / 0.680424 (-0.492902) | 0.020201 / 0.534201 (-0.514000) | 0.394778 / 0.579283 (-0.184505) | 0.408393 / 0.434364 (-0.025971) | 0.470965 / 0.540337 (-0.069373) | 0.667974 / 1.386936 (-0.718962) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#3b3333d790800ddaa3bf386ee71dc800258c921c \"CML watermark\")\n" ]

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[]

[]

"2023-10-18T16:10:59Z"

"2023-10-19T16:31:59Z"

"2023-10-19T16:23:07Z"

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[ "<details>\n<summary>Show benchmarks</summary>\n\nPyArrow==8.0.0\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006209 / 0.011353 (-0.005144) | 0.003708 / 0.011008 (-0.007300) | 0.080435 / 0.038508 (0.041926) | 0.060105 / 0.023109 (0.036995) | 0.392962 / 0.275898 (0.117064) | 0.429381 / 0.323480 (0.105902) | 0.003596 / 0.007986 (-0.004390) | 0.003849 / 0.004328 (-0.000480) | 0.062377 / 0.004250 (0.058127) | 0.048718 / 0.037052 (0.011666) | 0.400906 / 0.258489 (0.142417) | 0.440335 / 0.293841 (0.146494) | 0.027807 / 0.128546 (-0.100739) | 0.008066 / 0.075646 (-0.067580) | 0.262542 / 0.419271 (-0.156730) | 0.045513 / 0.043533 (0.001980) | 0.399608 / 0.255139 (0.144469) | 0.418007 / 0.283200 (0.134807) | 0.023475 / 0.141683 (-0.118208) | 1.476563 / 1.452155 (0.024409) | 1.528898 / 1.492716 (0.036182) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.223798 / 0.018006 (0.205792) | 0.430526 / 0.000490 (0.430036) | 0.009232 / 0.000200 (0.009032) | 0.000082 / 0.000054 (0.000028) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.024921 / 0.037411 (-0.012490) | 0.077692 / 0.014526 (0.063166) | 0.085382 / 0.176557 (-0.091174) | 0.146220 / 0.737135 (-0.590915) | 0.086396 / 0.296338 (-0.209943) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.439986 / 0.215209 (0.224777) | 4.384552 / 2.077655 (2.306897) | 2.373697 / 1.504120 (0.869577) | 2.176138 / 1.541195 (0.634943) | 2.225914 / 1.468490 (0.757424) | 0.505776 / 4.584777 (-4.079001) | 3.053744 / 3.745712 (-0.691968) | 3.080443 / 5.269862 (-2.189419) | 1.904392 / 4.565676 (-2.661285) | 0.058112 / 0.424275 (-0.366163) | 0.006631 / 0.007607 (-0.000976) | 0.503409 / 0.226044 (0.277365) | 5.053375 / 2.268929 (2.784447) | 2.789963 / 55.444624 (-52.654661) | 2.452659 / 6.876477 (-4.423818) | 2.512353 / 2.142072 (0.370280) | 0.590095 / 4.805227 (-4.215132) | 0.126267 / 6.500664 (-6.374397) | 0.061246 / 0.075469 (-0.014223) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.249884 / 1.841788 (-0.591903) | 17.684730 / 8.074308 (9.610422) | 13.967467 / 10.191392 (3.776075) | 0.144202 / 0.680424 (-0.536222) | 0.017004 / 0.534201 (-0.517197) | 0.333634 / 0.579283 (-0.245649) | 0.387251 / 0.434364 (-0.047113) | 0.390189 / 0.540337 (-0.150148) | 0.535662 / 1.386936 (-0.851274) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006379 / 0.011353 (-0.004974) | 0.003681 / 0.011008 (-0.007327) | 0.063005 / 0.038508 (0.024497) | 0.064221 / 0.023109 (0.041112) | 0.446074 / 0.275898 (0.170176) | 0.471997 / 0.323480 (0.148517) | 0.005074 / 0.007986 (-0.002911) | 0.002945 / 0.004328 (-0.001383) | 0.063305 / 0.004250 (0.059054) | 0.050608 / 0.037052 (0.013556) | 0.443260 / 0.258489 (0.184771) | 0.478497 / 0.293841 (0.184656) | 0.028980 / 0.128546 (-0.099566) | 0.008145 / 0.075646 (-0.067502) | 0.068412 / 0.419271 (-0.350859) | 0.041552 / 0.043533 (-0.001980) | 0.436649 / 0.255139 (0.181510) | 0.462397 / 0.283200 (0.179198) | 0.019929 / 0.141683 (-0.121753) | 1.530248 / 1.452155 (0.078093) | 1.611117 / 1.492716 (0.118401) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.232894 / 0.018006 (0.214888) | 0.421451 / 0.000490 (0.420961) | 0.003984 / 0.000200 (0.003784) | 0.000084 / 0.000054 (0.000030) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027776 / 0.037411 (-0.009635) | 0.081632 / 0.014526 (0.067106) | 0.094031 / 0.176557 (-0.082526) | 0.147930 / 0.737135 (-0.589206) | 0.094226 / 0.296338 (-0.202112) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.471722 / 0.215209 (0.256513) | 4.713241 / 2.077655 (2.635587) | 2.662660 / 1.504120 (1.158540) | 2.490778 / 1.541195 (0.949583) | 2.555786 / 1.468490 (1.087296) | 0.512209 / 4.584777 (-4.072568) | 3.210612 / 3.745712 (-0.535100) | 2.863346 / 5.269862 (-2.406516) | 1.884664 / 4.565676 (-2.681012) | 0.058514 / 0.424275 (-0.365761) | 0.006473 / 0.007607 (-0.001134) | 0.543279 / 0.226044 (0.317235) | 5.441485 / 2.268929 (3.172556) | 3.145398 / 55.444624 (-52.299226) | 2.749603 / 6.876477 (-4.126874) | 2.925738 / 2.142072 (0.783666) | 0.598725 / 4.805227 (-4.206502) | 0.125616 / 6.500664 (-6.375048) | 0.061314 / 0.075469 (-0.014155) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.384270 / 1.841788 (-0.457518) | 18.307618 / 8.074308 (10.233310) | 14.635768 / 10.191392 (4.444376) | 0.148787 / 0.680424 (-0.531637) | 0.018191 / 0.534201 (-0.516010) | 0.333166 / 0.579283 (-0.246117) | 0.405116 / 0.434364 (-0.029247) | 0.392798 / 0.540337 (-0.147540) | 0.582299 / 1.386936 (-0.804637) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#7004f0f2ec59832fe53af033efdca10d00377760 \"CML watermark\")\n" ]

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"2023-10-18T09:38:05Z"

"2024-02-06T19:24:20Z"

"2024-02-06T19:24:20Z"

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

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"2023-10-17T13:36:57Z"

"2023-11-27T21:11:14Z"

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