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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) | 0.008368 / 0.011353 (-0.002985) | 0.004754 / 0.011008 (-0.006254) | 0.096646 / 0.038508 (0.058138) | 0.088980 / 0.023109 (0.065871) | 0.374532 / 0.275898 (0.098633) | 0.404840 / 0.323480 (0.081360) | 0.006026 / 0.007986 (-0.001960) | 0.005716 / 0.004328 (0.001387) | 0.076297 / 0.004250 (0.072047) | 0.072335 / 0.037052 (0.035283) | 0.379435 / 0.258489 (0.120946) | 0.423449 / 0.293841 (0.129608) | 0.041344 / 0.128546 (-0.087202) | 0.009758 / 0.075646 (-0.065889) | 0.341550 / 0.419271 (-0.077721) | 0.068559 / 0.043533 (0.025026) | 0.368313 / 0.255139 (0.113174) | 0.415147 / 0.283200 (0.131947) | 0.028692 / 0.141683 (-0.112990) | 1.816198 / 1.452155 (0.364044) | 1.983351 / 1.492716 (0.490635) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.222712 / 0.018006 (0.204706) | 0.517850 / 0.000490 (0.517360) | 0.004436 / 0.000200 (0.004236) | 0.000094 / 0.000054 (0.000040) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033168 / 0.037411 (-0.004243) | 0.101353 / 0.014526 (0.086827) | 0.113235 / 0.176557 (-0.063322) | 0.180308 / 0.737135 (-0.556827) | 0.114604 / 0.296338 (-0.181734) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.454415 / 0.215209 (0.239206) | 4.500355 / 2.077655 (2.422701) | 2.188223 / 1.504120 (0.684103) | 1.974256 / 1.541195 (0.433061) | 2.067331 / 1.468490 (0.598841) | 0.572982 / 4.584777 (-4.011795) | 4.239160 / 3.745712 (0.493448) | 3.836812 / 5.269862 (-1.433049) | 2.367022 / 4.565676 (-2.198655) | 0.066886 / 0.424275 (-0.357389) | 0.009111 / 0.007607 (0.001504) | 0.539881 / 0.226044 (0.313837) | 5.362247 / 2.268929 (3.093319) | 2.784044 / 55.444624 (-52.660580) | 2.320975 / 6.876477 (-4.555502) | 2.543108 / 2.142072 (0.401036) | 0.685751 / 4.805227 (-4.119477) | 0.156840 / 6.500664 (-6.343824) | 0.071764 / 0.075469 (-0.003705) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.549830 / 1.841788 (-0.291958) | 22.799622 / 8.074308 (14.725314) | 16.750692 / 10.191392 (6.559300) | 0.196192 / 0.680424 (-0.484232) | 0.024518 / 0.534201 (-0.509683) | 0.479302 / 0.579283 (-0.099981) | 0.522256 / 0.434364 (0.087892) | 0.545809 / 0.540337 (0.005471) | 0.748437 / 1.386936 (-0.638499) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007789 / 0.011353 (-0.003564) | 0.004563 / 0.011008 (-0.006445) | 0.074631 / 0.038508 (0.036123) | 0.086892 / 0.023109 (0.063783) | 0.427014 / 0.275898 (0.151116) | 0.463257 / 0.323480 (0.139777) | 0.005987 / 0.007986 (-0.001999) | 0.003803 / 0.004328 (-0.000526) | 0.074799 / 0.004250 (0.070549) | 0.063473 / 0.037052 (0.026420) | 0.429905 / 0.258489 (0.171416) | 0.468967 / 0.293841 (0.175127) | 0.036768 / 0.128546 (-0.091778) | 0.009675 / 0.075646 (-0.065971) | 0.082546 / 0.419271 (-0.336725) | 0.058027 / 0.043533 (0.014494) | 0.429813 / 0.255139 (0.174674) | 0.449200 / 0.283200 (0.166001) | 0.026713 / 0.141683 (-0.114969) | 1.812022 / 1.452155 (0.359867) | 1.847305 / 1.492716 (0.354589) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.320383 / 0.018006 (0.302377) | 0.485995 / 0.000490 (0.485505) | 0.024365 / 0.000200 (0.024165) | 0.000156 / 0.000054 (0.000101) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.036341 / 0.037411 (-0.001071) | 0.104635 / 0.014526 (0.090110) | 0.119456 / 0.176557 (-0.057101) | 0.182042 / 0.737135 (-0.555093) | 0.118944 / 0.296338 (-0.177395) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.506410 / 0.215209 (0.291201) | 5.061119 / 2.077655 (2.983465) | 2.756557 / 1.504120 (1.252437) | 2.546504 / 1.541195 (1.005309) | 2.585509 / 1.468490 (1.117019) | 0.564291 / 4.584777 (-4.020486) | 4.281219 / 3.745712 (0.535507) | 3.919439 / 5.269862 (-1.350423) | 2.588788 / 4.565676 (-1.976889) | 0.066900 / 0.424275 (-0.357375) | 0.008680 / 0.007607 (0.001073) | 0.598435 / 0.226044 (0.372390) | 5.976054 / 2.268929 (3.707125) | 3.260211 / 55.444624 (-52.184414) | 2.874597 / 6.876477 (-4.001880) | 3.105769 / 2.142072 (0.963697) | 0.692938 / 4.805227 (-4.112289) | 0.157777 / 6.500664 (-6.342887) | 0.073128 / 0.075469 (-0.002341) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.559380 / 1.841788 (-0.282408) | 22.986540 / 8.074308 (14.912232) | 16.305564 / 10.191392 (6.114172) | 0.174939 / 0.680424 (-0.505485) | 0.021932 / 0.534201 (-0.512269) | 0.468162 / 0.579283 (-0.111121) | 0.472610 / 0.434364 (0.038246) | 0.574574 / 0.540337 (0.034237) | 0.783505 / 1.386936 (-0.603431) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#550923b5d6ae64eb20b8f66da843395e9fa404ac \"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) | 0.012553 / 0.011353 (0.001201) | 0.005358 / 0.011008 (-0.005650) | 0.108338 / 0.038508 (0.069830) | 0.101105 / 0.023109 (0.077995) | 0.416808 / 0.275898 (0.140910) | 0.454599 / 0.323480 (0.131119) | 0.006665 / 0.007986 (-0.001321) | 0.004186 / 0.004328 (-0.000143) | 0.084900 / 0.004250 (0.080649) | 0.062881 / 0.037052 (0.025829) | 0.424423 / 0.258489 (0.165934) | 0.482651 / 0.293841 (0.188810) | 0.055740 / 0.128546 (-0.072807) | 0.014469 / 0.075646 (-0.061177) | 0.383267 / 0.419271 (-0.036005) | 0.067487 / 0.043533 (0.023955) | 0.414983 / 0.255139 (0.159844) | 0.459437 / 0.283200 (0.176237) | 0.038679 / 0.141683 (-0.103004) | 1.828002 / 1.452155 (0.375847) | 1.951946 / 1.492716 (0.459230) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.288033 / 0.018006 (0.270027) | 0.603536 / 0.000490 (0.603046) | 0.004874 / 0.000200 (0.004674) | 0.000138 / 0.000054 (0.000084) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031988 / 0.037411 (-0.005423) | 0.095807 / 0.014526 (0.081281) | 0.113459 / 0.176557 (-0.063098) | 0.182012 / 0.737135 (-0.555123) | 0.113121 / 0.296338 (-0.183217) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.620709 / 0.215209 (0.405500) | 6.096569 / 2.077655 (4.018915) | 2.754612 / 1.504120 (1.250492) | 2.449786 / 1.541195 (0.908591) | 2.470694 / 1.468490 (1.002204) | 0.837016 / 4.584777 (-3.747761) | 5.237290 / 3.745712 (1.491578) | 4.713220 / 5.269862 (-0.556642) | 3.020934 / 4.565676 (-1.544743) | 0.096892 / 0.424275 (-0.327383) | 0.009423 / 0.007607 (0.001816) | 0.720313 / 0.226044 (0.494269) | 7.369673 / 2.268929 (5.100744) | 3.550384 / 55.444624 (-51.894241) | 2.868868 / 6.876477 (-4.007609) | 3.081469 / 2.142072 (0.939397) | 1.042968 / 4.805227 (-3.762259) | 0.232530 / 6.500664 (-6.268134) | 0.080805 / 0.075469 (0.005336) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.645777 / 1.841788 (-0.196011) | 24.590862 / 8.074308 (16.516554) | 21.315496 / 10.191392 (11.124104) | 0.228796 / 0.680424 (-0.451628) | 0.028479 / 0.534201 (-0.505722) | 0.494413 / 0.579283 (-0.084870) | 0.582773 / 0.434364 (0.148409) | 0.552575 / 0.540337 (0.012238) | 0.787217 / 1.386936 (-0.599719) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008743 / 0.011353 (-0.002609) | 0.005253 / 0.011008 (-0.005755) | 0.083766 / 0.038508 (0.045257) | 0.086305 / 0.023109 (0.063195) | 0.520171 / 0.275898 (0.244273) | 0.565812 / 0.323480 (0.242332) | 0.006465 / 0.007986 (-0.001520) | 0.004585 / 0.004328 (0.000257) | 0.085344 / 0.004250 (0.081094) | 0.063418 / 0.037052 (0.026366) | 0.519759 / 0.258489 (0.261270) | 0.552770 / 0.293841 (0.258929) | 0.049439 / 0.128546 (-0.079107) | 0.017564 / 0.075646 (-0.058082) | 0.092713 / 0.419271 (-0.326559) | 0.065837 / 0.043533 (0.022305) | 0.516133 / 0.255139 (0.260994) | 0.539813 / 0.283200 (0.256613) | 0.036531 / 0.141683 (-0.105152) | 1.919275 / 1.452155 (0.467121) | 2.039987 / 1.492716 (0.547271) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.297978 / 0.018006 (0.279972) | 0.608243 / 0.000490 (0.607753) | 0.006611 / 0.000200 (0.006411) | 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) | 0.033909 / 0.037411 (-0.003503) | 0.106370 / 0.014526 (0.091844) | 0.119032 / 0.176557 (-0.057524) | 0.180319 / 0.737135 (-0.556816) | 0.122826 / 0.296338 (-0.173513) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.639265 / 0.215209 (0.424056) | 6.248430 / 2.077655 (4.170775) | 2.944760 / 1.504120 (1.440640) | 2.654005 / 1.541195 (1.112811) | 2.733625 / 1.468490 (1.265134) | 0.837172 / 4.584777 (-3.747605) | 5.245084 / 3.745712 (1.499372) | 4.722614 / 5.269862 (-0.547248) | 3.008286 / 4.565676 (-1.557391) | 0.102340 / 0.424275 (-0.321935) | 0.009433 / 0.007607 (0.001826) | 0.762991 / 0.226044 (0.536946) | 7.385020 / 2.268929 (5.116092) | 3.787648 / 55.444624 (-51.656977) | 3.234345 / 6.876477 (-3.642132) | 3.394444 / 2.142072 (1.252371) | 1.023472 / 4.805227 (-3.781756) | 0.208199 / 6.500664 (-6.292465) | 0.081513 / 0.075469 (0.006043) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.795864 / 1.841788 (-0.045923) | 25.270852 / 8.074308 (17.196544) | 23.356413 / 10.191392 (13.165021) | 0.228002 / 0.680424 (-0.452422) | 0.031851 / 0.534201 (-0.502350) | 0.499424 / 0.579283 (-0.079859) | 0.588027 / 0.434364 (0.153664) | 0.581746 / 0.540337 (0.041408) | 0.814183 / 1.386936 (-0.572753) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#33ee536876a667403ee44574bd685073261c4903 \"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) | 0.006477 / 0.011353 (-0.004876) | 0.003878 / 0.011008 (-0.007130) | 0.084085 / 0.038508 (0.045577) | 0.071297 / 0.023109 (0.048188) | 0.309176 / 0.275898 (0.033278) | 0.342830 / 0.323480 (0.019350) | 0.005189 / 0.007986 (-0.002796) | 0.003263 / 0.004328 (-0.001065) | 0.063920 / 0.004250 (0.059670) | 0.052233 / 0.037052 (0.015180) | 0.324830 / 0.258489 (0.066341) | 0.357956 / 0.293841 (0.064115) | 0.030459 / 0.128546 (-0.098087) | 0.008350 / 0.075646 (-0.067297) | 0.287330 / 0.419271 (-0.131942) | 0.051005 / 0.043533 (0.007473) | 0.309227 / 0.255139 (0.054088) | 0.346184 / 0.283200 (0.062984) | 0.023961 / 0.141683 (-0.117722) | 1.463983 / 1.452155 (0.011829) | 1.573036 / 1.492716 (0.080319) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.205653 / 0.018006 (0.187647) | 0.457336 / 0.000490 (0.456846) | 0.005347 / 0.000200 (0.005147) | 0.000079 / 0.000054 (0.000025) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028080 / 0.037411 (-0.009332) | 0.081755 / 0.014526 (0.067229) | 0.095716 / 0.176557 (-0.080841) | 0.151340 / 0.737135 (-0.585795) | 0.097174 / 0.296338 (-0.199164) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.390725 / 0.215209 (0.175516) | 3.899114 / 2.077655 (1.821459) | 1.895352 / 1.504120 (0.391232) | 1.716072 / 1.541195 (0.174877) | 1.784952 / 1.468490 (0.316462) | 0.477247 / 4.584777 (-4.107530) | 3.606641 / 3.745712 (-0.139071) | 3.203337 / 5.269862 (-2.066524) | 2.017003 / 4.565676 (-2.548674) | 0.056182 / 0.424275 (-0.368094) | 0.007508 / 0.007607 (-0.000099) | 0.461965 / 0.226044 (0.235921) | 4.605926 / 2.268929 (2.336997) | 2.466695 / 55.444624 (-52.977929) | 2.136376 / 6.876477 (-4.740100) | 2.277334 / 2.142072 (0.135261) | 0.576119 / 4.805227 (-4.229109) | 0.131497 / 6.500664 (-6.369167) | 0.060068 / 0.075469 (-0.015401) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.262681 / 1.841788 (-0.579107) | 19.411572 / 8.074308 (11.337264) | 14.383421 / 10.191392 (4.192029) | 0.166115 / 0.680424 (-0.514308) | 0.018366 / 0.534201 (-0.515835) | 0.393903 / 0.579283 (-0.185380) | 0.408788 / 0.434364 (-0.025576) | 0.461796 / 0.540337 (-0.078541) | 0.628460 / 1.386936 (-0.758476) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006501 / 0.011353 (-0.004852) | 0.003915 / 0.011008 (-0.007093) | 0.065245 / 0.038508 (0.026737) | 0.073146 / 0.023109 (0.050037) | 0.363537 / 0.275898 (0.087639) | 0.391571 / 0.323480 (0.068092) | 0.005181 / 0.007986 (-0.002805) | 0.003272 / 0.004328 (-0.001056) | 0.065060 / 0.004250 (0.060810) | 0.054302 / 0.037052 (0.017249) | 0.361571 / 0.258489 (0.103082) | 0.400221 / 0.293841 (0.106380) | 0.030762 / 0.128546 (-0.097784) | 0.008449 / 0.075646 (-0.067197) | 0.071148 / 0.419271 (-0.348123) | 0.048111 / 0.043533 (0.004578) | 0.360327 / 0.255139 (0.105188) | 0.379073 / 0.283200 (0.095874) | 0.024367 / 0.141683 (-0.117316) | 1.451080 / 1.452155 (-0.001074) | 1.510818 / 1.492716 (0.018102) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.267078 / 0.018006 (0.249072) | 0.454074 / 0.000490 (0.453584) | 0.015055 / 0.000200 (0.014855) | 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) | 0.030916 / 0.037411 (-0.006496) | 0.089212 / 0.014526 (0.074686) | 0.100005 / 0.176557 (-0.076552) | 0.155100 / 0.737135 (-0.582035) | 0.101759 / 0.296338 (-0.194580) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.412826 / 0.215209 (0.197616) | 4.122520 / 2.077655 (2.044865) | 2.107870 / 1.504120 (0.603750) | 1.911936 / 1.541195 (0.370741) | 1.984936 / 1.468490 (0.516446) | 0.483835 / 4.584777 (-4.100942) | 3.641860 / 3.745712 (-0.103852) | 3.220540 / 5.269862 (-2.049322) | 2.015521 / 4.565676 (-2.550155) | 0.056913 / 0.424275 (-0.367362) | 0.007285 / 0.007607 (-0.000322) | 0.484886 / 0.226044 (0.258842) | 4.854734 / 2.268929 (2.585805) | 2.593550 / 55.444624 (-52.851074) | 2.233904 / 6.876477 (-4.642572) | 2.438858 / 2.142072 (0.296785) | 0.580880 / 4.805227 (-4.224347) | 0.133891 / 6.500664 (-6.366773) | 0.061678 / 0.075469 (-0.013791) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.336843 / 1.841788 (-0.504944) | 19.731571 / 8.074308 (11.657263) | 14.290228 / 10.191392 (4.098836) | 0.167635 / 0.680424 (-0.512789) | 0.018767 / 0.534201 (-0.515434) | 0.394953 / 0.579283 (-0.184330) | 0.407711 / 0.434364 (-0.026653) | 0.472371 / 0.540337 (-0.067966) | 0.655278 / 1.386936 (-0.731658) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#528b15f775a4724836bdefdc38d932c06484d702 \"CML watermark\")\n"
] | "2023-08-17T21:58:24" | "2023-08-17T22:11:30" | null | CONTRIBUTOR | null | 0 | {
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https://api.github.com/repos/huggingface/datasets/issues/6159 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6159/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6159/comments | https://api.github.com/repos/huggingface/datasets/issues/6159/events | https://github.com/huggingface/datasets/issues/6159 | 1,855,691,512 | I_kwDODunzps5um5r4 | 6,159 | Add `BoundingBox` feature | {
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] | open | false | null | [] | null | [] | "2023-08-17T20:49:51" | "2023-08-17T20:49:51" | null | CONTRIBUTOR | null | null | null | ... to make working with object detection datasets easier. Currently, `Sequence(int_or_float, length=4)` can be used to represent this feature optimally (in the storage backend), so I only see this feature being useful if we make it work with the viewer. Also, bounding boxes usually come in 4 different formats (explained [here](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/)), so we need to decide which one to support (or maybe all of them).
cc @NielsRogge @severo | {
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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) | 0.008219 / 0.011353 (-0.003134) | 0.005201 / 0.011008 (-0.005807) | 0.108542 / 0.038508 (0.070034) | 0.076427 / 0.023109 (0.053318) | 0.441257 / 0.275898 (0.165358) | 0.436477 / 0.323480 (0.112997) | 0.006915 / 0.007986 (-0.001071) | 0.004215 / 0.004328 (-0.000113) | 0.072517 / 0.004250 (0.068267) | 0.066906 / 0.037052 (0.029853) | 0.431153 / 0.258489 (0.172664) | 0.413359 / 0.293841 (0.119518) | 0.051112 / 0.128546 (-0.077435) | 0.014664 / 0.075646 (-0.060982) | 0.358385 / 0.419271 (-0.060887) | 0.069682 / 0.043533 (0.026149) | 0.434810 / 0.255139 (0.179671) | 0.484372 / 0.283200 (0.201172) | 0.035731 / 0.141683 (-0.105952) | 1.827648 / 1.452155 (0.375494) | 2.039761 / 1.492716 (0.547045) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.277386 / 0.018006 (0.259379) | 0.599771 / 0.000490 (0.599282) | 0.005033 / 0.000200 (0.004833) | 0.000091 / 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) | 0.030652 / 0.037411 (-0.006759) | 0.103435 / 0.014526 (0.088909) | 0.120072 / 0.176557 (-0.056485) | 0.177886 / 0.737135 (-0.559249) | 0.140636 / 0.296338 (-0.155702) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.603729 / 0.215209 (0.388520) | 6.144213 / 2.077655 (4.066558) | 2.785080 / 1.504120 (1.280960) | 2.368958 / 1.541195 (0.827763) | 2.409806 / 1.468490 (0.941316) | 0.836531 / 4.584777 (-3.748246) | 5.154035 / 3.745712 (1.408323) | 4.620224 / 5.269862 (-0.649638) | 2.879441 / 4.565676 (-1.686235) | 0.087322 / 0.424275 (-0.336953) | 0.007698 / 0.007607 (0.000090) | 0.678443 / 0.226044 (0.452399) | 7.431798 / 2.268929 (5.162869) | 3.589905 / 55.444624 (-51.854719) | 2.679349 / 6.876477 (-4.197127) | 3.100569 / 2.142072 (0.958496) | 1.021501 / 4.805227 (-3.783726) | 0.203150 / 6.500664 (-6.297514) | 0.073545 / 0.075469 (-0.001924) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.669981 / 1.841788 (-0.171806) | 23.379274 / 8.074308 (15.304966) | 19.811451 / 10.191392 (9.620059) | 0.197705 / 0.680424 (-0.482719) | 0.030112 / 0.534201 (-0.504089) | 0.501720 / 0.579283 (-0.077563) | 0.582413 / 0.434364 (0.148049) | 0.513261 / 0.540337 (-0.027076) | 0.729710 / 1.386936 (-0.657226) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.011493 / 0.011353 (0.000140) | 0.005478 / 0.011008 (-0.005530) | 0.070955 / 0.038508 (0.032447) | 0.073877 / 0.023109 (0.050768) | 0.425765 / 0.275898 (0.149867) | 0.440869 / 0.323480 (0.117389) | 0.008322 / 0.007986 (0.000337) | 0.004004 / 0.004328 (-0.000325) | 0.071968 / 0.004250 (0.067718) | 0.060576 / 0.037052 (0.023524) | 0.448731 / 0.258489 (0.190242) | 0.517038 / 0.293841 (0.223197) | 0.051542 / 0.128546 (-0.077005) | 0.013219 / 0.075646 (-0.062427) | 0.077933 / 0.419271 (-0.341339) | 0.072879 / 0.043533 (0.029346) | 0.436553 / 0.255139 (0.181414) | 0.510050 / 0.283200 (0.226850) | 0.037136 / 0.141683 (-0.104547) | 1.535706 / 1.452155 (0.083552) | 1.611909 / 1.492716 (0.119192) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.335648 / 0.018006 (0.317642) | 0.612787 / 0.000490 (0.612297) | 0.021934 / 0.000200 (0.021734) | 0.000113 / 0.000054 (0.000059) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.028164 / 0.037411 (-0.009247) | 0.097686 / 0.014526 (0.083160) | 0.093343 / 0.176557 (-0.083214) | 0.156871 / 0.737135 (-0.580264) | 0.102694 / 0.296338 (-0.193645) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.609348 / 0.215209 (0.394139) | 5.835798 / 2.077655 (3.758144) | 2.792700 / 1.504120 (1.288580) | 2.539597 / 1.541195 (0.998403) | 2.413003 / 1.468490 (0.944513) | 0.882404 / 4.584777 (-3.702372) | 5.170564 / 3.745712 (1.424852) | 4.621663 / 5.269862 (-0.648199) | 3.029683 / 4.565676 (-1.535993) | 0.097061 / 0.424275 (-0.327214) | 0.008940 / 0.007607 (0.001333) | 0.723052 / 0.226044 (0.497007) | 7.484947 / 2.268929 (5.216018) | 3.833049 / 55.444624 (-51.611575) | 3.019606 / 6.876477 (-3.856871) | 3.270503 / 2.142072 (1.128430) | 0.977870 / 4.805227 (-3.827357) | 0.210090 / 6.500664 (-6.290574) | 0.094723 / 0.075469 (0.019254) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.585278 / 1.841788 (-0.256510) | 22.769727 / 8.074308 (14.695419) | 19.503640 / 10.191392 (9.312248) | 0.231996 / 0.680424 (-0.448428) | 0.032641 / 0.534201 (-0.501560) | 0.429833 / 0.579283 (-0.149451) | 0.549606 / 0.434364 (0.115242) | 0.527405 / 0.540337 (-0.012933) | 0.713302 / 1.386936 (-0.673634) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#546c7bb5cbeff0f8673cf60c4432ea167283cc42 \"CML watermark\")\n"
] | "2023-08-17T17:02:11" | "2023-08-17T19:24:20" | "2023-08-17T19:13:15" | MEMBER | null | 0 | {
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} | Finishes the `to_iterable_dataset` documentation by adding it to the relevant sections in the tutorial and guide. | {
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https://api.github.com/repos/huggingface/datasets/issues/6157 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6157/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6157/comments | https://api.github.com/repos/huggingface/datasets/issues/6157/events | https://github.com/huggingface/datasets/issues/6157 | 1,855,265,663 | I_kwDODunzps5ulRt_ | 6,157 | DatasetInfo.__init__() got an unexpected keyword argument '_column_requires_decoding' | {
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"Thanks for reporting, but we can only fix this issue if you can provide a reproducer that consistently reproduces it.",
"@mariosasko Ok. What exactly does it mean to provide a reproducer",
"To provide a code that reproduces the issue :)",
"@mariosasko I complete the above code, is it enough?"
] | "2023-08-17T15:48:11" | "2023-08-17T17:39:00" | null | NONE | null | null | null | ### Describe the bug
When I was in load_dataset, it said "DatasetInfo.__init__() got an unexpected keyword argument '_column_requires_decoding'". The second time I ran it, there was no error and the dataset object worked
### Steps to reproduce the bug
```python
from logging import config
import datasets
import os
from PIL import Image
import csv
import json
class ImagesConfig(datasets.BuilderConfig):
def __init__(self, **kwargs):
super(ImagesConfig, self).__init__(**kwargs)
class Images(datasets.GeneratorBasedBuilder):
def _split_generators(self, dl_manager: datasets.DownloadManager):
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"split": datasets.Split.TRAIN},
)
]
BUILDER_CONFIGS = [
ImagesConfig(
name="similar_pairs",
description="simliar pair dataset,item is a pair of similar images",
),
ImagesConfig(
name="image_prompt_pairs",
description="image prompt pairs",
),
]
def _info(self):
if self.config.name == "similar_pairs":
return datasets.Features(
{
"image1": datasets.features.Image(),
"image2": datasets.features.Image(),
"similarity": datasets.Value("float32"),
}
)
elif self.config.name == "image_prompt_pairs":
return datasets.Features(
{"image": datasets.features.Image(), "prompt": datasets.Value("string")}
)
def _generate_examples(self, split):
data_path = os.path.join(self.config.data_dir, "data")
if self.config.name == "similar_pairs":
prompts = {}
with open(os.path.join(data_path ,"prompts.json"), "r") as f:
prompts = json.load(f)
with open(os.path.join(data_path, "similar_pairs.csv"), "r") as f:
reader = csv.reader(f)
for row in reader:
image1_path, image2_path, similarity = row
yield image1_path + ":" + image2_path + ":", {
"image1": Image.open(image1_path),
"prompt1": prompts[image1_path],
"image2": Image.open(image2_path),
"prompt2": prompts[image2_path],
"similarity": float(similarity),
}
```
```python
dataset = load_dataset(data_dir, data_dir=data_dir, name="similar_pairs")
```
### Expected behavior
The first execution gives an error, but it works fine
### Environment info
- `datasets` version: 2.14.3
- Platform: Linux-6.2.0-26-generic-x86_64-with-glibc2.35
- Python version: 3.11.4
- Huggingface_hub version: 0.16.4
- PyArrow version: 12.0.1
- Pandas version: 2.0.3 | {
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https://api.github.com/repos/huggingface/datasets/issues/6156 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6156/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6156/comments | https://api.github.com/repos/huggingface/datasets/issues/6156/events | https://github.com/huggingface/datasets/issues/6156 | 1,854,768,618 | I_kwDODunzps5ujYXq | 6,156 | Why not use self._epoch as seed to shuffle in distributed training with IterableDataset | {
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"@lhoestq ",
"`_effective_generator` returns a RNG that takes into account `self._epoch` and the current dataset's base shuffling RNG (which can be set by specifying `seed=` in `.shuffle() for example`).\r\n\r\nTo fix your error you can pass `seed=` to `.shuffle()`. And the shuffling will depend on both this seed and `self._epoch`",
"Thanks for the reply"
] | "2023-08-17T10:58:20" | "2023-08-17T14:33:15" | "2023-08-17T14:33:14" | CONTRIBUTOR | null | null | null | ### Describe the bug
Currently, distributed training with `IterableDataset` needs to pass fixed seed to shuffle to keep each node use the same seed to avoid overlapping.
https://github.com/huggingface/datasets/blob/a7f8d9019e7cb104eac4106bdc6ec0292f0dc61a/src/datasets/iterable_dataset.py#L1174-L1177
My question is why not directly use `self._epoch` which is set by `set_epoch` as seed? It's almost the same across nodes.
https://github.com/huggingface/datasets/blob/a7f8d9019e7cb104eac4106bdc6ec0292f0dc61a/src/datasets/iterable_dataset.py#L1790-L1801
If not using `self._epoch` as shuffling seed, what does this method do to prepare an epoch seeded generator?
https://github.com/huggingface/datasets/blob/a7f8d9019e7cb104eac4106bdc6ec0292f0dc61a/src/datasets/iterable_dataset.py#L1206
### Steps to reproduce the bug
As mentioned above.
### Expected behavior
As mentioned above.
### Environment info
Not related | {
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https://api.github.com/repos/huggingface/datasets/issues/6155 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6155/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6155/comments | https://api.github.com/repos/huggingface/datasets/issues/6155/events | https://github.com/huggingface/datasets/pull/6155 | 1,854,661,682 | PR_kwDODunzps5YI8Pc | 6,155 | Raise FileNotFoundError when passing data_files that don't exist | {
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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) | 0.009288 / 0.011353 (-0.002065) | 0.005950 / 0.011008 (-0.005058) | 0.122376 / 0.038508 (0.083868) | 0.093177 / 0.023109 (0.070068) | 0.448517 / 0.275898 (0.172619) | 0.474999 / 0.323480 (0.151520) | 0.005133 / 0.007986 (-0.002853) | 0.005123 / 0.004328 (0.000795) | 0.085479 / 0.004250 (0.081229) | 0.065613 / 0.037052 (0.028561) | 0.451179 / 0.258489 (0.192690) | 0.516876 / 0.293841 (0.223036) | 0.047536 / 0.128546 (-0.081010) | 0.013894 / 0.075646 (-0.061752) | 0.382149 / 0.419271 (-0.037122) | 0.067380 / 0.043533 (0.023848) | 0.419282 / 0.255139 (0.164143) | 0.482042 / 0.283200 (0.198842) | 0.041230 / 0.141683 (-0.100452) | 1.818127 / 1.452155 (0.365972) | 1.938123 / 1.492716 (0.445406) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.271824 / 0.018006 (0.253817) | 0.604933 / 0.000490 (0.604443) | 0.004953 / 0.000200 (0.004753) | 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) | 0.036682 / 0.037411 (-0.000729) | 0.095604 / 0.014526 (0.081078) | 0.116862 / 0.176557 (-0.059695) | 0.191335 / 0.737135 (-0.545800) | 0.116620 / 0.296338 (-0.179718) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.620735 / 0.215209 (0.405526) | 6.157119 / 2.077655 (4.079465) | 2.848548 / 1.504120 (1.344428) | 2.493731 / 1.541195 (0.952536) | 2.505801 / 1.468490 (1.037311) | 0.837315 / 4.584777 (-3.747462) | 5.360653 / 3.745712 (1.614941) | 4.908863 / 5.269862 (-0.360999) | 3.184672 / 4.565676 (-1.381004) | 0.105687 / 0.424275 (-0.318588) | 0.011350 / 0.007607 (0.003743) | 0.745729 / 0.226044 (0.519684) | 7.431584 / 2.268929 (5.162655) | 3.644670 / 55.444624 (-51.799954) | 2.910159 / 6.876477 (-3.966317) | 3.257137 / 2.142072 (1.115065) | 1.041377 / 4.805227 (-3.763851) | 0.213289 / 6.500664 (-6.287375) | 0.089208 / 0.075469 (0.013739) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.727274 / 1.841788 (-0.114513) | 25.448436 / 8.074308 (17.374128) | 23.016108 / 10.191392 (12.824716) | 0.219454 / 0.680424 (-0.460970) | 0.028531 / 0.534201 (-0.505670) | 0.500231 / 0.579283 (-0.079052) | 0.614631 / 0.434364 (0.180267) | 0.557926 / 0.540337 (0.017588) | 0.786261 / 1.386936 (-0.600675) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008608 / 0.011353 (-0.002745) | 0.006185 / 0.011008 (-0.004823) | 0.089258 / 0.038508 (0.050750) | 0.090109 / 0.023109 (0.067000) | 0.522200 / 0.275898 (0.246302) | 0.559218 / 0.323480 (0.235738) | 0.008983 / 0.007986 (0.000997) | 0.004488 / 0.004328 (0.000159) | 0.083658 / 0.004250 (0.079408) | 0.064962 / 0.037052 (0.027909) | 0.519477 / 0.258489 (0.260988) | 0.573842 / 0.293841 (0.280001) | 0.053984 / 0.128546 (-0.074562) | 0.014665 / 0.075646 (-0.060982) | 0.089438 / 0.419271 (-0.329834) | 0.065756 / 0.043533 (0.022223) | 0.525131 / 0.255139 (0.269992) | 0.568934 / 0.283200 (0.285734) | 0.037308 / 0.141683 (-0.104375) | 1.928790 / 1.452155 (0.476635) | 2.027926 / 1.492716 (0.535209) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.309595 / 0.018006 (0.291588) | 0.615675 / 0.000490 (0.615186) | 0.004869 / 0.000200 (0.004669) | 0.000116 / 0.000054 (0.000061) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033306 / 0.037411 (-0.004105) | 0.104429 / 0.014526 (0.089904) | 0.116989 / 0.176557 (-0.059568) | 0.183638 / 0.737135 (-0.553497) | 0.132624 / 0.296338 (-0.163714) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.644511 / 0.215209 (0.429302) | 6.425544 / 2.077655 (4.347889) | 3.079071 / 1.504120 (1.574951) | 2.720963 / 1.541195 (1.179769) | 2.835607 / 1.468490 (1.367117) | 0.863561 / 4.584777 (-3.721216) | 5.333462 / 3.745712 (1.587750) | 4.843183 / 5.269862 (-0.426678) | 3.106858 / 4.565676 (-1.458819) | 0.106790 / 0.424275 (-0.317485) | 0.008829 / 0.007607 (0.001222) | 0.759003 / 0.226044 (0.532958) | 7.771247 / 2.268929 (5.502318) | 3.896844 / 55.444624 (-51.547780) | 3.246671 / 6.876477 (-3.629806) | 3.486167 / 2.142072 (1.344094) | 1.071290 / 4.805227 (-3.733937) | 0.217972 / 6.500664 (-6.282692) | 0.089848 / 0.075469 (0.014379) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.816048 / 1.841788 (-0.025739) | 25.625084 / 8.074308 (17.550776) | 24.490882 / 10.191392 (14.299490) | 0.242356 / 0.680424 (-0.438067) | 0.027886 / 0.534201 (-0.506315) | 0.496997 / 0.579283 (-0.082286) | 0.613815 / 0.434364 (0.179451) | 0.607132 / 0.540337 (0.066795) | 0.833051 / 1.386936 (-0.553885) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#0adfa9ada14c38fce5973b5e3f196a2c46dc9170 \"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) | 0.011580 / 0.011353 (0.000227) | 0.004199 / 0.011008 (-0.006809) | 0.084055 / 0.038508 (0.045547) | 0.096824 / 0.023109 (0.073715) | 0.308755 / 0.275898 (0.032857) | 0.341717 / 0.323480 (0.018237) | 0.006018 / 0.007986 (-0.001968) | 0.003597 / 0.004328 (-0.000731) | 0.064953 / 0.004250 (0.060702) | 0.059577 / 0.037052 (0.022525) | 0.316292 / 0.258489 (0.057803) | 0.358991 / 0.293841 (0.065150) | 0.033925 / 0.128546 (-0.094621) | 0.008828 / 0.075646 (-0.066818) | 0.288673 / 0.419271 (-0.130599) | 0.055494 / 0.043533 (0.011961) | 0.311181 / 0.255139 (0.056042) | 0.345220 / 0.283200 (0.062021) | 0.024033 / 0.141683 (-0.117649) | 1.504709 / 1.452155 (0.052554) | 1.587920 / 1.492716 (0.095204) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.301099 / 0.018006 (0.283093) | 0.594497 / 0.000490 (0.594007) | 0.006244 / 0.000200 (0.006044) | 0.000228 / 0.000054 (0.000174) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.027663 / 0.037411 (-0.009748) | 0.081767 / 0.014526 (0.067241) | 0.097342 / 0.176557 (-0.079215) | 0.153200 / 0.737135 (-0.583935) | 0.097474 / 0.296338 (-0.198864) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.405929 / 0.215209 (0.190719) | 4.045398 / 2.077655 (1.967743) | 2.044669 / 1.504120 (0.540549) | 1.872889 / 1.541195 (0.331694) | 1.911901 / 1.468490 (0.443411) | 0.480939 / 4.584777 (-4.103838) | 3.652833 / 3.745712 (-0.092879) | 3.281659 / 5.269862 (-1.988202) | 2.038023 / 4.565676 (-2.527654) | 0.056501 / 0.424275 (-0.367775) | 0.007571 / 0.007607 (-0.000036) | 0.481053 / 0.226044 (0.255009) | 4.802048 / 2.268929 (2.533119) | 2.560479 / 55.444624 (-52.884145) | 2.164852 / 6.876477 (-4.711625) | 2.374595 / 2.142072 (0.232523) | 0.576309 / 4.805227 (-4.228918) | 0.134831 / 6.500664 (-6.365833) | 0.060649 / 0.075469 (-0.014820) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.254210 / 1.841788 (-0.587578) | 19.826143 / 8.074308 (11.751835) | 14.446391 / 10.191392 (4.254999) | 0.165707 / 0.680424 (-0.514717) | 0.018221 / 0.534201 (-0.515980) | 0.395996 / 0.579283 (-0.183287) | 0.424567 / 0.434364 (-0.009796) | 0.459836 / 0.540337 (-0.080501) | 0.635969 / 1.386936 (-0.750967) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006696 / 0.011353 (-0.004657) | 0.004131 / 0.011008 (-0.006877) | 0.064587 / 0.038508 (0.026079) | 0.079189 / 0.023109 (0.056080) | 0.359977 / 0.275898 (0.084079) | 0.389331 / 0.323480 (0.065851) | 0.005502 / 0.007986 (-0.002483) | 0.003492 / 0.004328 (-0.000837) | 0.064967 / 0.004250 (0.060716) | 0.055953 / 0.037052 (0.018901) | 0.363997 / 0.258489 (0.105508) | 0.398405 / 0.293841 (0.104564) | 0.031292 / 0.128546 (-0.097254) | 0.008693 / 0.075646 (-0.066953) | 0.070451 / 0.419271 (-0.348820) | 0.048965 / 0.043533 (0.005432) | 0.358288 / 0.255139 (0.103149) | 0.379136 / 0.283200 (0.095936) | 0.024364 / 0.141683 (-0.117319) | 1.478998 / 1.452155 (0.026843) | 1.547282 / 1.492716 (0.054566) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.328188 / 0.018006 (0.310182) | 0.525968 / 0.000490 (0.525478) | 0.003782 / 0.000200 (0.003582) | 0.000089 / 0.000054 (0.000034) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032528 / 0.037411 (-0.004883) | 0.087685 / 0.014526 (0.073159) | 0.100684 / 0.176557 (-0.075872) | 0.155944 / 0.737135 (-0.581192) | 0.101949 / 0.296338 (-0.194389) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.418591 / 0.215209 (0.203382) | 4.199235 / 2.077655 (2.121580) | 2.183880 / 1.504120 (0.679760) | 2.024502 / 1.541195 (0.483307) | 2.017435 / 1.468490 (0.548945) | 0.488881 / 4.584777 (-4.095896) | 3.635002 / 3.745712 (-0.110710) | 3.359992 / 5.269862 (-1.909870) | 2.089686 / 4.565676 (-2.475991) | 0.057813 / 0.424275 (-0.366462) | 0.007349 / 0.007607 (-0.000258) | 0.490719 / 0.226044 (0.264674) | 4.859950 / 2.268929 (2.591022) | 2.616711 / 55.444624 (-52.827914) | 2.238671 / 6.876477 (-4.637806) | 2.442262 / 2.142072 (0.300190) | 0.598368 / 4.805227 (-4.206859) | 0.135281 / 6.500664 (-6.365383) | 0.063072 / 0.075469 (-0.012397) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.356396 / 1.841788 (-0.485392) | 20.075123 / 8.074308 (12.000815) | 14.191317 / 10.191392 (3.999925) | 0.167691 / 0.680424 (-0.512732) | 0.018290 / 0.534201 (-0.515911) | 0.392881 / 0.579283 (-0.186402) | 0.413665 / 0.434364 (-0.020699) | 0.480766 / 0.540337 (-0.059571) | 0.655625 / 1.386936 (-0.731311) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a46ca9cc138754629be261522301e725c7d14152 \"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) | 0.007834 / 0.011353 (-0.003519) | 0.004744 / 0.011008 (-0.006264) | 0.102061 / 0.038508 (0.063553) | 0.089246 / 0.023109 (0.066137) | 0.399936 / 0.275898 (0.124038) | 0.436974 / 0.323480 (0.113494) | 0.004791 / 0.007986 (-0.003195) | 0.005976 / 0.004328 (0.001647) | 0.079336 / 0.004250 (0.075086) | 0.065947 / 0.037052 (0.028894) | 0.403747 / 0.258489 (0.145258) | 0.460249 / 0.293841 (0.166408) | 0.038065 / 0.128546 (-0.090482) | 0.010179 / 0.075646 (-0.065467) | 0.403620 / 0.419271 (-0.015652) | 0.066439 / 0.043533 (0.022906) | 0.412123 / 0.255139 (0.156984) | 0.452121 / 0.283200 (0.168921) | 0.033533 / 0.141683 (-0.108150) | 1.858650 / 1.452155 (0.406495) | 1.916248 / 1.492716 (0.423532) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.237180 / 0.018006 (0.219174) | 0.526844 / 0.000490 (0.526354) | 0.004220 / 0.000200 (0.004020) | 0.000123 / 0.000054 (0.000069) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.033860 / 0.037411 (-0.003552) | 0.105054 / 0.014526 (0.090528) | 0.116494 / 0.176557 (-0.060063) | 0.185990 / 0.737135 (-0.551145) | 0.119072 / 0.296338 (-0.177266) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.488549 / 0.215209 (0.273340) | 4.884950 / 2.077655 (2.807295) | 2.521819 / 1.504120 (1.017699) | 2.329382 / 1.541195 (0.788188) | 2.413710 / 1.468490 (0.945220) | 0.568325 / 4.584777 (-4.016452) | 4.243505 / 3.745712 (0.497793) | 3.785983 / 5.269862 (-1.483879) | 2.387146 / 4.565676 (-2.178531) | 0.067176 / 0.424275 (-0.357099) | 0.009145 / 0.007607 (0.001538) | 0.571482 / 0.226044 (0.345437) | 5.688822 / 2.268929 (3.419894) | 3.067346 / 55.444624 (-52.377278) | 2.688723 / 6.876477 (-4.187754) | 2.883785 / 2.142072 (0.741713) | 0.679326 / 4.805227 (-4.125901) | 0.156018 / 6.500664 (-6.344646) | 0.070947 / 0.075469 (-0.004522) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.556611 / 1.841788 (-0.285177) | 23.545074 / 8.074308 (15.470766) | 17.125108 / 10.191392 (6.933716) | 0.180180 / 0.680424 (-0.500244) | 0.021420 / 0.534201 (-0.512781) | 0.466888 / 0.579283 (-0.112395) | 0.485746 / 0.434364 (0.051383) | 0.606181 / 0.540337 (0.065843) | 0.776691 / 1.386936 (-0.610245) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007820 / 0.011353 (-0.003533) | 0.004531 / 0.011008 (-0.006478) | 0.076142 / 0.038508 (0.037634) | 0.086367 / 0.023109 (0.063258) | 0.456150 / 0.275898 (0.180252) | 0.499712 / 0.323480 (0.176232) | 0.006545 / 0.007986 (-0.001441) | 0.003760 / 0.004328 (-0.000568) | 0.076400 / 0.004250 (0.072150) | 0.069689 / 0.037052 (0.032637) | 0.459732 / 0.258489 (0.201243) | 0.504217 / 0.293841 (0.210376) | 0.037838 / 0.128546 (-0.090709) | 0.009804 / 0.075646 (-0.065843) | 0.084654 / 0.419271 (-0.334617) | 0.060301 / 0.043533 (0.016768) | 0.452984 / 0.255139 (0.197845) | 0.479956 / 0.283200 (0.196757) | 0.029674 / 0.141683 (-0.112009) | 1.814059 / 1.452155 (0.361904) | 1.878886 / 1.492716 (0.386170) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.326174 / 0.018006 (0.308168) | 0.539722 / 0.000490 (0.539232) | 0.025637 / 0.000200 (0.025437) | 0.000209 / 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) | 0.036328 / 0.037411 (-0.001084) | 0.106369 / 0.014526 (0.091843) | 0.118598 / 0.176557 (-0.057958) | 0.182760 / 0.737135 (-0.554376) | 0.120013 / 0.296338 (-0.176326) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.507328 / 0.215209 (0.292119) | 5.092689 / 2.077655 (3.015034) | 2.962334 / 1.504120 (1.458214) | 2.507699 / 1.541195 (0.966504) | 2.612245 / 1.468490 (1.143755) | 0.568625 / 4.584777 (-4.016152) | 4.296484 / 3.745712 (0.550772) | 4.037788 / 5.269862 (-1.232073) | 2.579826 / 4.565676 (-1.985850) | 0.068558 / 0.424275 (-0.355717) | 0.008916 / 0.007607 (0.001309) | 0.601054 / 0.226044 (0.375010) | 6.016061 / 2.268929 (3.747133) | 3.311880 / 55.444624 (-52.132744) | 2.912926 / 6.876477 (-3.963551) | 3.101465 / 2.142072 (0.959393) | 0.686848 / 4.805227 (-4.118380) | 0.160243 / 6.500664 (-6.340421) | 0.074084 / 0.075469 (-0.001385) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.754343 / 1.841788 (-0.087444) | 24.215302 / 8.074308 (16.140994) | 17.211007 / 10.191392 (7.019615) | 0.188370 / 0.680424 (-0.492054) | 0.028157 / 0.534201 (-0.506044) | 0.490879 / 0.579283 (-0.088404) | 0.501508 / 0.434364 (0.067144) | 0.599719 / 0.540337 (0.059381) | 0.852438 / 1.386936 (-0.534498) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#d84cd1d6f51ca75ec5f5c3db3f372f093758cac9 \"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) | 0.009736 / 0.011353 (-0.001617) | 0.004761 / 0.011008 (-0.006247) | 0.100069 / 0.038508 (0.061561) | 0.077944 / 0.023109 (0.054835) | 0.419944 / 0.275898 (0.144046) | 0.459803 / 0.323480 (0.136323) | 0.006296 / 0.007986 (-0.001689) | 0.005375 / 0.004328 (0.001047) | 0.089457 / 0.004250 (0.085207) | 0.060585 / 0.037052 (0.023532) | 0.437988 / 0.258489 (0.179499) | 0.482676 / 0.293841 (0.188835) | 0.049126 / 0.128546 (-0.079420) | 0.015043 / 0.075646 (-0.060603) | 0.342500 / 0.419271 (-0.076771) | 0.067088 / 0.043533 (0.023555) | 0.418364 / 0.255139 (0.163225) | 0.458259 / 0.283200 (0.175059) | 0.034091 / 0.141683 (-0.107592) | 1.721589 / 1.452155 (0.269434) | 1.823142 / 1.492716 (0.330426) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.212110 / 0.018006 (0.194103) | 0.530957 / 0.000490 (0.530467) | 0.003581 / 0.000200 (0.003382) | 0.000112 / 0.000054 (0.000058) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030202 / 0.037411 (-0.007210) | 0.100552 / 0.014526 (0.086026) | 0.108150 / 0.176557 (-0.068407) | 0.173203 / 0.737135 (-0.563932) | 0.108624 / 0.296338 (-0.187715) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.577340 / 0.215209 (0.362131) | 5.794197 / 2.077655 (3.716543) | 2.396285 / 1.504120 (0.892165) | 2.151972 / 1.541195 (0.610777) | 2.109485 / 1.468490 (0.640995) | 0.873906 / 4.584777 (-3.710871) | 5.083302 / 3.745712 (1.337589) | 4.600756 / 5.269862 (-0.669105) | 2.891731 / 4.565676 (-1.673945) | 0.096293 / 0.424275 (-0.327982) | 0.008651 / 0.007607 (0.001044) | 0.719095 / 0.226044 (0.493051) | 7.193225 / 2.268929 (4.924297) | 3.220145 / 55.444624 (-52.224479) | 2.496715 / 6.876477 (-4.379762) | 2.672972 / 2.142072 (0.530900) | 1.031656 / 4.805227 (-3.773571) | 0.207854 / 6.500664 (-6.292810) | 0.074507 / 0.075469 (-0.000962) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.552821 / 1.841788 (-0.288967) | 22.573015 / 8.074308 (14.498707) | 21.074321 / 10.191392 (10.882929) | 0.231911 / 0.680424 (-0.448513) | 0.027761 / 0.534201 (-0.506440) | 0.474644 / 0.579283 (-0.104639) | 0.563780 / 0.434364 (0.129416) | 0.527593 / 0.540337 (-0.012745) | 0.732299 / 1.386936 (-0.654637) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008675 / 0.011353 (-0.002678) | 0.005268 / 0.011008 (-0.005741) | 0.079078 / 0.038508 (0.040570) | 0.073505 / 0.023109 (0.050395) | 0.453982 / 0.275898 (0.178083) | 0.487839 / 0.323480 (0.164359) | 0.005950 / 0.007986 (-0.002035) | 0.003848 / 0.004328 (-0.000481) | 0.076004 / 0.004250 (0.071754) | 0.058410 / 0.037052 (0.021358) | 0.460099 / 0.258489 (0.201610) | 0.514860 / 0.293841 (0.221019) | 0.048843 / 0.128546 (-0.079703) | 0.014275 / 0.075646 (-0.061371) | 0.090243 / 0.419271 (-0.329029) | 0.060092 / 0.043533 (0.016559) | 0.455669 / 0.255139 (0.200530) | 0.484738 / 0.283200 (0.201538) | 0.033012 / 0.141683 (-0.108671) | 1.738854 / 1.452155 (0.286699) | 1.852552 / 1.492716 (0.359835) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.245453 / 0.018006 (0.227447) | 0.519929 / 0.000490 (0.519439) | 0.007262 / 0.000200 (0.007062) | 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) | 0.031446 / 0.037411 (-0.005965) | 0.094236 / 0.014526 (0.079710) | 0.114457 / 0.176557 (-0.062100) | 0.167448 / 0.737135 (-0.569687) | 0.108791 / 0.296338 (-0.187548) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.603331 / 0.215209 (0.388122) | 6.051556 / 2.077655 (3.973902) | 2.797110 / 1.504120 (1.292990) | 2.500517 / 1.541195 (0.959322) | 2.531421 / 1.468490 (1.062931) | 0.852075 / 4.584777 (-3.732702) | 5.034140 / 3.745712 (1.288427) | 4.576573 / 5.269862 (-0.693289) | 2.973541 / 4.565676 (-1.592135) | 0.101303 / 0.424275 (-0.322972) | 0.008467 / 0.007607 (0.000860) | 0.707143 / 0.226044 (0.481098) | 7.262803 / 2.268929 (4.993874) | 3.548841 / 55.444624 (-51.895783) | 2.895975 / 6.876477 (-3.980502) | 3.063521 / 2.142072 (0.921449) | 1.014961 / 4.805227 (-3.790266) | 0.208527 / 6.500664 (-6.292137) | 0.074939 / 0.075469 (-0.000530) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.670708 / 1.841788 (-0.171080) | 22.685227 / 8.074308 (14.610919) | 20.393017 / 10.191392 (10.201625) | 0.239303 / 0.680424 (-0.441121) | 0.027742 / 0.534201 (-0.506459) | 0.467230 / 0.579283 (-0.112053) | 0.564169 / 0.434364 (0.129805) | 0.554859 / 0.540337 (0.014522) | 0.767471 / 1.386936 (-0.619465) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#72a57356a46ded67f4d7a02741141a96061246a8 \"CML watermark\")\n"
] | "2023-08-17T09:49:48" | "2023-08-17T21:04:20" | null | MEMBER | null | 0 | {
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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) | 0.006829 / 0.011353 (-0.004524) | 0.004535 / 0.011008 (-0.006473) | 0.085255 / 0.038508 (0.046747) | 0.080861 / 0.023109 (0.057752) | 0.366023 / 0.275898 (0.090125) | 0.403095 / 0.323480 (0.079615) | 0.005615 / 0.007986 (-0.002370) | 0.003830 / 0.004328 (-0.000498) | 0.064502 / 0.004250 (0.060251) | 0.053916 / 0.037052 (0.016863) | 0.366010 / 0.258489 (0.107521) | 0.414565 / 0.293841 (0.120724) | 0.031500 / 0.128546 (-0.097046) | 0.009252 / 0.075646 (-0.066394) | 0.289584 / 0.419271 (-0.129688) | 0.052984 / 0.043533 (0.009451) | 0.352626 / 0.255139 (0.097487) | 0.390964 / 0.283200 (0.107764) | 0.025118 / 0.141683 (-0.116565) | 1.462316 / 1.452155 (0.010161) | 1.565682 / 1.492716 (0.072966) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.294432 / 0.018006 (0.276426) | 0.618366 / 0.000490 (0.617876) | 0.003270 / 0.000200 (0.003071) | 0.000081 / 0.000054 (0.000027) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031194 / 0.037411 (-0.006217) | 0.088892 / 0.014526 (0.074366) | 0.102580 / 0.176557 (-0.073977) | 0.159449 / 0.737135 (-0.577686) | 0.104434 / 0.296338 (-0.191905) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.385690 / 0.215209 (0.170481) | 3.832782 / 2.077655 (1.755128) | 1.862521 / 1.504120 (0.358401) | 1.685674 / 1.541195 (0.144479) | 1.724984 / 1.468490 (0.256494) | 0.483700 / 4.584777 (-4.101077) | 3.664154 / 3.745712 (-0.081558) | 3.323023 / 5.269862 (-1.946839) | 2.055958 / 4.565676 (-2.509718) | 0.056990 / 0.424275 (-0.367285) | 0.007674 / 0.007607 (0.000067) | 0.460642 / 0.226044 (0.234598) | 4.609964 / 2.268929 (2.341036) | 2.434868 / 55.444624 (-53.009756) | 2.003347 / 6.876477 (-4.873130) | 2.209520 / 2.142072 (0.067448) | 0.629363 / 4.805227 (-4.175864) | 0.135434 / 6.500664 (-6.365230) | 0.060498 / 0.075469 (-0.014971) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.253917 / 1.841788 (-0.587870) | 19.988953 / 8.074308 (11.914645) | 14.353739 / 10.191392 (4.162347) | 0.165987 / 0.680424 (-0.514437) | 0.018299 / 0.534201 (-0.515902) | 0.395532 / 0.579283 (-0.183751) | 0.418708 / 0.434364 (-0.015656) | 0.460865 / 0.540337 (-0.079472) | 0.633925 / 1.386936 (-0.753011) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006631 / 0.011353 (-0.004722) | 0.004109 / 0.011008 (-0.006899) | 0.065003 / 0.038508 (0.026495) | 0.080407 / 0.023109 (0.057297) | 0.362966 / 0.275898 (0.087068) | 0.389727 / 0.323480 (0.066247) | 0.005588 / 0.007986 (-0.002397) | 0.003517 / 0.004328 (-0.000812) | 0.065821 / 0.004250 (0.061570) | 0.057614 / 0.037052 (0.020561) | 0.367422 / 0.258489 (0.108932) | 0.400706 / 0.293841 (0.106865) | 0.031560 / 0.128546 (-0.096986) | 0.008659 / 0.075646 (-0.066987) | 0.070756 / 0.419271 (-0.348516) | 0.049821 / 0.043533 (0.006288) | 0.360836 / 0.255139 (0.105697) | 0.383981 / 0.283200 (0.100781) | 0.023719 / 0.141683 (-0.117963) | 1.485197 / 1.452155 (0.033043) | 1.544899 / 1.492716 (0.052182) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.336480 / 0.018006 (0.318474) | 0.532839 / 0.000490 (0.532349) | 0.003767 / 0.000200 (0.003567) | 0.000087 / 0.000054 (0.000032) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.034132 / 0.037411 (-0.003280) | 0.090131 / 0.014526 (0.075605) | 0.104086 / 0.176557 (-0.072471) | 0.158385 / 0.737135 (-0.578751) | 0.106417 / 0.296338 (-0.189922) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.416462 / 0.215209 (0.201253) | 4.160409 / 2.077655 (2.082755) | 2.195355 / 1.504120 (0.691235) | 2.051234 / 1.541195 (0.510040) | 2.012116 / 1.468490 (0.543626) | 0.477414 / 4.584777 (-4.107363) | 3.590326 / 3.745712 (-0.155386) | 3.318490 / 5.269862 (-1.951371) | 2.064124 / 4.565676 (-2.501553) | 0.057040 / 0.424275 (-0.367235) | 0.007283 / 0.007607 (-0.000324) | 0.480490 / 0.226044 (0.254445) | 4.804013 / 2.268929 (2.535084) | 2.625940 / 55.444624 (-52.818685) | 2.231537 / 6.876477 (-4.644939) | 2.441649 / 2.142072 (0.299576) | 0.573207 / 4.805227 (-4.232020) | 0.131685 / 6.500664 (-6.368979) | 0.060112 / 0.075469 (-0.015357) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.358587 / 1.841788 (-0.483200) | 20.457562 / 8.074308 (12.383254) | 14.236304 / 10.191392 (4.044912) | 0.152860 / 0.680424 (-0.527563) | 0.018466 / 0.534201 (-0.515735) | 0.401391 / 0.579283 (-0.177893) | 0.410252 / 0.434364 (-0.024111) | 0.484335 / 0.540337 (-0.056002) | 0.663818 / 1.386936 (-0.723118) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#acac88873abcb585892dc361eb9f6a70a1fd9a59 \"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) | 0.007725 / 0.011353 (-0.003628) | 0.004448 / 0.011008 (-0.006560) | 0.098689 / 0.038508 (0.060180) | 0.082919 / 0.023109 (0.059809) | 0.380707 / 0.275898 (0.104809) | 0.452977 / 0.323480 (0.129497) | 0.004430 / 0.007986 (-0.003555) | 0.003712 / 0.004328 (-0.000616) | 0.076675 / 0.004250 (0.072425) | 0.062281 / 0.037052 (0.025228) | 0.403370 / 0.258489 (0.144881) | 0.464557 / 0.293841 (0.170716) | 0.035646 / 0.128546 (-0.092900) | 0.009776 / 0.075646 (-0.065870) | 0.341955 / 0.419271 (-0.077316) | 0.059515 / 0.043533 (0.015983) | 0.388421 / 0.255139 (0.133282) | 0.439496 / 0.283200 (0.156296) | 0.029090 / 0.141683 (-0.112593) | 1.727473 / 1.452155 (0.275319) | 1.810448 / 1.492716 (0.317732) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.221215 / 0.018006 (0.203208) | 0.486660 / 0.000490 (0.486171) | 0.005467 / 0.000200 (0.005267) | 0.000110 / 0.000054 (0.000056) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032491 / 0.037411 (-0.004920) | 0.094446 / 0.014526 (0.079920) | 0.110339 / 0.176557 (-0.066217) | 0.175004 / 0.737135 (-0.562131) | 0.109209 / 0.296338 (-0.187129) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.453966 / 0.215209 (0.238757) | 4.515842 / 2.077655 (2.438187) | 2.240512 / 1.504120 (0.736392) | 2.059911 / 1.541195 (0.518717) | 2.150635 / 1.468490 (0.682145) | 0.564509 / 4.584777 (-4.020268) | 4.055208 / 3.745712 (0.309496) | 3.614084 / 5.269862 (-1.655778) | 2.295760 / 4.565676 (-2.269917) | 0.066507 / 0.424275 (-0.357768) | 0.008909 / 0.007607 (0.001302) | 0.542604 / 0.226044 (0.316560) | 5.412162 / 2.268929 (3.143233) | 2.758757 / 55.444624 (-52.685867) | 2.430693 / 6.876477 (-4.445784) | 2.669866 / 2.142072 (0.527793) | 0.681756 / 4.805227 (-4.123471) | 0.156524 / 6.500664 (-6.344140) | 0.069499 / 0.075469 (-0.005970) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.571591 / 1.841788 (-0.270197) | 22.543437 / 8.074308 (14.469129) | 16.068426 / 10.191392 (5.877034) | 0.169860 / 0.680424 (-0.510564) | 0.021216 / 0.534201 (-0.512985) | 0.468745 / 0.579283 (-0.110538) | 0.475924 / 0.434364 (0.041560) | 0.535574 / 0.540337 (-0.004763) | 0.733823 / 1.386936 (-0.653113) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.008038 / 0.011353 (-0.003315) | 0.004565 / 0.011008 (-0.006443) | 0.076892 / 0.038508 (0.038384) | 0.089559 / 0.023109 (0.066450) | 0.456752 / 0.275898 (0.180854) | 0.497282 / 0.323480 (0.173802) | 0.005991 / 0.007986 (-0.001995) | 0.003784 / 0.004328 (-0.000545) | 0.076339 / 0.004250 (0.072089) | 0.066050 / 0.037052 (0.028998) | 0.462708 / 0.258489 (0.204219) | 0.503711 / 0.293841 (0.209870) | 0.037098 / 0.128546 (-0.091448) | 0.009869 / 0.075646 (-0.065777) | 0.083678 / 0.419271 (-0.335594) | 0.058166 / 0.043533 (0.014633) | 0.461839 / 0.255139 (0.206700) | 0.481546 / 0.283200 (0.198347) | 0.027755 / 0.141683 (-0.113928) | 1.738490 / 1.452155 (0.286335) | 1.832276 / 1.492716 (0.339560) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.329935 / 0.018006 (0.311929) | 0.497438 / 0.000490 (0.496949) | 0.034644 / 0.000200 (0.034444) | 0.000199 / 0.000054 (0.000145) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.035427 / 0.037411 (-0.001984) | 0.105689 / 0.014526 (0.091163) | 0.117706 / 0.176557 (-0.058850) | 0.177862 / 0.737135 (-0.559273) | 0.116791 / 0.296338 (-0.179547) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.484851 / 0.215209 (0.269642) | 4.804346 / 2.077655 (2.726691) | 2.494801 / 1.504120 (0.990681) | 2.320185 / 1.541195 (0.778990) | 2.374090 / 1.468490 (0.905600) | 0.567397 / 4.584777 (-4.017380) | 4.087402 / 3.745712 (0.341690) | 3.794245 / 5.269862 (-1.475616) | 2.378481 / 4.565676 (-2.187195) | 0.068228 / 0.424275 (-0.356047) | 0.008740 / 0.007607 (0.001133) | 0.574876 / 0.226044 (0.348832) | 5.742644 / 2.268929 (3.473716) | 3.047661 / 55.444624 (-52.396963) | 2.729742 / 6.876477 (-4.146735) | 2.852510 / 2.142072 (0.710438) | 0.679450 / 4.805227 (-4.125777) | 0.156162 / 6.500664 (-6.344502) | 0.074051 / 0.075469 (-0.001418) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.576182 / 1.841788 (-0.265605) | 23.298147 / 8.074308 (15.223839) | 16.344621 / 10.191392 (6.153229) | 0.167571 / 0.680424 (-0.512852) | 0.021423 / 0.534201 (-0.512778) | 0.464511 / 0.579283 (-0.114772) | 0.453257 / 0.434364 (0.018893) | 0.563439 / 0.540337 (0.023102) | 0.764759 / 1.386936 (-0.622177) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#e8dc4b32b0d91bdb0971f8203ee37e6588c7770e \"CML watermark\")\n",
"This should also fix https://github.com/huggingface/datasets/issues/6140, so please link it with this PR before merging.",
"Done !",
"<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) | 0.006719 / 0.011353 (-0.004634) | 0.004299 / 0.011008 (-0.006709) | 0.085296 / 0.038508 (0.046788) | 0.085144 / 0.023109 (0.062035) | 0.361703 / 0.275898 (0.085805) | 0.397721 / 0.323480 (0.074241) | 0.005920 / 0.007986 (-0.002065) | 0.003853 / 0.004328 (-0.000476) | 0.065633 / 0.004250 (0.061383) | 0.057000 / 0.037052 (0.019947) | 0.379981 / 0.258489 (0.121492) | 0.419041 / 0.293841 (0.125200) | 0.031225 / 0.128546 (-0.097322) | 0.008868 / 0.075646 (-0.066779) | 0.288808 / 0.419271 (-0.130463) | 0.052391 / 0.043533 (0.008859) | 0.362349 / 0.255139 (0.107210) | 0.399858 / 0.283200 (0.116658) | 0.025843 / 0.141683 (-0.115840) | 1.498988 / 1.452155 (0.046834) | 1.547290 / 1.492716 (0.054574) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.278091 / 0.018006 (0.260085) | 0.621794 / 0.000490 (0.621305) | 0.003770 / 0.000200 (0.003570) | 0.000084 / 0.000054 (0.000029) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.029128 / 0.037411 (-0.008283) | 0.082061 / 0.014526 (0.067536) | 0.101758 / 0.176557 (-0.074799) | 0.155724 / 0.737135 (-0.581411) | 0.102173 / 0.296338 (-0.194165) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.387145 / 0.215209 (0.171935) | 3.868262 / 2.077655 (1.790607) | 1.886440 / 1.504120 (0.382320) | 1.723305 / 1.541195 (0.182111) | 1.805411 / 1.468490 (0.336921) | 0.485024 / 4.584777 (-4.099753) | 3.637859 / 3.745712 (-0.107853) | 3.319593 / 5.269862 (-1.950269) | 2.087860 / 4.565676 (-2.477817) | 0.056992 / 0.424275 (-0.367283) | 0.007623 / 0.007607 (0.000016) | 0.468182 / 0.226044 (0.242138) | 4.681112 / 2.268929 (2.412183) | 2.407010 / 55.444624 (-53.037614) | 2.026604 / 6.876477 (-4.849872) | 2.298158 / 2.142072 (0.156086) | 0.581839 / 4.805227 (-4.223388) | 0.132101 / 6.500664 (-6.368563) | 0.060472 / 0.075469 (-0.014997) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.236422 / 1.841788 (-0.605365) | 20.505168 / 8.074308 (12.430860) | 14.356081 / 10.191392 (4.164689) | 0.148808 / 0.680424 (-0.531616) | 0.018433 / 0.534201 (-0.515768) | 0.391323 / 0.579283 (-0.187960) | 0.413142 / 0.434364 (-0.021222) | 0.453484 / 0.540337 (-0.086853) | 0.620771 / 1.386936 (-0.766165) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007030 / 0.011353 (-0.004323) | 0.004430 / 0.011008 (-0.006578) | 0.065578 / 0.038508 (0.027070) | 0.090751 / 0.023109 (0.067642) | 0.389121 / 0.275898 (0.113223) | 0.424657 / 0.323480 (0.101177) | 0.006575 / 0.007986 (-0.001410) | 0.003855 / 0.004328 (-0.000473) | 0.066175 / 0.004250 (0.061925) | 0.063255 / 0.037052 (0.026202) | 0.397161 / 0.258489 (0.138672) | 0.435291 / 0.293841 (0.141450) | 0.031622 / 0.128546 (-0.096925) | 0.008900 / 0.075646 (-0.066747) | 0.071694 / 0.419271 (-0.347577) | 0.049161 / 0.043533 (0.005628) | 0.386214 / 0.255139 (0.131075) | 0.404571 / 0.283200 (0.121372) | 0.024821 / 0.141683 (-0.116862) | 1.489514 / 1.452155 (0.037359) | 1.576139 / 1.492716 (0.083423) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.289884 / 0.018006 (0.271878) | 0.629342 / 0.000490 (0.628852) | 0.004799 / 0.000200 (0.004599) | 0.000160 / 0.000054 (0.000106) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.032081 / 0.037411 (-0.005331) | 0.088152 / 0.014526 (0.073626) | 0.107289 / 0.176557 (-0.069267) | 0.164598 / 0.737135 (-0.572537) | 0.108395 / 0.296338 (-0.187944) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.426723 / 0.215209 (0.211514) | 4.267719 / 2.077655 (2.190064) | 2.289657 / 1.504120 (0.785537) | 2.117435 / 1.541195 (0.576240) | 2.187292 / 1.468490 (0.718802) | 0.478387 / 4.584777 (-4.106390) | 3.625096 / 3.745712 (-0.120616) | 3.408036 / 5.269862 (-1.861826) | 2.124117 / 4.565676 (-2.441559) | 0.056537 / 0.424275 (-0.367738) | 0.007489 / 0.007607 (-0.000118) | 0.502434 / 0.226044 (0.276389) | 5.025357 / 2.268929 (2.756428) | 2.740554 / 55.444624 (-52.704070) | 2.418841 / 6.876477 (-4.457635) | 2.730764 / 2.142072 (0.588691) | 0.600013 / 4.805227 (-4.205214) | 0.133039 / 6.500664 (-6.367625) | 0.061466 / 0.075469 (-0.014003) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.330211 / 1.841788 (-0.511577) | 21.092100 / 8.074308 (13.017792) | 14.463054 / 10.191392 (4.271662) | 0.154149 / 0.680424 (-0.526274) | 0.018891 / 0.534201 (-0.515310) | 0.393078 / 0.579283 (-0.186205) | 0.415279 / 0.434364 (-0.019085) | 0.479469 / 0.540337 (-0.060868) | 0.659953 / 1.386936 (-0.726983) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#5ca2ba050340829b4dd44791afc15db0d82a3276 \"CML watermark\")\n"
] | "2023-08-17T09:17:05" | "2023-08-17T20:46:25" | "2023-08-17T20:37:19" | MEMBER | null | 0 | {
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} | This would make the data files resolution faster: no need to list all the data files to infer the dataset builder to use.
fix https://github.com/huggingface/datasets/issues/6140 | {
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"@lhoestq ",
"Makes sense, I guess this can be fixed in the load_dataset_builder method.\r\nIt concerns every packaged builder I think (see values in `_PACKAGED_DATASETS_MODULES`)",
"I think the behavior is related to these lines, which short circuited the error handling.\r\nhttps://github.com/huggingface/datasets/blob/664a1cb72ea1e6ef7c47e671e2686ca4a35e8d63/src/datasets/load.py#L946-L952\r\n\r\nSo should data_dir be checked here or still delegating to actual `DatasetModule`? In that case, how to properly set `data_files` here.",
"This is location in PackagedDatasetModuleFactory.get_module seems the be the right place to check if at least data_dir or data_files are passed"
] | "2023-08-16T04:38:09" | "2023-08-17T13:45:18" | null | CONTRIBUTOR | null | null | null | ### Describe the bug
FolderBase Dataset automatically resolves under current directory when data_dir is not specified.
For example:
```
load_dataset("audiofolder")
```
takes long time to resolve and collect data_files from current directory. But I think it should reach out to this line for error handling https://github.com/huggingface/datasets/blob/cb8c5de5145c7e7eee65391cb7f4d92f0d565d62/src/datasets/packaged_modules/folder_based_builder/folder_based_builder.py#L58-L59
### Steps to reproduce the bug
```
load_dataset("audiofolder")
```
### Expected behavior
Error report
### Environment info
- `datasets` version: 2.14.4
- Platform: Linux-5.15.0-78-generic-x86_64-with-glibc2.17
- Python version: 3.8.15
- Huggingface_hub version: 0.16.4
- PyArrow version: 12.0.1
- Pandas version: 1.5.3 | {
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"`Dataset.sort` essentially does the same thing except it uses `pyarrow.compute.sort_indices` which doesn't involve copying the data into python objects (saving memory)\r\n\r\n```python\r\nsort_keys = [(col, \"ascending\") for col in column_names]\r\nindices = pc.sort_indices(self.data, sort_keys=sort_keys)\r\nreturn self.select(indices)\r\n```"
] | "2023-08-15T14:02:31" | "2023-08-15T14:17:09" | null | NONE | null | null | null | ### Feature request
A faster way to sort a dataset which contains a large number of rows.
### Motivation
The current sorting implementations took significantly longer than expected when I was running on a dataset trying to sort by timestamps.
**Code snippet:**
```python
ds = datasets.load_dataset( "json", **{"data_files": {"train": "path-to-jsonlines"}, "split": "train"}, num_proc=os.cpu_count(), keep_in_memory=True)
sorted_ds = ds.sort("pubDate", keep_in_memory=True)
```
However, once I switched to a different method which
1. unpacked to a list of tuples
2. sorted tuples by key
3. run `.select` with the sorted list of indices
It was significantly faster (orders of magnitude, especially with M's of rows)
### Your contribution
I'd be happy to implement a crude single key sorting algorithm so that other users can benefit from this trick. Broadly, this would take a `Dataset` and perform;
```python
# ds is a Dataset object
# key_name is the sorting key
class Dataset:
...
def _sort(key_name: str) -> Dataset:
index_keys = [(i,x) for i,x in enumerate(self[key_name])]
sorted_rows = sorted(row_pubdate, key=lambda x: x[1])
sorted_indicies = [x[0] for x in sorted_rows]
return self.select(sorted_indicies)
``` | {
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"```\r\n dataset = IterableDataset(dataset) if type(dataset) != IterableDataset else dataset # to force dataset.take(batch_size) to work in non-streaming mode\r\n ```\r\n",
"hf discuss: https://discuss.huggingface.co/t/how-does-one-make-dataset-take-512-work-with-streaming-false-with-hugging-face-data-set/50770",
"so: https://stackoverflow.com/questions/76902824/how-does-one-make-dataset-take512-work-with-streaming-false-with-hugging-fac",
"Feel free to work on this. In addition, `IterableDataset` supports `skip`, so we should also add this method to `Dataset`."
] | "2023-08-15T00:17:51" | "2023-08-17T13:49:37" | null | NONE | null | null | null | ### Feature request
I want to do:
```
dataset.take(512)
```
but it only works with streaming = True
### Motivation
uniform interface to data sets. Really surprising the above only works with streaming = True.
### Your contribution
Should be trivial to copy paste the IterableDataset .take to use the local path in the data (when streaming = False) | {
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https://api.github.com/repos/huggingface/datasets/issues/6149 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6149/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6149/comments | https://api.github.com/repos/huggingface/datasets/issues/6149/events | https://github.com/huggingface/datasets/issues/6149 | 1,850,700,624 | I_kwDODunzps5uT3NQ | 6,149 | Dataset.from_parquet cannot load subset of columns | {
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"Looks like this regression was introduced in `datasets==2.13.0` (`2.12.0` could load a subset of columns)\r\n\r\nThis does not appear to be fixed by https://github.com/huggingface/datasets/pull/6045 (bug still exists on `main`)"
] | "2023-08-14T23:28:22" | "2023-08-17T17:12:47" | null | CONTRIBUTOR | null | null | null | ### Describe the bug
When using `Dataset.from_parquet(path_or_paths, columns=[...])` and a subset of columns, loading fails with a variant of the following
```
ValueError: Couldn't cast
a: int64
-- schema metadata --
pandas: '{"index_columns": [], "column_indexes": [], "columns": [{"name":' + 273
to
{'a': Value(dtype='int64', id=None), 'b': Value(dtype='int64', id=None)}
because column names don't match
The above exception was the direct cause of the following exception:
```
Looks to be triggered by https://github.com/huggingface/datasets/blob/c02a44715c036b5261686669727394b1308a3a4b/src/datasets/table.py#L2285-L2286
### Steps to reproduce the bug
```
import pandas as pd
from datasets import Dataset
pd.DataFrame([{"a": 1, "b": 2}]).to_parquet("test.pq")
Dataset.from_parquet("test.pq", columns=["a"])
```
### Expected behavior
A subset of columns should be loaded without error
### Environment info
- `datasets` version: 2.14.4
- Platform: Linux-5.10.0-23-cloud-amd64-x86_64-with-glibc2.2.5
- Python version: 3.8.16
- Huggingface_hub version: 0.16.4
- PyArrow version: 12.0.1
- Pandas version: 2.0.3 | {
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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) | 0.006818 / 0.011353 (-0.004534) | 0.004166 / 0.011008 (-0.006842) | 0.086756 / 0.038508 (0.048248) | 0.084444 / 0.023109 (0.061335) | 0.319249 / 0.275898 (0.043351) | 0.358689 / 0.323480 (0.035209) | 0.004344 / 0.007986 (-0.003641) | 0.003564 / 0.004328 (-0.000765) | 0.065021 / 0.004250 (0.060771) | 0.055991 / 0.037052 (0.018939) | 0.319573 / 0.258489 (0.061084) | 0.373239 / 0.293841 (0.079398) | 0.031431 / 0.128546 (-0.097115) | 0.008671 / 0.075646 (-0.066975) | 0.288484 / 0.419271 (-0.130788) | 0.053501 / 0.043533 (0.009968) | 0.316934 / 0.255139 (0.061795) | 0.354233 / 0.283200 (0.071034) | 0.028088 / 0.141683 (-0.113595) | 1.510905 / 1.452155 (0.058750) | 1.568614 / 1.492716 (0.075898) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.292343 / 0.018006 (0.274337) | 0.592309 / 0.000490 (0.591819) | 0.003850 / 0.000200 (0.003650) | 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) | 0.033510 / 0.037411 (-0.003901) | 0.089546 / 0.014526 (0.075020) | 0.104909 / 0.176557 (-0.071648) | 0.162219 / 0.737135 (-0.574916) | 0.104137 / 0.296338 (-0.192202) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.407993 / 0.215209 (0.192784) | 4.063423 / 2.077655 (1.985768) | 2.050237 / 1.504120 (0.546117) | 1.888939 / 1.541195 (0.347744) | 2.015195 / 1.468490 (0.546704) | 0.492617 / 4.584777 (-4.092160) | 3.595871 / 3.745712 (-0.149841) | 3.320467 / 5.269862 (-1.949395) | 2.099987 / 4.565676 (-2.465690) | 0.058513 / 0.424275 (-0.365762) | 0.007709 / 0.007607 (0.000102) | 0.479277 / 0.226044 (0.253233) | 4.790712 / 2.268929 (2.521783) | 2.517292 / 55.444624 (-52.927332) | 2.167461 / 6.876477 (-4.709016) | 2.432011 / 2.142072 (0.289939) | 0.600537 / 4.805227 (-4.204690) | 0.133538 / 6.500664 (-6.367126) | 0.059621 / 0.075469 (-0.015848) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.280375 / 1.841788 (-0.561413) | 20.777971 / 8.074308 (12.703663) | 14.869539 / 10.191392 (4.678147) | 0.159372 / 0.680424 (-0.521052) | 0.018096 / 0.534201 (-0.516105) | 0.393945 / 0.579283 (-0.185338) | 0.409598 / 0.434364 (-0.024766) | 0.459202 / 0.540337 (-0.081136) | 0.632298 / 1.386936 (-0.754638) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.006694 / 0.011353 (-0.004659) | 0.004299 / 0.011008 (-0.006709) | 0.064880 / 0.038508 (0.026372) | 0.083233 / 0.023109 (0.060124) | 0.366488 / 0.275898 (0.090590) | 0.405049 / 0.323480 (0.081569) | 0.005602 / 0.007986 (-0.002384) | 0.003623 / 0.004328 (-0.000705) | 0.064410 / 0.004250 (0.060160) | 0.057962 / 0.037052 (0.020910) | 0.365318 / 0.258489 (0.106829) | 0.403151 / 0.293841 (0.109310) | 0.031285 / 0.128546 (-0.097261) | 0.008867 / 0.075646 (-0.066780) | 0.071137 / 0.419271 (-0.348135) | 0.048398 / 0.043533 (0.004865) | 0.360187 / 0.255139 (0.105048) | 0.383872 / 0.283200 (0.100673) | 0.023232 / 0.141683 (-0.118451) | 1.526980 / 1.452155 (0.074826) | 1.587265 / 1.492716 (0.094549) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.362603 / 0.018006 (0.344596) | 0.557034 / 0.000490 (0.556544) | 0.025303 / 0.000200 (0.025103) | 0.000562 / 0.000054 (0.000508) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.030636 / 0.037411 (-0.006775) | 0.088085 / 0.014526 (0.073559) | 0.103238 / 0.176557 (-0.073318) | 0.155208 / 0.737135 (-0.581928) | 0.106661 / 0.296338 (-0.189678) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.413660 / 0.215209 (0.198451) | 4.122717 / 2.077655 (2.045063) | 2.097656 / 1.504120 (0.593536) | 1.931995 / 1.541195 (0.390801) | 2.071497 / 1.468490 (0.603007) | 0.490257 / 4.584777 (-4.094520) | 3.588076 / 3.745712 (-0.157636) | 3.423087 / 5.269862 (-1.846774) | 2.147974 / 4.565676 (-2.417703) | 0.058783 / 0.424275 (-0.365492) | 0.007456 / 0.007607 (-0.000151) | 0.492350 / 0.226044 (0.266305) | 4.935935 / 2.268929 (2.667006) | 2.604217 / 55.444624 (-52.840407) | 2.333723 / 6.876477 (-4.542754) | 2.585293 / 2.142072 (0.443220) | 0.608800 / 4.805227 (-4.196427) | 0.135806 / 6.500664 (-6.364858) | 0.062716 / 0.075469 (-0.012753) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.347359 / 1.841788 (-0.494429) | 21.420505 / 8.074308 (13.346197) | 14.325914 / 10.191392 (4.134522) | 0.159617 / 0.680424 (-0.520806) | 0.018769 / 0.534201 (-0.515432) | 0.399677 / 0.579283 (-0.179606) | 0.402992 / 0.434364 (-0.031372) | 0.484629 / 0.540337 (-0.055709) | 0.656007 / 1.386936 (-0.730929) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#ac94bb10d5c00ce8fdaf461eb1ff4b8572cfe956 \"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) | 0.007291 / 0.011353 (-0.004062) | 0.004501 / 0.011008 (-0.006508) | 0.097529 / 0.038508 (0.059021) | 0.079257 / 0.023109 (0.056147) | 0.356390 / 0.275898 (0.080492) | 0.390065 / 0.323480 (0.066585) | 0.006071 / 0.007986 (-0.001914) | 0.003783 / 0.004328 (-0.000546) | 0.074598 / 0.004250 (0.070348) | 0.059626 / 0.037052 (0.022574) | 0.395344 / 0.258489 (0.136855) | 0.418564 / 0.293841 (0.124723) | 0.041843 / 0.128546 (-0.086704) | 0.009293 / 0.075646 (-0.066354) | 0.332668 / 0.419271 (-0.086604) | 0.065753 / 0.043533 (0.022220) | 0.357285 / 0.255139 (0.102146) | 0.402974 / 0.283200 (0.119775) | 0.028714 / 0.141683 (-0.112968) | 1.733913 / 1.452155 (0.281759) | 1.802574 / 1.492716 (0.309858) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.253114 / 0.018006 (0.235108) | 0.606338 / 0.000490 (0.605848) | 0.006871 / 0.000200 (0.006671) | 0.000126 / 0.000054 (0.000072) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.031850 / 0.037411 (-0.005562) | 0.095148 / 0.014526 (0.080622) | 0.111499 / 0.176557 (-0.065057) | 0.174653 / 0.737135 (-0.562483) | 0.109396 / 0.296338 (-0.186943) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.440442 / 0.215209 (0.225233) | 4.408792 / 2.077655 (2.331137) | 2.149778 / 1.504120 (0.645658) | 1.922430 / 1.541195 (0.381235) | 2.029281 / 1.468490 (0.560791) | 0.611586 / 4.584777 (-3.973191) | 4.204571 / 3.745712 (0.458859) | 3.638194 / 5.269862 (-1.631668) | 2.336146 / 4.565676 (-2.229531) | 0.065383 / 0.424275 (-0.358892) | 0.008441 / 0.007607 (0.000834) | 0.527357 / 0.226044 (0.301313) | 5.247892 / 2.268929 (2.978963) | 2.654005 / 55.444624 (-52.790620) | 2.256596 / 6.876477 (-4.619881) | 2.432191 / 2.142072 (0.290119) | 0.672759 / 4.805227 (-4.132469) | 0.148494 / 6.500664 (-6.352170) | 0.068248 / 0.075469 (-0.007221) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.544250 / 1.841788 (-0.297538) | 21.882016 / 8.074308 (13.807708) | 16.470182 / 10.191392 (6.278790) | 0.166107 / 0.680424 (-0.514317) | 0.021305 / 0.534201 (-0.512896) | 0.445069 / 0.579283 (-0.134214) | 0.500631 / 0.434364 (0.066267) | 0.525801 / 0.540337 (-0.014536) | 0.806534 / 1.386936 (-0.580402) |\n\n</details>\nPyArrow==latest\n\n<details>\n<summary>Show updated benchmarks!</summary>\n\n### Benchmark: benchmark_array_xd.json\n\n| metric | read_batch_formatted_as_numpy after write_array2d | read_batch_formatted_as_numpy after write_flattened_sequence | read_batch_formatted_as_numpy after write_nested_sequence | read_batch_unformated after write_array2d | read_batch_unformated after write_flattened_sequence | read_batch_unformated after write_nested_sequence | read_col_formatted_as_numpy after write_array2d | read_col_formatted_as_numpy after write_flattened_sequence | read_col_formatted_as_numpy after write_nested_sequence | read_col_unformated after write_array2d | read_col_unformated after write_flattened_sequence | read_col_unformated after write_nested_sequence | read_formatted_as_numpy after write_array2d | read_formatted_as_numpy after write_flattened_sequence | read_formatted_as_numpy after write_nested_sequence | read_unformated after write_array2d | read_unformated after write_flattened_sequence | read_unformated after write_nested_sequence | write_array2d | write_flattened_sequence | write_nested_sequence |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.007322 / 0.011353 (-0.004030) | 0.004206 / 0.011008 (-0.006802) | 0.074827 / 0.038508 (0.036319) | 0.084759 / 0.023109 (0.061650) | 0.421204 / 0.275898 (0.145306) | 0.464442 / 0.323480 (0.140962) | 0.006523 / 0.007986 (-0.001463) | 0.003613 / 0.004328 (-0.000716) | 0.073796 / 0.004250 (0.069545) | 0.066609 / 0.037052 (0.029557) | 0.430108 / 0.258489 (0.171619) | 0.463165 / 0.293841 (0.169324) | 0.036015 / 0.128546 (-0.092532) | 0.009696 / 0.075646 (-0.065951) | 0.083326 / 0.419271 (-0.335946) | 0.056804 / 0.043533 (0.013271) | 0.423333 / 0.255139 (0.168194) | 0.450538 / 0.283200 (0.167338) | 0.027067 / 0.141683 (-0.114616) | 1.700563 / 1.452155 (0.248408) | 1.748738 / 1.492716 (0.256021) |\n\n### Benchmark: benchmark_getitem\\_100B.json\n\n| metric | get_batch_of\\_1024\\_random_rows | get_batch_of\\_1024\\_rows | get_first_row | get_last_row |\n|--------|---|---|---|---|\n| new / old (diff) | 0.395682 / 0.018006 (0.377675) | 0.540192 / 0.000490 (0.539702) | 0.140049 / 0.000200 (0.139849) | 0.000694 / 0.000054 (0.000639) |\n\n### Benchmark: benchmark_indices_mapping.json\n\n| metric | select | shard | shuffle | sort | train_test_split |\n|--------|---|---|---|---|---|\n| new / old (diff) | 0.036643 / 0.037411 (-0.000769) | 0.104422 / 0.014526 (0.089896) | 0.113072 / 0.176557 (-0.063484) | 0.179561 / 0.737135 (-0.557575) | 0.118620 / 0.296338 (-0.177718) |\n\n### Benchmark: benchmark_iterating.json\n\n| metric | read 5000 | read 50000 | read_batch 50000 10 | read_batch 50000 100 | read_batch 50000 1000 | read_formatted numpy 5000 | read_formatted pandas 5000 | read_formatted tensorflow 5000 | read_formatted torch 5000 | read_formatted_batch numpy 5000 10 | read_formatted_batch numpy 5000 1000 | shuffled read 5000 | shuffled read 50000 | shuffled read_batch 50000 10 | shuffled read_batch 50000 100 | shuffled read_batch 50000 1000 | shuffled read_formatted numpy 5000 | shuffled read_formatted_batch numpy 5000 10 | shuffled read_formatted_batch numpy 5000 1000 |\n|--------|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 0.476547 / 0.215209 (0.261338) | 4.716009 / 2.077655 (2.638354) | 2.412111 / 1.504120 (0.907991) | 2.246389 / 1.541195 (0.705194) | 2.307058 / 1.468490 (0.838568) | 0.552759 / 4.584777 (-4.032018) | 4.172484 / 3.745712 (0.426771) | 3.848419 / 5.269862 (-1.421443) | 2.310338 / 4.565676 (-2.255339) | 0.071757 / 0.424275 (-0.352518) | 0.011206 / 0.007607 (0.003599) | 0.609526 / 0.226044 (0.383482) | 5.583065 / 2.268929 (3.314136) | 3.081227 / 55.444624 (-52.363397) | 2.637782 / 6.876477 (-4.238695) | 2.887561 / 2.142072 (0.745489) | 0.667227 / 4.805227 (-4.138000) | 0.154421 / 6.500664 (-6.346243) | 0.070772 / 0.075469 (-0.004697) |\n\n### Benchmark: benchmark_map_filter.json\n\n| metric | filter | map fast-tokenizer batched | map identity | map identity batched | map no-op batched | map no-op batched numpy | map no-op batched pandas | map no-op batched pytorch | map no-op batched tensorflow |\n|--------|---|---|---|---|---|---|---|---|---|\n| new / old (diff) | 1.605500 / 1.841788 (-0.236288) | 22.872717 / 8.074308 (14.798409) | 15.865333 / 10.191392 (5.673941) | 0.170353 / 0.680424 (-0.510071) | 0.021854 / 0.534201 (-0.512347) | 0.461467 / 0.579283 (-0.117816) | 0.477743 / 0.434364 (0.043379) | 0.597234 / 0.540337 (0.056896) | 0.800416 / 1.386936 (-0.586520) |\n\n</details>\n</details>\n\n![](https://cml.dev/watermark.png#a7f8d9019e7cb104eac4106bdc6ec0292f0dc61a \"CML watermark\")\n"
] | "2023-08-14T10:43:41" | "2023-08-17T08:54:06" | "2023-08-17T08:43:58" | MEMBER | null | 0 | {
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} | This warning message was shown every time you pass num_proc to `load_dataset` because of `map_nested`
```
parallel_map is experimental and might be subject to breaking changes in the future
```
This PR removes it for `map_nested`. If someone uses another parallel backend they're already warned when `parallel_backend` is called anyway | {
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"The cause of the error seems to be that `datasets` adds \"gcs://\" as a schema, while `beam` checks only \"gs://\".\r\n\r\ndatasets: https://github.com/huggingface/datasets/blob/c02a44715c036b5261686669727394b1308a3a4b/src/datasets/builder.py#L822\r\n\r\nbeam: [link](https://github.com/apache/beam/blob/25e1a64641b1c8a3c0a6c75c6e86031b87307f22/sdks/python/apache_beam/io/filesystems.py#L98-L101)\r\n```\r\n systems = [\r\n fs for fs in FileSystem.get_all_subclasses()\r\n if fs.scheme() == path_scheme\r\n ]\r\n```"
] | "2023-08-14T03:11:34" | "2023-08-14T03:19:43" | null | NONE | null | null | null | ### Describe the bug
When running the BeamBasedBuilder with a GCS path specified in the cache_dir, the following ValueError occurs:
```
ValueError: Unable to get filesystem from specified path, please use the correct path or ensure the required dependency is installed, e.g., pip install apache-beam[gcp]. Path specified: gcs://my-bucket/huggingface_datasets/my_beam_dataset/default/0.0.0/my_beam_dataset-train [while running 'train/Save to parquet/Write/WriteImpl/InitializeWrite']
```
Same error occurs after running `pip install apache-beam[gcp]` as instructed.
### Steps to reproduce the bug
Put `my_beam_dataset.py`:
```python
import datasets
class MyBeamDataset(datasets.BeamBasedBuilder):
def _info(self):
features = datasets.Features({"value": datasets.Value("int64")})
return datasets.DatasetInfo(features=features)
def _split_generators(self, dl_manager, pipeline):
return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={})]
def _build_pcollection(self, pipeline):
import apache_beam as beam
return pipeline | beam.Create([{"value": i} for i in range(10)])
```
Run:
```bash
datasets-cli run_beam my_beam_dataset.py --cache_dir=gs://my-bucket/huggingface_datasets/ --beam_pipeline_options="runner=DirectRunner"
```
### Expected behavior
Running the BeamBasedBuilder with a GCS cache path without any errors.
### Environment info
- `datasets` version: 2.14.4
- Platform: macOS-13.4-arm64-arm-64bit
- Python version: 3.9.17
- Huggingface_hub version: 0.16.4
- PyArrow version: 9.0.0
- Pandas version: 2.0.3 | {
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"I've tried clear the .cache file, doesn't work.",
"This issue happens on AWS sagemaker",
"This issue can happen if there is a directory named \"glue\" relative to the Python script with the `load_dataset` call (similar issue to this one: https://github.com/huggingface/datasets/issues/5228). Is this the case?"
] | "2023-08-13T05:17:56" | "2023-08-17T17:19:41" | null | NONE | null | null | null | ### Describe the bug
Package version: datasets-2.14.4
When I run the codes:
```
from datasets import load_dataset
dataset = load_dataset("glue", "ax")
```
I got the following errors:
---------------------------------------------------------------------------
SchemaInferenceError Traceback (most recent call last)
File ~/anaconda3/envs/python3/lib/python3.10/site-packages/datasets/builder.py:1949, in ArrowBasedBuilder._prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id)
1948 num_shards = shard_id + 1
-> 1949 num_examples, num_bytes = writer.finalize()
1950 writer.close()
File ~/anaconda3/envs/python3/lib/python3.10/site-packages/datasets/arrow_writer.py:598, in ArrowWriter.finalize(self, close_stream)
597 self.stream.close()
--> 598 raise SchemaInferenceError("Please pass `features` or at least one example when writing data")
599 logger.debug(
600 f"Done writing {self._num_examples} {self.unit} in {self._num_bytes} bytes {self._path if self._path else ''}."
601 )
SchemaInferenceError: Please pass `features` or at least one example when writing data
The above exception was the direct cause of the following exception:
DatasetGenerationError Traceback (most recent call last)
Cell In[5], line 3
1 from datasets import load_dataset
----> 3 dataset = load_dataset("glue", "ax")
File ~/anaconda3/envs/python3/lib/python3.10/site-packages/datasets/load.py:2136, 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, token, use_auth_token, task, streaming, num_proc, storage_options, **config_kwargs)
2133 try_from_hf_gcs = path not in _PACKAGED_DATASETS_MODULES
2135 # Download and prepare data
-> 2136 builder_instance.download_and_prepare(
2137 download_config=download_config,
2138 download_mode=download_mode,
2139 verification_mode=verification_mode,
2140 try_from_hf_gcs=try_from_hf_gcs,
2141 num_proc=num_proc,
2142 storage_options=storage_options,
2143 )
2145 # Build dataset for splits
2146 keep_in_memory = (
2147 keep_in_memory if keep_in_memory is not None else is_small_dataset(builder_instance.info.dataset_size)
2148 )
File ~/anaconda3/envs/python3/lib/python3.10/site-packages/datasets/builder.py:954, in DatasetBuilder.download_and_prepare(self, output_dir, download_config, download_mode, verification_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, file_format, max_shard_size, num_proc, storage_options, **download_and_prepare_kwargs)
952 if num_proc is not None:
953 prepare_split_kwargs["num_proc"] = num_proc
--> 954 self._download_and_prepare(
955 dl_manager=dl_manager,
956 verification_mode=verification_mode,
957 **prepare_split_kwargs,
958 **download_and_prepare_kwargs,
959 )
960 # Sync info
961 self.info.dataset_size = sum(split.num_bytes for split in self.info.splits.values())
File ~/anaconda3/envs/python3/lib/python3.10/site-packages/datasets/builder.py:1049, in DatasetBuilder._download_and_prepare(self, dl_manager, verification_mode, **prepare_split_kwargs)
1045 split_dict.add(split_generator.split_info)
1047 try:
1048 # Prepare split will record examples associated to the split
-> 1049 self._prepare_split(split_generator, **prepare_split_kwargs)
1050 except OSError as e:
1051 raise OSError(
1052 "Cannot find data file. "
1053 + (self.manual_download_instructions or "")
1054 + "\nOriginal error:\n"
1055 + str(e)
1056 ) from None
File ~/anaconda3/envs/python3/lib/python3.10/site-packages/datasets/builder.py:1813, in ArrowBasedBuilder._prepare_split(self, split_generator, file_format, num_proc, max_shard_size)
1811 job_id = 0
1812 with pbar:
-> 1813 for job_id, done, content in self._prepare_split_single(
1814 gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
1815 ):
1816 if done:
1817 result = content
File ~/anaconda3/envs/python3/lib/python3.10/site-packages/datasets/builder.py:1958, in ArrowBasedBuilder._prepare_split_single(self, gen_kwargs, fpath, file_format, max_shard_size, job_id)
1956 if isinstance(e, SchemaInferenceError) and e.__context__ is not None:
1957 e = e.__context__
-> 1958 raise DatasetGenerationError("An error occurred while generating the dataset") from e
1960 yield job_id, True, (total_num_examples, total_num_bytes, writer._features, num_shards, shard_lengths)
DatasetGenerationError: An error occurred while generating the dataset
### Steps to reproduce the bug
from datasets import load_dataset
dataset = load_dataset("glue", "ax")
### Expected behavior
When generating the train split:
Generating train split:
0/0 [00:00<?, ? examples/s]
It raise the error:
DatasetGenerationError: An error occurred while generating the dataset
### Environment info
datasets-2.14.4.
Python 3.10 | {
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https://api.github.com/repos/huggingface/datasets/issues/6153 | https://api.github.com/repos/huggingface/datasets | https://api.github.com/repos/huggingface/datasets/issues/6153/labels{/name} | https://api.github.com/repos/huggingface/datasets/issues/6153/comments | https://api.github.com/repos/huggingface/datasets/issues/6153/events | https://github.com/huggingface/datasets/issues/6153 | 1,852,630,074 | I_kwDODunzps5ubOQ6 | 6,153 | custom load dataset to hub | {
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"This is an issue for the [Datasets repo](https://github.com/huggingface/datasets).",
"> This is an issue for the [Datasets repo](https://github.com/huggingface/datasets).\r\n\r\nThanks @sgugger , I guess I will wait for them to address the issue . Looking forward to hearing from them ",
"You can use `.push_to_hub(\"<username>/<repo>\")` to push a `Dataset` to the Hub."
] | "2023-08-13T04:42:22" | "2023-08-17T14:17:05" | null | NONE | null | null | null | ### System Info
kaggle notebook
i transformed dataset:
```
dataset = load_dataset("Dahoas/first-instruct-human-assistant-prompt")
```
to
formatted_dataset:
```
Dataset({
features: ['message_tree_id', 'message_tree_text'],
num_rows: 33143
})
```
but would like to know how to upload to hub
### Who can help?
@ArthurZucker @younesbelkada
### Information
- [ ] The official example scripts
- [ ] My own modified scripts
### Tasks
- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)
### Reproduction
shared above
### Expected behavior
load dataset to hub | {
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